# Vstorm - Engineering consultancy firm. Applied AI Agents > Vstorm is a boutique AI Agent engineering consultancy recognized by Deloitte and EY. We transform business operations with tailored RAG and Agentic automations that go beyond standard solutions, delivering proven ROI through practical, hands-on implementation --- ## Ebook - [The LLM Book](https://vstorm.co/ebook/the-llm-book/): Ebook by Vstorm introducing recruitment AI guildelines that can help Builidng AI team inbusiness - [Practical Guideline for Building AI Team](https://vstorm.co/ebook/practical-guideline-for-building-ai-team/): Ebook by Vstorm introducing recruitment AI guildelines that can help Builidng AI team inbusiness - [Generative AI for your startup & business](https://vstorm.co/ebook/generative-ai-in-your-startup-and-business/): Ebook by Vstorm introducing generative AI technologies that can help skyrocket your startup and business --- ## Posts - [Top 10 Applied AI Consulting firms for SMBs 2025](https://vstorm.co/agentic-ai/top-10-applied-ai-consulting-firms-for-smbs-2025/): A deep dive into the top 10 applied AI consultation companies for SMBs to bridge the mid-market gap in 2025... - [Top 10 Applied AI & ML Consulting Service firms 2025](https://vstorm.co/ai-agents/top-10-applied-ai-ml-consulting-service-firms-2025/): A deep dive into the top 10 applied AI & ML consultation companies for SMBs to help bridge the mid-market... - [Old-School Keyword Search to the Rescue When Your RAG Fails](https://vstorm.co/rag/old-school-keyword-search-to-the-rescue-when-your-rag-fails/): With all the AI hype, semantic search powered by language models has become the default choice for retrieval augmented generation,... - [Agentic AI Engineering Consultancy vs General Custom Software Developer: Pricing and Service Comparison 2025](https://vstorm.co/agentic-ai/agentic-ai-engineering-consultancy-vs-general-custom-software-developer-pricing-and-service-comparison-2025/): Within you will find a side-by-side comparison of the capabilities and limitations of dedicated engineering consultancy firms vs general custom... - [How Vstorm supports Saudi Arabia Vision 2030?](https://vstorm.co/ai-agents/how-vstorm-supports-saudi-arabia-vision-2030/): A closer look at how Vstorm, the boutique Agentic AI consultancy company located in Poland, is equipped to accelerate AI... - [When clean text is not enough: structured extraction for RAG](https://vstorm.co/rag/when-clean-text-is-not-enough-structured-extraction-for-rag/): Garbage in, garbage out – every seasoned data scientist knows poor data can derail Retrieval-Augmented Generation (RAG). Yet there is... - [Why RAG is not dead: a case for context engineering over massive context windows](https://vstorm.co/rag/why-rag-is-not-dead-a-case-for-context-engineering-over-massive-context-windows/): Amid the current AI boom, you may have recently heard that RAG is dead as major AI labs compete with... - [Top 10 AI Agent Development & Consulting companies for SMBs and Enterprises 2025](https://vstorm.co/ai-agents/top-10-ai-agent-development-consulting-companies-for-smbs-and-enterprises-2025/): A deep dive into the top 10 AI Agent development and consultation companies of 2025 To understand the landscape of... - [Top 5 tips from Lucian Puca of Mixam on launching Agentic AI transformation](https://vstorm.co/ai/top-5-tips-from-lucian-puca-of-mixam-on-launching-agentic-ai-transformation/): Lucian Puca, Digital Product Manager and Automation and Workflow Lead of Mixam, has been at the forefront of the digital... - [Introduction to Information Retrieval in RAG pipelines](https://vstorm.co/rag/introduction-to-information-retrieval-in-rag-pipelines/): In this article, we’ll pull back the curtain and share the tools, methodologies, and tricks we use to build robust... - [Advanced RAG pipeline, part 1: Rerankers](https://vstorm.co/rag/advanced-rag-pipeline-part-1-rerankers/): Standard RAG systems face a common problem: they can quickly find documents, but often provide irrelevant files that lead to... - [What is Agentic AI? A simple guide for Small and Medium businesses](https://vstorm.co/agentic-ai/what-is-agentic-ai-a-simple-guide-for-small-and-medium-businesses/): The numbers tell a story that most business leaders recognize: Eight out of ten companies now use generative AI, yet... - [Top 10 Agentic AI Development & Consulting companies for SMBs and Enterprises 2025](https://vstorm.co/agentic-ai/top-10-agentic-ai-development-consulting-companies-for-smbs-and-enterprises-2025/): A deep dive into the top 10 Agentic AI development and consultation companies of 2025 To understand the landscape of... - [Top 10 RAG Development service firms in 2025](https://vstorm.co/rag/top-10-rag-development-service-firms-in-2025/): A deep dive into the top 10 RAG development companies of 2025, complete with a weighted list of top RAG... - [The use of AI by AI engineers](https://vstorm.co/ai/the-use-of-ai-by-ai-engineers/): — So, how do you do it? — We’re often asked by our customers — Being experts in AI, how do you... - [Choosing the right LLM model for the job](https://vstorm.co/model-evaluation/choosing-the-right-llm-model-for-the-job/): We get it; it’s hard to decide which large language model should be used for a specific business application. Many... - [Top 10 Custom AI Agent Development Companies](https://vstorm.co/ai/top-10-custom-ai-agent-development-companies/): Artificial Intelligence (AI) agents are revolutionizing industries by enabling automation, enhancing decision-making, and improving user experiences. Companies worldwide are leveraging... - [Off-the-shelf AI platform or Custom AI Agent solution?](https://vstorm.co/ai/off-the-shelf-ai-platform-or-custom-ai-agent-solution/): At Vstorm, we’ve noticed a new trend in the AI world: Companies that initially jumped on off-the-shelf Agentic AI platforms... - [Vstorm: Leader in LLMs solutions recognized by Deloitte Technology Fast 50](https://vstorm.co/community/vstorm-leader-in-llms-solutions-recognized-by-deloitte-technology-fast-50/): We are proud to announce that Vstorm has been recognized as one of the leading companies in Deloitte’s prestigious Technology... - [From idea to Agentic AI solution](https://vstorm.co/ai-advisory/how-to-assess-the-value-of-ai-vsa-in-practice/): Each AI-related project is a journey into a bit of the unknown. It is a learning experience with a magnitude... - [AI Agentic Workflows: What they offer?](https://vstorm.co/ai/ai-agentic-workflows-what-they-offer/): Artificial intelligence (AI) has revolutionized various industries, and one of the most transformative advancements is the development of agentic AI... - [Technologies behind AI Agents](https://vstorm.co/ai-agents/technologies-behind-ai-agents/): AI agents are autonomous or semi-autonomous systems that perform tasks, make decisions, and interact with users or environments. These systems... - [How to implement AI Agents in your company](https://vstorm.co/ai/how-to-implement-ai-agents-in-your-company/): AI Agents are transforming businesses by automating tasks, improving decision-making, and optimizing workflows. Unlike traditional automation tools, AI-driven agents can... - [How companies really implement AI and measure success](https://vstorm.co/ai/ai-in-business-separating-facts-from-myths/): When you think about AI, do you see it as a powerful tool to embrace or a disrupting force? Many... - [What are AI Agents?](https://vstorm.co/ai/ai-agents-the-next-step-in-business-automation/): Many companies have spent years implementing automation solutions—CRM integrations, scripted chatbots, and workflow automation tools—to improve efficiency. But these systems... - [Data Annotation and its role in AI](https://vstorm.co/llms/data-annotation-and-its-role-in-ai/): Imagine building a house. You can have the best design, the most advanced materials, and a skilled team, but if... - [RAG: When it makes sense and how to prepare?](https://vstorm.co/ai/rag-when-it-makes-sense-and-how-to-prepare/): Retrieval-Augmented Generation (RAG) is a transformative technology that blends the capabilities of Large Language Models (LLMs) with real-time retrieval systems.... - [vLLM: A smarter alternative to traditional LLMs?](https://vstorm.co/ai/vllm-a-smarter-alternative-to-traditional-llms/): For many businesses, large language models (LLMs) like GPT and BERT represent untapped potential. They promise to automate repetitive tasks,... - [RAG 's Role in Data Privacy and Security for LLMs](https://vstorm.co/rag/rag-s-role-in-data-privacy-and-security-for-llms/): In the digital era, data protection and security are critical components of any AI-based technology. Retrieval-augmented generation (RAG), a technique... - [When to choose vLLM or RAG?](https://vstorm.co/ai/when-to-choose-vllm-or-rag/): Imagine this: Your company’s customer service team is overwhelmed by an influx of inquiries. They’re swamped, struggling to respond promptly,... - [PyTorch Developer: How to Choose One?](https://vstorm.co/ai/pytorch-developer-how-to-choose-one/): Operationalizing machine learning models effectively is critical for businesses seeking to unlock the full potential of AI-powered solutions. PyTorch developers... - [What is PyTorch in AI & LLM Projects?](https://vstorm.co/ai/what-is-pytorch-in-ai-llm-projects/): In the ever-evolving world of Artificial Intelligence (AI), organizations constantly seek reliable tools that can bring their innovative ideas to... - [Top 10 PyTorch Development Companies](https://vstorm.co/ai/top-10-pytorch-development-companies/): PyTorch development companies play a pivotal role in building, optimizing, and deploying machine learning and deep learning solutions for businesses.... - [Data Preparation: The Key to AI and LLM Success](https://vstorm.co/ai/data-preparation-the-key-to-ai-and-llm-success/): Integrating AI and Large Language Models (LLMs) into business workflows requires much more than implementing advanced algorithms. It demands a... - [What is LangGraph and how to use it?](https://vstorm.co/ai/what-is-langgraph-and-how-to-use-it/): Imagine a tool that doesn’t just organize your data but actually understands it—a tool that connects the dots, identifies patterns,... - [How to Build a Large Language Models](https://vstorm.co/ai/how-to-build-a-large-language-models/): Large Language Models (LLMs) have transformed how AI systems process human language using natural language processing techniques. These models perform... - [How to Leverage LLM and AI in Telecommunications?](https://vstorm.co/ai/llm-and-ai-in-telecommunications/): The telecommunications sector is one of the most dynamic and critical areas of the modern economy. As technology and customer... - [MLOps vs LLMOps: Key differences for businesses](https://vstorm.co/ai/mlops-vs-llmops/): The modern business world is rapidly evolving with the advancement of artificial intelligence technologies. Concepts like MLOps (Machine Learning Operations)... - [Top 10 MLOps Companies](https://vstorm.co/machine-learning-ml/top-10-mlops-companies/): What is MLOps? MLOps (Machine Learning Operations) is a set of practices that combine operational management (DevOps) with the lifecycle... - [How to enhance LLMs through LLM Ops?](https://vstorm.co/ai/how-to-enhance-llms-through-llm-ops/): Large Language Models (LLMs) have revolutionized business processes by enabling advanced automation and intelligent decision-making. However, their implementation and management... - [LLMOps company: How to choose one?](https://vstorm.co/llms/llmops-company-how-to-choose-one/): Operationalizing a large language model ( LLM ) effectively is critical for businesses seeking to unlock the full potential of... - [What is MLOps? - Machine Learning Operations](https://vstorm.co/ai/what-is-mlops-machine-learning-operations/): What is MLOps? MLOps, short for Machine Learning Operations, is transforming how businesses leverage artificial intelligence to achieve strategic goals.... - [LLM Ops: How to manage Large Language Models?](https://vstorm.co/ai/llm-ops-how-to-manage-large-language-models/): The rise of Large Language Models (LLMs), such as GPT and BERT, has transformed how businesses leverage AI for automation,... - [Top 10 LLM Ops Companies](https://vstorm.co/ai/top-10-llm-ops-companies-for-2025/): LLM Ops (Large Language Model Operations) refers to the set of practices, tools, and frameworks used to manage, monitor, and... - [AI Chatbot Developer: How to choose one?](https://vstorm.co/ai/ai-chatbot-developer-how-to-choose-one/): AI chatbots are reshaping how businesses interact with customers, automate workflows, and provide personalized experiences. By leveraging technologies like natural... - [How to use AI Chatbots in your company?](https://vstorm.co/ai/how-to-use-ai-chatbots/): AI-powered chatbots are no longer just an innovative idea—they’ve become a key tool for businesses looking to enhance efficiency, improve... - [Top 10 Custom LLM Development Companies](https://vstorm.co/llms/top-10-custom-llm-development-companies-for-2025/): The demand for tailored Large Language Model (LLM) solutions is rapidly growing as businesses across every industry seek to transform... - [Comparing custom LLM software to LLM development](https://vstorm.co/llms/difference-between-custom-llm-software-and-llm-development/): Large Language Models (LLM) have become a cornerstone of modern technological solutions, enabling businesses to automate processes, personalize experiences, and... - [The role of RAG in automating enterprise workflows](https://vstorm.co/ai/the-role-of-rag-in-automating-enterprise-workflows/): Introduction to Retrieval-Augmented Generation Retrieval-Augmented Generation (RAG) is a technology that combines the best features of two approaches: information retrieval... - [Why should you secure LLM?](https://vstorm.co/ai/why-should-you-secure-llm/): Securing your Large Language Models (LLMs) is crucial for protecting both your data and your business from a wide range... - [RAG Developer: How to choose one?](https://vstorm.co/ai/how-to-choose-rag-developer/): In today’s fast-paced world of AI, Retrieval-Augmented Generation (RAG) is transforming how businesses leverage data for efficiency, personalization, and innovation.... - [What is Natural Language Processing (NLP)?](https://vstorm.co/ai/what-is-nlp/): In the fast-paced digital world, businesses are constantly looking for ways to improve efficiency, enhance customer experiences, and make data-driven... - [How to prompt? Build the perfect prompt for your LLM](https://vstorm.co/ai/how-to-prompt-build-the-perfect-prompt-for-your-llm/): Designing an effective prompt is essential for unlocking the full potential of large language models (LLMs). A well-structured prompt guides... - [What are Large Language Models (LLMs)?](https://vstorm.co/ai/what-are-large-language-models-llms/): Large Language Models (LLMs) are a form of AI designed to understand and generate human language. Trained on vast amounts... - [What is LlamaIndex? New possibilities in development with LLMs](https://vstorm.co/ai/what-is-llamaindex-new-possibilities-in-development-with-llms/): Welcome to LLM and LlamaIndex capabilities Large Language Models (LLMs) have transformed the way businesses interact with language, enabling computers... - [How to choose the right LlamaIndex developer?](https://vstorm.co/ai/how-to-choose-the-right-llamaindex-developer/): In today’s rapidly advancing field of AI and Natural Language Processing (NLP), leveraging large language models (LLMs) effectively is crucial... - [How to secure data in LLM? [GUIDE]](https://vstorm.co/ai/data-security-using-llms-guide/): Why is data security so important when working with LLMs? The rise of Large Language Models (LLMs) in artificial intelligence... - [How to choose the right LangChain developer?](https://vstorm.co/ai/how-to-choose-the-right-langchain-developer/): In today’s fast-paced world of AI and natural language processing (NLP), the effective use of large language models (LLMs) has... - [What is LangChain? New possibilities for LLMs](https://vstorm.co/langchain/what-is-langchain-new-possibilities-in-development-with-llms/): LangChain is paving the way for a new era in AI development, making it easier than ever for businesses to... - [How to data scraping using LangChain?](https://vstorm.co/ai/how-to-scrape-data-using-langchain/): Who are we? We are at the forefront of helping startups and tech companies grow as a dedicated LangChain development... - [Top 10 best LangChain development companies](https://vstorm.co/langchain/top-10-best-langchain-development-companies/): What is LangChain? LangChain is an open-source development framework designed to simplify the creation of applications using large language models... - [Strategies for fostering High-Performance AI in enterprises](https://vstorm.co/ai/ai_in_enterprises/): Artificial intelligence (AI) is revolutionizing industries by enhancing automation, personalization, and decision-making processes. As AI technologies continue to advance, enterprises... - [Advancing Text Analysis on images with LLMs](https://vstorm.co/ai/advancing-text_analysis-on-images-with-llms/): Text analysis on images, also known as Optical Character Recognition (OCR), has become a significant aspect of artificial intelligence (AI).... - [Advancing LLM Text Clustering](https://vstorm.co/ai/advancing_text_clustering_with_llms/): Text clustering is a technique in natural language processing (NLP) that enables the grouping of similar texts based on their... - [Advancing AI Translation with LLMs](https://vstorm.co/ai/what-is-translation/): Artificial Intelligence has significantly impacted various fields, including natural language processing (NLP). One of the most transformative applications of NLP... - [Advancing Sentiment Analysis with LLMs](https://vstorm.co/ai/what-is-sentiment-analysis/): Sentiment analysis, a prominent application of text classification, has gained significant traction in recent years. By analyzing text to determine... - [Advancing Reasoning with LLMs](https://vstorm.co/ai/the-impact-of-llms-on-reasoning/): Reasoning is a cornerstone of artificial intelligence, enabling systems to process information, conclude, and make informed decisions. Unlike simple data... - [Advancing LLM Semantic Search](https://vstorm.co/ai/semantic-search/): In the modern era of information overload, finding the right information quickly and accurately is crucial. Traditional keyword-based search systems... - [Advancing AI Question Answering with LLMs](https://vstorm.co/ai/what-is-question-answering-llms/): Question-answering (QA) systems are a cornerstone of artificial intelligence (AI) and Large Language Models (LLMs), designed to automatically answer questions... - [Advancing Text Summarization with LLMs](https://vstorm.co/ai/all-you-have-to-know-about-text-summarization/): In our digital era, the volume of information available can be overwhelming. Text Summarization (TS) has emerged as a crucial... - [What is Information Extraction using LLMs?](https://vstorm.co/ai/implementing-information-extraction/): In our increasingly digital world, the sheer volume of data generated every day presents both opportunities and challenges. Information Extraction... - [How Large Action Models Are Transforming Industries?](https://vstorm.co/ai/how-large-action-models-are-transforming-industries/): In a time of rapid technological progression, Large Action Models (LAMs) stand as transformative forces reshaping industry landscapes. This article... - [What Is Retrieval-Augmented Generation (RAG) for LLMs](https://vstorm.co/ai/the-power-rag-in-llm/): In the evolving field of AI, new technologies continually expand what’s possible. One significant advancement is Retrieval-Augmented Generation (RAG), which... - [Happy Easter Holiday](https://vstorm.co/community/happy-ester-holiday-vstorm/): As the spring season blossoms, we at Vstorm are filled with gratitude and want to extend our warmest Easter greetings... - [The Impact of LLM on Sales Strategies](https://vstorm.co/ai/the-impact-of-llm-on-sales-strategies-in-2024/): In the swiftly evolving world of technology, Large Language Models (LLMs) have carved out a niche, becoming a cornerstone in... - [How can LLM be integrated with CRM to boost the sales team?](https://vstorm.co/ai/how-can-llm-be-integrated-with-crm-to-boost-the-sales-team/): The modern business world is fiercely competitive, requiring sales teams to constantly find new ways to improve their performance and... - [FinTech in 2024: the role of LLMs and AI in finance](https://vstorm.co/ai/ai-and-llm-in-fintech-transforming-the-future-of-finance/): The financial sector is undergoing a transformative shift, driven by the adoption of Artificial Intelligence (AI) and Large Language Models... - [Happy Christmas and Cheers to 2024!](https://vstorm.co/community/happy-christmas-and-cheers-to-2024/): As the year wraps up, we at Vstorm just wanted to take a moment to say a big thank you... - [Use-Cases of LLMs in AdTech](https://vstorm.co/ai/use-cases-large-language-models-in-adtech/): Imagine a world where every advertisement you see feels like it’s speaking directly to you, where brands understand your needs... - [Large Language Models in Telecommunication](https://vstorm.co/ai/large-language-models-in-telecommunication/): The telecommunication sector has always been at the forefront of technological innovation. The recent integration of Large Language Models (LLMs)... - [Collaborative synergy: Vstorm x Generative AI Conference](https://vstorm.co/ai/collaborative-synergy-vstorm-x-generative-ai-conference/): The partnership between Vstorm and the Generative AI Conference represents a collaboration in the field of artificial intelligence (AI). This... - [Vstorm Honored as a Clutch Champion for 2023](https://vstorm.co/ai/vstorm-honored-as-a-clutch-champion-for-2023/): In the competitive landscape of AI development, recognition for exceptional service and expertise is a significant achievement. Vstorm has recently... - [The Power of LangChain in LLM-Based Applications](https://vstorm.co/ai/the-power-of-langchain-in-llm-based-applications/): LangChain, a framework specifically designed for Large Language Model (LLM) applications, has emerged as a major tool in enhancing the... - [LangChain Development Services](https://vstorm.co/ai/langchain-development-services/): At Vstorm, our decision to adopt LangChain was driven by its unparalleled potential in the application of Large Language Models... - [AI talent recruitment challenges in Enterprises](https://vstorm.co/ai/ai-talent-recruitment-challenges-in-enterprises/): Hiring AI Developers in Enterprises According to McKinsey, generative AI is a key driver of productivity in the modern economy,... - [A guide to finding the right AI developer on Upwork](https://vstorm.co/ai/a-guide-to-finding-the-right-ai-developer-on-upwork/): The increasing demand for AI expertise In an era where artificial intelligence (AI) is reshaping industries, businesses are increasingly seeking... - [Strategies for choosing the best AI development vendor on Clutch](https://vstorm.co/ai/strategies-for-choosing-the-best-ai-development-vendor-on-clutch/): In the dynamic world of Artificial Intelligence, selecting a top-tier vendor is paramount to the success of your projects. The... - [AI Integration](https://vstorm.co/ai/ai-integration/): The digital age has shifted the boundaries of what’s possible in business. One of the most transformative drivers of this... - [Instant customer service. AI chatbots in e-commerce](https://vstorm.co/ai/instant-customer-service-ai-chatbots-in-e-commerce/): The e-commerce landscape is evolving at an unprecedented pace, with artificial intelligence (AI) standing at the forefront of this transformation.... - [The Manifest Honors Vstorm as Toronto’s Most Reviewed AI Leader for 2023](https://vstorm.co/ai/the-manifest-honors-vstorm-as-torontos-most-reviewed-ai-leader-for-2023/): Here at Vstorm, we’re dedicated to helping businesses harness and maximize the power of generative AI to fuel their growth.... - [Generative AI in Tech Sectors](https://vstorm.co/ai/generative-ai-in-tech-sectors/): In the ever-evolving landscape of technology, generative AI stands as one of the most groundbreaking advancements. It is not just... - [Navigating the AI wilderness: tools for small businesses and startups](https://vstorm.co/ai/navigating-the-ai-wilderness-tools-for-small-businesses-and-startups/): Navigating the world of technology, especially AI, is a bit like venturing into a dense forest for small businesses and... - [AI Chatbot Built in Public by Vstorm 01](https://vstorm.co/ai/ai-chatbot-build-in-public-by-vstorm-01/): In 2023, if you’re a business owner not considering the integration of AI chatbots, you might be missing out on... - [Artificial Intelligence and its Dance with Data Privacy: Unpacking the ChatGPT Conundrum](https://vstorm.co/ai/artificial-intelligence-and-its-dance-with-data-privacy-unpacking-the-chatgpt-conundrum/): In the digital age, the relationship between AI and data privacy stands as a testament to the dual-edged nature of... - [Simple How-To with AI. GPT in Google Docs and Spreadsheets 01](https://vstorm.co/ai/simple-how-to-with-ai-gpt-in-google-docs-and-spreadsheets-01/): Welcome to the start of our How-To with AI series for everyday users. Here, we’ll break down how to use... - [Meet our COO! Interview with Kamil Włodarczyk](https://vstorm.co/uncategorized/meet-our-coo-interview-with-kamil-wlodarczyk/): We are pleased to introduce Kamil Włodarczyk, the recently appointed Chief Operating Officer (COO) of Vstorm. With an impressive background... - [Generative AI for startup owners... and not only!](https://vstorm.co/ai/generative-ai-technologies-for-startups/): As you embark on your entrepreneurial journey, it’s essential to keep up with the latest technologies to stay ahead of... - [AI in programming. Separating hype from reality with Code Interpreter and Copilot](https://vstorm.co/ai/ai-in-programming-separating-hype-from-reality-with-code-interpreter-and-copilot/): AI in programming, really? Let’s dive into it. Artificial intelligence (AI) has been touted as a game changer in many... - [Getting to know the new Chairman: an interview with Piotr Krzysztofik](https://vstorm.co/uncategorized/getting-to-know-the-new-chairman-an-interview-with-piotr-krzysztofik/): We are excited to announce a new addition to the Vstorm community: Piotr Krzysztofik become Chairman of Vstorm. In this... - [Will AI eliminate Recruitment departments?](https://vstorm.co/ai/will-ai-eliminate-recruitment-departments/): As technology continues to evolve, so too does the way businesses are run. As Artificial Intelligence (AI) becomes increasingly sophisticated,... - [Harnessing technology to craft a data strategy for your business](https://vstorm.co/data-experts/harnessing-technology-to-craft-a-winning-data-strategy-for-your-business/): Business owners in today’s world must understand the importance of data. Data is the lifeblood of modern business, and the... --- ## Pages - [Agentic AI in Real Estate](https://vstorm.co/agentic-ai/agentic-ai-in-real-estate/) - [Custom Agentic AI Development](https://vstorm.co/custom-agentic-ai-development/) - [Agentic AI development](https://vstorm.co/agentic-ai-development/) - [Agentic AI in Mining](https://vstorm.co/agentic-ai-in-mining/) - [Agentic AI in Defence](https://vstorm.co/agentic-ai-in-defence/) - [Agentic AI in Construction Engineering](https://vstorm.co/agentic-ai-in-construction-engineering/) - [Agentic Process Automation services](https://vstorm.co/agentic-ai/agentic-process-automation-services/) - [Agentic Process Automation](https://vstorm.co/agentic-ai/agentic-process-automation/) - [AI automation agency](https://vstorm.co/agentic-ai/ai-automation-agency/) - [AI business automation](https://vstorm.co/agentic-ai/ai-business-automation/) - [AI for Business Process Automation](https://vstorm.co/agentic-ai/ai-for-business-process-automation/) - [Agentic AI in Trevel](https://vstorm.co/agentic-ai/agentic-ai-in-travel/) - [Agentic AI for ITSM](https://vstorm.co/agentic-ai/agentic-ai-for-itsm/) - [Agentic AI in Smart City](https://vstorm.co/agentic-ai/agentic-ai-in-smart-city/) - [Agentic AI for Government](https://vstorm.co/agentic-ai/agentic-ai-for-government/) - [Agentic AI in Logistics](https://vstorm.co/agentic-ai/agentic-ai-in-logistics/) - [Agentic AI in MEDIA](https://vstorm.co/agentic-ai/agentic-ai-in-media/) - [Agentic AI in Insurance](https://vstorm.co/agentic-ai/agentic-ai-in-insurance/) - [Agentic AI in Telecommunication](https://vstorm.co/agentic-ai/agentic-ai-in-telecommunication/) - [Agentic AI in Education](https://vstorm.co/agentic-ai/agentic-ai-in-education/) - [Agentic AI in Ecommerce](https://vstorm.co/agentic-ai/agentic-ai-in-ecommerce/) - [Agentic AI in Energy](https://vstorm.co/agentic-ai/agentic-ai-in-energy/) - [Agentic AI in Automotive](https://vstorm.co/agentic-ai/agentic-ai-in-automotive/) - [Agentic AI in Agriculture](https://vstorm.co/agentic-ai/agentic-ai-in-agriculture/) - [Agentic AI in Supply Chain](https://vstorm.co/agentic-ai-in-supply-chain/) - [Agentic AI in Retail](https://vstorm.co/agentic-ai-in-retail/) - [Agentic AI for Manufacturing](https://vstorm.co/agentic-ai-for-manufacturing/) - [Agentic AI consalting](https://vstorm.co/agentic-ai-consulting/) - [Agentic AI company in Saudi Arabia](https://vstorm.co/agentic-ai-company-in-saudi-arabia/) - [AI Agent Development in Saudi Arabia](https://vstorm.co/ai-agent-development-in-saudi-arabia/) - [Agentic AI consulting in Saudi Arabia](https://vstorm.co/agentic-ai-consulting-in-saudi-arabia/) - [GenAI development in Saudi Arabia](https://vstorm.co/genai-development-in-saudi-arabia/) - [Agentic AI in banking](https://vstorm.co/agentic-ai-in-banking/) - [Agentic AI Company](https://vstorm.co/agentic-ai/agentic-ai-company/) - [Agentic AI development services](https://vstorm.co/agentic-ai-development-services/) - [Gen AI Pilot Development](https://vstorm.co/genai-pilot-development/) - [Riyadh RAG Consulting](https://vstorm.co/riyadh-rag-consulting/) - [AI Agents government](https://vstorm.co/ai-agents-government/) - [Custom AI Agent Development](https://vstorm.co/custom-ai-agent-development/) - [Enterprise AI Agent Development](https://vstorm.co/enterprise-ai-agent-development/) - [AI Agent Development for startups](https://vstorm.co/ai-agent-development-for-startups/) - [Langchain AI agent development](https://vstorm.co/langchain-ai-agent-development/) - [Agentic AI Services](https://vstorm.co/agentic-ai-services/) - [Generative AI Development Company](https://vstorm.co/generative-ai-development-company/) - [Agentic AI in Finances](https://vstorm.co/agentic-ai-in-finances/) - [Agentic AI in Customer Service](https://vstorm.co/agentic-ai-in-customer-service/) - [Generative AI Consulting](https://vstorm.co/generative-ai-consulting/) - [Generative AI Consulting Services](https://vstorm.co/generative-ai-consulting-services/) - [Customer Service AI Agent](https://vstorm.co/customer-aervice-ai-agent/) - [AI Customer Service Agent](https://vstorm.co/ai-customer-service-agent/) - [Custom AI Agent Software Development](https://vstorm.co/custom-ai-agent-software-development/) - [AI Agent development company](https://vstorm.co/ai-agent-development-company/) - [Agentic Process Automation](https://vstorm.co/agentic-process-automation/) - [Agentic AI development company](https://vstorm.co/agentic-ai-development-company/) - [RAG Development Company](https://vstorm.co/rag-development-company/) - [RAG Development](https://vstorm.co/rag-development/) - [RAG AI Agent Development ](https://vstorm.co/rag-ai-agent-development/) - [RAG Agent Development Company](https://vstorm.co/rag-agent-development-company/) - [Generative AI Development](https://vstorm.co/generative-ai-development/) - [AI Agent development](https://vstorm.co/ai-agent-development/) - [Agentic AI](https://vstorm.co/agentic-ai/) - [AI Agent development company services](https://vstorm.co/ai-agent-development-company-services/) - [AI for Technology Providers](https://vstorm.co/ai-for-technology-providers/) - [AI in Ecommerce and Retail](https://vstorm.co/ai-in-ecommerce-and-retail/) - [Die Pragmatik der KI](https://vstorm.co/die-pragmatik-der-ki/) - [Agentic AI in Healthcare](https://vstorm.co/agentic-ai-in-healthcare/) - [Pragmatics of Agentic AI](https://vstorm.co/pragmatics-of-ai-workshop/) - [1-pager VSA](https://vstorm.co/1-pager-vsa/) - [Schedule a meeting](https://vstorm.co/schedule-a-meeting/) - [Fill out the form](https://vstorm.co/fill-out-the-form/) - [PyTorch development](https://vstorm.co/pytorch-development/) - [ML Ops service](https://vstorm.co/ml-ops-service/) - [LLM Ops service](https://vstorm.co/llm-ops-service/) - [AI Chatbot development](https://vstorm.co/ai-chatbot-development/) - [RAG Advanced Engineering](https://vstorm.co/rag-development-service/) - [LLM Development](https://vstorm.co/large-language-models-development/) - [LLM software: Custom Large Language Model](https://vstorm.co/custom-llm-based-software/) - [AI Consultancy in New York](https://vstorm.co/ai-consultancy-in-new-york/) - [AI Consulting & Advisory](https://vstorm.co/ai-consultancy/) - [LlamaIndex Development Company](https://vstorm.co/llamaindex-development-company/) - [Universe | Vstorm](https://vstorm.co/universe/): - [AI Vstorm Book](https://vstorm.co/the-llm-book/) - [AI Community Vstorm](https://vstorm.co/ai-community/) - [Home](https://vstorm.co/) - [LangChain Development Company](https://vstorm.co/langchain-development-company/) - [Berlin](https://vstorm.co/berlin/): Berlin, long celebrated as the beating heart of Europe’s startup culture and technological renaissance, has witnessed a surge in its... - [NLP Development Company](https://vstorm.co/nlp-development-company/) - [AI Chatbot](https://vstorm.co/ai-chatbot/) - [AI Business Assistance](https://vstorm.co/ai-business-assistance/) - [GPT-4 Customization](https://vstorm.co/gpt-4-customization/): Welcome to Vstorm, your trusted partner for Chat GPT-4 customization services. We understand the unique needs of startups and SMBs.... - [Stable Diffusion](https://vstorm.co/stable-diffusion/): Unlock the Power of AI-Generated Content Discover how Stable Diffusion integration can revolutionize your startup or SMB by unlocking the... - [AI Semantic search: The Future of Information Retrieval](https://vstorm.co/ai-semantic-search-the-future-of-information-retrieval/): Introduction Artificial Intelligence (AI) has become an integral part of our society, influencing various sectors from transportation to healthcare. One... - [AI Semantic Translation: The Bridge Between Languages](https://vstorm.co/ai-semantic-translation-the-bridge-between-languages/): Introduction Artificial Intelligence (AI) has revolutionized numerous fields, and translation is no exception. By applying semantic understanding, AI has significantly... - [AI Information Extraction: Revolutionizing Data Processing](https://vstorm.co/ai-information-extraction-revolutionizing-data-processing/): Artificial Intelligence (AI) is transforming the way we extract information from a myriad of sources. By leveraging advancements in machine... - [AI Documentation Automation](https://vstorm.co/ai-documentation-automation/): Artificial Intelligence (AI) is revolutionizing many aspects of our lives, and one area it’s making significant strides in is the... - [AI Customer Support: The Future of Customer Engagement](https://vstorm.co/ai-customer-support/): Artificial Intelligence (AI) is playing a transformative role in customer service, improving engagement, enhancing user experiences, and streamlining processes. Despite... - [AI Content personalization](https://vstorm.co/ai-content-personalization/): AI’s capability to revolutionize content personalization has gained significant attention in recent years, particularly within the startup ecosystem. Leveraging AI... - [Large Language Models (LLMs) - Unlocking the potential](https://vstorm.co/large-language-models/): What are Large Language Models? Large Language Models or LLMs are the frontier of artificial intelligence. By generating texts that... - [Text-to-Image Generation: Visual Content Creation for SMBs and Startups](https://vstorm.co/text-to-image/): A New Era: Text-to-Image Generation Welcome to the future of content creation where text-to-image technology is redefining the way we... - [AI Proof of Concept Delivery for startups](https://vstorm.co/ai-proof-of-concept/): In the fast-paced world of tech startups, creating a viable product is a challenge. This is particularly true when the... - [AI Application Discovery: A Pathway to Innovation](https://vstorm.co/ai-application-discovery/): AI application discovery? How do I get started? As the business world rapidly embraces artificial intelligence (AI), the opportunities for... --- ## Career - [AI Team Lead](https://vstorm.co/career/ai-team-lead/): We seek an experienced AI Team Lead Engineer to join our mission. This is more than just a statement –... - [Python AI Developer](https://vstorm.co/career/python-developer-ai/): We seek an experienced Python AI/LLM Engineer to join our mission. This is more than just a statement – it’s... - [AI Automation Project Manager](https://vstorm.co/career/pm/): We’re seeking a technical Project Manager who can bridge the gap between AI technology and business transformation, working directly with... - [Python AI/LLM Engineer](https://vstorm.co/career/python-ai-llm-engineer/): We seek an experienced Python AI/LLM Engineer to join our mission. This is more than just a statement – it’s... --- ## Case Study - [AI agent for order recommendation and completion](https://vstorm.co/case-study/ai-agent-for-order-recommendation-and-completion/): What does Mixam do? Mixam is a self-publishing company that primarily provides printing and fulfillment services for independent authors, publishers,... - [Intelligent automation with actionable AI Agents for the US telecommunication company](https://vstorm.co/case-study/intelligent-automation-with-actionable-ai-agents-for-the-us-telecommunication-company/): What does the client do? The US-based telecommunications provider with over 45 years of industry experience delivers fiber-powered internet and... - [Mapping out architecture for Machine Learning-based software](https://vstorm.co/case-study/mapping-out-future-architecture-for-machine-learning-based-software/): What does Spectrally do? Spectrally is a deep-tech startup based in Poland, EU, specializing in real-time chemical analysis using Raman... - [Swapping Iron; making AI code designed from Nvidia run on Intel Gaudi](https://vstorm.co/case-study/swapping-iron-from-nvidia-to-intel/): Migrating Machine Learning and LLM solutions designed to run on Nvidia hardware to a different architecture: Intel Gaudi AI accelerators - [Multi-channel AI Agent for personalized appointments in Healthcare](https://vstorm.co/case-study/multi-channel-ai-agent-in-healthcare/): What does the company do? The US-based healthcare company has a mission to provide high-quality, affordable, and easy-to-understand healthcare plans... - [Advanced RAG Engineering for real estate due diligence AI Agent](https://vstorm.co/case-study/advanced-rag-engineering-for-real-estate-due-diligence-ai-agent/): What does Mapline do? Mapline. AI is a US-based startup on a mission to transform how real estate developers conduct... - [LLM-powered voice assistant for call-center.](https://vstorm.co/case-study/llm-powered-voice-assistant-for-call-center/): What does a company do? The company develops and implements AI-powered voice assistants that automate tasks such as call verification... - [AI-powered text summarization for vacation rentals using LLMs](https://vstorm.co/case-study/text-summarization-for-vacation-rentals-using-llms/): Text summarization for marketing agency using LLMs What does Guesthook do? Guesthook is a marketing agency specializing in the vacation... - [RAG : Automation e-mail response with AI and LLMs](https://vstorm.co/case-study/rag-automation-e-mail-response-with-ai-and-llms/): RAG to automate email responses in the IT industry What does Senetic do? Senetic is a global provider of IT... - [Automated data scraping platform powered by AI and LLMs](https://vstorm.co/case-study/automated-data-scraping-platform-powered-by-ai-and-llms/): Collecting data from thousands of sites using AI and LLMs What does Rotwand do? Rotwand is a boutique PR agency... - [Collaborative Conversational AI assistant with automation](https://vstorm.co/case-study/collaborative-conversational-ai-assistant-with-automation/): What does the company do? Established in 2011, a California-based startup has emerged to reshape online discussions through open-source technology.... - [Reduced time-to-market with hyper-automated reports using AI Translation with LLMs](https://vstorm.co/case-study/ai-translation-with-llms/): Using AI Translation with LLMs solution for achieving hyper-automation What does MindSonar do? MindSonar measures mindsets. It is a complete... - [Fight against diabetes with data and advanced AI](https://vstorm.co/case-study/fight-against-diabetes-with-data-and-advanced-ai/): Innovation in medical healthcare and the fight against diabetes with advanced AI. What does GlucoActive do? Glucoactive is a Research... --- ## Glossary - [Agent-to-Human Handoff](https://vstorm.co/glossary/agent-to-human-handoff/): Agent-to-Human Handoff is the systematic process where an AI agent transfers control of an ongoing interaction, task, or decision-making process... - [Agentic Workflow Patterns](https://vstorm.co/glossary/agentic-workflow-patterns/): Agentic Workflow Patterns are standardized, reusable architectural designs that define how AI agents execute complex tasks, make decisions, and interact... - [Orchestrator-Worker Pattern](https://vstorm.co/glossary/orchestrator-worker-pattern/): Orchestrator-Worker Pattern is a distributed AI agent architecture where a central orchestrator agent coordinates and manages multiple specialized worker agents... - [Agent Washing](https://vstorm.co/glossary/agent-washing/): Agent Washing is the deceptive marketing practice where companies falsely label traditional automation tools, simple chatbots, or rule-based systems as... - [Autopilot Selling](https://vstorm.co/glossary/autopilot-selling/): Autopilot Selling is an autonomous AI Agent system that independently manages and executes sales processes with minimal human intervention. These... - [Digital Labor/Digital Worker](https://vstorm.co/glossary/digital-labor-digital-worker/): Digital Labor, also known as Digital Workers, refers to software-based automation technologies that perform cognitive and repetitive tasks traditionally executed... - [Complexity Threshold](https://vstorm.co/glossary/complexity-threshold/): Complexity Threshold is the critical point at which a task, process, or problem exceeds the capabilities of current automation approaches... - [Long-term Coherence](https://vstorm.co/glossary/long-term-coherence/): Long-term Coherence is the ability of AI Agents to maintain consistent reasoning, decision-making, and behavioral patterns across extended periods of... - [Headless AI Agent](https://vstorm.co/glossary/headless-ai-agent/): Headless AI Agent is an AI system that operates without a direct user interface, designed to be integrated programmatically into... - [Open Agentic Web](https://vstorm.co/glossary/open-agentic-web/): Open Agentic Web is a vision of the internet where AI Agents can autonomously navigate, interact, and perform tasks across... - [Multi-Agent Systems (MAS)](https://vstorm.co/glossary/multi-agent-systems-mas/): Multi-Agent Systems (MAS) are distributed computing environments where multiple autonomous AI Agents interact, coordinate, and collaborate to solve complex problems... - [Polyphonic AI](https://vstorm.co/glossary/polyphonic-ai/): Polyphonic AI is an architectural approach where multiple AI Agents, models, or processing streams operate simultaneously in coordinated harmony to... - [Agentive AI](https://vstorm.co/glossary/agentive-ai/): Agentive AI is artificial intelligence that autonomously takes actions and makes decisions to complete complex tasks, rather than simply responding... - [Agent-to-Agent (A2A) Protocol](https://vstorm.co/glossary/agent-to-agent-a2a-protocol/): Agent-to-Agent (A2A) Protocol is a standardized communication framework that enables AI agents to interact, coordinate, and collaborate with each other... - [Model Context Protocol (MCP)](https://vstorm.co/glossary/model-context-protocol-mcp/): Model Context Protocol (MCP) is a standardized framework that governs how AI models and agents maintain, share, and utilize contextual... - [Agent Cards](https://vstorm.co/glossary/agent-cards/): Agent Cards are structured metadata documents that define the capabilities, specifications, and operational parameters of AI agents within multi-agent systems.... - [ChatGPT 5](https://vstorm.co/glossary/chatgpt-5/): ChatGPT 5 is OpenAI’s most advanced large language model, representing a significant leap in artificial intelligence capabilities beyond its predecessors.... - [SWE Langchain](https://vstorm.co/glossary/swe-langchain/): SWE Langchain is a specialized implementation of the LangChain framework designed for Software Engineering (SWE) applications and automated code development... - [Genie 3](https://vstorm.co/glossary/genie-3/): Genie 3 is Google’s advanced generative interactive environment model that creates controllable 2D worlds from visual observations and text prompts.... - [LangChain](https://vstorm.co/glossary/langchain-2/): LangChain is an open-source framework for developing applications powered by large language models (LLMs). LangChain simplifies every stage of the... - [Retrieval - Augmented Generation RAG configuration](https://vstorm.co/glossary/retrieval-augmented-generation-rag-configuration-2/): Retrieval-Augmented Generation RAG configuration is the set of tunable parameters that shapes how a RAG pipeline finds knowledge and feeds... - [Zero shot training](https://vstorm.co/glossary/zero-shot-training/): Zero shot training is a machine learning paradigm where models are trained to perform tasks on categories or domains they... - [Explainability meaning](https://vstorm.co/glossary/explainability-meaning/): Explainability meaning refers to the fundamental concept of making artificial intelligence systems’ decision-making processes, reasoning patterns, and internal mechanisms comprehensible... - [What's a TTS](https://vstorm.co/glossary/whats-a-tts/): What’s a TTS refers to Text-to-Speech technology, an artificial intelligence system that converts written text into natural-sounding synthetic speech through... - [What is an AGI AI](https://vstorm.co/glossary/what-is-an-agi-ai/): What is an AGI AI refers to Artificial General Intelligence AI, hypothetical systems that possess human-level cognitive abilities across all... - [What OpenAI](https://vstorm.co/glossary/what-openai/): What OpenAI refers to the artificial intelligence research organization founded in 2015 that develops advanced AI systems including GPT language... - [Knowledge generation](https://vstorm.co/glossary/knowledge-generation/): Knowledge generation is the artificial intelligence process of creating new information, insights, and understanding from existing data, patterns, and experiences... - [What is zero-shot](https://vstorm.co/glossary/what-is-zero-shot/): What is zero-shot refers to the machine learning capability where AI systems perform tasks or classify categories they have never... - [What is Whisper OpenAI](https://vstorm.co/glossary/what-is-whisper-openai/): What is Whisper OpenAI refers to OpenAI’s robust automatic speech recognition system that converts spoken language into text across 99... - [What is unsupervised learning in AI](https://vstorm.co/glossary/what-is-unsupervised-learning-in-ai/): What is unsupervised learning in AI refers to machine learning algorithms that discover hidden patterns, structures, and relationships in data... - [Zeroshot learning](https://vstorm.co/glossary/zeroshot-learning/): Zeroshot learning is a machine learning paradigm where models perform classification or prediction tasks on categories they have never encountered... - [What does collective learning mean](https://vstorm.co/glossary/what-does-collective-learning-mean/): What does collective learning mean refers to a distributed machine learning paradigm where multiple autonomous agents, systems, or entities collaborate... - [Zero shot models](https://vstorm.co/glossary/zero-shot-models/): Zero shot models are artificial intelligence systems capable of performing tasks on categories, domains, or scenarios they have never encountered... - [How does stacking work](https://vstorm.co/glossary/how-does-stacking-work/): How does stacking work refers to the ensemble learning technique where multiple base models’ predictions are combined using a meta-learner... - [What is Stacking?](https://vstorm.co/glossary/what-is-stacking/): What is stacking refers to an ensemble learning technique that combines predictions from multiple diverse base models using a meta-learner... - [Zero-Shot Transfer](https://vstorm.co/glossary/zero-shot-transfer/): Zero-shot transfer is the machine learning capability where models apply knowledge learned from source domains to completely different target domains... - [OpenAI explained](https://vstorm.co/glossary/openai-explained/): OpenAI explained encompasses the artificial intelligence research organization founded in 2015 that has revolutionized AI development through breakthrough technologies including... - [GPT4 meaning](https://vstorm.co/glossary/gpt4-meaning/): GPT4 meaning refers to Generative Pre-trained Transformer 4, OpenAI’s fourth-generation large language model that demonstrates advanced reasoning, multimodal capabilities, and... - [Define explainability](https://vstorm.co/glossary/define-explainability/): Define explainability refers to the capacity of artificial intelligence systems to provide clear, understandable explanations for their decisions, predictions, and... - [Why is computer vision important](https://vstorm.co/glossary/why-is-computer-vision-important/): Why is computer vision important becomes evident through its transformative impact across industries, enabling machines to interpret and understand visual... - [Zero shot learning explained](https://vstorm.co/glossary/zero-shot-learning-explained/): Zero shot learning explained describes machine learning systems that can classify or perform tasks on categories they have never encountered... - [What is this type of technology called that uses this conversational AI](https://vstorm.co/glossary/what-is-this-type-of-technology-called-that-uses-this-conversational-ai/): Conversational AI technology encompasses artificial intelligence systems that enable natural language interactions between humans and machines through text or voice... - [How does Zero shot learning work](https://vstorm.co/glossary/how-does-zero-shot-learning-work/): How does zero shot learning work through semantic knowledge transfer mechanisms that enable models to classify unseen categories by leveraging... - [Strong Artificial Intelligence is](https://vstorm.co/glossary/strong-artificial-intelligence-is/): Strong artificial intelligence refers to hypothetical AI systems that possess human-level cognitive abilities across all domains, including reasoning, learning, creativity,... - [gpt-4 meaning](https://vstorm.co/glossary/gpt-4-meaning-3/): GPT-4 meaning refers to Generative Pre-trained Transformer 4, OpenAI’s fourth-generation large language model that demonstrates advanced reasoning, multimodal capabilities, and... - [What does computer vision do](https://vstorm.co/glossary/what-does-computer-vision-do/): What does computer vision do encompasses analyzing, interpreting, and understanding visual information from digital images and videos to enable automated... - [0 shot learning](https://vstorm.co/glossary/0-shot-learning/): 0 shot learning is a machine learning paradigm where models perform tasks on categories or domains they have never encountered... - [Stochastic Parrots meaning](https://vstorm.co/glossary/stochastic-parrots-meaning/): Stochastic parrots meaning refers to the critique that large language models are sophisticated pattern matching systems that generate plausible text... - [Probabilistic model vs Deterministic model](https://vstorm.co/glossary/probabilistic-model-vs-deterministic-model/): Probabilistic model vs deterministic model represents two fundamental approaches to mathematical modeling where probabilistic models incorporate uncertainty and randomness through... - [What is OpenAI company](https://vstorm.co/glossary/what-is-openai-company/): What is OpenAI company refers to the artificial intelligence research organization founded in 2015 that develops advanced AI systems including... - [Adapters](https://vstorm.co/glossary/adapters/): Adapters are lightweight neural network modules inserted into pre-trained models to enable efficient task-specific fine-tuning without modifying the original model... - [What is Stable Diffusion model](https://vstorm.co/glossary/what-is-stable-diffusion-model/): What is Stable Diffusion model refers to an open-source latent diffusion neural network architecture that generates high-quality images from text... - [Interpretability](https://vstorm.co/glossary/interpretability/): Interpretability is the degree to which humans can understand and explain the decision-making processes, internal mechanisms, and predictions of artificial... - [What is Probabilistic](https://vstorm.co/glossary/what-is-probabilistic/): What is probabilistic refers to systems, models, or approaches that incorporate uncertainty, randomness, and probability distributions rather than producing deterministic... - [NLU tasks](https://vstorm.co/glossary/nlu-tasks/): NLU tasks are specific natural language understanding functions that enable AI systems to extract structured information and meaning from unstructured... - [What is Probabilistic modeling](https://vstorm.co/glossary/what-is-probabilistic-modeling/): What is probabilistic modeling refers to a mathematical framework that uses probability theory to represent and quantify uncertainty in data,... - [Zero shot machine learning](https://vstorm.co/glossary/zero-shot-machine-learning/): Zero shot machine learning is a paradigm where models perform tasks on classes or domains they have never encountered during... - [Synthesize Voice](https://vstorm.co/glossary/synthesize-voice/): Synthesize voice is the artificial intelligence process of converting written text into natural-sounding human speech through neural networks and digital... - [Define collective learning](https://vstorm.co/glossary/define-collective-learning/): Define collective learning refers to the distributed machine learning paradigm where multiple autonomous agents, systems, or entities collaborate to acquire... - [Voice processing](https://vstorm.co/glossary/voice-processing/): Voice processing is the computational analysis and manipulation of human speech signals through digital signal processing and artificial intelligence techniques... - [What is stablediffusion](https://vstorm.co/glossary/what-is-stablediffusion/): What is Stable Diffusion refers to an open-source latent diffusion model that generates high-quality images from text descriptions through a... - [Model Chaining](https://vstorm.co/glossary/model-chaining/): Model chaining is an architectural approach that connects multiple AI models in sequence or parallel to accomplish complex tasks requiring... - [Probabilistic Model Example](https://vstorm.co/glossary/probabilistic-model-example/): Probabilistic model example encompasses concrete implementations like Bayesian networks for medical diagnosis, Hidden Markov Models for speech recognition, Gaussian Mixture... - [Parameter efficient tuning](https://vstorm.co/glossary/parameter-efficient-tuning/): Parameter efficient tuning is a family of machine learning techniques that adapt large pre-trained models to new tasks by training... - [Automatic Speech](https://vstorm.co/glossary/automatic-speech/): What is automatic speech refers to artificial intelligence systems that process, analyze, and understand human speech without manual intervention, primarily... - [NLU definition](https://vstorm.co/glossary/nlu-definition/): NLU definition refers to Natural Language Understanding, a branch of artificial intelligence that enables machines to comprehend, interpret, and extract... - [Language ambiguity](https://vstorm.co/glossary/language-ambiguity/): Language ambiguity refers to the phenomenon where linguistic expressions have multiple possible interpretations or meanings, creating challenges for natural language... - [Speech synthesizers use to determine context before outputting](https://vstorm.co/glossary/speech-synthesizers-use-to-determine-context-before-outputting/): Speech synthesis context analysis refers to the computational processes that text-to-speech systems employ to understand linguistic context, semantic meaning, and... - [What is collective learning](https://vstorm.co/glossary/what-is-collective-learning/): What is collective learning refers to a distributed machine learning paradigm where multiple autonomous agents, systems, or entities collaborate to... - [What is Stable Diffusion?](https://vstorm.co/glossary/what-is-stable-diffusion/): Stable Diffusion is an open-source latent diffusion model that generates high-quality images from text descriptions through a denoising process. This... - [Deterministic in statistics](https://vstorm.co/glossary/deterministic-in-statistics/): Deterministic in statistics refers to models or processes where outcomes are precisely determined by initial conditions and parameters, with no... - [N-shot learning](https://vstorm.co/glossary/n-shot-learning/): N-shot learning is a machine learning paradigm where models learn to perform new tasks using only n examples per class,... - [Benchmark tests AI models](https://vstorm.co/glossary/benchmark-tests-ai-models/): Benchmark tests for AI models are standardized evaluation frameworks that measure model performance across specific tasks, datasets, and metrics to... - [GPT-4 Meaning](https://vstorm.co/glossary/gpt-4-meaning-2/): GPT-4 (Generative Pre-trained Transformer 4) is OpenAI’s fourth-generation large language model that demonstrates advanced reasoning, multimodal capabilities, and enhanced safety... - [What is Stable Diffusion Trained On?](https://vstorm.co/glossary/what-is-stable-diffusion-trained-on/): Stable Diffusion is trained on LAION (Large-scale Artificial Intelligence Open Network) datasets, primarily LAION-5B containing 5. 85 billion image-text pairs... - [What is Overfitting Data](https://vstorm.co/glossary/what-is-overfitting-data/): Overfitting Data occurs when a machine learning model learns training data patterns too specifically, including noise and irrelevant details, resulting... - [Generative Pre-trained Transformers](https://vstorm.co/glossary/generative-pre-trained-transformers/): Generative pre-trained transformers are neural network architectures that generate human-like text by predicting the next word in a sequence based... - [Instruction Fine-Tuning](https://vstorm.co/glossary/instruction-fine-tuning-2/): Instruction fine-tuning is a supervised learning technique that trains pre-trained language models to better follow human instructions and complete specific... - [Instruction tuning LLM](https://vstorm.co/glossary/instruction-tuning-llm/): Instruction tuning LLM is a post-training method that adapts large language models to follow human instructions and perform diverse tasks... - [What is Deterministic?](https://vstorm.co/glossary/what-is-deterministic/): Deterministic refers to systems, processes, or algorithms where identical inputs always produce identical outputs, with no randomness or unpredictability involved.... - [Is AI Self Learning](https://vstorm.co/glossary/ai-self-learning/): AI self-learning refers to systems that can acquire new knowledge, skills, or behaviors autonomously without explicit human supervision or programming... - [Define Summarization](https://vstorm.co/glossary/define-summarization/): Summarization is the process of condensing large amounts of text or information into shorter, coherent representations that preserve essential meaning... - [Deterministic Process](https://vstorm.co/glossary/deterministic-process/): Deterministic process is a computational or mathematical procedure where identical inputs invariably produce identical outputs through a fixed sequence of... - [TTS output](https://vstorm.co/glossary/tts-output/): TTS output (Text-to-Speech output) is synthesized audio generated from written text using artificial intelligence models that convert linguistic input into... - [Latency](https://vstorm.co/glossary/latency/): Latency is the time delay between initiating a request and receiving the corresponding response in computational systems, measured in milliseconds... - [Zero-shot AI](https://vstorm.co/glossary/zero-shot-ai/): Zero-shot AI refers to artificial intelligence systems that can perform tasks or make predictions without having seen specific examples of... - [Generative transformer](https://vstorm.co/glossary/generative-transformer/): Generative transformer is a neural network architecture that uses self-attention mechanisms to generate sequential data, primarily text, by predicting subsequent... - [What are Adapters](https://vstorm.co/glossary/what-are-adapters/): Adapters are lightweight neural network modules inserted into pre-trained models to enable task-specific adaptation without modifying the original model parameters.... - [What is Text to Speech used for?](https://vstorm.co/glossary/what-is-text-to-speech/): Text to speech is used for creating accessible interfaces, voice-enabled applications, and automated communication systems across diverse industries and use... - [What is K-Shot?](https://vstorm.co/glossary/what-is-k-shot/): K-shot is a machine learning terminology where k represents the number of labeled examples available per class during training or... - [Multi Hop](https://vstorm.co/glossary/multi-hop/): Multi-hop refers to reasoning or information retrieval processes that require multiple sequential steps or “hops” through different data sources, documents,... - [Instruction Tuning vs Fine Tuning](https://vstorm.co/glossary/instruction-tuning-vs-fine-tuning/): Instruction tuning vs fine tuning represents two distinct approaches to adapting pre-trained language models, with instruction tuning focusing on teaching... - [LLM Instruction Tuning](https://vstorm.co/glossary/llm-instruction-tuning/): LLM instruction tuning is a specialized training methodology that adapts large language models to follow human instructions and complete diverse... - [Weak-to-Strong Generalization](https://vstorm.co/glossary/weak-to-strong-generalization-2/): Weak-to-strong generalization is the phenomenon where more capable AI models can learn to perform better than their less capable supervisors... - [Pre-train](https://vstorm.co/glossary/pre-train/): Pre-train refers to the initial training phase where AI models learn foundational representations from large, unlabeled datasets before being adapted... - [AI lingo](https://vstorm.co/glossary/ai-lingo/): AI lingo is the specialized vocabulary, terminology, and jargon used within artificial intelligence research, development, and deployment that encompasses technical... - [Transformer GPT](https://vstorm.co/glossary/transformer-gpt/): Transformer GPT (Generative Pre-trained Transformer) is a family of autoregressive language models built on decoder-only transformer architecture, designed for text... - [Automatic speech recognition technology](https://vstorm.co/glossary/automatic-speech-recognition-technology/): Automatic speech recognition technology is a computational system that converts spoken language into written text through signal processing, acoustic modeling,... - [What is Text Speech?](https://vstorm.co/glossary/what-is-text-speech/): Text speech refers to text-to-speech (TTS) technology that converts written text into synthesized spoken audio using artificial intelligence and signal... - [Artificial Intelligence Glossary](https://vstorm.co/glossary/artificial-intelligence-glossary/): Artificial intelligence glossary is a comprehensive reference resource that defines and explains technical terms, concepts, methodologies, and technologies within the... - [Collective learning meaning](https://vstorm.co/glossary/collective-learning-meaning/): Collective learning meaning refers to the fundamental concept of distributed intelligence where multiple entities collaborate to acquire knowledge, solve problems,... --- ## Events --- # # Detailed Content ## Ebook --- ## Posts - Published: 2025-11-20 - Modified: 2025-11-26 - URL: https://vstorm.co/agentic-ai/top-10-applied-ai-consulting-firms-for-smbs-2025/ - Categories: Agentic AI, AI, AI Agents - Translation Priorities: Optional A deep dive into the top 10 applied AI consultation companies for SMBs to bridge the mid-market gap in 2025 When entering the market in search of an applied AI consulting firm to suit your business needs and budget, it is crucial to understand the cost to benefit relationship innate to any attempted AI implementation. The truth of the AI market is quickly coming to light, with MIT finding that only 5% of implementations across the board manage to ever produce any meaningful impact on ROI. With big-consultancy fees high enough to break the budget of all but the largest enterprises and off-the-self tools quickly capping out as their lack of meaningful impact becomes apparent, mid-market companies and SMBs often find themselves in a brutal mid-market gap. Primed to benefit from custom tailored AI solutions, they often lack the means and internal knowledge base to form an adequate strategy for implementation. This is further impacted by the fact that many companies of all sizes choose to launch ambitious AI projects with expectations running high, often treating it like ordinary software or app development. But without true Agentic AI expertise in the room, 80% of all initiatives stall before going into production. This is fueled by the tendency to mistake vision for strategy. Companies pick processes, set bold targets, and fund them. But when implementation begins, they discover their "strategy" is disconnected from the technological reality. Meanwhile, the potential benefits are hard to ignore. For SMBs, implementing a perfectly tailored agentic... --- - Published: 2025-11-20 - Modified: 2025-11-20 - URL: https://vstorm.co/ai-agents/top-10-applied-ai-ml-consulting-service-firms-2025/ - Categories: Agentic AI, AI, AI Agents - Translation Priorities: Optional A deep dive into the top 10 applied AI & ML consultation companies for SMBs to help bridge the mid-market gap in 2025 When entering the market in search of an applied AI & ML consulting firm to suit your business needs and budget, it is crucial to understand the cost to benefit relationship innate to any attempted AI implementation. The truth of the AI market is quickly coming to light, with MIT finding that only 5% of implementations across the board manage to ever produce any meaningful impact on ROI. With big-consultancy fees high enough to break the budget of all but the largest enterprises and off-the-self tools quickly capping out as their lack of meaningful impact becomes apparent, mid-market companies and SMBs often find themselves in a brutal mid-market gap. Primed to benefit from custom tailored AI & ML solutions, they often lack the means and internal knowledge base to form an adequate strategy for implementation. 25% of enterprises using GenAI are forecast to deploy AI agents in 2025, growing to 50% by 2027... This evolution will enable AI agents to tackle a broader range of applications, providing businesses with valuable tools to drive productivity of knowledge workers and efficiency gains in workflows of all kinds. Deloitte Global’s 2025 Predictions Report This is further impacted by the fact that many companies of all sizes choose to launch ambitious AI projects with expectations running high, often treating it like ordinary software or app development. But without true Agentic AI... --- - Published: 2025-11-03 - Modified: 2025-11-14 - URL: https://vstorm.co/rag/old-school-keyword-search-to-the-rescue-when-your-rag-fails/ - Categories: RAG - Translation Priorities: Optional With all the AI hype, semantic search powered by language models has become the default choice for retrieval augmented generation, or RAG, systems. But is it always the best approach? While semantic search excels at understanding meaning and different phrasing, it often stumbles on precise identifiers with a tendency to confuse similar product names or mixing up date ranges. In this post, we explore how hybrid search combines semantic embeddings with keyword matching to handle the conceptual understanding and literal precision that real-world queries demand. Vector search and retrieval-augmented generation have emerged as critical AI workflow tools to make business data more structured and address (and amend) the disconnect between enterprise models and execution. Bianca Lewis, executive director of the OpenSearch Software Foundation, October 3 2025, on How RAG continues to ‘tailor’ well-suited AI Why your RAG might confuse similar products or dates Clients often reach out in frustration saying that their RAG systems produce hallucinated or inaccurate results despite providing clean data and properly configured vector databases. In these cases, unstructured data is often not to blame, and the culprit is frequently the retrieval strategy itself, which must match the type of content to what you are actually searching for. Pure semantic search can struggle with precise identifiers and exact matches, for instance a query about "IBM 5150" might return results about the similar "IBM 5100," or searching for "Q3 2024 revenue" could mistakenly pull up Q4 2023 or Q2 2024 results instead. This happens because semantic embeddings capture... --- - Published: 2025-10-22 - Modified: 2025-11-06 - URL: https://vstorm.co/agentic-ai/agentic-ai-engineering-consultancy-vs-general-custom-software-developer-pricing-and-service-comparison-2025/ - Categories: Agentic AI, AI Agents - Translation Priorities: Optional Within you will find a side-by-side comparison of the capabilities and limitations of dedicated engineering consultancy firms vs general custom software developers in providing AI solutions so you can choose the provider that best suits your business needs. *Many companies launch ambitious AI projects with high expectations, treating them like ordinary software or apps. Mistaking vision for strategy, they pick processes, set bold targets, and fund them. Yet when implementation begins, they discover their "strategy" was disconnected from the technological reality. I do think of it as a workforce. This is a workforce that will conduct end-to-end processes, replacing many tasks being performed today by the human workforce. Jorge Amar, McKinsey Senior Partner, June 3 2025, on The future of work is agentic According to market estimates presented by Rand, more than 80% of AI projects fail. That is double the rate of failure for information technology projects that do not involve AI. But having the right development partner on board from the beginning who can provide tailored AI agents with a custom fit approach can yield production-grade agents for your core workflows, greatly enhancing efficiency while providing significant gains in ROI. According to Deloitte predictions for 2025, 25% of enterprises using GenAI are forecast to deploy AI Agents by 2025, growing to 50% by 2027. The wide adoption of agentic AI solutions to fill the expectation gap of AI implementations across industries is ever more clear. But how do you choose the right agentic AI development partner to best... --- - Published: 2025-10-17 - Modified: 2025-11-05 - URL: https://vstorm.co/ai-agents/how-vstorm-supports-saudi-arabia-vision-2030/ - Categories: AI Agents - Translation Priorities: Optional A closer look at how Vstorm, the boutique Agentic AI consultancy company located in Poland, is equipped to accelerate AI initiatives in Saudi Arabia’s government and private sectors Saudi Arabia's Vision 2030 has proven to be one of the most successful national transformations on record, having achieved 93% of its key performance indicators at the program's midpoint. The Kingdom has fundamentally restructured its economy, society, and international positioning while successfully maintaining political stability and cultural identity. Saudi Arabia is now on the verge of achieving unprecedented economic transformation through AI with the commitment of over $100 billion through Project Transcendence, with early projections estimating $235. 2 billion in contributions to GDP by 2030 (12. 4% of total GDP). This substantial investment, if combined with comprehensive sectoral AI deployments and strategic global partnerships, can create a realistic pathway for the success of Vision 2030. “I hereby invite all dreamers, innovators, investors and thinkers to join us, here in the Kingdom, to achieve our ambitions together and to build a pioneering model; to unlock the value of data and AI in order to build knowledge-based economies and advance our present and future generations,”- Saudi Arabia’s Crown Prince Mohammed bin Salman in calls for global collaboration to unlock benefits of AI for all The stakes placed upon success are enormous: as the Kingdom stands to gain significant economic diversification, breaking their reliance on oil prices, while positioning the Kingdom as a global AI superpower by 2030. However, success depends on addressing talent pipeline... --- - Published: 2025-10-17 - Modified: 2025-11-05 - URL: https://vstorm.co/rag/when-clean-text-is-not-enough-structured-extraction-for-rag/ - Categories: RAG - Translation Priorities: Optional Garbage in, garbage out – every seasoned data scientist knows poor data can derail Retrieval-Augmented Generation (RAG). Yet there is a gap between having clean text and having retrieval-ready content. While raw text in a vector database may suffice for basic use cases, this article explores how LLMs can restructure that text into retrieval-ready formats for more refined search. Is perfectly parsed text good enough for RAG? Real-world data rarely arrives in perfect condition, regardless of its source. Data quality extends beyond just file formats or OCR accuracy. Even if you have clean text, perfectly parsed from an image or PDF, it still might not be in the best shape for building an effective RAG system. The key often lies in how you structure that extracted data, making it more comprehensible and easier to leverage in different contexts. Fortunately, large language models excel at structured data extraction. Vector search and retrieval-augmented generation have emerged as critical AI workflow tools to make business data more structured and address (and amend) the disconnect between enterprise models and execution. Bianca Lewis, executive director of the OpenSearch Software Foundation, October 3 2025, on How RAG continues to ‘tailor’ well-suited AI Step-by-step pipeline illustrating document ingestion, text extraction, content structuring, chunking, and vector storage for RAG systems. Note: The order of steps 3 and 4 can vary depending on implementation. You might chunk first and then extract structure from each chunk, or extract structure from the full document and then chunk the enriched content. What... --- - Published: 2025-09-23 - Modified: 2025-11-05 - URL: https://vstorm.co/rag/why-rag-is-not-dead-a-case-for-context-engineering-over-massive-context-windows/ - Categories: RAG - Translation Priorities: Optional Amid the current AI boom, you may have recently heard that RAG is dead as major AI labs compete with the capabilities of new models, offering context windows of 1M tokens and sometimes more. If a model can process the content of several Harry Potter novels at once, why use RAG? This increasingly popular argument suggests that RAG was only necessary as a workaround for earlier models with limited context capacity. But reality is quite the opposite. Research reveals two major arguments critics make against RAG. First, long context windows in modern large language models can now handle entire knowledge bases directly, eliminating the need for intricate retrieval systems. Second, RAG systems are overly complex, requiring the orchestration and detailed calibration of multiple components when simpler single-system solutions based on LLMs with a wide context window should suffice. But is RAG really dead? Reality tells a different story – RAG solutions can be found in ChatGPT for storing user memories and in leading code generation tools like Cursor and Claude Code. This text will explain why the most important players in the AI market still employ some form of information retrieval tool in their most cutting-edge solutions. What is RAG (Retrieval Augmented Generation) and what is a context window? Before diving into the details, let us establish what RAG and context window actually mean. In its simplest form, RAG (Retrieval Augmented Generation) is a technique that allows an AI model to search for necessary information in external databases before providing... --- - Published: 2025-09-19 - Modified: 2025-11-10 - URL: https://vstorm.co/ai-agents/top-10-ai-agent-development-consulting-companies-for-smbs-and-enterprises-2025/ - Categories: AI Agents - Translation Priorities: Optional A deep dive into the top 10 AI Agent development and consultation companies of 2025 To understand the landscape of agentic AI companies, it is crucial to first grasp the agentic definition and explore what is agentic in the context of artificial intelligence systems. Agentic AI describes autonomous AI systems that act independently to achieve predetermined goals. While traditional AI follows predefined rules, agentic systems operate proactively and complete complex tasks without constant human oversight. The term "agentic" refers to the system's agency - its ability to act independently while remaining goal-oriented. These intelligent AI systems analyze situations, make decisions, set objectives, reason through problems, and implement solutions with minimal human intervention. Rather than simply generating content or providing data insights, they take the initiative to drive decision-making processes through autonomous execution. For SMBs, agentic AI functions like your most reliable business advisor - one who understands your specific challenges, learns from every customer interaction, and makes decisions aligned with your business goals. These cognitive AI agents integrate with your existing tools (such as your CRM) and actively use your data to make decisions and execute actions on your behalf. Agentic AI systems follow a four-step operational process: Perceive. Collect data from various sources and identify meaningful elements Reason. Determine necessary tasks and find solutions to challenges Act. Execute tasks by connecting with external tools and platforms Learn. Continuously monitor results to improve decision-making 25% of enterprises using GenAI are forecast to deploy AI agents in 2025, growing to 50%... --- - Published: 2025-09-19 - Modified: 2025-10-27 - URL: https://vstorm.co/ai/top-5-tips-from-lucian-puca-of-mixam-on-launching-agentic-ai-transformation/ - Categories: AI, AI Advisory - Tags: ai, code, no code - Translation Priorities: Optional   Lucian Puca, Digital Product Manager and Automation and Workflow Lead of Mixam, has been at the forefront of the digital transformation of Mixam’s printing services. Enabling Mixam to lead the way in packaging printing with innovative solutions and a commitment to quality, setting new standards in the industry. Interview with Lucian Puca Mixam is a leading online printing platform that creates customizable print products for audiences all over the world, with operations across the United States and Canada, crossing overseas to service the UK, Ireland and Germany, and spanning continents to Australia. Their mission statement is to make print easy, accessible and affordable. Operating in the traditionally conservative industry of print, Mixam chose to take a revolutionary step in embarking upon the Agentic AI transformation. And it began its AI transformation by finding an agentic AI development partner which had the deep expertise and practical knowledge required to turn their ambitions into a reality, revolutionizing printing quotes with an instant, automated price calculator. Mixam, with Lucian leading the way, turned to Vstorm. The core challenge which needed to be addressed was in communicating specific needs with customers who lacked knowledge of printing or graphic design jargon (e. g. , paper types, CMYK). These customers were possessed of a clear vision of what they wanted with no means of communicating how it should be achieved. This presented an opportunity for AI to simplify the process and guide customers to the end product they truly wanted, translating the customer’s needs into... --- - Published: 2025-09-09 - Modified: 2025-11-10 - URL: https://vstorm.co/rag/introduction-to-information-retrieval-in-rag-pipelines/ - Categories: RAG - Translation Priorities: Optional In this article, we'll pull back the curtain and share the tools, methodologies, and tricks we use to build robust and reliable RAG systems so you can get a firm handle on what is going on under the hood. In the article to follow, we will explain precisely what IR (Information Retrieval) in RAG is, how it works, its various applications; covering keyword search, semantic search, hybrid search, and metadata filtering. I do think of it as a workforce. This is a workforce that will conduct end-to-end processes, replacing many tasks being performed today by the human workforce. Jorge Amar, McKinsey Senior Partner, June 3 2025, on The future of work is agentic What is RAG? RAG, Retrieval Augmented Generation, is an AI-enhancing tool that combines the power of information retrieval and text generation to fetch relevant data from a large database before generating a response. This RAG process helps improve the quality and relevance of generated responses by incorporating specific and citable facts. The RAG approach is particularly useful for chatbots, AI assistants, and other applications that require accurate and contextually relevant information. RAG allows LLMs to access and reference information outside the LLMs own training data, such as an organization's specific knowledge base, before generating a response—and, crucially, with citations included. This capability enables LLMs to produce highly specific outputs without extensive fine-tuning or training, delivering some of the benefits of a custom LLM at considerably less expense. Lareina Yee, Senior Partner of McKinsey, October 30, 2024, on... --- - Published: 2025-09-08 - Modified: 2025-11-13 - URL: https://vstorm.co/rag/advanced-rag-pipeline-part-1-rerankers/ - Categories: RAG - Translation Priorities: Optional - Osoby: Antoni Kozelski Standard RAG systems face a common problem: they can quickly find documents, but often provide irrelevant files that lead to low-quality answers. This analysis shows how adding a reranking step solves this challenge, using real-world regulatory documents to demonstrate clear improvements in finding the right information fast. Announcing our new series: Advanced RAG Techniques Welcome to the first post in our new series on Advanced Retrieval-Augmented Generation (RAG) Techniques! While many have heard of RAG, getting it to work well in the real world is another story. Standard RAG pipelines are a great start, but they often fall short when faced with complex, domain-specific documents. In this series, we'll pull back the curtain and share the tools, methodologies, and tricks we use to build robust and reliable RAG systems. We'll show you how to move beyond the basics and truly master your RAG pipeline. RAG allows LLMs to access and reference information outside the LLMs own training data, such as an organization's specific knowledge base, before generating a response—and, crucially, with citations included. This capability enables LLMs to produce highly specific outputs without extensive fine-tuning or training, delivering some of the benefits of a custom LLM at considerably less expense. Lareina Yee, Senior Partner of McKinsey, October 30, 2024, on What is retrieval-augmented generation (RAG)? Our testing ground: the EurLex project For this series, we've chosen to work with the EurLex dataset, a collection of European Union regulations including more than 700 different documents. This dataset presents many of the... --- - Published: 2025-09-06 - Modified: 2025-11-05 - URL: https://vstorm.co/agentic-ai/what-is-agentic-ai-a-simple-guide-for-small-and-medium-businesses/ - Categories: Agentic AI, AI - Translation Priorities: Optional The numbers tell a story that most business leaders recognize: Eight out of ten companies now use generative AI, yet just as many report no meaningful impact on their bottom line (by McKinsey). We see this disconnect every day in our work as businesses and partners strive to bridge the gap between AI adoption and achieving practical results. We build and have built agentic AI systems that bridge this generative AI gap. These systems are equipped to make autonomous decisions and pursue complex goals without the need for constant supervision. Companies implementing our AI Agents report up to 36% increases in operational value. But what makes this particularly compelling is that 62% of executives expect returns above 100% from agentic AI as compared to standard generative AI approaches, according to a recent report by PagerDuty. Agentic AI markets will grow from $7. 28 billion in 2025 to over $41 billion in 2030 (stats by Mordor Intelligence). Small and medium businesses face unique challenges that agentic AI addresses directly. Common resource constraints disappear when AI agents can handle customer inquiries and manage invoices autonomously. The market validates this, with 35% of customers actually preferring to work with AI agents in order to avoid repeating their concerns to multiple representatives. This guide explains what agentic AI is, how it differs from technologies you might already use, and the specific ways it can improve your business operations today. Our approach follows our "Practice > Theory" philosophy – every recommendation comes from hands-on experience... --- - Published: 2025-09-06 - Modified: 2025-11-04 - URL: https://vstorm.co/agentic-ai/top-10-agentic-ai-development-consulting-companies-for-smbs-and-enterprises-2025/ - Categories: Agentic AI - Translation Priorities: Optional A deep dive into the top 10 Agentic AI development and consultation companies of 2025 To understand the landscape of agentic AI companies, it is crucial to first grasp the agentic definition and explore what is agentic in the context of artificial intelligence systems. Agentic AI describes autonomous AI systems that act independently to achieve predetermined goals. While traditional AI follows predefined rules, agentic systems operate proactively and complete complex tasks without constant human oversight. The term "agentic" refers to the system's agency - its ability to act independently while remaining goal-oriented. These intelligent AI systems analyze situations, make decisions, set objectives, reason through problems, and implement solutions with minimal human intervention. Rather than simply generating content or providing data insights, they take the initiative to drive decision-making processes through autonomous execution. For SMBs, agentic AI functions like your most reliable business advisor - one who understands your specific challenges, learns from every customer interaction, and makes decisions aligned with your business goals. These cognitive AI agents integrate with your existing tools (such as your CRM) and actively use your data to make decisions and execute actions on your behalf. Agentic AI systems follow a four-step operational process: Perceive. Collect data from various sources and identify meaningful elements Reason. Determine necessary tasks and find solutions to challenges Act. Execute tasks by connecting with external tools and platforms Learn. Continuously monitor results to improve decision-making 25% of enterprises using GenAI are forecast to deploy AI agents in 2025, growing to 50%... --- - Published: 2025-08-26 - Modified: 2025-10-28 - URL: https://vstorm.co/rag/top-10-rag-development-service-firms-in-2025/ - Categories: RAG - Translation Priorities: Optional A deep dive into the top 10 RAG development companies of 2025, complete with a weighted list of top RAG development companies. RAG (Retrieval Augmented Generation) enhances AI by combining information retrieval and text generation to produce relevant, fact-based responses. Leading RAG development providers, spread across India, Poland, the US, and Germany, serve diverse market needs - from enterprise-level solutions to SMB-focused services. This article explores RAG's business impact, selection criteria for development firms, and highlights 2025's top 10 RAG development companies. What is Retrieval-Augmented Generation (RAG) and Why Might You Need It? RAG enhances business AI by incorporating real-time data into decision-making. It retrieves and synthesizes current information, crucial for sectors like healthcare, finance, and legal services where accuracy is paramount. By reducing retraining needs, it cuts costs and enhances adaptability. Its scalability enables efficient processing of large datasets and complex queries, making it suitable for growing organizations. Its versatility spans multiple industries, from customer service to knowledge management. According to Deloitte's 2025 predictions, 25% of enterprises using GenAI will deploy AI Agents by 2025, reaching 50% by 2027. RAG allows LLMs to access and reference information outside the LLMs own training data, such as an organization's specific knowledge base, before generating a response—and, crucially, with citations included. This capability enables LLMs to produce highly specific outputs without extensive fine-tuning or training, delivering some of the benefits of a custom LLM at considerably less expense. - Lareina Yee, Senior Partner, McKinsey, October 30, 2024, on What is retrieval-augmented generation... --- - Published: 2025-05-27 - Modified: 2025-11-06 - URL: https://vstorm.co/ai/the-use-of-ai-by-ai-engineers/ - Categories: AI, Coding AI - Tags: ai, code, no code - Translation Priorities: Optional — So, how do you do it? — We’re often asked by our customers — Being experts in AI, how do you actually support yourself with AI when coding AI solutions?   To understand how such a question puts us in an uncomfortable spot, try to ask a magician about his trick. Wizards, too, rarely speak of their magic, but since the question reoccurs constantly, we decided to give you a sneak peek of what’s behind the curtain where the magic happens. Let’s begin by dispelling a myth first. Is the coding no more with AI? Andrej Karpathy, one of the godfathers of the AI field, gave birth to an idea that coding might become irrelevant at some point, as we might soon fully rely on LLMs for coding. He called this the “Vibe Coding” — where all that it would be to unlease oneself from the burden of analytical thinking and just go with the flow, follow the vibe, and embrace the exponentials that code-generators offer. The trend caught momentum with the proliferation of low-code and no-code platforms that made the headlines, offering a chance to build entire SaaS platforms with zero handwritten code, all thanks to AI code generators. Welcome to the future. A future in which users, such as Leo (known as @leojr94_ on “X” ) stress-tested this potential by building with AI tools a fully functional solution. With initial pride, he shared that: “AI is no longer just an assistant, it’s also the builder. You can continue to... --- - Published: 2025-03-28 - Modified: 2025-11-27 - URL: https://vstorm.co/model-evaluation/choosing-the-right-llm-model-for-the-job/ - Categories: LLM Scoring, Model Evaluation - Translation Priorities: Optional We get it; it’s hard to decide which large language model should be used for a specific business application. Many new ones are coming out each month, and without being technically versed, it’s hard to see the forest for the trees. That is why every business-oriented decision-maker can count on our help in discerning — should you rely on Claude Sonnet, Gemini Flash, GPT-4o, or Llama, what are their strengths and weaknesses in real-life business applications, and how such applications differ from what you see in what you see in existing benchmarks. What we’re excited about when thinking of models?   From a pragmatic perspective, it’s more important how a model supports a business use case and its costs, from how high its evaluation metrics are positioned in the model comparisons. It is a different vantage point to what benchmarks show. But let’s start with how to read the benchmarks that we are exposed LLM Benchmarks: How are models typically compared? Recently, xAI and Anthropic released new models: Grok 3 and Claude 3. 7 Sonnet. Let’s take a closer look at their release notes, focusing on the benchmarks used to measure model performance. Both companies evaluated their models using eight different benchmarks, but only three — MMMU, AIME’24, and GPQA — were common across both reports. One of the biggest challenges in benchmarking large language models is the sheer variety of available tests, many of which are frequently updated. Different models excel in different areas, so companies strategically select benchmarks that highlight their model’s strengths and demonstrate competitiveness. This... --- - Published: 2025-03-06 - Modified: 2025-08-27 - URL: https://vstorm.co/ai/top-10-custom-ai-agent-development-companies/ - Categories: AI, AI Agents - Translation Priorities: Optional Artificial Intelligence (AI) agents are revolutionizing industries by enabling automation, enhancing decision-making, and improving user experiences. Companies worldwide are leveraging AI-driven solutions to build intelligent agents that streamline workflows, provide insights, and drive operational efficiency. In this article, we explore the top AI agent development companies, highlighting their expertise, services, and why they stand out in the field. What Are AI Agents and their business impact? AI agents are advanced software programs that utilize machine learning, natural language processing (NLP), and deep learning models to automate tasks, analyze data, and interact with users in real time. Unlike traditional software, AI agents can learn from interactions, adapt to changing conditions, and provide predictive insights. Businesses implement AI agents in various domains, including customer service, data analysis, healthcare, and finance, to enhance efficiency, reduce costs, and drive innovation. Why do businesses need custom AI agents? Custom AI agents provide businesses with tailored solutions that align with specific industry needs. Unlike off-the-shelf AI tools, custom-built agents can be fine-tuned to integrate seamlessly with existing systems, ensuring security, scalability, and high performance. Companies investing in AI agents benefit from improved automation, enhanced customer engagement, and data-driven decision-making. Criteria for selecting top custom AI Agent development companies To identify the top AI agent development companies, we evaluated firms based on the following criteria: Expertise in AI Development: Companies with a strong focus on AI agent development, machine learning, NLP, and automation. Proven Track Record: A history of successfully delivered AI projects with positive client feedback.... --- - Published: 2025-03-03 - Modified: 2025-11-20 - URL: https://vstorm.co/ai/off-the-shelf-ai-platform-or-custom-ai-agent-solution/ - Categories: AI, AI Agents - Translation Priorities: Optional At Vstorm, we’ve noticed a new trend in the AI world: Companies that initially jumped on off-the-shelf Agentic AI platforms (like Botpress) are now turning to custom-built solutions. This move isn’t just a random pivot; it’s the natural evolution of businesses that demand tangible, trustworthy outcomes. While platforms like Rivet, Botpress, Vellum, EngAIge, and dataRobot enable the rapid development of AI agents, they often fall short of meeting more complex and critical business requirements. And in a world where your AI agent might be the face of your brand, “close enough” simply doesn’t cut it. What is an AI Agent? An AI Agent is an intelligent system powered by a Large Language Models (e. g. , GPT-4o) capable of making decisions, reasoning through problems, and taking relevant actions without human intervention. For example, imagine a customer service AI that independently decides to review product documentation, check internal databases, and generate a personalized response based on a customer’s order history. It’s not just a chatbot — it’s an autonomous decision-maker representing your company around the clock. Off-the-shelf AI solutions Agentic AI SaaS platforms promise a quick start. With pre-configured workflows, no-code interfaces, and easy integrations, these tools help businesses deploy AI without hiring an army of developers. For straightforward tasks, that’s great. But many organizations discover that these platforms become restrictive the moment they need: Customization that aligns perfectly with unique business processes. Advanced reliability to avoid humiliating AI “hallucinations” that erode trust. Control over data to ensure security and compliance... --- - Published: 2025-02-21 - Modified: 2025-10-28 - URL: https://vstorm.co/community/vstorm-leader-in-llms-solutions-recognized-by-deloitte-technology-fast-50/ - Categories: Community - Translation Priorities: Optional We are proud to announce that Vstorm has been recognized as one of the leading companies in Deloitte’s prestigious Technology Fast 50 ranking for Central Europe. This achievement is not only a milestone for us but also a confirmation that we are the fastest-growing company in this field. Our dynamic expansion and leading role in implementing AI-based solutions, particularly in the area of Large Language Models (LLMs), are the foundation of our success. Exceptional growth and leadership in AI Over the past years, Vstorm has achieved an impressive growth rate of 333%, securing our place among the region’s top-performing companies in AI innovation. What sets us apart is our specialization in LLM solutions, a rapidly evolving area where we’ve established ourselves as true experts. This achievement reflects our commitment to pushing boundaries and delivering cutting-edge solutions to our partners. A message from our CEO Antoni Kozelski, CEO and Co-founder of Vstorm shared his thoughts on this accomplishment: “This recognition is a huge honor for us and a reflection of the hard work and dedication of our entire team. But we’re not stopping here. Our mission is to keep evolving, ensuring that our partners grow and succeed alongside us. This award inspires us to keep raising the bar in the AI industry, especially when it comes to advancing LLM technologies. ” Turning AI vision into reality The past year has been transformative for AI. What was once a futuristic concept has now become a powerful, real-world tool driving value across industries.... --- - Published: 2025-02-21 - Modified: 2025-10-28 - URL: https://vstorm.co/ai-advisory/how-to-assess-the-value-of-ai-vsa-in-practice/ - Categories: AI Advisory - Translation Priorities: Optional Each AI-related project is a journey into a bit of the unknown. It is a learning experience with a magnitude of unknowns in the process, which range from technical feasibility to real value delivered with automation. That is why at Vstorm, we work according to a specific method that spans the entire purchase process — from the moment we first get in touch with our potential customers to the moment we wrap up implementation projects, delivering either a single AI agent or an entire agentic framework. How to deal with the unknowns of AI adoption? During our conversations with customers, it is uncommon to face questions such as: Is the AI agent I need technically feasible? Would running it be economically viable? How may it help me to differentiate from the competition? What could be the long-term systemic impact on my team? To answer them, we blend decades of experience in IT solutions deployment from Agile to Scrum with risk-preventing measures necessary when working with novel, cutting-edge technologies. In this hotbed of a field, solutions, and technology tend to evolve as fast as customer expectations. Thanks to the methodology we work with, we're able to manage expectations across the entire scope of engagement, from proposal to deployment of custom solutions. From idea to Agentic AI one pager Familiarize yourself with your Vstorm’s unique approach that secures business results from custom AI agent deployment Download At Vstorm, we manage customer expectations by blending low-level expertise with high-level business advisory and entrepreneurial... --- - Published: 2025-02-19 - Modified: 2025-11-27 - URL: https://vstorm.co/ai/ai-agentic-workflows-what-they-offer/ - Categories: AI, AI Agents - Translation Priorities: Optional Artificial intelligence (AI) has revolutionized various industries, and one of the most transformative advancements is the development of agentic AI workflows. These advanced automated systems function independently without human intervention, significantly enhancing efficiency and adaptability. By handling complex tasks autonomously, agentic AI workflows are reshaping traditional operational methods, making them indispensable in modern organizations. AI Agentic Workflows are transforming business operations by automating complex tasks, enhancing efficiency, and supporting data-driven decision-making. However, while these systems are powerful, they are not a one-size-fits-all solution. Their success depends on careful implementation, high-quality data, and ongoing optimization. In this article, we’ll take a balanced look at what AI Agentic Workflows can do, how they work, and what it takes to make them effective—without hype or overpromising, just the facts. What are Agentic AI Workflows? At their core, AI Agentic Workflows are systems that use intelligent agents to automate and manage tasks. These agents can analyze data, make decisions, and even collaborate with each other. Unlike traditional AI, which follows fixed rules, agentic AI is designed to adapt and learn over time. Large language models enable these agents to understand natural language, reason, and perform complex tasks autonomously. This adaptability makes them particularly useful for complex workflows—tasks that involve multiple steps, require real-time decision-making, or need to adjust to changing conditions. For example, they can optimize supply chain management, automate customer service, or enhance software development. Natural language processing plays a crucial role in enhancing AI capabilities, allowing these workflows to manage intricate tasks... --- - Published: 2025-02-07 - Modified: 2025-10-27 - URL: https://vstorm.co/ai-agents/technologies-behind-ai-agents/ - Categories: AI, AI Agents, LLMs - Translation Priorities: Optional AI agents are autonomous or semi-autonomous systems that perform tasks, make decisions, and interact with users or environments. These systems have found applications in various domains, from customer service chatbots to advanced robotic process automation (RPA). As businesses increasingly integrate AI agents into their workflows, understanding the key technologies that power them is essential for effective deployment. Core components of AI Agents AI agents operate through multiple layers that work together to perceive, process, and act on data. The Perception layer enables agents to interpret information from various sources. Natural Language Processing (NLP) allows them to understand human language, while computer vision enables recognition and interpretation of visual inputs. Technologies like speech-to-text and text-to-speech further enhance the interaction between humans and AI. At the Reasoning & Decision-making layer, agents analyze data and determine actions. Some rely on predefined business rules and logic, while others use reinforcement learning to improve decision-making over time. Knowledge graphs and ontologies help organize and retrieve information efficiently, making reasoning more structured and effective. The Action layer is where agents execute decisions. This includes generating responses in conversational AI, integrating with external systems, and automating business processes. AI-powered workflows allow businesses to streamline repetitive tasks, freeing up human resources for more complex work. Key technologies for AI Agents Natural Language Processing (NLP) NLP is fundamental to AI agents, as it allows them to understand and generate human language. Large Language Models (LLMs) such as GPT-4, Claude, and LLaMA enable sophisticated conversational AI. Advanced techniques like tokenization,... --- - Published: 2025-02-07 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/how-to-implement-ai-agents-in-your-company/ - Categories: AI, AI Agents, LLMs - Translation Priorities: Optional AI Agents are transforming businesses by automating tasks, improving decision-making, and optimizing workflows. Unlike traditional automation tools, AI-driven agents can adapt, learn from data, and execute tasks independently, making them highly valuable across various industries. However, implementing Agents is not just about deploying a chatbot or an automated system. It requires strategic planning, selecting the right use cases, ensuring data readiness, and choosing the right technology stack. This guide will take you through the step-by-step process of integrating AI Agents into your organization effectively. Understanding AI Agents and their capabilities AI Agents are software entities capable of perceiving their environment, making decisions, and taking actions to achieve specific goals. They vary in complexity, from simple rule-based agents to sophisticated systems powered by large language models (LLMs) and reinforcement learning. Types of AI Agents Rule-based agents – Operate based on predefined conditions and workflows. Machine learning-based agents – Improve decision-making over time using data-driven models. LLM-powered agents – Leverage natural language processing to interact with users in a human-like way. Autonomous multi-agent systems – Collaborate with other AI Agents to solve complex tasks. The effectiveness of Agents depends on the underlying technology. Natural language processing (NLP) enables them to understand human speech and text, while reinforcement learning allows them to optimize decisions through trial and error. Retrieval-augmented generation (RAG) further enhances AI Agents by providing access to external knowledge sources, ensuring more accurate and context-aware responses. Identifying use cases for AI Agents in your business To successfully implement AI Agents, businesses... --- - Published: 2025-02-04 - Modified: 2025-10-28 - URL: https://vstorm.co/ai/ai-in-business-separating-facts-from-myths/ - Categories: AI, AI Advisory - Translation Priorities: Optional When you think about AI, do you see it as a powerful tool to embrace or a disrupting force? Many businesses struggle with this very question. To set the stage for our AI workshops, we often start with a thought-provoking comparison: How does Paul Atreides of Dune differ from Prometheus of Greek myth? Dune envisions a future where AI resembling humans was outlawed, ultimately resulting in a rejection of AI technology due to its risks. Mythical Prometheus did the opposite — fearlessly embraced godly powers (symbolized by fire) defying all related risks for human empowerment. Where does your organization stand in comparison — cautious of new technologies to the point of refusal, or self-made defiant, Promethean-style? For each decisionmaker seeking the answer — we help to strike the right balance, avoiding blind adoption while unlocking AI’s full potential for growth. Case Study: Tauron Dystrybucja Workshop Many large organizations, such as Tauron Dystrybucja, already use AI-based tools but are looking for a comprehensive environment tailored to their needs. Tauron Dystrybucja is a key player in the Polish energy sector, responsible for the distribution of electricity to millions of customers across southern Poland. As part of the Tauron Group, one of the largest energy companies in the country, the company ensures a stable and efficient energy supply while continuously modernizing its power grid infrastructure. Their focus on innovation and digital transformation drives their interest in AI-based solutions for optimizing energy distribution, predictive maintenance, and operational efficiency. And when they do so, they... --- - Published: 2025-02-03 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/ai-agents-the-next-step-in-business-automation/ - Categories: AI, LangChain, LlamaIndex, LLMs, Machine Learning (ML), RAG - Translation Priorities: Optional Many companies have spent years implementing automation solutions—CRM integrations, scripted chatbots, and workflow automation tools—to improve efficiency. But these systems have limits. They follow predefined rules, struggle with dynamic situations, and require frequent updates whenever business needs change. Not like AI Agents. Agents take automation to a new level. Unlike static rule-based systems, they analyze data, make decisions, and act autonomously. Instead of following rigid instructions, they learn from interactions, adapt to new inputs, and optimize workflows over time. This makes them invaluable for businesses dealing with complex processes, large datasets, or unpredictable customer interactions. While early adopters are already gaining efficiency and cost advantages, many companies still hesitate, unsure how to integrate AI into their operations. Understanding how AI agents work, their business applications, and how to implement them effectively is crucial for staying competitive in an increasingly automated world. How AI Agents work: From data to intelligent decisions At their core, AI agents perceive, analyze, and act—just like a human decision-maker would, but at a much larger scale. Perception – The agent gathers data from various sources: CRM systems, IoT sensors, user interactions, emails, or financial reports. Unlike traditional automation, which follows strict input-output rules, AI agents process information dynamically. Example: A chatbot handling customer service queries doesn’t just match keywords to pre-set responses. It analyzes sentiment, past interactions, and intent, allowing it to tailor responses based on the customer’s history. Decision-making – Agents use machine learning models, rule-based logic, or reinforcement learning to determine the best course... --- - Published: 2025-01-29 - Modified: 2025-11-19 - URL: https://vstorm.co/llms/data-annotation-and-its-role-in-ai/ - Categories: AI, LLMs - Translation Priorities: Optional Imagine building a house. You can have the best design, the most advanced materials, and a skilled team, but if the foundation is weak, the entire structure will collapse. The same applies to Artificial Intelligence (AI) and Large Language Models (LLM). Data Annotation is that invisible but crucial foundation that determines whether your AI solutions will work or fail. But what exactly is Data Annotation, and why should you care? Data Annotation in a nutshell – What it is and why it works Data Annotation is the process of labeling data—text, images, sounds, or video—in a way that allows machines to understand it. It's like explaining to a child what they see in a picture: "This is a cat, and this is a tree. " With this labeled data, AI learns to recognize patterns and make decisions. Imagine you want to build a chatbot for your business. Without Data Annotation, the model won't know whether a customer is asking about a price, product availability, or filing a complaint. Data Annotation teaches the model how to interpret user intent, ensuring that the chatbot responds accurately to customer needs. Why Data Annotation is your new best friend AI is only as good as the data it learns from. If the data is poorly labeled, the model will behave like a driver with a blindfold—it might move forward, but sooner or later, it will crash. High-quality annotations guarantee that your AI will operate precisely and reliably. Every business is unique. Data Annotation allows... --- - Published: 2025-01-24 - Modified: 2025-11-14 - URL: https://vstorm.co/ai/rag-when-it-makes-sense-and-how-to-prepare/ - Categories: AI, LlamaIndex, LLMs, RAG - Translation Priorities: Optional Retrieval-Augmented Generation (RAG) is a transformative technology that blends the capabilities of Large Language Models (LLMs) with real-time retrieval systems. Unlike traditional LLMs, which rely on data embedded during their training, RAG dynamically accesses external data sources to generate accurate and contextually relevant responses. This approach not only enhances the quality of information retrieval but also strengthens data security by minimizing the need to store sensitive data within the model itself. RAG’s potential lies in its ability to empower organizations to work smarter, react faster, and adapt to ever-changing demands. However, to harness its full benefits, businesses must understand when it makes sense to implement RAG and how to prepare effectively for its deployment. When does implementing RAG make sense? RAG is not a one-size-fits-all solution. It excels in specific scenarios where dynamic and secure data retrieval is essential. Understanding these scenarios will help organizations evaluate if RAG aligns with their goals and operational needs. Managing large and dynamic datasets Organizations handling vast and frequently updated data—such as customer databases, technical documentation, or analytical reports—can benefit significantly from RAG. The ability to pull real-time data ensures accuracy without constant model retraining. Real-time access to information Industries that require immediate access to relevant information, such as customer support, legal research, or financial analysis, can leverage RAG to improve response times and service quality. Enhancing data security By retrieving information directly from external sources rather than embedding it in the model, RAG ensures sensitive data remains securely stored in controlled environments, reducing... --- - Published: 2025-01-22 - Modified: 2025-10-08 - URL: https://vstorm.co/ai/vllm-a-smarter-alternative-to-traditional-llms/ - Categories: AI, LLMs - Translation Priorities: Optional For many businesses, large language models (LLMs) like GPT and BERT represent untapped potential. They promise to automate repetitive tasks, enhance decision-making, and generate meaningful insights. However, the cost and complexity of deploying these models have kept them out of reach for many organizations. This gap has left smaller businesses relying on outdated processes while larger corporations dominate the AI space. But there’s a shift happening. Enter vLLM (Virtual Large Language Model)—a solution designed to make AI more accessible and practical without requiring excessive resources. This article explores how vLLM addresses real-world challenges and opens the door for businesses of all sizes to leverage AI effectively. Why traditional LLMs are hard to implement Large language models are undeniably powerful, but their implementation often comes with steep challenges: Hardware demands: Running a traditional LLM requires GPUs with extensive memory, which are costly to purchase and maintain. High costs: Beyond hardware, operational expenses for deploying LLMs can reach tens or hundreds of thousands of dollars annually. Complex integration: Adapting these models to existing business systems often requires specialized teams and technical expertise. These barriers have made AI adoption slow, especially for small and medium-sized businesses. Even larger organizations struggle to deploy these systems efficiently at scale. What makes vLLM different? Unlike traditional LLMs, vLLM is built for efficiency. By optimizing how data and computation are handled, vLLM ensures businesses can benefit from AI without the need for extensive infrastructure upgrades or budgets. Here's what sets it apart: Smaller memory footprint vLLM uses... --- - Published: 2025-01-22 - Modified: 2025-11-11 - URL: https://vstorm.co/rag/rag-s-role-in-data-privacy-and-security-for-llms/ - Categories: AI, LLMs, RAG - Translation Priorities: Optional In the digital era, data protection and security are critical components of any AI-based technology. Retrieval-augmented generation (RAG), a technique that combines Large Language Models (LLMs) with retrieval systems, not only improves data processing efficiency but also strengthens RAG security by safeguarding sensitive information. Traditional generative models, such as GPT, rely on storing vast amounts of data within their structure during training. This storage increases the risk of data leaks. In contrast, RAG dynamically retrieves information from external databases in real time, eliminating the need to store sensitive data within the model. This approach significantly reduces privacy risks. Why is data security crucial in RAG? RAG operates in environments where data flows continuously between users, models, and knowledge bases. Each stage introduces potential vulnerabilities if not adequately secured. Sensitivity of user input: Users may provide confidential information that, without appropriate safeguards, could be intercepted. Security of data sources: The external sources used by RAG, such as documents or databases, often contain critical organizational data, making them an attractive target for attackers. Communication between systems: Data transmitted between the model and the knowledge base must be encrypted to prevent unauthorized access. Examples of RAG applications with a focus on data security Certain industries, such as healthcare, finance, and law, are highly sensitive to data security due to the nature of the information they handle. Patient records, financial transactions, and legal documents are all prime targets for data breaches. However, even in other industries, every piece of information—whether customer data or internal... --- - Published: 2025-01-20 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/when-to-choose-vllm-or-rag/ - Categories: AI, LLMs, RAG - Translation Priorities: Optional Imagine this: Your company’s customer service team is overwhelmed by an influx of inquiries. They’re swamped, struggling to respond promptly, and customers are growing impatient. At the same time, your legal team is trying to navigate an ever-changing landscape of regulations, where a missed update could cost the company millions. Two different problems, two different solutions—but both could benefit from advanced AI. Enter vLLM and RAG, two technologies that are reshaping how businesses leverage artificial intelligence. But how do you choose the right one? Let’s break it down. What are vLLM and RAG? Before we dive into which one to choose, let’s clarify what we’re talking about. vLLM (velocity Large Language Models): Think of a model finely tuned for speed and efficiency. It’s like a sprinter—ready to deliver results in record time, perfect for applications where every second counts. RAG (Retrieval-Augmented Generation): Now imagine a seasoned researcher. RAG doesn’t rely solely on pre-learned knowledge. Instead, it dynamically fetches the latest, most relevant data from external sources to generate precise, up-to-date answers. Both are powerful. Both are transformative. But they serve very different purposes. When to choose vLLM? Let’s revisit our customer service scenario. A vLLM-powered chatbot can handle repetitive, straightforward inquiries in milliseconds. Use Cases: Chatbots: Answering common customer questions instantly. Content generation: Creating marketing materials, reports, or summaries at scale. Recommendation systems: Providing personalized product or service suggestions based on historical data. Why vLLM works: vLLMs shine when speed and efficiency are critical. They don’t need to consult external... --- - Published: 2025-01-17 - Modified: 2025-11-24 - URL: https://vstorm.co/ai/pytorch-developer-how-to-choose-one/ - Categories: AI, LLMs - Translation Priorities: Optional Operationalizing machine learning models effectively is critical for businesses seeking to unlock the full potential of AI-powered solutions. PyTorch developers play a pivotal role in building, deploying, and optimizing these models for various applications. Success depends on selecting the right PyTorch developer who understands your unique goals, offers technical expertise, and provides ongoing support. This guide outlines the key steps to finding and partnering with the ideal PyTorch developer. What is PyTorch development? PyTorch development refers to the practices, tools, and workflows involved in designing, training, deploying, and maintaining machine learning models using the PyTorch framework. These operations include: Model development Building custom neural networks and machine learning solutions. Training and tuning Optimizing models for accuracy and performance. Deployment Efficiently setting up models in production environments. Monitoring and maintenance Ensuring models perform reliably and adapt to new data or requirements. A skilled PyTorch developer integrates these elements into seamless workflows, ensuring your AI systems remain robust, scalable, and cost-effective. Benefits of partnering with a PyTorch developer Working with a PyTorch developer offers several advantages: Custom solutions Tailored machine learning models for your specific business needs. Scalability Solutions designed to handle growing user demands and data volumes. Enhanced performance Continuous optimization ensures accurate and efficient outputs. Cost efficiency Resource-efficient setups reduce infrastructure and operational expenses. Future-proofing Expertise in emerging PyTorch features keeps your solution up to date. Define your goals Every business has unique goals and challenges. Before hiring a PyTorch developer, it’s crucial to define your objectives. What do you... --- - Published: 2025-01-15 - Modified: 2025-09-24 - URL: https://vstorm.co/ai/what-is-pytorch-in-ai-llm-projects/ - Categories: AI, LLMs - Translation Priorities: Optional In the ever-evolving world of Artificial Intelligence (AI), organizations constantly seek reliable tools that can bring their innovative ideas to life. PyTorch has emerged as a powerful framework that balances flexibility and efficiency, making it a preferred choice for building Machine Learning (ML) and Large Language Model (LLM) projects. Since its inception by Meta AI (formerly Facebook AI), PyTorch has been a game-changer, particularly for teams looking to experiment, iterate, and deploy models quickly. This article provides an in-depth look at what PyTorch is, why it matters, and how businesses can harness its potential to create impactful AI solutions. From key components to practical use cases, we’ll explore the unique features that make PyTorch a standout in the AI ecosystem. What is PyTorch? PyTorch is an open-source machine learning framework designed to simplify the development of neural networks and deep learning models. Unlike traditional frameworks that use static computation graphs, PyTorch builds dynamic graphs that adjust in real time during execution. This flexibility makes it easier for developers to debug and refine their models. Key features of PyTorch Dynamic computation graph: Provides flexibility to modify computations during runtime, making iteration faster and more intuitive. Automatic differentiation: Automatically calculates gradients needed for model optimization, saving time and reducing manual coding errors. Seamless GPU integration: PyTorch allows developers to leverage powerful GPUs with minimal configuration changes, accelerating model training. TorchScript support: Converts PyTorch models into deployable formats optimized for production environments. Why it matters: PyTorch’s adaptability enables companies to rapidly prototype and... --- - Published: 2025-01-15 - Modified: 2025-11-13 - URL: https://vstorm.co/ai/top-10-pytorch-development-companies/ - Categories: AI, LLMs - Translation Priorities: Optional PyTorch development companies play a pivotal role in building, optimizing, and deploying machine learning and deep learning solutions for businesses. By leveraging PyTorch's flexibility and dynamic computation graph, these companies create custom AI solutions that address industry-specific challenges. Their expertise enables businesses to integrate AI models seamlessly into their operations, improving efficiency, scalability, and decision-making. Key responsibilities of PyTorch development companies Custom model development. Designing and building tailored machine learning and deep learning models. Model training and Fine-tuning. Ensuring models are accurately trained using domain-specific datasets. Deployment and integration. Implementing models into production environments. Performance optimization. Regularly monitoring and enhancing models for better performance. Data security compliance. Ensuring AI systems adhere to data privacy regulations. Why PyTorch development matters for businesses As businesses increasingly adopt AI to transform their operations, the demand for efficient model development and management has grown. PyTorch’s ease of use and support for research and production make it a preferred choice for many companies. Benefits of PyTorch development: Flexibility and scalability. Supports seamless scaling of AI solutions. Rapid prototyping. Enables quick testing and iteration of machine learning models. Open-source community. Backed by a large, active community that continuously improves the framework. Cost-effectiveness. Reduces development costs through faster iteration and integration. Selection criteria for the ranking The following companies were evaluated based on: Experience in PyTorch development. Track record of delivering impactful AI projects. Innovation. Use of state-of-the-art technologies to create cutting-edge solutions. Client portfolio. Success stories and case studies demonstrating expertise. Global reach. Ability to serve... --- - Published: 2025-01-08 - Modified: 2025-10-24 - URL: https://vstorm.co/ai/data-preparation-the-key-to-ai-and-llm-success/ - Categories: AI, LLMs - Translation Priorities: Optional Integrating AI and Large Language Models (LLMs) into business workflows requires much more than implementing advanced algorithms. It demands a structured, consistent, and compliant approach to data preparation. High-quality data is the foundation of any successful AI model—it improves accuracy, reduces operational costs, and accelerates deployment. In this guide, we’ll walk you through the essential steps involved in preparing data for AI and LLM integration, addressing the most common challenges, tools, and best practices. The importance of data preparation for AI and LLMs Data preparation is the backbone of AI projects. It ensures that the system can understand, learn, and generate meaningful outputs. When data is disorganized, incomplete, or inconsistent, even the most advanced LLMs will struggle to deliver accurate results. This can lead to issues such as incorrect customer insights, misleading predictions, or biased decision-making. Clean, labeled, and well-structured data enables the model to: Recognize meaningful patterns in vast datasets. Learn efficiently from smaller datasets by avoiding noise. Reduce the time and cost needed for re-training and maintenance. Additionally, a robust data preparation process helps organizations comply with regulatory frameworks, ensuring that AI solutions remain transparent and trustworthy. Types of data used in AI and LLM projects Structured data Structured data is highly organized and stored in predefined formats, such as tables or databases. Examples include customer purchase records, sales history, and financial reports. A retail company, for instance, might store transaction details in a database with columns for "purchase ID," "date," "item," and "amount. " This format makes... --- - Published: 2024-12-30 - Modified: 2025-09-26 - URL: https://vstorm.co/ai/what-is-langgraph-and-how-to-use-it/ - Categories: AI, LLMs - Translation Priorities: Optional Imagine a tool that doesn’t just organize your data but actually understands it—a tool that connects the dots, identifies patterns, and answers your questions in plain language. That’s LangGraph. Whether you’re trying to analyze a social network, uncover hidden fraud, or deliver better recommendations, LangGraph is here to simplify the way you work with data. But what is it exactly, and how does it work? Introduction to LangGraph In today’s world, data is everywhere. From social media interactions to business operations, we’re surrounded by complex webs of information. However, making sense of these webs can feel overwhelming. This is where LangGraph comes in. Think of it as your personal data translator, capable of turning tangled datasets into clear, actionable insights. LangGraph combines the structure of graph databases—which represent data as nodes (entities) and edges (relationships)—with the power of Large Language Models (LLMs) that understand context and meaning. This marriage of technologies unlocks new possibilities, making it easier than ever to see not just the data itself, but the story it’s telling. How LangGraph works Let’s break it down. Imagine you’re looking at a massive network of dots and lines. Each dot represents a person, a product, or an idea, while the lines show how they’re connected. At first glance, it’s chaos. LangGraph turns that chaos into clarity by following a structured and intuitive process: 1. Data ingestion LangGraph begins by importing your data and organizing it into nodes (the dots) and edges (the lines). For instance, in a customer database,... --- - Published: 2024-12-19 - Modified: 2025-10-15 - URL: https://vstorm.co/ai/how-to-build-a-large-language-models/ - Categories: AI, LLMs - Translation Priorities: Optional Large Language Models (LLMs) have transformed how AI systems process human language using natural language processing techniques. These models perform tasks such as language translation, sentiment analysis, and text generation, showcasing their potential across industries. This guide explores how to create an LLM by breaking down the necessary steps in a practical and accessible way. Imagine starting with a blank canvas—a system that knows nothing about language—and transforming it into a model that generates coherent essays, summarizes articles, or engages in meaningful conversations. This guide will teach you practical steps, including defining a use case, collecting relevant and high-quality training data, and choosing an architecture. By the end, you will gain essential skills to effectively build and deploy an LLM. Understanding the basics of Large Language Models What are LLMs? Large Language Models are advanced AI systems designed to process and generate human-like text through natural language understanding and text generation. Think of them as powerful tools that can "read," "write," and "understand" text. But unlike humans, LLMs rely on patterns rather than true comprehension. For instance, GPT (Generative Pre-trained Transformer) is a pre-trained model capable of generating human-like text by predicting the next token in a sequence. BERT (Bidirectional Encoder Representations from Transformers), on the other hand, focuses on understanding context, making it ideal for tasks like answering questions or detecting sentiment. How do LLMs work? Large Language Models work by analyzing input text through attention mechanisms, identifying key patterns and relationships. For instance, when processing the sentence "The... --- - Published: 2024-12-16 - Modified: 2025-10-12 - URL: https://vstorm.co/ai/llm-and-ai-in-telecommunications/ - Categories: AI, LLMs - Translation Priorities: Optional The telecommunications sector is one of the most dynamic and critical areas of the modern economy. As technology and customer expectations evolve, telecom companies must seek new ways to adapt to changing market demands. One of the most effective tools revolutionizing the telecom industry is advanced Artificial Intelligence (AI) and Large Language Models (LLMs). These technologies enable telecom operators to automate processes, enhance customer service, and optimize network operations while reducing operational costs. By adopting AI technology, telecom providers can unlock new revenue streams and deliver a superior customer experience. However, integrating these solutions requires strategic planning to overcome challenges and fully leverage their capabilities. Challenges in the Telecommunications Industry Telecom companies face several challenges, including: Growing data volume. Massive data generation from customers, IoT devices, and network infrastructure requires advanced data analytics tools. Ensuring high-quality customer service. Companies must maintain quick response times and efficient customer service to meet increasing demand. Fraud detection. Combating real-time fraud is a major challenge that requires robust AI models for network security and revenue protection. Optimizing network infrastructure. The development of technologies like 5G and network slicing demands intelligent resource management and predictive maintenance solutions. These challenges highlight the importance of adopting AI solutions to enhance efficiency and competitiveness in the telecommunications industry. Applications of AI and LLM in Telecommunications Automating customer service AI and LLMs enable the automation of customer interactions through chatbots and voice assistants. These technologies handle customer queries, resolve technical issues, and support sales processes while reducing operational costs.... --- - Published: 2024-12-13 - Modified: 2025-10-20 - URL: https://vstorm.co/ai/mlops-vs-llmops/ - Categories: AI, LLMs, Machine Learning (ML) - Translation Priorities: Optional The modern business world is rapidly evolving with the advancement of artificial intelligence technologies. Concepts like MLOps (Machine Learning Operations) and LLMOps (Large Language Model Operations) have gained prominence, becoming critical elements in effectively managing AI-based projects. This article explores what these two approaches are, the benefits they offer to businesses, and their key differences. We will also examine which types of enterprises can benefit the most from each. The basics of MLOps MLOps is a set of practices, tools, and processes designed to manage the lifecycle of machine learning (ML) models. It addresses the growing needs of companies leveraging machine learning in their operations. Key features of MLOps include: Automation. Key processes such as building, testing, deploying, and monitoring models Efficient data management. Improved business outcomes through better handling of data Standardization of processes. Minimization of errors and enabling faster scaling What is LLMOps Large Language Model Operations is a specialized approach to operations involving large language models (LLMs), such as GPT or BERT. This relatively new area focuses on managing, training, and optimizing models that analyze vast amounts of textual data. Benefits of investing in LLMOps include: Advanced systems. Enhanced tools for text analysis Implementation of chatbots. Development of personalization tools Competitive advantages. Better customer engagement through advanced capabilities However, LLMOps requires advanced infrastructure and significant computational resources, making it accessible primarily to businesses with substantial budgets and technological expertise. The benefits of MLOps for businesses Implementing MLOps allows businesses to leverage the potential of machine learning models... --- - Published: 2024-12-13 - Modified: 2025-11-21 - URL: https://vstorm.co/machine-learning-ml/top-10-mlops-companies/ - Categories: AI, Machine Learning (ML) - Translation Priorities: Optional What is MLOps? MLOps (Machine Learning Operations) is a set of practices that combine operational management (DevOps) with the lifecycle of machine learning models. It involves automating, deploying, monitoring, and managing ML models in production environments. With MLOps services, organizations can achieve greater efficiency, scalability, and reliability in their AI solutions. Why is MLOps important? Effective implementation of MLOps enables organizations to: Increase the efficiency of ML processes. Scale models to large-scale applications. Monitor and maintain models in real-time. Ensure compliance with regulations and data security standards. Criteria for selecting the best MLOps companies We selected these criteria to ensure a comprehensive evaluation of the companies offering MLOps services. These factors highlight the ability of each firm to deliver reliable, scalable, and innovative solutions that meet the unique needs of their clients. By focusing on experience, client success stories, and innovation, we aim to identify the best partners for organizations looking to implement or optimize MLOps. Experience and expertise Choosing the right MLOps company depends on its experience in implementing ML projects and its expertise with tools such as Kubernetes, TensorFlow, and PyTorch. Client portfolio and case studies Analyzing completed projects and portfolios provides insight into how companies tackle challenges related to MLOps. Innovation and unique features The best companies offer innovative solutions, such as automated pipelines, model personalization, and comprehensive support throughout the entire ML lifecycle. Top 10 companies offering MLOps services Here is our curated list of the top 10 companies providing MLOps services. These firms excel in... --- - Published: 2024-12-11 - Modified: 2025-10-07 - URL: https://vstorm.co/ai/how-to-enhance-llms-through-llm-ops/ - Categories: AI, LLMs - Translation Priorities: Optional Large Language Models (LLMs) have revolutionized business processes by enabling advanced automation and intelligent decision-making. However, their implementation and management require a sophisticated approach known as LLM Ops—the operationalization of LLMs. Unlike many approaches that delay operationalization and deployment until later phases, we prioritize these aspects from the very beginning. This approach minimizes technical debt and ensures clients avoid unforeseen costs when transitioning to production environments. As an end-to-end solution provider, we support our clients throughout the lifecycle of their models, from development and optimization to deployment and monitoring. In this article, we outline how our approach to LLM Ops improves model efficiency, addresses key challenges, and delivers tangible benefits to our clients. Our approach to LLM Ops We specialize in delivering comprehensive end-to-end LLM solutions that seamlessly integrate development, deployment, optimization, and scaling into a unified process. By leveraging our deep expertise in LLMs, we ensure efficient implementation tailored to specific project needs. Our approach minimizes risks, addresses potential challenges early, and avoids the technical pitfalls associated with incomplete operationalization. This holistic process empowers clients to unlock the full potential of their LLM-based systems, ensuring long-term scalability and success. Optimization Optimization is critical to ensuring LLMs operate at peak efficiency. Before diving into specific improvements, it’s essential to understand that optimization encompasses refining algorithms, improving model outputs, and utilizing hardware effectively to ensure peak performance. Reducing computational overhead: We refine algorithms to minimize unnecessary operations, ensuring faster response times and lower resource consumption. Improving model accuracy: By analyzing performance... --- - Published: 2024-12-11 - Modified: 2025-11-20 - URL: https://vstorm.co/llms/llmops-company-how-to-choose-one/ - Categories: AI, LLMs - Translation Priorities: Optional Operationalizing a large language model ( LLM ) effectively is critical for businesses seeking to unlock the full potential of AI-powered solutions. LLMOps (Large Language Model Operations) ensure these models are deployed, monitored, and maintained to deliver optimal performance at scale. However, success depends on selecting the right LLMOps company that understands your unique goals, offers technical expertise, and provides ongoing support. This guide outlines the key steps to finding and partnering with the ideal LLMOps provider. What is LLMOps? LLMOps refers to the practices, tools, and workflows involved in managing large language models in production environments. These operations include: Deployment. Efficiently setting up LLMs in production. Monitoring. Ensuring models perform reliably, with low latency and high accuracy. Optimization. Regularly fine-tuning models to improve performance. Maintenance. Updating models to handle new data or evolving requirements. A skilled LLMOps company integrates these elements into seamless workflows, ensuring your AI systems remain robust, scalable, and cost-effective. Benefits of partnering with an LLMOps company Working with an LLMOps company offers several advantages: Streamlined Operations. Automated workflows reduce complexity and manual intervention. Improved Scalability. Systems are designed to handle growing user demands and data loads. Enhanced Performance. Continuous optimization ensures accurate and efficient outputs. Cost Savings. Resource-efficient setups reduce infrastructure and operational expenses. Compliance Assurance. Expertise in GDPR, HIPAA, and other standards protects sensitive data. 1. Define Your Goals Every business has unique goals and challenges Before starting an LLMOps project, it’s crucial to define your goals. What do you expect the large language... --- - Published: 2024-12-11 - Modified: 2025-11-26 - URL: https://vstorm.co/ai/what-is-mlops-machine-learning-operations/ - Categories: AI, Machine Learning (ML) - Translation Priorities: Optional What is MLOps? MLOps, short for Machine Learning Operations, is transforming how businesses leverage artificial intelligence to achieve strategic goals. It acts as the bridge between data science and IT operations, ensuring that Machine Learning (ML) models are not only developed effectively but also deployed, monitored, and maintained with precision. For organizations looking to maximize ROI from AI initiatives, MLOps provides the critical framework needed to scale efficiently and deliver consistent value. By integrating automation, collaboration, and monitoring tools, MLOps enables businesses to stay ahead in a competitive, data-driven world. The importance of MLOps Scaling machine learning within an organization often involves navigating a maze of challenges, from operational inefficiencies to unpredictable costs. MLOps emerges as the solution by aligning people, processes, and technology to streamline the entire lifecycle of ML models. It offers businesses key advantages that directly impact their bottom line: Scalability. Seamlessly expands infrastructure to accommodate growing workloads and user demands. Optimization. Enhances team productivity by automating routine tasks and minimizing bottlenecks. Deployment flexibility. Adapts to various deployment environments, including cloud, on-premises, or hybrid architectures, ensuring compatibility with existing systems. Performance monitoring. Proactively identifies potential issues, minimizing downtime and preserving customer experience. Cost reduction. Dynamically manages resources, activating computational power only when necessary to save on operational costs. By addressing these core challenges, MLOps ensures that machine learning initiatives are not only successful but also sustainable in the long term. Core components of MLOps To achieve its transformative potential, MLOps relies on several foundational components that work... --- - Published: 2024-12-06 - Modified: 2025-11-23 - URL: https://vstorm.co/ai/llm-ops-how-to-manage-large-language-models/ - Categories: AI, LLMs - Translation Priorities: Optional The rise of Large Language Models (LLMs), such as GPT and BERT, has transformed how businesses leverage AI for automation, personalization, and decision-making. These models, trained on vast datasets, possess capabilities that can significantly enhance business processes. However, deploying, maintaining, and scaling such powerful tools comes with challenges—ranging from computational costs to ensuring consistent performance in dynamic environments. LLM Ops— Operationalizing Large Language Models is a specialized approach to managing the lifecycle of these models—addresses these challenges by providing a structured framework for operation and optimization. In this article, we explore what LLM Ops is, why it is essential for businesses, and how it enables organizations to maximize the potential of LLMs. By the end of this guide, you’ll have a clear understanding of how LLM Ops can empower your business to overcome the challenges of modern AI deployment while ensuring sustainable growth and innovation. What is LLM Ops? LLM Ops is the backbone of effective AI deployment for Large Language Models. At its core, LLM Ops encompasses the tools, methodologies, and best practices required to manage LLMs throughout their lifecycle. It ensures these models function optimally from the moment they are introduced to a business environment to their real-world application and scaling. Core Components of Operationalizing Large Language Models Lifecycle Management. Support from training and fine-tuning to real-time deployment and updates. Data Optimization. High-quality, relevant, and secure data usage. Performance Monitoring. Tracks model accuracy, efficiency, and relevance to avoid pitfalls like performance degradation. LLM Ops bridges the gap between... --- - Published: 2024-12-06 - Modified: 2025-07-02 - URL: https://vstorm.co/ai/top-10-llm-ops-companies-for-2025/ - Categories: AI, LLMs - Translation Priorities: Optional LLM Ops (Large Language Model Operations) refers to the set of practices, tools, and frameworks used to manage, monitor, and optimize large language models throughout their lifecycle. These practices ensure efficient deployment, fine-tuning, and seamless integration of LLMs into production environments. LLM Ops addresses key operational aspects such as model versioning, latency management, and scaling, ensuring businesses can maximize the potential of these powerful AI tools while maintaining security and performance. The role of LLM Ops companies LLM Ops companies provide specialized services to deploy and manage large language models in real-world scenarios. They focus on creating tailored workflows that optimize model performance, enhance data security, and deliver measurable business outcomes. These companies enable businesses to seamlessly integrate LLMs into their existing systems while ensuring ongoing performance monitoring and optimization. Key Responsibilities of LLM Ops Companies: Model deployment. Efficiently transitioning models from development to production. Performance monitoring. Continuous tracking of model performance to ensure high accuracy. Scalability management. Adapting models to handle varying loads and complex tasks. Security compliance. Ensuring data privacy and adhering to industry regulations. Why is LLM Ops important for businesses? As businesses increasingly adopt large language models to transform operations, the need for efficient management and optimization becomes crucial. LLM Ops ensures that these models operate reliably, securely, and cost-effectively while delivering high performance tailored to specific business requirements. 1. Efficiency and scalability LLM Ops ensures seamless operation of large language models even under heavy workloads, enabling businesses to scale their operations without compromising on performance.... --- - Published: 2024-12-04 - Modified: 2025-11-06 - URL: https://vstorm.co/ai/ai-chatbot-developer-how-to-choose-one/ - Categories: AI, LLMs - Translation Priorities: Optional AI chatbots are reshaping how businesses interact with customers, automate workflows, and provide personalized experiences. By leveraging technologies like natural language processing (NLP) and machine learning (ML), AI-powered chatbots offer scalable, interactive solutions that drive efficiency and engagement. However, the success of an AI chatbot project depends on choosing the right developer—one who can align the chatbot’s capabilities with your goals, deliver tailored solutions, and ensure long-term scalability. This guide outlines the essential steps to finding and partnering with the ideal AI chatbot developer. What is an AI Chatbot? An AI chatbot is a computer program that uses artificial intelligence (AI) to simulate human-like conversations with users. These chatbots are designed to understand and respond to customer inquiries, provide information, and assist with tasks in a conversational manner. By leveraging large language models (LLMs) and natural language processing (NLP) algorithms, AI chatbots can comprehend and generate human-like text responses, making interactions seamless and efficient. Benefits of AI Chatbots AI chatbots offer numerous benefits that can significantly enhance business operations: 24/7 Customer Support: AI chatbots provide instant responses to customer inquiries at any time of day, reducing wait times and improving overall customer satisfaction. Increased Efficiency: By automating routine tasks, AI chatbots free up human agents to focus on more complex issues, thereby increasing operational efficiency. Personalized Experiences: Utilizing customer data and preferences, AI chatbots can deliver personalized responses and recommendations, enhancing user engagement. Cost Savings: Implementing AI chatbots can reduce the need for a large human customer support team, leading... --- - Published: 2024-11-27 - Modified: 2025-10-31 - URL: https://vstorm.co/ai/how-to-use-ai-chatbots/ - Categories: AI, LLMs - Translation Priorities: Optional AI-powered chatbots are no longer just an innovative idea—they’ve become a key tool for businesses looking to enhance efficiency, improve customer service, and drive growth. AI chat allows for the creation of customizable chatbots and AI assistants. From handling routine tasks to boosting engagement, chatbots can transform how your company operates. This guide will show you step-by-step how to implement an AI chatbot in your business, with practical tips, real-life examples, and a roadmap for success. What is an AI Chatbot? An AI chatbot is a tool designed to simulate human-like conversations with users. Unlike traditional bots, AI chatbots use advanced technologies such as natural language processing (NLP) and machine learning to understand user intent, remember context, and provide natural, dynamic responses. They can: Operate 24/7 without fatigue. Juggle multiple conversations simultaneously. Continuously learn and improve over time. Whether integrated into websites, apps, or messaging platforms, chatbots are used for tasks like answering customer inquiries, guiding purchases, or streamlining internal workflows. Definition of an AI Chatbot An AI chatbot is a sophisticated computer program that leverages artificial intelligence to simulate human-like conversations with users. An AI writer, on the other hand, focuses on generating written content like articles, stories, or poetry based on user input. Unlike traditional bots, AI chatbots utilize advanced technologies such as natural language processing (NLP) and machine learning algorithms to understand and respond to user inputs, whether they are text or voice commands. This allows AI chatbots to engage in more natural and dynamic interactions, providing... --- - Published: 2024-11-26 - Modified: 2025-11-23 - URL: https://vstorm.co/llms/top-10-custom-llm-development-companies-for-2025/ - Categories: AI, LLMs - Translation Priorities: Optional The demand for tailored Large Language Model (LLM) solutions is rapidly growing as businesses across every industry seek to transform their operations and leverage AI for personalized, efficient systems. Custom LLMs provide unparalleled advantages by automating processes, improving decision-making, and addressing unique industry-specific challenges. Here is a ranking of the top 10 companies specializing in custom LLM development, with Vstorm clearly leading the field. What is a custom Large Language Model? A custom LLM (Large Language Model) is a tailored version of an AI-powered language model designed to meet the specific needs of a business or industry. Unlike generic AI models, custom LLMs are fine-tuned using proprietary or specialized datasets to deliver highly relevant, accurate, and context-aware results. These models leverage natural language understanding to perform error-free document analysis and generate contextually relevant content, enhancing various business processes. The process involves: Fine-tuning: Adapting pre-trained models to fit unique use cases. Integration: Embedding the custom LLM seamlessly into existing systems. Optimization: Enhancing performance for real-world requirements. Custom LLMs are essential for maximizing efficiency, improving customer experiences, and ensuring data security. The role of custom LLM development companies Custom LLM development companies specialize in creating and fine-tuning tailored Large Language Models that address specific business needs. By leveraging advanced AI and NLP technologies, they help organizations enhance customer engagement, improve operational efficiency, and drive growth. Their work begins with data preparation and utilizes industry-specific data and minimal training data to develop highly accurate and optimized custom LLMs. These companies create solutions that... --- - Published: 2024-11-25 - Modified: 2025-11-18 - URL: https://vstorm.co/llms/difference-between-custom-llm-software-and-llm-development/ - Categories: AI, LLMs - Translation Priorities: Optional Large Language Models (LLM) have become a cornerstone of modern technological solutions, enabling businesses to automate processes, personalize experiences, and analyze data at scale. Models like GPT and BERT are widely used to build innovative tools that support business growth. In this article, we explain the distinction between custom LLM-based software—tailored software solutions built on language models—and LLM development, which focuses on developing and optimizing these models. Understanding this difference, along with the importance of data security, will help you decide which approach best suits your needs. Understanding Large Language Models Large Language Models (LLMs) are a groundbreaking advancement in artificial intelligence, designed to understand, interpret, and generate human language. These models leverage deep learning techniques and Natural Language Processing (NLP) to perform a wide array of language-related tasks, such as text generation, translation, summarization, and sentiment analysis. By training on vast amounts of text data, LLMs learn intricate patterns and relationships within language, enabling them to generate human-like text and respond to natural language prompts with remarkable accuracy. How do LLMs work? At the core of LLMs is their ability to process and generate human language. They achieve this through deep learning algorithms that analyze extensive datasets, identifying patterns and structures in the text. This process allows LLMs to understand context, predict subsequent words, and generate coherent and contextually relevant responses. The training process involves feeding the model massive amounts of text data, which helps it learn the nuances of human language, including grammar, syntax, and semantics. As a... --- - Published: 2024-11-21 - Modified: 2025-11-08 - URL: https://vstorm.co/ai/the-role-of-rag-in-automating-enterprise-workflows/ - Categories: AI, LLMs, RAG - Translation Priorities: Optional Introduction to Retrieval-Augmented Generation Retrieval-Augmented Generation (RAG) is a technology that combines the best features of two approaches: information retrieval and answer generation using language models. In a business context, RAG can play a crucial role in workflow automation, as it enables the rapid processing of large volumes of data and the use of this information for decision-making and business process management. With RAG, we can efficiently find and utilize the necessary information from various sources, such as databases, company documentation, or internal knowledge repositories. This is particularly useful when employees need quick access to reliable information or analysis to effectively manage complex tasks. Applications of RAG in business process automation One of the key applications of RAG in companies is the automation process in business process automation, which significantly enhances business operations. RAG works well in activities that require understanding context and processing large amounts of information, making it an ideal tool for optimizing work in various areas of business. 1. Customer service automation By using RAG, customer service operations can automatically respond to customer inquiries, using access to historical data and context. This approach allows for personalized responses and shorter problem-solving times, resulting in higher customer satisfaction. RAG not only provides quick responses but also learns from previous interactions, which continuously improves service quality. Customers receive more accurate answers, increasing their loyalty to the company. 2. HR support Human resources departments can use Retrieval-Augmented Generation technology in the recruitment process by automatically analyzing candidates’ resumes and matching them... --- - Published: 2024-11-20 - Modified: 2025-11-20 - URL: https://vstorm.co/ai/why-should-you-secure-llm/ - Categories: AI, LLMs - Translation Priorities: Optional Securing your Large Language Models (LLMs) is crucial for protecting both your data and your business from a wide range of threats. Without proper security measures, a language model can become vulnerable to data breaches, unauthorized access, and malicious attacks. Failing to secure these systems can result in financial loss, damage to your organization’s reputation, and severe legal consequences, especially in industries with strict data protection regulations, such as healthcare or finance. When LLMs are used in environments that handle sensitive information, such as personal identifiers, financial records, or proprietary business data, any security lapse can lead to severe breaches. Moreover, unprotected LLMs can be manipulated to leak confidential information or be exploited through prompt injection attacks. Securing LLMs involves not only protecting the data they process but also ensuring that the models themselves, the infrastructure they run on, and the entire pipeline are safeguarded from external threats. Implementing a structured security framework is essential for businesses aiming to maintain regulatory compliance and ensure smooth operations. Security risks and threats Large language models (LLMs) come with several security risks and threats that need to be addressed to ensure their safe deployment. One of the primary concerns is the potential for data breaches, especially when sensitive information is stored or transmitted by the model. LLMs can also be targets for attacks such as prompt injection and model exploitation, which can compromise the model’s integrity and the quality of its outputs. Another significant risk is the misuse of LLMs for malicious purposes,... --- - Published: 2024-11-20 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/how-to-choose-rag-developer/ - Categories: AI, LLMs, RAG - Translation Priorities: Optional In today’s fast-paced world of AI, Retrieval-Augmented Generation (RAG) is transforming how businesses leverage data for efficiency, personalization, and innovation. Custom RAG solutions enhance the capabilities of generative AI by incorporating external information sources, thereby improving the quality and accuracy of responses. Large language models (LLMs) play a crucial role in RAG, as they generate content based on extensive datasets and utilize RAG techniques to provide timely and contextually relevant answers. RAG solutions are powering advanced chatbots, personalized customer support, and real-time data analysis, making it a critical tool for companies looking to stay competitive. However, successfully implementing custom RAG technology requires a specialized developer who can navigate its complexities. The right RAG developer can tailor solutions to your specific needs, ensure seamless integration, and provide long-term support. But how do you find the right company for such a crucial task? This guide is designed to help you through the process of choosing the right RAG developer. From defining your goals to making the final decision, here are the steps to ensure your project is in expert hands. 1. Define your goals Before starting any RAG development project, it’s crucial to establish clear objectives. This step is the foundation for all subsequent decisions and ensures that your project is aligned with your business priorities. Every business has unique goals and challenges No two businesses are alike. Your goals will depend on your industry, the size of your operations, and the specific problems you aim to solve. For instance, an e-commerce... --- - Published: 2024-10-25 - Modified: 2025-11-05 - URL: https://vstorm.co/ai/what-is-nlp/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski In the fast-paced digital world, businesses are constantly looking for ways to improve efficiency, enhance customer experiences, and make data-driven decisions. One of the most transformative technologies driving these changes is Natural Language Processing (NLP). This branch of artificial intelligence focuses on enabling machines to understand, interpret, and generate human language, bridging the gap between people and technology in ways that were once unimaginable. Introduction to NLP Natural Language Processing (NLP) is a fascinating subfield of artificial intelligence (AI) that focuses on the interaction between computers and humans using natural language. By combining the expertise of computer science, linguistics, and machine learning, NLP enables machines to process, understand, and generate human language in a way that feels remarkably intuitive. The ultimate goal of NLP is to develop sophisticated algorithms and statistical models that can perform tasks requiring human-level understanding of language, such as text analysis, sentiment analysis, language translation, and speech recognition. Imagine a world where computers can not only understand what we say but also respond in a meaningful way. This is the promise of NLP, and it’s already transforming how businesses operate. From analyzing customer feedback to automating customer service interactions, NLP is making it possible for machines to understand and respond to human language with unprecedented accuracy and efficiency. Understanding Natural Language Processing: The foundation of human-machine communication Natural Language Processing combines computational linguistics and machine learning to analyze language in its natural form. Unlike traditional programming languages, human communication is complex, and filled with nuances, context,... --- - Published: 2024-10-21 - Modified: 2025-11-05 - URL: https://vstorm.co/ai/how-to-prompt-build-the-perfect-prompt-for-your-llm/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Designing an effective prompt is essential for unlocking the full potential of large language models (LLMs). A well-structured prompt guides the model to generate accurate, relevant, and context-aware responses. In this article, we’ll walk through the key components of crafting the perfect prompt and provide practical examples of how to implement them. 1. Understanding Large Language Models Large language models (LLMs) are a groundbreaking type of artificial intelligence designed to process and generate human-like language. These models are trained on vast amounts of text data, allowing them to learn intricate patterns and relationships within human language. At the core of LLMs are artificial neural networks, specifically transformer models, which enable them to grasp the context and nuances of language with remarkable accuracy. The architecture of LLMs is built on the transformer model, a sophisticated framework that excels in understanding and generating natural language. This model uses a token vocabulary to break down text into manageable pieces, making it easier for the AI to process and generate coherent responses. The training data for LLMs is extensive, encompassing a wide range of texts from books, articles, websites, and more, which helps the model learn diverse language patterns. LLMs have a multitude of applications, from language translation and text summarization to question answering and content creation. They power chatbots, virtual assistants, and other generative AI tools, making them a valuable resource in various fields. However, it’s important to note that LLMs are not without limitations. They can sometimes produce errors, exhibit biases, or... --- - Published: 2024-10-16 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/what-are-large-language-models-llms/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Large Language Models (LLMs) are a form of AI designed to understand and generate human language. Trained on vast amounts of text, they can process and create meaningful, coherent content, which makes them useful in automating tasks that involve language. LLMs are important because they enable businesses to increase efficiency and reduce costs by automating language-based tasks. Their accessibility allows companies of all sizes to benefit from AI technology, transforming how we interact with digital systems and making technology more intuitive and scalable. Definition and Importance Large Language Models Definition A large language models (LLMs) is a type of artificial intelligence algorithm that leverages deep learning techniques and vast datasets to understand, summarize, generate, and predict new content. These models are a form of generative AI specifically designed to generate text-based content. By analyzing patterns in the data they are trained on, LLMs can produce coherent and contextually relevant text, making them invaluable for a variety of applications, from automated content creation to sophisticated conversational agents. Importance of LLMs in Modern Technology Large language models are becoming increasingly crucial in modern technology due to their ability to enhance efficiency, effectiveness, and user experience across various domains. One of the key advantages of LLMs is their capability to generate high-quality content, including text, images, and even videos. This makes them indispensable for content creation, marketing, and media industries. Additionally, LLMs significantly improve customer service by providing 24/7 support, answering frequently asked questions, and handling customer inquiries with high accuracy. Their ability... --- - Published: 2024-10-11 - Modified: 2025-10-05 - URL: https://vstorm.co/ai/what-is-llamaindex-new-possibilities-in-development-with-llms/ - Categories: AI, LlamaIndex, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Welcome to LLM and LlamaIndex capabilities Large Language Models (LLMs) have transformed the way businesses interact with language, enabling computers to understand and generate human-like text. These AI models excel in tasks like content creation, language translation, and customer support automation. However, the true potential of LLMs is realized when they can seamlessly integrate with external data sources, a task made easier by LlamaIndex. Integrating own private data with public data is crucial in the development of applications utilizing LLMs. LlamaIndex development serves as a bridge, facilitating the connection between LLMs and external databases, documents, or APIs, which allows businesses to create advanced Natural Language Processing (NLP) applications. This makes LlamaIndex particularly useful in industries ranging from healthcare to finance, where processing large datasets is crucial. What is LlamaIndex, and how can it be used for large language models development? LlamaIndex is a framework that simplifies the development of applications powered by LLMs. It can handle various types of data, including unstructured data, which is crucial for improving the models' ability to recognize and interpret complex information. It streamlines the process by offering tools to connect LLMs with various external data sources, making it easier for developers to build intelligent applications. Whether it’s incorporating real-time data from a CRM or extracting insights from large text files, LlamaIndex allows businesses to harness the full potential of LLMs without needing deep technical expertise. It’s particularly effective for automating data-driven tasks and enhancing user interactions through chatbots or virtual assistants. Introducing LlamaIndex: A... --- - Published: 2024-10-11 - Modified: 2025-09-29 - URL: https://vstorm.co/ai/how-to-choose-the-right-llamaindex-developer/ - Categories: AI, LlamaIndex, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski In today's rapidly advancing field of AI and Natural Language Processing (NLP), leveraging large language models (LLMs) effectively is crucial for businesses aiming to stay competitive. Whether it's streamlining customer interactions, refining data analysis, or enhancing user experiences, LLM offer significant potential across sectors. However, to harness this potential, especially with LlamaIndex—a powerful tool designed to integrate LLMs into various applications—businesses need specialized expertise. Choosing the right development partner is key to achieving a successful LlamaIndex implementation. This guide will walk you through the steps to select a LlamaIndex development company that aligns with your needs. We'll cover critical considerations, potential challenges, and important questions to ask during the selection process to ensure your project is in good hands. 1. Define your goals Before starting a LlamaIndex project, it’s essential to have a clear understanding of your business objectives. This foundational step guides all subsequent decisions, ensuring that the technology delivers on your specific needs. Without a clear set of goals, even the most advanced solution may not achieve the desired outcomes. Unique goals and challenges for each business Each business has its own objectives and challenges. For some, the focus might be on automating customer service, while others might aim to improve decision-making through advanced data analysis. Clearly defining these priorities helps in setting realistic expectations and identifying the best path forward with LlamaIndex. Why businesses need LLMs and LlamaIndex Here are a few reasons why businesses are turning to LLMs and LlamaIndex: Automating complex language processing tasks Enhancing... --- - Published: 2024-09-16 - Modified: 2025-10-25 - URL: https://vstorm.co/ai/data-security-using-llms-guide/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Why is data security so important when working with LLMs? The rise of Large Language Models (LLMs) in artificial intelligence has transformed the way businesses operate, offering advanced capabilities in data analysis, automation, and decision-making. However, with these advancements come significant data security and privacy concerns. Safeguarding sensitive information is now a top priority, especially for industries like healthcare, finance, public administration, e-commerce, and cloud service providers. These sectors manage vast amounts of confidential data, making it crucial to adopt robust risk management strategies to protect against potential threats, manage business risks, and maintain regulatory compliance. Understanding Risk Management Definition of risk management Risk management is a systematic process designed to identify, assess, and mitigate potential risks that could adversely affect an organization’s objectives, assets, or reputation. This comprehensive approach involves recognizing potential threats, evaluating their likelihood and impact, and implementing strategies to minimize their adverse effects. Effective risk management ensures that organizations are prepared to handle uncertainties and can safeguard their operations and interests. Importance of risk management in LLMs In the realm of Large Language Models (LLMs), risk management is indispensable. LLMs, with their intricate components and vast data processing capabilities, present unique challenges, particularly concerning data privacy. Effective risk management in LLMs is essential to prevent data breaches, mitigate biases, and address other potential threats. By implementing robust risk management strategies, organizations can ensure that their LLMs operate securely and efficiently, maintaining the integrity and confidentiality of the data they handle. Brief overview of the risk management... --- - Published: 2024-09-06 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/how-to-choose-the-right-langchain-developer/ - Categories: AI, LangChain, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski In today's fast-paced world of AI and natural language processing (NLP), the effective use of large language models (LLMs) has become essential for businesses looking to stay competitive. Whether it's automating customer service, enhancing data analysis, or improving user experiences, LLMs are unlocking incredible potential across industries. However, successfully integrating these models into your business requires specialized expertise for LangChain development company. This is where LangChain, a cutting-edge framework designed to connect LLMs with your applications, comes into play. Choosing the right development company to implement LangChain solutions for your business can be a game-changer. The right partner will not only help you leverage the power of LLMs but also tailor the solutions to meet your specific needs and objectives. But how do you find the right company for such a crucial task? This article was created to guide you through the process of selecting a LangChain development company. We’ll break down the key considerations, potential challenges, and critical questions to ask to ensure your project is in good hands. 1. Define your goals Before embarking on any LangChain development project, it's crucial to begin with a clear understanding of your business objectives. This first step is perhaps the most important, as it lays the foundation for all subsequent decisions. Without clear goals, even the best technology won't deliver the desired results. Every business has unique goals and challenges No two businesses are the same, and each one comes with its own set of goals and obstacles. Whether you're looking... --- - Published: 2024-08-26 - Modified: 2025-11-16 - URL: https://vstorm.co/langchain/what-is-langchain-new-possibilities-in-development-with-llms/ - Categories: AI, LangChain, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski LangChain is paving the way for a new era in AI development, making it easier than ever for businesses to harness the power of Large Language Models (LLMs). LangChain enables AI developers to integrate language models with external data sources, emphasizing its role as an open-source framework that connects powerful large language models to various external components for the development of advanced NLP applications. This article not only explores the framework’s potential but also highlights why now is the perfect time for companies to start integrating the framework into their AI strategies. Welcome to LLM and LangChain capabilities As AI continues to transform industries, businesses face the challenge of integrating advanced technologies like large language models (LLMs) into their operations. LangChain emerges as a crucial tool that not only simplifies this process but also empowers companies to stay competitive in an increasingly AI-driven world. The rise of AI has transformed industries across the globe, with language models leading the charge in innovation. From customer service to content creation, the capabilities of large language models (LLMs) have reshaped how businesses operate. As companies strive to harness the power of AI, the need for frameworks that simplify the development of LLM-based applications has become more apparent. This is where LangChain steps in—a framework designed to streamline and enhance the process of creating applications powered by LLMs. A key feature of LangChain is its ability to perform data analysis, which enhances the efficiency and accuracy of AI interactions. This capability allows LLMs to... --- - Published: 2024-08-12 - Modified: 2025-10-20 - URL: https://vstorm.co/ai/how-to-scrape-data-using-langchain/ - Categories: AI, LangChain, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Who are we? We are at the forefront of helping startups and tech companies grow as a dedicated LangChain development company. Using AI and software solutions, we tailor services, automate tasks, and improve decision-making in application development. This approach allows our clients to work smarter and more efficiently. In this case, we develop LLM-based solutions with LangChain. Specializing in LangChain development, we are excited to lead in adopting this technology. The LangChain framework plays a crucial role in creating AI workflows through interconnected components, enabling sophisticated application development. Our focus is on helping clients enhance integration, performance, and scalability using LangChain as one of our main technologies. Why do we choose LangChain technology as a core tech stack for data scraping? We use LangChain technology as a core component of our tech stack because of its open-source nature and the flexibility it offers. This choice aligns with our philosophy of adaptability and innovation in the fast-paced tech industry. Additionally, the LangChain libraries simplify the development of AI workflows by providing off-the-shelf chains and customizable components. We are particularly fond of LangChain due to the supportive and resourceful community surrounding it. The community’s willingness to help and the collaborative relationships we’ve built with LangChain’s development team and key engineers enhance our understanding and implementation of the technology. This allows for quicker and more effective commercial applications, including the fine tuning of language models to adapt to changing requirements and address post-deployment issues. In summary, our choice of LangChain development technology shows... --- - Published: 2024-08-08 - Modified: 2025-11-18 - URL: https://vstorm.co/langchain/top-10-best-langchain-development-companies/ - Categories: AI, LangChain - Translation Priorities: Optional - Osoby: Antoni Kozelski What is LangChain? LangChain is an open-source development framework designed to simplify the creation of applications using large language models (LLMs). It provides a set of tools, components, and interfaces that enable developers to build complex, interactive AI systems with ease. Why is LangChain Important? LangChain plays a crucial role in the context of AI development and natural language processing for several reasons. It streamlines the process of integrating LLMs into various applications, offering a modular approach that allows developers to combine different components flexibly. The framework facilitates the creation of more sophisticated AI systems by providing tools for memory management, prompt engineering, and chain-of-thought reasoning. Additionally, LangChain enables developers to build applications that can interact with external data sources and APIs, significantly expanding the capabilities of language models. Criteria for selecting best LangChain development companies. To determine the top LangChain development companies, we focused on these three key criteria: Developer expertise. Prioritized firms with a large number of highly skilled LangChain developers. Proven track record. Selected companies with a history of successful LangChain project implementations. Specialized knowledge. Chose firms with deep expertise in LangChain processing, including LLM integration and memory management. These criteria ensure the chosen companies are best equipped to harness LangChain's capabilities and deliver exceptional AI solutions. Top LangChain development companies 1. Vstorm Number of employees: 25+ Year of foundation: 2017 Country: Poland Vstorm is a custom AI and software development company that specializes in creating solutions powered by large language models (LLMs). Vstorm has quickly gained... --- - Published: 2024-07-30 - Modified: 2025-11-19 - URL: https://vstorm.co/ai/ai_in_enterprises/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski Artificial intelligence (AI) is revolutionizing industries by enhancing automation, personalization, and decision-making processes. As AI technologies continue to advance, enterprises can adopt robust AI strategies to remain competitive in an increasingly digital world. This article provides comprehensive guidelines for fostering a high-performance AI startup within an enterprise, highlighting the benefits, challenges, and successful case studies that illustrate best practices. Current state of AI deployment Where are we now? Adopting AI technologies is becoming increasingly critical for maintaining a competitive edge. According to recent research, approximately 62% of enterprises have yet to explore or use AI/ML deployment, 22% are piloting these technologies, and only 7% have fully deployed them. Where will we be in 2030? Organizations implementing AI technologies have high expectations for the benefits they will gain. A significant 56% of these organizations expect AI to improve efficiency and productivity. Meanwhile, 32% are looking forward to cost reductions, and 31% anticipate that AI will drive innovation and growth within their operations. The AI market is set to experience remarkable growth over the next six years. It is projected to expand tenfold, with an annual growth rate (CAGR 2024-2030) of 46. 47%. This substantial growth is expected to increase the market volume from US$36 billion in 2024 to US$356. 10 billion by 2030. Vstorm This presentation was used and presented at the EIC conference in Berlin. Europe's leading event for the future of digital identities and cybersecurity. Here you can watch the speech of our CEO & Founder Antoni Kozelski https://www.... --- - Published: 2024-07-03 - Modified: 2025-11-24 - URL: https://vstorm.co/ai/advancing-text_analysis-on-images-with-llms/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Text analysis on images, also known as Optical Character Recognition (OCR), has become a significant aspect of artificial intelligence (AI). This technology enables the extraction and analysis of text from images, such as scanned documents, photographs, and other visual media. With the proliferation of digital content, the ability to automatically read and interpret text from images has numerous applications, ranging from digitizing historical documents to enhancing accessibility features for the visually impaired. As digital transformation accelerates across industries, OCR technology is becoming increasingly essential for automating data entry, improving document management, and enhancing the accessibility of digital content. What is Text Analysis on images? Text analysis on images refers to the use of AI and machine learning techniques to identify and extract textual information from images. This process involves detecting text regions within an image, recognizing the characters, and converting them into machine-readable text. OCR is commonly used in various fields, including document management, data entry automation, and content indexing, enabling efficient data extraction from visual media. For instance, OCR can be used to convert printed books and articles into digital formats, allowing them to be easily searched and accessed online. Additionally, OCR technology can assist in translating text from images in different languages, aiding in international communication and collaboration. How does Text Analysis on Images work? Text analysis on images typically involves several stages: Image preprocessing The image is prepared for analysis through various preprocessing steps, such as noise reduction, binarization (converting the image to black and white), and... --- - Published: 2024-06-27 - Modified: 2025-10-16 - URL: https://vstorm.co/ai/advancing_text_clustering_with_llms/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Text clustering is a technique in natural language processing (NLP) that enables the grouping of similar texts based on their content. This method has wide-ranging applications, from organizing large volumes of documents to improving search engines and enhancing customer service. By automatically categorizing texts, AI-powered text clustering helps in managing and extracting meaningful insights from massive textual data, addressing a few common problems such as customer segmentation, anomaly detection, and text classification, using efficiency and innovation across various industries. What is Text Clustering? Text clustering involves the automatic grouping of a collection of text documents into clusters, where documents within the same cluster are more similar to each other than to those in other clusters. This unsupervised machine learning technique does not require labeled data, making it particularly useful for exploratory data analysis. Text clustering can be applied to emails, articles, social media posts, customer reviews, and any other text-based data to uncover patterns, trends, and relationships. For example, in a customer service setting, clustering can help identify common issues faced by customers by highlighting which feature is the most significant, enabling businesses to address these problems with AI more effectively. How does Text Clustering work? Text clustering generally involves the following steps: Preprocessing Text data is cleaned and standardized, involving steps like tokenization (breaking text into words or phrases), removing stop words (common words like “and”, and “the”), and stemming or lemmatization (reducing words to their root forms). This step is crucial to ensure that the data is in... --- - Published: 2024-06-11 - Modified: 2025-09-27 - URL: https://vstorm.co/ai/what-is-translation/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Artificial Intelligence has significantly impacted various fields, including natural language processing (NLP). One of the most transformative applications of NLP is translation. AI-powered translation tools have revolutionized the way we communicate across different languages, breaking down language barriers and facilitating global interactions. The ability to translate text and speech accurately and efficiently has vast implications for businesses, governments, education, and personal communication. This chapter delves into the intricacies of AI translation, exploring its mechanisms, tools, benefits, and challenges. What is Translation? AI Translation refers to the use of artificial intelligence, particularly machine learning and deep learning techniques, to translate text or speech from one language to another. Unlike traditional translation methods that rely heavily on human translators, AI translation leverages large datasets and advanced algorithms to perform translations quickly and accurately. For instance, AI translation systems can process millions of documents in seconds, providing immediate results that would take human translators much longer to achieve. Additionally, AI can handle a wide array of languages and dialects, making it a versatile tool for global communication. How does Translation work? AI Translation typically involves several stages: Data collection Large datasets of parallel texts (same text in different languages) are gathered. These datasets often include texts from books, websites, and other sources that have been translated by humans, providing a rich source of information for training AI models. Training models Machine learning models, such as neural networks, are trained on these datasets to understand the nuances and patterns of languages. For example, a... --- - Published: 2024-06-07 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/what-is-sentiment-analysis/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski Sentiment analysis, a prominent application of text classification, has gained significant traction in recent years. By analyzing text to determine the sentiment behind it—whether positive, negative, or neutral—sentiment analysis provides invaluable insights across various domains such as customer service, market research, and social media monitoring. This chapter delves into the intricacies of sentiment analysis, exploring its workings, implementation, techniques, and benefits. As organizations increasingly seek to understand their customers and stakeholders, sentiment analysis emerges as a crucial tool for capturing the nuances of human emotions and opinions embedded in textual data. What is Sentiment Analysis? Sentiment analysis (SA), also known as opinion mining, is a subfield of natural language processing (NLP) that focuses on identifying and categorizing opinions expressed in a piece of text. The primary goal is to determine the writer's attitude toward a particular subject. This can range from assessing customer feedback on a product to gauging public opinion on social issues. For instance, companies might analyze product reviews on e-commerce websites to gauge customer satisfaction, or political analysts might study social media posts to understand public sentiment about a policy or candidate. The ability to automatically process vast amounts of text and derive meaningful insights makes sentiment analysis a powerful tool in various sectors. How does Sentiment Analysis work? SA operates by processing and analyzing text data through various NLP techniques. Initially, the text is preprocessed to remove noise and normalize the data. This involves tokenization, lemmatization, and the removal of stop words. Following this, machine learning... --- - Published: 2024-06-05 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/the-impact-of-llms-on-reasoning/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Reasoning is a cornerstone of artificial intelligence, enabling systems to process information, conclude, and make informed decisions. Unlike simple data processing or pattern recognition, reasoning involves a deeper understanding of relationships and the application of logical rules to infer new knowledge. This capability is crucial in AI systems that must solve complex problems, explain their actions, and adapt to new situations. What is Reasoning? Reasoning in AI refers to the process of drawing logical conclusions from available information. It involves using known data, rules, and logic to infer new knowledge, make predictions, or decide on the best course of action. This AI can be categorized into several types, including deductive, inductive, and abductive reasoning, each serving different purposes in AI systems. Deductive reasoning involves deriving specific conclusions from general principles, such as using mathematical theorems to solve problems. Inductive reasoning, on the other hand, involves generalizing from specific instances to broader generalizations, such as predicting future trends based on historical data. Abductive AI involves forming the most likely explanation for a set of observations, commonly used in diagnostic systems. How does Reasoning work? Reasoning systems typically operate by applying logical rules to a knowledge base. These rules can be predefined by experts or learned from data using machine learning techniques. The reasoning process involves: Data gathering Collecting relevant information from various sources, such as databases, sensors, or user inputs. Rule application Using logical rules to process the information, which may involve if-then statements, probabilistic rules, or fuzzy logic. Inference Drawing... --- - Published: 2024-05-30 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/semantic-search/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski In the modern era of information overload, finding the right information quickly and accurately is crucial. Traditional keyword-based search systems have their limitations, often failing to understand the context and meaning behind user queries. This is where Large Language Models (LLMs) and information retrieval (IR) come into play, enhancing semantic search capabilities. They revolutionize the way we access data, making searches more intuitive and results more relevant. This chapter delves into the concepts, workings, implementation, techniques, benefits, and challenges of information retrieval and semantic searching. Understanding these advanced search mechanisms is essential for businesses and individuals alike, as they navigate vast amounts of data in search of actionable insights. What is Semantic Search? Information retrieval (IR) refers to obtaining relevant information from a large repository, typically a database or the internet, in response to a user query. Semantic searching, on the other hand, enhances IR by understanding the context and intent behind the search terms. Unlike traditional searches that rely heavily on exact keyword matches, semantic search aims to comprehend the meaning and relationships between words, providing more accurate and contextually relevant results. For example, if a user searches for “apple,” a traditional search might return results about the fruit and the technology company indiscriminately, while a semantic search would use contextual clues to prioritize results that match the user’s intended meaning. Embedding models, such as BERT or sentence-transformers, are critical for generating text embeddings and optimizing applications for semantic search, thereby enhancing overall performance. An example of such a... --- - Published: 2024-05-29 - Modified: 2025-11-25 - URL: https://vstorm.co/ai/what-is-question-answering-llms/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski Question-answering (QA) systems are a cornerstone of artificial intelligence (AI) and Large Language Models (LLMs), designed to automatically answer questions posed by humans in natural language. These systems have evolved from simple keyword-based searches to sophisticated models capable of understanding and generating human-like responses. As a key application of natural language processing (NLP), QA systems are widely used in customer service, search engines, and virtual assistants. Over the past few decades, machine learning and deep learning advancements have significantly enhanced the capabilities of QA systems, enabling them to handle a wider variety of queries with greater accuracy and efficiency. These advancements have also enabled the creation and deployment of tailored QA projects through custom question answering, which includes features such as authoring, training, and publishing processes, as well as integration into the development lifecycle. What is Question answering? Question answering involves the extraction and generation of information in response to queries. Unlike traditional information retrieval systems that provide a list of documents, QA systems aim to give precise answers to questions. This involves understanding the context, semantics, and intent behind the question to deliver accurate and relevant responses. For example, if a user asks, “What is the capital of France? ” a QA system would directly answer “Paris” rather than providing links to articles about France. This ability to provide concise and direct answers makes QA systems highly valuable in various applications, from virtual assistants like Siri and Alexa to customer support chatbots that handle routine inquiries. How does it... --- - Published: 2024-05-22 - Modified: 2025-10-05 - URL: https://vstorm.co/ai/all-you-have-to-know-about-text-summarization/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski In our digital era, the volume of information available can be overwhelming. Text Summarization (TS) has emerged as a crucial component in the field of Text Generation, simplifying complex content into manageable, digestible summaries. This capability is vital for professionals like journalists, content creators, educators, and researchers who deal with copious amounts of information, enabling them to distill essential insights and communicate them efficiently. What is Text Summarization? Text Summarization refers to the process within Artificial Intelligence focused on reducing a lengthy document into a brief, concentrated version that encapsulates its core information and overall meaning. This technology is crucial in numerous contexts: It allows for the rapid understanding of lengthy articles, research papers, or detailed reports by distilling their essential content. It supports readers in grasping the main ideas without the necessity of navigating through dense material, thereby saving time and enhancing comprehension. In professional settings, Text Summarization aids in managing the deluge of information by transforming extensive documentation into easy-to-digest summaries, thus boosting efficiency and focus. How does Text Summarization work? The methodology behind Text Summarization involves several intricate steps that utilize advanced natural language processing (NLP) techniques to transform full texts into summaries: Pre-processing This critical initial step involves preparing the text for summarization. It includes cleansing the text by removing superfluous elements like excessive formatting, irrelevant data, and errors. This phase also involves standardizing the text to ensure consistency in presentation and readability. Identification of key points Algorithms analyze the text to determine which parts carry... --- - Published: 2024-05-15 - Modified: 2025-11-21 - URL: https://vstorm.co/ai/implementing-information-extraction/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski In our increasingly digital world, the sheer volume of data generated every day presents both opportunities and challenges. Information Extraction (IE) emerges as a critical technology in making sense of this data deluge, transforming unstructured or semi-structured data into structured, actionable information. This capability is indispensable for professionals like data scientists, AI researchers, software developers, and business analysts. They rely on IE to parse through vast amounts of textual data, extract meaningful insights, and ultimately enhance business intelligence and analytics initiatives. What is Information Extraction? Information Extraction refers to a set of methodologies within Artificial Intelligence (AI) aimed at systematically extracting specific information from unstructured and semi-structured data sources. This process is needed in: Identifying and categorizing unique data elements called entities—such as names, dates, places, financial figures, and product names—allows different industries to tailor information to specific operational needs, enhancing data accuracy and relevance in decision-making processes. Organizing data into a structured format Enabling easy storage, search, and analysis of data IE is crucial for creating efficient databases, enhancing content indexing, and supporting complex decision-making processes across various industries. Large Language Models (LLMs) significantly enhance the capabilities of Information Extraction (IE) systems. These advanced AI tools are trained on diverse and extensive datasets, allowing them to perform complex language understanding tasks, including accurately identifying and categorizing various data elements known as entities—such as names, dates, places, financial figures, and product names—within unstructured or semi-structured texts. How does Information Extraction work? The process of AI like Information Extraction (IE) involves... --- - Published: 2024-04-16 - Modified: 2025-10-23 - URL: https://vstorm.co/ai/how-large-action-models-are-transforming-industries/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski In a time of rapid technological progression, Large Action Models (LAMs) stand as transformative forces reshaping industry landscapes. This article explores the significant role and impact of LAMs, highlighting their features, uses, and the challenges they introduce. LAMs mark a significant advancement by broadening AI capabilities from mere understanding to executing sophisticated tasks, offering remarkable improvements in operational efficiency, decision-making processes, and customer service personalization across different sectors. What is LAM? Large Action Models are advanced technological systems designed to analyze big data, predict trends, and automate tasks with high precision. They're built using complex algorithms and machine learning, allowing them to understand and act upon vast amounts of information quickly. LAMs can adapt and learn from new data, making them incredibly efficient at improving processes, personalizing experiences, and making smart decisions in various industries. Essentially, they're like highly intelligent assistants that can help businesses operate more effectively and stay ahead in their market. Capabilities of Language Action Models The concept of LAM represents a significant advancement of natural language processing, positioning itself as the next step following the foundational Large Language Models (LLM). This transition from LLM to LAM marks a leap forward in the development of language technologies. LLM and LAM might look similar at first but we'll take a closer look at what sets them apart, focusing on the unique things each one can do. Learning basis: LLM: Trained primarily on textual data to understand and generate human-like text. LAM: Trained not only on textual data but... --- - Published: 2024-04-09 - Modified: 2025-10-29 - URL: https://vstorm.co/ai/the-power-rag-in-llm/ - Categories: AI, LangChain, LLMs, RAG - Translation Priorities: Optional - Osoby: Antoni Kozelski In the evolving field of AI, new technologies continually expand what's possible. One significant advancement is Retrieval-Augmented Generation (RAG), which combines extensive databases with powerful computing to advance AI. RAG improves Large Language Models (LLMs) by enabling them to use external data instantly, offering a more versatile approach to AI applications. This discussion delves into how RAG works, its influence across various sectors, and how it might shape the future of AI. What is RAG? Retrieval-augmented generation is a sophisticated approach that merges the capabilities of large language models with advanced retrieval techniques. This architecture enables LLMs to pull in and utilize information from external, specific sources like proprietary databases or the internet in real time. By doing so, RAG significantly improves the accuracy and relevance of the outputs produced by these AI applications. The inclusion of such precise, contextually relevant data into the AI's workflow allows it to perform tasks with a higher degree of precision. This is particularly valuable when dealing with proprietary, private, or constantly changing information, where the direct application of AI can greatly benefit from the most current and specific data available. In essence, RAG enriches the foundation upon which AI models operate, providing them with a wider pool of information to draw from. This not only enhances their performance in generating accurate and contextually appropriate responses but also broadens the scope of tasks they can effectively tackle. Through RAG, AI applications become more intelligent, adaptable, and capable of handling complex, dynamic scenarios, making this... --- - Published: 2024-04-04 - Modified: 2025-09-07 - URL: https://vstorm.co/community/happy-ester-holiday-vstorm/ - Categories: Community - Translation Priorities: Optional - Osoby: Antoni Kozelski As the spring season blossoms, we at Vstorm are filled with gratitude and want to extend our warmest Easter greetings to you. This time of year, as nature reawakens, symbolizes renewal and growth, and we hope your Easter is brimming with joy, peace, and rejuvenation. Whether you’re embarking on an outdoor adventure to enjoy the fresh spring air or finding serenity in the comfort of your home, we wish you a delightful Easter and a season filled with prosperity and well-being. Reflecting on the past few months, we’ve journeyed through exciting developments and breakthroughs, embraced the ever-evolving landscape of AI & LLMs, and have been honored with industry accolades that underscore our commitment to innovation. These strides forward were not solitary achievements but were made possible through the strength of our collective efforts and, most importantly, your unwavering support and collaboration. As we look forward to the unfolding chapters of 2024, we’re energized by the prospects that lie ahead. Your engagement and partnership are the keystones of our existence and success. We are genuinely thrilled to continue this journey with you by our side, fostering growth and exploring new horizons together. Here’s to celebrating the spirit of Easter and welcoming a splendid spring season. Your participation in our Vstorm community enriches us in countless ways, and we eagerly anticipate crafting an even more remarkable year together. Thank you for being an indispensable part of our story. Let’s embark on this next phase with hope and enthusiasm, aiming to make 2024... --- - Published: 2024-02-29 - Modified: 2025-10-11 - URL: https://vstorm.co/ai/the-impact-of-llm-on-sales-strategies-in-2024/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski In the swiftly evolving world of technology, Large Language Models (LLMs) have carved out a niche, becoming a cornerstone in the advancement of various sectors. These AI-driven tools, known for their ability to understand, generate, and interact with human language at an unparalleled scale, are now making significant inroads into the sales industry. This exploration seeks to demystify the application of LLMs within sales, spotlighting their advantages, practical uses, and the challenges they present. Large Language Models (LLM) represent a subset of artificial intelligence, specifically within natural language processing (NLP), that are trained on extensive corpora of text data. Through techniques like deep learning and transformer architectures, LLMs are capable of understanding, generating, and engaging with human language at a level that approaches natural human comprehension. These models utilize massive datasets to learn linguistic patterns, context, and semantics, enabling them to perform a wide range of language-based tasks. In the context of sales, LLMs apply this capability to automate customer communications, analyze customer feedback and behavior, and generate actionable insights from unstructured data, thereby supporting more personalized and data-informed sales strategies. The use cases of integrating LLMs into Sales Crafting effective sales strategies: Effective sales strategies blend digital engagement, thought leadership, and targeted outreach like inbound lead prioritization, detailed prospect analysis, and the strategic use of free trials, alongside the traditional cold calling approach. These components act as navigational tools that guide sales teams through the competitive marketplace, driving them toward their goals. By adopting such a multifaceted approach, sales... --- - Published: 2024-02-28 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/how-can-llm-be-integrated-with-crm-to-boost-the-sales-team/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski The modern business world is fiercely competitive, requiring sales teams to constantly find new ways to improve their performance and keep a step ahead of their rivals. Among the technological advancements making waves recently, Large Language Models stand out. These powerful tools offer a wealth of possibilities to reshape how sales teams communicate with their customers trought CRM, manage their daily tasks, and enhance their ability to forecast and predict outcomes accurately. In this article, we'll delve into the advantages and real-world uses of integrating LLMs with Customer Relationship Management (CRM) systems. We'll explain how LLMs can make customer interactions more personalized, streamline sales operations, refine sales forecasting and predictions, and redefine sales strategies. Additionally, we'll look at how LLMs can foster better teamwork within sales departments, offer tailored training suggestions, improve the management of sales territories, and encourage innovation and experimentation. What is an LLM? Large Language Models are advanced artificial intelligence systems designed to understand, generate, and interpret human language at scale. They are trained on extensive datasets, encompassing a wide range of human discourse, enabling them to perform tasks such as text generation, translation, summarization, and sentiment analysis with high accuracy. LLMs leverage deep learning techniques, particularly transformer architectures, to analyze and produce language, making them invaluable tools for natural language processing (NLP) applications across various industries. The amazing thing about LLMs is that they get better the more they're used. They adapt and learn, making them great for personalizing responses and automating tasks that involve language.... --- - Published: 2023-12-22 - Modified: 2025-11-17 - URL: https://vstorm.co/ai/ai-and-llm-in-fintech-transforming-the-future-of-finance/ - Categories: AI, LLMs - Translation Priorities: Optional - Osoby: Antoni Kozelski The financial sector is undergoing a transformative shift, driven by the adoption of Artificial Intelligence (AI) and Large Language Models (LLMs). These technologies are redefining financial services, bringing innovative perspectives and capabilities. An International Monetary Fund report highlights AI's significant impact on financial operations and decision-making, showcasing its pivotal role in the industry. Explore the future of finance through the lens of AI and LLMs in FinTech. This transformation is revolutionizing the way financial institutions operate, interact with customers, and make strategic decisions, signaling a new era in the industry where traditional practices are being augmented or completely redefined by more efficient, AI-driven methods. Key benefits and impact AI in Finance The financial sector is at the cusp of a revolutionary transformation, largely driven by the integration of cutting-edge technologies like Artificial Intelligence (AI) and Language Language Models (LLMs). This evolution is not just a fleeting trend but a substantial shift in how financial services operate and interact with their customers. Delving into the core aspects of this transformation reveals the multifaceted impact of AI and LLM in FinTech: Strategic decision-making enhanced The role of AI in finance goes beyond basic analytics. It encompasses a sophisticated analysis of complex datasets, providing unparalleled insights. This capability enables financial institutions to make more informed, strategic decisions, particularly in areas such as investment strategies and financial planning. By leveraging AI, firms can forecast market trends and customer behaviors, leading to proactive and precise financial decision-making. Operational efficiency boosted AI's influence in streamlining operations... --- - Published: 2023-12-21 - Modified: 2025-11-08 - URL: https://vstorm.co/community/happy-christmas-and-cheers-to-2024/ - Categories: Community - Translation Priorities: Optional As the year wraps up, we at Vstorm just wanted to take a moment to say a big thank you and send some holiday cheer your way. We hope your holidays are filled with fun and relaxation. Whether you're out there braving the cold for a winter run or just kicking back at home, we wish you a wonderful Christmas time and a New Year full of good health and happiness. This year has been pretty busy, marked by new ventures and partnerships, a pivot towards AI & LLMs, and recognition as an AI leader by Clutch. We faced challenges head-on, together. Looking ahead to 2024, we're excited about what's to come. Your presence means the world to us, and we can’t wait to keep the momentum going into the new year. Here's to a fantastic holiday season and an even better New Year. Thanks for being part of our Vstorm circle. Let's make 2024 amazing! Vstorm Team. --- - Published: 2023-11-16 - Modified: 2025-10-12 - URL: https://vstorm.co/ai/use-cases-large-language-models-in-adtech/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski Imagine a world where every advertisement you see feels like it's speaking directly to you, where brands understand your needs before you even articulate them. This is not a futuristic fantasy; it's the present reality in the realm of AdTech, thanks to Large Language Models (LLMs) like GPT-3. In 2023, the AdTech industry, valued at over $400 billion (Statista), is undergoing a seismic shift. LLMs are at the heart of this transformation, heralding a new era of personalized and efficient advertising. A report by McKinsey & Company reveals that businesses that adopt AI technologies in marketing and sales can see a 15-20% increase in ROI (McKinsey & Company). LLMs, with their ability to analyze and process vast amounts of data, are playing a pivotal role in realizing these gains. By crafting messages that resonate on a personal level, these models are not just changing how ads are made; they're redefining the relationship between brands and consumers. Personalized content creation Large Language Models (LLMs) in AdTech are revolutionizing ad copy creation, tailoring content to individual audience segments. This personalized approach, driven by consumer behavior and preference data, enhances user experience and campaign effectiveness. A real-life example is Spotify's personalized playlists, which use similar technology to curate music based on individual listening habits, significantly boosting user engagement. Research by Forbes corroborates the effectiveness of personalized ads, noting they significantly boost engagement rates (Forbes). Predictive analysis and trend forecasting LLMs analyze extensive datasets to accurately predict trends and consumer behaviors. This capability is... --- - Published: 2023-11-15 - Modified: 2025-11-15 - URL: https://vstorm.co/ai/large-language-models-in-telecommunication/ - Categories: AI - Translation Priorities: Optional The telecommunication sector has always been at the forefront of technological innovation. The recent integration of Large Language Models (LLMs) marks another leap forward. These sophisticated AI systems are not just reshaping customer interactions but are also redefining operational efficiencies. A 2022 study by Deloitte highlights a 30% increase in customer satisfaction in companies employing AI-driven solutions, underscoring the burgeoning role of LLMs in telecom. LLMs like OpenAI's GPT series have transcended traditional Natural Language Processing applications. Their adoption in the telecom sector, though in nascent stages, is progressively unfolding, showcasing their potential in diverse operational aspects . Looking ahead, the influence of LLMs in telecommunications is poised to grow exponentially. McKinsey & Company's research indicates a transformative potential for AI in reshaping service delivery and enhancing customer experiences in the telecom sector. The prospect of AI-driven personalized telecom services is not just a possibility but an impending reality. Understanding LLMs and their capabilities Large Language Models like GPT-3 represent the pinnacle of AI's ability to process and generate human-like text. These models absorb vast amounts of data, learning intricate language patterns and nuances. Their proficiency in mimicking human conversation makes them invaluable in telecommunications, where effective communication is crucial. GPT-3, for instance, has demonstrated remarkable capabilities in generating responses that are indistinguishable from human text, opening new avenues for automated yet personalized customer interactions. At their core, LLMs utilize transformer-based architectures with self-attention mechanisms. This architecture allows them to process vast amounts of text data, capture long-range dependencies, and... --- - Published: 2023-11-15 - Modified: 2025-10-27 - URL: https://vstorm.co/ai/collaborative-synergy-vstorm-x-generative-ai-conference/ - Categories: AI - Translation Priorities: Optional The partnership between Vstorm and the Generative AI Conference represents a collaboration in the field of artificial intelligence (AI). This article provides an overview of this alliance, focusing on the roles of both entities and the influence of Francesca Tabor, the Conference’s founder. The Generative AI Conference's contributions to AI The Generative AI Conference, a hub for AI advancements, plays a keyl role in shaping the discussion in the field. It serves as a vital platform for exchanging knowledge and showcasing emerging technologies, bringing together a diverse array of AI experts, researchers, and practitioners. These gatherings are instrumental in shaping the discussion around AI's trends, challenges, and potential future paths. The Genesis of the partnership The partnership took root at the Generative AI Conference held virtually on October 25th. Among the attendees was Vstorm, represented by CEO Antoni Kozelski, who brought to the table profound insights into the integration of Large Language Models (LLMs) in marketing technology (MarTech). Vstorm's role at the conference Vstorm's engagement in the conference was a reflection of its deep commitment to advancing AI in the business sphere. Kozelski’s presentation, "Utilizing LLMs in Your Company," offered a practical perspective on AI applications, resonating well with the conference's goal of bridging theory and practice in AI. A Convergence of ideas and innovation The conference served as a melting pot for innovative thoughts, featuring industry leaders like Rifah Nawar from Writesonic and Sasha Wallinger from Blockchain Style Lab. This gathering underscored the versatile applications of AI across various... --- - Published: 2023-11-15 - Modified: 2025-10-28 - URL: https://vstorm.co/ai/vstorm-honored-as-a-clutch-champion-for-2023/ - Categories: AI - Translation Priorities: Optional In the competitive landscape of AI development, recognition for exceptional service and expertise is a significant achievement. Vstorm has recently been honored as a 2023 Clutch Champion, a leading global marketplace for B2B service providers. The Clutch Champion title is awarded to the top 10% of companies on the platform, highlighting those who have demonstrated industry leadership and delivered outstanding results compared to their peers. Vstorm Clutch Champion achivement in context This award for Vstorm comes as a result of diligent work in the field of AI development. The selection criteria for the Clutch Champion award are rigorous, focusing on industry expertise and consistent performance excellence. Vstorm's inclusion in the 2023 Fall Clutch Champions was driven by new, verified client reviews, emphasizing the company's commitment to client satisfaction and high-quality service delivery. Client reviews as a measure of success Vstorm’s recognition is largely attributed to positive client feedback. These reviews not only commend Vstorm for its technical capabilities in AI development but also highlight the company’s dedication to understanding and effectively meeting client needs. Such client endorsements are crucial in establishing Vstorm's reputation as a reliable and competent player in the AI development sector. Remarks from Clutch CEO Sonny Ganguly Sonny Ganguly, CEO of Clutch, commented on the significance of the award: “The Clutch Champion designation marks a high level of achievement on our platform. This year’s honorees, through their exceptional service, have set a benchmark for excellence and client satisfaction. We are proud to acknowledge their contributions and successes.... --- - Published: 2023-11-10 - Modified: 2025-10-13 - URL: https://vstorm.co/ai/the-power-of-langchain-in-llm-based-applications/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski LangChain, a framework specifically designed for Large Language Model (LLM) applications, has emerged as a major tool in enhancing the capabilities of natural language processing (NLP). A recent study highlighted by ProjectPro points out the significant improvements LangChain brings in accuracy and efficiency for NLP tasks. This is especially relevant as businesses and technology sectors increasingly rely on advanced language processing for better communication and data analysis. One of the key strengths of LangChain, as detailed by Towards Data Science, lies in its function-calling feature. This capability not only makes AI more usable but also opens up new avenues for integrating complex functionalities within LLM applications. This has been instrumental in driving forward the potential of language models beyond conventional boundaries. These insights underscore the growing importance of LangChain in the realm of LLMs, indicating a trend towards more sophisticated, data-rich applications in the field of AI and language processing. How does it work? LangChain, an innovative application development. Its technological strength lies in its ability to facilitate the building of sophisticated applications powered by LLMs like GPT. LangChain does this by providing a framework that simplifies the integration of complex language models into varied applications. Its structure comprises several parts, including Python and JavaScript libraries, which are essential for developers looking to harness the advanced capabilities of NLP. By enabling precise context-based actions and decision-making processes, LangChain offers a unique advantage in developing applications that require nuanced understanding and generation of language. This feature is crucial in creating more... --- - Published: 2023-11-10 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/langchain-development-services/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski At Vstorm, our decision to adopt LangChain was driven by its unparalleled potential in the application of Large Language Models (LLMs) for our clients. LangChain development enables us to efficiently integrate and orchestrate LLMs, enhancing workflows and creating customized solutions that meet specific business needs. LLMs, such as GPT, are advanced AI technologies capable of understanding and generating human-like text. They have been transforming the way we interact with digital content, making AI interactions more natural and intuitive. By leveraging LangChain, we can build advanced AI-driven systems that process vast amounts of data, deliver accurate results, and streamline operations for our clients. Why LangChain? LangChain, as a framework, excels in making the integration of these powerful LLMs into various applications faster and more efficient. This efficiency means that our clients can benefit from advanced AI capabilities without the need for extensive development time or resources. LangChain’s ability to combine LLMs with other information sources also allows for more context-specific AI solutions. Research and reports have consistently highlighted the significance of LLMs in the current AI landscape. Their ability to process and generate language has opened up new possibilities in AI application, from automating customer service interactions to creating content and analyzing large sets of data. LangChain development serves as a bridge, bringing these sophisticated AI capabilities into practical, everyday business use. Key LangChain development services we offer: AI-Powered application development Imagine having an application that not only understands your needs but also anticipates them. That's what we offer with our... --- - Published: 2023-11-08 - Modified: 2025-11-26 - URL: https://vstorm.co/ai/ai-talent-recruitment-challenges-in-enterprises/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski Hiring AI Developers in Enterprises According to McKinsey, generative AI is a key driver of productivity in the modern economy, radically transforming how businesses operate and compete . According to an additional report by LinkedIn, the role of AI specialist ranks as the #1 emerging job, highlighting the growing demand for AI expertise in various industries. This surge in demand is prompting companies to invest significantly in employee training and development to fill AI roles, emphasizing the critical need for AI developers in the current job market. Furthermore, a majority of respondents in a McKinsey survey reported difficulty in hiring for each AI-related role in the past year, indicating a significant gap in the availability of skilled AI professionals . This gap presents a clear opportunity for enterprises to focus on hiring and nurturing AI talent to meet their evolving technological needs. The visualization above represents the challenges faced by enterprises in recruiting AI talent, based on a McKinsey survey. It shows that a significant majority (60%) of respondents reported difficulty in hiring for AI-related roles in the past year. This disparity underscores the need for enterprises to focus more on hiring and nurturing AI talent to meet their evolving technological requirements. According to Deloitte, team augmentation with AI developers is an essential strategy for businesses looking to stay ahead . It involves not just expanding the workforce but enriching it with specialized skills and new perspectives. This approach is particularly beneficial in tackling complex AI projects and fostering innovation.... --- - Published: 2023-10-31 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/a-guide-to-finding-the-right-ai-developer-on-upwork/ - Categories: AI - Translation Priorities: Optional The increasing demand for AI expertise In an era where artificial intelligence (AI) is reshaping industries, businesses are increasingly seeking skilled AI developers to drive innovation. Recent statistics indicate that AI-related job postings have surged by over 70% in the last two years alone, highlighting the growing demand for this expertise. For companies and entrepreneurs aiming to harness the power of AI, finding the right talent is crucial. Upwork, a leading platform for freelancers, offers a vast pool of AI professionals, but the challenge lies in identifying the best match for your specific needs. The search begins: navigating Upwork’s AI talent pool Understanding AI developer roles AI development is a broad field, encompassing roles from machine learning engineers to data scientists. Before diving into Upwork’s talent pool, it’s essential to understand the specific skill set required for your project. An AI engineer on Upwork, for example, might specialize in algorithm development, while an AI agency on Upwork could offer a more comprehensive range of services, including data analysis and model deployment. Criteria for selecting an AI developer When searching for an AI developer on Upwork, consider factors such as experience, portfolio quality, client feedback, and specific technical skills. Look for professionals who have a proven track record in projects similar to yours. Also, assess their communication skills and ability to understand your project's unique requirements. The role of AI agencies on Upwork For more complex projects, collaborating with an AI agency on Upwork can be advantageous. Agencies like Vstorm offer... --- - Published: 2023-10-31 - Modified: 2025-11-23 - URL: https://vstorm.co/ai/strategies-for-choosing-the-best-ai-development-vendor-on-clutch/ - Categories: AI - Translation Priorities: Optional In the dynamic world of Artificial Intelligence, selecting a top-tier vendor is paramount to the success of your projects. The journey to finding the right partner, especially on platforms like Clutch, can be intricate and demands a strategic approach. This detailed guide aims to equip you with the knowledge and tools to navigate this terrain, ensuring that your choice in AI development vendor is nothing short of excellent. Crystalize your vision: Defining your AI project requirements The foundation of a successful vendor search begins with a clear understanding of what you need. Define the objectives, scope, and intricacies of your AI project. What specific AI technologies are necessary? What is the expected timeline for completion? What budget constraints are in play? Answering these questions ahead of time establishes a clear roadmap, facilitating a smoother selection process. Crafting a stellar project brief: Your introduction to vendors Your project brief serves as the initial point of contact between you and potential AI development vendors. Make it count. Clearly outline the scope of your project, the technical expertise required, and your expectations. A detailed and transparent project brief not only attracts vendors with the right skill set but also sets the tone for a productive relationship. Leveraging Clutch’s review system: Digging deeper Clutch is renowned for its extensive review and rating system, which provides valuable insights into a vendor’s past performance. Don’t just skim the surface; delve into the reviews to understand the experiences of previous clients. Focus on aspects such as the... --- - Published: 2023-10-30 - Modified: 2025-11-22 - URL: https://vstorm.co/ai/ai-integration/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski The digital age has shifted the boundaries of what's possible in business. One of the most transformative drivers of this change is artificial intelligence (AI). As AI technology matures, company owners are continuously seeking ways to leverage its power to optimize processes, gain competitive advantages, and craft new business paradigms. If "AI integration" or "LLM integration" are on your radar, understanding their evolutionary stages is crucial. As we explore the process of AI integration, it's impossible to sideline the emerging power of Language Models, particularly Large Language Models (LLMs) like OpenAI's GPT series. These mammoth computational models can process and generate human-like text based on a vast array of data, offering significant advantages to businesses when integrated properly. For company owners intrigued by LLM's potential, the journey might seem daunting. But fret not. In this section, we’ll answer some pivotal questions about LLM integration into your organizational processes. How to start LLM integration? Let's begin with identifying the need: Begin by pinpointing areas in your organization that can benefit from natural language processing. Customer service, content creation, and data analysis are common starting points. Then there is a time for research and choosing the right model: While there are several LLMs available, their capabilities vary. Understand the strengths of each and choose one that aligns with your business needs. What we strongly recommend are pilot projects: Instead of an organization-wide rollout, start with a small-scale pilot project. This allows you to gauge the effectiveness and ROI of the LLM without... --- - Published: 2023-10-24 - Modified: 2025-11-27 - URL: https://vstorm.co/ai/instant-customer-service-ai-chatbots-in-e-commerce/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski The e-commerce landscape is evolving at an unprecedented pace, with artificial intelligence (AI) standing at the forefront of this transformation. AI chatbots, in particular, have become integral in enhancing customer service, providing instant, personalized responses to customer inquiries, and significantly improving user experience. The adoption of these AI-powered tools is not just a trend; it’s a necessary step forward. According to a report by Grand View Research, the global chatbot market size is expected to reach $1. 25 billion by 2025, growing at a compound annual growth rate (CAGR) of 24. 3%. Source: Precedence Research In this comprehensive guide, we will delve deep into how AI chatbots are changing customer service in e-commerce and retail, the benefits they offer, real-life use cases, potential downsides and how the industry is overcoming them, and finally, how you can get aid in integrating AI into your customer service. Let’s embark on this journey to uncover why AI chatbots are not just beneficial but essential in today’s digital age. Benefits of conversational chatbots in customer service Source: LitsLink 24/7 availability The digital age demands immediate responses and constant availability. AI chatbots are meticulously designed to meet these needs, providing unparalleled service around the clock. Whether it’s the crack of dawn or the dead of night, these digital assistants are ready and waiting, ensuring that every customer inquiry is promptly addressed. This tireless work ethic is crucial, especially when dealing with customers from around the globe, effectively eliminating the challenges posed by time zones. The... --- - Published: 2023-10-24 - Modified: 2025-10-28 - URL: https://vstorm.co/ai/the-manifest-honors-vstorm-as-torontos-most-reviewed-ai-leader-for-2023/ - Categories: AI - Translation Priorities: Optional Here at Vstorm, we’re dedicated to helping businesses harness and maximize the power of generative AI to fuel their growth. Founded in 2017, our team has been delivering top-notch development services to businesses across the globe. We proudly serve as their strategic and technological partners for their respective endeavors. Today, we’re extremely excited to share with you all an esteemed award that was made possible by our gracious clients. During the annual The Manifest Company Awards, Vstorm was officially spotlighted as one of the most reviewed and recommended technology firms. According to their latest report, our team is among Toronto’s go-to partners for artificial intelligence solutions this 2023! For those of you who don’t know, The Manifest is an independent business news resource designed to guide browsers through different B2B industries. The site holds a yearly awards cycle to spotlight the finest service providers who share exceptional bonds with their clients. The awardees are chosen based on the number of testimonials they’ve earned over the preceding twelve months. This incredible award reflects the amazing support and trust of our beloved clients. If it weren’t for their brilliant projects, we wouldn’t be celebrating this moment right now. We’d like to seize this opportunity to extend our sincerest gratitude to everyone who believed in Vstorm. Thank you so much for everything! We hope that we made you proud and we genuinely look forward to seizing more fantastic opportunities with you all in the future.   Drive your digital potential with Vstorm! Connect... --- - Published: 2023-10-03 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/generative-ai-in-tech-sectors/ - Categories: AI - Translation Priorities: Optional In the ever-evolving landscape of technology, generative AI stands as one of the most groundbreaking advancements. It is not just a buzzword but is reshaping various tech sectors, transforming business processes, and redefining customer experiences. This article delves deep into the tech sectors that are immensely benefiting from generative AI services and their notable use cases. 1. HRTech: The Human Touch to Automation The Human Resources sector is not just about recruiting and payroll anymore. With the integration of generative AI, HR processes are witnessing unprecedented levels of automation and precision. AI Chatbots: Serving as virtual HR assistants, chatbots answer employee queries related to policies, benefits, or leave applications, providing 24/7 support. NLP for Feedback Analysis: Generative AI can analyze open-text feedback from employees, identifying areas of improvement and predicting potential issues. Automated Candidate Screening with LLMs: Generative models can sift through thousands of resumes, matching job descriptions to the most fitting candidates, thus simplifying the recruitment process. 2. HealthTech: The New Age of Patient Care Generative AI has given a fresh dimension to healthcare, making patient care more personalized and diagnosis more accurate. NLP for Patient Records: By analyzing patient records, generative AI can predict health outcomes, aiding in proactive care. AI Chatbots for Health Advice: Patients can now receive immediate advice or book appointments, streamlining the administrative processes of hospitals. 3. FinTech: The Financial Facelift The financial sector thrives on data. With generative AI, this data is not just analyzed but also used to make predictions, offer personalized... --- - Published: 2023-09-21 - Modified: 2025-10-18 - URL: https://vstorm.co/ai/navigating-the-ai-wilderness-tools-for-small-businesses-and-startups/ - Categories: AI - Translation Priorities: Optional Navigating the world of technology, especially AI, is a bit like venturing into a dense forest for small businesses and startups. There’s a lot to explore, and finding the right path—that is, the right AI vendor—can make all the difference in reaching your destination, your business goals. The stakes are high, and the journey is filled with decisions and details, each one as important as the next. Choosing the right companion: finding a good AI vendor It starts with looking around and doing your homework. Think of it as scouting. Find vendors with a good reputation and proven experience. Look for those who get your business, who understand your industry and its unique challenges. It’s about finding a companion who knows the terrain and can guide you through it. Mapping the journey: preparing a good project brief Before stepping into the forest, you need a map. That’s your project brief. It should clearly outline what you want to achieve, what you expect, and any specific things you have in mind, like certain technologies or frameworks. This map helps the vendor understand your path and provide the right guidance and solutions. Calculating the travel costs: getting price estimations Having a good map— a clear, detailed brief— is also key to knowing how much your journey will cost. It helps vendors gauge the scope and complexity of your project and allocate resources accordingly, giving you a fair and accurate price estimation. Using the right tools: RTP, AI-powered excel template tool Every explorer... --- - Published: 2023-08-30 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/ai-chatbot-build-in-public-by-vstorm-01/ - Categories: AI - Translation Priorities: Optional In 2023, if you're a business owner not considering the integration of AI chatbots, you might be missing out on a revenue. AI chatbots are not just about automating customer support. Their potential spans across sales, marketing, and even internal processes. 24/7 Availability: The most evident benefit, and often the primary reason businesses opt for chatbots, is their non-stop availability . While human agents need breaks, chatbots are there round-the-clock, ensuring customers from any time zone receive timely responses. Cost efficiency: Chatbots are projected to save a staggering $209M in banking alone, with businesses witnessing up to 20-40% boost in sales due to chatbot integration. Personalized customer experience: Gone are the days when bots provided generic answers. With advancements in Natural Language Processing (NLP), Machine Learning (ML), and Natural Language Understanding (NLU), chatbots now offer highly personalized interactions that mimic human-like conversations, fostering loyalty. Multilingual and omnichannel support: As businesses go global, the demand for multilingual support rises. Chatbots cater to this need efficiently, ensuring consistent interactions across various digital platforms. Enhanced analytics and feedback collection: Chatbots don't just respond. They learn. By monitoring user data, tracking behaviors, and collecting feedback, they continually refine their interactions, providing businesses with invaluable insights for improvement . Sales and marketing boost: From the eCommerce sector, where Generative AI chatbots shape a whopping $5. 9T market by refining recommendations and reviews, to banking, where chatbots detect fraud and promote products, the impact on sales and marketing is undeniable. In conclusion, the integration of AI... --- - Published: 2023-08-14 - Modified: 2025-11-07 - URL: https://vstorm.co/ai/artificial-intelligence-and-its-dance-with-data-privacy-unpacking-the-chatgpt-conundrum/ - Categories: AI - Translation Priorities: Optional In the digital age, the relationship between AI and data privacy stands as a testament to the dual-edged nature of progress. The marvel that is ChatGPT by OpenAI sits at the heart of this debate, illustrating the tightrope walk between immense potential and pertinent concerns. AI-driven chatbots, once belonging to the realm of futurist fantasies, have become an everyday reality. ChatGPT, with its capacity to generate text that’s coherent and contextually sound, has set new standards in this space. From engaging users in detailed conversations to aiding in design projects, ChatGPT's applications seem boundless. Businesses across the globe have been quick to catch on, recognizing the utility of a tool that can not only enhance customer service but also derive rich insights from complex data. Yet, with all its merits, ChatGPT embodies the inherent challenges of AI. For starters, while it can generate information and even predict user intent, it doesn’t truly "understand" in a human way. This gap can lead to unforeseen misinterpretations. Furthermore, the potential for unintentionally perpetuating biases remains. But paramount among these concerns is the challenge of data protection. A common misconception is that AI models, like ChatGPT, store or remember user-specific inputs. This is not the case. While ChatGPT is trained on a plethora of data up until 2021, ensuring its responses are rich and diverse, it doesn’t retain personal conversations. OpenAI, true to its commitment to user privacy, ensures that personal conversations aren't utilized for further model refinement. Yet, assurances aside, the regulatory machinery... --- - Published: 2023-08-09 - Modified: 2025-09-22 - URL: https://vstorm.co/ai/simple-how-to-with-ai-gpt-in-google-docs-and-spreadsheets-01/ - Categories: AI - Translation Priorities: Optional Welcome to the start of our How-To with AI series for everyday users. Here, we'll break down how to use the "GPT for Sheets and Docs" extension in easy-to-follow steps. If you're curious about integrating AI into your regular tasks, analyzing the content of your document or the data in a spreadsheet, this guide will help you get started without the jargon. Let's dive in. Crucial step 0 First, make sure you have an account on OpenAI. com. To sign up go to https://platform. openai. com/signup? launch You can use Google or Microsoft accounts to sign up. Upon signing up, you are provided with $18 in free credits. Ideal for a test run. Remember to set up payment for regular use once your initial credits are exhausted. Btw. you can and should also set usage limits to manage your expenses. Step 1. Finding secret API key After signing up and logging into your account, go to user settings to access your Secret API key. https://platform. openai. com/account/api-keys . Generate a new one, name it for example Google Docs, and copy it to the notepad. Step 2. Open your doc and install GPT for Sheets and Docs into your Google extensions A. What is GPT for Sheets and Docs? It's an AI tool for Google Sheets and Docs. With it, you can write better, extract data, translate text, and more, all powered by ChatGPT. There are many on the market, but this one makes a really good job. B. How to... --- - Published: 2023-05-29 - Modified: 2025-10-01 - URL: https://vstorm.co/uncategorized/meet-our-coo-interview-with-kamil-wlodarczyk/ - Categories: Uncategorized - Translation Priorities: Optional We are pleased to introduce Kamil Włodarczyk, the recently appointed Chief Operating Officer (COO) of Vstorm. With an impressive background in the IT industry spanning more than two decades, Kamil brings extensive leadership experience to the team. His career has encompassed diverse sectors, including the public sector, retail, and international organizations. Kamil has successfully nurtured multiple businesses, guiding them from their inception to becoming well-established players in their respective markets. His wide-ranging expertise in operations, delivery management, finance, administration, and customer and sales relations adds valuable depth to Vstorm's capabilities. In this interview, we explore Kamil's insights, experiences, and his vision for driving innovation and growth in the dynamic realm of technology. What do you believe are the key success factors for the company? I believe that every company is similar to a wristwatch. Just like a good wristwatch, a company should be well-designed, assembled, and have a well-turned mechanism. Additionally, it should be developed according to modern trends and provide a warranty to its wearer. To ensure growth and establish meaningful relations with partners, all operational areas such as planning and control, workload management, benchmarking, support, client relationship and continuous improvement must work together effectively. At Vstorm, we have taken all of these factors into account since the company's founding. Moreover, our flexibility allows us to efficiently examine new projects and enter into new areas by utilizing the capacity and expertise of our talented team. I strongly believe that the key to our success lies in a good mix... --- - Published: 2023-05-19 - Modified: 2025-10-06 - URL: https://vstorm.co/ai/generative-ai-technologies-for-startups/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski As you embark on your entrepreneurial journey, it's essential to keep up with the latest technologies to stay ahead of the game. In this chapter, we'll introduce you to some key generative AI technologies that can help skyrocket your startup. Before we get started, let's be clear: AI is not a silver bullet that can solve all your problems overnight. However, when used correctly, AI can help you automate mundane tasks, streamline your operations, and make better-informed decisions. It's all about using the right AI technologies for your specific needs. Let’s make note: with AI landscape currently evolving we are focusing only on the most-up-to-date technologies at the moment of writing this ebook.   This is an excerpt from our ebook " From zero to AI Hero. Generative AI for startups & business". Read more here! Generative AI You might have heard about generative AI, which is all about creating something new from scratch. Imagine having a buddy who can write engaging articles, design eye-catching graphics, or even compose catchy tunes. Generative AI can be that buddy for your startup. It uses advanced algorithms to generate content, code, designs, or music, based on the data it has been trained on. Think of it as a creative partner that can help you scale your content production without breaking the bank. Text-to-image The next cool kids on the block are Stable Diffusion, Midjourney and Dall-e. Have you ever wished you could simply describe an image and have it magically appear? That's where... --- - Published: 2023-05-08 - Modified: 2025-11-13 - URL: https://vstorm.co/ai/ai-in-programming-separating-hype-from-reality-with-code-interpreter-and-copilot/ - Categories: AI - Tags: ai, generative ai - Translation Priorities: Optional AI in programming, really? Let's dive into it. Artificial intelligence (AI) has been touted as a game changer in many industries, including coding. AI-powered tools like Copilot, and Amazon Code Whisperer have been praised for their ability to generate code and help programmers save time. Also, ChatGPT has just released its latest innovation in the form of the Code Interpreter. This new tool promises to revolutionize the way developers work. It streamlines the coding process, making it faster and more efficient than ever before. But is AI in programming really the magic bullet it’s made out to be? Let’s take a closer look at the pros and cons of AI in coding. GPT-4 and AI in programming On the one hand, AI in programming tools can be incredibly useful. They can automate repetitive tasks, generate code based on existing patterns, and help catch errors that might otherwise go unnoticed. This can save time and improve efficiency, allowing programmers to focus on more complex tasks. The ChatGPT Code Interpreter is an all-in-one tool that can create charts, perform basic video editing, and even convert files. It is a game-changer for developers (and not only! ) who are looking for ways to speed up their workflow and save time. One of the most impressive features of the ChatGPT Code Interpreter is its ability to generate code based on natural language. This means that developers can simply type in plain English what they want the code to do. As a result the tool... --- - Published: 2023-02-23 - Modified: 2025-10-26 - URL: https://vstorm.co/uncategorized/getting-to-know-the-new-chairman-an-interview-with-piotr-krzysztofik/ - Categories: Uncategorized - Translation Priorities: Optional - Osoby: Antoni Kozelski We are excited to announce a new addition to the Vstorm community: Piotr Krzysztofik become Chairman of Vstorm. In this role, Piotr will head up, helping our company to continue to expand and grow. Piotr Krzysztofik is a business angel, investor, founder, and strategic advisor with over 22 years of experience in IT and managing large, international organizations with over $100M budgets and strategic business initiatives such as the growth of Siemens, Atos, and GlobalLogic. Successfully contribute to scaling Siemens Development Center department and Global Delivery Center of Atos. The last position in the big enterprises he held was the VP, CEO, Country Head, and Advisory Board member at GlobalLogic in Poland, Croatia, and Slovakia. After succeeding acquisition of GlobalLogic by Hitachi he started his activities as a founder, investor, and business advisor. In addition, he has completed an MBA degree and is a certified coach and mentor. In 2023 he has been nominated for the Business Angel of the Year 2022 (BAY )award. We are delighted that Piotr has decided to join Vstorm. He will be on the road soon, meeting with as many customers and partners as possible. So we decided to ask him a few questions! What do you believe are the key success factors for the company? As an IT business professional with over 20 years of experience and a vast network, I strongly believe that the success of any company lies in the mix of business understanding, people and company energy. These factors are particularly... --- - Published: 2023-02-15 - Modified: 2025-11-15 - URL: https://vstorm.co/ai/will-ai-eliminate-recruitment-departments/ - Categories: AI - Translation Priorities: Optional - Osoby: Antoni Kozelski As technology continues to evolve, so too does the way businesses are run. As Artificial Intelligence (AI) becomes increasingly sophisticated, it is being more widely adopted in recruitment and HR departments. AI is being used to automate certain processes and tasks, as well as to make more accurate predictions about job applicants. This article will explore the potential for AI to completely replace recruitment departments.   Screening process There are a variety of AI-based tools available for recruitment and HR departments. These tools are designed to automate certain processes and tasks, such as sourcing and screening job applicants, and making accurate predictions about job performance.   AI-driven recruitment and talent acquisition tools use machine learning algorithms and Natural Language Processing (NLP) to analyze large amounts of data and provide insights into the best candidates for any given job. AI-driven tools can be used to automate the screening process and to accurately match job seekers with the right positions. AI-based tools can quickly and accurately assess job applicants’ resumes and CVs, eliminating the need for manual screening. AI can also be used to predict job performance, as it can identify patterns in data that are not easily visible to humans. AI-based tools can also be used to identify the best channels to source candidates, and to track the progress of a candidate's application and interview.   Tracking One of the most popular types of AI-based tools is applicant tracking systems (ATS). ATSs are used to organize and manage the recruitment process,... --- - Published: 2023-01-19 - Modified: 2025-11-24 - URL: https://vstorm.co/data-experts/harnessing-technology-to-craft-a-winning-data-strategy-for-your-business/ - Categories: Data Experts - Translation Priorities: Optional Business owners in today’s world must understand the importance of data. Data is the lifeblood of modern business, and the tools used to create and manage it are more important than ever. A comprehensive data strategy is essential for every business to stay competitive and manage its data effectively. Data strategy is the process of creating a plan for collecting, managing, and analyzing data for business purposes. It involves understanding the data needs of the organization, identifying data sources, developing a data architecture, and establishing governance policies. With the right data strategy, businesses can maximize their data’s value, create competitive advantages, and make better decisions. The technology tools used to create a data strategy are varied and often depend on the specific needs of the organization. Businesses must consider the data they need to collect, their current technology infrastructure, their budget, and their timeline. Steps in creating a data strategy Data collection is the first step in creating a data strategy. Businesses need to be able to collect data from multiple sources, such as customer surveys, web and mobile analytics, sales reports, and other sources. Technology tools such as data warehouses, databases, and software-as-a-service (SaaS) solutions can help businesses collect and store data. Once the data is collected, businesses must be able to analyze and interpret it. This is where data analytics tools come in. Data analytics can help businesses uncover patterns, trends, and correlations in their data. Business intelligence (BI) tools, machine learning (ML) algorithms, and predictive analytics tools... --- --- ## Pages - Published: 2025-08-28 - Modified: 2025-08-29 - URL: https://vstorm.co/custom-agentic-ai-development/ - Translation Priorities: Optional Custom Agentic AI Development | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeCustom Agentic AI Development Custom Agentic AI Development Custom Agentic AI Development: Build agentic AI solutions & deploy custom AI agents Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Custom Agentic AI Development When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia Vision 2030? Read... --- - Published: 2025-08-28 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai-development/ - Translation Priorities: Optional Agentic AI development | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI development Agentic AI development Agentic AI development: Explore autonomous artificial intelligence. how AI agents use decision-making for real-time problem-solving Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI Development  When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia Vision 2030? Read article Why... --- - Published: 2025-08-28 - Modified: 2025-09-01 - URL: https://vstorm.co/agentic-ai-in-mining/ - Translation Priorities: Optional Agentic AI in Mining | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Mining Agentic AI in Mining Agentic AI is set to transform mining operations. Discover how autonomous agents and AI can create more efficient and autonomous workflows Recognized by Book a free consultation Why leading companies automate processes with Agentic AI in Mining? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Mining When clean text is not enough: structured extraction for RAG... --- - Published: 2025-08-28 - Modified: 2025-09-01 - URL: https://vstorm.co/agentic-ai-in-defence/ - Translation Priorities: Optional Agentic AI in Defence | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Defence Agentic AI in Defence Explore agentic AI in defence: AI agents enhancing decision-making, optimising workflow, and countering the adversary in national security Recognized by Book a free consultation Why leading companies automate processes with Agentic AI in Defence? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Defence When clean text is not enough: structured extraction for RAG Read article How... --- - Published: 2025-08-28 - Modified: 2025-09-01 - URL: https://vstorm.co/agentic-ai-in-construction-engineering/ - Translation Priorities: Optional Agentic AI in Construction Engineering | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Construction Engineering Agentic AI in Construction Engineering Explore how AI agents automate workflows, enhance project management, and offer autonomous solutions in the construction industry Recognized by Book a free consultation Why leading companies automate processes with Agentic AI in Construction Engineering? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Construction Engineering When clean text is not enough: structured extraction for... --- - Published: 2025-08-27 - Modified: 2025-09-01 - URL: https://vstorm.co/agentic-ai/agentic-ai-in-travel/ - Translation Priorities: Optional Agentic AI in Trevel | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Trevel Agentic AI in Trevel Agentic AI in Travel: Discover how AI agents personalize and automate travel, transforming experiences with intelligent AI technology Recognized by Book a free consultation Why leading companies automate processes with Agentic AI in Trevel? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Trevel When clean text is not enough: structured extraction for RAG Read article How... --- - Published: 2025-08-26 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai/agentic-ai-for-government/ - Translation Priorities: Optional Agentic AI for Government | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI for Government Agentic AI for Government Agentic AI for Government:Learn how AI agents and AI systems are transforming the public sector with minimal human intervention Recognized by Agentic AI for Government Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI for Government When clean text is not enough: structured extraction for RAG... --- - Published: 2025-08-26 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai/agentic-ai-in-logistics/ - Translation Priorities: Optional Agentic AI in Logistics | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Logistics Agentic AI for Logistics Explore agentic AI use cases in logistics & supply chain management. Learn how autonomous agents optimize processes and improve efficiency Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI for Logistics When clean text is not enough: structured extraction for RAG Read article How... --- - Published: 2025-08-26 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai/agentic-ai-in-media/ - Translation Priorities: Optional Agentic AI in MEDIA | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in MEDIA Agentic AI in Media Agentic AI in Media: Streamline digital marketing, personalize ads and empower your marketing team with successful AI Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Media When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi... --- - Published: 2025-08-26 - Modified: 2025-09-01 - URL: https://vstorm.co/agentic-ai/agentic-ai-in-telecommunication/ - Translation Priorities: Optional Agentic AI in Telecommunication | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Telecommunication Agentic AI in Telecommunication Agentic AI in Telecommunication: Unlock the power of agentic AI in the telecommunications industry Recognized by Book a free consultation Why leading companies automate processes with Agentic AI in Telecommunication? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Telecommunication When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia... --- - Published: 2025-08-26 - Modified: 2025-09-01 - URL: https://vstorm.co/agentic-ai/agentic-ai-in-education/ - Translation Priorities: Optional Agentic AI in Education | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Education Agentic AI in Education Explore Agentic AI in education. Discover how artificial intelligence is transforming learning and teaching in higher education Recognized by Book a free consultation Why leading companies automate processes with Agentic AI in Education? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Education When clean text is not enough: structured extraction for RAG Read article How Vstorm... --- - Published: 2025-08-25 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai/agentic-ai-in-ecommerce/ - Translation Priorities: Optional Agentic AI in Ecommerce | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Ecommerce Agentic AI in Ecommerce Agentic AI in Ecommerce: AI Agents create autonomous shopper experiences, redefine commerce, and personalize product discovery Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Ecommerce When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia... --- - Published: 2025-08-25 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai/agentic-ai-in-energy/ - Translation Priorities: Optional Agentic AI in Energy | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Energy Agentic AI in Energy AI agents for energy enhance energy optimization & transform the energy landscape. Drive energy transition initiatives now Recognized by Agentic AI in Energy Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Energy When clean text is not enough: structured extraction for RAG Read article... --- - Published: 2025-08-25 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai/agentic-ai-in-automotive/ - Translation Priorities: Optional Agentic AI in Automotive | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Automotive Agentic AI in Automotive Agentic AI in Automotive: Discover how agentic AI is transforming the automotive industry, automate processes & enable smarter, faster solutions Recognized by Agentic AI in Automotive Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Automotive When clean text is not enough: structured extraction for... --- - Published: 2025-08-25 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai/agentic-ai-in-agriculture/ - Translation Priorities: Optional Agentic AI in Agriculture | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Agriculture Agentic AI in Agriculture Agentic AI in Agriculture: Discover how AI Agents automate agricultural tasks, transform farming, optimize crops & revolutionize the farm with AI Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Agriculture When clean text is not enough: structured extraction for RAG Read article... --- - Published: 2025-08-22 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai-in-supply-chain/ - Translation Priorities: Optional Agentic AI in Supply Chain | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Supply Chain Agentic AI in Supply Chain Agentic AI is revolutionizing the Supply Chain! Unlock autonomous automation & build resilience with agentic AI for smart decisions Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Supply Chain When clean text is not enough: structured extraction for RAG... --- - Published: 2025-08-22 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai-in-retail/ - Translation Priorities: Optional Agentic AI in Retail | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in Retail Agentic AI in Retail Agentic AI in Retail: Discover how Agentic AI Agents transform retail and consumer experiences. Automate processes and empower retailers now Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in Retail When clean text is not enough: structured extraction for RAG Read article How... --- - Published: 2025-08-22 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai-for-manufacturing/ - Translation Priorities: Optional Agentic AI for Manufacturing | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI for Manufacturing Agentic AI for Manufacturing Agentic AI to revolutionize manufacturing. AI agents transform productivity with real-time decisions. Explore how agentic AI redefines manufacturing. Recognized by Agentic AI for Manufacturing Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI for Manufacturing When clean text is not enough: structured extraction for RAG Read... --- - Published: 2025-08-21 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai-consulting/ - Translation Priorities: Optional Agentic AI consulting | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI consalting Agentic AI consulting Agentic AI consulting services and solutions. Leverage AI agent technology beyond GenAI for intelligent automation. Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI consulting When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia Vision 2030? Read article Why... --- - Published: 2025-08-20 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai-company-in-saudi-arabia/ - Translation Priorities: Optional Agentic AI company in Saudi Arabia | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI company in Saudi Arabia Agentic AI company in Saudi Arabia Agentic AI company in Saudi Arabia. Shaping the future of AI in Saudi Arabia, 2025 and beyond Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI company in Saudi Arabia When clean text is not enough: structured extraction... --- - Published: 2025-08-20 - Modified: 2025-09-02 - URL: https://vstorm.co/genai-development-in-saudi-arabia/ - Translation Priorities: Optional GenAI development in Saudi Arabia | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeGenAI development in Saudi Arabia GenAI development in Saudi Arabia GenAI development in Saudi Arabia. The potential of generative AI across sectors, leveraging its power to transform Saudi under Vision 2030. Recognized by Book a free consultation Why leading companies automate processes with GenAI development in Saudi Arabia? Generative AI is a category of artificial intelligence that creates new, original content by learning patterns from vast datasets and generating human-like text, images, code, audio, and other media formats. Rather than simply analyzing or categorizing existing information, Generative AI produces novel outputs that didn't previously exist, enabling organizations to automate creative processes, accelerate content production, and unlock new forms of value creation across business functions 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about GenAI development in... --- - Published: 2025-08-20 - Modified: 2025-08-29 - URL: https://vstorm.co/agentic-ai-in-banking/ - Translation Priorities: Optional Agentic AI in banking | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI in banking Agentic AI in banking Agentic AI in banking automates complex workflows, enhances decision-making and compliance Recognized by Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about Agentic AI in banking When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia Vision 2030? Read article Why... --- - Published: 2025-08-13 - Modified: 2025-09-01 - URL: https://vstorm.co/ai-agent-development-company/ - Translation Priorities: Optional AI Agent development company | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAI Agent development company AI Agent development company Discover how Agentic Process Automation leverages AI agents for intelligent workflow execution. Explore APA & advanced AI automation Recognized by Book a free consultation Why leading companies automate processes with AI Agent development company? An AI Agent is an autonomous software system that can perceive its environment, make decisions, and take actions to achieve specific goals without constant human supervision. Unlike traditional automation that follows pre-programmed rules, AI Agents use advanced reasoning, learning capabilities, and contextual understanding to adapt their behavior based on changing conditions and complex scenarios 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5X Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about AI Agent Development Company When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia Vision... --- - Published: 2025-08-12 - Modified: 2025-09-01 - URL: https://vstorm.co/ai-agent-development/ - Translation Priorities: Optional AI Agent development | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAI Agent development AI Agent development AI agent development: Explore the creation of intelligent agents, their capabilities, and the frameworks used. Revolutionize tasks with AI Recognized by Book a free consultation Why leading companies automate processes with AI Agent development? An AI Agent is an autonomous software system that can perceive its environment, make decisions, and take actions to achieve specific goals without constant human supervision. Unlike traditional automation that follows pre-programmed rules, AI Agents use advanced reasoning, learning capabilities, and contextual understanding to adapt their behavior based on changing conditions and complex scenarios 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5X Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about AI Agent development When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia Vision 2030? Read article Why... --- - Published: 2025-08-12 - Modified: 2025-08-13 - URL: https://vstorm.co/agentic-ai/ - Translation Priorities: Optional Agentic AI | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAgentic AI Agentic AI Agentic AI: Autonomous systems that act independently to achieve goals, transforming business processes. Discover how AI Agents revolutionize workflows. Recognized by Book a free consultation Insights about Agentic AI When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia Vision 2030? Read article Why RAG is not dead: a case for context engineering over massive context windows Read article Advanced RAG pipeline, part 1: Rerankers Read article Frequently Asking Questions What is agentic AI and how does it differ from traditional AI solutions? Agentic AI represents autonomous systems that independently plan, execute, and adapt to achieve specific business goals without constant human oversight. Unlike traditional AI that responds to prompts, our agentic AI solutions proactively identify opportunities, make decisions, and take action. These intelligent ai agents continuously learn from outcomes, making them ideal for dynamic business environments. Vstorm’s agentic ai applications transform reactive processes into proactive, goal-oriented automation that drives measurable results. How can Agentic AI work within our existing business systems and workflows? Our Agentic AI systems integrate seamlessly with your current infrastructure through APIs and existing software platforms. These AI Agents operate within established workflows while... --- - Published: 2025-08-11 - Modified: 2025-09-01 - URL: https://vstorm.co/ai-agent-development-company-services/ - Translation Priorities: Optional AI Agent development company services | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAI Agent development company services AI Agent Development Company Services AI Agent Development company engineering custom solutions that deliver measurable ROI. Explore proven results Recognized by Book a free consultation Why leading companies automate processes with AI Agent development company services? An AI Agent is an autonomous software system that can perceive its environment, make decisions, and take actions to achieve specific goals without constant human supervision. Unlike traditional automation that follows pre-programmed rules, AI Agents use advanced reasoning, learning capabilities, and contextual understanding to adapt their behavior based on changing conditions and complex scenarios 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Insights about AI Agent development company services When clean text is not enough: structured extraction for RAG Read article How Vstorm supports Saudi Arabia... --- - Published: 2025-05-29 - Modified: 2025-05-29 - URL: https://vstorm.co/ai-for-technology-providers/ - Translation Priorities: Optional Custom AI & LLM-based software development company 🚀 Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAI for Technology Providers AI for Technology Providers Each high-technological product embraces new capabilities provided by LLMs. We help companies that aim to make it right from the get-go. Book a free consultation AI is becoming essential to advanced products, transforming static systems into intelligent, adaptive ones that are easier to navigate and more accessible than ever before. Personalization and User Experience AI enables products to understand individual user behavior, preferences, and context to deliver tailored experiences. From recommendation engines in streaming platforms to adaptive interfaces in smartphones, AI creates products that feel intuitive and personally relevant, dramatically improving user satisfaction and engagement. Automation and Efficiency AI automates complex decision-making processes that previously required human intervention, making products more efficient and capable. This includes everything from smart home systems that optimize energy usage based on occupancy patterns to enterprise software that automates workflow routing and resource allocation, reducing operational overhead while improving performance. Predictive and Proactive Functionality: AI transforms reactive products into proactive ones by analyzing patterns to predict future needs or problems. This shift from responding to problems to preventing them creates immense value for users and businesses alike. Adding AI Capabilities to high-tech... --- - Published: 2025-05-29 - Modified: 2025-09-17 - URL: https://vstorm.co/ai-in-ecommerce-and-retail/ - Translation Priorities: Optional Custom AI & LLM-based software development company 🚀 Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeIndustriesAI in Ecommerce and Retail AI in E-commerce and Retail Each high-technological product embraces new capabilities provided by LLMs. We help companies that aim to make it right from the get-go. Ask us how — book a free consultation AI is becoming essential to advanced products, transforming static systems into intelligent, adaptive ones that are easier to navigate and more accessible than ever before. Personalization and User Experience AI makes it possible to better understand individual user behavior, preferences, and, in result, deliver tailored experiences. From recommendation engines in streaming platforms to adaptive interfaces in smartphones, AI creates products that feel intuitive and personally relevant, dramatically improving user satisfaction and engagement. Automation and Efficiency AI automates complex decision-making processes that previously required human intervention, making products more efficient and capable. This includes everything from smart home systems that optimize energy usage based on occupancy patterns to enterprise software that automates workflow routing and resource allocation, reducing operational overhead while improving performance. Proactive Sales With the help of AI, it’s possible to leverage patterns to predict future customer needs. This allows proactive positioning of products, creating immense value for both buyers and sellers. AI for faster... --- - Published: 2025-04-24 - Modified: 2025-04-24 - URL: https://vstorm.co/die-pragmatik-der-ki/ - Translation Priorities: Optional Die Pragmatik der KI - Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeDie Pragmatik der KI Die Pragmatik der KI Workshop München, 17. Juni   Eine Sitzung, die Unternehmen dabei helfen soll, das Potenzial von KI für ihr Geschäft zu verstehen. Organisiert als kostenloses Treffen mit Praktikern der KI (Ingenieuren und Geschäftsarchitekten), um die Kommunikation zwischen Managern zu öffnen, die pragmatische Ansätze für KI-Lösungen suchen und jenen, die bereits Wert aus KI in Unternehmen gezogen haben. https://vstorm.co/app/uploads/2025/04/Workshop-invitation-comp.mp4 Für wen  Für jeden C-Level-Manager, Leiter oder Verantwortlichen, der Innovation innerhalb einer Organisation oder eines digitalen Produkts vorantreiben möchte, aber strengen Zeitmangel, kein Fachwissen oder kein erfahrenes Team im Bereich KI hat. Die Größe Ihrer Organisation spielt dabei keine Rolle, ebenso wenig wie Ihre Erfahrung. Wir haben eine gute Erfolgsbilanz mit Unternehmen jeder Größe. Hauptvorteile des Workshops Sie werden über KI-Anwendungsfälle erfahren, die für Ihre Branche relevant sind, einschließlich Beispiele, die zeigen, wie ähnliche Organisationen KI erfolgreich einsetzen.   Sie werden lernen, wo und wie KI realistisch in Ihrer Organisation/ihrem Produkt implementiert werden kann.   Sie werden über die Kosten der Implementierung von KI informiert, wobei Ihre Fähigkeiten und Bedürfnisse berücksichtigt werden.   Ihre Teilnahme sollte Ihnen helfen, die Risiken, die mit Investitionen in oder der Nichtimplementierung von KI verbunden sind, zu... --- - Published: 2025-04-24 - Modified: 2025-09-17 - URL: https://vstorm.co/agentic-ai-in-healthcare/ - Translation Priorities: Optional Agentic AI in Healthcare | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeIndustriesAgentic AI in Healthcare Agentic AI in Healthcare Agentic AI in Healthcare: Autonomous systems revolutionizing patient care, diagnosis, and treatment workflows. Explore intelligent healthcare solutions. Book a free consultation Why leading companies automate processes with Agentic AI? Agentic AI is a paradigm that empowers artificial intelligence systems with genuine agency - the ability to independently set priorities, develop strategies, and execute complex multi-step plans to achieve business objectives. Unlike reactive AI that responds to prompts or follows predetermined workflows, Agentic AI proactively identifies opportunities, anticipates challenges, and takes initiative to optimize outcomes across entire business ecosystems 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Ready to see how Agentic AI in Healthcare transforms business workflows? Meet directly with our founders and PhD AI engineers. We will demonstrate real... --- - Published: 2025-04-02 - Modified: 2025-04-24 - URL: https://vstorm.co/pragmatics-of-ai-workshop/ - Translation Priorities: Optional Pragmatics of Agentic AI - Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomePragmatics of Agentic AI Pragmatics of Agentic IA Workshop in Munich, Germany July 17th A session designed to help companies understand the potential of AI for their business.  Organized as a free meeting with practicians of AI (engineers and business architects)  to open communication between managers who seek pragmatic approaches toward AI solutions and those who have already provided value from AI in businesses.  https://vstorm.co/app/uploads/2025/04/Workshop-invitation-comp.mp4 For who For every C-level executive, leader, or manager who wants to drive innovation within an organization or digital product, but strictly does not have time,  know-how, or experienced team in the AI area. The size of your organization doesn’t matter, nor does your experience. We have a clean track record with every size of companies. Main benefits of the workshop You’ll learn about AI use cases relevant to your industry, including examples that show how similar organizations are successfully using AI You’ll learn where and how AI can realistically implement AI in your organization/product You’ll learn about the costs of implementing AI, taking into account your capabilities and needs Your participation should help you mitigate the risks associated with investing in or not implementing AI. Sign-up & download agenda ✕ What... --- - Published: 2025-02-28 - Modified: 2025-05-02 - URL: https://vstorm.co/1-pager-vsa/ - Translation Priorities: Optional 1-pager VSA - Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us Home1-pager VSA Download VSA 1-pager Familiarize yourself with your Vstorm’s unique approach that secures business results from custom AI agent deployment Download ✕ Fill out this form to get free access What value do you get from the VSA? Clarity in AI implementation You will understand the entire AI deployment journey, from initial purchase decisions to full implementation, ensuring a structured and informed approach. Risk reduction and smarter decision-making You will learn how to identify and mitigate uncertainties in AI projects, assessing both technical feasibility and real business value before committing resources. Seamless AI procurement and deployment You will be able to streamline the transition from purchase to implementation, eliminating inefficiencies and ensuring optimal resource allocation. Greater transparency and predictability You will gain the ability to plan, track, and measure AI project success, ensuring alignment with business goals and a structured execution process. Learn what else we can do for you Top 5 tips from Lucian Puca of Mixam on launching Agentic AI transformation Read article From idea to Agentic AI solution Read article How companies really implement AI and measure success Read article Company About us Blog Privacy Policy Contact us Press For candidates For Candidates Candidates: Frequently... --- - Published: 2025-02-03 - Modified: 2025-04-29 - URL: https://vstorm.co/schedule-a-meeting/ - Translation Priorities: Optional Schedule a meeting - Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us Schedule a meeting Select a convenient time to meet with our experts and get personalized insights. 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Your continued use of the site means that you agree to their use.AcceptPrivacy Policy --- - Published: 2025-02-03 - Modified: 2025-02-03 - URL: https://vstorm.co/fill-out-the-form/ - Translation Priorities: Optional Fill out the form - Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeFill out the form Let's discuss your project Fill out the form below, and our team will get back to you shortly to discuss your project, answer any questions, and explore how we can collaborate. 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Your continued use of the site means that you agree to their use.AcceptPrivacy Policy --- - Published: 2025-01-17 - Modified: 2025-01-17 - URL: https://vstorm.co/pytorch-development/ - Translation Priorities: Optional PyTorch Development | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomePyTorch development PyTorch Development Build, optimize, and scale AI solutions that drive measurable results. Estimate your project Our PyTorch development services What we can help you with: Custom Model Development Our custom model development service leverages the power of PyTorch to build advanced AI solutions tailored to your unique business challenges. We analyze your requirements, design bespoke models, and ensure seamless integration into your existing systems to maximize efficiency and impact. Optimization and Acceleration Our optimization and acceleration service focuses on enhancing your AI models for maximum performance. Using techniques such as TorchScript and quantization, we streamline resource utilization, reduce latency, and improve computational efficiency to support your scaling needs. Training Pipeline Automation Our training pipeline automation service simplifies and accelerates the end-to-end training process. From data preparation to monitoring and version control, we design robust workflows that save time, reduce errors, and keep your models production-ready. Deployment at Scale Our deployment service ensures your PyTorch models perform optimally in large-scale production environments. Leveraging tools like PyTorch Serve, Kubernetes, and cloud platforms, we enable seamless deployment and scalability for your AI solutions. Maintenance and Scalability Provide regular updates, monitor performance, and scale the chatbot to meet growing demands and... --- - Published: 2024-12-13 - Modified: 2024-12-16 - URL: https://vstorm.co/ml-ops-service/ - Translation Priorities: Optional ML Ops service | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeML Ops service ML Ops service Efficiently optimize, scale, and manage your Machine Learning models with tailored ML Ops solutions Optimize your ML model Our MLOps services What we can help you with: Consultation and strategy We provide expert consultations to help you navigate the complexities of ML operations. This service includes: Assessing your current ML workflows and infrastructure to identify bottlenecks and areas for improvement. Recommending best practices for deploying, optimizing, and managing ML pipelines. Tailoring strategies to align with your business goals and technical requirements. Our advisory services ensure you make informed decisions to maximize the value and efficiency of your ML investments. Data pipeline automation and integration We streamline your data management processes to support seamless ML operations. This service includes: Automating data preprocessing, feature engineering, and pipeline workflows. Ensuring smooth integration with your existing enterprise data systems. Establishing scalable and robust data pipelines to handle increasing data volumes. Our solutions enable reliable data flow, ensuring your ML models operate on consistent and high-quality datasets. Custom ML deployment solutions We specialize in deploying ML models in environments tailored to your unique needs. This service includes: Building custom pipelines for continuous integration and deployment (CI/CD). Implementing... --- - Published: 2024-12-11 - Modified: 2025-09-17 - URL: https://vstorm.co/llm-ops-service/ - Translation Priorities: Optional LLM Ops service | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeServicesLLM Ops service LLM Ops service Efficiently optimize, scale, and manage your Large Language Models with tailored LLM Ops solutions Optimize your LLM Why leading companies automate processes with Generative AI? Generative AI is a category of artificial intelligence that creates new, original content by learning patterns from vast datasets and generating human-like text, images, code, audio, and other media formats. Rather than simply analyzing or categorizing existing information, Generative AI produces novel outputs that didn't previously exist, enabling organizations to automate creative processes, accelerate content production, and unlock new forms of value creation across business functions 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months. 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Our LLMOps services What we can help you with: Consultation We provide expert consultations to help you navigate the complexities of LLM operations. This... --- - Published: 2024-12-05 - Modified: 2025-08-22 - URL: https://vstorm.co/ai-chatbot-development/ - Translation Priorities: Optional Custom AI chatbot development | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAI Chatbot development Custom AI chatbot development Intelligent custom AI chatbot solution tailored to your business needs Estimate your AI Chatbot project Our AI Chatbot development services What we can help you with: AI Chatbot Consultancy Our consultancy service provides expert guidance to help businesses understand and implement AI chatbot solutions effectively. We analyze your needs, design tailored strategies, and ensure your chatbot aligns perfectly with your operational goals. Chatbot Design and Development Create intelligent chatbots with customized conversational flows and advanced NLP capabilities, delivering seamless, human-like interactions. Integration with Existing Systems Connect chatbots to CRMs, ERPs, or APIs for real-time data access, streamlined workflows, and a consistent user experience across platforms. Testing and Deployment Ensure the chatbot’s reliability through functionality and performance testing, followed by smooth deployment on chosen platforms. Maintenance and Scalability Provide regular updates, monitor performance, and scale the chatbot to meet growing demands and evolving business needs. Our clients achieve Hyper-automation Hyper-personalization Enhanced decision-making processes Hyper-automation Hyper-automation leads to significantly higher operational efficiency and reduced costs by automating complex processes across the organization. It allows businesses to scale their operations faster, minimize human errors, and optimize resource allocation, resulting in improved productivity and... --- - Published: 2024-11-27 - Modified: 2025-09-17 - URL: https://vstorm.co/rag-development-service/ - Translation Priorities: Optional Retrieval-Augmented Generation (RAG) - development | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeServicesRAG Advanced Engineering RAG development Service Delivering advanced RAG development solutions to integrate your data, enhance efficiency, and achieve measurable business outcomes Estimate your RAG project Why leading companies automate processes with RAG? Retrieval-Augmented Generation (RAG) is an approach that combines the generative capabilities of large language models with information retrieval from external sources. Rather than relying solely on the static knowledge embedded in an LLM’s training data, RAG pulls information in real time from external sources — whether proprietary databases, private document collections, or web resources. By integrating current, context-specific data into the model’s workflow, RAG improves accuracy, relevance, and reliability of generated responses 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Our RAG development services What we can help you with: RAG Consultancy Our RAG consultancy... --- - Published: 2024-11-25 - Modified: 2025-09-17 - URL: https://vstorm.co/large-language-models-development/ - Translation Priorities: Optional Large Language Model development Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeServicesLLM Development Large Language Models (LLM) development Transform operations with hyper-automation, hyper-personalization, and smarter decision-making using Large Language Model Get a free consultation Our Large Language Model development Consultancy & Strategy LLM Audits & Insights Selecting the Optimal LLM Data Preparation & Management Model Fine-Tuning Scalable Deployment & MLOps Maintenance & Optimization Consultancy & Strategy This service includes an in-depth analysis of your business needs, challenges, and goals. We guide you through the process of identifying where LLM-based solutions can bring the most value. This includes: Understanding your business domain and objectives. Identifying use cases where LLMs can optimize processes or enhance outcomes. Recommending tailored strategies and technical approaches. Outlining the implementation steps, timelines, and expected ROI. LLM Audits & Insights This service includes an in-depth analysis of your business needs, challenges, and goals. Comprehensive evaluation of your language model systems throughout development and deployment. We ensure your LLM solutions are safe, reliable, and optimal through: safety and bias assessments, performance evaluations across benchmarks and real scenarios, behavioral analysis to identify risks, and actionable insights for optimization. Our process guides you through systematic auditing protocols that maximize model effectiveness while minimizing potential issues and compliance risks. Selecting the Optimal... --- - Published: 2024-11-20 - Modified: 2025-09-17 - URL: https://vstorm.co/custom-llm-based-software/ - Translation Priorities: Optional LLM software: Custom Large Language Model | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeServicesLLM software: Custom Large Language Model LLM software: Custom Large Language Model We develop advanced software based on LLM models tailored to your needs. Estimate your LLM project Why leading companies automate processes with Generative AI? Generative AI is a category of artificial intelligence that creates new, original content by learning patterns from vast datasets and generating human-like text, images, code, audio, and other media formats. Rather than simply analyzing or categorizing existing information, Generative AI produces novel outputs that didn't previously exist, enabling organizations to automate creative processes, accelerate content production, and unlock new forms of value creation across business functions 70% CEOs expect business transformation Seven of ten CEOs say that AI will significantly change the way their company creates, delivers, and captures value over the next three years (PwC’s 28th CEO Survey) 3-5x Delivering ROI on automation On average, Agentic Process Automation delivers a 3- to 6-fold return on investment within months 80%+ Projects fail without proper expertise Most AI initiatives fail due to implementation challenges, underscoring the critical need for experienced transformation partners (by RAND) Our LLM-based software services What we can help you with: LLM Consultation Proof of Concept (PoC)... --- - Published: 2024-10-30 - Modified: 2024-10-30 - URL: https://vstorm.co/ai-consultancy-in-new-york/ - Translation Priorities: Optional AI Consultancy | New York Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAI Consultancy in New York AI Consultancy in New York Consult your current and future projects with Vstorm - specialists in the field of AI Schedule free 1-1 meeting Our AI Consultancy services What we can help you with: AI Consultation Service Our AI consultation for New York service helps companies leverage artificial intelligence to optimize operations and drive innovation. We analyze your business needs and goals, recommend the best AI tools and technologies, and guide you in maximizing your return on investment while supporting long-term growth. AI workshop Our AI workshops are interactive sessions designed to help teams understand how AI can be implemented in their organization. Participants gain hands-on knowledge of the latest tools, methods, and strategies, enabling them to quickly adopt AI and apply it effectively in daily operations. Design AI strategy Our AI consultancy in New York crafts comprehensive strategies tailored to your company’s specific needs, ensuring AI becomes a core component of your long-term business development and success. Technology consultancy Our technology consultancy service offers a detailed audit of your current systems, an analysis of available AI technologies, and expert guidance on selecting the optimal tools for your project. We help you mitigate... --- - Published: 2024-10-17 - Modified: 2025-09-17 - URL: https://vstorm.co/ai-consultancy/ - Translation Priorities: Optional AI Consultancy - Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeServicesAI Consulting & Advisory AI Consulting Experts in Artificial Intelligence Solutions Vstorm Consult your current and future projects with Vstorm - specialists in the field of AI Schedule free consultation Consultancy in the field of AI solutions Business benefits AI Consultation Service Our AI consultation service helps companies leverage artificial intelligence to optimize operations and drive innovation. We analyze your business needs and goals, recommend the best AI tools and technologies, and guide you in maximizing your return on investment while supporting long-term growth. AI workshop Our AI workshops are interactive sessions designed to help teams understand how AI can be implemented in their organization. Participants gain hands-on knowledge of the latest tools, methods, and strategies, enabling them to quickly adopt AI and apply it effectively in daily operations. Designe AI strategy We craft comprehensive AI strategies tailored to your company’s specific needs. From identifying key business objectives to developing implementation plans, our approach ensures that AI becomes a core component of your long-term business development and success. Technology consultancy Our technology consultancy service offers a detailed audit of your current systems, an analysis of available AI technologies, and expert guidance on selecting the optimal tools for your project.... --- - Published: 2024-10-10 - Modified: 2025-08-21 - URL: https://vstorm.co/llamaindex-development-company/ - Translation Priorities: Optional LlamaIndex Development Company | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeLlamaIndex Development Company LlamaIndex Development Company LlamaIndex Development Company: Expert RAG solutions and knowledge base systems for enterprise AI applications. Discover advanced data indexing. Estimate your project What is LlamaIndex? LlamaIndex is a data framework designed to streamline the process of connecting large language models (LLMs) to external data sources. It provides tools to efficiently organize, query, and retrieve information from various datasets, enabling LLMs to access and utilize relevant data in real-time applications. By integrating LlamaIndex, developers can enhance the capabilities of LLM-powered systems, improving their ability to handle specific data-driven tasks such as retrieval-augmented generation (RAG). With the growing adoption of AI-powered applications, LlamaIndex is becoming an essential tool for building more context-aware and data-rich LLM solutions across multiple industries. Read more Our LlamaIndex services LlamaIndex consultation Custom LlamaIndex-based software development LlamaIndex project audit LlamaIndex consultation We offer expert guidance to integrate LlamaIndex into your AI solutions. Our team evaluates your data needs and designs a tailored strategy to optimize data access and retrieval, ensuring seamless integration and enhanced performance of your LLM applications. Custom LlamaIndex-based software development Leveraging our expertise in AI and LLM technologies, we build custom software solutions using LlamaIndex, designed specifically for... --- - Published: 2024-09-06 - Modified: 2025-07-26 - URL: https://vstorm.co/universe/ - Translation Priorities: Optional   window. onload = function { Calendly. initBadgeWidget({ url: 'https://calendly. com/info-vstorm/60min? primary_color=fb3640', text: 'Book a free AI consultation! ', color: '#fb3640', textColor: '#ffffff', branding: undefined }); } --- - Published: 2024-05-29 - Modified: 2025-05-02 - URL: https://vstorm.co/the-llm-book/ - Translation Priorities: Optional The LLM Book - Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAI Vstorm Book The LLM Book The LLM Book explores the world of Artificial Intelligence and Large Language Models, examining their capabilities, technology, and adaptation. Perfect for tech enthusiasts and professionals, this book provides a clear understanding of AI & LLMs and their impact on various fields. Read it now ✕ Fill out this form to get free access What is included in the book? Text and empty words alone won’t help you use AI in your business or even understand it. That’s why we created a book that combines theoretical knowledge with practical application. In the book, we covered topics such as: AI basic History of AI Large Language Models LLM Capabilities Technologies Team composition & positions FAQs Answers to your most frequently asked questions Is "The LLM Book" free? Yes, “The LLM Book” is completely free and will always remain free. Our mission is to educate and demonstrate the importance of AI now and in the future. By providing this book at no cost, we aim to make valuable knowledge accessible to everyone, fostering a deeper understanding of AI and its transformative potential. What are the key benefits of reading "The LLM Book"? Reading “The LLM... --- - Published: 2024-04-29 - Modified: 2025-05-02 - URL: https://vstorm.co/ai-community/ - Translation Priorities: Optional AI Community by Vstorm - Join now! Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeAI Community Vstorm Join the AI Community by Vstorm Become a member of the AI Community to connect with like-minded people, gain insights, and receive support on adopting AI and LLMs. Driven by our mission to help people focus on what matters most leveraging AI. Join now Vstorm Community in a shortcut The AI Community by Vstorm is a vibrant network of tech entrepreneurs, innovators, and thinkers who are united by a shared passion for adopting and advancing AI technologies. Our platform is designed not just as a forum, but as a launchpad for actionable insights, support, and transformative projects. Members of our community can tap into the collective knowledge of others who are equally dedicated to AI and LLM technologies. They can also collaborate on projects, share insights, and refine strategies with peers who are passionate about AI. Additionally, our community fosters a supportive environment where members receive constructive feedback from insiders. What I value most about this community is the practical focus. Everything discussed is applicable, not just theoretical knowledge that doesn’t translate to real-world use Matt CEO & Co-founder Woodpeaker Community FAQs Is there a membership fee? NO, it’s free forever, as we... --- - Published: 2024-03-05 - Modified: 2025-10-27 - URL: https://vstorm.co/ - Translation Priorities: Optional The Leading Agentic AI Company Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us Your browser does not support the video tag. Applied Agentic AI Engineering Consultancy Boutique Vstorm is a boutique AI Agent engineering consultancy recognized by EY, Deloitte and Forbes. We transform business operations with tailored RAG and Agentic automations that go beyond standard solutions, delivering proven ROI through practical, hands-on implementation Recognized by Book a free consultation Trusted by world-renowned brands We strive to lead the field and we will not stop until we are the best at what we do At Vstorm, our mission isn’t corporate rhetoric—it is deeply personal commitment. As recognized founders, we know that technology is only transformative when it solves real human problems. That is why, we are laser-focused on one thing—help organizations implement Agentic AI in mission-critical processes and workplace pain points, enabling people to focus on what truly matters. Who we help We’re ready to join hands with various teams on artificial intelligence projects, no matter how complex. By teaming up, we’ll build new systems, solutions, and perform integrations to help you stand out from your competitors. Companies planning AI transformation We’re helping decision makers arrive at clarity of goals and expectations for their initial AI deployments. Our team can assist you... --- - Published: 2023-11-06 - Modified: 2025-09-17 - URL: https://vstorm.co/langchain-development-company/ - Translation Priorities: Optional LangChain Development Experts — Your Trusted AI Company | Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeServicesLangChain Development Company LangChain Development Experts Let's make LLMs simple! Get better integration, performance and scalability with the help of a LangChain Development Consultant. Estimate your project What is LangChain? LangChain is a framework for developing applications powered by large language models (LLMs). LangChain can help you build AI-powered applications that integrate with external data sources, perform complex reasoning, and manage long-term memory, enabling more dynamic and context-aware user interactions. With wide adoption in over 100,000 projects across various industries and a large community, LangChain has emerged as a leading framework in developing AI and LLM solutions. Read more Our LangChain services What we can help you with: LangChain consultation Leveraging our deep expertise in AI-driven applications, we help you create optimal AI solutions using LangChain. Our team carefully evaluates your needs to craft customized AI strategies specifically designed for your business. Custom LangChain-based software development With our deep expertise in AI and LLM-based solution development, we can create custom LangChain-based software tailored to meet your business needs. Our solutions integrate advanced technology to ensure maximum efficiency and intuitive user experience. LangChain project audit Our LangChain project audit service is designed to ensure... --- - Published: 2023-10-03 - Modified: 2023-10-30 - URL: https://vstorm.co/berlin/ - Translation Priorities: Optional Berlin, long celebrated as the beating heart of Europe's startup culture and technological renaissance, has witnessed a surge in its AI development sector. In the matrix of cobblestone streets and modern glass façades, artificial intelligence has found its newest playground. But as Berlin's enterprises increasingly adopt AI, an interesting trend is shaping up: the inclination to outsource development, especially to neighboring Poland. At the helm of this shift is a standout company, Vstorm. co. Berlin: The Silicon Allee of AI Development Coined as the 'Silicon Allee', Berlin's tech scene is not just about startups and venture capitalists anymore. AI has rapidly become a cornerstone. From chatbots enhancing customer service in eCommerce platforms to sophisticated deep learning models predicting financial market nuances, AI's integration in Berlin is profound. However, as with any booming technology, AI development brings its own challenges: Talent Pool: While Berlin boasts a significant number of tech-savvy individuals, the specific expertise required for nuanced AI projects can sometimes be scarce. Budget Constraints: AI development, with its requirement for specialized skills and tools, can be an expensive endeavor, especially for startups and mid-sized enterprises. This brings us to a pivotal question: With Berlin's resources, why is there a drift towards external assistance? Poland's Rising Dominance in AI and IT Poland, traditionally known for its historical landmarks and scenic beauty, is rapidly carving its identity in the tech world. Cities like Warsaw, Krakow, and Wroclaw are bustling with tech events, hackathons, and a young, tech-enthused population. Here are reasons why... --- - Published: 2023-10-03 - Modified: 2023-10-30 - URL: https://vstorm.co/nlp-development-company/ - Translation Priorities: Optional NLP Development Company | Leading NLP Developers Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeNLP Development Company Transforming Text to Triumph: NLP Development Use the potential of Natural Language Processing to gain deeper insights from customer feedback, streamline customer support with chatbots, enhance user experience through tailored content, automate tedious text-based tasks, and drive more informed decision-making by analyzing vast amounts of textual data. Book Free AI Consultation NLP in practice Natural Language Processing (NLP) is a fascinating intersection of artificial intelligence and linguistics that enables machines to understand, interpret, and generate human language. It's the driving force behind voice assistants, chatbots, and many text analysis tools. For business owners, NLP offers a competitive edge, enabling them to analyze customer sentiments, automate support, personalize content, and unearth insights from vast textual data, ultimately leading to informed decisions and increased profitability. Find also other branches of Generative AI: Large Language Models (LLMs) Imagine you can save hours on extract and expand information from different sources with new way of semantic search approach, creating documentation. At the same time, develop your company’s brain that knows your industry’s specification and is trained on your data, or simply implement existing solutions to your systems. Read more about LLMs Our NLP development services Let’s dive... --- - Published: 2023-06-15 - Modified: 2023-10-30 - URL: https://vstorm.co/gpt-4-customization/ - Translation Priorities: Optional Welcome to Vstorm, your trusted partner for Chat GPT-4 customization services. We understand the unique needs of startups and SMBs. We are here to help you unlock the full potential of this cutting-edge technology. Vstorm offers assistance in harnessing the power of Chat GPT-4. Let's enhance your customer interactions, streamline operations, and drive business growth. Why Choose Chat GPT-4 Customization? Elevate Customer Interactions with Conversational AI With Chat GPT-4, you can take your customer interactions to the next level. Our team specializes in customizing this advanced AI model to match your specific industry and business requirements. By training Chat GPT-4 with your company's unique data and using it to understand and respond to customer queries, you can deliver personalized and engaging experiences. Whether it's addressing support tickets, providing product recommendations, or nurturing leads, Chat GPT-4 will seamlessly interact with your customers, providing accurate and helpful responses. Streamline Operations and Boost Efficiency Efficiency is the key to success for startups and SMBs, and Chat GPT-4 customization can help you achieve it. Our experts will work closely with you to integrate Chat GPT-4 into your internal processes, automating tasks and freeing up valuable human resources. From administrative duties to project management and collaboration, Chat GPT-4 becomes a virtual assistant that handles repetitive and time-consuming tasks. By optimizing your workflows, you can focus on strategic initiatives, drive innovation, and propel your business forward. How Our Customization Services Work At Vstorm, we follow a comprehensive approach to Chat GPT-4 customization to ensure maximum effectiveness... --- - Published: 2023-06-15 - Modified: 2023-10-30 - URL: https://vstorm.co/stable-diffusion/ - Translation Priorities: Optional Unlock the Power of AI-Generated Content Discover how Stable Diffusion integration can revolutionize your startup or SMB by unlocking the power of AI-generated content. Streamline your visual assets, accelerate growth, and captivate your audience like never before. The Benefits of Stable Diffusion Integration With SD's integration, you gain access to cutting-edge AI technology that can transform your business. Experience the seamless generation of high-quality images based on text prompts and input images. Revolutionize your content creation process and drive engagement with captivating visuals. Revolutionize Your Visual Assets Stable Diffusion integration empowers you to create stunning visual assets effortlessly. From logos and illustrations to product images and social media content, AI-generated visuals can take your branding to the next level. Stand out from the competition and captivate your audience with visually striking and professional-looking designs. Streamline Content Generation By integrating Stable Diffusion into your startup or SMB, you streamline content generation and save valuable time and resources. Say goodbye to tedious and time-consuming manual creation processes. With AI-generated content, you can quickly generate a wide variety of visuals to meet your marketing and communication needs. Drive Growth and Engagement Engaging visuals are key to capturing your audience's attention and driving growth. SD's integration allows you to create eye-catching visuals that resonate with your target market. Whether it's social media posts, blog graphics, or website banners, AI-generated content can help you stand out and leave a lasting impression. Seamless Integration Process At Vstorm, we specialize in seamlessly integrating Stable Diffusion into startups... --- - Published: 2023-06-15 - Modified: 2023-10-30 - URL: https://vstorm.co/ai-semantic-search-the-future-of-information-retrieval/ - Translation Priorities: Optional Introduction Artificial Intelligence (AI) has become an integral part of our society, influencing various sectors from transportation to healthcare. One area where AI is making significant strides is search technology, particularly semantic search . Understanding AI Semantic Search AI Semantic Search is an advanced search technology that employs AI, particularly Natural Language Processing (NLP), and machine learning algorithms to understand the meaning, context, and intent behind search queries, thus providing more accurate and relevant search results. Trends in AI Semantic Search Several trends are shaping the future of AI Semantic Search: Large Language Models: Models like GPT-4 and ChatGPT are changing the face of search technology by enabling more advanced conversational search. They can understand and respond to queries in a human-like manner, providing more contextual and relevant results . Vector Search: As text content continues to grow, dense vector search is becoming mainstream. It offers superior speed and accuracy by transforming text into dense vector spaces . Hybrid Search Models: Combining traditional keyword-based search with AI-driven technologies is seen as the most pragmatic model for the future, capitalizing on the strengths of both to enhance search capabilities . Ethics and Bias: As AI-driven search continues to evolve, concerns persist around potential biases in AI models and the ethical implications of their use. Efforts to build explainable AI models aim to tackle these issues, creating a more transparent and trustworthy AI ecosystem . Democratization of AI: AI is becoming increasingly accessible through apps and low-code/no-code platforms, and this includes AI-driven... --- - Published: 2023-06-15 - Modified: 2023-10-30 - URL: https://vstorm.co/ai-semantic-translation-the-bridge-between-languages/ - Translation Priorities: Optional Introduction Artificial Intelligence (AI) has revolutionized numerous fields, and translation is no exception. By applying semantic understanding, AI has significantly improved the accuracy and nuance of machine translation, enabling more effective communication across languages and cultures. What is AI Semantic Translation? AI Semantic Translation is an advanced application of machine learning and natural language processing (NLP) that goes beyond literal translation. While traditional machine translation typically maps words from the source language to the target language based on predefined rules or statistical models, semantic translation goes a step further. It aims to understand the meaning and context behind sentences, phrases, and words in the source language to provide a more accurate and contextually appropriate translation in the target language . Trends and Advancements in AI Semantic Translation There are several key trends and advancements: Embeddings: Embeddings are numerical representations of concepts that can represent semantic similarity between different pieces of text or code. OpenAI, for example, has developed advanced embedding models derived from GPT-3 to map text and code into high-dimensional space, facilitating more accurate understanding and comparison of concepts. This technology has found applications in a variety of domains, such as astrophysics data analysis, textbook content retrieval, and customer conversation analysis, and has shown improved accuracy and efficiency in these applications . Semantic Data Science (SDS): The use of Semantic Data Science in AI model development is another important trend. This involves automating the discovery of relevant concepts, linking them to external knowledge and code, and suggesting new features... --- - Published: 2023-06-15 - Modified: 2023-10-30 - URL: https://vstorm.co/ai-information-extraction-revolutionizing-data-processing/ - Translation Priorities: Optional Artificial Intelligence (AI) is transforming the way we extract information from a myriad of sources. By leveraging advancements in machine learning, deep learning, and natural language processing (NLP), AI-based information extraction systems can decipher and classify information from complex documents, drastically improving efficiency and accuracy in various industries. What is AI Information Extraction? It is a field that involves using AI techniques, such as machine learning and NLP, to extract structured information from unstructured data sources like text documents, images, and web pages. The extracted data can be utilized in downstream applications, such as building knowledge graphs, performing analytics, or powering decision-making systems. Trends and Advancements in AI Information Extraction Several key advancements and trends are shaping the future: Deep Learning Architectures: Innovative deep learning architectures allow for sophisticated data extraction from documents, even those with uncommon fonts, misaligned text, and complex visuals. IBM Research, for instance, has introduced technologies like TableLab, which leverages user feedback to fine-tune pre-trained models, resulting in improved accuracy for table extraction. Other advancements include synthetic data generation and unsupervised extraction of document layouts . Automation and Efficiency: AI has the potential to drastically reduce errors and costs associated with traditional data extraction methods. This can lead to faster document processing, simpler operational procedures, and significant productivity gains. Despite this potential, many companies are yet to prioritize AI and machine learning for information extraction . OCR, Deep Learning, and NLP: Techniques like Optical Character Recognition (OCR), deep learning, and NLP are being increasingly utilized in... --- - Published: 2023-06-15 - Modified: 2023-10-30 - URL: https://vstorm.co/ai-documentation-automation/ - Translation Priorities: Optional Artificial Intelligence (AI) is revolutionizing many aspects of our lives, and one area it's making significant strides in is the automation of documentation. From processing and managing documents to automatically generating documentation from code, AI technologies are enhancing efficiency, reducing costs, and improving the accuracy and consistency of documentation. AI Documentation Automation: The State of Play Several key technologies and trends are shaping the future of AI-driven documentation automation: Comprehensive Document Processing Solutions: Tools such as Microsoft's Document automation toolkit comprising AI Builder, Power Automate, Power Apps, and Microsoft Dataverse, are enabling the setup of a complete document processing solution. Power Automate orchestrates the process, AI Builder extracts information intelligently, Power Apps facilitate manual document review and approval, while Dataverse manages data, files, and configurations. Such comprehensive solutions are revolutionizing how organizations handle document processing, from creation to archival . Structured Data Extraction and Management: Platforms such as Google's Document AI solutions suite offer features that enable structured data extraction from documents, along with analysis, search, and storage capabilities. These platforms leverage Google's AI technologies, offering a unified console for document processing, data enrichment, and human-in-the-loop reviews. The benefits of such a system include cost-effectiveness, operational efficiency, data accuracy and compliance, and leveraging document data for customer insights . Automatic Documentation Generation from Code: Startups like Mintlify are using AI techniques such as natural language processing and web scraping to automatically generate documentation from code. This not only helps improve documentation quality but also offers additional features such as scanning... --- - Published: 2023-06-15 - Modified: 2023-10-30 - URL: https://vstorm.co/ai-customer-support/ - Translation Priorities: Optional Artificial Intelligence (AI) is playing a transformative role in customer service, improving engagement, enhancing user experiences, and streamlining processes. Despite the challenges associated with use case selection, technology integration, and self-service complexity, the benefits of AI-powered customer support far outweigh the risks. Key Benefits and Applications of AI in Customer Support Improved Engagement: Financial institutions are leveraging AI-powered customer service to increase satisfaction and enhance customer engagement. By aligning AI tools with the customer engagement vision, these institutions are successfully navigating the complexity of self-service and the limitations of the labor market, ultimately deepening relationships with their customers and anticipating their needs . Optimal User Experiences and Process Improvement: AI enhances user experiences by assisting agents and empowering customers to resolve issues effectively. Tools like chatbots and sentiment analysis not only help in automating processes but also improve the efficiency of customer service. This augmentation of physical and software processes reduces costs and increases efficiency. However, the successful implementation of AI requires high-quality and relevant data . Automation and Personalization: AI applications like chatbots, ticket organization, opinion mining, and multilingual support have revolutionized customer service automation. They improve efficiency, reduce costs, and enhance the customer experience. By analyzing past interactions, AI can provide dynamic wait times, automate action recommendations for agents, and personalize experiences. Though challenges associated with AI implementation exist, the benefits include reduced ticket volume, improved resolution efficiency, lower costs, and enhanced customer satisfaction and retention . Conclusion As advancements in AI and machine learning continue, we can... --- - Published: 2023-06-15 - Modified: 2023-10-30 - URL: https://vstorm.co/ai-content-personalization/ - Translation Priorities: Optional AI's capability to revolutionize content personalization has gained significant attention in recent years, particularly within the startup ecosystem. Leveraging AI for content personalization allows businesses to refine customer engagement strategies, leading to increased growth and success. Introduction In the fast-paced world of startups, differentiation is key. One area that offers significant potential for differentiation is the personalization of content, with AI providing the necessary tools to achieve this . AI-driven creative content generation and decision-making are powerful tools that startups can harness to engage customers at a personalized level, significantly enhancing customer experience and brand loyalty . Benefits and Applications of AI Content Personalization AI Creative-Content Generation Startups often face the challenge of limited resources, with teams needing to wear multiple hats. In this context, AI is a game-changer. AI algorithms, drawing from extensive databases, can generate creative content that deeply resonates with target customers, driving conversion rates and contributing to growth. Startups, by leveraging AI creative-content generation, can maximize engagement while minimizing time and effort spent on content creation and curation . AI-driven content personalization has found success across numerous industries, from e-commerce and customer service to marketing and beyond. For example, a retailer successfully boosted its revenue and order count by employing AI to use achievement-oriented language in its ads. AI Tools for Non-Technical Marketers The use of AI in content personalization is not limited to those with technical expertise. AI tools are available that allow even non-technical marketers to create personalized content journeys, increasing customer engagement and... --- - Published: 2023-06-14 - Modified: 2023-11-15 - URL: https://vstorm.co/large-language-models/ - Translation Priorities: Optional What are Large Language Models? Large Language Models or LLMs are the frontier of artificial intelligence. By generating texts that mirror human conversation, these advanced AI models offer unparalleled capabilities. Their capacity to understand context, nuances, and deliver pertinent responses, sets LLMs apart. Ever since the advent of digital transformation, artificial intelligence has played a significant role in transforming business operations across the globe. One such AI technology that is reshaping business processes is the concept of Large Language Models (LLMs). These models, are neural networks that are trained to understand patterns and relationships in languages. They offer immense potential for businesses, startups, and SMBs, enabling them to generate high-quality text and optimize content for better online visibility. The science behind LLMs Before delving into the applications of Large Language Models in the business context, it is important to understand what they are and how they operate. LLMs, such as GPT-3, are neural networks that learn patterns in language through training on diverse datasets. This learning process involves complex techniques such as cross-validation, fine-tuning, and transfer learning. The fascinating part about these models is their use of transformers, a neural network architecture that captures context and dependencies in language effectively. This ability to understand context and generate contextually relevant text is the essence of these language models' functionality. Ethical considerations for Large Language Models While LLMs are a promising technology, they come with certain ethical considerations. These models rely on training data and can reflect the biases in those data... --- - Published: 2023-06-14 - Modified: 2023-10-30 - URL: https://vstorm.co/text-to-image/ - Translation Priorities: Optional  A New Era: Text-to-Image Generation Welcome to the future of content creation where text-to-image technology is redefining the way we create visuals. Leveraging the power of AI and machine learning, AI image generation offers an innovative solution for producing captivating visual content. As businesses continue to harness the power of artificial intelligence, one area that's gathering momentum is Image Generation, particularly text-to-image synthesis. This technology has found a crucial role in various sectors, aiding businesses, startups, and small-to-medium-sized businesses (SMBs) in a myriad of ways. Powered by machine learning models, it provides an innovative approach to generating visually appealing and relevant imagery based on descriptive text inputs. Decoding Image Generation: The Science Behind Text-to-Image Synthesis Image generation is a complex process, often facilitated by Generative Adversarial Networks (GANs). In text-to-image synthesis, AI models are trained to convert textual descriptions into corresponding visual elements. Despite the complexity, recent advancements have made it possible to generate high-quality images that align semantically with the input descriptions . Some of the groundbreaking models in this field include the StyleDrop and the Semantic-Spatial Aware GAN. StyleDrop is a method introduced by Kihyuk Sohn and colleagues to synthesize images with specific design patterns, textures, or materials . On the other hand, Semantic-Spatial Aware GAN is a novel framework designed to effectively fuse text features and image features for text-to-image synthesis . Ethical Considerations for Image Generation Like many advanced AI technologies, image generation comes with its ethical considerations. These include issues related to copyright infringement, misinformation,... --- - Published: 2023-06-14 - Modified: 2023-10-30 - URL: https://vstorm.co/ai-proof-of-concept/ - Translation Priorities: Optional In the fast-paced world of tech startups, creating a viable product is a challenge. This is particularly true when the technology involved is as cutting-edge as generative AI. With this in mind, an AI proof of concept (PoC) becomes essential. It's here we guide you on how startups can use an AI PoC. This tool validates innovative ideas and makes visions a reality. The Essence Before diving into the benefits and steps of implementing an AI proof of concept, understanding it is key. Essentially, an AI PoC is a working model. It demonstrates the practicality of an AI-based idea. The main goal is to evaluate how the generative AI product will operate in a real-world environment. Consequently, stakeholders gain a tangible representation of the innovation. The Importance of AI Proof of Concept to Startups For startups, an AI proof of concept offers multiple benefits. Firstly, it verifies technical feasibility. It makes sure the proposed AI model functions as planned. Secondly, it helps secure funding. Investors are more likely to back a startup with a functioning product model. Lastly, an AI PoC allows for valuable feedback. Gathering this feedback can refine the AI model before a full-scale rollout. Creating Your AI PoC Developing an AI proof of concept is a process with several stages. Initially, defining the concept and setting objectives is vital. Then, creating a model to evaluate the AI's performance is necessary. Finally, you must conduct testing in a controlled environment. For a successful AI PoC, consider: Clear Objectives:... --- - Published: 2023-06-14 - Modified: 2023-10-30 - URL: https://vstorm.co/ai-application-discovery/ - Translation Priorities: Optional AI application discovery? How do I get started? As the business world rapidly embraces artificial intelligence (AI), the opportunities for its application are nearly limitless. From enhancing efficiency to reducing costs, AI has demonstrated its ability to revolutionize industries. As a result, startups, businesses, and e-commerce platforms can particularly benefit from AI if they can discover the most relevant applications for their needs. Decoding AI Application Discovery AI application discovery is the process of identifying areas within a business where AI can make a significant impact. This discovery process is multifaceted and involves understanding various types of AI applications and their potential evolution. It also requires categorization based on the intelligence level and whether it's a standalone or integrated platform . As per a survey by Forbes Advisor, businesses are utilizing AI across customer service, CRM, inventory management, and content production . Meanwhile, AI's application in e-commerce includes areas such as website personalization, recommendation systems, pricing optimization, retail analytics, and cybersecurity . How to Discover AI Applications for Your Business Assess Your Needs: Evaluate your business needs, challenges, and objectives. Identifying your key requirements will guide your discovery of the most relevant AI applications. Research and Benchmarking: Look at what similar businesses in your industry are doing with AI. Learn from their experiences, successes, and challenges. Consult with Experts: Engage with AI experts, consultants, or technology vendors to gain insights into the latest AI applications relevant to your business. Experimentation: Trial and error can be a great way to discover... --- - Published: 2023-06-14 - Modified: 2023-10-30 - URL: https://vstorm.co/prompt-design-engineering/ - Translation Priorities: Optional Prompt design and engineering have emerged as pivotal aspects of working with advanced artificial intelligence (AI) systems, such as the OpenAI GPT models. They serve as key factors in controlling the behavior of these models, influencing their output in significant ways. These techniques ensure more accurate, relevant, and value-driven responses from AI models. What is Prompt Design and Engineering? Prompt engineering involves the creation of informative, diverse, and relevant prompts that guide AI models, especially language models, to generate the desired outputs. It's the art and science of carefully formulating input prompts to evoke targeted responses from an AI model . Core Techniques in Prompt Design and Engineering Advanced techniques in prompt engineering as offered by Azure OpenAI Service include strategies like: Clear Instructions: Prompts should have explicit and unambiguous instructions to get precise responses from the model. System Messages: These can be used to remind the AI model of its identity and function, helping it stay in character and within the desired context . Recency Bias and Priming the Output: AI models tend to pay more attention to recent inputs, hence, essential instructions can be placed closer to the end of the prompt. Breaking Tasks Down: For complex tasks, breaking them down into simpler subtasks can help the AI model generate more accurate outputs . Advanced Services Offered Companies like Vstorm provide a comprehensive suite of prompt engineering services which include: Custom Prompt Design: Crafting custom prompts tailored to the specific requirements of the business. Fine-Tuning: Adjusting and refining... --- - Published: 2023-06-14 - Modified: 2023-10-30 - URL: https://vstorm.co/ai-model-training/ - Translation Priorities: Optional In today's rapidly evolving technological landscape, AI Model Training has emerged as a game-changer for startups and SMBs. This revolutionary approach enables businesses to harness the potential of artificial intelligence and drive innovation. Let's explore the significance of AI Model Training and its applications for startups and SMBs. Understanding AI Model Training AI Training involves the process of training artificial intelligence models to analyze data, recognize patterns, and make informed decisions. It empowers businesses to develop intelligent systems that can automate tasks, extract valuable insights, and enhance decision-making processes. Benefits for Startups and SMBs AI Training offers numerous benefits for startups and SMBs, propelling them towards success in the competitive market. Let's explore some key advantages: Data-Driven Decision Making: By leveraging AI models trained on relevant data, startups and SMBs can make data-driven decisions, enabling them to stay ahead of the curve and optimize their operations. Enhanced Efficiency: AI-powered systems can automate repetitive tasks, streamline workflows, and improve overall efficiency. This allows startups and SMBs to focus on strategic initiatives and achieve more in less time. Improved Customer Experience: AI models can analyze customer behavior, preferences, and interactions to provide personalized experiences. This leads to higher customer satisfaction and loyalty, contributing to business growth. Competitive Edge: It equips startups and SMBs with powerful tools to gain a competitive edge. By leveraging AI algorithms, businesses can identify market trends, predict customer needs, and adapt their strategies accordingly. The Process AI Model Training involves several steps to ensure optimal results. Let's take... --- - Published: 2023-01-25 - Modified: 2023-11-22 - URL: https://vstorm.co/team-extension/ - Translation Priorities: Optional Team Extension - Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeTeam extension AI Developers for your Team Extension Let us take the guesswork out of recruiting and streamline your hiring process with our data-driven recruitment services. 🚀Check also our AI Developers recruitment services. Our AI developers work with Contact sales Teams extension We hire pre-vetted senior IT specialists with strong technical backgrounds and excellent communication skills at an unbeatable price. 🚀Also, we will augment your team with the AI developers. Pay just one invoice – no hidden fees. AI talent recruitment challenges in Enterprises Staff augmentation In this model, the client pays for the time actually worked by the employee, there are no hidden costs or commissions. Full and part-time recruitment We specialize in data-driven recruitment processes, with favorable pricing, improved time-to-hire, and retention program to ensure business continuity. AI Developers Recruitment At the crossroads of technology and innovation lies the rapidly evolving realm of artificial intelligence (AI). For businesses to make most of its full potential, partnering with the right talent becomes crucial. Our AI Developers Recruitment service is designed to bridge this very gap. Objective: Our primary goal is to connect businesses with top-tier AI Developers, Machine Learning Engineers, Data Scientists and LLM/NLP Engineers, ensuring a  match... --- - Published: 2023-01-23 - Modified: 2023-10-30 - URL: https://vstorm.co/web-product-development/ - Translation Priorities: Optional Web Product Development for Startups | Leading Web Developers Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeWeb Product Development Product builder for startups As an early-stage startup, time and resources are precious, and you've got big ambitions to turn your digital product dreams into reality. That's where we come in! We've got your back with our end-to-end web product development process, starting small and scaling smart. We're all about achieving big results with lean resources - just like you. Contact sales Web Product Development Vstorm partners with early-stage startups to build their digital products. We help achieve the goal with limited time and resources to move fast, effortlessly, and scale it easily. PoC Proof of concept is a demonstration of the feasibility of a product or solution in web product development and is typically used to prove that a concept is possible. It is a prototype developed to validate a concept or process and is usually done before any development or coding begins. MVP Minimum viable product (MVP) is a development technique in which a new product is introduced in the market with basic features, enough to get customer feedback for future product development. It is used to quickly launch a product, gather customer insights and validate a product idea... --- - Published: 2022-05-05 - Modified: 2025-05-02 - URL: https://vstorm.co/digital-nomadopedy/ - Translation Priorities: Optional Digital Nomadopedia - Vstorm Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomeDigital Nomadopedia Here you will find a guide to digital nomading, key points, and ideas, which will guide you on your path. Digital Nomads The term for the first time appeared in 1997 in a book called The Digital Nomad, written by Tsugio Makimoto and David Manners. Their book described the invention of a singular, all-powerful communication device that would allow employees the ability to work from anywhere, among other hypotheses. Digital nomads are people that live a nomadic lifestyle and are location-independent. They use technology to do their jobs. Instead of being physically present at a company’s headquarters or office, digital nomads telecommute. Content management software, inexpensive Internet connectivity via WiFi, smartphones, and Voice-over-Internet Protocol (VoIP) to contact clients and employers have all contributed to the digital nomad lifestyle. In addition, the rise of the gig economy has had an impact. Digital Familists Digital nomads may also be families who work and educate their children in nomadic lifestyle. We have coined the term Digital Familists. Digital nomads are not necessarily only young people. The average age, according to one survey, is 35 years old. They have the potential to raise a generation that knows how to live and coexist... --- - Published: 2022-04-20 - Modified: 2025-05-02 - URL: https://vstorm.co/press/ - Translation Priorities: Optional Vstorm in Press Skip to content Services LLM software: Custom Large Language Model RAG Advanced Engineering LangChain Development AI Consulting & Advisory LLM Development LLM Ops service Industries Healthcare E-commerce & Retail Technology Case studies About us Insights Blog AI Glossary Career For Candidates Open positions Contact us Join us HomePress Welcome to our press room. See our latest news, events, and appearances. Download brand materials and contact our press liaison. Here we would like to provide easy access to visitors and journalists to information about the Vstorm community, its accomplishments and development. Contact for media-related queries They write about us Selected articles Dolnośląskie firmy podbijają światowe rynki Read article Firmy z Dolnego Śląska i ich pomysły na unowocześnianie świata. Read article Tokio Game Show 2022. Dolny Śląsk chce podbić technologiczne centrum świata Read article Firmy z Dolnego Śląska promowały się na targach DMEA w Berlinie Read article Download our presspack Logotype Our logotype in 3 color versions. Only authorised usage allowed, All Rights Registered @Vstorm. Download logotype Brand guidelines About Vstorm and the brand Download here Your contact point Got questions? Feel free to contact Jagoda! Jagoda Malanin Contact me on Linkedin Company About us Blog Privacy Policy Contact us Press For candidates For Candidates Candidates: Frequently Asked Questions Remote practices for better work-life balance For business Services Case studies AI Newsletter AI Glossary AI Community The LLM Book Social Media Facebook Twitter Linkedin All rights reserved This website uses cookies to provide you with the best possible service.... --- - Published: 2022-02-08 - Modified: 2023-06-15 - URL: https://vstorm.co/gdpr-compliance-note/ - Translation Priorities: Optional We inform you that with regard to your providing your personal data in order to participate in the recruitment procedure: 1) Your data controller is VSTORM Sp. z o. o. with the registered office at Waniliowa 48/6 Street, 51-180 in Wrocław, entered in the Register of Entrepreneurs of the National Court Register conducted by the District Court for the City Wroclaw, VI-th Commercial Division of the National Court Register under number KRS 0000853793, having tax identification number (NIP) 8952220262, which can be contacted via email: info@vstorm. co; 2) You can contact our Data Protection Officer via email address: info@vstorm. co; 3) Your personal data shall be processed in order to contact and invite you, carry out and decide on the recruitment process, based on article 221 § 1 of the Labor Code with regard to article 88 of the GDPR and article 6 section 1 point c of the GDPR and the agreement declared by yourself (article 6 section 1 point a of the GDPR); 4) For some of our job offers we may require video material from you, including your image. Your personal data in this scope shall be processed in order to carry out and decide the recruitment process for the position you are applying for, based on article 221 § 4 of the Labor Code with regard to article 88 of the GDPR and the agreement declared by yourself (article 6 section 1 point a of the GDPR); 5) We shall process your personal data in order... --- - Published: 2022-02-04 - Modified: 2023-06-16 - URL: https://vstorm.co/frequently-asked-questions/ - Translation Priorities: Optional What are the values of the Vstorm community? “Community that drives your full potential” is what we believe. We follow remote-first culture, a workplace 4. 0 approach; we care about work-life balance and our impact on the societies we live in. In Vstorm, you will find people who enjoy their jobs while working for both big enterprises and smart SMBs; people who live their full potential and do what they promise. What are your perks? Apart from working 100% remotely with flextime, each Vstorm community member receives an annual budget of $2,500 to drive their full potential and achieve their life goals. We would like you to follow your passion, take risks and try new things, including traveling and self-development. Where is your company’s headquarter? We are a remote-first company, which means that we don’t have a headquarter, although our primary location is in Wroclaw, Poland, where our core team is. We have engineers from all parts of Poland and other countries.   How does Vstorm communicate daily? We use tools like Slack, Hangouts, Google Docs, Notion, etc. , to communicate daily. We communicate with partners via private channels, regular video #coffee-small-talks, or sharing your news or fun things on #social Slack channels. We also have regular meetings online events to play games, explore themes and share our great news to stay updated with the community.   Where do I receive the work equipment from? Our partners are providing the workstation and phone (if needed). Applying for a job: How... --- - Published: 2022-02-03 - Modified: 2025-02-04 - URL: https://vstorm.co/contact-us/ - Translation Priorities: Optional About us Vstorm Sp. z o. o. VAT ID: 895-222-02-62ul. Waniliowa 48/651-180 Wrocław,Poland, EUE-mail: info@vstorm. co Request for Proposal Use our Request for Proposal tool and make the process of sending requests easier! https://bit. ly/ai-powered-RFP     --- - Published: 2022-01-17 - Modified: 2025-07-27 - URL: https://vstorm.co/about-us/ - Translation Priorities: Optional   window. onload = function { Calendly. initBadgeWidget({ url: 'https://calendly. com/info-vstorm/60min? primary_color=fb3640', text: 'Book a free AI consultation! ', color: '#fb3640', textColor: '#ffffff', branding: undefined }); } --- - Published: 2022-01-12 - Modified: 2022-04-06 - URL: https://vstorm.co/privacy-policy/ - Translation Priorities: Optional How do we process your personal data? When as a natural person you contact us or use our services, regardless of whether you act on your behalf or on behalf of another entity (e. g. our client, supplier, etc. ) or when we have obtained your personal data from other sources (e. g. from publicly available industry websites or when your personal data have been disclosed to us as a contact for the purpose of execution of the contracts) we start to process your personal data. We approach all information about you responsibly and in accordance with the law – in particular with GDPR. Your privacy is important to us. This policy explains what personal data we collect from you and how we process it. It also explains how our website uses cookies. I. Glossary – basic concepts Your data controller is VSTORM Sp. z o. o. with the registered office at Waniliowa 48/6 Street, 51-180 in Wrocław, entered in the Register of Entrepreneurs of the National Court Register conducted by the District Court for the City Wroclaw, VI-th Commercial Division of the National Court Register under number KRS 0000853793, having tax identification number (NIP) 8952220262, which can be contacted via email: info@vstorm. co. Personal data – all information that we process related to you. For example: name, surname, email address, consumption data, payment details etc; Processing – all operations that we perform on your personal data. This includes e. g. : collecting, storage, updating, sending correspondence, analysing in order to issue... --- --- ## Career - Published: 2025-10-15 - Modified: 2025-10-16 - URL: https://vstorm.co/career/ai-team-lead/ - Custom Taxonomies: AI Agents - Tags: AI Agents, Lead We seek an experienced AI Team Lead Engineer to join our mission. This is more than just a statement - it’s our daily practice and a core part of our DNA. We’re seeking an A-player who thrives on continuous learning, personal growth, and building AI Agentic solutions that impact businesses. All our projects focus on building custom agentic AI solutions. Projects center on developing agentic AI as a cornerstone of AI-driven business transformation. Every system is engineered from scratch – no GPTs, no off-the-shelf components. This is pure engineering, where each solution is fully customized to address specific business needs. We act as long-term partners, embedding ourselves in our clients’ processes to automate and optimize them step by step. You'll collaborate directly with the client's team in the US and our five AI Engineers on multiple projects in sprint-based iterations. Our mission is to drive the AI Agents transformation, addressing immediate pain points while aligning with long-term strategic goals. What will you do? Define technical vision and AI strategy in collaboration with business consultants and clients, translating business objectives into scalable technical architectures and implementation roadmaps Architect and develop Retrieval-Augmented Generation (RAG) pipelines and end-to-end AI workflows. Evaluate and select technology stacks, assessing LLM frameworks, vector databases, orchestration tools, and cloud services to build maintainable, future-proof solutions Design validation and evaluation frameworks for LLMs, defining metrics, testing methodologies, and continuous monitoring to ensure AI system quality and reliability Mentor and grow the engineering team, conducting code reviews, providing technical guidance,... --- - Published: 2025-08-20 - Modified: 2025-10-15 - URL: https://vstorm.co/career/python-developer-ai/ - Custom Taxonomies: AI Agents, LLM, Python - Tags: AI Agents, LLMs, Python We seek an experienced Python AI/LLM Engineer to join our mission. This is more than just a statement – it’s our daily practice and a core part of our DNA. We’re seeking an A-player who thrives on continuous learning, personal growth, and building AI Agentic solutions that impact businesses. All our projects focus on building custom agentic AI solutions. Projects center on developing agentic AI as a cornerstone of AI-driven business transformation. Every system is engineered from scratch – no GPTs, no off-the-shelf components. This is pure engineering, where each solution is fully customized to address specific business needs. We act as long-term partners, embedding ourselves in our clients’ processes to automate and optimize them step by step. You’ll collaborate directly with the client’s team and our AI Tech Lead in sprint-based iterations. Our mission is to drive the AI Agents transformation, addressing immediate pain points while aligning with long-term strategic goals. What will you do? Develop and maintain AI-powered applications using Python. Support the design and implementation of AI agents and workflows using frameworks such as PydanticAI, LangChain, LangGraph, or LlamaIndex. Build and maintain RESTful and WebSocket APIs to expose application functionalities. Engineering of Retrieval-Augmented Generation (RAG) pipelines and working with vector databases (e. g. , Qdrant, Pinecone). Contribute to preparing validation datasets and running basic evaluation tests for LLMs. Ensure reliability, maintainability, and performance of services in production environments. Collaborate with business consultants to understand client requirements and translate them into technical solutions. Lead projects, work directly with... --- - Published: 2025-07-24 - Modified: 2025-10-15 - URL: https://vstorm.co/career/pm/ - Custom Taxonomies: AI Agents, LLM, Project Manager - Tags: AI Agents, LLMs, Project Manager We're seeking a technical Project Manager who can bridge the gap between AI technology and business transformation, working directly with clients to automate their processes while driving significant account growth What makes this role unique? You're not just delivering projects - you're transforming how businesses operate while building strategic partnerships that drive our company's growth. If you love the intersection of technical problem-solving and business strategy, this role is for you. What you will do: You will focus on automating processes using AI Agents in different industries for a US/UK/Germany-based companies You should be available from 14:00 to 18:00 Polish time during working days Contributing to building VstormPedia (our internal technical knowledge database) on a weekly basis Collaborate with AI Tech Lead, AI Engineers (our team members) to help potential clients design a strategy This role is hybrid – you’ll work from our Wrocław office 2 days per week, so being based in or near Wrocław is required. Requirements 3+ years of professional experience in Project Management with a strong focus on Process Automation, Data, AI, or related technical fields Solid software development background - we need someone who's been in the trenches and understands Python, frameworks, and technical trade-offs (Yes, we are looking for an ex-developer who gets it) Track record of account growth and business development - we expect our PMs to identify expansion opportunities and drive revenue growth Resource management and team optimization experience - you'll be responsible for maximizing engineer utilization and project profitability Proven client-facing... --- - Published: 2025-01-13 - Modified: 2025-10-23 - URL: https://vstorm.co/career/python-ai-llm-engineer/ - Custom Taxonomies: AI Agents, LLM - Tags: AI Agents, LLMs We seek an experienced Python AI/LLM Engineer to join our mission. This is more than just a statement - it’s our daily practice and a core part of our DNA. We’re seeking an A-player who thrives on continuous learning, personal growth, and building AI Agentic solutions that impact businesses. All our projects focus on building custom agentic AI solutions. Projects center on developing agentic AI as a cornerstone of AI-driven business transformation. Every system is engineered from scratch – no GPTs, no off-the-shelf components. This is pure engineering, where each solution is fully customized to address specific business needs. We act as long-term partners, embedding ourselves in our clients’ processes to automate and optimize them step by step. You'll collaborate directly with the client's team and our AI Tech Lead in sprint-based iterations. Our mission is to drive the AI Agents transformation, addressing immediate pain points while aligning with long-term strategic goals. What will you do? Design & Implement production-grade AI Agents using PydanticAI. Develop Retrieval-Augmented Generation (RAG) pipelines and end-to-end AI workflows. Build RESTful and WebSocket APIs to expose AI functionalities. Prepare & Automate validation datasets, evaluation suites, and continuous testing for LLMs. Containerize services (Docker) and deploy on cloud platforms (AWS, GCP, or Azure). Optimize performance and ensure reliability, security, and observability in production Collaborate with business consultants (our team members) to help potential clients design a strategy Creating documentation for projects Conduct code reviews to ensure best practices, optimize performance, and maintain high code quality Contributing to... --- --- ## Case Study - Published: 2025-08-27 - Modified: 2025-10-16 - URL: https://vstorm.co/case-study/ai-agent-for-order-recommendation-and-completion/ - Industries: Retail & E-commerce - Business Functions: Customer Service / Support, Sales & Marketing What does Mixam do? Mixam is a self-publishing company that primarily provides printing and fulfillment services for independent authors, publishers, and creators. They specialize in high-quality print production, including books, magazines, and other printed materials. Their services are designed to make it easier for individuals and small publishers to produce and distribute their works without the need for large-scale traditional publishing houses. Mixam was established in 2007 in the United Kingdom but operates on a global scale, expanding its services to meet the needs of the global market. One of the key aspects of the expansion is the usage of AI in accordance with the user-friendliness of their self-publishing platform. How did Vstorm help? Vstorm designed and implemented an AI agent designed to help Mixam’s customers navigate the company’s complex printing offers, smoothing the customer experience in navigating complex publication processes. Cooperation with Vstorm began when Mixam had already begun using AI elements and AI-based platforms in various operations and services. However, the company's ambitious goals required reaping the full potential of AI in increasingly demanding and complex processes. The initial Vstorm project was centered around creating a satisfying experience for new users who were just starting their self-publishing journey and beginning to explore the range of options available to them. From book format to paper thickness and structure, it’s easy for any non-publishing professional to get lost in the variety of choices that need to be made before their first publication materializes in the desired form. That is why... --- - Published: 2025-04-25 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/intelligent-automation-with-actionable-ai-agents-for-the-us-telecommunication-company/ - Case Category: Advanced RAG - Industries: Technology, Telecommunication - Business Functions: IT & Engineering, Operations What does the client do? The US-based telecommunications provider with over 45 years of industry experience delivers fiber-powered internet and video services to 150,000+ households in 500+ master-planned communities across two south states. Award-winning for both their technology and service, the company delivers ultra-fast internet speeds with exceptional customer support, setting a new standard for connected living. How does Vstorm cooperate with the client? The client was looking for a strategic consulting and engineering partner to transform its AI adoption vision into reality complexly. They initially had identified over 80 potential use cases across the organization where Agentic AI automation could create significant value. We started with a collaborative identification of the first two high-impact opportunities that would demonstrate the real-world grounded value of automation with AI Agents. These initial projects delivered immediate benefits like ROI, as well as unleashed growth potential, proving both the technical feasibility and business value of intelligent automation. Process #1 - Field installation automation Vstorm transformed the client's device installation process by replacing a manual, corporate call-center dependent workflow with an intelligent multi-agent system. Before automation, each field installation required technicians to call a support center where three agents manually assisted them in real-time in activating devices using multiple systems. This outdated process created several critical business constraints, including high labor costs for every installation; limited service hours that delay jobs outside office hours; as well as a structural bottleneck that prevents expansion beyond other states. Vstorm engineered an actionable AI Agent with a multi-agent... --- - Published: 2025-04-24 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/mapping-out-future-architecture-for-machine-learning-based-software/ - Industries: Manufacturing, Technology - Business Functions: IT & Engineering, Operations What does Spectrally do? Spectrally is a deep-tech startup based in Poland, EU, specializing in real-time chemical analysis using Raman spectroscopy. Their unique technology enables non-destructive, rapid monitoring of chemical compositions directly within industrial processes, eliminating the need for traditional laboratory testing. Spectrally's systems utilize laser-based Raman spectroscopy to analyze the molecular composition of substances in real time. What was the challenge the Vstorm team addressed? With the commercial success and growth of Spectrally's industrial products, the team was looking to reimagine software architecture capable of meeting the most ambitious customers' requirements. The software, fueled by Machine Learning models, is becoming increasingly part of the business. Spectrally was looking to enhance its scope, adding a wide range of options — from quality assurance support to customer-facing usage metrics and analytics. Foresight set by experts To gain a perspective on software evolution, Spectrally Vstorm offered a consulting service based on a workshop format. The meeting was used to collect requirements and discuss potential opportunities that could be addressed in the process of evolving software architecture. The workshop was led by B. A Gonczarek, a Vstorm co-founder in the role of Solution Architect, who brought consulting experience from similar projects and companies that excelled thanks to their software portfolio. The meeting was supported by Bartosz Rogulski, senior engineer at Vstorm, who offered his ML & Agentic AI experience on the subject. "Working with the Vstorm team was a really positive experience for us. They quickly understood what we do, what challenges we... --- - Published: 2025-04-17 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/swapping-iron-from-nvidia-to-intel/ - Industries: Manufacturing, Technology - Business Functions: IT & Engineering Migrating Machine Learning and LLM solutions designed to run on Nvidia hardware to a different architecture: Intel Gaudi AI accelerators There’s a growing trend in deploying MML solutions on premise, using open source models and many existing hardware solutions. Sometimes, however, the model to be used doesn’t match the bare metal it is to be running on. In the case study below, Vstorm engineering team was tasked to swap iron. Without calling our customer by name, we’ll illustrate what that entails by showing how we ported Llama, made for NVidia, to run locally on Intel Gaudi architecture. What is LLama? The Lama. cpp is a groundbreaking open-source project that has revolutionized how we run Large Language Models on personal computers and various hardware setups. At its core, it's a lightweight C/C++ implementation designed to run LLMs with remarkable efficiency and minimal complexity. The popularity of llama. cpp is evident in its impressive GitHub metrics, with over 74,500 stars and 10,800 forks, making it one of the most prominent AI infrastructure projects in the open-source community. Its influence extends far beyond its direct usage, as its core technology has become a fundamental building block for numerous other AI projects, including popular tools like oLlama, effectively establishing Llama. cpp as a cornerstone of local LLM deployment. On what can you run Llama? At the heart of Llama. cpp lies ggml, a specialized C/C++ library designed specifically for Transformer model inference. While ggml started as part of the Llama. cpp project it has evolved into a powerful foundation that handles the core tensor operations and hardware acceleration features. Think of ggml as the... --- - Published: 2025-04-10 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/multi-channel-ai-agent-in-healthcare/ - Case Category: Advanced RAG - Industries: Healthcare - Business Functions: Customer Service / Support What does the company do? The US-based healthcare company has a mission to provide high-quality, affordable, and easy-to-understand healthcare plans for seniors. It specializes in Medicare Advantage offerings and leverages advanced technology to enhance healthcare delivery. Operating across multiple states in the United States, this organization serves over 100,000 members, reflecting its expanding market presence. By the end of that year, the organization employed over 550 individuals, and it maintains a public listing on the NASDAQ. How does Vstorm cooperate with the client? Challenge As the organization expanded, the need for scalable, personalized solutions became increasingly urgent to improve efficiency across multiple clinics and enhance communication with senior patients. They sought a consulting and engineering partner capable of delivering AI Agents and integrating them into existing workflows and healthcare systems. The overarching goal was to create an AI Agent that would, in a personalized manner, analyze a patient’s entire record, including medical history, physician notes, active health issues, visit history, personal details, and current medications - while also gathering any additional information directly from the patient using multi-channel methods tailored to older adults. At the same time, the solution would continue building a robust data foundation for future use, all in compliance with HIPAA standards. How did we approach? Incremental rollout. To avoid technical debt, we began with a Proof of Concept (PoC) tested by a select group of doctors. They were impressed by how much time it saved, validating our approach before scaling up. Semi-manual approach. In line with... --- - Published: 2025-04-07 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/advanced-rag-engineering-for-real-estate-due-diligence-ai-agent/ - Case Category: Advanced RAG - Industries: Constructor Engineering, Real estate - Business Functions: IT & Engineering, Operations What does Mapline do? Mapline. AI is a US-based startup on a mission to transform how real estate developers conduct due diligence. By utilizing the power of artificial intelligence, the tool drastically reduces the time and costs associated with traditional due diligence processes. Instead of weeks of research and site visits, Mapline. AI enables remote, comprehensive evaluations of potential developments in a matter of minutes. This innovation not only accelerates decision-making but also lowers the barrier to entry, making professional due diligence more accessible to developers of all sizes—without requiring them to be on-site or deeply familiar with local legal complexities. How does Vstorm cooperate with Mapline? The overarching goal was to create an AI Agent capable of conducting in-depth due diligence for real estate development projects across various U. S. municipalities. This required integrating multiple high-volume data streams - geospatial, environmental, municipal, legal, and infrastructural - while ensuring the accuracy of insight-based reports. Percel-specific variables include land use, zoning class, total acres, proposed land use, proposed zoning class, open space, greenways, wetlands, surface water, watersheds, adjacent roads, transportation plans, major roads, local roads, conservation elements, greenways & trails and access easements. Off-the-shelf AI tools were prone to the limitation of adjusting to specific workflows and technical environments and, as a result, “hallucination” when faced with the sheer complexity and variability of these data sets. Further, the differences in municipal regulations added another layer of difficulty, necessitating a custom AI solution Vstorm’s mission was to build a robust AI Agent... --- - Published: 2024-10-07 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/llm-powered-voice-assistant-for-call-center/ - Case Category: Data mangement - Industries: Retail & E-commerce - Business Functions: Customer Service / Support, Sales & Marketing What does a company do? The company develops and implements AI-powered voice assistants that automate tasks such as call verification and routing for inbound customer calls. Their solutions integrate with existing telecommunication systems to make customer service operations more efficient. By automating these processes, the company helps businesses handle calls faster, reduce errors, and lower operational costs while improving the overall customer experience. The company's goal is to improve how businesses handle customer interactions, making communication smoother, reducing costs, and ensuring better service for customers. How does Vstorm cooperate with Call-center? At Vstorm, we partner with our clients to solve complex challenges through innovative AI and LLM-based technologies. Our collaboration with the company showcases our ability to deliver tailored solutions that help scale the business by addressing challenges related to operational productivity, ensuring long-term impact. The client approached us with a primary goal: to automate the verification and routing of inbound customer calls. Their existing system required significant manual intervention, which was both time-consuming and error-prone, especially during peak times. Additionally, the client faced difficulties scaling their process to accommodate a growing global customer base, with the need for support across multiple languages. We began our collaboration by conducting a thorough audit of their existing solution. Through this process, we identified several key challenges, including: The manual handling of calls was inefficient and led to delays in response times. There was an increased error rate due to the manual verification and routing processes. The system struggled to scale to meet... --- - Published: 2024-09-04 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/text-summarization-for-vacation-rentals-using-llms/ - Case Category: Data mangement - Industries: Real estate - Business Functions: Operations Text summarization for marketing agency using LLMs What does Guesthook do? Guesthook is a marketing agency specializing in the vacation rental industry, offering tailored solutions to help property owners increase bookings and revenue. Since its inception, the company has focused on creating compelling property descriptions, managing social media, designing websites, and running email campaigns for vacation rentals. Guesthook’s services aim to enhance the online presence of rental properties, making them stand out on platforms like Airbnb and Vrbo. By providing strategic marketing and content creation, the company supports property owners in building stronger brands and attracting more guests, ultimately maximizing their rental income. How does Vstorm cooperate with Guesthook? For Guesthook, collaborating with AI and Large Language Models (LLMs) was a new experience, so our first priority was to clearly explain the potential benefits these technologies could bring to their operations. We focused on understanding Guesthook’s needs, objectives, and challenges to identify the areas where AI could provide the most value. Through our analysis, we identified that one of the main challenges for Guesthook was the manual process of creating property descriptions for vacation rentals. Property owners would provide specific guidelines, and Guesthook would outsource the task to external experts, who would write the content. This process was not only time-consuming but also costly, and the quality and consistency of the descriptions varied depending on the skills of individual copywriters. Our goal was to automate this process using AI and LLMs, enabling Guesthook to generate engaging property descriptions more efficiently... --- - Published: 2024-08-28 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/rag-automation-e-mail-response-with-ai-and-llms/ - Case Category: Data mangement - Industries: Retail & E-commerce - Business Functions: Customer Service / Support, Sales & Marketing RAG to automate email responses in the IT industry What does Senetic do? Senetic is a global provider of IT solutions, supporting companies and public institutions in optimizing their daily tasks by creating intuitive digital ecosystems. Operating since 2009, the company has established itself as a leader in delivering high-end networking, server, software, and IT hardware solutions for small and medium-sized businesses worldwide. With 27 subsidiaries across the globe and sales in 151 countries, Senetic serves over 2 million customers annually. The company has been recognized with multiple awards and is a trusted Microsoft partner. To further enhance and develop its services, Senetic has partnered with Vstorm. How does Vstorm cooperate with Senetic? RAG and automated emails For Senetic, collaborating with AI was a new experience, so our first priority was to clearly explain the potential benefits that Large Language Models (LLMs) could bring to their operations. Our goal was to thoroughly understand Senetic’s needs, objectives, and challenges while identifying the areas where artificial intelligence could most effectively enhance its processes. We identified a key area that was consuming significant resources and time for Senetic’s employees: email communication with clients. Since Senetic operates globally, customer inquiries arrive from all over the world, in various languages, which presents a significant challenge for email management. We proposed a solution to Senetic that would greatly streamline this process. Previously, when a customer sent an inbound inquiry via email, the message would land in the company’s Microsoft Outlook inbox, where it awaited manual processing.... --- - Published: 2024-07-11 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/automated-data-scraping-platform-powered-by-ai-and-llms/ - Case Category: Data mangement - Industries: Media - Business Functions: IT & Engineering, Operations Collecting data from thousands of sites using AI and LLMs What does Rotwand do? Rotwand is a boutique PR agency in Munich, Germany, that focuses on data-driven public relations. The agency combines traditional PR methods with advanced SEO techniques to create effective digital PR solutions. This approach increases visibility, generates leads, and provides measurable results for their clients. Rotwand has been featured in notable publications such as PRWeek, The Holmes Report, Handelsblatt, ARD, Frankfurter Allgemeine Zeitung, BR, WIRED, and HORIZONT. Rotwand, an independent and progressive company, wants to become a leader in the high-tech PR industry. The company improves public relations with creative methods and strategic insights. To stay competitive, Rotwand is dedicated to using AI to refine its approach and offer better solutions for its clients. How does Vstorm cooperate with Rotwand? A few years ago, Rotwand started a project using traditional data scraping techniques to collect unstructured data from many different sources. This approach required a lot of budget, time, and development work. Additionally, the accuracy of data collection was unsatisfactory. Rotwand faced a big challenge: how to efficiently and accurately scrape unstructured data from numerous sources while minimizing costs and development efforts. Seeing the limitations of their traditional methods, they approached us at Vstorm, as experts in AI and custom LLM-based software. Our goal was clear: to develop a data scraping technique using advanced Natural Language Processing (NLP), Machine Learning (ML), and Large Language Models (LLMs). This approach aimed to significantly reduce development costs, increase accuracy, and... --- - Published: 2024-06-11 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/collaborative-conversational-ai-assistant-with-automation/ - Case Category: Remote IT Resources - Industries: Education & EduTech - Business Functions: Operations What does the company do? Established in 2011, a California-based startup has emerged to reshape online discussions through open-source technology. Their mission is to enable conversations over the world’s knowledge. Drawing from their founder's expertise in climate change, this organization has made a name for itself by developing applications that powerfully enhance online interactions through annotations. How does Vstorm cooperate with the company? In collaboration with Vstorm, the California-based startup embarked on a project marked by ambition and innovation. This initiative aimed to craft an AI platform that is open-source and user-friendly, addressing the growing need for a versatile platform for Large Language Models (LLMs). The project's design goal was to allow multiple users to collaborate in real-time using various state-of-the-art LLMs (API-based and custom models with their infrastructure). The primary objective is to enable self-hosted applications and utilize custom-developed LLMs to enhance data security, safety, transparency, and control over the LLMs trained on the company's data, ensuring the accuracy of results. The platform stands out with its dual chat+layer system, one for organizational communication and another dedicated to prompt design and chaining. The project prides itself on the capability for prompt library additions to enhance its utility and functionalities that interface with diverse LLMs. Transparent collaboration is at its core, with immediate usability for entities integrated with external applications like G-suite. The platform's memory function enables it to perform in the context of previous messages, offering appropriate responses and support. An essential part of the project was the use... --- - Published: 2023-02-10 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/ai-translation-with-llms/ - Case Category: Data mangement - Industries: Education & EduTech, Healthcare - Business Functions: Customer Service / Support, Operations Using AI Translation with LLMs solution for achieving hyper-automation What does MindSonar do? MindSonar measures mindsets. It is a complete software platform for data collection, synchronization, and visualization of multimodal human behavior. It is a psychological instrument that measures people's thoughts (their Meta Programs) and what they find important (their Graves Drives). Your mindset influences how you evaluate things, what you notice, and what you overlook. And that, in turn, determines your results, also at work. MindSonar uses several online applications for administering tests, generating reports, and managing professional users. Clients receive a 30-page in-depth profile of how individuals think in a given context that makes the invisible visible. MindSonar is used in prestigious organizations such as the Dutch armed forces, a top European automobile manufacturer, the Olympic Dressage team, and one of Europe's premier banks. Top-level recruitment, choosing key decision makers, solving internal conflicts, improving team (group) process & composition, offering insights into problems and resources, and clarifying training goals are among the many usages of this revolutionary authorial approach. How does Vstorm cooperate with MindSonar? AI Translation with LLMs The MindSonar team was looking for a way to scale their current solution due to new improvements they wanted to implement. They started working with clients from new countries and needed to speed up the scaling phase worldwide. Facing challenges in scaling their solution and entering new markets, MindSonar turned to Vstorm, a leading AI and LLM-based software company. We quickly identified the key areas where MindSonar could optimize... --- - Published: 2022-12-08 - Modified: 2025-08-27 - URL: https://vstorm.co/case-study/fight-against-diabetes-with-data-and-advanced-ai/ - Case Category: Data mangement - Industries: Healthcare - Business Functions: IT & Engineering Innovation in medical healthcare and the fight against diabetes with advanced AI. What does GlucoActive do? Glucoactive is a Research and Development start-up, featured on TechCrunch with an estimated worth of above 6 million euros that is working on a revolutionary medical care product that will change the way we treat diabetes. The GlucoStation uses laser light waves (optical and spectrophotometric) that can get through the skin and measure blood glucose levels. This is done without hurting the person. Anyone who has ever struggled with diabetes will welcome this new product. How does Vstorm cooperate with GlucoActive? How does the GlucoActive device work? It measures given parameters, tracking various types of spectrophotometric and sensor data. In the next step, the algorithms analyze the information and give results to the user. But, to put such a complex system into motion, you obviously need a team of highly skilled Data & AI specialists. The goal of GlucoActive was to create a complex system in the field of medical technology that uses machine learning and other data analysis. For this high-tech data management system to gather, store, share, and analyze information, it needs powerful software. The objective was to better manage data for further AI & LLM work via efficient development, facilitate the R&D process (both for the current device and future devices), support data analysis and machine learning, and maximize the quality of the resulting data. Outsourcing was intended to make the company more flexible, reduce paperwork, become cost-effective, and maintain high... --- --- ## Glossary - Published: 2025-08-25 - Modified: 2025-08-25 - URL: https://vstorm.co/glossary/agent-to-human-handoff/ - Glossary Categories: Agentic AI Agent-to-Human Handoff is the systematic process where an AI agent transfers control of an ongoing interaction, task, or decision-making process to a human operator when the agent encounters limitations in its capabilities, requires human judgment, or faces complex scenarios outside its training parameters. This critical mechanism ensures service continuity and quality by recognizing when human expertise, empathy, or authority is needed. The handoff typically includes context preservation, where all relevant conversation history, user data, and situational information are seamlessly transferred to the human agent. Effective handoffs maintain user experience while leveraging the complementary strengths of both AI efficiency and human nuanced understanding, making them essential components in hybrid automation systems across customer service, healthcare, financial services, and complex decision-making processes. --- - Published: 2025-08-21 - Modified: 2025-08-21 - URL: https://vstorm.co/glossary/agentic-workflow-patterns/ - Glossary Categories: Agentic AI Agentic Workflow Patterns are standardized, reusable architectural designs that define how AI agents execute complex tasks, make decisions, and interact with systems and other agents. These proven patterns include sequential processing, parallel execution, conditional branching, feedback loops, and human-in-the-loop validation. Common patterns like Chain-of-Thought, ReAct (Reasoning + Acting), and Multi-Agent Collaboration provide structured approaches for breaking down complex problems into manageable steps. By implementing these established patterns, organizations can build more reliable, predictable, and scalable AI agent systems that consistently deliver business value while minimizing hallucinations and errors. --- - Published: 2025-08-21 - Modified: 2025-08-21 - URL: https://vstorm.co/glossary/orchestrator-worker-pattern/ - Glossary Categories: AI Agent, Architecture, Automation, Frameworks Orchestrator-Worker Pattern is a distributed AI agent architecture where a central orchestrator agent coordinates and manages multiple specialized worker agents to complete complex tasks. The orchestrator handles task decomposition, work distribution, progress monitoring, error handling, and result aggregation, while worker agents focus on executing specific subtasks within their areas of expertise. This pattern enables horizontal scaling, fault tolerance, and specialization by allowing different workers to handle distinct capabilities like data processing, external API calls, or domain-specific reasoning. The orchestrator maintains overall workflow state and ensures proper sequencing and dependencies between worker outputs, making it ideal for large-scale automation scenarios requiring multiple specialized AI capabilities. --- - Published: 2025-08-21 - Modified: 2025-08-21 - URL: https://vstorm.co/glossary/agent-washing/ - Glossary Categories: AI Agent Agent Washing is the deceptive marketing practice where companies falsely label traditional automation tools, simple chatbots, or rule-based systems as "AI Agents" to capitalize on market enthusiasm for agentic AI technology. Unlike genuine AI Agents that demonstrate autonomous reasoning, decision-making, tool usage, and adaptive behavior, agent-washed products typically follow predetermined scripts, lack true autonomy, and cannot handle complex, multi-step workflows. This practice misleads buyers into purchasing inferior solutions that cannot deliver the sophisticated capabilities of authentic agentic systems. Agent washing undermines market trust and creates unrealistic expectations, making it crucial for organizations to evaluate vendors based on actual autonomous capabilities, reasoning depth, and real-world performance rather than marketing claims. --- - Published: 2025-08-21 - Modified: 2025-08-21 - URL: https://vstorm.co/glossary/autopilot-selling/ - Glossary Categories: AI Agent, Automation Autopilot Selling is an autonomous AI Agent system that independently manages and executes sales processes with minimal human intervention. These sophisticated agents handle lead qualification, prospect engagement, objection handling, proposal generation, negotiation, and deal closure across multiple channels including email, chat, phone, and social media. Unlike basic chatbots or CRM automation, autopilot selling agents use advanced reasoning to adapt conversations in real-time, personalize messaging based on prospect behavior, and make strategic decisions about pricing and terms. The system continuously learns from successful interactions, optimizes conversion rates, and maintains detailed pipeline management while seamlessly escalating complex scenarios to human sales professionals when necessary. --- - Published: 2025-08-21 - Modified: 2025-08-21 - URL: https://vstorm.co/glossary/digital-labor-digital-worker/ - Glossary Categories: AI, AI Agent, Automation Digital Labor, also known as Digital Workers, refers to software-based automation technologies that perform cognitive and repetitive tasks traditionally executed by human employees. These virtual workers encompass a spectrum of technologies including AI agents, robotic process automation (RPA) bots, intelligent document processing systems, and chatbots that can handle data entry, customer service, financial processing, and complex decision-making workflows. Advanced digital workers powered by AI agents demonstrate reasoning capabilities, tool usage, and adaptive behavior, while simpler forms follow rule-based processes. Digital labor operates 24/7 without breaks, reduces operational costs, minimizes human error, and scales instantly to meet demand fluctuations, making it essential for modern business process optimization and competitive advantage. --- - Published: 2025-08-21 - Modified: 2025-08-21 - URL: https://vstorm.co/glossary/complexity-threshold/ - Glossary Categories: Architecture, Automation Complexity Threshold is the critical point at which a task, process, or problem exceeds the capabilities of current automation approaches and requires more sophisticated AI Agent architectures or human intervention. This threshold typically manifests when simple rule-based systems fail due to ambiguous inputs, multi-step reasoning requirements, contextual dependencies, or dynamic environmental changes. Tasks below the complexity threshold can be handled by traditional automation or basic AI tools, while those above require advanced agentic capabilities like reasoning, tool usage, memory, and adaptive decision-making. Understanding complexity thresholds helps organizations choose appropriate automation strategies, determine when to implement AI Agents versus simpler solutions, and identify optimal human-AI collaboration points for maximum efficiency and reliability. --- - Published: 2025-08-21 - Modified: 2025-08-21 - URL: https://vstorm.co/glossary/long-term-coherence/ - Glossary Categories: AI Agent, Automation, Frameworks Long-term Coherence is the ability of AI Agents to maintain consistent reasoning, decision-making, and behavioral patterns across extended periods of operation or complex multi-step workflows. This critical capability ensures agents remain aligned with their original objectives, maintain logical consistency between actions, preserve contextual understanding across session boundaries, and avoid contradictory decisions when handling lengthy processes. Long-term coherence requires sophisticated memory management, state persistence, goal tracking, and conflict resolution mechanisms. Agents with strong long-term coherence can handle enterprise-scale workflows spanning days or weeks, maintain relationship context in customer interactions, and execute complex projects without degrading performance or losing strategic focus over time. --- - Published: 2025-08-21 - Modified: 2025-10-25 - URL: https://vstorm.co/glossary/headless-ai-agent/ - Glossary Categories: AI Agent Headless AI Agent is an AI system that operates without a direct user interface, designed to be integrated programmatically into existing applications, workflows, or backend systems through APIs and code interfaces. Unlike conversational AI Agents with chat interfaces, headless agents function entirely behind the scenes, processing data, making decisions, and executing actions through programmatic calls rather than human interaction. These agents excel at automating complex business logic, data processing pipelines, system integrations, and workflow orchestration without requiring user-facing components. Headless AI Agents offer maximum flexibility for developers, enabling seamless embedding into existing software architectures, custom automation scenarios, and enterprise systems where direct user interaction is unnecessary or undesirable. --- - Published: 2025-08-21 - Modified: 2025-08-21 - URL: https://vstorm.co/glossary/open-agentic-web/ - Glossary Categories: AI Agent, Architecture, Automation Open Agentic Web is a vision of the internet where AI Agents can autonomously navigate, interact, and perform tasks across different web services, platforms, and applications through standardized protocols and open APIs. This ecosystem enables agents to seamlessly access web resources, communicate with other agents, execute cross-platform workflows, and provide services without being confined to proprietary systems or closed platforms. The Open Agentic Web relies on interoperability standards, semantic web technologies, and agent communication protocols that allow diverse AI systems to collaborate effectively. Unlike siloed AI platforms, this approach promotes innovation, reduces vendor lock-in, and creates a distributed network of specialized agents that can be composed into complex, multi-service workflows spanning the entire web ecosystem. --- - Published: 2025-08-21 - Modified: 2025-08-21 - URL: https://vstorm.co/glossary/multi-agent-systems-mas/ - Glossary Categories: AI Agent, Automation Multi-Agent Systems (MAS) are distributed computing environments where multiple autonomous AI Agents interact, coordinate, and collaborate to solve complex problems that exceed the capabilities of individual agents. These systems feature agents with distinct roles, goals, and capabilities that communicate through message passing, shared environments, or coordination protocols to achieve collective objectives. MAS enable distributed problem-solving, parallel processing, fault tolerance, and emergent intelligent behavior through agent cooperation, competition, or negotiation. Key advantages include scalability, robustness, specialization, and the ability to handle dynamic, uncertain environments. Applications span autonomous vehicles, smart grids, supply chain management, and distributed manufacturing, where decentralized intelligence and coordination deliver superior performance compared to centralized approaches. --- - Published: 2025-08-21 - Modified: 2025-10-08 - URL: https://vstorm.co/glossary/polyphonic-ai/ - Glossary Categories: Agentic AI, AI, AI Agent Polyphonic AI is an architectural approach where multiple AI Agents, models, or processing streams operate simultaneously in coordinated harmony to generate unified, coherent outputs. Like musical polyphony where different voices contribute distinct melodies to create rich compositions, polyphonic AI systems blend diverse AI capabilities—reasoning, creativity, analysis, and domain expertise—running in parallel rather than sequentially. This approach enables real-time multi-perspective processing, where specialized agents contribute their unique "voices" to complex problem-solving while maintaining overall coherence and consistency. Polyphonic AI excels at handling multifaceted challenges requiring simultaneous consideration of multiple factors, constraints, or viewpoints, delivering more nuanced and comprehensive solutions than single-threaded AI approaches. --- - Published: 2025-08-20 - Modified: 2025-10-14 - URL: https://vstorm.co/glossary/agentive-ai/ - Glossary Categories: Agentic AI, AI Agentive AI is artificial intelligence that autonomously takes actions and makes decisions to complete complex tasks, rather than simply responding to prompts. These systems can interact with tools, access data sources, execute workflows, and adapt their approach based on real-time feedback. Unlike traditional AI that generates responses, agentive AI operates as digital workers that can handle multi-step processes, integrate with existing software systems, and maintain context across extended interactions. This technology enables businesses to automate sophisticated workflows that require reasoning, decision-making, and dynamic problem-solving capabilities. --- - Published: 2025-08-20 - Modified: 2025-08-20 - URL: https://vstorm.co/glossary/agent-to-agent-a2a-protocol/ - Glossary Categories: Agentic AI, AI Agent Agent-to-Agent (A2A) Protocol is a standardized communication framework that enables AI agents to interact, coordinate, and collaborate with each other in multi-agent systems. These protocols define the messaging formats, interaction patterns, and coordination mechanisms that allow autonomous agents to share information, delegate tasks, negotiate resources, and work collectively toward complex objectives. A2A protocols ensure interoperability between different AI agents, enabling them to form dynamic networks that can solve problems beyond individual agent capabilities. Key features include message routing, task allocation, conflict resolution, and distributed decision-making processes that maintain system coherence while preserving individual agent autonomy. --- - Published: 2025-08-20 - Modified: 2025-08-20 - URL: https://vstorm.co/glossary/model-context-protocol-mcp/ - Glossary Categories: Agentic AI, AI Agent Model Context Protocol (MCP) is a standardized framework that governs how AI models and agents maintain, share, and utilize contextual information across interactions and sessions. This protocol ensures consistent memory retention, enabling AI agents to access previous conversations, task states, and learned preferences while maintaining coherent understanding throughout extended workflows. MCP defines the structure for context storage, retrieval mechanisms, and context sharing between different AI systems or agent instances. It addresses critical challenges in conversational AI and agent-based systems by preventing context loss, enabling seamless handoffs between agents, and maintaining personalization across multiple touchpoints within complex automation workflows. --- - Published: 2025-08-20 - Modified: 2025-08-20 - URL: https://vstorm.co/glossary/agent-cards/ - Glossary Categories: Agentic AI, AI, AI Agent Agent Cards are structured metadata documents that define the capabilities, specifications, and operational parameters of AI agents within multi-agent systems. These standardized profiles contain essential information including agent functions, input/output formats, resource requirements, interaction protocols, and performance characteristics. Agent Cards serve as digital identity documents that enable system administrators and other agents to understand what each agent can accomplish, how to communicate with it, and under what conditions it operates optimally. They facilitate agent discovery, task routing, and dynamic composition of agent workflows by providing machine-readable descriptions of agent capabilities and constraints. --- - Published: 2025-08-08 - Modified: 2025-08-08 - URL: https://vstorm.co/glossary/chatgpt-5/ - Glossary Categories: AI Agent, Deep Learning, LLM, ML ChatGPT 5 is OpenAI's most advanced large language model, representing a significant leap in artificial intelligence capabilities beyond its predecessors. This conversational AI system demonstrates enhanced reasoning, multimodal processing, and improved contextual understanding across diverse domains. ChatGPT 5 features superior code generation, mathematical problem-solving, and creative writing abilities while maintaining more consistent and reliable outputs. The model incorporates advanced safety measures and alignment techniques to reduce hallucinations and biased responses. With expanded context windows and faster processing speeds, ChatGPT 5 enables more sophisticated AI agent implementations for enterprise applications, from customer service automation to complex analytical tasks requiring nuanced decision-making and multi-step reasoning processes. --- - Published: 2025-08-08 - Modified: 2025-08-08 - URL: https://vstorm.co/glossary/swe-langchain/ - Glossary Categories: AI Agent, Automation, Data, LLM, ML SWE Langchain is a specialized implementation of the LangChain framework designed for Software Engineering (SWE) applications and automated code development tasks. This AI-powered tool combines LangChain's orchestration capabilities with software engineering workflows to create intelligent agents that can analyze, generate, debug, and refactor code across multiple programming languages. SWE Langchain enables developers to build sophisticated AI systems that understand software architecture, perform code reviews, generate documentation, and execute complex programming tasks through natural language interfaces. The framework integrates with development environments, version control systems, and testing frameworks to streamline software development processes. By leveraging large language models within structured workflows, SWE Langchain transforms traditional programming approaches and accelerates development cycles while maintaining code quality standards. --- - Published: 2025-08-08 - Modified: 2025-08-08 - URL: https://vstorm.co/glossary/genie-3/ - Glossary Categories: AI Agent, Architecture, Automation, Deep Learning, Generative Models Genie 3 is Google's advanced generative interactive environment model that creates controllable 2D worlds from visual observations and text prompts. This foundation model represents a significant evolution in AI-driven world generation, enabling users to interact with dynamically created environments through learned action spaces. Genie 3 demonstrates sophisticated understanding of physics, object relationships, and environmental dynamics while generating diverse interactive scenarios from minimal input data. The model excels at creating game-like environments, simulations, and virtual worlds that respond realistically to user inputs. Built on transformer architecture with enhanced spatiotemporal reasoning capabilities, Genie 3 serves as a powerful tool for AI research, game development, and interactive content creation, offering unprecedented control over generated environments and supporting complex multi-agent interactions within its created worlds. --- - Published: 2025-08-01 - Modified: 2025-09-21 - URL: https://vstorm.co/glossary/langchain-2/ LangChain is an open-source framework for developing applications powered by large language models (LLMs). LangChain simplifies every stage of the LLM application lifecycle, providing interoperable components and third-party integrations to simplify AI application development. Available in both Python and JavaScript libraries, LangChain's tools and APIs streamline the process of building LLM-driven applications like chatbots and AI agents. The framework connects LLMs to private data and APIs to build context-aware, reasoning applications, enabling rapid movement from prototype to production through popular methods like retrieval-augmented generation (RAG) and chain architectures. LangChain is used by major companies including Google and Amazon for its versatility, performance, and extensive community support. --- - Published: 2025-08-01 - Modified: 2025-08-01 - URL: https://vstorm.co/glossary/retrieval-augmented-generation-rag-configuration-2/ - Glossary Categories: LLM, RAG, Vector Database Retrieval-Augmented Generation RAG configuration is the set of tunable parameters that shapes how a RAG pipeline finds knowledge and feeds it to a large language model. It spans four layers: data prep (chunk size, overlap, embedding model, metadata), retrieval strategy (vector or hybrid search, filters, rerankers), generation context (prompt template, token budget, citation style), and orchestration logic (fallback LLMs, confidence thresholds, caching, security). Engineers adjust these levers to trade off latency, accuracy, and cost. Larger chunks boost semantic coverage but risk context overflow; hybrid BM25-plus-vector search improves recall at the expense of compute. A robust configuration also defines evaluation metrics—precision, citation precision, hallucination rate—and iterates via automated A/B tests. Version-controlled YAML or JSON files store the settings so teams can reproduce builds, roll back quickly, and swap vector databases or models without code rewrites, turning RAG from an experiment into maintainable production software. --- - Published: 2025-07-29 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/zero-shot-training/ - Glossary Categories: AI, ZSL Zero shot training is a machine learning paradigm where models are trained to perform tasks on categories or domains they have never encountered during training, leveraging learned representations and transferable knowledge to generalize beyond their training distribution. This approach enables models to handle novel scenarios without requiring task-specific examples by utilizing semantic embeddings, attribute-based learning, and cross-modal knowledge transfer. The training process focuses on learning generalizable patterns and relationships that can be applied to unseen categories through compositional understanding and semantic reasoning. Common techniques include pre-training on diverse datasets, learning shared feature spaces, and utilizing auxiliary information like textual descriptions or ontologies. For AI agents, zero shot training provides immediate adaptation capabilities without retraining, enabling deployment across diverse domains and handling of unexpected scenarios cost-effectively. --- - Published: 2025-07-29 - Modified: 2025-08-19 - URL: https://vstorm.co/glossary/explainability-meaning/ - Glossary Categories: AI, ZSL Explainability meaning refers to the fundamental concept of making artificial intelligence systems' decision-making processes, reasoning patterns, and internal mechanisms comprehensible and interpretable to humans in actionable terms. This core AI principle encompasses the ability to provide clear, logical explanations for model predictions, feature importance, and algorithmic behavior that stakeholders can understand and validate. Explainability meaning extends beyond simple output generation to include transparency in model architecture, training processes, and decision pathways. The concept encompasses both global explainability that reveals overall system behavior and local explainability that explains individual predictions. This fundamental requirement enables trust building, regulatory compliance, bias detection, and system debugging. For AI agents, explainability meaning ensures transparent autonomous decision-making, supports accountability frameworks, and enables human oversight essential for responsible AI deployment. --- - Published: 2025-07-29 - Modified: 2025-11-22 - URL: https://vstorm.co/glossary/whats-a-tts/ - Glossary Categories: ML, TTS What's a TTS refers to Text-to-Speech technology, an artificial intelligence system that converts written text into natural-sounding synthetic speech through neural networks and digital signal processing. TTS systems employ deep learning architectures like Tacotron, WaveNet, and neural vocoders to generate human-like audio that captures prosody, intonation, and emotional expression. The synthesis process involves text preprocessing, phonetic analysis, prosody prediction, and audio generation using sophisticated neural models. Modern TTS technology supports multiple languages, voice characteristics, and speaking styles while achieving near-human quality output. Applications include virtual assistants, accessibility tools, audiobook generation, and automated announcements. Advanced implementations enable real-time generation, voice cloning, and custom speaker creation. For AI agents, TTS provides essential voice interface capabilities enabling natural spoken communication, multilingual support, and hands-free interaction. --- - Published: 2025-07-29 - Modified: 2025-07-29 - URL: https://vstorm.co/glossary/what-is-an-agi-ai/ - Glossary Categories: AI What is an AGI AI refers to Artificial General Intelligence AI, hypothetical systems that possess human-level cognitive abilities across all domains, capable of understanding, learning, and applying intelligence to any problem that humans can solve. AGI AI represents the theoretical achievement of machine intelligence that matches or exceeds human cognitive capabilities without domain-specific limitations. Unlike narrow AI systems optimized for specific tasks, AGI AI would demonstrate flexible reasoning, transfer learning across diverse domains, self-awareness, and autonomous goal formation. This concept encompasses machine consciousness, creative problem-solving, emotional understanding, and adaptability indistinguishable from human intelligence. Current AI systems represent narrow or weak AI, while AGI remains a research goal requiring breakthroughs in machine learning, cognitive architectures, and consciousness understanding. For AI agents, AGI AI represents the ultimate aspiration of fully autonomous, general-purpose systems. --- - Published: 2025-07-29 - Modified: 2025-09-28 - URL: https://vstorm.co/glossary/what-openai/ - Glossary Categories: AI What OpenAI refers to the artificial intelligence research organization founded in 2015 that develops advanced AI systems including GPT language models, ChatGPT conversational interfaces, DALL-E image generators, and Whisper speech recognition technology. Originally established as a non-profit research laboratory, OpenAI transitioned to a capped-profit structure in 2019 to secure funding for large-scale AI development while maintaining its mission of ensuring artificial general intelligence benefits humanity. The organization focuses on developing safe, beneficial AI through research in natural language processing, computer vision, robotics, and AI alignment. Key contributions include transformer-based language models, reinforcement learning from human feedback methodologies, and API services that democratize access to advanced AI capabilities. OpenAI emphasizes responsible AI development through safety research, gradual deployment strategies, and international collaboration. For AI agents, OpenAI provides foundational models, development tools, and safety frameworks. --- - Published: 2025-07-29 - Modified: 2025-10-12 - URL: https://vstorm.co/glossary/knowledge-generation/ - Glossary Categories: AI Knowledge generation is the artificial intelligence process of creating new information, insights, and understanding from existing data, patterns, and experiences through computational methods and machine learning algorithms. This process encompasses knowledge extraction from unstructured data, automated reasoning to derive new conclusions, information synthesis from multiple sources, and representation learning that captures semantic relationships. Knowledge generation employs techniques including natural language processing for text mining, graph neural networks for relationship discovery, and generative models that produce novel content based on learned patterns. The process enables AI systems to move beyond pattern recognition toward creating actionable insights, hypotheses, and solutions. Applications span scientific discovery, business intelligence, content creation, and decision support systems. For AI agents, knowledge generation provides essential capabilities for autonomous learning, creative problem-solving, and adaptive reasoning. --- - Published: 2025-07-29 - Modified: 2025-07-29 - URL: https://vstorm.co/glossary/what-is-zero-shot/ - Glossary Categories: AI, ML What is zero-shot refers to the machine learning capability where AI systems perform tasks or classify categories they have never encountered during training, leveraging learned representations and semantic knowledge to generalize beyond their training distribution. This paradigm enables models to handle novel scenarios without requiring task-specific examples by exploiting semantic embeddings, attribute-based learning, and cross-modal knowledge transfer. Zero-shot approaches utilize techniques like mapping textual descriptions to visual features, leveraging pre-trained embeddings, and exploiting compositional understanding of concepts. Common implementations include vision-language models classifying unseen object categories, language models following novel instructions, and recommendation systems handling new items. The capability emerges from models learning generalizable patterns and relationships that transfer across domains. For AI agents, zero-shot capabilities provide immediate deployment to new tasks and cost-effective scaling. --- - Published: 2025-07-29 - Modified: 2025-11-10 - URL: https://vstorm.co/glossary/what-is-whisper-openai/ - Glossary Categories: OpenAI What is Whisper OpenAI refers to OpenAI's robust automatic speech recognition system that converts spoken language into text across 99 languages with remarkable accuracy and noise tolerance. This transformer-based neural network was trained on 680,000 hours of multilingual audio data from the internet, enabling zero-shot performance without language-specific fine-tuning. Whisper handles diverse audio conditions including background noise, accents, and technical terminology through its encoder-decoder architecture. The model supports multiple tasks including transcription, translation to English, language identification, and voice activity detection. Available in several sizes from tiny (39M parameters) to large (1550M parameters), Whisper balances computational efficiency with accuracy requirements. Its open-source availability enables integration into custom workflows and applications. For AI agents, Whisper provides essential speech-to-text capabilities enabling voice interfaces, real-time transcription, and multilingual communication. --- - Published: 2025-07-29 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/what-is-unsupervised-learning-in-ai/ - Glossary Categories: AI, ML What is unsupervised learning in AI refers to machine learning algorithms that discover hidden patterns, structures, and relationships in data without labeled examples or explicit target outputs. This paradigm enables AI systems to learn from unlabeled datasets by identifying underlying data distributions, clustering similar instances, and extracting meaningful representations. Unsupervised learning employs techniques including clustering algorithms like k-means and hierarchical clustering, dimensionality reduction methods such as principal component analysis and t-SNE, and association rule mining for pattern discovery. Advanced approaches include autoencoders for feature learning, generative adversarial networks for data synthesis, and self-supervised learning that creates supervision signals from data structure. Applications span anomaly detection, market segmentation, data compression, and feature engineering. For AI agents, unsupervised learning enables autonomous pattern discovery, environmental understanding, and knowledge acquisition without human supervision. --- - Published: 2025-07-29 - Modified: 2025-10-04 - URL: https://vstorm.co/glossary/zeroshot-learning/ - Glossary Categories: ML, ZSL Zeroshot learning is a machine learning paradigm where models perform classification or prediction tasks on categories they have never encountered during training, leveraging semantic knowledge and learned representations to generalize beyond their training distribution. This approach enables immediate adaptation to unseen classes by exploiting attribute-based learning, semantic embeddings, and cross-modal knowledge transfer between different data modalities. Zeroshot learning employs techniques like mapping textual descriptions to visual features, utilizing pre-trained word embeddings, and exploiting compositional understanding of concepts. Common implementations include computer vision models classifying novel object categories, natural language systems handling new domains, and recommendation engines processing previously unseen items. The capability emerges from models learning transferable patterns and relationships during training. For AI agents, zeroshot learning provides rapid deployment capabilities, cost-effective scaling across domains, and handling of unexpected scenarios. --- - Published: 2025-07-29 - Modified: 2025-11-24 - URL: https://vstorm.co/glossary/what-does-collective-learning-mean/ - Glossary Categories: AI, ML What does collective learning mean refers to a distributed machine learning paradigm where multiple autonomous agents, systems, or entities collaborate to acquire knowledge and improve performance through coordinated learning processes while maintaining individual operational capabilities and data privacy. This approach enables knowledge sharing and skill acquisition across networked systems without centralizing raw data, preserving privacy and security. Collective learning encompasses federated learning where edge devices train models locally while sharing only parameter updates, multi-agent reinforcement learning where agents learn from each other's experiences, and swarm intelligence where simple agents collectively solve complex problems. Key mechanisms include consensus algorithms for model synchronization, knowledge distillation between agents, and emergent behavior arising from distributed interactions. For AI agents, collective learning enables collaborative skill acquisition, distributed problem-solving, and adaptive coordination. --- - Published: 2025-07-29 - Modified: 2025-07-29 - URL: https://vstorm.co/glossary/zero-shot-models/ - Glossary Categories: ML, ZSL Zero shot models are artificial intelligence systems capable of performing tasks on categories, domains, or scenarios they have never encountered during training by leveraging learned representations and semantic knowledge to generalize beyond their training distribution. These models achieve zero-shot capabilities through techniques like semantic embeddings that map textual descriptions to learned feature spaces, cross-modal knowledge transfer between different data modalities, and compositional understanding of concepts. Common implementations include vision-language models like CLIP that classify unseen object categories, large language models that follow novel instructions, and multimodal systems that handle diverse input types. Zero shot models eliminate the need for task-specific training data, enabling immediate deployment across new domains and rapid adaptation to emerging requirements. For AI agents, zero shot models provide flexible reasoning capabilities essential for autonomous operation in unpredictable environments. --- - Published: 2025-07-29 - Modified: 2025-09-18 - URL: https://vstorm.co/glossary/how-does-stacking-work/ - Glossary Categories: ML How does stacking work refers to the ensemble learning technique where multiple base models' predictions are combined using a meta-learner that learns optimal weighting strategies from cross-validated outputs. This process involves training diverse base models on the original dataset, generating out-of-fold predictions through cross-validation to avoid overfitting, then training a meta-model (often called a blender) on these base model predictions as features. The stacking process creates a two-level architecture where base models capture different aspects of the data while the meta-learner discovers optimal combination strategies. Common base models include random forests, support vector machines, and neural networks, while meta-learners employ linear regression, logistic regression, or neural networks. Stacking typically outperforms individual models and simple averaging by exploiting complementary strengths and reducing prediction variance. For AI agents, stacking enables robust decision-making through diverse model perspectives. --- - Published: 2025-07-29 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/what-is-stacking/ - Glossary Categories: ML What is stacking refers to an ensemble learning technique that combines predictions from multiple diverse base models using a meta-learner to achieve superior performance compared to individual models. This approach involves training several heterogeneous models on the same dataset, then using their predictions as input features for a higher-level meta-model that learns optimal combination strategies. The stacking process employs cross-validation to generate out-of-fold predictions, preventing data leakage and overfitting while training the meta-learner. Base models typically include different algorithms like random forests, support vector machines, gradient boosting, and neural networks to capture diverse patterns. The meta-model, often a linear regression or neural network, discovers how to best weight and combine base model outputs. Stacking leverages model diversity to reduce prediction variance and bias. For AI agents, stacking enables robust decision-making through ensemble intelligence. --- - Published: 2025-07-29 - Modified: 2025-10-03 - URL: https://vstorm.co/glossary/zero-shot-transfer/ - Glossary Categories: ML, ZSL Zero-shot transfer is the machine learning capability where models apply knowledge learned from source domains to completely different target domains without any training examples from the target, leveraging transferable representations and semantic understanding. This process enables cross-domain generalization by exploiting shared feature spaces, semantic embeddings, and learned abstractions that bridge different but related tasks. Zero-shot transfer employs techniques including cross-modal knowledge mapping, domain adaptation through shared representations, and compositional understanding that generalizes across contexts. Common implementations include transferring visual knowledge to unseen object categories, applying language understanding to new domains, and cross-lingual transfer without target language examples. The approach relies on learning universal patterns and relationships during training that remain valid across diverse scenarios. For AI agents, zero-shot transfer enables immediate deployment across new domains and applications. --- - Published: 2025-07-29 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/openai-explained/ - Glossary Categories: OpenAI OpenAI explained encompasses the artificial intelligence research organization founded in 2015 that has revolutionized AI development through breakthrough technologies including GPT language models, ChatGPT conversational interfaces, DALL-E image generation, and Whisper speech recognition systems. Originally established as a non-profit research laboratory, OpenAI transitioned to a capped-profit structure in 2019 to secure funding for large-scale AI development while maintaining its mission of ensuring artificial general intelligence benefits humanity. The organization pioneered transformer-based architectures, reinforcement learning from human feedback methodologies, and API services that democratize access to advanced AI capabilities. OpenAI's research spans natural language processing, computer vision, robotics, and AI safety, emphasizing responsible development through gradual deployment strategies and safety research. For AI agents, OpenAI provides foundational models, development frameworks, and safety standards. --- - Published: 2025-07-28 - Modified: 2025-09-12 - URL: https://vstorm.co/glossary/gpt4-meaning/ - Glossary Categories: AI GPT4 meaning refers to Generative Pre-trained Transformer 4, OpenAI's fourth-generation large language model that demonstrates advanced reasoning, multimodal capabilities, and enhanced safety compared to predecessors. This transformer-based architecture processes both text and images, enabling complex problem-solving across mathematics, coding, creative writing, and visual analysis. GPT-4 employs constitutional AI training and reinforcement learning from human feedback (RLHF) to improve alignment and reduce harmful outputs. Key improvements include expanded context windows, better factual accuracy, reduced hallucinations, and enhanced instruction-following capabilities. The model powers various applications through API access, enabling integration into AI agents, chatbots, and automated workflows. For agentic systems, GPT-4 serves as a reasoning engine capable of multi-step planning, tool usage, and complex decision-making, making it foundational for sophisticated AI agent implementations. --- - Published: 2025-07-28 - Modified: 2025-09-30 - URL: https://vstorm.co/glossary/define-explainability/ - Glossary Categories: AI Define explainability refers to the capacity of artificial intelligence systems to provide clear, understandable explanations for their decisions, predictions, and internal processes in human-comprehensible terms. This critical AI property encompasses interpretability methods that reveal how models process inputs and generate outputs, enabling stakeholders to understand, trust, and validate AI system behavior. Explainability techniques include feature importance analysis, attention visualization, LIME (Local Interpretable Model-agnostic Explanations), and SHAP (SHapley Additive exPlanations) values that highlight influential factors in decision-making. The concept spans global explainability revealing overall model behavior and local explainability explaining individual predictions. Regulatory frameworks increasingly mandate explainable AI in high-stakes domains like healthcare, finance, and criminal justice. For AI agents, explainability ensures transparent decision-making, enables debugging and improvement, builds user trust, and supports regulatory compliance essential for responsible deployment. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/why-is-computer-vision-important/ - Glossary Categories: AI Why is computer vision important becomes evident through its transformative impact across industries, enabling machines to interpret and understand visual information for automated decision-making and human augmentation. Computer vision importance stems from its ability to process visual data at superhuman speed and accuracy, revolutionizing healthcare through medical imaging analysis, autonomous vehicles through real-time environment perception, and manufacturing through quality control automation. This technology enables accessibility solutions for visually impaired individuals, enhances security through facial recognition and surveillance systems, and drives innovation in augmented reality applications. Computer vision reduces human error in critical tasks, processes vast amounts of visual data impossible for manual analysis, and operates continuously without fatigue. Economic benefits include increased productivity, cost reduction, and new business model creation. For AI agents, computer vision provides essential perception capabilities enabling autonomous navigation, object manipulation, environmental understanding, and visual reasoning necessary for real-world interaction and task completion. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/zero-shot-learning-explained/ - Glossary Categories: ML Zero shot learning explained describes machine learning systems that can classify or perform tasks on categories they have never encountered during training, leveraging learned semantic relationships and transferable knowledge. This paradigm enables models to generalize to unseen classes by mapping high-level descriptions or attributes to visual or textual features through embedding spaces. Zero shot learning employs techniques like attribute-based learning where models learn relationships between semantic descriptions and observable features, cross-modal knowledge transfer between different data modalities, and semantic embeddings that bridge seen and unseen categories. Common applications include image classification with novel object categories, natural language understanding for new domains, and recommendation systems handling new items. The approach relies on auxiliary information such as class descriptions, ontologies, or pre-trained representations to enable generalization. For AI agents, zero shot learning provides immediate adaptation capabilities without retraining, enabling deployment across diverse domains and handling of unexpected scenarios. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/what-is-this-type-of-technology-called-that-uses-this-conversational-ai/ - Glossary Categories: AI, ML Conversational AI technology encompasses artificial intelligence systems that enable natural language interactions between humans and machines through text or voice interfaces, combining natural language processing, machine learning, and dialogue management. This technology integrates multiple AI components including natural language understanding (NLU) for intent recognition, natural language generation (NLG) for response creation, and dialogue state tracking for context maintenance across conversations. Modern conversational AI employs large language models, transformer architectures, and reinforcement learning from human feedback to achieve human-like communication capabilities. Key features include context awareness, multi-turn dialogue handling, personality consistency, and integration with external systems for task completion. Applications span virtual assistants, customer service chatbots, voice interfaces, and intelligent tutoring systems. For AI agents, conversational AI technology provides essential communication capabilities enabling natural human-computer interaction, instruction interpretation, and collaborative task execution. --- - Published: 2025-07-28 - Modified: 2025-11-12 - URL: https://vstorm.co/glossary/how-does-zero-shot-learning-work/ - Glossary Categories: AI, ML How does zero shot learning work through semantic knowledge transfer mechanisms that enable models to classify unseen categories by leveraging learned relationships between attributes and features. The process begins with training on seen classes while simultaneously learning semantic embeddings that map class descriptions, attributes, or auxiliary information to visual or textual features. During inference, the model matches unseen class descriptions to learned feature representations through similarity computation in embedding spaces. Key mechanisms include attribute-based learning where models learn mappings between semantic properties and observable features, cross-modal knowledge transfer between text and images, and prototype-based approaches using class centroids. The system relies on auxiliary information such as word embeddings, ontologies, or human-provided descriptions to bridge the gap between seen and unseen categories. For AI agents, this enables immediate adaptation to new domains without retraining by exploiting compositional understanding. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/strong-artificial-intelligence-is/ - Glossary Categories: AI Strong artificial intelligence refers to hypothetical AI systems that possess human-level cognitive abilities across all domains, including reasoning, learning, creativity, and consciousness, capable of understanding and performing any intellectual task that humans can accomplish. Also known as Artificial General Intelligence (AGI), strong AI represents the theoretical achievement of machine intelligence that matches or exceeds human cognitive capabilities without domain-specific limitations. Unlike narrow AI systems optimized for specific tasks, strong artificial intelligence would demonstrate flexible reasoning, transfer learning across diverse domains, self-awareness, and autonomous goal formation. This concept encompasses machine consciousness, emotional understanding, and creative problem-solving abilities indistinguishable from human intelligence. Current AI systems represent weak or narrow AI, while strong AI remains a research goal requiring breakthroughs in machine learning, cognitive architectures, and understanding of consciousness. For AI agents, strong artificial intelligence represents the ultimate aspiration of fully autonomous, general-purpose systems. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/gpt-4-meaning-3/ - Glossary Categories: AI GPT-4 meaning refers to Generative Pre-trained Transformer 4, OpenAI's fourth-generation large language model that demonstrates advanced reasoning, multimodal capabilities, and enhanced safety compared to its predecessors. This transformer-based architecture processes both text and images, enabling complex problem-solving across mathematics, coding, creative writing, and visual analysis. GPT-4 employs constitutional AI training methods and reinforcement learning from human feedback (RLHF) to improve alignment and reduce harmful outputs. Key improvements include expanded context windows supporting longer conversations, better factual accuracy, reduced hallucinations, and enhanced instruction-following capabilities. The model powers various applications through API access, enabling integration into AI agents, chatbots, and automated workflows. For AI agents, GPT-4 serves as a reasoning engine capable of multi-step planning, tool usage, and complex decision-making, making it foundational for sophisticated AI agent implementations. --- - Published: 2025-07-28 - Modified: 2025-11-03 - URL: https://vstorm.co/glossary/what-does-computer-vision-do/ - Glossary Categories: AI What does computer vision do encompasses analyzing, interpreting, and understanding visual information from digital images and videos to enable automated decision-making and intelligent responses. Computer vision performs core functions including object detection and recognition, image classification, semantic segmentation, motion tracking, and depth estimation. The technology processes visual data through convolutional neural networks and deep learning algorithms to extract meaningful patterns, identify objects, measure dimensions, and analyze spatial relationships. Key capabilities include real-time video analysis, quality inspection, facial recognition, optical character recognition, and scene understanding. Computer vision enables autonomous navigation, medical image analysis, manufacturing quality control, security surveillance, and augmented reality applications. The technology converts unstructured visual data into structured information that machines can process and act upon. For AI agents, computer vision provides essential perception capabilities enabling environmental awareness, object manipulation, visual reasoning, and autonomous interaction with physical environments. --- - Published: 2025-07-28 - Modified: 2025-10-01 - URL: https://vstorm.co/glossary/0-shot-learning/ - Glossary Categories: ML 0 shot learning is a machine learning paradigm where models perform tasks on categories or domains they have never encountered during training, leveraging learned representations and semantic knowledge to generalize beyond their training distribution. This approach enables immediate adaptation to new classes without requiring additional training examples by utilizing semantic embeddings, attribute-based learning, and cross-modal knowledge transfer. 0 shot learning employs techniques like mapping textual descriptions to visual features, leveraging pre-trained embeddings, and exploiting compositional understanding of concepts. Common implementations include vision-language models that classify unseen object categories, language models following novel instructions, and recommendation systems handling new items. The capability emerges from models learning generalizable patterns and relationships that transfer across domains. For AI agents, 0 shot learning provides immediate deployment capabilities, rapid adaptation to unforeseen scenarios, and cost-effective scaling across diverse applications without retraining requirements. --- - Published: 2025-07-28 - Modified: 2025-08-16 - URL: https://vstorm.co/glossary/stochastic-parrots-meaning/ - Glossary Categories: LLM Stochastic parrots meaning refers to the critique that large language models are sophisticated pattern matching systems that generate plausible text through statistical correlations without genuine understanding or meaning comprehension. This concept, introduced by Bender et al. , characterizes language models as stochastic systems that probabilistically recombine training data patterns, similar to parrots mimicking speech without comprehending content. The term highlights fundamental limitations where models excel at surface-level linguistic patterns while lacking true semantic understanding, reasoning capabilities, or world knowledge. Stochastic parrots produce coherent outputs by exploiting statistical regularities in massive text corpora rather than developing genuine intelligence or intentionality. This critique addresses concerns about AI systems appearing more capable than their actual understanding warrants, potentially misleading users about model capabilities. For AI agents, the stochastic parrots concept emphasizes the importance of robust evaluation, limitation awareness, and careful deployment strategies. --- - Published: 2025-07-28 - Modified: 2025-08-31 - URL: https://vstorm.co/glossary/probabilistic-model-vs-deterministic-model/ - Glossary Categories: ML Probabilistic model vs deterministic model represents two fundamental approaches to mathematical modeling where probabilistic models incorporate uncertainty and randomness through probability distributions, while deterministic models produce identical outputs from identical inputs without random variation. Probabilistic models use statistical methods to handle noise, uncertainty, and incomplete information, employing techniques like Bayesian inference, Monte Carlo sampling, and stochastic processes. Examples include Gaussian mixture models, hidden Markov models, and Bayesian neural networks that quantify prediction confidence. Deterministic models follow exact mathematical relationships with predictable outcomes, including linear regression equations, differential equations, and rule-based systems. Probabilistic models excel in handling real-world uncertainty and providing confidence estimates, while deterministic models offer reproducibility and computational efficiency. For AI agents, probabilistic models enable robust decision-making under uncertainty, while deterministic components provide reliable, testable functionality. --- - Published: 2025-07-28 - Modified: 2025-11-05 - URL: https://vstorm.co/glossary/what-is-openai-company/ - Glossary Categories: AI What is OpenAI company refers to the artificial intelligence research organization founded in 2015 that develops advanced AI systems including GPT language models, ChatGPT conversational interfaces, DALL-E image generators, and Whisper speech recognition technology. Originally established as a non-profit research laboratory, OpenAI transitioned to a capped-profit structure in 2019 to secure funding for large-scale AI development while maintaining its mission of ensuring artificial general intelligence benefits humanity. The company focuses on developing safe, beneficial AI through research in natural language processing, computer vision, robotics, and AI alignment. Key contributions include transformer-based language models, reinforcement learning from human feedback methodologies, and API services that democratize access to advanced AI capabilities. OpenAI emphasizes responsible AI development through safety research, gradual deployment strategies, and international collaboration. For AI agents, OpenAI provides foundational models, development tools, and safety frameworks essential for building sophisticated autonomous systems. --- - Published: 2025-07-28 - Modified: 2025-08-14 - URL: https://vstorm.co/glossary/adapters/ - Glossary Categories: ML Adapters are lightweight neural network modules inserted into pre-trained models to enable efficient task-specific fine-tuning without modifying the original model parameters, allowing rapid customization while preserving base model capabilities. These parameter-efficient techniques add small trainable layers between existing model components, typically reducing trainable parameters by over 95% compared to full fine-tuning. Common adapter architectures include bottleneck adapters with down-projection and up-projection layers, Low-Rank Adaptation (LoRA) that decomposes weight updates into low-rank matrices, and prefix tuning approaches. Adapters enable multi-task learning where different modules handle specialized capabilities, prevent catastrophic forgetting, and support modular system design. Benefits include reduced computational costs, faster training times, and memory efficiency for deploying multiple task-specific variants. For AI agents, adapters provide cost-effective personalization, domain adaptation, and skill acquisition without expensive retraining. --- - Published: 2025-07-28 - Modified: 2025-09-22 - URL: https://vstorm.co/glossary/what-is-stable-diffusion-model/ - Glossary Categories: Deep Learning What is Stable Diffusion model refers to an open-source latent diffusion neural network architecture that generates high-quality images from text prompts through a progressive denoising process in compressed latent space. This deep learning model consists of three core components: a variational autoencoder that compresses images into latent representations, a U-Net neural network that performs iterative denoising guided by text embeddings, and a CLIP text encoder that processes natural language descriptions. The model operates through forward and reverse diffusion processes, learning to remove Gaussian noise while conditioned on textual inputs. Stable Diffusion model architecture enables efficient computation compared to pixel-space alternatives, supporting various generation tasks including text-to-image synthesis, inpainting, outpainting, and image-to-image translation. Its open-source nature allows customization, fine-tuning, and commercial deployment. For AI agents, Stable Diffusion model provides foundational visual generation capabilities. --- - Published: 2025-07-28 - Modified: 2025-08-11 - URL: https://vstorm.co/glossary/interpretability/ - Glossary Categories: AI, ML Interpretability is the degree to which humans can understand and explain the decision-making processes, internal mechanisms, and predictions of artificial intelligence systems in meaningful, actionable terms. This fundamental AI property encompasses both global interpretability that reveals overall model behavior patterns and local interpretability that explains individual predictions or decisions. Interpretability techniques include feature importance analysis, attention visualization, gradient-based methods, and model-agnostic approaches like LIME and SHAP that identify influential factors in AI decision-making. The concept spans intrinsically interpretable models like decision trees and linear regression, as well as post-hoc explanation methods for complex neural networks. Interpretability enables stakeholders to validate model reasoning, identify biases, ensure compliance with regulations, and build trust in AI systems. For AI agents, interpretability provides transparency essential for debugging, safety assurance, regulatory compliance, and user acceptance. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/what-is-probabilistic/ - Glossary Categories: ML What is probabilistic refers to systems, models, or approaches that incorporate uncertainty, randomness, and probability distributions rather than producing deterministic outcomes. Probabilistic methods use statistical frameworks to quantify uncertainty, model incomplete information, and make decisions under ambiguous conditions. These approaches employ probability theory, Bayesian inference, and stochastic processes to represent and reason about uncertain events. Key techniques include Monte Carlo sampling, variational inference, and probabilistic graphical models that capture dependencies between random variables. Probabilistic systems output confidence estimates alongside predictions, enabling risk assessment and robust decision-making. Applications span machine learning models like Gaussian processes, natural language processing with probabilistic parsing, and computer vision with uncertainty quantification. For AI agents, probabilistic approaches enable handling of noisy sensor data, uncertain environments, and incomplete information while providing confidence measures essential for safe autonomous operation. --- - Published: 2025-07-28 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/nlu-tasks/ - Glossary Categories: AI, NLP NLU tasks are specific natural language understanding functions that enable AI systems to extract structured information and meaning from unstructured human language input. These computational tasks include intent classification for determining user goals, named entity recognition for identifying specific entities like names and locations, slot filling for extracting relevant parameters, sentiment analysis for emotional tone detection, and dependency parsing for grammatical relationship identification. Modern NLU tasks employ transformer-based architectures, pre-trained language models, and multi-task learning frameworks to achieve robust performance across diverse linguistic patterns. Key challenges include handling ambiguity, context dependence, and domain adaptation. Advanced NLU tasks encompass coreference resolution, relation extraction, and semantic role labeling for deeper language understanding. For AI agents, NLU tasks provide essential language comprehension capabilities enabling natural communication interfaces, instruction interpretation, and contextual reasoning. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/what-is-probabilistic-modeling/ - Glossary Categories: ML What is probabilistic modeling refers to a mathematical framework that uses probability theory to represent and quantify uncertainty in data, systems, and predictions by modeling variables as probability distributions rather than fixed values. This approach enables systems to capture and reason about inherent randomness, incomplete information, and measurement noise through statistical methods. Probabilistic modeling incorporates prior knowledge through Bayesian inference, updating beliefs as new evidence emerges using techniques like Markov Chain Monte Carlo sampling, variational inference, and expectation-maximization algorithms. Core applications include Gaussian processes, hidden Markov models, and Bayesian neural networks that provide uncertainty estimates alongside predictions. The framework enables robust decision-making under uncertainty, handles missing data gracefully, and supports interpretable AI systems. For AI agents, probabilistic modeling provides essential capabilities for risk assessment, confidence estimation, and reliable operation in uncertain environments. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/zero-shot-machine-learning/ - Glossary Categories: ML Zero shot machine learning is a paradigm where models perform tasks on classes or domains they have never encountered during training, leveraging learned representations and semantic knowledge to generalize beyond their training distribution. This approach enables immediate adaptation to new categories without requiring additional training examples by exploiting semantic embeddings, attribute-based learning, and cross-modal knowledge transfer. Zero shot learning employs techniques like mapping textual descriptions to visual features, utilizing pre-trained embeddings, and exploiting compositional understanding of concepts. Common implementations include vision-language models classifying unseen object categories, language models following novel instructions, and recommendation systems handling new items. The capability emerges from models learning generalizable patterns and relationships that transfer across domains. For AI agents, zero shot machine learning provides immediate deployment capabilities, rapid adaptation to unforeseen scenarios, and cost-effective scaling. --- - Published: 2025-07-28 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/synthesize-voice/ - Glossary Categories: TTS Synthesize voice is the artificial intelligence process of converting written text into natural-sounding human speech through neural networks and digital signal processing techniques. This text-to-speech technology employs deep learning models like Tacotron, WaveNet, and neural vocoders to generate synthetic audio that mimics human vocal characteristics including intonation, rhythm, and emotional expression. The synthesis process involves text analysis and preprocessing, phonetic transcription, prosody prediction for natural speech patterns, and final audio generation through sophisticated neural architectures. Modern voice synthesis systems support multiple languages, speaker identities, and emotional styles while achieving near-human quality output. Advanced implementations enable real-time generation, voice cloning, and custom speaker creation. For AI agents, voice synthesis provides essential communication capabilities enabling natural spoken interfaces, accessibility features, multilingual support, and hands-free interaction. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/define-collective-learning/ - Glossary Categories: AI Define collective learning refers to the distributed machine learning paradigm where multiple autonomous agents, systems, or entities collaborate to acquire knowledge and improve performance through coordinated learning processes while maintaining individual operational capabilities. This approach enables knowledge sharing and skill acquisition across networked systems without centralizing raw data or compromising privacy. Collective learning encompasses federated learning where edge devices train models locally while sharing only parameter updates, multi-agent reinforcement learning where agents learn from each other's experiences, and swarm intelligence where simple agents collectively solve complex problems. Key mechanisms include consensus algorithms for model synchronization, knowledge distillation between agents, and emergent behavior arising from distributed interactions. For AI agents, collective learning enables collaborative skill acquisition, distributed problem-solving, and adaptive coordination essential for autonomous systems. --- - Published: 2025-07-28 - Modified: 2025-08-26 - URL: https://vstorm.co/glossary/voice-processing/ - Glossary Categories: Voice AI Voice processing is the computational analysis and manipulation of human speech signals through digital signal processing and artificial intelligence techniques to extract information, enhance audio quality, and enable voice-based interactions. This field encompasses multiple domains including automatic speech recognition for converting speech to text, text-to-speech synthesis for generating artificial speech, speaker identification and verification for biometric applications, and voice activity detection for audio segmentation. Voice processing employs signal processing methods like spectral analysis, feature extraction using mel-frequency cepstral coefficients, and noise reduction algorithms. Modern approaches utilize deep learning architectures including recurrent neural networks, transformers, and convolutional networks for robust performance across diverse acoustic conditions. Applications span virtual assistants, telecommunication systems, hearing aids, and security systems. For AI agents, voice processing provides essential capabilities for natural speech interfaces, multilingual communication, and acoustic scene understanding. --- - Published: 2025-07-28 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/what-is-stablediffusion/ - Glossary Categories: AI What is Stable Diffusion refers to an open-source latent diffusion model that generates high-quality images from text descriptions through a denoising process in compressed latent space rather than directly in pixel space. This deep learning architecture employs a variational autoencoder to compress images into lower-dimensional representations, then uses a U-Net neural network to progressively remove noise guided by CLIP text embeddings. The model operates through forward diffusion that adds Gaussian noise to training images and reverse diffusion that learns to denoise, enabling controllable image generation. Stable Diffusion supports various tasks including text-to-image synthesis, image-to-image translation, inpainting, and outpainting through different sampling methods like DDIM and DPM-Solver. Its open-source nature enables customization, fine-tuning, and commercial deployment without licensing restrictions. For AI agents, Stable Diffusion provides visual content generation capabilities essential for creative workflows. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/model-chaining/ - Glossary Categories: Generative Models Model chaining is an architectural approach that connects multiple AI models in sequence or parallel to accomplish complex tasks requiring diverse specialized capabilities beyond what single models can achieve. This technique enables sophisticated workflows where each model contributes specific expertise, such as combining speech recognition, natural language understanding, reasoning, and text-to-speech models for conversational AI systems. Model chaining employs orchestration mechanisms to manage data flow, error handling, and coordination between heterogeneous models with different input-output formats and processing requirements. Common implementations include pipeline architectures for sequential processing, ensemble methods for parallel model execution, and hybrid systems combining rule-based and neural components. Benefits include modularity, specialized optimization, fault tolerance, and scalable system design. For AI agents, model chaining enables sophisticated multi-modal reasoning, complex task decomposition, and integration of specialized models. --- - Published: 2025-07-28 - Modified: 2025-10-01 - URL: https://vstorm.co/glossary/probabilistic-model-example/ - Glossary Categories: Generative Models Probabilistic model example encompasses concrete implementations like Bayesian networks for medical diagnosis, Hidden Markov Models for speech recognition, Gaussian Mixture Models for clustering customer segments, and Naive Bayes classifiers for spam detection. These models represent uncertainty through probability distributions rather than deterministic outputs. A Gaussian process example predicts stock prices with confidence intervals, while a Kalman filter tracks object positions with measurement uncertainty. Topic models like Latent Dirichlet Allocation discover document themes probabilistically, and Monte Carlo methods simulate complex systems through random sampling. Reinforcement learning agents use probabilistic policies for exploration-exploitation balance, while Bayesian neural networks provide prediction confidence estimates. These examples demonstrate uncertainty quantification, belief updating, and robust decision-making under incomplete information. For AI agents, probabilistic model examples provide frameworks for risk assessment, sensor fusion, and reliable operation in uncertain environments. --- - Published: 2025-07-28 - Modified: 2025-09-25 - URL: https://vstorm.co/glossary/parameter-efficient-tuning/ - Glossary Categories: ML Parameter efficient tuning is a family of machine learning techniques that adapt large pre-trained models to new tasks by training only a small subset of parameters while keeping the majority of model weights frozen. This approach dramatically reduces computational costs, memory requirements, and training time compared to full fine-tuning. Key methods include Low-Rank Adaptation (LoRA) that decomposes weight updates into low-rank matrices, adapter layers that insert small trainable modules between existing components, and prompt tuning that optimizes soft prompts while maintaining frozen model parameters. These techniques typically require less than 1% of trainable parameters compared to full fine-tuning while achieving comparable performance. Benefits include faster training, reduced storage requirements for multiple task-specific variants, and prevention of catastrophic forgetting. For AI agents, parameter efficient tuning enables cost-effective customization, rapid domain adaptation, and scalable deployment across diverse applications. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/automatic-speech/ - Glossary Categories: ASR What is automatic speech refers to artificial intelligence systems that process, analyze, and understand human speech without manual intervention, primarily encompassing automatic speech recognition (ASR) technology that converts spoken language into text. This computational process employs signal processing, acoustic modeling, and language understanding to interpret audio signals and extract meaningful information. Automatic speech systems utilize deep learning architectures including recurrent neural networks, transformers, and attention mechanisms to handle diverse speakers, accents, and acoustic conditions. Key components include voice activity detection, feature extraction using spectrograms, acoustic modeling through neural networks, and language modeling for word sequence prediction. Applications span voice assistants, transcription services, call center automation, and accessibility tools. Modern automatic speech technology enables real-time processing, multilingual support, and domain-specific vocabulary adaptation. For AI agents, automatic speech provides essential voice interface capabilities. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/nlu-definition/ - Glossary Categories: NLU NLU definition refers to Natural Language Understanding, a branch of artificial intelligence that enables machines to comprehend, interpret, and extract meaning from human language in its natural form. This computational process goes beyond simple keyword matching to understand context, intent, entities, and semantic relationships within text or speech. NLU systems employ deep learning models, transformer architectures, and linguistic analysis to perform core tasks including intent classification, named entity recognition, slot filling, sentiment analysis, and semantic parsing. The technology combines syntactic analysis for grammatical structure with semantic analysis for meaning extraction, enabling machines to understand ambiguity, context dependencies, and implicit information. NLU serves as the foundation for conversational AI, chatbots, voice assistants, and automated text analysis systems. For AI agents, NLU provides essential language comprehension capabilities enabling natural human-computer interaction, instruction interpretation, and contextual reasoning. --- - Published: 2025-07-28 - Modified: 2025-07-28 - URL: https://vstorm.co/glossary/language-ambiguity/ - Glossary Categories: NLP Language ambiguity refers to the phenomenon where linguistic expressions have multiple possible interpretations or meanings, creating challenges for natural language processing and human-computer communication. This complexity manifests in several forms: lexical ambiguity where words have multiple meanings (bank as financial institution vs riverbank), syntactic ambiguity arising from grammatical structure variations, semantic ambiguity involving different conceptual interpretations, and pragmatic ambiguity depending on contextual factors. Language ambiguity poses significant challenges for AI systems that must disambiguate intended meanings through contextual analysis, world knowledge, and statistical inference. Resolution techniques include word sense disambiguation, syntactic parsing, semantic role labeling, and contextual embedding models that capture multiple meaning representations. Modern NLP systems employ transformer architectures and large language models to handle ambiguous expressions through learned contextual understanding. For AI agents, managing language ambiguity is essential for accurate instruction interpretation and natural communication. --- - Published: 2025-07-28 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/speech-synthesizers-use-to-determine-context-before-outputting/ - Glossary Categories: TTS Speech synthesis context analysis refers to the computational processes that text-to-speech systems employ to understand linguistic context, semantic meaning, and pragmatic factors before generating appropriate vocal output. This analysis encompasses multiple stages including text preprocessing to handle abbreviations and numbers, syntactic parsing to identify grammatical relationships, semantic analysis to determine emphasis and emotional tone, and discourse analysis to maintain consistent speaking style. Modern speech synthesizers utilize natural language processing techniques, transformer models, and linguistic rules to analyze sentence structure, punctuation cues, and contextual relationships that influence prosody, rhythm, and intonation patterns. Context analysis enables appropriate pause placement, stress assignment, question intonation, and emotional expression in synthesized speech. Advanced systems incorporate speaker modeling, style adaptation, and multi-modal context from surrounding text or dialogue history. For AI agents, speech synthesis context analysis ensures natural, contextually appropriate voice output. --- - Published: 2025-07-28 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/what-is-collective-learning/ - Glossary Categories: AI, ML What is collective learning refers to a distributed machine learning paradigm where multiple autonomous agents, systems, or entities collaborate to acquire knowledge and improve performance through coordinated learning processes while maintaining individual operational capabilities. This approach enables knowledge sharing and skill acquisition across networked systems without centralizing raw data or compromising privacy. Collective learning encompasses federated learning where edge devices train models locally while sharing only parameter updates, multi-agent reinforcement learning where agents learn from each other's experiences, and swarm intelligence where simple agents collectively solve complex problems. Key mechanisms include consensus algorithms for model synchronization, knowledge distillation between agents, and emergent behavior arising from distributed interactions. For AI agents, collective learning enables collaborative skill acquisition, distributed problem-solving, and adaptive coordination. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/what-is-stable-diffusion/ - Glossary Categories: AI Stable Diffusion is an open-source latent diffusion model that generates high-quality images from text descriptions through a denoising process. This deep learning architecture operates in latent space rather than pixel space, making it computationally efficient while producing detailed visual outputs. The model uses a variational autoencoder to compress images into lower-dimensional representations, then applies a U-Net neural network to progressively remove noise guided by text embeddings. Stable Diffusion employs CLIP (Contrastive Language-Image Pre-training) for text encoding, enabling precise semantic understanding of prompts. Unlike proprietary alternatives, its open-source nature allows customization, fine-tuning, and integration into diverse applications. The model supports various sampling methods including DDIM and DPM-Solver, offering control over generation speed and quality. Its architecture enables inpainting, outpainting, and image-to-image translation, making it versatile for creative workflows, content generation, and AI-powered design systems. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/deterministic-in-statistics/ - Glossary Categories: ML Deterministic in statistics refers to models or processes where outcomes are precisely determined by initial conditions and parameters, with no random variation involved. Unlike stochastic models, deterministic statistical models produce identical results when given the same inputs, following exact mathematical relationships without probabilistic components. These models assume that observed relationships can be fully explained by measurable variables and their interactions. Examples include linear regression equations (excluding error terms), mathematical optimization functions, and differential equation models. In statistical analysis, deterministic components represent the systematic, predictable portions of relationships between variables. While pure deterministic models rarely exist in real-world applications, they serve as foundational building blocks within hybrid models that combine deterministic relationships with stochastic error terms. For AI agents, deterministic statistical models provide reproducible decision rules, enable precise causal inference, and support interpretable algorithmic behavior essential for regulated industries and mission-critical applications. --- - Published: 2025-07-25 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/n-shot-learning/ - Glossary Categories: ML N-shot learning is a machine learning paradigm where models learn to perform new tasks using only n examples per class, where n represents a small, finite number. This approach encompasses zero-shot learning (no examples), one-shot learning (single example), few-shot learning (typically 2-10 examples), and many-shot learning (hundreds of examples). N-shot learning leverages meta-learning techniques, where models learn how to learn efficiently from limited data by training on diverse task distributions. Core methods include model-agnostic meta-learning (MAML), prototypical networks, and in-context learning with large language models. For AI agents, n-shot learning enables rapid adaptation to new domains, personalization without extensive retraining, and deployment in data-scarce environments. This capability is crucial for autonomous systems that must quickly acquire new skills, handle novel scenarios, and operate effectively when collecting large training datasets is impractical or expensive. --- - Published: 2025-07-25 - Modified: 2025-08-12 - URL: https://vstorm.co/glossary/benchmark-tests-ai-models/ - Glossary Categories: AI, ML Benchmark tests for AI models are standardized evaluation frameworks that measure model performance across specific tasks, datasets, and metrics to enable objective comparison and validation. These systematic assessments use curated datasets like ImageNet for computer vision, GLUE/SuperGLUE for natural language processing, and specialized benchmarks for reasoning, code generation, and multimodal capabilities. Common evaluation metrics include accuracy, F1-score, BLEU scores, and task-specific measures. Benchmark tests assess capabilities such as mathematical reasoning (GSM8K), common sense understanding (CommonsenseQA), and safety alignment (HarmBench). For AI agents, specialized benchmarks evaluate autonomous decision-making, tool usage, and multi-step reasoning abilities. Leading benchmark suites include MLPerf for training efficiency, HELM for holistic evaluation, and AgentBench for agentic capabilities. These standardized tests enable researchers to track progress, identify model limitations, validate claims, and guide development priorities while providing transparency for deployment decisions in production environments. --- - Published: 2025-07-25 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/gpt-4-meaning-2/ - Glossary Categories: AI GPT-4 (Generative Pre-trained Transformer 4) is OpenAI's fourth-generation large language model that demonstrates advanced reasoning, multimodal capabilities, and enhanced safety compared to its predecessors. This transformer-based architecture processes both text and images, enabling complex problem-solving across diverse domains including mathematics, coding, creative writing, and visual analysis. GPT-4 employs constitutional AI training methods and reinforcement learning from human feedback (RLHF) to improve alignment and reduce harmful outputs. The model features an expanded context window, supporting longer conversations and document analysis. Key improvements include better factual accuracy, reduced hallucinations, and enhanced instruction-following capabilities. GPT-4 powers various applications through API access, enabling integration into AI agents, chatbots, and automated workflows. For agentic systems, GPT-4 serves as a reasoning engine capable of multi-step planning, tool usage, and complex decision-making, making it foundational for sophisticated AI agent implementations. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/what-is-stable-diffusion-trained-on/ - Glossary Categories: AI, ML Stable Diffusion is trained on LAION (Large-scale Artificial Intelligence Open Network) datasets, primarily LAION-5B containing 5. 85 billion image-text pairs scraped from the internet. The training process uses LAION-400M and LAION-2B subsets for initial stages, followed by LAION-5B for final training. These datasets include images with associated alt-text, captions, and metadata from web sources like Common Crawl. Training employs a three-stage process: autoencoder training on ImageNet, text encoder training using CLIP methodology, and diffusion model training in latent space. The model learns associations between textual descriptions and visual concepts through contrastive learning and denoising objectives. Additional fine-tuning uses curated datasets and safety filtering to reduce harmful content generation. For AI agents, understanding Stable Diffusion's training data helps predict model capabilities, limitations, and potential biases in generated outputs. --- - Published: 2025-07-25 - Modified: 2025-10-11 - URL: https://vstorm.co/glossary/what-is-overfitting-data/ - Glossary Categories: ML Overfitting Data occurs when a machine learning model learns training data patterns too specifically, including noise and irrelevant details, resulting in poor generalization to new, unseen data. This phenomenon manifests when models achieve high accuracy on training datasets but demonstrate significantly lower performance on validation or test sets. Overfitting typically results from excessive model complexity relative to available training data, insufficient regularization, or prolonged training without proper early stopping. Common indicators include large gaps between training and validation accuracy, perfect or near-perfect training performance, and declining validation metrics during training. Prevention techniques include cross-validation, regularization methods (L1/L2), dropout, data augmentation, and ensemble approaches. For AI agents, overfitting can lead to brittle decision-making that fails in real-world scenarios, making robust validation and generalization testing critical for deployment. Proper overfitting detection ensures AI systems maintain reliable performance across diverse operational conditions. --- - Published: 2025-07-25 - Modified: 2025-10-20 - URL: https://vstorm.co/glossary/generative-pre-trained-transformers/ - Glossary Categories: LLM Generative pre-trained transformers are neural network architectures that generate human-like text by predicting the next word in a sequence based on learned patterns from vast text corpora. These models undergo unsupervised pre-training on billions of tokens, learning language structure, grammar, and factual knowledge without explicit supervision. The transformer architecture employs self-attention mechanisms to process input sequences in parallel, capturing long-range dependencies and contextual relationships effectively. Pre-training involves next-token prediction objectives, enabling models to understand and generate coherent text across diverse domains. Following pre-training, these models can be fine-tuned for specific tasks through supervised learning or reinforcement learning from human feedback. Popular implementations include GPT series, PaLM, and LLaMA models. For AI agents, generative pre-trained transformers serve as reasoning engines, enabling natural language understanding, instruction following, and complex problem-solving capabilities essential for autonomous decision-making and human-AI interaction. --- - Published: 2025-07-25 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/instruction-fine-tuning-2/ - Glossary Categories: ML Instruction fine-tuning is a supervised learning technique that trains pre-trained language models to better follow human instructions and complete specific tasks through natural language prompts. This process uses curated datasets containing instruction-response pairs, where models learn to map diverse instruction formats to appropriate outputs. Unlike traditional fine-tuning on single tasks, instruction fine-tuning employs multi-task datasets like InstructGPT, Alpaca, or FLAN collections that cover reasoning, summarization, question-answering, and creative tasks. The training objective typically uses supervised fine-tuning followed by reinforcement learning from human feedback (RLHF) to align outputs with human preferences. This approach enables zero-shot generalization to unseen instruction types and improves model helpfulness, harmlessness, and honesty. For AI agents, instruction fine-tuning is essential for creating systems that reliably interpret and execute complex user commands, enabling autonomous task completion, workflow automation, and natural human-AI collaboration. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/instruction-tuning-llm/ - Glossary Categories: LLM Instruction tuning LLM is a post-training method that adapts large language models to follow human instructions and perform diverse tasks through supervised learning on instruction-response datasets. This process transforms base LLMs trained on next-token prediction into instruction-following assistants capable of understanding and executing complex commands. Instruction tuning employs datasets like Alpaca, Vicuna, or custom collections containing thousands of instruction-output pairs covering reasoning, coding, summarization, and creative tasks. The training methodology typically combines supervised fine-tuning with reinforcement learning from human feedback (RLHF) to align model behavior with human preferences and safety requirements. Key improvements include enhanced zero-shot task performance, better instruction comprehension, and reduced need for few-shot examples. For AI agents, instruction-tuned LLMs enable reliable task execution, natural language interfaces, and autonomous decision-making based on human directives, making them essential components for building responsive and controllable AI systems. --- - Published: 2025-07-25 - Modified: 2025-11-15 - URL: https://vstorm.co/glossary/what-is-deterministic/ - Glossary Categories: AI, ML Deterministic refers to systems, processes, or algorithms where identical inputs always produce identical outputs, with no randomness or unpredictability involved. In deterministic systems, outcomes are completely determined by initial conditions and governing rules, following precise mathematical relationships without probabilistic elements. This concept spans computer science, mathematics, and AI, where deterministic algorithms execute the same sequence of operations given identical inputs, ensuring reproducible results. Examples include sorting algorithms, mathematical functions, and rule-based systems. Deterministic models contrast with stochastic systems that incorporate randomness or uncertainty. In AI applications, deterministic components provide predictable behavior essential for debugging, testing, and regulatory compliance. For AI agents, deterministic decision-making ensures consistent responses to identical scenarios, enables reliable system behavior, and supports interpretable reasoning chains. While pure determinism is rare in real-world AI systems, deterministic components serve as building blocks within hybrid architectures. --- - Published: 2025-07-25 - Modified: 2025-10-07 - URL: https://vstorm.co/glossary/ai-self-learning/ - Glossary Categories: AI AI self-learning refers to systems that can acquire new knowledge, skills, or behaviors autonomously without explicit human supervision or programming for each learning instance. This capability encompasses several approaches including self-supervised learning, where models learn from unlabeled data by creating their own training signals, continual learning that adapts to new information while retaining previous knowledge, and meta-learning that develops learning strategies applicable to novel tasks. Self-learning AI systems employ techniques like curiosity-driven exploration, active learning for strategic data selection, and transfer learning to apply existing knowledge to new domains. Examples include reinforcement learning agents that improve through trial-and-error interaction with environments, language models that learn from internet text, and computer vision systems that discover patterns in visual data. For AI agents, self-learning enables autonomous skill acquisition, adaptation to changing environments, and continuous improvement without constant human intervention, making systems more independent and capable of handling unforeseen scenarios. --- - Published: 2025-07-25 - Modified: 2025-09-10 - URL: https://vstorm.co/glossary/define-summarization/ - Glossary Categories: NLP Summarization is the process of condensing large amounts of text or information into shorter, coherent representations that preserve essential meaning and key insights. This natural language processing task employs two primary approaches: extractive summarization, which selects and combines existing sentences from source documents, and abstractive summarization, which generates new text that captures core concepts using paraphrasing and synthesis. Modern summarization systems utilize transformer-based models like BART, T5, and GPT variants, employing attention mechanisms to identify salient information and maintain coherence across generated summaries. Techniques include sequence-to-sequence learning, reinforcement learning for optimization, and multi-document summarization for synthesizing information across sources. For AI agents, summarization enables efficient information processing, rapid document analysis, meeting transcription, and knowledge distillation from large datasets, making complex information accessible for decision-making and workflow automation. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/deterministic-process/ - Glossary Categories: ML Deterministic process is a computational or mathematical procedure where identical inputs invariably produce identical outputs through a fixed sequence of operations, without any random or probabilistic elements. This process follows predetermined rules and algorithms, ensuring complete predictability and reproducibility across multiple executions. In deterministic processes, each step is uniquely determined by the current state and governing functions, creating a causal chain where future states can be precisely calculated from initial conditions. Examples include mathematical computations, sorting algorithms, finite state machines, and rule-based decision trees. Deterministic processes contrast with stochastic processes that incorporate randomness or uncertainty. For AI agents, deterministic processes provide reliable, testable components essential for mission-critical applications, regulatory compliance, and system debugging. They enable consistent behavior in production environments, facilitate unit testing, and support transparent decision-making where explainability and auditability are required. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/tts-output/ - Glossary Categories: TTS TTS output (Text-to-Speech output) is synthesized audio generated from written text using artificial intelligence models that convert linguistic input into natural-sounding human speech. This process involves multiple stages: text analysis and preprocessing, phonetic transcription, prosody prediction for rhythm and intonation, and final audio synthesis through neural vocoders or concatenative methods. Modern TTS systems employ deep learning architectures like Tacotron, WaveNet, and neural vocoders to produce high-quality, expressive speech with natural cadence, emotion, and speaker characteristics. TTS output quality is measured by naturalness, intelligibility, and prosodic accuracy. Advanced systems support multiple voices, languages, speaking styles, and real-time generation. For AI agents, TTS output enables voice interfaces, accessibility features, multilingual communication, and hands-free interaction. Applications include virtual assistants, audiobook generation, customer service automation, and assistive technologies for visually impaired users. --- - Published: 2025-07-25 - Modified: 2025-11-11 - URL: https://vstorm.co/glossary/latency/ - Glossary Categories: TTS, Voice AI Latency is the time delay between initiating a request and receiving the corresponding response in computational systems, measured in milliseconds or seconds. This metric encompasses multiple components including network transmission delays, processing time, queue waiting periods, and input/output operations. In AI systems, latency affects user experience and system responsiveness, with types including inference latency (model prediction time), network latency (data transmission delays), and end-to-end latency (total request-response cycle). Factors influencing latency include model complexity, hardware specifications, batch processing, caching strategies, and geographic distance between components. Optimization techniques involve model quantization, edge deployment, asynchronous processing, and load balancing. For AI agents, low latency enables real-time decision-making, responsive user interactions, and seamless workflow automation. Critical applications like autonomous vehicles, trading systems, and conversational AI require sub-second latency to maintain effectiveness and user satisfaction. --- - Published: 2025-07-25 - Modified: 2025-11-23 - URL: https://vstorm.co/glossary/zero-shot-ai/ - Glossary Categories: AI Zero-shot AI refers to artificial intelligence systems that can perform tasks or make predictions without having seen specific examples of those tasks during training. This capability emerges from models learning generalizable representations and patterns that transfer to novel scenarios without additional fine-tuning or examples. Zero-shot learning typically relies on semantic embeddings, cross-modal knowledge transfer, or learned meta-representations that bridge the gap between seen and unseen categories. Large language models demonstrate zero-shot capabilities by following instructions for tasks they weren't explicitly trained on, leveraging their broad knowledge base and reasoning abilities. Implementation approaches include attribute-based learning, where models learn relationships between semantic descriptions and visual features, and prompt engineering that guides pre-trained models to novel tasks. For AI agents, zero-shot capabilities enable immediate deployment across diverse domains, rapid adaptation to new requirements, and handling of unexpected scenarios without retraining, making systems more flexible and cost-effective. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/generative-transformer/ - Glossary Categories: Deep Learning, LLM Generative transformer is a neural network architecture that uses self-attention mechanisms to generate sequential data, primarily text, by predicting subsequent tokens based on preceding context. This decoder-only architecture employs masked self-attention to prevent information leakage from future positions during training, enabling autoregressive generation where each token depends on previously generated content. The model processes input through multiple transformer layers containing multi-head attention, feed-forward networks, and residual connections with layer normalization. Key innovations include positional encoding for sequence understanding, attention heads that capture different linguistic relationships, and parallel processing capabilities that enable efficient training on large corpora. Generative transformers power applications like text completion, dialogue systems, code generation, and creative writing. For AI agents, generative transformers serve as reasoning engines that produce coherent responses, generate plans, and communicate naturally with humans through structured language generation and instruction following. --- - Published: 2025-07-25 - Modified: 2025-11-01 - URL: https://vstorm.co/glossary/what-are-adapters/ - Glossary Categories: NLP Adapters are lightweight neural network modules inserted into pre-trained models to enable task-specific adaptation without modifying the original model parameters. These parameter-efficient fine-tuning techniques add small trainable layers while keeping the base model frozen, allowing rapid customization for new domains or tasks. Common adapter architectures include bottleneck adapters with down-projection and up-projection layers, Low-Rank Adaptation (LoRA) that decomposes weight updates into low-rank matrices, and prefix tuning that prepends learnable tokens to input sequences. Adapters typically add less than 5% additional parameters while achieving performance comparable to full fine-tuning. Benefits include reduced computational costs, faster training, prevention of catastrophic forgetting, and support for multi-task learning where different adapters handle different capabilities. For AI agents, adapters enable efficient personalization, domain adaptation, and skill acquisition without expensive retraining, making systems more modular and cost-effective. --- - Published: 2025-07-25 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/what-is-text-to-speech/ - Glossary Categories: TTS Text to speech is used for creating accessible interfaces, voice-enabled applications, and automated communication systems across diverse industries and use cases. Primary applications include accessibility solutions for visually impaired users, dyslexia support, and assistive technologies that convert written content into audible format. TTS powers virtual assistants, customer service automation, navigation systems, and smart home devices requiring voice interaction. Educational applications encompass language learning platforms, audiobook generation, and pronunciation training tools. Enterprise uses include automated phone systems, voice notifications, real-time translation services, and hands-free workflow interfaces. Gaming and entertainment leverage TTS for character voices, interactive narratives, and dynamic content generation. Healthcare applications include patient communication systems, medication reminders, and therapeutic tools. For AI agents, TTS enables natural voice interfaces, multilingual communication, and seamless human-computer interaction essential for conversational AI and autonomous systems deployment. --- - Published: 2025-07-25 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/what-is-k-shot/ - Glossary Categories: ML K-shot is a machine learning terminology where k represents the number of labeled examples available per class during training or adaptation, defining the data constraint under which a model must learn new tasks. The variable k typically ranges from 0 (zero-shot) to small integers like 5-10 (few-shot), with higher values indicating more available training examples. This notation originated from computer vision classification tasks but now spans natural language processing, reinforcement learning, and multimodal applications. The k-shot framework enables researchers to systematically study how model performance scales with increasing data availability and to develop algorithms optimized for data-scarce scenarios. Common variations include 1-shot learning (single example per class), 5-shot learning (five examples per class), and k-shot generalization studies. For AI agents, k-shot capabilities determine how quickly systems can adapt to new domains, personalize to user preferences, and handle novel scenarios with minimal supervision. --- - Published: 2025-07-25 - Modified: 2025-11-12 - URL: https://vstorm.co/glossary/multi-hop/ - Glossary Categories: ML Multi-hop refers to reasoning or information retrieval processes that require multiple sequential steps or "hops" through different data sources, documents, or logical connections to reach a final answer or conclusion. This approach is essential for complex question-answering tasks where no single source contains complete information, requiring systems to gather and synthesize evidence from multiple locations. Multi-hop reasoning involves iterative information gathering, where each step informs the next query or reasoning operation, creating chains of logical dependencies. Common applications include multi-document question answering, knowledge graph traversal, and complex problem-solving that requires connecting disparate facts. Implementation techniques include graph neural networks, attention mechanisms that track reasoning paths, and retrieval-augmented generation with iterative refinement. For AI agents, multi-hop capabilities enable sophisticated analytical tasks, comprehensive research automation, and complex decision-making that mirrors human reasoning patterns by building conclusions through systematic evidence accumulation. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/instruction-tuning-vs-fine-tuning/ - Glossary Categories: ML Instruction tuning vs fine tuning represents two distinct approaches to adapting pre-trained language models, with instruction tuning focusing on teaching models to follow diverse natural language instructions while traditional fine tuning optimizes performance on specific tasks. Instruction tuning employs multi-task datasets containing instruction-response pairs across various domains, enabling models to generalize to new instruction types and perform zero-shot task completion. Traditional fine tuning adapts models using task-specific datasets with examples of input-output pairs for particular objectives like sentiment analysis or named entity recognition. Instruction tuning prioritizes instruction-following capabilities and broad generalization, while fine tuning maximizes performance on targeted tasks. Instruction tuning typically uses conversational formats and diverse prompts, whereas fine tuning employs structured task-specific data. For AI agents, instruction tuning creates more versatile systems capable of interpreting and executing varied commands, while traditional fine tuning optimizes specialized capabilities. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/llm-instruction-tuning/ - Glossary Categories: LLM LLM instruction tuning is a specialized training methodology that adapts large language models to follow human instructions and complete diverse tasks through supervised learning on instruction-response datasets. This process transforms base LLMs trained on next-token prediction into instruction-following assistants capable of understanding and executing complex natural language commands. The methodology typically involves supervised fine-tuning on curated instruction datasets like Alpaca, Dolly, or OpenAssistant, followed by reinforcement learning from human feedback (RLHF) to align outputs with human preferences. LLM instruction tuning employs multi-task learning where models learn to handle reasoning, summarization, coding, creative writing, and question-answering through diverse instruction formats. This approach enables zero-shot generalization to unseen instruction types while maintaining the broad knowledge acquired during pre-training. For AI agents, LLM instruction tuning creates reliable systems that interpret user commands, execute multi-step tasks, and provide helpful responses. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/weak-to-strong-generalization-2/ - Glossary Categories: AI Weak-to-strong generalization is the phenomenon where more capable AI models can learn to perform better than their less capable supervisors by generalizing beyond the supervisor's demonstrated abilities. This concept addresses the alignment challenge of training advanced AI systems using weaker oversight, where the supervisory signal comes from less capable models or limited human feedback. The approach leverages the strong model's inherent capabilities while using weak supervision for guidance, enabling performance that exceeds the supervisor's baseline. Implementation techniques include reward modeling where weak models provide training signals for stronger ones, constitutional AI methods that use simple rules to guide complex behavior, and iterative amplification where weak models help train stronger successors. This paradigm is crucial for superalignment research, addressing how to maintain AI safety and alignment as models become more capable than their human supervisors. For AI agents, weak-to-strong generalization enables scalable oversight methods and safety alignment strategies essential for deploying increasingly sophisticated autonomous systems. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/pre-train/ - Glossary Categories: LLM Pre-train refers to the initial training phase where AI models learn foundational representations from large, unlabeled datasets before being adapted for specific tasks through fine-tuning. This unsupervised or self-supervised learning process enables models to acquire general knowledge, patterns, and features that transfer across diverse applications. Pre-training typically employs objectives like next-token prediction for language models, masked language modeling for BERT-style architectures, or contrastive learning for vision models. The process requires massive computational resources and datasets containing billions of examples, creating versatile base models with broad capabilities. Popular pre-training approaches include autoregressive generation, denoising autoencoders, and multi-modal learning across text, images, and audio. Pre-trained models serve as starting points for subsequent fine-tuning, enabling faster convergence and better performance on downstream tasks. For AI agents, pre-training provides the foundational intelligence necessary for reasoning, language understanding, and multi-domain knowledge essential for autonomous decision-making. --- - Published: 2025-07-25 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/ai-lingo/ - Glossary Categories: AI AI lingo is the specialized vocabulary, terminology, and jargon used within artificial intelligence research, development, and deployment that encompasses technical concepts, methodologies, and system components. This domain-specific language includes foundational terms like neural networks, machine learning, and deep learning, alongside advanced concepts such as transformer architectures, attention mechanisms, and reinforcement learning. AI lingo spans multiple disciplines including computer science, statistics, cognitive science, and engineering, creating a comprehensive lexicon for describing algorithmic processes, model architectures, training methodologies, and performance metrics. Common categories include model types (CNNs, RNNs, LLMs), training processes (fine-tuning, pre-training, RLHF), evaluation metrics (accuracy, F1-score, perplexity), and deployment concepts (inference, latency, scalability). For AI agents, understanding AI lingo is essential for effective communication between technical teams, stakeholders, and end-users, enabling precise specification of requirements, capabilities, and limitations. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/transformer-gpt/ - Glossary Categories: LLM Transformer GPT (Generative Pre-trained Transformer) is a family of autoregressive language models built on decoder-only transformer architecture, designed for text generation through next-token prediction. Unlike encoder-decoder transformers, GPT models use only the decoder stack with masked self-attention to prevent information leakage from future tokens during training. The architecture employs multi-head attention mechanisms, feed-forward networks, and positional embeddings to process sequential text data. GPT models undergo unsupervised pre-training on vast text corpora using causal language modeling objectives, learning to predict subsequent words based on preceding context. Key innovations include scaling to billions of parameters, in-context learning capabilities, and emergence of complex reasoning abilities. The GPT series demonstrates how transformer architecture can achieve remarkable language understanding and generation through scale and architectural refinements. For AI agents, transformer GPT models serve as powerful reasoning engines enabling natural language understanding, instruction following, and complex task completion. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/automatic-speech-recognition-technology/ - Glossary Categories: ASR Automatic speech recognition technology is a computational system that converts spoken language into written text through signal processing, acoustic modeling, and language understanding techniques. This technology employs multiple processing stages: audio preprocessing to filter noise and normalize signals, feature extraction using spectrograms or mel-frequency cepstral coefficients, acoustic modeling through neural networks that map audio features to phonetic units, and language modeling to predict likely word sequences. Modern ASR systems utilize deep learning architectures including recurrent neural networks, transformer models, and attention mechanisms for improved accuracy across diverse speakers, accents, and acoustic conditions. Key components include voice activity detection, speaker adaptation algorithms, and confidence scoring mechanisms. Advanced systems support real-time processing, multilingual recognition, and domain-specific vocabulary adaptation. For AI agents, automatic speech recognition technology enables voice interfaces, hands-free operation, conversational interactions, and accessibility features essential for natural human-computer communication. --- - Published: 2025-07-25 - Modified: 2025-08-24 - URL: https://vstorm.co/glossary/what-is-text-speech/ - Glossary Categories: TTS Text speech refers to text-to-speech (TTS) technology that converts written text into synthesized spoken audio using artificial intelligence and signal processing techniques. This technology analyzes input text through natural language processing to understand linguistic structure, pronunciation rules, and contextual meaning before generating corresponding audio output. The process involves text normalization to handle abbreviations and symbols, phonetic analysis to determine pronunciation, prosody prediction for natural rhythm and intonation, and audio synthesis using neural vocoders or concatenative methods. Modern text speech systems employ deep learning models like Tacotron, WaveNet, and FastSpeech to produce human-like voices with emotional expression and speaker characteristics. Key features include multilingual support, voice customization, speaking rate control, and real-time generation capabilities. For AI agents, text speech technology enables voice interfaces, accessibility features, automated narration, and natural spoken communication essential for conversational AI systems and hands-free user interactions. --- - Published: 2025-07-25 - Modified: 2025-07-31 - URL: https://vstorm.co/glossary/artificial-intelligence-glossary/ - Glossary Categories: AI Artificial intelligence glossary is a comprehensive reference resource that defines and explains technical terms, concepts, methodologies, and technologies within the AI domain, serving as a knowledge base for practitioners, researchers, and business stakeholders. This structured collection encompasses fundamental concepts like machine learning and neural networks, advanced topics including transformer architectures and reinforcement learning, and practical applications such as AI agents and natural language processing. AI glossaries typically organize entries alphabetically or thematically, providing clear definitions, contextual examples, and cross-references between related concepts. Key categories include model architectures, training methodologies, evaluation metrics, deployment strategies, and emerging technologies. These resources bridge knowledge gaps between technical and non-technical audiences, standardize terminology usage, and support educational initiatives. For AI agents, glossaries serve as knowledge repositories enabling systems to understand domain-specific vocabulary, provide accurate explanations, and maintain consistent terminology across documentation and user interactions. --- - Published: 2025-07-25 - Modified: 2025-07-25 - URL: https://vstorm.co/glossary/collective-learning-meaning/ - Glossary Categories: AI Collective learning meaning refers to the fundamental concept of distributed intelligence where multiple entities collaborate to acquire knowledge, solve problems, and improve performance through shared experiences and coordinated learning processes. This paradigm emphasizes emergent intelligence arising from group interactions rather than individual capabilities, drawing inspiration from biological systems like ant colonies, bee swarms, and neural networks. The meaning encompasses both technical implementations such as federated learning, multi-agent systems, and ensemble methods, as well as theoretical frameworks for understanding how collective behavior generates superior outcomes compared to isolated learning. Key principles include knowledge sharing without centralized data storage, distributed decision-making, and adaptive coordination mechanisms. The significance lies in enabling scalable learning that preserves privacy, leverages diverse perspectives, and creates robust systems through redundancy and collaboration. For AI agents, collective learning meaning defines how autonomous systems can form intelligent networks that collectively solve complex problems beyond individual agent capabilities. --- --- ## Events ---