Transform operations with hyper-automation, hyper-personalization, and smarter decision-making using Large Language Model
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:
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 business agility.
Over 90 completed projects since 2017, specializing in enterprise transformation with Large Language Models. Our 25 AI specialists deliver custom, scalable solutions tailored to business needs.
We leverage a range of specialized tools designed for Large Language Model development, ensuring efficient, innovative, and tailored solutions for every project.
We provide full support from consultation and proof of concept to deployment and maintenance, ensuring scalable, secure, and future-ready solutions.
California-based startup emerged as an organization dedicated to reshaping online discussions with open-source technology
Conversational AI platform that allows multiple users to collaboratively work in real time for an array of state-of-the-art self-hosted LLMs in a secure and safety way.
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Germany’s PR agency specializes in digital public relations, focusing on creating and managing online PR strategies, social media marketing, and content creation for brands and businesses.
An all-in-one AI-powered platform enabling digital journalists to request and scrape domain-specific web content, leveraging LLMs for multi-category expertise.
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Global provider of IT solutions for businesses and public organizations seeking to create a collaborative digital environment and ensure seamless daily operations.
An AI-driven internal sales platform that interprets inbound sales emails, utilizing LLM and RAG connection to different sources from product information while allowing manual customization of responses.
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LLM development is the end-to-end process of designing, training, fine-tuning, and deploying a large language model so it solves a specific business problem—from data engineering to MLOps.
The “black box” refers to the difficulty of explaining how billions of parameters arrive at a prediction. Mitigating it with interpretability tools builds trust and uncovers hidden biases in the neural network.
Active learning reduces labeling costs by letting the model query the most uncertain samples in the dataset, accelerating performance gains on smaller, high-value data slices.
Spin up a sandbox using open-source checkpoints on services like Hugging Face Hub, fine-tune with a small framework such as LoRA, and iterate on prompts to validate ROI before scaling.
LangChain offers composable abstractions—prompts, memory, and agents—that hide boilerplate and let you chain together language model calls, tools, and data sources without reinventing the wheel.
Ollama packages popular open-source LLMs into one-command Docker images, enabling offline AI experimentation, faster iteration, and cost-free inference during prototyping.
Developers transition from writing boilerplate to reviewing and guiding generated text. Productivity spikes while code quality improves through automated unit-test scaffolding and inline documentation.
Scale. Traditional ML rarely exceeds millions of parameters, whereas LLM projects manage billions, demanding specialized distributed training, data pipelines, and inference optimization.
Transformer models process entire sequences simultaneously using self-attention, while RNNs handle tokens one-by-one. Transformers therefore parallelize computation and capture long-range context more effectively.
Focus on domain relevance, language diversity, licensing compliance, and size. A balanced dataset ensures the LLM learns nuanced patterns without inheriting unwanted biases.
