Efficiently optimize, scale, and manage your Large Language Models with tailored LLM Ops solutions
What we can help you with:
We provide expert consultations to help you navigate the complexities of LLM operations.
This service includes:
Our advisory services ensure you make informed decisions to maximize the value of your AI investments.
We enhance the performance of your LLMs by fine-tuning their parameters and improving their computational efficiency.
Our optimization services include:
By optimizing your models, we help reduce operational costs and improve user satisfaction, delivering faster and more precise results for your business.
Ensure your systems are ready to handle high traffic with our scalability solutions.
This service includes:
With our scalability solutions, your business can confidently grow while maintaining seamless and efficient operations.
We specialize in deploying LLMs tailored to your infrastructure requirements, ensuring seamless integration.
This service covers:
Our deployment services ensure your models are operational and ready to deliver value from day one.
Stay ahead of potential issues with continuous monitoring of your LLMs’ performance.
Key features include:
With our performance monitoring, you can trust your LLMs to operate at their best, always.
Optimize operational costs while maintaining high performance.
Our cost optimization solutions include:
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 LLM Ops, 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.
Call-center automates its inbound customer call verification and routing processes using AI-powered voice assistants.
By integrating advanced technologies such as LLMs, speech recognition, and Retrieval-Augmented Generation (RAG), the system handles calls more efficiently, reduces human intervention, supports multiple languages, and improves overall operational scalability.
Read more
Guesthook, a specialized marketing agency in the vacation rental industry, focuses on creating compelling property descriptions and enhancing the online presence of rental properties.
An AI-driven platform automates the creation of personalized property descriptions using LLMs, enabling hyper-automation and hyper-personalization. This solution allows property owners to efficiently generate tailored listings, reducing costs and improving booking potential.
Read more
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.
Read moreDon’t you see the question you have in your mind here? Ask it to us via the contact form
LLMOps focuses on managing and scaling large language models, while MLOps addresses the broader lifecycle of traditional ml models. LLMOps requires specialized infrastructure and optimization strategies due to the size and complexity of llms.
LLMOps refers to the set of practices, tools, and workflows designed to manage, deploy, monitor, and optimize large language models throughout their lifecycle. It extends the concepts of mlops to accommodate the specific needs of foundation models.
A key aspect of llmops is efficient orchestration of compute resources to support model training, deployment, and real-time inference. It also involves maintaining high model performance while minimizing operational costs.
Major challenges include managing large volumes of training data, ensuring reproducibility, securing sensitive data, and optimizing infrastructure for high-performance llms. Teams also face hurdles in aligning llm outputs with business and ethical expectations.
LLMOps are operational processes and systems tailored for deploying, optimizing, and maintaining large language models. They help teams integrate llm capabilities into real-world applications with reliability and efficiency.
One key aspect of llmops is continuous performance monitoring, which ensures that llms deliver accurate and efficient results in dynamic environments. This includes anomaly detection, logging, and ongoing performance audits.
LLMOps employs techniques like quantization, model pruning, and feedback-driven optimization to improve model accuracy and speed. These enhancements reduce latency and computational demand.
No, llmops is specifically designed for projects involving large language models. Traditional ml models can typically be managed with standard mlops practices.
Industries dealing with high-volume unstructured data—such as finance, healthcare, legal, and e-commerce—see significant benefits from llmops. It enables reliable deployment of natural language interfaces and automation tools.
Yes, llmops can be implemented for both proprietary and open-source llms. It supports custom model fine-tuning, deployment, and performance tracking across platforms.
Modern llmops practices allow teams to deploy large language models securely while maintaining high model accuracy and compliance. Through advanced model and data monitoring pipelines and automated llmops pipelines, performance stays optimal even under heavy usage. The llmops platform provides end-to-end transparency across your deployment and training stack.
A full-featured llmops platform supports everything from training a foundation model to production-ready deployment. It enables rest api model endpoints, tracks model performance over time, and integrates with mlops platform such as MLflow. Organizations can manage model and pipeline management more effectively and adapt quickly to new requirements.
The future of llmops lies in automation and interoperability across systems. As teams work with llm chains or pipelines that span multiple tools, llmops automates the operational overhead, from language model training to fine-tuning. This allows for continuous updates, future fine-tuning of your llm, and streamlined adaptation to user feedback.
