Enterprises are increasingly hesitant to deploy generative AI agents to production due to the fear of real-life consequences - issues such as hallucinations, misleading outputs, and security threats that can turn your AI agent into a sitting duck. LLMs may display inconsistencies, unreliability, and unethical or dangerous suggestions. Coupled with their vulnerability to cyber attacks, world’s most significant technology risks slowing down its imminent potential for enterprises and their end users.
Develop, scale and fine-tune your LLMs in a secured environment set for growth
Let us guide you through the brave new world of reliable, secured, and continuously evaluated generative AI agents your users can trust.
Great possibilities, even greater obstacles
Build better AI and turn your model into a trusted one
QCerris’ senior technologies, engineers and advisors are on a mission to industrialize AI and make it available at scale for enterprises. We’ve taken the uncharted routes of deep learning and LLM security to master building RAG architecture for improved development of Gen AI solutions. Such a system bolsters models during development, defends them during operation, and consistently validates their dependency and safety. The result is an optimized output of a LLM model, more credible and accurate responses, and greater resiliency to cyber-attacks.
Leave your fears of Gen AI weaknesses behind and let your solutions wield unlimited power.
All AI dev services you may need, in one place, with one partner
We will help you assess your AI readiness, identify your gaps and opportunities, closely work with you to develop your AI strategy and build all the necessary software infrastructure for your apps.
Expect nothing less than:
Deep expertise for tailor-made solutions
As AI project builders, our engineers can extend what you can do with your AI solutions, by developing an environment fitting uniquely to your vertical and your customers' needs.
As AI project builders, our engineers can extend what you can do with your AI solutions, by developing an environment fitting uniquely to your vertical and your customers' needs.
Any technology, any framework
We are platform agnostic and will talk you through the best choices in terms of LLM models (OpenAI, Llama, Mosaic, ...) and development frameworks and tools that best suit your goals.
We are platform agnostic and will talk you through the best choices in terms of LLM models (OpenAI, Llama, Mosaic, ...) and development frameworks and tools that best suit your goals.
Continuous optimization
Your AI agents require constant validation for trustworthiness, for which we will provide both favorable and adverse conditions within a secured and efficient private cloud infrastructure.
Your AI agents require constant validation for trustworthiness, for which we will provide both favorable and adverse conditions within a secured and efficient private cloud infrastructure.
FAQ
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is a hybrid approach that enhances the capabilities of large language models (LLMs) by incorporating external, authoritative knowledge bases into the response generation process. Traditional LLMs, trained on vast amounts of data with billions of parameters, can perform various tasks such as answering questions, translating languages, and completing sentences. However, their responses are limited to the scope of their training data, which may not always be correct, up-to-date, domain-specific or even ethical.
Without RAG, the LLM provides responses based on information it was trained on, while with RAG, the model is instructed to first check a new data source, with user query and the relevant information both given to the LLM.
Why is Retrieval-Augmented Generation (RAG) important?
RAG is a framework made to tackle the biggest and most frequent challenges and downsides of LLMs. We are talking about retrieving irrelevant, outdated, inacurrate responses based from non-credible sources, that a model presents to the user with utmost confidence. No need to elaborate why this is a deal-breaker for both developers and end users.
Enter RAG.
RAG process feeds the model with external data, provides more secure, relevant and accurate facts, enriches the prompt and makes sure all the sources are continuously up-to-date. The latter can be done with the beauty of automation of periodic data batch processing. The end result of successfully implemented RAG approach include optimized LLM output that retains user’s trust and provides more control to developers and MLOps. Another great advantage of RAG is that it’s economical; compared to retraining a foundation model, RAG proves to be a cost-efficient approach that makes gen AI more accessible.
Should I choose fine-tuning or retrieval augmented generation?
Choosing between fine-tuning and retrieval-augmented generation RAG for your Gen AI project depends on your specific goals, the nature of your data, and the resources you have available. RAG is a more resource-respectable solution with less data dependency, better generalization, and overall flexibility and adaptibility. RAG is particularly beneficial when you need a cost-effective solution that can handle dynamic information and adapt to changes in the knowledge base with minimal retraining. It is well-suited for applications requiring real-time updates, flexibility in knowledge integration, and reduced dependence on large, domain-specific datasets. Fine-tuning, on the other hand, offers deep customization but comes with higher costs and more intensive resource requirements.