AI & Generative AI

Develop, scale, and fine-tune your LLMs in a secured environment set for growth.

The challenge

Great possibilities. Even greater obstacles.

Enterprises are increasingly hesitant to deploy generative AI agents to production due to the fear of real-life consequences - hallucinations, misleading outputs, and security threats that can turn your AI agent into a liability.

QCerris senior engineers and advisors are on a mission to industrialize AI and make it available at scale for enterprises. We've mastered building RAG architecture for improved development of Gen AI solutions - bolstering models during development, defending them during operation, and consistently validating their dependability and safety.

What you get

Deep expertise for tailor-made solutions

Our engineers can extend what you can do with your AI solutions, building an environment fitting uniquely to your vertical and your customers' needs.

Platform agnostic

We will guide you through the best LLM model choices (OpenAI, Llama, Mistral, Anthropic...) and development frameworks that best suit your goals.

Continuous optimization

Your AI agents require constant validation for trustworthiness, provided within a secured and efficient infrastructure.

FAQ

Common questions about AI development

What is Retrieval-Augmented Generation (RAG)?+

RAG is a hybrid approach that enhances LLM capabilities by incorporating external, authoritative knowledge bases into the response generation process. Without RAG, the LLM provides responses based on its training data alone. With RAG, the model first checks a current knowledge source - resulting in more accurate, up-to-date, and trustworthy outputs with dramatically fewer hallucinations.

Why is RAG important for enterprise AI?+

RAG tackles the biggest challenges of LLMs: irrelevant, outdated, or inaccurate responses presented with full confidence. RAG feeds the model from verified external data, makes sources continuously up-to-date through automation, and is far more cost-efficient than retraining a foundation model.

Should I choose fine-tuning or RAG?+

RAG is a more resource-efficient solution with less data dependency, better generalization, and overall flexibility - especially for real-time updates and dynamic information. Fine-tuning offers deep customization but comes with higher costs and more intensive resource requirements. For most enterprise use cases, RAG is the right starting point.

How do you ensure AI output is secure?+

We implement continuous validation, adversarial red-teaming, and output monitoring as standard parts of our LLM deployment process. Every AI system we ship is hardened at both application and infrastructure levels, governed by our ISO/IEC 27001-aligned security program.

Ready to get started?

Consultation is free. Let's talk.