AI Engineering
RAG, AI agents, LLMOps: we build with your teams the architectures and systems that put AI to work at scale in production.
- Design robust AI architectures (RAG, agents, LLM) suited to your use cases
- Industrialize your AI systems with proven LLMOps practices
- Set up systematic evaluation of your models and systems
- Upskill your teams in AI Engineering
Key factors for a successful AI transformation
RAG, AI agents, LLMOps: we build with your teams the architectures and systems that put AI to work at scale in production.
AI system architecture & design
Choose the right patterns (RAG, agentic AI, MCP) and architect AI systems suited to your use cases and constraints.
RAG & knowledge management
High-performing RAG pipelines: ingestion, chunking, retrieval, evaluation. Context engineering at the heart of accuracy.
LLMOps & production deployment
CI/CD, prompt versioning, monitoring and observability. Your AI systems deserve the same standards as your APIs.
Evaluation & quality
Systematically evaluate your AI systems: LLM-as-judge, the RAGAS framework, business benchmarks. Measure to improve.
The steps of our support
A sequential methodology to design, build and run your AI systems in production, from initial scoping to expert coaching.
Technical diagnostic
Analysis of your existing stack, identification of priority AI use cases, and technical scoping.
What we do
- Map your technical stack and existing data flows
- Identify the AI use cases with the strongest business impact
- Assess your organization's AI Engineering maturity
- Scope the technical, regulatory and cost constraints
Deliverables
- Mapping of the stack and AI use cases
- AI Engineering maturity audit
- Prioritized roadmap with quick wins
Ready to accelerate your transformation?
Our experts build the support that matches your challenges with you.