Why RAG is the foundation of enterprise AI
Every organisation wants AI that knows its own data — internal policies, product documentation, regulatory texts, client dossiers. Fine-tuning is expensive and fragile. RAG is the pragmatic alternative: retrieve relevant chunks from your knowledge base, then let the LLM generate a grounded answer.
But RAG is not plug-and-play. Chunking strategy, embedding model selection, vector store architecture, re-ranking, evaluation and hallucination detection all require real expertise. I train your teams on the full RAG lifecycle — from document ingestion to production monitoring.
For Swiss organisations handling sensitive data (banking, legal, healthcare), I specifically address data residency, on-premises vector stores and nFADP-compliant architectures.
RAG lets AI speak your language, from your data, without making things up: that is the promise, and the training delivers.