Enterprise RAG has become the most reliable way to put generative AI to work: the assistant first searches your own documents, then answers citing its sources. Easy to demo, it needs rigour in production.
What is RAG?
Retrieval-Augmented Generation: index documents as embeddings, retrieve relevant passages for each question, generate an answer from those passages only, with references.
Use cases that work
Internal support, pre-sales, customer service, legal and purchasing, digital asset search.
From prototype to production
- Source quality: select, date and de-duplicate documents
- Chunking and retrieval: hybrid search, re-ranking
- Access rights inherited from source tools and enforced at query time
- Evaluation with a reference question set
- Hosting and confidentiality: Europe, no training on your data, usage logs
Production RAG is not a stronger model; it is clean documents, respected rights and measured answers.
Limits
Weak on questions that combine many documents or require calculation; for numbers, query the database directly.
Not sure where to start? See AI maturity audit and automating invoice entry.
Enterprise RAG: FAQ
RAG or classic chatbot?
A RAG searches your documents first and cites sources, limiting made-up answers.
Do we need to train a model?
No — RAG uses your documents at question time, without retraining.
Your enterprise RAG
We deploy enterprise RAG on your data, alongside supplier invoice OCR, hosted in Europe. Let’s talk.