Private RAG: Let Your Team Chat With Company Documents Without Leaking Data
What a private RAG system is, when a business needs one, and how we build them so your documents never leave infrastructure you control.
Most companies we talk to want the same thing: ask a question in plain language and get an answer from their own manuals, contracts, tickets and wikis. The obvious route — pasting documents into a public chatbot — is often not allowed. That is where a private retrieval-augmented generation (RAG) system fits.
What RAG actually does
A RAG system does not retrain a model on your data. It works in three steps:
- Index — documents are split into passages and turned into vectors stored in a vector database.
- Retrieve — when someone asks a question, the most relevant passages are found.
- Generate — a language model writes an answer using only those passages, and cites them.
Because the model reads your documents at question time instead of memorising them, you can add, update or remove knowledge instantly.
What makes it “private”
“Private” means you decide where each part runs:
| Component | Private option |
|---|---|
| Document storage | Your own server or an EU cloud region |
| Vector database | Self-hosted (e.g. PostgreSQL with pgvector) |
| Language model | Open-weight model on your hardware, or an EU-hosted API with no training on your data |
| Access control | Answers only use documents the user is allowed to see |
When you need one
- Staff spend hours searching shared drives and wikis.
- Support agents answer the same product questions every day.
- Your data is covered by GDPR, NDAs or industry rules that forbid sending it to public AI tools.
How we build them
We start with a two-week prototype on a sample of your real documents and a list of real questions. That list becomes an evaluation set, so every later change is measured against it rather than judged by feel.
A RAG system is only as good as its retrieval. Most of our work goes into chunking, metadata and access control — not the chat window.
If you are considering a private AI assistant, tell us about your documents and we will tell you honestly whether RAG is the right tool.