Guide
Private RAG over internal documents
People already find this page-adjacent query around position ten: a private system that answers from your files. This is how we build that, from Montreal, without uploading the company drive to a public chatbot.
What “private RAG” actually means
RAG (retrieval-augmented generation) is not a smarter search box and it is not ChatGPT with your logo. When someone asks a question, the system retrieves passages from documents you designated — PDFs, SOPs, contracts, wiki pages — then writes an answer that cites those passages. Private means the index and the model sit in your cloud or on your network. The documents do not become training data for a consumer product.
Why teams stop pasting into ChatGPT
Pasting a policy into a public model is fast until it is a breach. It is also wrong in the ways that matter: the model invents a clause, mixes last year’s PDF with this year’s, and cannot show the page it used. A private RAG over internal docs is slower to stand up and stricter about sources. That is the point. Employees get an answer they can open. Legal and IT get a boundary.
What changes when the agency is Canadian
Québec and federal teams ask two questions before they ask about embeddings: where does the data live, and can the system answer in both languages from a mixed corpus. We deploy in a VPC you control (or on-prem). We do not need the files to leave Canada. Bilingual retrieval is not a toggle — French questions over English SOPs, and the reverse, have to be designed, not hoped for. That is ordinary work for a Montreal studio. It is unusual for a generic “enterprise AI” deck.
When RAG is the wrong tool
If you have twenty pages and one author, a well-tagged intranet wins. If you need the model to invent strategy, you want a person. RAG earns its keep when the library is large, the answers must be sourced, and the alternative is a senior staffer hunting SharePoint. Hospitals, professional services, and operations teams with decades of PDFs are the usual fit — the same profile as the long-tail queries already ranking near this site.
What a first conversation covers
We start with where the files live, who is allowed to see which shelf, and whether French and English must retrieve from the same index. Then we talk hosting (your AWS, Azure, GCP, or a machine in the office) and what “good” looks like: a cited paragraph, not a vibe. If that matches, we scope a private RAG over a slice of the library before anyone promises the whole archive.