Custom RAG, explained: answers from your own documents
A language model that answers from your contracts, manuals and tickets, cites every source and respects who may read what. What custom RAG is, and what separates one that works from a demo.
Ask a general chatbot about your supplier contracts and it will answer confidently, from nothing. Retrieval-augmented generation, RAG, changes the order. It first finds the passages in your own documents that are relevant, then answers from those only, citing where each statement comes from. When nothing relevant is found, it says so.
Why custom matters
Pointing a model at a folder of PDFs makes a convincing demo. Making it reliable enough that people stop double-checking it is engineering. Four things separate the two.
Your sources, kept in sync
Knowledge lives in SharePoint, mailboxes, ticket systems, wikis and scanned PDFs. We index the systems you already use and keep the index current as documents change, so an answer does not come from last year's version.
Access rights carried through
If a person may not open the board minutes, the assistant must not quote them. Permissions are carried into the search itself, so people only get answers from documents they are allowed to read.
Every answer cites its sources
Each statement links back to the passage it came from. The answer becomes something that can be checked in one click, and that is what makes people rely on it in daily work.
Measured before launch
Answer quality is tested against a set of your real questions, before go-live and after every change. Without that, nobody can say whether a change made the system better or worse.
Where the data lives
Hosting follows the data: in the EU, or on your own infrastructure where the data must not leave. The choice of model stays open, so you are not tied to one provider.
Where to start
Pick one body of documents people search every day, collect the questions they actually ask, and build from there. A discovery workshop is the usual first step, and for a Luxembourg SME it can qualify for the SME AI package.