RAG platform: answers with sources from your own documentation
Your people search through manuals, work instructions and procedures every day. The answer is in there, but who finds it depends on who happens to be looking and how recently they read it. A language model answering from general knowledge does not solve that: it gives an answer that sounds plausible and cannot be verified.
What a RAG platform does differently
For a national professional association we built a knowledge environment that answers questions from their own documentation. Employees ask their question in plain language and get an answer from their own regulations, with the source underneath. If it is not in there, the system says so. No answer without a source.
The value is not in the chat
The chat is the front end. The value sits in the layer underneath: a maintained knowledge base that keeps your documentation searchable, even as it changes. Sources are kept up to date, outdated material stands out, and every answer traces back to the document it came from.
The overview you did not have yet
Then the part organisations do not see coming. You get a view of which questions you could not answer from your own documentation. For a trade body or professional association that is not a technical log but board-level information. It is the gap map of your own knowledge, and until now you did not have it. It shows where your members expect something from you that is not written down anywhere, and that is exactly the list a next guideline, training course or publication gets based on.
Sound familiar? Let us take a look.
Get in touch →Your data stays where you want it
Because the knowledge base is separate from the language model, you decide where the processing happens: at a cloud provider or on your own infrastructure in the Netherlands. Switch models and the knowledge base stays put.
Where you start
Does your organisation work with its own regulations, guidelines or professional documentation that members ask questions about? Then the first question is not which AI you pick, but how well your documentation can be searched. We are happy to spend an hour looking at that with you.
Marketing Coordinator at Wabber B.V.