A retrieval-augmented assistant that answers from the client's own documents.
A proof of concept pairing a chat interface with retrieval over a private knowledge base, so answers are grounded in the client's material rather than in model training data. The client has asked not to be named.
How it is put together
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What we built.
A language model on its own knows nothing about your organisation. Ask it about your policies, your products or your procedures and it will answer confidently from whatever resembles them in its training data. That is the failure mode retrieval-augmented generation exists to fix.
Moweb built a proof of concept that puts the client's own documents in front of the model at answer time: the question retrieves the relevant passages, and the model answers from those rather than from memory.
This was a proof of concept. It was built to establish whether the approach held up on the client's material, not as a production rollout, and it is published here on that basis.
Key challenges
These are the constraints that decide whether a RAG assistant is useful or merely demonstrable.
- 1Retrieval quality sets the ceiling. If the right passage is not retrieved, no model can answer correctly from it, so the limiting factor is almost never the model.
- 2Source documents are written for people, not for retrieval. Long documents, tables and inconsistent structure all have to be handled before anything can be looked up reliably.
- 3An assistant that answers confidently when it should not is worse than one that declines. Knowing when the knowledge base does not cover a question is part of the job.
- 4Answers have to be checkable. Without the retrieved passages shown alongside, nobody can tell a grounded answer from a fluent guess.
Our solutions
What the proof of concept covered:
- A chat interface over the client's knowledge base, so questions are asked in the way people actually ask them.
- Retrieval that finds the relevant passages for a question and supplies them to the model as context.
- Answers grounded in the retrieved material, so the assistant works from the client's documents rather than from model training data.
Technology stack.
More case studies.
Proof
Work we have shipped.
Wondering whether RAG fits your knowledge base?
A proof of concept answers that faster than a specification does. We can scope one against your own documents.