Before building an AI knowledge base, decide which documents it should use, who may access them and what happens when the answer isn't there. Those decisions matter as much as the model.
Which version of the service procedure is current? Is the product specification in the shared drive newer than the one attached to last month's email? These are ordinary questions, but finding a reliable answer can take longer than it should.
A private AI knowledge base is one way to help people find their way through business documents. For a UAE company considering custom software, the attraction is understandable: ask a question instead of searching folder after folder. The difficult part is making sure the answer comes from the right material.
Start with one collection people can trust
Resist the temptation to upload everything. A smaller collection of approved service procedures is a better starting point than a mixture of drafts, old contracts and unlabelled PDFs.
Give each document an owner and a version. Decide when it needs review and what happens when it's replaced. If two departments disagree about which policy applies, settle that before asking an assistant to explain it. Otherwise, the system may simply make the disagreement harder to spot.
What RAG does—and what it doesn't
Retrieval-augmented generation, or RAG, retrieves relevant material and supplies it to a model as context for an answer. Think of it as helping the model find something to read before it responds.
That can be useful, but the retrieval can still select the wrong passage. A fluent answer won't tell you that it happened. Test the search step separately: write a question, identify the document that should answer it and check which passage the system actually retrieves.
Anthropic's contextual retrieval research explores the importance of context around retrieved passages. Treat it as a useful reference, not proof that one technique will suit every collection. Your own tests should include product codes, abbreviations, tables and scans with imperfect text extraction.
A login screen doesn't settle document access
Suppose a salesperson can ask about product specifications but cannot open internal pricing approvals. The assistant must respect that distinction before it passes source material to the model. Telling the model not to reveal confidential information isn't a substitute for filtering what it receives.
Test permissions with more than one role. Then remove a user's access and try again. Check indexes, cached results and conversation history as well as the original file store. A change in the document portal should not leave an unnoticed route through the assistant.
| Area | Ask this | Look for |
|---|---|---|
| Sources | Which version should it use? | A named owner and revision |
| Access | Who is allowed to read it? | Tests across user roles |
| Answers | Where did this statement come from? | A relevant source passage |
| Removal | What if we withdraw the document? | No stale retrieval result |
Make the answer easy to verify
A source link should open the relevant document version, ideally at the section supporting the answer. A filename at the bottom of a response isn't enough if the document doesn't support the claim.
Sometimes the right response is that the information isn't available. Agree how the assistant should handle missing evidence and conflicting policies. It should point someone towards the responsible team rather than use general model knowledge to guess a company rule.
Be specific about what “private” means
Ask where files are stored and processed, who at the provider can access them, what gets logged and how long it stays there. Include embeddings, backups and diagnostic records in that discussion. A restricted account doesn't answer all those questions.
Use approved, anonymised or minimised documents for demonstrations. Don't copy customer or employee records into a test simply because the product has “private” in its name. Your data owners should approve the handling arrangements.
Check whether it actually helps the team
Try questions with clear answers, questions the documents can't answer and requests for material the user shouldn't see. Measure supported answers and the time people spend verifying them. Plenty of chat activity isn't evidence that the system saves work.
If you're exploring an AI document assistant with Gligx, bring a small approved collection and a handful of real questions. That's enough to start a useful conversation about scope, access and testing.
Sources and further reading
References checked on 23 October 2026.
Frequently Asked Questions
What does RAG actually do?
It retrieves source material and passes it to a model as context for an answer. That can help, but the retrieved passage or the generated answer can still be wrong.
Does a private knowledge base have to run on our premises?
No. Restricted access and on-premises hosting are different things. Ask separately about storage, processing locations, provider access and retention.
Can staff see different information depending on their role?
They can if the application enforces those permissions during retrieval and when opening sources. Telling the model to keep information confidential isn't enough.
What happens when a document becomes outdated?
Give someone responsibility for replacing or withdrawing it, then check that the old version disappears from search results and relevant caches—not just the original folder.