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GullySystem
A practical guide

How AI Assistants Can Answer Questions from Company Documents

Ganesh HSStrategy and Technology, GullySystem
Practical advice · 4 min read

In this guide 6 sections
The short answer

A document-grounded assistant searches your approved company files, selects the passages that match the question, and writes an answer from those passages alone, citing each source. A general chatbot works differently. It answers from training data alone. The citation is what makes checking possible.

How a document-grounded assistant works

The technique is called retrieval-augmented generation. It never learned your policies during training. Each time someone asks, it turns the question into an embedding, retrieves the closest passages from approved documents, and writes an answer from those passages, citing each one.

Outside questions get a refusal. A properly built version replies that nothing in the approved documents covers the question, rather than filling the silence with a plausible guess from general knowledge. Traceability is the advantage.

Scanned PDFs need text extraction first. Gather the leave policy, lockout-tagout procedure, product datasheets and supplier correspondence from SharePoint folders and shared drives, then mark each file approved, superseded or archived. Superseded versions mislead the index. The assistant cannot tell a superseded leave policy from the current one, so it may quote either with the same calm confidence.

Take a mid-sized manufacturer whose shop-floor supervisors keep phoning HR with leave and safety questions, and who build an assistant to answer them instead. Before launch, someone deletes the 2023 leave policy from the index, confirms the current safety manual is the one indexed, and archives anything that should never be searchable. Indexing comes after cleanup.

Access control for documents

Unfiltered search leaks payroll data. An assistant that searches every folder can show staff a salary band from an HR policy or a client contract that only one department is meant to open. ACLs must travel with every file. When a supervisor asks about leave, the search covers only what that supervisor may open, while a finance clerk asking about vendor contracts searches a different set.

Build this in from day one. Adding permission checks after the assistant already answers company-wide means re-testing every answer it has given, which is harder than inheriting the source system’s rules.

Testing citations, refusals and gaps

Test three kinds of question. The first has a clear answer in the documents, and you check that the cited passage supports it, not merely that the answer sounds right. The second is covered nowhere, and the assistant should admit that instead of guessing, while the third can be answered two ways depending on which policy applies.

The second kind matters most. Teams often skip it. An assistant that never says “I don’t have that” will eventually fill a gap in your documents with a plausible but wrong answer. Safety questions are where this hurts.

Keeping content current and watching for failures

Stale indexes decay quietly. A revised handbook replaces the old PDF in SharePoint, but nobody re-runs the indexing job, so the assistant keeps citing last year’s wording with complete confidence. Give the document set a named owner. Re-index after every policy update.

Also keep a log. Record each question the assistant could not answer or answered with low confidence, because the list shows where your documentation has gaps and what employees keep trying to find. Review it every month.

Permission-aware knowledge assistant diagram

A diagram of a question entering the assistant, a retrieval step limited to documents the employee’s role may open, and a generation step that returns a cited answer. A fallback path covers the empty result.

Open a blank worksheet to print

Questions owners ask

Can employees see restricted documents?

Not in a correct build. The assistant inherits the permissions of the document store, so each employee receives answers drawn only from files that person could already open. Design it in early.

What happens when the answer is missing?

It admits it found nothing. It must not guess from general knowledge, and a run of such misses tells you which documents to write or update, instead of exposing a flaw to hide.

Who should own the document set?

Give it one named owner. That person approves new versions, triggers the re-index after every change, and reads the unanswered-question log each month to decide what documentation is missing.

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