---
title: "Internal knowledge assistant: AI Use Case"
description: "Answer staff questions from approved policies, procedures and product documents, with the source shown and a clear handoff when the answer is not there."
url: https://lidvizion.ai/use-cases/internal-knowledge-assistant/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Use cases](https://lidvizion.ai/use-cases/) · Operations

# Internal knowledge assistant

Answer staff questions from approved policies, procedures and product documents, with the source shown and a clear handoff when the answer is not there.

[Talk through this workflow](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fuse-cases%2Finternal-knowledge-assistant%2F#write) · [Configure your AI](https://lidvizion.ai/services/ai-setup/)

Where it starts

## The trigger.

A staff member has a policy, process or product question.

How it runs today

- The same questions reach the same few people every week.
- Answers depend on who is asked.
- Documents exist but nobody can find the current version.

The workflow

## Four bounded steps.

Each step has an owner and a stop rule.

1. 01 / Scope
   Choose the approved documents and who may see each.
2. 02 / Connect
   Give the assistant read access to those sources only.
3. 03 / Answer
   Questions are answered with the source document linked.
4. 04 / Hand off
   Unanswered or sensitive questions go to the named owner.

Where people stay in control

## Approval points.

- Document owners approve what the assistant can read.
- HR, legal and pay questions route to a person.

How we would measure it

## Against a baseline.

Measured before and after, on the same work.

### Repeat questions reaching experts.

### Share of answers with a source staff accepted.

### Time to answer, before and after.

Related

## Go deeper.

- [Configure your AI](https://lidvizion.ai/services/ai-setup/): Turn individual AI use into a repeatable way of working.
- [Embeddings and feature vectors](https://lidvizion.ai/resources/basics/embeddings-feature-vectors/): How models turn text, images and records into numbers that can be compared.
- [Prepare your documents for an AI assistant](https://lidvizion.ai/guides/prepare-your-documents-for-an-ai-assistant/): A four-step playbook to get policies, procedures and product documents ready for an AI assistant, so its answers are current, sourced and limited to what each person may see.
- [What Is Retrieval-Augmented Generation (RAG)?](https://lidvizion.ai/answers/what-is-retrieval-augmented-generation/): What is retrieval-augmented generation, and when does a business need it?

More use cases

## Other places to start.

[All use cases](https://lidvizion.ai/use-cases/)

- [Meeting notes to tracked actions](https://lidvizion.ai/use-cases/meeting-notes-to-actions/): Operations Turn meeting notes or transcripts into a short summary, decisions and owned action items in the tools the team already uses.
- [Administrative letters from approved templates](https://lidvizion.ai/use-cases/administrative-letters-from-approved-templates/): Operations Draft routine administrative letters, such as referral acknowledgements, appointment confirmations and records-request replies, from approved templates and the details already on file, so staff review and send them instead of typing each one.
- [Document intake and review](https://lidvizion.ai/use-cases/document-intake-review/): Operations Pull the fields out of incoming documents, check them against your rules, and route only the exceptions to a person.

An illustrative workflow pattern, not a result from a specific customer.

Bring the messy version

## One workflow. A better way to run it.

Show us the tools, the handoffs, and the part that keeps slowing your team down.

[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)