Internal Q&A agents

An internal Q&A agent is an assistant that lives in Slack or Teams and answers employee questions from your own documents. Instead of searching through a wiki or pinging a colleague, someone asks a question in plain language and gets a short, direct answer with a link to the source it came from. It reads your handbooks, wikis, drives, and past threads, and it respects your existing permissions so people only get answers from documents they are already allowed to see. The point is not a chatbot. It is a way to stop the same few experts being everyone's help desk for questions that already have a documented answer.

Built-in search gives you a list of documents and leaves you to open each one, judge which is current, and find the passage yourself. An internal Q&A agent reads across all of those sources and hands you the actual answer, in the chat tool you already use, with a link back to the source so you can check it. It also handles the case where the answer is spread across several docs or a policy doc plus an old thread. You ask a question and get an answer, instead of getting a folder to dig through. It connects to the tools you already run, so there is no new place to go looking.

It inherits your existing permissions. During setup the agent is wired to your access rules, so it will only ever answer someone from documents that person already has the right to see. A salary file or a board memo will not surface in an answer for someone who should not see it. That mapping is the careful part of the build, and it is tested before anyone relies on the agent. The failure mode to avoid is a system that indexes everything and answers from all of it regardless of who is asking. This one is scoped to respect the same walls your document tools already enforce.

It is built to answer only from what it actually finds in your documents, and every answer links to the source so you can verify it in one click. When your docs do not cover a question, it is set to say it does not have that rather than guess, which is the single most important behavior to get right. It can still be wrong if the underlying document is out of date, which is why part of setup is deciding which source is authoritative when two disagree, and testing the agent against real questions before it goes live. You are getting an answer with a receipt, not a black box.

On the asking side, Slack and Microsoft Teams, so questions get answered where your team already talks. On the source side, Notion, Confluence, Google Drive and Docs, SharePoint, internal wikis and handbooks, and support tools like Zendesk, Intercom, and Guru. Most tools with an API can be added. The audit maps your exact stack and picks the sources that hold your real answers, so the agent is reading from the documents that matter rather than everything at once.

Usually two to three weeks. The first days go to connecting your document sources and mapping permissions so the agent respects who can see what. Then it runs against a set of real questions so the answers get checked for accuracy and the grounding is tuned, meaning it answers from the docs and admits when it does not know, before anyone depends on it. You are live fairly quickly, and that testing window is what makes the answers trustworthy instead of just plausible.

Two parts. Tooling runs as a monthly cost for the model usage and connectors, typically a per-user range that scales with how many people use it. Implementation is a fixed scope, quoted once the audit maps your document sources, permission rules, and where the agent should live, so you are pricing a defined build rather than an open-ended retainer. The audit itself is where the scope and price get set against every other opportunity in your business, and it is priced as part of that engagement, so you are not guessing at effort up front.