AI knowledge base
Put your company's brain in one place AI can use.
A folder dump is not a knowledge base. We turn approved prices, processes, and exceptions into a source of truth people and agents can both use.
The problem
Your real answers live in inboxes, old quotes, a shared Drive, and one employee's head. Connecting ChatGPT to that pile makes the conflicts easier to retrieve. It does not make them true.
How we work
Step 1
Name the job it must support
We start with one workflow: quoting, lead reply, or a process people keep asking about. The knowledge base follows that job. It is not a filing project.
Step 2
Inventory sources and owners
We list where each fact lives, who can approve it, and what happens when two docs disagree. Drafts and old proposals stay labeled as history.
Step 3
Publish a first source of truth
People can browse it. An agent can retrieve it. You keep ownership. We can then install a managed employee on top if the job is worth it.
What you get
- One approved answer for prices, process, and exceptions
- Owners and review dates so the system does not rot
- Useful to humans on day one, not only to an agent
- Ready for a managed employee when you want it run
Questions
- What belongs in an AI knowledge base?
- The facts, rules, and decisions needed for a named job: current pricing, process steps, exceptions, and who approves what. Not every file you have ever saved.
- Is this just uploading docs to ChatGPT?
- No. Upload-and-chat treats the newest file as truth. A real knowledge base marks governing sources, owners, and what to do when the answer is missing or stale.
- Can we do this ourselves?
- Yes. Use the free prompt library to inventory sources and draft an SOP. If you want it built and kept current, start with the $999 assessment.
- What do we get if we hire you?
- A scoped knowledge system for one workflow: source map, approved records, and a maintenance cadence. The knowledge stays yours. Optional next step is a managed AI employee that runs on it.
