Example / Private knowledge

A private knowledge helper answers from the right documents.

An internal assistant is not a folder search box with a chat skin. It needs to know who is asking, which documents they may see, and when an answer should become a handoff instead of a guess.

Direct answer

A private knowledge helper should authenticate the person, retrieve only the sources they can access, cite those sources in the response, and route unresolved work to the team that owns the decision.

Reference workflow. The access, retention, and audit requirements must be defined with the organization that owns the documents.

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01 / Identity first

Make access part of the request.

Document access is not an afterthought. The search request must carry the identity, tenant or team scope, and any role constraints needed to filter the result set before text is returned to the AI host.

That keeps the policy in the application boundary, where it can be reviewed and changed without rewriting conversational instructions.

Model Context Protocol · authorization
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02 / Answer with evidence

Citations make the answer checkable.

When a response relies on an internal handbook, contract, or runbook, show the document title and the relevant section. The user can decide whether the source fits the situation instead of treating the answer as an unexplained authority.

This also makes source maintenance visible: an outdated answer points back to the document that needs review.

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03 / Keep actions separate

Knowing a policy is not permission to change a system.

The helper may explain the approved path and offer a next step, but a write action belongs behind its own named tool, validation, and confirmation. A policy answer should never silently become a payroll, access, or customer-record change.

This distinction lets teams release useful knowledge access before they decide which operational actions are safe to expose.

04

04 / Measure unanswered work

Use gaps as an editorial queue.

Track the questions that had no source, produced conflicting sources, or required a person. Those patterns identify missing documentation and candidates for a narrower, approved workflow.

The point is not to maximize chat volume. It is to make the next answer more reliable and the next product action more deliberate.

Continue reading

Private enterprise workflow

See the broader internal operating model for identity, scoped tools, confirmation, and auditability.

B2C versus enterprise AI apps

Choose between customer-facing distribution and an internal, governed operating model before implementation.

Examples catalog

Return to the workflow catalog for customer follow-up and order support patterns.

AI / Second opinion

Ask your AI first.

Use this prepared question to assess where an AI Product Interface could remove friction from your product.

“I run a software product. Help me identify one high-value workflow customers could complete through an AI Product Interface. Ask me about the product, the user, the action, required data, permissions, and the safest small first release.”

The prompt is copied as a backup. Some AI hosts may ask you to paste it after sign-in.

FAQ04

Questions, answered.

Is this only an MCP server?

No. The MCP layer is one part. We also scope the product actions, permissions, host-specific behavior, UI, testing, and release path.

Do we need to rebuild our product?

Usually no. The interface sits in front of approved capabilities in your existing product. We start with a narrow workflow and expand from evidence.

Will it work in ChatGPT, Claude, and Gemini?

The shared architecture can support all three. Each host still has its own UI, authentication, approval, and publishing rules, so we verify them separately.

How do you keep actions safe?

We define explicit tools, validate inputs, keep user approval visible, and preserve the product's existing authorization rules.