Reference case / Private enterprise workflow
A private enterprise AI workflow needs your product in charge, not a public chat link.
This is a reference pattern, not a client case study. It lays out the decisions a company has to make when an AI chat can reach private knowledge or start work inside an internal product.
Reference pattern, not a client result. The final controls depend on the company's systems, policies, and regulatory obligations.
- Works in
- ChatGPT
- Claude
- Gemini
- Coding agents
Direct answer
For a private enterprise workflow, check who the person is, limit every action to their role and their workspace, make them confirm anything with real consequences, and keep a record in your product that someone can go back and check
Sign-in01
The request has to say who is really asking.
A message in a chat is not enough to decide who may read a document or run an action. Your product needs three things first: who is signed in, which workspace or organization they belong to, and what their role is. Only then does it pick the data and the actions.
Who is allowed to do what is your product's job, not the model's. You should be able to point to that rule in the design and test it on its own, apart from anything the model is told.
Model Context Protocol · authorizationThe request carries
Who is signed in
Dana R.Which workspace or organization
Workspace #17What their role is
Support leadBefore your product picks any data or action.
Limited actions02
Turn on the smallest set of actions that finishes the job.
An internal product may have hundreds of ways in, but a first workflow should offer only the few named actions an employee needs. For each one, write down what goes in, what comes back, what gets checked, what happens when it fails, and who is allowed to run it.
That limits how far the AI can reach by accident. It also gives your reviewers something exact to read, instead of a broad claim that the AI can “use the system.”
Model Context Protocol · tools specificationThe layer · open for this workflow
records.read_case
Readrecords.draft_update
Draftrecords.update_case
Confirmed writeHundreds of other ways in stay closed.
Confirmation and records03
Anything sensitive needs a clear yes from a person.
When something changes customer data, access, money, or operations, show what it will change and what that means before it happens. Once it runs, the result should say exactly what changed and where it was recorded.
Your business system stays the source of truth. The chat makes the request easy to read. It does not take responsibility away from your product.
records.update_caseRecordCase #588
OwnerDana R.
ProposedSet the status to “Resolved” · notify the account owner
Who owns it04
Build the workflow with the people who own it.
Releasing this inside a company takes named owners and a plan for what could go wrong, not just access to a model. The right first release puts four together: a business owner, a technical owner, a data or security reviewer, and one task you can measure after launch.
That gives you a way to grow, but only once the first workflow proves clear and controlled in real use.
NIST · AI Risk Management FrameworkThe first release pairs
A business owner
A technical owner
A data or security reviewer
A measurable task, checked after launch
Grow only after the first workflow proves controlled in real use.
AI / Second opinion05
Not sure where to start? Ask an AI.
Paste this question into ChatGPT, Claude, or Gemini. It walks you through finding the one task your customers would most want to finish by chatting, instead of clicking through your app.
“I run a software product. Help me identify one high-value workflow my customers could finish inside ChatGPT instead of switching between tabs. Ask me about the product, the user, the action, the data it needs, permissions, and the safest small first release.”
The prompt is copied as a backup. Some AI chats may ask you to paste it after sign-in.
FAQ06
Questions, answered.
Is this just an MCP server?
No. The server is one piece. We do the whole job: we pick the customer task, set who can do what, handle the quirks of each AI chat, make the actions run fast, add monitoring, test it, and ship it.
Do we have to rebuild our product?
Almost never. Our layer sits in front of what your product already does. We start with one small workflow and grow from what works.
Will it work in ChatGPT, Claude, and Gemini?
The same build can serve all three. Each one has its own sign-in, look, approval step, and publishing rules, so we set up and test each one separately.
How do you keep it safe?
Your product decides what is allowed, and we keep that in charge. Every action is spelled out, every input is checked, and the customer sees an approval step before anything happens. Our layer sits beside your product, not inside it.
Where does it run? Does it touch our code?
It runs as a separate service in front of your product. It never goes into your code, and you still own your data.
What does it cost, and how fast?
A first workflow starts at €3,200 and goes live in about 7 days.
Contact us07
Now build yours
Send us your product, who uses it, and the action you have in mind. We will tell you the smallest version worth building, and whether it is a €3,200 first workflow or something that needs a closer look first.