Insight / ChatGPT apps and MCP
Three decisions to make before you build a ChatGPT app.
An MCP server can connect your product to an AI chat. It does not tell you which customer job belongs in a conversation, what the customer should approve, or how your team will run the result day to day.
- Works in
- ChatGPT
- Claude
- Gemini
- Coding agents
Direct answer
Before you build a ChatGPT app, pick one customer job, decide which actions your product will own, and plan the sign-in, the look, the testing, and the release work each AI chat needs
Start with the task01
Pick one job that ends inside your product.
The first question is not “which tools can we expose?” It is “which customer task gets shorter when the request starts as a sentence?” A good first job touches a few parts of your product you already know well. It ends in a result a person can check.
A narrow job also lets you say no to the rest of your product until you have evidence that the first release is useful.
Keep the product in charge02
MCP is a way in. It does not replace what your product does.
The AI chat can find and run named actions through the layer. Your product still owns sign-in, who is allowed to do what, input checks, your own rules, your own state, and the record of a finished action. Keep all of that visible in how each action is defined and in your product's own logs.
That is what lets you add an easier way in without ending up with a second version of your product that behaves differently.
OpenAI Apps SDK · build an MCP serverTreat each AI chat as its own surface03
ChatGPT still has its own rules for behavior and release.
One build can serve every AI chat. But the sign-in, the look inside the chat, the review, and the publishing rules can differ from one to the next. Plan real testing for the chat your customers will actually use.
What you can honestly promise is one workflow you have tested, not a vague claim that every AI chat behaves the same way.
Keep people in control04
The name and description of an action are part of the interface.
What an action is called and how it is described tell both the AI and the customer what it can do. For anything sensitive, say what will happen and keep the approval step visible. Avoid one friendly-sounding command that quietly makes several changes nobody can undo.
A smaller action that explains itself is easier to ship, check, and improve.
Model Context Protocol · tools specificationAI / 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
Start with one customer task
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.