Product / Managed integration

YOUR PRODUCT, AVAILABLE IN AI

Give AI a controlled way to understand and use your product.

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Scope

Start with one valuable customer task.

We define the user, action, data, permission, confirmation, and failure state before the first tool is exposed.

Architecture

MCP Gateway plus an LDK-based integration layer.

MCP Gateway makes approved product tools searchable and executable. The LDK-based integration boundary keeps host-specific authentication, UI, approvals, and publishing work explicit.

Existing APIs and product rules stay authoritative
Every action has a named input and result
The host receives only the context it needs

Delivery

A release your team can inspect and scale.

You get reviewed code, tool contracts, tests, and deployment notes. Your hosting application keeps ownership of compute, state, and scaling rather than hiding them inside the gateway library.

Evidence / Primary sources

OpenAI · Apps SDK

Official guide to MCP servers, tools, authentication, and UI for ChatGPT apps.

Model Context Protocol · tools specification

Protocol reference for named, discoverable tools and their input and output contracts.

Model Context Protocol · authorization

Protocol reference for authorization responsibilities at the connection boundary.

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.”

Ask ChatGPT Ask Claude Ask Gemini

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.