ChatGPT App & Plugin Development: How to Build Software That Lives Inside ChatGPT

ChatGPT is no longer just a chat window. At DevDay on September 29, OpenAI said it is opening up ChatGPT as a shared surface where developers can launch native experiences directly to its 1.2 billion weekly users.

For businesses, that's a new distribution channel. Your scheduling system, product catalog, quoting tool, or client portal can now show up inside the AI assistant your customers and staff already use every day.

This guide explains what changed, what you can build, and how the technology works, in plain language first and technical detail second.

Four DevDay announcements matter most for businesses building on ChatGPT. All four are described in OpenAI's DevDay 2026 recap.

OpenAI also says users choose which plugins to use and approve the access each one receives. That permission step is important for trust, and it's something your plugin design needs to plan for.

A few realistic examples, based on the kinds of software we build at Design Develop Now:

• A healthcare practice lets patients check appointment availability and request a booking from inside ChatGPT, with the request landing in the practice's existing scheduling system. (See our healthcare work.)
• A home services company lets homeowners describe a problem, get a rough estimate range, and book a technician without leaving the chat. (See home services.)
• A law firm gives staff a ChatGPT panel that searches its own case management system with the right permissions. (See law firms.)
• A real estate brokerage shows live listings in an interactive panel instead of a wall of text. (See real estate.)
• An ecommerce store lets shoppers browse products and check order status conversationally. (See eCommerce solutions.)

The common thread: a plugin is a new front door to software you already have. The real value sits in the systems behind it.

Most ChatGPT plugins are built on the Model Context Protocol, or MCP. Think of MCP as a universal adapter. Instead of every AI tool needing a custom connection to every app, the app publishes one MCP "server" that describes what it can do, and any compatible AI assistant can use it.

An MCP server usually exposes three kinds of things:

1. Tools — actions the AI can take, like "create appointment" or "look up order."
2. Resources — information the AI can read, like a product catalog or a policy document.
3. Prompts — reusable instructions that help the AI use the tools well.

The new MCP Events proposal adds a fourth idea: your app can notify the AI when something happens. That's what lets a plugin kick off an automation while nobody is watching.

The practical upside of MCP is reuse. A well-built MCP server can also work with other AI assistants that support the protocol, including Anthropic's Claude. You're not building for one vendor.

If you're scoping a plugin, here's the order we work in:

1. Define three to five jobs. Plugins with a narrow, clear purpose get approved and used. "Do everything" plugins confuse both users and the model.
2. Design the tools. Give each one a clear name, a plain description, and strict input rules. The AI decides when to call a tool based on that description, so write it carefully.
3. Handle authentication. Users must connect their own account with the right permissions. Never let the AI see more data than the logged-in person could.
4. Build the interface. Use plugin extensions for a sidebar home and interactive panels when plain text isn't enough.
5. Add events if you need automation. OpenAI's MCP events guide covers triggering work from outside changes.
6. Test with real conversations. People phrase requests in ways you won't predict. Test messy, real wording before submitting.
7. Submit and monitor. Follow OpenAI's plugin submission process, then watch logs for failed or confusing tool calls.

• Exposing too much data. Every tool should return the minimum needed. This matters doubly in healthcare, legal, and finance.
• Letting the AI take irreversible actions without confirmation. Charging a card or deleting a record should require a clear yes from the user.
• Vague tool descriptions. If the description is fuzzy, the model will call the wrong tool or none at all.
• Skipping your website. A plugin is a channel, not a replacement. Customers still need a fast, trustworthy website to land on.

Do I need a developer to build a ChatGPT plugin? OpenAI's Plugin Creator helps with simple ones. Anything connected to your real business data, logins, or payments needs a developer who understands security.

Is a ChatGPT plugin the same as a custom GPT? No. A custom GPT is mainly instructions and files. A plugin connects ChatGPT to live software that can read and take actions.

Will my plugin work with Claude or other AI assistants? If it's built on an MCP server, much of the work carries over. The interface pieces are specific to each platform.

What does it cost? A focused plugin on top of an existing system is a modest project. The cost grows with the number of systems it connects to and the security review required.

DDN builds ChatGPT plugins, MCP servers, and the integrations behind them as part of our AI automation and custom software work. If you have a system your customers or staff already rely on, it may be a strong candidate.

Book a 15-minute discovery call and we'll tell you what a plugin could do for you.