How we build GPTApps

Every project at GPTAPPS begins with an idea, a way to bring an existing product, service, or platform into the conversational world of AI. We don’t create assistants; we make the apps that live inside them. These are tools that users can access directly from environments like ChatGPT, Gemini, Claude, or Copilot to do things like buy products directly within these platforms, find nearby stores on a map, check inventory, or trigger complex workflows, all through natural conversation.

The process starts by understanding what the app should do inside the assistant: what service it connects to, what kind of user actions it enables, and how it should appear during the chat. We translate these goals into a structure of interactions that feels intuitive and human, turning your platform into something people can talk to and act through instantly.

We have been building all kinds of technical platforms and projects for over ten years at MarsBased, and we apply this accumulated knowledge to the AI projects we're building now at GPTApps.

Data integration and security

Once the concept is clear, we move to the data layer. Here, our role is to integrate your systems, APIs, or databases with the AI environment securely and efficiently. We have up until now build an MCP server, the bridge that lets your app communicate with the language model without ever exposing sensitive data, but now, with the advent of OpenClaw, the possibilities are endless (with the strictest security standards - very important ⚠️).

This ensures your services can operate safely inside assistants while keeping performance, security, and control at the core. The result is a seamless connection between your platform and the conversational world, and your customer base, at large.

Designing the conversational experience

Then comes design, not the interface of a traditional website or app, but the experience of interaction inside the assistant. We design how information is presented in the chat, how options and actions appear, and how users move through a task using simple, natural prompts. Whether they’re adding an item to a cart, filtering data, or locating a store, everything must feel effortless, as if your service was built to exist inside conversation.

Engineering and deployment

When the experience feels right, our engineers bring it to life. We develop the connectivity/orchestration server and any supporting components using modern technologies like TypeScript and React, following best practices for scalability, maintainability, and speed.

Each integration is built to grow, so your app inside ChatGPT or any other assistant remains reliable as usage expands, fine-tuning to pick the most optimal model for each task, so you can keep costs under control.

Once the app is ready, we prepare it for launch. We handle hosting and deployment, whether in the cloud or on-premises, with automated pipelines, authentication, and monitoring systems that ensure stability. We make sure your app is always available, secure, and ready to respond inside the assistant environment.

Quality assurance and launch

Before anything goes live, we test everything. Because AI assistants can behave unpredictably, we run extensive interaction testing to ensure your app responds correctly in every possible context. We also perform full security audits to guarantee data protection and system reliability. Only when every piece performs flawlessly do we release it to the world.

That is our method: a path from idea to integrated app, designed to bring real-world platforms into the conversational ecosystems of AI. It’s not about building new assistants; it’s about expanding what existing ones can do, and helping users connect with your service through the most natural interface there is: language.