ChatGPT & Claude Apps
Distribute capability through ChatGPT, Claude, and MCP channels with trust boundaries, logging, and launch packaging.
We help organizations reach customers and operators inside trusted AI surfaces. The work covers consumer and employee-facing apps, secure tool channels into company systems, and go-to-live packaging: clear jobs, onboarding, and approval on sensitive actions. Content and landing experiences can ship under the same governed model when growth teams need speed without losing brand control.
Who this is for
Consumer and B2B brands in regulated and fast-moving markets, including fintech, medtech, consumer, and wholesale.
Approach
- Identify the channel opportunity and the first high-value user job
- Design the experience, trust boundaries, and operating model
- Launch a pilot that proves first value under real usage
- Enable the organization to expand channel coverage from evidence
What we deliver
- AI application design and launch (ChatGPT, Claude, and related surfaces)
- Secure company tool channels for external and internal agents
- Cold-start packaging: connect flows, jobs, and first-run guidance
- Governed content and landing-page drafting for growth teams
- Usage measurement, allowlists, and approval paths
Common questions
What is MCP (Model Context Protocol), and do we need one?
MCP is an open protocol that lets an assistant call your tools and read your data through a declared, permissioned interface. It is how your product becomes usable from inside ChatGPT or Claude instead of only from your own UI. You need one when customers or staff already work in an assistant and your product is a tab they have to leave. If nobody reaches your system from an assistant yet, a regular API may be the better first investment.
What is the difference between an MCP server and a regular API?
An MCP server is usually a governed wrapper over APIs you already have, not a replacement. It declares tools, arguments, and permissions in a form a model can discover, with an explicit boundary for what an agent may do without asking. A REST API assumes a developer read the docs. MCP assumes a model chooses among tools at runtime, which changes how tightly you scope each one.
How do we get our product into ChatGPT?
Expose tools an assistant can call, publish as a remote MCP server over Streamable HTTP or as a ChatGPT app, and decide which actions require a person. Engineering is often the smaller half. Choosing the first three or four tools matters more: a surface with too many tools performs worse than a narrow one. We start from the workflow a user is already trying to complete.
Is it safe to let an AI assistant call our internal tools?
It is safe when the boundary is explicit: tool allowlists, scoped credentials, logging on every call, and required approval on sending, publishing, paying, deleting, or deploying. The failure mode worth designing against is a model being confidently wrong on a step nobody was watching. Scoped tools and approval on high-impact actions address that class of risk.
How is this different from building a custom GPT?
A custom GPT is a prompt and some files; it cannot reliably act on your systems and it does not survive a schema change. An MCP channel is a deployed service with typed tools, authentication, logging, and versioning. The distinction matters as soon as the assistant needs to write anything, or you need to answer what it did last Tuesday and why.
Based in Sarasota, Florida. We work with companies anywhere in the United States and internationally; most engagements are remote.
Contact us about ChatGPT & Claude Apps or book a 30-minute call.