AI integrations for protocols
A growing share of integration questions about your protocol are answered by Claude, Cursor, and ChatGPT before a developer ever opens your docs. If a model answers wrong about your API, that is your surface failing, not the model. We build the integrations and the documentation structure that make models answer correctly.
Why this work is different
Developer surfaces were planned for a human reading a page. Models read differently. They need typed interfaces, stable naming, one canonical statement of each fact, and examples that actually run, and they fail quietly when they get prose instead. The failure shows up as a developer pasting code the model invented against an API that never existed, then filing the confusion as an issue against your protocol.
This is engineering, not technical writing. The people building it need to read the client source to know what is true, and they need to test the surface the way models consume it. That combination is what we do.
How we work
We start from a question set written with your team. The questions a developer actually asks in your Discord, the integrations that actually go wrong. The surface is built and then measured against that set, so correct answers are a tested property rather than a hope.
Everything ships in your repos, under your org and your licence. Typed tool interfaces versioned with the protocol, docs maintained against a running client, and examples wired into CI so they break loudly when a release changes behaviour.
What that means we build
Tool interfaces for AI agents. Typed schemas over your RPC surface, contracts, and SDKs, so Claude, Cursor, and other agents call the real API instead of inventing one.
Agent-readable documentation. Reference restructured for retrieval, one canonical page per fact, runnable examples, and an llms.txt that tells crawlers what is true. Measured against the question set, re-measured on every release.
Common questions
- What is an agent-readable developer surface?
- Documentation and interfaces structured for how models consume them. Typed schemas, stable naming, one canonical statement of each fact, and runnable examples. A model reading an agent-readable surface answers correctly about the protocol instead of inventing an API.
- Do you build integrations for Claude and Cursor?
- Yes. We build typed tool interfaces that let Claude, Cursor, ChatGPT, and other agents call a protocol's real API, plus the documentation structure those tools rely on. Everything is published to your org under your licence.
- How do you know the AI answers are actually correct?
- We write a question set with your team before building, drawn from what developers actually ask. The surface is measured against that set when it ships and re-measured on every release, so correctness is tested rather than assumed.
- Is this technical writing or engineering?
- Engineering. The surface is built by engineers who read your client source, ships in your repos, and is wired into CI. Prose that drifts from the code is the failure mode this work exists to prevent.
Have something hard to build?
Tell us the hard part: the protocol, the client, the mechanism, or the system that has to hold.