Eight specialist agents plan, build, test, rework, and ship a pull request to your GitHub. Each one owns its lane — PM, UX, Backend, Frontend, Mobile, QA, DevOps, Validator — and hands off through a real dependency graph.
You already know how to ship. What you don't want is to wire the eighth React state management package, write the same Dockerfile for the fourth time, and remember which test runner this stack uses. The boilerplate is solved; the design fidelity, the API contracts, and the deploy targets aren't. That's the part eight specialists can handle while you stay the architect.
Team-AI is built because Claude is finally good enough to coordinate that work. Not a pair-programmer that autocompletes lines — eight specialists that hand work off through a dependency graph, each with their own system prompt, reasoning budget, and tool permissions.
The platform doesn't hide Claude behind a gloss layer. It uses every capability Claude ships: extended thinking for planning, prompt caching to keep runs cheap, 1M context so every agent sees the full codebase, and native tool use for structured output.
Projects go to your GitHub via OAuth. No proxy, no shared pool — every artifact lands in a repo you own and control.
Bring your Anthropic API key. We don't proxy it, don't cache it on shared infrastructure, and never use it for another user.
The Integration Validator actually runs the build. If tests fail, it loops back into the model's 1M context for a rework round — not a TODO comment.
The pipeline is built around Claude — extended thinking for planning, prompt caching for cost, 1M context for cross-agent awareness, native tool use for structured output. Single-provider by design; llm_client.py is the source of truth.
Sign in with GitHub. Paste your Anthropic key. Describe your app.
