ABOUT TEAM-AI

An engineering team
you don't have to manage.

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.

WHY WE BUILT IT

Generating code was never the hard part.

By early 2026, most new code is AI-generated and the developer's real job is intent, architecture, and judgment. But speed came with a catch the industry now calls the Illusion of Speed: an agent writes a thousand lines in minutes and introduces mistakes at the same rate. Vibe coding is not production. The moment an autonomous system writes the code, "it compiles and the tests pass" stops being automatic, and that gap is exactly where large apps fall apart. Your job is to stay the architect; the team handles the implementation.

That gap is why Team-AI is built the way it is. One model guessing at a whole app drifts across files, so instead eight specialists each own a lane and hand work off through a real dependency graph, keeping the app coherent as it grows. The last agent in that graph is a validation layer: the Integration Validator runs the real build and loops back for a rework round until the code actually works, instead of leaving a broken file behind. This is agentic engineering, AI as an implementation engine kept inside real constraints, not a demo that looks done.

The engine underneath is Claude. The design around it, the specialists, the dependency graph, and the validator in the loop, is what turns fast generation into software you can actually ship.

PRINCIPLES

How we think about the product.

01

Your code, your infrastructure.

Projects go to your GitHub via OAuth. No proxy, no shared pool. Every artifact lands in a repo you own and control.

02

Your API key, your bill.

Bring your Anthropic API key. We don't proxy it, don't cache it on shared infrastructure, and never use it for another user.

03

Ship working code, not skeletons.

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.

04

Bet on Claude.

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.

THE STACK

What runs under the hood.

Claude Sonnet 4.6
Code generation
Anthropic SDK 0.40+
Extended thinking, tool use
FastAPI
Async orchestration
PostgreSQL
Projects, tasks, artifacts
SQLAlchemy 2.0
Async ORM
Railway
Hosting + DB
GitHub OAuth
Per-user auth
PyGithub
Commit & PR pipeline
8
Specialist agents
1M
Context window
10%
Anthropic cache price
3
Auto-rework rounds

A sentence is enough to start.

Sign in with GitHub. Paste your Anthropic key. Describe your app.