Describe a product.
Ship the software.From idea to shipped product.
Two approvals.
GreatCTO is an AI Product Builder — describe a software product and it runs the whole build: architecture, data model, backend, frontend, tests, deploy. Two checkpoints — you approve the design, then the deploy. Everything between runs unattended, to a shipped repo and a live URL.
41k downloads on npm15 industries · 60 products · 6 reusable pipelines
The 60 products collapse into 6 reusable build pipelines (CRUD vertical-SaaS, booking, CRM, dashboard, marketplace, content) — each ships through one CTO gate, then automated to a live URL. See how it works ↗
Next.js, Postgres, shadcn, Stripe, Tailwind — GreatCTO builds on the tools your team already trusts, and ships a repo you fully own.
Building a product is now two approvals.
The next wave isn't a faster code editor — it's a builder that takes a product from idea to shipped software. You describe what you want; specialist agents handle architecture, the data model, the backend, the frontend, the tests and the deploy. The two things you do are approve the design and approve the deploy. Everything between runs unattended, to a repo you own and a live URL. And every model improvement makes the build faster and cheaper — the economics only bend your way.
A pipeline of specialist agents — with one gate that's yours.
Spec → build → test → deploy.
A chain of specialist agents runs the real steps of shipping software — architect, design, build, QA, deploy — not one chat window. You watch the whole pipeline on one board.
You approve the spec. Once.
The first checkpoint: the architecture and plan. Approve it and scaffold, backend, frontend, integrations, tests and deploy all run automatically — no per-line babysitting.
A repo and a live URL.
Not a prototype in a sandbox — a working product: a git repo you own, a deployed app on Vercel or Cloudflare, and the tests that keep it honest.
Generated tests, green before ship.
Every build writes its own tests and must pass CI. You sign the direction; the test suite catches the regressions — so automation stays safe without a human reading every diff.
Next.js, Postgres, shadcn, Stripe.
Built on the tools your team already trusts, with a React Native option for mobile. No bespoke framework to learn — it's a codebase any developer can pick up.
One template ships many products.
The 60 products collapse into 6 build archetypes — CRUD, booking, CRM, dashboard, marketplace, content. See the pipelines ↗ Self-hosted, MIT, your code stays on your machine.
What do you want to build?
Pick a product — see the pipeline, the agents at each stage, and the one CTO gate.
Describe it. Approve the spec. It ships.
Say what you want to build.
A dispatch app, a booking portal, a CRM, a dashboard — name the product and the industry. The architect and design-advisor draft the spec, data model and screens.
One gate — the spec.
You review the architecture and plan, and sign off. That is the first of two checkpoints; the second is the deploy.
Build → test → deploy, automated.
Scaffold, backend, frontend, integrations, generated tests and deploy run end to end — to a repo you own and a live URL.
Automated to the deploy.
The rails keep it honest.
Letting a build run unattended is only safe with rails. Every pipeline ships with three: two human checkpoints — the design and the deploy — generated tests that must pass CI before anything ships, and a repo you fully own and can read, diff, and roll back.
The design, and the deploy.
The default stops twice and nowhere else. You sign the direction — what gets built and how — before a line is generated. Nothing irreversible runs before that.
Generated tests, green before ship.
Every build writes its own tests and must pass CI. The suite — not a human reading every diff — catches regressions, so the automation stays safe at speed.
Readable, diffable, reversible.
The output is a normal git repo on a modern stack — not a black box. Review the diff, run it locally, fork it, or roll it back. Self-hosted, MIT, your code stays on your machine.
A CTO dashboard — that runs itself.
great-cto board opens a live board on your own machine — the pipeline, cost, and project memory in one place. No account, no SaaS, no telemetry by default. And you never set it up: it fills in as you work.
No /audit, no /save.
The first session maps your codebase automatically; every agent run records a verdict that feeds the metrics; every session auto-saves a log and extracts lessons on exit. You just work — the memory, metrics, and logs fill in.
Runs on localhost, yours.
No sign-up, no seats, no cloud dashboard. It runs on your machine against your repo, offline-capable, and your code stays on it. Open source, MIT.
Pipeline, cost, memory.
The live pipeline with its risk-tier gate, per-agent cost, 30-day LLM spend vs a human-team baseline, and browsable project memory — PROJECT.md, archetypes, lessons.
Tokens per build.
Not seats. Not agencies.
A build costs the tokens it uses — against a human baseline of weeks and tens of thousands of dollars per product.
Pay for the work, not seats.
Every model improvement makes the same product faster and cheaper to build. The economics only bend your way.
MIT. Self-hosted. Yours.
You run it; your data and your repo never leave your machine. No per-seat tax, no SaaS lock-in.
You pay your tokens — we don't bill you.
No GreatCTO invoice. Bring your own Anthropic / OpenAI key.
One feature, end to end:
1h 26m and $3.40 in LLM cost.
A real run, fully public: spec → build → review → tests → merged PR. Every stage timestamped, every artifact links to a real GitHub PR — no screenshots, no marketing math.
median $171 · 70/100
The open benchmark built 7 products end to end: median $171 in tokens, median quality 70/100 (range 58–86). Reproduce it with scripts/bench-run.sh.
1h 26m · $3.40 LLM
Architect → plan → implementation → review → tests → merged PR. Wall-clock from prompt to ship, with one human signing the spec.
Timestamps, PRs, costs.
The full stage-by-stage timeline with public GitHub links. Walk the run on /proof →
Frequently asked.
What is GreatCTO?
What can it build?
How much is automated?
What does it cost?
Where does my data go?
How do I start?
Describe the product.
Ship the software.
Open source · MIT · self-hosted · your code stays on your machine