The AI software factory
Hand agents a spec. They plan, build, test and open the pull request. Your team reviews and merges.
Works with
- GitHub
- Linear
- Slack
- Claude Code
- Codex
- Grok
- Cursor
Ship the work. Keep the review.
Everything you need to make agents ship
The engine under every run.
Repository-aware
Agents read your code, your history and your knowledge base before they write a line.
Native integrations
GitHub, Linear, Jira, Slack and Sentry. Connect the tools your team already uses in a minute.
Run trace
Every decision, tool call and test result, recorded with a time. A run without a trace is a guess.
Scheduled workflows
Weekly dependency updates, nightly flaky-test sweeps, monthly cleanup of dead code.
Delivery reporting
Merge rate, first-time CI pass and review time, per team and per repository.
API and MCP
Drive the factory from your own tools. A REST API and an MCP server, with scoped tokens.
Set the rules your agents build by
anturno learns your conventions and keeps to them.
Explore KnowledgeShowing Handle escalations
Built for enterprise security and privacy
Preparing for SOC 2 and ISO 27001
No auditor is engaged yet, and we hold no certifications.
Controls on by default
Isolated runs, repository-scoped tokens, encrypted secrets and an audit log.
Not used for training
anturno never trains a model on your content.
What teams ask before the first run
Every answer is what the product does today.
It takes a ticket or a spec, reads your repository and your knowledge base, plans the change, builds it, runs your tests and opens a pull request. When it needs a decision it stops and asks, with the options it considered and a link to the exact line.
No. Agents open the pull request, answer review comments and keep it green. The merge is a person’s: neither the API nor the MCP server can merge one.
Only if you turn that on, and only for an author your workspace trusts. Off by default, every piece of work waits in intake as a proposal until a person starts it.
Yes. A REST API and an MCP server cover the same ground: start a run, read its trace, search and write Knowledge. Point Claude Code, Claude Desktop or Cursor at the server and the client gets its own token, scoped to what you granted and revocable in settings. A run started that way still waits for a person to approve it.
Into the session’s own isolated environment. Its disk is kept between the session’s runs so work can resume, and deleted when the work ends. On a paired device, the checkout stays on your machine.
anturno never trains a model on your code, pages or traces. What a model provider may keep or train on is set by the terms of the key or subscription you connect.