Engineering
How we build the DODIL AI cloud — architecture, internals, and deep-dives.
Platform
4 tutorialsGetting Started
4 tutorialsBuild a module on DODIL: from 'I want a CRM' to a running system, in seven steps
The DODIL enterprise packages are reference implementations, not product. An agent reads one, interviews the customer, and GENERATES their system — their objects, their stages, their approvals, their org chart — copying the platform layer verbatim. This is the canonical flow: discover, fetch, derive, generate, provision, deploy, verify; the per-customer topology (one pool, one bucket, one app per module by default); what adding a module actually means (same pool, same bucket, one more router); and the invariants you copy rather than regenerate — each one a scar from a live failure.
Our Thesis, and Why Now
Why we're building one agent-native AI-data platform on sovereign EMEA hardware — the three curves that made now the moment, what we collapse, and what actually ships today.
From Zero to Building in Two Minutes: CLI, Login, and Your Agent
Register, install the dodil CLI, log in, and connect your agent (Claude Code, Cursor, VS Code, Codex) over MCP — the whole DODIL setup, once.
Inside the DODIL AI Cloud: One Bucket, Three Planes — On Our Own Hardware
An engineer's tour of how DODIL is built: a data, compute, and inference plane glued by typed pipelines — running on our own hardware and network.