Local AI, specialized.
Nomi is a local AI system built around open-source models, task-specific pipelines, and orchestrators that present one dependable interface while breaking large jobs into efficient, specialized steps.
Cloud AI is expensive, centralized, and rarely fit for purpose.
Calling a frontier model for every small task burns money and bandwidth. Sensitive data leaves your machine. And one giant model is rarely the right shape for a narrow, repeated job.
Run the right model for each step — locally, by default.
A local-first runtime that routes between open-source models, composes task-specific pipelines, and lets orchestrators fan out work like agents — but cheaper, faster, and reproducible.
// a typical Nomi pipeline routes between a small router model, a domain-specific worker, and a verifier — no cloud calls required.
Built for developers who want control.
Local model routing
Route each request to the cheapest model that meets the bar. Small models for the easy work, larger ones only when the task demands it.
Task-specific pipelines
Compose narrow, deterministic steps into pipelines. Predictable, debuggable, and far cheaper than a single giant prompt.
Open-source model support
First-class support for the Llama, Qwen, Mistral, and Phi families — plus any GGUF or MLX model you can serve locally.
Composable orchestrators
Keep one model-compatible interface while routing, branching, and chaining underneath. Reduce hardware strain without complicating the application layer.
Reduced API dependency
Use cloud APIs only when they earn it. Keep ownership of your prompts, your data, and your bill.
Developer-friendly architecture
Type-safe SDK, declarative pipeline graphs, and a runtime that runs the same on your laptop and your server.
Help us shape the developer preview.
Get early access, share feedback, and influence the architecture. Nomi is being built with developers, not for them.