A model per element, not a blob
Top-down generators create a finished mix and try to split it apart afterwards. We train a separate model per sound family - the drums model learns only drums - so every element is born clean, as its own layer.
Technology
Generative audio models built for control
The hard problem in AI audio is not generating sound - it is generating sound you can still edit. Everything we build starts from that constraint.
11 fundamental sound parameters
The product of 2.5 years of research: every sound is reconstructed from 11 fundamental parameters. That representation is what makes real editing possible after generation - not just re-prompting and hoping.
MIDI-first, stems intact
Generation is MIDI-first and bottom-up. The notes, the stems, and the structure survive the process - so what you get is not a rendering of an idea, it is the idea, still open for you to change.
A render chain worthy of release
Every sound you hear passes a mastering-grade render pipeline - normalization, resonance correction, dynamic EQ, limiting - and the export runs the same chain, so what you download matches what you auditioned.
Trained on licensed and public-domain sound
Our models are trained on licensed and public-domain sound libraries - built for creators, cleared for creating.
Where it is heading: audio for the agent era
The engine is built to be called - text to sound over API and MCP, so creator suites, video and game tools, and AI agents can generate editable audio inside their own workflows. We are not a plugin; we plug into the most-used AI interfaces in the world.
For platforms & developers
We are not a plugin. We plug into the most-used AI interfaces in the world.
We are building toward text-to-sound over API and MCP - generative audio models that creator suites, video and game tools, and AI agents can call directly. If that is a future you are building too, we want to talk.
Talk to us
