Drums
Kit, percussion and transients kept intact — attack stays sharp instead of smearing into a wash of cymbals.
Drums, bass, vocals, everything else — pulled out of a finished record at studio quality, on your own machine, inside the DAW you already use. A proprietary separation engine written in C++, trained only on audio we actually have the right to train on.
AU · VST3 · Standalone — macOS & Windows
Not a novelty filter — separations clean enough to drop straight into a session, re-balance, re-verb, resample, or rebuild the whole arrangement around.
Kit, percussion and transients kept intact — attack stays sharp instead of smearing into a wash of cymbals.
Low end lifted out whole, with the sub kept where it belongs and kick bleed pushed back into the drum stem.
Lead and backing voices, with breath, sibilance and tails preserved — not gated into a robot.
Guitars, keys, synths, strings, horns — everything that makes the arrangement, in one coherent bed.
A full walkthrough — load a track, separate, solo each stem, and print it back to the timeline.
Demo reel — coming soon
We are cutting the walkthrough now. Join the waitlist and it lands in your inbox the day it goes up.
A native C++ engine with a proprietary model at its core. No queue, no upload, no monthly credit balance — the separation happens on the machine in front of you.
The engine and the model live inside the plugin. Your audio is never uploaded, never queued on someone else's GPU, never used to train anything.
100% localA low-latency causal mode for monitoring stems while the transport rolls, and a full-quality offline pass when you need the best result on the print.
Realtime + offlineAU, VST3 and a standalone app, built on JUCE. Four stem outputs routed to their own buses so your DAW sees real tracks.
AU · VST3 · StandaloneSeparation that happens in the background against the whole timeline, at full quality, instead of re-rendering every time you move the playhead.
On the roadmapComplex ratio masks instead of magnitude-only, so what comes back out still lines up with what went in. Null-tested against the source.
Proprietary modelEvery dataset, sample library and soundfont behind the shipping model is licensed for it — and the register that proves it is public.
AuditableOurs isn't. That decision cost us months and a measurable amount of quality, and we are not quietly walking it back later.
Every dataset, sample library, soundfont and audio source gets a row — source, license, allowed use, proof link, date — before a single byte reaches a disk. No row, no download.
Synthetic multitrack renders from CC0 and CC-BY instrument libraries, plus vocals from professional singers who consented to their recordings being used this way — and who are credited for it.
Public benchmark sets and pretrained models are useful for measuring ourselves. They are marked as reference-only, they never train a shipping model, and nothing is distilled from them — distillation counts as training.
Ambiguous terms, viral share-alike clauses, or a maintainer who can't vouch for their own samples: rejected, logged, and not re-litigated because the model would have scored better with it.
We are letting producers and mixing engineers in a handful at a time, and the people on this list go first. One email when the beta opens — nothing else.
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