We think a crucial part of the musical experience is the instant feedback loop between a musician’s hands and their ears; this is how music is made. The challenge with machine learning algorithms is that they all work asynchronously: input something, wait, get output. This is because most algorithms are optimized for large asynchronous batches, not low-latency throughput.

Machine learning algorithms like ours require a software stack which can be unwieldy to get up and running. Instead of creating a VST or standalone software and relying on the user to have a computer built for machine learning with a specific graphics card, CUDA version and drivers installed, we packaged the entire stack in a little box. No install or updates necessary.

We designed our models to be as close to real-time as possible, and put the results into a piece of dedicated hardware to open up new forms of expression with machine learning. The result is unlike any other instrument.


At the core of the processor is our style-transfer algorithm trained on vocal and instrumental recordings. These models have the capability to turn one instrument into another in near real-time. For example, a guitarist could play their guitar into the hardware and the output could sound like a violin, or a singer could sing into the hardwate and it could produce the sound of a Gamelan Orchestra.

The hardware is a single-tasker with no extra features. It has a TRS/XLR combo input and a TRS output. Currently we have a handful of models which come preloaded on the hardware. Simply select your model and start playing.

Currently the hardware is not for sale. We have a handful of prototypes which we are actively developing. If you have a specific project or need for one of these instruments, please fill out our form and get in touch.

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