Products
Applied AI.
Built to ship.
Each product starts with a well-defined engineering problem, a simulation or verification bottleneck, and a clear path to measurable speedup with calibrated confidence.
Active
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Fluxus
Neural design verification for superconducting RSFQ circuits
A learned physics model trained on your process design kit. Given any RSFQ cell layout, it predicts the outputs that matter — timing, current, and design rule compliance — with a confidence bound on every result, in milliseconds per cell.
The model is PDK-agnostic. We train a custom surrogate on each client's cell library and process node. Proof of concept is complete on MIT-LL SFQ5ee+. Additional process nodes are onboarding now.
Key results
chip-scale
validated types
SPLIT/DFF/AND2
every output
low-confidence
Compatible processes
In development
02
New product coming
03
New product coming
Your domain?
If you have a simulation bottleneck in a physics-constrained engineering domain, we want to hear about it.
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