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Six reasons this is hard to copy
built into the architecture, the data, or the eight years of research behind them.
not cloud
Detection runs on the device, under 2 MB. No cloud dependency for detection, no connectivity requirement, no audio leaving the chip.
by architecture
Because inference happens on-device, audio is not stored or streamed. GDPR and EU AI Act friendly by construction, which matters in European health and safety.
proprietary data
A sound dataset built over eight years of research, not available online, protected by a patent filed at INPI. Few-shot learning turns it into around 100 contexts per event.
not promised
Vocal distress runs at TRL 9 in production today, with under one false alarm per device per week and detection in about three seconds.
learning loop
Every deployment feeds model improvement, so the models get harder to replicate with each device in the field.
sound detection AI researchers
Led by a recognized reference in the sound AI research community, with PhD-level scientists and peer-reviewed publications behind the library.
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