The model learns what your signal should be.
Classical DSP assumes a fixed noise model. Our architecture doesn't. Adaptive inference corrects for sensor drift, supplier changes, and shifting noise floors—without a domain expert on retainer.
Down to –112 dBm
Sub-8 ms on-device
Zero manual re-tuning
Accurate classification maintained at SNR levels that break hand-tuned filter banks—validated across accelerometer and RF sensor classes.
Deployed on edge MCUs with no cloud round-trip. Train once on representative hardware; the compiled model runs anywhere in your target fleet.
When your sensor supplier changes or your PCB layout shifts the noise profile, the system detects the drift and corrects—no engineer intervention required.
From raw sensor data to reliable output.
We cover adaptive noise suppression, multi-axis signal classification, and thermal array denoising—each built around your hardware constraints, not a generic benchmark dataset.
Give us your target accuracy and your current noise floor. We scope feasibility and cost in concrete terms before any contract is signed.
Tell us your noise floor. We'll tell you what's solvable.
Describe your sensor type, target accuracy, and the degradation blocking your roadmap. We return a concrete feasibility read within one business day.