Edge AI brings machine learning directly to embedded devices — microcontrollers and sensors that operate at the boundary between the digital and physical worlds. Instead of sending data to the cloud for processing, the intelligence lives on the device itself.
This means real-time decisions made in microseconds, not milliseconds. Data that never leaves the device. Systems that work without an internet connection. And power consumption measured in microwatts, not watts.
We work with ultra-low-power microcontrollers — ARM Cortex-M class devices running optimized inference engines like ExecuTorch. Our focus is making AI practical on hardware where every microamp and every kilobyte counts.
Data stays on the device. No cloud uploads, no external servers, no privacy trade-offs. Sensitive information never leaves the hardware it was generated on.
On-device processing eliminates network latency entirely. Decisions happen in microseconds at the point of sensing — critical for responsive, safety-aware systems.
Optimized for microcontrollers that run on coin cells and small batteries. Months or years of operation — not hours. AI that fits within a strict power budget.
No network dependency. Edge AI devices work in remote locations, aircraft, underground, or anywhere connectivity is unreliable or unavailable.