AI at the Edge: Building Smarter Devices
Artificial intelligence no longer lives only in the cloud. Increasingly, the models run on the device itself — a microcontroller, a camera, a sensor node — and that shift, called edge AI, changes what small hardware can do.
Why move AI to the edge?
Sending every reading to a server and waiting for a response is slow, costly, and useless when the network drops. Running inference locally solves all three:
- Latency — decisions happen in milliseconds, not round-trips.
- Privacy — raw data never leaves the device.
- Resilience — the device keeps working offline.
- Cost — no per-request cloud bill for every measurement.
What actually runs on-device today
Modern toolchains quantize models down to a few hundred kilobytes, small enough for a microcontroller:
| Task | Typical model | Footprint |
|---|---|---|
| Keyword spotting | Tiny CNN | < 50 KB |
| Anomaly detection | Autoencoder | < 200 KB |
| Image classification | MobileNet (quantized) | ~1 MB |
Where we use it
At Sightek, edge inference lets a water-level controller learn a household's usage pattern and pre-empt a dry tank, without ever phoning home. The same approach powers predictive maintenance on pumps and motors.
The best cloud request is the one you never had to make.
Edge AI is not a replacement for the cloud — it is the layer that makes connected devices trustworthy when the connection is not.