Edge AI: Processing at the Source

The Cloud Latency Problem

Send data to the cloud, wait for inference, receive response. In industrial automation or autonomous driving, this round-trip latency is unacceptable. The decision must be made now, at the edge.

TinyML: Shrinking the Brain

We specialize in TinyML—running machine learning models on microcontrollers with kilobytes of RAM. This involves techniques like quantization (reducing precision from float32 to int8) and pruning (removing unnecessary connections in the neural net).

Privacy by Default

Edge AI is also a privacy solution. By processing voice or video data locally on the device, we ensure that sensitive user data never leaves the premises. It's processed, acted upon, and discarded.

Real-World Application: Predictive Maintenance

We developed a vibration sensor for factory motors that uses a localized anomaly detection model. It learns the "normal" vibration pattern of the motor and alerts operators to deviations that suggest bearing failure—weeks before a catastrophic breakdown occurs.