What is edge AI?
Edge AI means running machine-learning inference on the device itself (a microcontroller or small SoC) instead of sending raw data to the cloud. It cuts latency, bandwidth and power use, and lets the product keep working offline.
What does an edge AI project include?
- Sensor selection and data-acquisition hardware.
- Signal processing and feature extraction.
- Model development, quantization and deployment on the target MCU.
- Anomaly detection and predictive-maintenance logic.
- IoT connectivity for reporting results.
What about AI beyond the device?
Not every model has to run on a microcontroller. We also develop AI models and tools around the data your product collects: anomaly detection, classification and data pipelines that take readings from the sensor to a dashboard or to your own systems.
Example projects
Frequently asked questions
Can machine learning run on a microcontroller?
Yes. With quantized models and well-chosen features, anomaly detection and classification run on Cortex-M class MCUs within tight RAM and power budgets.
Last updated: 2026-10-20