Embedded AI and intelligent edge inference for cameras, sensors, and industrial controllers. Built to run where cloud can’t reach.
Edge AI brings intelligence directly to where it is needed: on the factory floor, in the field, or embedded in camera systems. The benefits are lower latency, reduced compute costs from lightweight models at the edge, and the ability to operate where connectivity is limited. As an AI consultancy, our embedded AI systems run on smart sensors and edge devices across industrial IoT environments where the work happens. We handle quantisation, pruning, and architecture selection so that on-device AI operates independently and reliably.
Intelligent video analytics on smart cameras running entirely on-device. We have deployed edge inference for livestock monitoring, ANPR systems achieving 98.3% accuracy with one-second inference in rural settings, and battery-powered object detection with no mains power.
AI on industrial controllers processing sensor data from smart sensors in real time. Models running in industrial IoT environments that monitor conveyor throughput, detect crusher downtime, and feed decisions back in milliseconds. Smart manufacturing keeps process control local and responsive.
Embedded AI on edge devices that works where connectivity does not. Models deployed to the device run autonomously, syncing results when connectivity returns. Offline AI keeps operations running regardless of network conditions, and because data stays local, it meets requirements for isolation and privacy.
Getting an inference engine to run on a Coral TPU, a Jetson Nano, or a microcontroller with 256KB of RAM is the engineering challenge. TinyML techniques, quantisation, pruning, and knowledge distillation compress models for target hardware without losing accuracy. This embedded machine learning discipline underpins every edge analytics deployment.
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