Edge AI for Embedded & IoT Systems
On-device machine learning for latency-sensitive and privacy-first applications — including TinyML, model quantisation, and lifecycle management for AI-powered smart infrastructure and embedded systems development.
Why Edge AI
Local inference for higher reliability and efficiency
Moving inference to the edge reduces latency, bandwidth usage, and privacy risks. We develop efficient models that run on microcontrollers and constrained SoCs for reliable AI-enabled IoT solutions.
Who benefits
Organisations requiring anomaly detection, event recognition, vision-based alerts, and predictive maintenance — where rapid local decisions are critical.
Our ML pipeline
Model selection → compression/quantisation → benchmarking on target hardware → runtime integration → remote update & retraining strategy.
Deliverables & outcomes
TinyML model prototyping and quantisation report.
On-device inference demo (ESP32/nRF/STM32).
Inference latency & power tradeoff table.
Retraining & remote update plan.
Metrics: precision/recall and field validation plan.
