Airs ML

AIRS ML emerged from hardware and machine learning engineering rooted at Imperial College London with the insight that industrial machines could be monitored continuously on the device itself. It builds edge artificial intelligence for real-time asset monitoring and predictive maintenance, analysing sensor and operational data to detect faults and their causes.

Its focus is on-device intelligence that processes temperature, pressure and vibration signals where they arise rather than relying on constant cloud transfer. That edge mechanism sets it apart from cloud-connected monitoring approaches, and it directs its systems at operators of high-value manufacturing assets seeking continuous oversight.