Autonomy for Asset Management

Count on each asset for high availability and reliable output

Autonomously predict, diagnose, prescribe, decide, and act to increase the reliability and performance of capital-intensive equipment. Maximize asset lifetimes and ensure production output while reducing the cost required to achieve these outcomes.

How it works

Grounded in the knowledge base
Sensor histories, maintenance records, manuals, and the asset hierarchy resolve into one graph — every asset carries its own context: what it is, what feeds it, what it has done before.
Reasoned over by agents
Normal behavior models score failure probability per equipment class; agents trace an anomaly through the graph and weigh remaining life against cost, risk, and the schedule.
Delivered as a work order
The prediction leaves as prescribed work — parts, crew, and window attached — and the outcome feeds back into the model that called it.