Predictive Insights and Prescriptive Actions for Critical Assets, Processes, and Systems

Reactive, time-based, and expensive maintenance programs

AI-based failure risk predictions detect anomalous operations, reduce expensive unscheduled maintenance, and provide continuous visibility into asset health

Too many false alerts generated by rules-based systems

At-risk assets and processes prioritized using early warnings from machine learning models and codified domain expertise

Difficult to track asset risks across thousands of systems and sensors

System-of-systems AI approach scalable to any number of assets, processes, and systems

Operational knowledge siloed within an aging workforce

Automatic failure mode identification to prescribe mitigation steps and augmented by AI feedback loops

Disparate and unconnected reliability systems

Closed-loop workflow for all asset, maintenance, and operational systems