Stop unplanned downtime before it happens. We deploy machine-learning models on your equipment data — vibration, motor current, temperature, pressure and PLC tags — to detect degradation and predict failures days or weeks ahead, so maintenance becomes planned instead of reactive.
We start with the assets that hurt the most when they stop. We connect to existing sensors and controllers (no rip-and-replace), build and validate failure models on your historical data, and deliver alerts straight into your CMMS and dashboards with clear remaining-useful-life and recommended actions.
Anonymized project profiles across industries. No client names or sensitive data are disclosed.
Recurrent unplanned stops on high-tonnage presses were disrupting JIT delivery. We modeled motor current and vibration to flag bearing and clutch wear early.
Result: ~38% fewer unplanned stops and six-figure annual savings in avoided downtime.
Critical conveyor gearboxes were failing without warning in a remote site with limited connectivity. Edge thermal + vibration models ran locally.
Result: 3 failures caught before breakdown; +12% line availability.
Tool and spindle failures were driving scrap and rework on precision parts.
Result: ~25% less scrap from unplanned tool failures.