Stop fixing yesterday's failures.
Predict asset failures, optimize tool lifecycle and cut energy waste — all from your existing machine telemetry. Predictions land inside your maintenance workflow, not in another dashboard nobody opens.
For
Reliability / Plant 1
Asset health · CNC-07
Predicted RUL
Health score
Signals
Recommended action
Schedule bearing inspection in next planned stop · WO-3142 drafted.
Reactive maintenance is the most expensive maintenance.
Unplanned downtime costs more than the part. Most plants already have the data to predict failures — they don't have the platform to act on it.
Built for the assets you actually run.
Six asset classes covered out of the box, each with the signal types we monitor and the failure modes the models learn first. Your CMMS feeds them work, our models feed your CMMS predictions.
Motors & drives
Spindle, conveyor and pump motors — bearings, windings, alignment.
Signals we watch
Pumps
Centrifugal and positive-displacement — cavitation, seal wear, blockage.
Signals we watch
Ovens & furnaces
Heat-treatment, curing, drying — uniformity, element life, leakage.
Signals we watch
Robots
Articulated arms — joint torque, position drift, gearbox health.
Signals we watch
Presses
Stamping, forming, injection — die wear, force curves, dwell.
Signals we watch
Compressors
Air and refrigeration — valves, lubrication, energy creep.
Signals we watch
Capabilities included.
Predictive maintenance, tool management, energy management and machine data collection.
Predictive Maintenance
Leverage AI to predict machine failures before they happen. Analyze vibration and telemetry to optimize maintenance.
Tool Management
Monitor tool usage and lifecycle. Automate reordering and maintenance alerts to prevent quality issues.
Energy Management
Monitor energy consumption per machine. Identify waste and optimize usage to reduce costs.
Machine Data Collection
Connect to any PLC or sensor using vendor-independent standard protocols. Centralize data for analysis and reporting without vendor lock-in.
Telemetry to ticket in four steps.
Stream
Vibration, temperature, current — pulled in over OPC UA, MQTT or Modbus.
Predict
Use AutoML for time-series or your own models — both run side by side.
Surface
Push predictions into the maintenance workflow as planned tickets.
Close the loop
Outcomes feed back into retraining — the model keeps getting better.
Outcomes our customers see.
−40%
Unplanned downtime
Failures predicted before they happen.
−25%
Maintenance cost
Right action, right time, right part.
−15%
Energy consumption
Anomaly detection on energy meters catches silent waste.
Powered by the RockQ platform
This solution composes these platform capabilities. Each one is also available standalone.
An expert behind every solution.
Real engineers, real factory experience. Drop them a line — they'll respond, scope and propose a working architecture, not a sales deck.
Predict one failure on your line.
Bring two months of vibration data from one critical asset. We'll come back with a model and the dollars saved.