Catch failures before they cascade.
Predictive maintenance agents detect degradation patterns across factory floors, reducing downtime by catching failures before they cascade.
The challenge
Unplanned downtime costs manufacturers an estimated $50 billion annually. Traditional maintenance schedules are either too aggressive (wasting parts and labour) or too conservative (allowing failures). Sensor data exists but sits unused in historians and SCADA systems.
How JITM.ai helps
Upload vibration data, temperature logs, pressure readings, and maintenance records. JITM.ai builds models that score equipment health in real time, flagging degradation trends before they become breakdowns.
What you can predict
Remaining useful life, failure probability within N days, quality defect rates, production throughput anomalies, and optimal maintenance windows based on actual equipment condition.
Why it matters
Moving from reactive to predictive maintenance typically reduces unplanned downtime by 30-50% and maintenance costs by 10-25%. One prevented line stoppage often pays for an entire year of predictive capability.