Predictive Maintenance with Agentic Monitoring
Agentic monitoring system that predicts equipment failures 72 hours in advance, schedules maintenance autonomously, and coordinates parts inventory across 4 facilities.
Reduction in Unplanned Downtime
Industrial Equipment Manufacturer
The Challenge
An industrial equipment manufacturer with 4 production facilities experienced an average of 23 hours of unplanned downtime per month per facility. Each hour of downtime cost $45,000. Maintenance was reactive — they fixed things after they broke.
Our Solution
Deployed an agentic monitoring system that ingests sensor data from 2,400+ IoT endpoints, predicts failures using time-series models, and autonomously coordinates maintenance scheduling and parts logistics.
System Architecture
Sensor Agent
Ingests vibration, temperature, pressure, and power data from 2,400+ endpoints via MQTT
Prediction Agent
Time-series anomaly detection with 72-hour failure prediction window
Scheduling Agent
Coordinates maintenance windows with production schedules to minimize impact
Parts Agent
Checks inventory, orders parts proactively, routes to correct facility
Reporting Agent
Generates maintenance briefs, tracks KPIs, surfaces trends to plant managers
Results
Technology Stack
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