Case Study
Nexus Manufacturing
Predictive maintenance system reducing unplanned equipment failures.
Manufacturing
22% uptime gain
Outcome
$11.5M avoided downtime
Quantified Impact
Challenge
Reactive maintenance led to production line stoppages and lost capacity.
Strategy
Created a sensor data pipeline with ML-based anomaly detection and alert routing.
Execution
- • Connected 400+ industrial sensors via MQTT to a central streaming platform.
- • Developed anomaly detection models trained on 2 years of operational data.
- • Built mobile alerts for maintenance teams with diagnostic context.
Tech stack
KafkaTensorFlowInfluxDBPrometheusReact Native
Results
- • 22% increase in equipment uptime
- • 35% fewer emergency repairs
- • 50% faster MTTR on fault detection
Testimonial
"We caught critical faults before they became production emergencies."
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