Problem
A network of manufacturing facilities lacked real-time visibility into equipment health. Unplanned downtime was costing millions annually, and maintenance teams relied on fixed schedules rather than actual machine condition.
Approach
We deployed a sensor network across critical equipment, built edge gateways for local processing, and created a cloud platform for centralized monitoring. ML models were trained on vibration and temperature data to predict failures 48 hours in advance.
Outcome
73% reduction in unplanned downtime. Maintenance costs decreased by 40%. The system now monitors 2,400+ sensors across 12 facilities.
73%
reduction in unplanned downtime
400+
sensors across 12 facilities
Technology Stack
- Custom sensor firmware (C/Rust)
- Edge gateway (Linux, MQTT, Docker)
- Time-series database (TimescaleDB)
- ML pipeline (Python, scikit-learn, TFLite)
- Dashboard (React, WebSocket)
Ready to build?
Let's discuss how we can engineer the right system for your use case.