Predict AI*
Real-Time Machine Health Monitoring & Failure Prediction
- Runs AI algorithms directly at the edge, enabling continuous, real-time machine monitoring without cloud dependency.
- Monitors multiple physical and operational parameters including vibration, temperature, current, acoustics, pressure, and machine signals.
- Learns normal machine behaviour and detects subtle deviations that indicate early-stage faults.
- Integrates seamlessly with factory systems to deliver real-time alerts and actionable maintenance insights.
- Supports a shift from reactive firefighting to planned, condition-based maintenance strategies.
- Improves machine uptime and asset lifespan while reducing unplanned downtime and maintenance costs.
- Delivered as a privacy-preserving, low-latency edge deployment suitable for always-on industrial environments.
- Log based root cause analysis

Use cases
Predict Machine Failures Before They Happen
Uses AI to forecast failures based on real-time machine behaviour.
Monitor Multiple Machine Health Parameters
Analyzes vibration, temperature, current, acoustics, and sensor data together.
Real-Time Alerts for Maintenance Teams
Notifies teams instantly when abnormal patterns or risk thresholds are detected.
Edge AI Monitoring Without Cloud Dependency
Runs fully on-device for low latency, offline operation, and data privacy.
Scalable Across Machines, Lines, and Plants
Deploy once and replicate predictive logic across multiple assets and factories.
*Coming soon