This is like putting smart ears and eyes on your machines so they can tell you when something sounds or looks wrong—before it breaks. Small sensor boxes sit on the equipment, watch and listen in real time, and warn you early so you can fix problems during planned downtime instead of after a costly failure.
Unplanned equipment downtime, high maintenance costs, and safety risks due to late detection of mechanical issues. It enables continuous, on-site (edge) monitoring of machine health and early anomaly detection without needing to stream all data to the cloud.
Domain-specific know‑how in vibration and vision-based machine health, integration with industrial sensors and edge hardware, and pre-built predictive models tuned for manufacturing equipment create switching costs and defensibility against generic AI platforms.
Hybrid
Time-Series DB
High (Custom Models/Infra)
On-device compute limits for running real-time analytics and vision models at the edge, plus data throughput and storage constraints for high-frequency sensor and video streams.
Early Majority
Focus on real-time, edge-native analytics for both sensor (edgeRX) and vision (edgeRX Vision) streams, reducing dependence on constant cloud connectivity and enabling deployment in bandwidth- or privacy-constrained industrial environments.