Market opportunity
The global predictive maintenance market is projected to reach tens of billions of dollars — driven by industrial manufacturers demanding earlier, more cost-effective fault detection. The critical enabler: anomaly detection that operates at the machine, not in the cloud. Latency-sensitive applications cannot afford round-trip cloud inference. SensiML solves this with on-device anomaly models deployable across new and legacy equipment alike.
What SensiML brings to market
SensiML specializes in customized anomaly detection algorithms that execute within the limited computing footprint available at the sensor node. The approach is uniquely practical: models are trained on baseline normal operation and configured to alert on outliers — making deployment feasible even when fault data is unavailable, unsafe to collect, or too costly to reproduce. This unlocks predictive maintenance for a vastly larger set of real-world industrial applications than supervised fault-classification approaches.
Capabilities & IP
Real-time
Anomaly detection at the IoT endpoint — zero cloud latency
Critical for high-speed processing lines, safety systems, and high-value machinery where milliseconds matter.
No fault data
Baseline-only training approach
Models learn normal operation and flag deviations — eliminating the need to reproduce dangerous or costly fault states for training.
Legacy + new
Over-the-top deployment on existing equipment
Smart edge sensor networks add monitoring capability without requiring changes to existing controls or connectivity infrastructure.
Proven use cases
- High-value machinery fault detection
- Robotic arm motion path integrity monitoring
- High-speed production line outlier isolation
- Building and perimeter security sensors
- Fan and pump acoustic anomaly detection
- Factory-wide distributed process monitoring
IP & technical differentiators
- Baseline-only training — no fault reproduction required
- Decentralized sensor network architecture for factory-scale coverage
- Fast model re-training as equipment behavior evolves
- Supports acoustic, vibration, and motion anomaly inputs
- Deployable on commodity MCUs — no specialized AI hardware needed
SensiML's baseline-only anomaly detection approach dramatically expands the addressable customer base — any manufacturer can deploy without first engineering fault datasets, a barrier that eliminates most competing solutions from consideration.
See it in action
Robotic Anomaly Detection
Baseline-trained models detect motion path deviations without fault data.
See tutorial →
Distributed Process Monitoring
Factory-scale edge AI sensor networks for consistent equipment monitoring.
