Industrial Applications

Predictive maintenance moves beyond threshold monitoring by identifying the true causal paths from early sensor degradation to equipment failure. Causal discovery algorithms on historical maintenance records and telemetry reveal which sensor combinations predict failure modes, enabling targeted inspection and replacement before catastrophic breakdown. Quality control leverages causal inference to identify which raw material properties, process parameters, and equipment conditions actually influence defect rates, enabling upstream intervention rather than downstream sorting. Process optimization uses causal analysis to identify true bottlenecks and leverage points where small changes generate outsized efficiency gains, avoiding investing in constraints that aren't actually binding. Our research on [optimal rework policies](/research#optimal-rework-policy) demonstrates these methods in practice.

Manufacturers using our platform experience measurable yield improvements, reduction in unplanned downtime, and energy efficiency gains through optimized process parameters. Supply chain resilience improves because you understand which supplier quality variations actually impact production and can negotiate accordingly. Equipment vendors and manufacturers use insights to improve designs. Multi-facility operators confidently transfer best practices across sites while accounting for local differences in equipment age, configuration, and operators.

Our industrial IoT integration handles streaming sensor data, processes it through causal analysis, and provides real-time alerts and recommendations to production engineering teams.