Predictive Maintenance
Predictive Maintenance
Predictive maintenance only creates value when it changes outcomes on the ground: fewer unplanned stoppages, safer operations, and maintenance work that is planned rather than reactive. We build predictive maintenance systems that convert equipment signals into actionable risk, integrated into CMMS/EAM workflows with monitoring, governance, and measurable reliability gains.
15–30%
of maintenance spend is wasted on calendar-based work that does not reduce failure risk.20–40%
of unplanned downtime is driven by repeatable failure modes that can be detected earlier.30–55%
of condition data remains unused because it is not integrated into planning and work orders.Areas we support
Condition Monitoring and Asset Health Intelligence
We unify sensor, SCADA, PLC, historian, and maintenance data into reliable health signals. This includes data quality controls, feature pipelines, anomaly detection, and failure mode modelling so asset risk is measurable, explainable, and stable across shifting operating conditions.
Maintenance Planning and Work Order Automation
We connect predictions to action by integrating with CMMS/EAM: risk-based work orders, prioritization, spares planning signals, and technician workflows. The outcome is a closed loop where interventions are planned, tracked, and continuously improved based on what actually prevented failures.
Edge-to-Enterprise Reliability Delivery
We deploy predictive maintenance across distributed sites and constrained networks: edge inference where required, secure updates, monitoring, drift controls, and audit-ready model/version governance. Systems are designed to run daily without specialist dependency and to remain reliable as operations evolve.