Traditional predictive maintenance relies on sensor data and requires building separate models for each specific failure type. Risk Score works with the unstructured operational data you already have – maintenance logs, fault codes, work orders – and discovers patterns across your entire operation without per-fault model development.

It works with CMMS systems (Maximo, SAP, etc.), maintenance logs, fault codes from equipment or data acquisition boxes, and operational records. No new infrastructure or sensors needed to start.

In operational validation with DB Regio, the system correctly identified 80% of at-risk first trains at least 60 minutes before their scheduled departure. This gave dispatchers sufficient time to investigate issues, resolve problems proactively, or implement contingency plans before passenger impact.

Yes. The same core technology validated with DB Regio for rail operations also identified at-risk airport ground equipment. Risk Score adapts to your operational data structure and discovers patterns specific to your fleet and maintenance practices.

Every risk score comes with clear reasoning showing which specific factors contributed to the prediction—recent maintenance events, historical patterns, operational context. This transparency builds trust with engineering teams and dispatchers who need to understand and act on the system’s recommendations.

Risk Score works alongside existing OEM monitoring systems, adding an operational intelligence layer across mixed-OEM fleet that OEMs can’t provide. While OEM systems monitor their own equipment or component, Risk Score assesses overall service readiness by analysing maintenance patterns, operational history, and cross-fleet correlations in your CMMS data.