Rank assets by risk of failure, so maintenance goes where it is needed first.
Sensor readings, operating data and work-order history are combined to spot early signs of wear and rank assets by risk. Planners get a prioritized list with the evidence behind each item and decide what to schedule. It starts with equipment that is already instrumented, or can be.
- 01Collect signals
- 02Detect anomalies and estimate risk
- 03Rank and explain
- 04Plan the work
- 05Record findings
The workflow
Maintenance planners, reliability engineers and technicians responsible for production, fleet or utility assets, and the operations managers who schedule around downtime.
- Maintenance runs on fixed calendars, so some parts are replaced early and others fail early.
- Unplanned stops are found by operators, not predicted.
- Sensor data is collected but only watched against simple alarm limits.
- Work-order history is inconsistent, so nobody can link past failures to their warning signs.
Collect signals
SystemVibration, temperature, pressure, current, runtime and similar readings are collected from sensors, controllers or historians with consistent timestamps.
Detect anomalies and estimate risk
AIModels compare each asset with its own normal behavior and with similar assets, and estimate risk of failure over the planning horizon.
Rank and explain
SystemAssets are ranked by risk and criticality, with the signals and trends behind each ranking shown.
Plan the work
PersonA planner or reliability engineer reviews the list, checks the evidence and decides whether to inspect, schedule repair or keep watching.
Record findings
PersonTechnicians record what they found, and those outcomes are used to check and retrain the models.
Where people stay in control
The system ranks assets and shows evidence; planners and engineers decide what work to do. Safety-critical inspections and regulatory maintenance continue on their required schedules regardless of model output. Technician findings are recorded against each alert so the team can see which alerts proved useful.
- Unplanned downtime on assets in scope, against the prior baseline
- Share of alerts confirmed by inspection
- Failures that occurred without a prior alert
- Planned versus reactive maintenance hours
Data and integrations
- Sensor or controller data at a sampling rate suited to the failure modes in scope
- Asset register with type, age, location and criticality
- Work-order and failure history, ideally with failure codes
- Operating context such as load, speed or production schedule
Realistic boundaries
- Assets without sensors, or with too few readings, need instrumentation first; we assess this before building models.
- Rare failure modes with little history are handled by anomaly detection and engineering rules rather than failure prediction.
- The system informs maintenance decisions; it does not replace mandated inspections or safety procedures.
How we build it
- Machine Learning & Predictive AnalyticsForecasting, classification, anomaly detection and recommendation models trained on your data and evaluated against the decisions they support.
- IoT & Edge SoftwareDevice connectivity, edge processing and data pipelines that turn equipment signals into records your teams and models can use.
- Data EngineeringData architecture, pipelines, quality and access that make your data usable for reporting, operations and AI.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
- ManufacturingMaintenance, visual quality inspection, knowledge transfer and connected operations — designed around plant-floor conditions and the systems that run production.
- Energy & UtilitiesAsset operations, field knowledge, reliability planning and customer service — designed for critical infrastructure, long-lived assets and strict security boundaries.
- Logistics & Supply ChainShipping documents, exception handling, planning and visibility across carriers, warehouses and partner systems that were never designed to talk to each other.
Questions buyers ask
We start with a survey of what signals already exist in controllers and historians. Often there is enough to begin on critical assets. Where there is not, we specify the sensors and sampling rates needed and can build the collection software through our IoT & edge service.
More is better, but it is rarely complete. With few recorded failures we begin with anomaly detection against normal behavior, then add failure prediction as confirmed findings build up.
Map this workflow with us.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.