• You are replacing parts on a fixed schedule whether they need it or not, generating unnecessary downtime and maintenance cost?

  • Equipment is failing unexpectedly because your current monitoring only alerts you after a threshold is already breached, not while the fault is developing?

Predictive Maintenance Software

RaftLabs builds predictive maintenance models that use equipment sensor data to forecast failures before they occur. Vibration, temperature, pressure, and current anomaly detection trained on your historical failure data -- integrated with your CMMS to trigger work orders automatically when the model detects a developing fault.
We start with a data and equipment audit: we assess what sensors you have, what failure history is available, and what failure modes matter most to your operations. The model is only worth building if the signal is there -- and we tell you that before you commit to building, not after.

  • Anomaly detection trained on your historical failure data -- not generic equipment benchmarks

  • Remaining useful life prediction so maintenance is scheduled at the right time, not too early or too late

  • CMMS integration that triggers work orders automatically when the model detects a developing fault

  • Model performance monitoring and retraining as equipment behaviour changes over time

RaftLabs builds predictive maintenance models using equipment sensor data to detect anomalies and forecast failures before they occur. We integrate with your CMMS to trigger work orders automatically and include model monitoring and retraining so the system stays accurate as equipment behaviour changes.

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Aldi
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Microsoft
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Calorgas
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Bank of America
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Valero
Techstars
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Time-based maintenance is a compromise: you service equipment on a fixed calendar schedule because you do not know when it will actually need attention. That means servicing some equipment unnecessarily early -- wasting parts, labour, and production time -- and other equipment too late, after a fault has already developed into a failure. The gap between those two errors is where predictive maintenance operates.

A predictive maintenance model watches the sensor signals that indicate developing faults -- rising bearing temperature, changing vibration spectrum, increased current draw -- and detects the deviation from normal behaviour weeks or months before the equipment fails. The result is maintenance scheduled when the equipment actually needs it, based on its actual condition rather than an arbitrary calendar interval. RaftLabs builds these models end to end: sensor data pipeline, anomaly detection model, remaining useful life estimation, and the CMMS integration that turns a model prediction into a work order your maintenance team can act on.

What we build

Sensor data anomaly detection

Unsupervised and semi-supervised anomaly detection models trained on your normal equipment operating data to establish the baseline behaviour of each asset. Statistical process control, autoencoder-based anomaly scoring, and isolation forest models -- selected based on your sensor count, data frequency, and the subtlety of the fault signatures you need to detect. Anomaly scores are produced continuously as new sensor data arrives.

Remaining useful life prediction

Regression models that estimate the remaining operating life of an asset based on its current sensor signature and degradation trajectory. Calibrated confidence intervals so your maintenance planners can schedule inspections with appropriate urgency -- not just a point estimate, but a range that tells them whether the asset needs attention in days or weeks. RUL predictions updated continuously as new sensor data arrives.

Failure mode classification models

Supervised classification models that identify not just that something is wrong, but which specific failure mode is developing. Bearing wear, imbalance, misalignment, electrical insulation degradation, and seal failure -- each produces a distinct sensor signature that a trained classifier can distinguish. Failure mode identification helps your maintenance team bring the right parts and skills to the inspection rather than arriving blind.

CMMS work order integration

Automated work order creation in your CMMS when the model exceeds a configurable risk threshold for a specific asset. IBM Maximo, SAP Plant Maintenance, Infor EAM, UpKeep, and custom CMMS integration via API. Work orders are created with the asset ID, detected fault type, recommended inspection action, priority level, and the sensor evidence that triggered the alert -- so your maintenance engineers have the context they need to act.

Predictive maintenance dashboard

An operational dashboard showing real-time asset health status, anomaly scores, RUL estimates, and pending work orders across your equipment fleet. Asset-level drill-down showing the sensor time series that triggered an alert, historical fault events, and maintenance action history. Fleet-level views for maintenance managers who need to prioritise across multiple assets competing for the same maintenance resource.

Model performance monitoring and retraining

Automated tracking of model prediction accuracy against actual maintenance outcomes -- whether anomalies that triggered work orders were confirmed as genuine faults during inspection. Precision and recall metrics by asset type and failure mode, surfaced in a reporting dashboard for your maintenance engineering team. Scheduled retraining pipelines that incorporate new inspection outcomes and sensor data so the model stays calibrated as equipment ages and operating conditions change.

Unexpected equipment failures are not inevitable.

Tell us what equipment you are trying to protect, what sensors you currently have, and what your current maintenance strategy is. We will assess whether a predictive maintenance model is worth building and what failure modes it can realistically detect.

Talk about your predictive maintenance project

  • IoT Development -- IoT sensor data infrastructure and pipelines feeding predictive maintenance models

  • AI Development -- custom ML model development for equipment failure prediction use cases

Frequently asked questions

The most useful sensors for predictive maintenance are vibration accelerometers, temperature sensors, current and power consumption monitors, pressure transducers, and acoustic emission sensors -- depending on the equipment type and the failure modes you are trying to predict. Rotating machinery failures are typically best detected through vibration and temperature. Electrical equipment failures are often detected through current signature analysis. You do not necessarily need all sensor types -- even a single well-placed vibration sensor on a critical rotating asset can provide enough signal to build a useful anomaly detection model. We review your existing sensor coverage in the first engagement phase and tell you whether it is sufficient or what you would need to add.

Limited failure history is the most common challenge in predictive maintenance -- most businesses have years of normal operation data and relatively few recorded failure events. We address this through two approaches. For anomaly detection, we train a model on your normal operating data to define what healthy looks like, then flag deviations from that baseline as potential developing faults -- this approach does not require failure examples. For failure classification and remaining useful life prediction, we supplement your historical data with physics-informed features derived from equipment specifications and failure mode analysis, which allows the model to generalise beyond the failure examples it has seen. We are transparent about the confidence level of predictions when training data is thin.

The last mile of predictive maintenance -- getting a model prediction into the hands of the maintenance engineer who needs to act on it -- is where most implementations fail. We build the CMMS integration as a core part of the engagement, not as an afterthought. When the model exceeds a configurable risk threshold for a specific asset, it automatically creates a work order in your CMMS (IBM Maximo, SAP PM, Infor EAM, or a custom system) with the asset ID, the fault type detected, the recommended inspection action, and the urgency level. Your maintenance team receives the work order through their existing workflow without needing to check a separate dashboard.

A predictive maintenance model for a single equipment type -- covering anomaly detection, a basic remaining useful life estimate, CMMS work order integration, and a monitoring dashboard -- typically runs $30,000 to $80,000. A multi-asset platform covering multiple equipment types, multiple failure modes per asset, and integration with a full CMMS workflow ranges from $80,000 to $200,000. We provide a fixed-cost quote after a data and equipment audit where we assess your sensor coverage and failure history.