Unplanned equipment downtime costing more per hour than the annual maintenance budget for that machine?
Calendar-based maintenance servicing equipment that doesn't need it while missing failures between service intervals?
AI for Predictive Maintenance
Equipment failure prediction from your sensor data -- vibration, temperature, pressure, power draw, and cycle time deviation -- with maintenance recommendations integrated into your CMMS before the breakdown happens.
The shift from calendar-based to condition-based maintenance that reduces unplanned downtime without over-servicing.
Failure prediction models trained on your equipment sensor data and historical maintenance records
Real-time anomaly detection on process parameters with early warning alerts
Maintenance schedule recommendations integrated with your CMMS for automated work order creation
Remaining useful life estimation for critical equipment components
RaftLabs builds AI-powered predictive maintenance systems for manufacturing operations -- equipment failure prediction from sensor data (vibration, temperature, pressure, power draw), anomaly detection on process parameters, and maintenance schedule recommendations integrated with your CMMS or ERP. Predictive maintenance shifts equipment servicing from calendar-based to condition-based, reducing unplanned downtime by 20--40% without over-servicing. Most predictive maintenance projects deliver in 10--14 weeks at a fixed cost.
20--40%Unplanned downtime reduction
·20+AI systems built
·24+Industries served
·FixedCost delivery
Reactive maintenance is the most expensive kind
Unplanned downtime costs more per hour than planned maintenance -- typically 5--10x more when you factor in emergency labour rates, expedited parts, production losses, and secondary damage from running failed equipment. Calendar-based maintenance reduces surprise failures but replaces components that still have useful life, adding cost without eliminating downtime.
Predictive maintenance uses the sensor data your equipment already generates to identify degradation patterns before failure. The maintenance happens when the equipment needs it, not on a fixed schedule. The failure doesn't happen at all.
What we build
Failure prediction models
Machine learning models trained on your historical sensor data and failure records to predict equipment failures before they occur. Vibration signature analysis, temperature trend modelling, power consumption anomaly detection, and cycle time degradation patterns. Models trained on your specific equipment types, operating conditions, and failure modes -- not generic benchmarks. Confidence-scored predictions with lead time estimates so your maintenance team knows how urgently to act.
Real-time condition monitoring
Real-time monitoring dashboards for equipment health across your plant. Sensor data ingested from PLCs, SCADA systems, and IoT edge devices. Current condition scores, trend visualisations, and anomaly flags for operations and maintenance teams. Machine-level and fleet-level views. Alert configuration for different severity thresholds. The operational visibility that gives your maintenance team a current picture of equipment health rather than waiting for a failure to signal a problem.
Remaining useful life estimation
Component-level remaining useful life (RUL) estimation for critical parts -- bearings, seals, drives, cutting tools, and wear components. Estimates based on current condition trends rather than elapsed time. Replacement scheduling recommendations that optimise part life without running to failure. Integration with your parts inventory and procurement system for automated reorder when RUL thresholds are reached. The shift from fixed replacement intervals to data-driven component management.
CMMS and ERP integration
Automated work order creation in your CMMS when predictive models flag maintenance requirements. Integration with SAP PM, IBM Maximo, Infor EAM, UpKeep, and custom maintenance management systems. Maintenance recommendations with priority, suggested action, and supporting sensor data attached to the work order. Post-maintenance data capture fed back into prediction models to improve accuracy over time. The closed loop between condition data and maintenance execution that makes predictive maintenance operationally real.
Sensor data pipelines
Data ingestion pipelines for high-frequency sensor data from existing equipment instrumentation. OPC-UA, Modbus, MQTT, and direct PLC integration. Time-series data storage and efficient querying for trend analysis and model training. Edge processing for environments with bandwidth or latency constraints. Data quality monitoring and anomaly detection on the data pipeline itself -- because a sensor reading zero is either equipment that's off or a sensor that's failed. Both matter differently.
Anomaly detection
Statistical and ML-based anomaly detection on process parameters -- for detecting novel failure modes that haven't occurred in your historical data. Unsupervised learning to identify unexpected patterns without labelled failure examples. Early warning alerts on process parameter deviations before they reach failure thresholds. Root cause analysis assistance that correlates anomalies across sensors to identify likely failure mechanisms. The detection that catches what rule-based threshold alerts miss.
Frequently asked questions
The minimum requirement is time-series sensor data from the equipment you want to monitor, plus historical records of failures and maintenance actions. Common sensors: vibration (accelerometers), temperature, pressure, power consumption, and cycle time. Most modern equipment generates this data via PLC or built-in instrumentation -- the challenge is usually accessing and centralising it rather than generating it. For older equipment without instrumentation, we can specify the sensor additions required. We assess your existing sensor coverage during discovery and design the system around what data is available.
The more failure examples in the historical data, the more accurate the model. As a practical minimum, we look for 12--24 months of sensor data with at least 5--10 failure events for each failure mode we're predicting. For equipment with rare failures or new installations, we combine transfer learning from pre-trained models, physics-based modelling of known degradation mechanisms, and anomaly detection approaches that don't require failure examples. We assess data availability during scoping and design the technical approach accordingly.
ROI from predictive maintenance comes from three sources: avoided unplanned downtime (typically $5,000--$50,000+ per hour for high-throughput production lines), reduced maintenance labour costs (condition-based scheduling reduces unnecessary preventive work by 20--30%), and extended equipment life (detecting degradation early prevents secondary damage from running failed components). A realistic 12-month ROI of 200--400% is achievable for high-value production equipment with significant current downtime costs. We model expected ROI during scoping based on your specific downtime cost and maintenance spend.
A focused system monitoring one equipment type or production line -- sensor integration, model development, dashboard, and CMMS integration -- typically delivers in 10--14 weeks. More complex builds covering multiple equipment types, multi-site deployment, or significant sensor infrastructure work run 16--24 weeks. We scope the project before pricing it so you know the timeline and delivery milestones before development starts.
Talk to us about your predictive maintenance project.
Tell us your equipment types, current sensor infrastructure, and what downtime is costing you. We'll design the system and give you a fixed cost.
