AI Predictive Maintenance Software

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 labor 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, so maintenance happens when the equipment needs it and the failure doesn't happen at all.

  • 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

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See our work

The problem

Sound familiar?

  • 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?

Short answer

RaftLabs builds predictive maintenance software 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.

Key takeaways

  • FFT-derived bearing fault frequencies (BPFI, BPFO, BSF, FTF) are typically detectable 4-8 weeks before audible failure symptoms appear.
  • Weibull-based remaining-useful-life models produce a confidence interval rather than a single number, targeting component replacement at 85-90% of estimated life.
  • Unsupervised anomaly detection (isolation forests, VAEs) catches novel failure modes that haven't appeared in historical failure records, complementing supervised models.
  • McKinsey benchmark data cites 10-40% maintenance cost reduction and 20-50% downtime reduction as the achievable range for mature implementations.

Trusted by

Vodafone logo
Aldi logo
Nike logo
Microsoft logo
Heineken logo
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Predictive maintenance delivery, by the numbers

unplanned downtime reduction
20-40%
AI systems built
20+
industries served
24+
cost delivery
Fixed

Reactive maintenance is the most expensive kind

Predictive maintenance uses the sensor data your equipment already generates to identify degradation patterns before failure. Maintenance happens when the equipment needs it, not on a fixed schedule, and the failure doesn't happen at all.

Capabilities

What we build

  • 01
    Failure prediction models

    FFT-based bearing fault frequency analysis and gradient-boosted models with SHAP explainability, trained on your equipment's specific failure modes.

    Built with
    XGBoost · SHAP
  • 02
    Real-time condition monitoring

    Kalman-filter denoised sensor ingestion with configurable informational/warning/critical alert thresholds by equipment criticality.

    Built with
    OPC-UA · MQTT
  • 03
    Remaining useful life estimation

    Weibull-based RUL confidence intervals targeting replacement at 85-90% of estimated life, with automated parts reorder integration.

  • 04
    CMMS and ERP integration

    Automated work orders with equipment ID, predicted failure mode, confidence score, and suggested action, closing the loop with post-maintenance feedback.

    Built with
    IBM Maximo · SAP PM
  • 05
    Sensor data pipelines

    Edge-processed FFT spectra reducing data volume by 100x, stored in a purpose-built time-series database with data-quality monitoring.

    Built with
    InfluxDB · TimescaleDB
  • 06
    Anomaly detection

    Unsupervised models (isolation forests, VAEs, LSTM reconstruction) catching novel failure modes 3-14 days before threshold-based alarms fire.

How we work

From scope to live predictive system

  1. Week 1
    01

    Sensor and equipment scoping

    We map your equipment types, current sensor infrastructure, and what downtime is costing you. You leave week 1 with a written scope document and a fixed-price quote.

  2. Weeks 2-5
    02

    Data pipeline and model design

    Sensor integration, feature engineering, and failure-prediction model architecture designed against your historical data.

  3. Weeks 4-12
    03

    Build and validate

    Models trained and validated against held-out failure events, with the dashboard and CMMS integration built in parallel.

  4. Final 2-3 weeks
    04

    UAT and go-live

    Maintenance teams trained on the alert workflow before production deployment.

Why us

Why manufacturers choose RaftLabs

  • 01
    Senior engineers build what they scope

    The engineers who assess your sensor infrastructure also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.

  • 02
    Fixed price before development starts

    We scope the work, calculate the cost, and lock it in writing before any development starts.

  • 03
    9 years and 100+ products shipped

    Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building predictive maintenance platforms.

  • 04
    ROI modelled from your actual downtime cost

    We calculate expected return from your specific baseline, not generalised benchmarks.

  • 05
    Explainable predictions, not a black box

    SHAP feature importance shows your engineers exactly which signals are driving each prediction.

Have a 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.

Talk about your predictive maintenance project

Related services

AI for Predictive Maintenance, scoped in one call.

Tell us what's broken. Within one business day you get a straight take on cost, timeline, and the right first step. No deck, no pressure.

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Frequently asked questions

The sensors required depend on the failure modes you want to predict. Rotating equipment is typically monitored with vibration accelerometers and bearing/winding temperature sensors. Hydraulic systems use pressure transducers and particle count sensors. Many failure modes can also be detected from existing PLC signals such as cycle time drift without adding new sensors.

For supervised models trained on labelled failure events, 12-24 months of sensor data containing confirmed failure instances is the minimum useful dataset, with at least 5-10 failure events per failure mode. For equipment with rare failures, transfer learning from similar equipment types or unsupervised anomaly detection bridges the data gap.

Unplanned downtime typically costs $5,000 to $50,000 per hour in lost production. Predictive maintenance typically reduces unplanned equipment failures by 20-40% in the first year. Most implementations pay back within 6 to 18 months. We model expected ROI based on your current downtime rate, equipment count, and production cost per hour.

A focused system covering one equipment class such as rotating assets across a single plant typically delivers in 12 to 16 weeks, covering sensor audit, data pipeline setup, model development, and CMMS integration. Broader implementations covering multiple equipment types run 20 to 30 weeks.

Work with us

Tell us what you need. We'll tell you what it would take.

We scope AI for Predictive Maintenance in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.

Go deeper

Browse our case studies

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