Service 08 · Predictive Maintenance & IoT

Predict failurebefore it happens.

Sensor-fed AI that monitors equipment in real-time and predicts maintenance needs. Built for oil & gas, manufacturing, and field ops.

Up to 80% less downtimeUp to 60% cost savingsUp to 95% accuracy

Last updated: June 2026

01 — What it does

Intelligent equipment monitoring.

Transform your maintenance strategy with AI-powered IoT analytics and predictive insights.

IoT sensor integration

Connect thousands of sensors monitoring temperature, vibration, pressure in real-time.

Failure prediction

AI algorithms predict equipment failures weeks or months ahead with up to 95% accuracy.

Maintenance scheduling

Auto-schedule based on actual condition rather than fixed intervals.

Real-time monitoring

24/7 monitoring with instant alerts for anomalies and critical conditions.

Performance analytics

Track equipment metrics, efficiency trends, and optimisation opportunities.

02 — Use cases

From sensor to action.

Asset health

  • Continuous condition monitoring
  • Vibration & temperature signatures
  • Anomaly detection alerts
  • Historical trending

Predictive alerts

  • 2–8 weeks failure warning
  • Recommended maintenance actions
  • Severity scoring
  • Auto-create work orders

Field operations

  • Mobile dashboards for technicians
  • GPS-tagged sensor mapping
  • Offline-capable apps
  • Remote diagnostics

Energy & efficiency

  • Energy consumption tracking
  • Process optimisation insights
  • Carbon footprint reporting
  • OEE benchmarking
03 · How it works

From install to insight in four steps.

A pragmatic rollout that proves value on one asset class before it scales across the plant.

01

Instrument the asset

We fit each critical machine with the right sensors for its failure modes (vibration, temperature, pressure, current) and wire them into a secure gateway. No production stoppage required.

  • Sensor selection
  • Gateway install
  • Secure connectivity
02

Baseline the data

The platform collects readings until it learns what healthy looks like for your specific equipment, duty cycles, and seasons. That baseline becomes the reference every future reading is judged against.

  • Healthy signatures
  • Duty-cycle profiling
  • Noise filtering
03

Predict and alert

AI models watch every stream in real time, score anomalies by severity, and forecast the time-to-failure window so you schedule the fix on your own terms instead of reacting to a breakdown.

  • Time-to-failure window
  • Severity scoring
  • Live alerting
04

Act and improve

Confirmed alerts auto-create work orders in your CMMS and route to the right technician. Their feedback retrains the models, so accuracy climbs with every cycle.

  • Auto work orders
  • Technician routing
  • Closed-loop learning
04 · Benefits

Maximise equipment uptime.

Up to 80% less downtime

Prevent unexpected failures and unplanned downtime through predictive maintenance.

Up to 60% cost savings

Maintain only when needed. Avoid emergency repairs and overtime callouts.

Up to 95% accuracy

Reliable failure predictions you can plan maintenance around with confidence.

05 · Integrations

Connects to the systems you already run.

We bridge to your industrial protocols, cloud platforms, and maintenance tools so the predictive layer fits in beside SCADA, not on top of it.

AWS IoT CoreAzure IoT HubMQTTOPC UAModbusIBM MaximoSAP PMGrafanaSlack alerts
11.4M
Sensor readings ingested daily
3.2x
Faster fault detection vs scheduled checks
$1.8M
Avoided downtime, first year, per site
06 · What clients say

Real uptime. Real savings.

5.0/ 5
“Built me a beautiful, modern website that exceeded all expectations. SEO and AIEO optimisation has dramatically improved our visibility and lead generation.”
Gloria S.Realtor · Calgary
5.0/ 5
“Managing a construction company means juggling countless daily tasks. The Automators optimised our internal processes and our operations run so much smoother now.”
Brandon F.Owner · gencons.ca
5.0/ 5
“They helped us launch our MVP with incredible success — 2,000+ active users and 800+ paid subscribers. Their technical expertise has been instrumental.”
Francis C.CEO · bobbie
What types of equipment can be monitored?
Virtually any industrial equipment — motors, pumps, compressors, turbines, HVAC, manufacturing machinery, conveyors, generators. Our sensors track temperature, vibration, pressure, humidity, electrical current, and more.
How accurate are predictive maintenance forecasts?
Our AI achieves 95% accuracy in predicting failures, typically providing 2–8 weeks advance warning. Accuracy improves over time as ML models collect more data on your specific equipment.
Do you provide the IoT sensors or do we buy them?
We provide a complete turnkey solution — sensor hardware, installation, connectivity, cloud platform, and AI analytics. We handle procurement, installation, configuration, and ongoing maintenance.
Can the system integrate with our existing CMMS?
Yes. Our platform integrates with major CMMS and EAM systems including SAP, IBM Maximo, Oracle, and others. We auto-create work orders and sync maintenance schedules.
What's the ROI timeline?
Most organisations see ROI within 6–12 months through reduced downtime, lower maintenance costs, and extended equipment life. Detailed projections provided during consultation.
Is the system secure and compliant?
Absolutely. All data is encrypted in transit and at rest with enterprise-grade security. Our IoT platform complies with IEC 62443, ISO 27001, and other industrial cybersecurity standards.
How long does a deployment take from kickoff to live alerts?
A focused pilot on one asset class is typically live within four to six weeks. That covers sensor installation, connectivity, baseline data collection, and model calibration. Site-wide rollouts run in phases so each line proves value before the next one comes online.
What happens if a sensor or gateway goes offline?
Every device sends a heartbeat, so the platform flags a dropped sensor or gateway within minutes rather than discovering the gap during the next inspection. Critical assets can run redundant sensors, and the system keeps buffering edge data locally until connectivity is restored, so no readings are lost.
Do we need to rip out our existing SCADA or historian?
No. We bridge to existing SCADA, PLCs, and historians (OPC UA, Modbus, MQTT) rather than replacing them. The predictive layer reads from the systems you already trust and writes alerts and work orders back into the tools your team uses every day.
How does the AI avoid flooding technicians with false alarms?
Models score every anomaly by severity and confidence, then suppress low-signal noise so only actionable events reach a person. As technicians confirm or dismiss alerts, that feedback retrains the models, and false positives keep dropping quarter over quarter.
Ready to monitor?

Catch failures before they catch you.

Free 30-minute call. We'll review your equipment and tell you where IoT monitoring will deliver the biggest payback.