Industrial AI, Monitoring & Predictive Maintenance
Sensor-backed monitoring, anomaly detection, and predictive maintenance for critical assets.
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What problem it solves
Unplanned downtime, inconsistent maintenance response, and limited visibility into asset health.
When it fits
- Repeatable downtime on identifiable assets
- Maintenance team can act on structured alerts
When it may not fit
- No maintenance capacity to respond to alerts
- Assets too diverse for a phased rollout plan
Typical solution stack
- Sensors and edge gateways
- Historian or cloud analytics
- Alert routing to maintenance
- Baseline model training workflow
Required provider roles
- IoT / analytics vendor
- Maintenance lead
- Controls integrator
Buyer data needed
- Critical assets list
- Existing sensor infrastructure
- Alert workflow
Validation checklist
- Baseline data collection period completed
- Alert thresholds validated with maintenance
Site readiness checklist
- Network connectivity to assets
- Maintenance workflow for alerts
Common risks
- Alert fatigue if thresholds are not tuned with maintenance
- Legacy PLCs lack data access without gateway investment
Preliminary cost / timeline note
Illustrative planning ranges often span roughly $120K–$850K CAD and 6–18 months from discovery to production ramp, depending on scope, site readiness, and integration complexity. Not a quote.
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