What is AI-Powered Predictive Maintenance?
Unplanned downtime is far more expensive than the maintenance that would have prevented it — but calendar-based maintenance either over-services healthy equipment or misses failures between intervals. Predictive maintenance reads the signals your equipment is already giving off. We build models on your sensor and telemetry data that forecast failures before they happen, prioritised by asset criticality and cost of failure, with alerts wired into your maintenance workflow. The result: less unplanned downtime, condition-based servicing, and longer asset life — maintenance driven by evidence, not the calendar.
Services provided
What the data says
Unplanned downtime is far more expensive than the maintenance that would have prevented it.
Condition-based beats calendar-based maintenance on both cost and reliability.
Most plants already collect the sensor data needed — the value is locked in unused telemetry.
Where Ganexa stands out
Works with your existing sensors and telemetry.
Prioritised by asset criticality and cost of failure.
Alerts wired into your maintenance workflow, not a dashboard nobody watches.
Validated on your failure history before it's trusted.
Monitored and retrained so predictions stay accurate.
Your engagement roadmap
Discover
1–2 weeksAssess assets, sensors and failure history for feasibility.
Feasibility
Design
2–3 weeksBuild and validate failure-prediction models on your data.
Model
Build & pilot
3–6 weeksPilot on critical assets and validate the alerts.
Piloted programme
Scale & embed
OngoingScale and integrate with maintenance operations.
Production predictive maintenance
Built for where you are
Manufacturing plant
“Unplanned outages on a key line cost us a fortune, and calendar maintenance isn't catching them.”
We built failure-prediction models on their existing sensor data for the critical assets, validated against failure history, and wired alerts into maintenance.
Early failure warnings that cut unplanned downtime on the critical line.
Utilities operator
“We service equipment on a fixed schedule — over-maintaining some assets and still getting surprised by others.”
We moved them to condition-based maintenance driven by predictive models prioritised by asset criticality.
Maintenance effort optimised — less over-servicing, fewer surprises.
Fleet operator
“Vehicle breakdowns disrupt schedules and we've no early warning.”
We modelled telemetry to predict component failures and surfaced alerts to the maintenance team.
Fewer roadside breakdowns through earlier, targeted maintenance.
What you walk away with
Failure-prediction models
Models on your sensor/telemetry data that forecast likely failures ahead of time.
Alerting & maintenance integration
Predictive alerts delivered into your maintenance workflow so action actually happens.
Asset-criticality prioritisation
Prioritisation of monitoring and action by asset criticality and cost of failure.
Model monitoring & retraining
Ongoing monitoring and retraining so predictions stay reliable.
Ready to put AI-Powered Predictive Maintenance to work?
Book a free 30-minute discovery call — you'll leave with a clear, costed next step, no obligation. Or ask us anything: we reply within one business day.