Predictive maintenance, powered by AI
Turn asset data into foresight with AI-driven maintenance workflows.
Why it matters
Downtime is expensive
Reactive maintenance leads to:
- 01
Unexpected outages and SLA breaches
- 02
Higher maintenance labor cost
- 03
Excess spare part stocking
- 04
Poor customer experience in B2B scenarios
- 05
Shortened equipment lifespan
How it works
From signal to decision
Calsoft builds end-to-end predictive maintenance pipelines:
What we cover
Across Assets & Industries
Applicable across:
Cut unplanned downtime from 60–90 hours to under 15 a year
Business outcomes
Less disruption. More value
| Metric | Before | After |
|---|---|---|
| Unplanned downtime/year | 60–90 hrs | <15 hrs |
| Maintenance cost / asset | High | ↓ by 25–35% |
| Mean time between failures (MTBF) | Low | ↑ by 40% |
| Spare parts usage | Overstocked | Demand-aligned |
| Warranty claims | Unoptimized | Proactive |
How to start
Predictive in 4 steps
Identify critical assets
Focus on those with high downtime cost or failure impact.
Ingest telemetry
Connect to IoT sensors or SCADA systems for data feeds.
Model failure modes
Train ML models to recognize anomaly patterns and forecast risks.
Integrate actions
Connect alerts to CMMS, workflows, or maintenance ticketing.
