Predictive maintenance, powered by AI
Turn asset data into foresight with AI-driven maintenance workflows.
Why it matters
Downtime is expensive
How it works
From signal to decision
Calsoft builds end-to-end predictive maintenance pipelines:
Real-time sensor/telemetry ingestion (vibration, temperature, RPM, flow, etc.)
Feature extraction and historical pattern learning
Predictive model training (time-series ML, LSTM, XGBoost, Prophet)
Risk scoring with lead-time forecasting
Automated notifications + workflow triggering
What we cover
Across Assets & Industries
Applicable across:
Manufacturing (robotics, pumps, CNC machines)
Utilities (transformers, pipelines, power grids)
Automotive (EV battery, brake pads, motors)
Datacenters (cooling, UPS, fan belts)
Telecom (towers, servers, switches)
Reduce MTTR by 40% via telemetry.
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.
