IoTGPT for Factories
Predictive Maintenance With What You Already Have
Runs on your existing SCADA data, on your own hardware, live in days.
0
new sensors needed
Days
to start
100%
on-premises
10-20%
estimated lower maintenance cost, and far fewer unplanned stops
Industry benchmark: US Department of Energy, McKinsey
The business story
- •Most predictive maintenance projects die on two demands: new sensors, years of labeled failures.
- •We skip both. We learn "healthy" from your existing SCADA data.
- •Drift from normal becomes an action item, not a dashboard.
- •Hundreds of assets on one standard CPU.
- •We bridge analytics, your domain knowledge and AI, in one system.
Why it works
Anomaly-first: no failure catalog needed.
Existing signals: temps, pressures, flows, currents.
Messy data OK: gaps and noise included.
Recommendations: not charts to interpret.
Analytics + domain + AI: your engineers' know-how encoded with the model.
Data to start
SCADA / PLC / historian tags: temps · pressures · flows · currents · power
No new instruments. No cleaning. No labeling.
Rollout
1
Connect
read-only, inside your network.
2
Learn
days of normal operation.
3
Recommend
plain-language next step.
4
Act
fix on planned downtime.
Key facts
New sensorsNone
History neededDays, unlabeled
Data prepNone
HardwareStandard CPU
OutputRecommendations
RunsOn-site
Next step: a read-only pilot
One line, one asset group, days to first recommendation.
IoTGPT: Predictive Maintenance for FactoriesExisting SCADA data · Runs on-site · Standard CPU