Case study / Energy
Predictive maintenance across 4,200 field assets
An AI-assisted operations platform that turns sensor noise into a ranked list of assets that need attention this week.
- Client
- Sentinel
- Sector
- Energy
- Year
- 2025
- Scope
- 3 services
- Services
- AI automation / Cloud & APIs / Product design
Sentinel / Energy
Case study / 2025
01
27%
Fewer unplanned outages
02
3.5M
Events processed daily
03
4,200
Assets monitored
Context
The problem we found
Technicians were servicing equipment on a fixed calendar while a growing share of failures happened between visits. The data existed, but it lived in three historians and nobody trusted the models enough to act on them.
Method
How we worked
Four decisions that shaped the build.
- 01
Built a feature pipeline over 3.5 million daily sensor events with data quality checks at the edge.
- 02
Trained and evaluated failure models against known field maintenance outcomes.
- 03
Designed a review workflow where every recommendation shows its evidence and a technician can overrule it.
- 04
Deployed with a shadow period before recommendations influenced the maintenance schedule.
What the client said
“They earned trust before they asked for it. The model shipped in shadow mode for a month, and the technicians were the ones who promoted it.”