UTILITIES, OIL & GAS
AI for , with safety built in.
Asset maintenance, outage response, and field operations with AI your engineers and regulators can trust.
What we hear
“Predictive maintenance models make recommendations our engineers don’t trust.”
“Sensor and asset data sits in systems that don’t agree with each other.”
“We can’t let AI anywhere near operational systems without strict controls.”
“Regulators and rate cases want to see what technology spending delivered.”
"Should we trust the maintenance recommendation?"
An ML model recommends replacing a transformer ahead of schedule. Field crews ignore it because they can’t see why. A3R sets it up so the recommendation earns trust or gets corrected:
- Show the evidence: each recommendation shows the readings and history behind it, in the terms the crew uses.
- Proven before use: the model is tested against past failures before go-live, and its hit rate is reported every month.
- AI recommends, engineers decide: a qualified engineer approves every action, and no AI writes to operational control systems.
- Learn from overrides: when crews override a recommendation, the reason is recorded and used to improve the model.
What you get
Data readiness assessment
For asset, sensor, and work-order data.
AI inventory with risk tiers
Including AI inside vendor software.
Separation from control systems
AI stays out of operational technology unless explicitly approved.
Performance tracking
Recommendation accuracy tied to cost and reliability results.
Regulator and rate case documentation
Showing what AI is used for and what it delivered.
Field adoption plan
So crews understand and use the recommendations.
Start with the assessment.
In about four weeks, you’ll know what AI you’re running, where you stand on the maturity curve, and what to fix first.