Energy & Utilities

A utility makes its hardest decisions under a clock. Storm approaching, crews finite, feeders prioritized, customers counting minutes. The information needed to make those calls well - live telemetry, network topology in GIS, asset condition and maintenance history, outage plans, crew availability and qualification, weather forecast, and the financial consequence of each choice - is distributed across systems built in different decades for different departments.

Ridgeline AI models the network and the work against it as one object. Condition and criticality inform which assets get hardened before a storm; topology and load inform restoration sequence; crew qualification and location inform dispatch. When the forecast changes, the plan changes with it, and the reasoning stays visible instead of being reconstructed later from memory.

Between events, the same model drives the unglamorous decisions that determine whether the next event is survivable: which assets to prioritize for inspection or replacement, where vegetation and condition risk overlap, how to phase capital so reliability and rate impact are both defensible. Because it draws on the same data the control room uses, the long-range plan and the daily plan finally agree.

The physical layer extends the reach. Line and substation inspection by autonomous capture, thermal and visual analytics on equipment condition, and edge inference at sites where sending raw video upstream is neither practical nor permitted. Every automated finding carries its provenance from sensor frame to recommendation.