Asset Intelligence & Field Copilot for a Utility
Illustrative reference architecture. This is a composite engagement template built to show how Mopshy structures enterprise work. It is not a real Mopshy client, and every figure, KPI, and quotation on this page is illustrative rather than measured client data.
Asset-health models across 3,100 substations, vegetation-risk vision, and a field-tech AI copilot — engineered with reliability + safety as first-order constraints.
−34%
Truck rolls
−41%
Vegetation-caused outages
−19%
SAIDI
The bottleneck
Reactive maintenance drove overtime and truck rolls. Vegetation-caused outages climbed 22% YoY. Field techs carried 40+ PDF manuals per truck.
The system
- Substation asset-health model on SCADA + PI historian data
- Vegetation-risk vision model on aerial + LiDAR imagery
- Field-tech AI copilot with offline-first mode across 40+ manuals
- Safety-critical guardrails: human confirmation required on any switching action
Outcomes measured, not promised
3,100
Substations monitored
0
Safety incidents attributed to AI
$47M
Annualized value
92%
Field-tech copilot adoption
What it cost, what it returned
Investment
$5.4M program (year one)
Payback
9 months
Annualized value
$47M O&M avoidance + reliability value
How it is built
- PI historian + SCADA unified for asset-health modeling
- Aerial + LiDAR imagery pipeline for vegetation-risk scoring
- Offline-first field-tech copilot with signed model updates
- Safety-critical guardrails with human confirmation
Representative engagement. This case study describes a composite of Mopshy AI's enterprise engagement patterns, delivery methodology, and observed outcome ranges. Company names, individual quotes, and specific figures are illustrative and used to communicate the enterprise pattern rather than to describe a single identified client.
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