// Grid Intelligence

You never label.
It never stops learning.

Our inspection AI generates and verifies its own training data, so new asset classes go live in days at zero labeling cost to you. Fully air-gapped: the entire pipeline runs inside your own network, and our on-prem verifier outperforms a leading cloud model on grid inspection.

Request a demo → See how it works
0 Labels from your team · ever
Days New class live · zero customer labeling
100% Inside your network · zero external calls
81% mAP@0.50 · held-out EPRI benchmark
// The Problem

The grid is failing
faster than anyone can see.

D+
US Grid Infrastructure Grade
ASCE report · America's power distribution networks
$1.3T
Capital gap before structural failure
Capex required to prevent compounding collapse
17Kmi
Patrolled manually per PSPS event
65 helicopters · 1,800 ground personnel · days lost
$200K+
Per NERC FAC-003 violation
Triggered by defects drone footage already captured

Your drone footage already has the answers.
Nobody is reading it.

// How It Works

From raw imagery
to operational intelligence.

Three phases. Zero customer labeling. On-premises or in your own cloud environment.

01
// Phase 01 · Day 1–14

Asset Inventory

Upload imagery. Get a GIS-ready inventory: 9 classes, 81% mAP@0.50. No labeling work from your team, ever.

02
// Phase 02 · Days per new class

Auto Expansion

A brand-new asset class onboarded in days, with zero customer labeling. The system detects, verifies, and self-labels it at 87–90% precision. No labeling work from you, and none of the months-long labeling projects competitors need.

03
// Phase 03 · Ongoing

Defect Intelligence

Anomalies flagged with confidence scores. Work orders and NERC evidence packages generated autonomously.

// The Platform

One system.
Raw imagery to operational intelligence.

Standard assets, novel classes, and defects — detected, self-improved, and delivered straight into the systems your team already uses.

// Why VisorVolt

Built for the grid.
Not adapted to it.

81%
mAP · EPRI benchmark

Proof the domain is hard

Off-the-shelf models score around 8% on aerial grid imagery. That is why every serious player has to train. The real question is what that training costs, and what happens to the model after it ships. Our production system: 81% mAP@0.50 (63.4% at the stricter COCO 0.50:0.95 average) on real EPRI Circuit 5 data. Held-out evaluation, mAP@0.50: Pole 97% · Transformer 94% · Crossarm 89% · Insulator 84% · Conductor 83% · Fuse 80%.

0
labels from you

You never label a single image

The system bootstraps a brand-new asset class from ~40 seed examples it generates and verifies itself — reaching 92% of a fully hand-labeled detector's accuracy — and adding it never costs you accuracy on the classes you already run. Your team labels nothing, ever. Competitors need thousands of hand-drawn boxes and months of onboarding; you upload imagery and get results.

0
migration required

Works inside what you already have

Detection writes natively into Esri ArcGIS, Hexagon, or any OGC-compatible platform. No parallel system. No export pipeline. No new infrastructure.

0
cloud required

Beats the cloud. Without the cloud.

Everyone else's inspection AI phones home to the cloud for the hard part: verifying what it found. Ours doesn't. Our air-gapped verifier outperforms a leading cloud model on our domain (F1 0.80 vs 0.71), with nothing leaving your network. The full pipeline, including AI-based verification, runs inside the utility's own NERC CIP boundary with zero external calls, on-premises or in your own cloud environment. BCSI compliant. The only inspection AI you can actually run inside a substation.

<2min
compliance packages

Compliance on autopilot

NERC FAC-003 evidence packages generated autonomously from detection output. Days of manual preparation collapse to minutes — every inspection, automatically documented.

// The Difference

Every serious vendor trains on utility data.
The question is who pays for it.

Buzz Solutions and AiDash both fine-tune models on customer-labeled data. That means every new asset class is a new labeling project the utility pays for, and every deployed model begins decaying the day it ships. We are architecturally different: our system generates and verifies its own training data. The difference is not a benchmark score. It is who pays to expand coverage, and what happens to accuracy after deployment.

$0
labeling cost to you

Who pays to expand coverage

With label-dependent vendors, every new asset class means a labeling project on the utility's budget and timeline. Our system generates and verifies its own training data, so new asset classes onboard in days at zero labeling cost to the customer.

↗
accuracy after deployment

What happens after it ships

A model trained on a fixed, hand-labeled dataset starts drifting from field conditions the day it ships, and the fix is another labeling round. Our deployed models feed the same self-labeling loop they were built with, so accuracy improves with every flight rather than degrading.

CIP
boundary · zero egress

Where it runs

The full pipeline, including AI-based verification, runs inside the utility's own NERC CIP boundary with zero external calls. It deploys on-premises or in the customer's own cloud environment. Cloud-dependent vendors cannot match this.

Same category. Different economics.

// Self-Improving

A research scientist
that never sleeps.

Self-improvement isn't a script — it's an autonomous agent that reads every model result and diagnoses the root cause: a model problem, or a data problem? If it's the model, the agent tunes hyperparameters, reads the latest research papers, and updates the model itself. If it's the data, it triggers the auto-label flywheel to harvest and self-verify new training examples. Proven, not promised: the flywheel has recovered a novel class's missing detections on its own — lifting its accuracy with zero new labels. All on-premises, overnight, with zero action from your team.

01 detect DETECT drone · satellite · LiDAR 02 diagnose DIAGNOSE model vs. data agent 03 fix FIX tune model · or · flywheel anomaly flagged › ‹ model deployed domain analysis LOOP CYCLE <24h flight → improvement
VisorVolt Agent
improvement-loop · running autonomously · overnight
● LIVE
COMPLETE
01
Detect
0.61conf
cracked_insulator · below threshold 0.72 · flagged for autonomous review
// aerial_frame_2847.jpg · <2s inference
›
COMPLETE
02
Diagnose
model
issue
agent ruled: model problem, not data · precision plateau on a thin class · reading recent papers to select the fix
// reasoning agent · read the results, chose the branch
›
RUNNING
03
Fix
+2.4pp
method from a recent paper applied · hyperparameters tuned · model updated · zero human touch
// tuned the model · read the literature
No client labeling — ever · Reads papers · tunes models · triggers the flywheel · <2s per frame · Accuracy compounds with every flight
// Imagery-First Intelligence

Starts with your first flight.
Grows smarter with every data layer.

No GIS exports required. No SCADA access. No 90-day onboarding project. Start with what you already fly.

Drone / Satellite Imagery
// Core — Day One
Cracked insulator detected → Dispatch flag generated
Condition detection: all 9 asset classes
+ GIS Metadata & Weather
// Roadmap
High-wind event + ageing insulator → Critical dispatch, 24h window
Roadmap: predictive risk scoring
+ Load History & Thermal Data
// Roadmap
Critical load circuit + thermal stress → Pre-emptive repair before failure
Roadmap: predictive maintenance
// Asset Coverage

One platform.
Any infrastructure.

New asset class onboarded in days, not the months competitors need. Proven on the EPRI benchmark: capacitor banks and junction boxes detected at 87–90% precision with zero customer labeling.

🗼
Power Distribution
Poles, crossarms, transformers, insulators, fuses, conductors + 3 more · 9 classes · 81% mAP
Production
⚡
Transmission Infrastructure
Towers, conductor bundles, high-voltage insulator assemblies
Production
🏭
Substations
Switchgear, transformers, bus structures · same architecture, same timeline
Deploy-Ready
🔥
Wildfire-Risk Circuits
Vegetation encroachment and conductor clearance across high-risk corridors
Active Use Case
// Use Case Spotlight

Post-PSPS Restoration.
The inspection problem no one has solved.

Restoration is gated by a manual, daylight-only patrol of every de-energised circuit mile.

17,000mi
Patrolled in a single PSPS event
PG&E · 65 helicopters · 1,800 ground personnel
$7.87M
SCE Fine — January 2025
Restoration notification failures
May
2026
FERC Wildfire AI Report
Federal regulator formally endorses predictive AI for wildfire risk
// 01
Shutoff Declared
Circuit de-energised ahead of wildfire-risk conditions.
// VisorVolt
Aerial Pass
Drone imagery processed — damage flagged in minutes, not days.
// 03
Restoration Authorised
Damaged sections isolated and repaired with confidence.
// 04
Power Restored
Faster restoration. Reduced regulatory exposure.
// Current Traction

Model validated.
US outreach beginning.

81%
Standard assets · mAP@0.50
9 asset classes · EPRI IEEE DataPort benchmark · 63.4% at the stricter COCO 0.50:0.95 average · zero client labeling
93.8%
Novel classes · verified precision
Discovered & auto-labeled with 0 new client labels · capacitor bank
0.80
On-prem verifier F1 · beats cloud
Air-gapped verifier outperforms a leading cloud model on our domain (F1 0.80 vs 0.71) · zero data egress
0
Client labels · ever
A new class bootstraps from ~40 seed examples the system generates & verifies itself · 92% of a fully hand-labeled detector · EPRI benchmark
0.74
Defect detection · F1
Insulator faults — broken discs, flashover · CPLID benchmark
~8%
Off-the-shelf AI on the same benchmark
Proof the domain is hard: everyone has to train · the question is what that training costs
2wk
POC to results
100 images from your programme · fixed fee · no commitment
9
US target accounts
Rural co-ops and wildfire-state IOUs · O-1 Visa · direct US presence
// The Founder

Built by someone who
has done this before.

Umang Sharma — Founder & CEO, VisorVolt
Umang Sharma
Founder & CEO · VisorVolt

A decade building production AI at scale: geospatial ML platforms at TomTom serving 500M+ users, co-founder and CTO of a US VC-backed technology company, and a published authority on deep learning.

  • Wiley Technical Author · Foreword by Google AI lead
  • Ex Lead Applied Scientist · TomTom · 500M+ users
  • Guest Faculty · IITs & IIMs
  • US O-1 Extraordinary Ability Visa
  • Co-Founder & CTO · ThreeV Technologies · Departed 2026
LinkedIn umang@visorvolt.ai
// Get Started

Start with imagery
you're already collecting.

100 images. 14 days. A structured asset inventory — no labeling, no infrastructure change, no commitment.

Request a demo → Talk to the founder

umang@visorvolt.ai · visorvolt.ai