SolarVerify

AI-assisted monitoring and verification for rural solar electrification projects, helping energy teams track performance, impact, and sustainability across dispersed solar installations.

The Problem

“Rural solar electrification projects lack scalable monitoring and verification across dispersed installations”

Organizations face these key challenges:

1

Remote sites are difficult and expensive to inspect regularly

2

Telemetry quality is inconsistent due to connectivity and device variability

3

Failures are often discovered only after community complaints

4

Maintenance teams lack a clear priority queue across many installations

5

Impact verification requires manual consolidation of fragmented data

6

Battery and inverter issues can go unnoticed until service quality drops significantly

7

Program managers cannot easily compare performance across regions or installers

Impact When Solved

Detect underperforming solar systems before complete failureReduce unnecessary field visits by prioritizing high-risk sitesImprove uptime and energy availability for rural communitiesAutomate verification reporting for funders, NGOs, and regulatorsTrack sustainability indicators such as battery degradation and maintenance responsivenessSupport portfolio-level planning across hundreds or thousands of installations

The Shift

Before AI~85% Manual

Human Does

  • •Gather community profiles, site notes, weather records, and baseline demand assumptions from scattered sources
  • •Conduct manual feasibility reviews and size candidate solar, battery, and backup systems in spreadsheets
  • •Compare site constraints, costs, and reliability tradeoffs across communities using static maps and consultant inputs
  • •Prepare planning packets and engineering recommendations for funding and deployment decisions

Automation

  • •No significant AI support in the legacy planning workflow
  • •No automated demand forecasting or community archetype classification
  • •No scenario simulation across weather, terrain, and logistics constraints
  • •No reusable replication recommendations from prior project outcomes
With AI~75% Automated

Human Does

  • •Review proposed community archetypes, demand assumptions, and design priorities for each site
  • •Approve final system configurations, rollout sequencing, and budget tradeoffs across communities
  • •Handle exceptions where local social, land, or logistics constraints require plan changes

AI Handles

  • •Consolidate local data, weather history, site conditions, and prior project records into planning profiles
  • •Estimate demand bands, classify community archetypes, and recommend candidate system designs with confidence scores
  • •Simulate design scenarios across cost, reliability, seasonal variability, and logistics constraints
  • •Generate replication templates, rank target communities, and produce standardized planning packets for rollout

Operating Intelligence

How it works

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence93%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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