Wayam Eye
AI inspection for solar plants: find defects before they cost yield.
Wayam Eye is Wayam's solar-inspection accelerator. It turns aerial and thermal imagery into defect findings O&M can act on: hotspots, soiling, module damage, and string issues, ranked by severity and mapped to the array, so inspection keeps pace with the size of the fleet.
Workflow
Problem
Solar fleets are too large to walk. Hotspots, soiling, cracked modules, and string failures stay hidden until generation drops.
Outcome
Plant-scale visual and thermal inspection that flags defects early and routes work orders to the right crews.
01
Capture
Bring in drone, robot, or fixed-camera flights for visual and thermal surveys.
02
Detect
Models flag defects and anomalies across modules, strings, and inverters.
03
Locate
Findings are pinned to plant layout so crews know exactly where to go.
04
Act
Prioritized reports and work-order ready outputs close the loop with O&M.
Modules
Aerial & thermal capture
Ingest drone, robot, and fixed-camera imagery across arrays and substations.
Defect detection
Spot hotspots, soiling, cracks, PID, and string anomalies automatically.
Inspection reports
Priority-ranked findings with location, severity, and evidence for O&M.
Fleet roll-up
Compare plants and campaigns so portfolio teams see risk, not just site folders.
Representative use cases
Portfolio-Wide Thermal Inspection Across a Multi-Gigawatt Solar Fleet
A solar independent power producer operates 40 sites across three states, each inspected manually on an annual cycle by walking crews.
Commissioning Inspection and Warranty Evidence for a New Solar Build
An EPC contractor completing a large utility-scale build must document module condition at handover to protect against later warranty disputes.
Directing O&M Crews to the Defects That Actually Cost Generation
A solar operations team receives inspection reports listing thousands of findings with no indication of which ones matter to output.
Detecting Progressive PID Degradation Before It Reaches String Level
A solar asset owner suspects potential-induced degradation across older arrays but cannot confirm it without invasive testing at scale.
Pairing Inspection Findings with Operating Data for Root-Cause Attribution
An asset owner sees an underperforming block in its SCADA data but cannot tell whether the cause is physical defect, soiling, or an inverter fault.
Evidence
What Wayam can show for this entry
Curated solution design with an owner and a review date. No performance or production claim.
Allowed claims at this tier: Possible workflow, typical stack, prerequisites.
- Evidence tier
- T4 · Reference pattern
- Integration status
- Typical enterprise system
- Metric status
- Scenario only
- Evidence owner
- Not yet attached
- Validated
- Not yet attached
- Valid until
- —
- Content version
- 2026.09
Pending evidence attachment
This entry represents an enterprise architectural reference pattern. Customer benchmarks, run replays, and metric verifications are established during technical discovery.
Limitations
- Headline metrics are representative until a customer result is attached.
Representative metrics · Reference · scenario only
Plant-scale
solar inspection
These describe the intended outcome of the design. They are not measured customer results until a validated case is attached above.
Architecture & controls
Composed across the operating loop
Ingest
Connect the systems of record and read the signals the work already produces.
Covered by the platform
Reason
Ground context, score options against policy and the stated goal, draft the next step.
1 agent role
Act
Execute an approved step in the system of record and keep the evidence.
7 agent roles
Govern
Set policy, gate consequential actions on a named approver, and audit what ran.
2 agent roles
Pack Architecture & Agent Composition Graph
10 Composed Agents- AGT-0781
Visual Asset Inspection Agent
Vision
- AGT-0445
Asset Inspection Agent
Energy
- AGT-0428
Maintenance Work Order Optimization Agent
Asset Management
- AGT-0345
Quality Inspection Reporting Agent
Quality
- AGT-0381
Warranty Claims Triage Agent
Quality
- AGT-0393
Field Technician Dispatch Optimizer
Field Operations
- AGT-0455
Substation Anomaly Detection Agent
Energy
- AGT-0284
Anomaly Explanation Agent
Data and Analytics
Control gates
- A named approver on every consequential write-back
- Evidence and audit trail kept with every run
Capability atoms
- vision-perception
- anomaly-detection
- risk-scoring
- alert-routing
- system-connectors
Typical enterprise systems
Typical stack for solution design; compatibility is validated during discovery.
- Cognex VisionPro
- NVIDIA Jetson
- AWS Panorama
- ROS
- GE Vernova GridOS
- Siemens Spectrum
- ENTSO-E API
- Sphera
Designed for
energy-utilities
Business case
Model a Wayam Eye scenario with your own baseline
Three scenarios from the numbers you enter. Capacity released is time; it becomes a saving only when roles or costs are actually removed or avoided.
| Scenario | Improvement | Capacity released | Cashable savings | Net annual | Payback |
|---|---|---|---|---|---|
| Conservative | 9% | — | — | — | — |
| Expected | 18% | — | — | — | — |
| Upside | 24% | — | — | — | — |
Enter an annual volume and a baseline cost to see figures.
Illustrative planning scenario, not a guarantee. Results depend on process baseline, adoption, data quality, integration scope, controls, and deployment costs.
Pilot this
From solution design to a bounded pilot
Step 1 · 3–4 weeks
Discovery Sprint
Validate the workflow, data, controls, baseline and business case before anything is built.
Gate: Blueprint and pilot plan signed by the business owner, technical owner and Wayam.
Step 2 · 6–10 weeks
Bounded Pilot
Prove quality and value on agreed data against agreed acceptance tests.
Gate: Acceptance thresholds met on the evaluation set; go/no-go decision recorded.
Agrani · evidence pending3 Agrani candidates in this solution: Maintenance Work Order Optimization Agent, Defect Root Cause Analysis Agent, Predictive Maintenance AgentPairs well with