Quality Assurance
Solar Quality Assurance & Inspection Software
Software for solar module and factory quality assurance — automated electroluminescence defect detection, inspection data pipelines, and factory system integration, built by the engineers who ran QA systems at scale.
Solar quality assurance produces enormous amounts of data and, too often, very little insight. Electroluminescence (EL) images reveal the cracks and dead cells that determine whether a module lasts twenty-five years or fails early — but when grading is manual, it’s slow, subjective, and disconnected from the factory systems that could act on it. QA should be a data pipeline, not a folder of images someone eyeballs.
Automated defect detection
Manual EL grading doesn’t scale and doesn’t stay consistent: two inspectors grade the same image differently, and throughput caps out at human speed. A trained computer-vision model changes the economics. In our own defect-detection work, the model classifies module defects in 1.8 seconds at 99.1% recall, inspects more than two million images per year, and cut missed-defect escapes by 87% — replacing subjective grading with consistent, auditable criteria.
Inspection data that connects to the factory
A defect classification is only useful if you can trace it back to a cause. We build QA systems that connect inspection to the rest of the operation:
- Module-level traceability — every result tied to the module, line, shift, and material lot
- Factory MES and production integration — so quality trends surface against production data, not in isolation
- Defect trend analytics — catch a drifting line or a bad material batch before it becomes a shipment of returns
- Inspection data pipelines — reliable capture and storage of high-volume image and measurement data
Built by clean energy QA engineers
This is not a domain we learned on your project. Our engineering team built the data, reporting, and client-portal systems at one of the largest clean energy quality assurance operations in the world — systems serving clients across 85+ countries. We know how factory QA actually works, where module defects hide, and how to turn inspection into software that makes the next module better. That’s the difference between a generic vision model and a QA system that fits how your line runs.
Frequently asked questions
What does solar QA software do?
It captures, analyzes, and tracks quality data across module inspection — most importantly electroluminescence (EL) imaging, which reveals cracks, dead cells, and defects invisible to the naked eye. Good QA software classifies defects consistently, connects each result to the module and production line it came from, and turns inspection into data you can act on rather than folders of images.
How does automated EL defect detection compare to manual grading?
Manual grading of EL images is slow and subjective — two inspectors can grade the same image differently. A trained computer-vision model classifies defects in under two seconds with high recall and consistent criteria. In our own work, an EL defect-detection model reached 99.1% recall at 1.8 seconds per image, inspecting over two million images per year and cutting missed-defect escapes by 87%.
Can it integrate with our factory systems?
Yes. Inspection data is only valuable when it connects to the rest of the factory — MES, production monitoring, and traceability systems — so a defect trend can be traced back to a line, a shift, or a material lot. We build those integrations rather than leaving QA as an island.
Who builds this?
Our engineering team built the data, reporting, and inspection systems at a leading clean energy quality assurance operation serving 85+ countries. Solar QA is not a domain we ramped into — it is where the team comes from.