The sector's friction
In manufacturing, quality control almost always depends on an inspector looking at parts at a pace the production line dictates, not the other way around. That means sampling (not 100% of parts) and criteria that vary between shifts and inspectors. A defect caught on the line costs far less than one that reaches the customer.
How we apply our technology
We train computer vision models specific to the defect or measurement that matters on your line — not a generic off-the-shelf "anomaly detection" model. The same technical approach we use in Spray Analyzer (image segmentation, per-pixel classification, decision threshold calculation) carries directly over to inspecting parts, surfaces or assemblies.
When the process justifies it, we combine visual inspection with AI process automation so that detecting a defect automatically triggers an alert, a reprocess or a log entry — without someone having to watch a dashboard for a whole shift.
Expected result
Automated inspection allows moving from partial sampling to near-100% coverage, with consistent criteria across shifts. We don't yet have a manufacturing case documented with our own figures — the verified data point we do have, on an equivalent technical principle, is Spray Analyzer's: ±0.5% error versus lab in UV fluorescence classification. We cite it as evidence of technical capability, not as a promised figure for manufacturing without a prior assessment.