An industrial manufacturer running high-speed strapping and yarn lines needed to catch defects the moment they start, not after scrap and downtime pile up. RTS Labs built a proof of concept showing it can be done with an off-the-shelf multimodal model that watches and listens to the line the way an experienced supervisor would, guided by a plain-English checklist instead of a labeled dataset.
Industrial Manufacturer (Proof of Concept)
Multimodal Defect Detection & Live Line Monitoring
Multimodal AI Model
Video + Audio Streaming
Browser App
Text-to-Speech Alerts
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The manufacturer runs high-speed strapping and yarn lines continuously across many machines. When something starts to go wrong (a spark, a puff of smoke, a tangle forming in the guides, a motor changing pitch), it’s obvious to a seasoned supervisor standing at the line.
But no supervisor can watch every line, every second. Problems surfaced late, after scrap and downtime had already piled up. And the usual fixes stalled: classic machine vision needs thousands of labeled examples per defect, per machine, which means months before anything ships. Sensor retrofits are costly and only see what they’re wired to see. And camera feeds without intelligence just move the watching problem to a screen.
High-speed lines run continuously across many machines, but no supervisor can watch every line, every second, so defects surface late.
Classic machine vision needs a large labeled dataset for every defect on every machine, which means months before anything ships.
Sensor retrofits are costly and only see what they’re wired to see. Camera feeds without intelligence just move the watching problem to a screen.
Instead of training a custom model, RTS Labs built a browser-based inspector on one general-purpose multimodal model that reads video and audio the way an experienced supervisor would. The AI’s instructions come from a plain-English checklist written with the floor supervisor. No labeled dataset, no fine-tuning, no months of data collection. The same app offers two ways to watch: upload a clip for an on-demand verdict, or go live and let it speak up only when something is wrong. Because the intelligence lives in the checklist, the pattern transfers: swap the checklist and keep the engine.
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