10 systems — computer vision, edge inference and LLM agents — from dataset to a monitored deployment.
Finished garments are size-checked by hand with a tape measure, so QC is slow, inconsistent between operators, and impossible to audit after the fact — and the brands, operators and purchase orders behind that QC line have no system of record at all.
Most tennis computer-vision systems assume a fixed camera, a regulation court, labelled training data and a GPU. Real coaching footage — a handheld camera, a driveway wall, no dataset — has none of those, so the tooling that exists does not run on it.
Fabric defects are caught by human inspectors watching moving cloth, so rare and unfamiliar defect types slip through and nothing is logged for the mill to act on.
In a disaster zone, finding people fast is the whole problem, and a human pilot watching a UAV feed cannot search a wide area quickly or reliably.
A loading dock’s turnaround - gate, docked, unloading, gone - is tracked by eye and on clipboards, so a slow stage is only noticed after the truck has left and the detention charge has already landed.
LLM / RAGScreening a stack of CVs against one job description is slow, inconsistent, and the reasoning behind a rejection is never written down.
Safety compliance on a work site is checked by supervisors who cannot watch every area at once, so violations are found after an incident rather than before.
Industrial vision has to run on whatever hardware is already bolted to the line - a GPU server is rarely on offer.
Company knowledge sits in documents nobody can find, and a chatbot that hallucinates the answer is worse than no chatbot.
In an emergency the useful thing is not a phone call - it is your location in someone else hands within seconds.
