Teaching a Catalog to See
A used-equipment catalog stuck at 5,000 items, a company going public, and one to two million photos nobody was using.
The company had a used-equipment business that could only sell what someone had hand-cataloged, and hand-cataloging is slow. Five thousand listings online. Tens of thousands of machines in the yard. Every one photographed, none of the photos doing any work. Meanwhile the company was heading toward going public, and the used business needed to look online like what it actually was in the yard.
Not "hire more people to catalog." Not "buy a vendor's tool." Train a model on the photos we already had. I conceived it, directed the technical design, and owned the testing: image recognition on Amazon Rekognition Custom Labels, trained on one to two million equipment photos, so a machine could be identified and listed from a picture instead of a form.
A pipeline that read the photos, recognized the equipment, and pushed it into the online catalog. The catalog grew from 5,000 items to 100,000. Alongside it, a second model read live data off the machines and flagged one drifting out of its normal range before it broke down: the same idea, pointed at uptime instead of inventory.
The AI wasn't the hard part. The hard part was deciding what "good enough to publish" meant, then testing it until it was true, and refusing to ship a demo. That's the standard I bring to every applied-AI build: it clocks in and does a job, or it doesn't go live.