16,000 products. One at a time. Always out of date.
Every product needs a confirmed photo in Amplience before a description can be written. The check had to be done manually: search Amplience for a style, read the result, type the status into a spreadsheet. Then move to the next one.
With 16,000+ products across seven department tabs, status was perpetually stale. A buyer asking "which of my styles still need photos?" had no fast answer. The only honest answer was "whatever someone checked last week, and some of it is probably wrong now."
There was no way to scale this without automation.
A Python script that reads Amplience the same way a browser does
The script walks through every product row in the Master Sample List, constructs the Amplience asset URL using the standard naming convention, and checks whether a real image exists at that address. Results write back directly to the Excel file as each department tab completes — so a crash mid-run never wipes completed work.
Results written directly into the Master Sample List
The script does not create a separate report. It writes status and photo links back into the source file — the same spreadsheet buyers, writers, and the photography team already use. No new tool to learn, no new file to check.
Two columns checked. Buyer-ready output in under 20 minutes.
Filtering column T now gives a buyer-ready NEEDS PHOTO list with zero manual work. Status reflects what is actually in Amplience, not what someone entered weeks ago.