AI product listing pipeline
Every drop at Transcend Vintage, my vintage label, means listing 40 to 70 one-of-a-kind pieces. This pipeline turns raw measurement notes and hundreds of unlabeled shoot photos into import-ready, SEO-optimized Shopify listings; I approve at two gates. It has run every drop since I built it.
Every drop cost an afternoon of manual work
Prepping one drop by hand took 3 to 4 hours: writing and pricing every listing, then matching about 600 photos to the right items and uploading them one at a time. The goal wasn't to remove me from the process. It was to remove the labor and keep the judgment.
Raw notes and photos in, finished listings out
Intake
Raw notes ("vintage black slip dress, 100% silk, fits small") become clean, on-brand listing copy, tags, and category.
Enrich
SEO title, meta description, and a data-driven price anchored to the store's real sales history from Shopify.
Segment
One flat photo folder is split into one group per item, using vision boundaries plus capture-time gaps.
Match & upload
Groups matched to listings, hero shot first, uploaded to the Shopify CDN, import CSV written.
Reliability comes from two design choices: two-pass matching (group first, then assign) so one bad guess can't cascade, and a confidence-gated fallback for leftover photos.
The AI does the work; I approve it
One live run of a real drop
The bigger win is capacity: listing volume no longer limits how often the store can drop. All of it runs on first-party data only.
Stack
A sanitized reference version, secrets and store data excluded, is on GitHub.