The generic-AI problem for collectibles

Most AI listing tools send your photo to a vision model with a generic prompt and accept whatever comes back. For commodity goods that is fine. For collectibles — stamps, postcards, coins, trading cards — it is a liability. A generic model will happily tell you it is "a vintage postage stamp" and price it like every other vintage postage stamp, completely missing that it is a specific catalog number with a rare watermark worth ten times the average.

Per-category prompts change the output

CollectorPursuit uses a separate AI prompt per category. A stamps prompt instructs the model to identify the catalog number, country, perforation, gum condition, and watermark before pricing; a trading-card prompt asks for the set, year, player, and grade-relevant flaws. The same photo, routed through the right prompt, produces a listing a specialist would recognize instead of a generic guess.

Draft-first, always reviewable

Crucially, nothing goes live until you have reviewed it. Every item generates as a draft — title, price, condition, item specifics, images, and description — where you can edit any field. The AI does the heavy lifting of extraction and structure; you do the final 5 percent that matters. You can read more about that review-first philosophy on the How It Works page.

The result

Category-specific prompts measurably reduce the edits a dealer has to make before publishing, and they catch the high-value specifics (catalog numbers, certified grades, error varieties) that generic tools silently drop. For specialists, that is the difference between a listing that sells and one that sits.

Browse the latest AI improvements on our changelog, or start with a free credit bundle to test it on your own inventory.