Catalog mistakes are silent revenue leaks. A missing attribute can stop a product from ranking. A wrong spec can trigger returns. A bad image can get an ad disapproved.
Generative AI makes catalog QA cheaper by handling the first pass: detect issues, propose fixes, and route exceptions to humans.
What to QA (beyond spelling)
- Completeness: required attributes per category
- Consistency: units, naming conventions, variant structure
- Compliance: claims, restricted terms, warranty language
- Image policy: dimensions, overlays, background, product visibility
- Feed readiness: Google Merchant and marketplace rules
A simple QA pipeline
- Ingest product data (CSV/PIM/Shopify export).
- Run deterministic checks (required fields, numeric ranges).
- Run AI checks (spec plausibility, claim detection, missing context).
- Generate a QA report with severity levels.
- Auto-fix safe issues; route risky issues to review.
Example: spec plausibility prompt
You are a product data QA assistant.
Given a product record, find likely issues.
Return JSON only with: issues[{field, severity(1-5), problem, suggested_fix}]
Rules:
- Do NOT invent new specifications.
- If data is missing, suggest which source to check (manual, manufacturer sheet, PIM).
- Flag incompatible units (e.g., inches vs mm mixed) or impossible values (e.g., negative weight).
Product record:
{product_json}The overlooked win: image-text compliance
Many ad platforms and marketplaces dislike certain overlays, claims, or watermarks. Automated image QA catches problems before you scale them.
How to roll this out safely
- Start with one category and 200–500 SKUs.
- Measure: returns, feed disapprovals, time spent on fixes.
- Keep a human approval step until false positives drop.
- Create a “gold set” of tricky SKUs to prevent regressions.
Catalog QA is one of those rare projects that makes teams feel instantly lighter. Less firefighting. More building.