AI image QA for e-commerce
Ship AI product imagery that matches the product
E-commerce teams generate more AI imagery than anyone — product-in-scene shots, seasonal variants, marketplace banners — and carry a risk nobody else has: the image is a promise about a physical object. A generated shot with the wrong number of buttons, invented stitching or a garbled label is not just ugly, it is a returns problem and a listing-policy problem.
What goes wrong in AI product imagery
- Product details that drift from the SKU — proportions, ports, stitching, materials
- Packaging and label text that garbles under generation
- Props and hands interacting impossibly with the product
- Reflections and shadows that contradict the staged scene
- Marketplace disclosure rules for AI imagery going unmet
How Chekr checks it
Chekr scans every creative with nine checks — text on packaging, anatomy of hands holding products, object physics, lighting, artifacts, brand rules, provenance and IP similarity — and returns pinned findings plus an integrity score in about four seconds per image.
At catalogue scale the scan runs through the API: every render is checked on arrival, scores gate what goes live, and bulk review in the queue handles the exceptions. One free check per image lets you evaluate the pipeline on your own catalogue first.
Frequently asked
Can Chekr verify the generated image matches our actual product?
Chekr flags physical implausibilities, garbled label text and detail defects that make a product shot wrong. Exact SKU comparison against a reference image is on the roadmap; today the checks catch the defect classes that cause most mismatch complaints.
Do marketplaces require AI disclosure?
Policies vary and are tightening — and in the EU, AI-generated content in ads faces transparency obligations from August 2026. Our blog covers the regulatory timeline in detail.
What volume can the API handle?
Scans complete in roughly four seconds per image and run in parallel, so nightly catalogue drops and continuous generation pipelines are both practical.