How to Review AI Product Photos Before Publishing

AI accelerates visual prep but does not know your warehouse shelf. Buyers receive physical goods, not pixels—mismatch drives returns and negative reviews. Marketplaces may reject cards when main images mislead about size, bundle, or category. Vitrina AI generates variants; sellers decide what ships.

Image, text, and video rules change—read documentation before large uploads. One frame may pass on one channel and fail on another. Do not copy files blindly between Kaspi, Wildberries, and Ozon without format and background checks. Log rejections with SKU, reason, and date.

Review is insurance against card blocks and reputation damage. Define who may upload after QA. During bulk runs, spot-check every fifth SKU. Build the checklist early to pay less in returns.

Compare silhouette, proportions, and bundle to the source and a warehouse sample. Check color in daylight on a calibrated monitor or neutral backdrop—AI often warms or cools tones. Patterns, stripes, and checks must not shift, duplicate, or vanish. Seams, zippers, buttons, hardware, logos, and labels must match.

For jewelry verify stone count, clasp shape, chain length, and metal tone. For shoes check profile, sole, laces, and material texture. For sets confirm every item in the description appears in frame. If anything drifts, reject—do not assume buyers will not notice.

Use large screens—phones hide distortion. Archive before/after per SKU. Add checklist items when a category repeats errors. Exact product card mode is usually safer than lifestyle for main images.

Background must not mislead about size, material, brand, or bundle. White frames should lack stains, stray objects, and watermarks. Lifestyle is fine for extra slots if the product stays recognizable. Shadows must not fake scale or hidden stands.

Inspect cutout edges: tassels, transparency, model hair fail often. Many platforms ban text on main images—read category rules. No fake brands, foreign logos, or invented awards. If background competes with the product, simplify or return to neutral.

For clothing on model, review fit, length, neckline, and proportions. For lingerie use adult commercial catalog style only—no minors or provocative framing. Video from photo is in development; do not publish unverified reels. When unsure, pick the more neutral frame.

Open current Kaspi, Wildberries, or Ozon image help for main photos. Check resolution, aspect ratio, allowed backgrounds, and category bans. Requirements change—last season pass may fail today. Vitrina AI is not an official partner.

For Kaspi align visuals with warehouse stock and description. Wildberries expects catalog consistency and plausible apparel fit. Ozon benefits from extra angles and honest bundles on every frame. Do not promise in copy what photos do not show.

Export in accepted formats without over-compression. If rejected, map the reason to your checklist—often it is product truth, not AI tooling. Keep sources beside exports for buyer disputes.

Step 1: open source and AI output side by side on a large screen. Step 2: verify shape, color, pattern, hardware, bundle. Step 3: check background, shadows, edges, stray objects. Step 4: read platform image rules and file spec.

Step 5: ask a colleague to review disputed frames. Step 6: log SKU, date, version, reviewer in your table. Step 7: upload only after all steps pass. Do not skip spot checks during batch uploads.

Keep originals for buyer claims. Quarterly, update the internal checklist from return data. Train marketplace owners, not only designers. One missed defect on a bestseller costs more than an hour of review.

Sending the first preview without source comparison is the leading mismatch cause. Bulk generation without spot checks spreads systematic errors. Mixing inconsistent main and lifestyle frames confuses buyers. Third-party photos and logos risk account issues.

Publishing AI frames with prettier but wrong color. Promising bundles not shown in images. Using video/Reels before confirming feature availability and SKU accuracy. Expecting AI to pass moderation automatically—responsibility stays with the seller.

Schedule QA in the SKU launch calendar like pricing and copy. Link checklists to white background and Kaspi guides. Honest simple cards beat cinematic wrong-SKU frames. Regeneration is minutes; disputes take days and hurt ratings.

When SKU count grows, do not skip comparison steps—batch Studio exports instead. Assign a rotating QA reviewer so one person does not approve their own work. Keep a shared rejection log: wrong color, clipped edge, misleading shadow, foreign logo.

For multi-marketplace sellers, run the same checklist on Kaspi, Wildberries, and Ozon exports even when file sizes differ. Amazon and Shopify listings benefit from the same discipline—buyers compare photos to received goods, not to your internal brief.

Quarterly, sample ten random live cards and compare to warehouse samples. Update internal guides when platforms change image help. Publication liability stays with the seller; Vitrina AI does not guarantee approval.