We Built a Product Image Enhancer. The Hardest Feature Was Teaching Sellers When NOT to Use It.
When we launched the image enhancer on LoomaDesign, we assumed the user flow would be straightforward: upload a photo, click enhance, download a better version.
Within the first few weeks, we noticed a pattern that broke that assumption. Sellers were uploading images that didn't need enhancement — they needed upscaling. Or retouching. Or, in the worst cases, a complete reshoot from a better source file. And they were disappointed when "enhance" didn't solve a problem it wasn't designed to solve.
This was a product education problem, not a product quality problem.
So we wrote a decision guide: Upscale vs Enhance vs Retouch vs Reshoot. Four paths, matched to four types of image failure. The guide now sits right before the upload flow, and here's what changed:
Support tickets about "the enhancer made my label look weird" dropped significantly. In most cases, the label was already unreadable in the source; an upscaler had hallucinated the fix.
Users started uploading better source files. The guide tells them: if the label is missing or the edge is cut off, go back and get a better original. No tool can help.
Batch processing got safer. Users now run one SKU through the full workflow, verify it against the real product, then batch the rest.
One concrete thing we learned: the most dangerous outcome isn't a bad-looking photo. It's a photo that looks great but misrepresents the product. An AI upscaler can generate a sharp, convincing label character that was never actually on the packaging. When the buyer opens the box and sees the real thing, that return is more expensive than the original bad photo ever was.
The decision table we built has five rows. The one that gets used the most — "small image with noise but intact product detail" — is the middle path. The "stop or escalate when" column is the real product. Processing can't add information that isn't in the source.
Teaching that took longer than building the enhancer itself.
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