AI image creation for Amazon, grounded in research.

On a search results page, your main image competes in a grid of near-identical thumbnails — it is effectively your storefront, your ad creative and your first impression at once. Inside the listing, most shoppers — especially on mobile — swipe the gallery and read the images, not the bullets.

That gives the gallery a job description:

• Main image — win the click without breaking Amazon's rules.
• Benefit frames — the two or three reasons this product wins, readable in a second.
• Scale and detail — size, fit and texture questions answered visually.
• Lifestyle — the product in its real context, so shoppers can picture owning it.
• Comparison / how-to — the objections and "will it work for me?" questions closed before they become returns.

The failure mode of AI product imagery is obvious: plastic-looking renders, hallucinated product details, text gibberish, a style that could belong to any brand. The studio avoids that by grounding every image in three inputs:

• Your actual product — generation works from your product photography as reference, so the item in the frame is your item, not an AI's guess at it.
• The research dossier — review mining and competitor analysis decide what each frame must communicate before any pixels are made (the same dossier behind listing copy).
• Your brand — colors, typography and tone carry across the gallery so the listing reads as one brand, not a collage.

Every image passes human review for accuracy — claims, details, legibility — and your approval before it ships. An image that misrepresents the product is a return machine; accuracy is non-negotiable.

Amazon's image requirements are strict, and the main image rules especially:

• Pure white background (RGB 255/255/255) for the main image.
• The product filling most of the frame, showing only what's actually in the box.
• No logos, badges, watermarks or overlay text on the main image.
• High resolution — 1,600px+ on the long side so zoom works (1,000px is the floor).

The studio produces to these rules by default — and where the rules leave room (angle, composition, props that are genuinely included, packaging visibility), that room is used deliberately to stand out in the search grid.

Because generation is fast, the constraint on image testing stops being design capacity and becomes traffic. Keplo uses that: main-image variants are tested through Amazon's A/B testing where eligible, gallery changes are measured against baseline, and each change gets a verdict — proven or killed. The verdicts accumulate into something most brands never build: evidence about what visual language actually sells your product.