A catalogue is a data problem first
Thousands of SKUs, each with variants, need main images, alternates, lifestyle scenes and copy for every market. A tool that works one image at a time turns that into downloading, renaming and uploading. Look for tools that read attributes and existing imagery from the PIM or feed, use the SKU or GTIN as the key, understand variant relationships so each colourway inherits the right scene, and write results back with consistent file names and alt text. Product accuracy is the other half: a generated image showing the wrong colour, a missing strap or a different cap is a returns problem and, in advertising terms, a misleading one.
Marketplace and feed rules differ by channel
Amazon's main image must be a realistic, professional-quality image of the actual product, true to its scale, quantity and colour and, for most product types, set on pure white, which makes edited real photography the safe route there. Google Merchant Center rejects promotional overlays, watermarks and borders, requires generated images to carry the IPTC metadata that marks them as AI-made, and requires AI-written titles and descriptions to be submitted through its structured title and description attributes. In the EU, the General Product Safety Regulation, applying since 13 December 2024, requires online listings to show the manufacturer, product identifiers and any warnings or safety information. A useful tool validates each asset against its destination before upload, instead of leaving rejections to surface in the seller console.
Descriptions from attributes, not imagination
Language models write fluent product copy and will invent features to fill a gap: water-resistant becomes waterproof, a cotton blend becomes pure cotton. Generate descriptions only from verified attributes, with a list of permitted claims and banned phrasing, and flag anything the attributes cannot support. Variants are the other trap. Thousands of near-identical descriptions across sizes and colours help neither shoppers nor search engines; describe what genuinely differs, and share copy where products really are the same. Localise units, sizing systems and legal wording for each market instead of translating them word for word.
Bulk jobs need review built in
Batch generation is only useful if review scales with it. Look for exception queues that route low-confidence or rule-breaking items to a person, sampling for the rest, and the ability to re-run only the SKUs that changed when packaging or specifications are updated. Synthetic White runs batch generation from a PIM or product feed, uses product-lock to keep real products pixel-accurate in geometry, labels and colours while scenes change, and connects to DAM, PIM and CMS systems, with copy localised into 30 languages and approved claims enforced.
Updated 25 September 2026