Team · AI content creation

AI content creation for e-commerce teams.

For e-commerce teams, the main job for AI content is closing the coverage gap: the long tail of SKUs with one supplier packshot and thin, copied text. The work runs on product data, so every image and description is built from the PIM record and must never contradict it. Accuracy decides whether it pays: a flattering image in the wrong colour turns a sale into a return.

Measuring the coverage gap

Start with a completeness score for every SKU: number of images and angles, an in-use or lifestyle shot, video where the category expects it, description quality, filled attributes and a translation for every market you sell in. The usual picture is lopsided. Hero ranges are fully dressed while the long tail carries a single supplier image and the manufacturer's text, word for word the same as every other retailer's. Rank gaps by traffic and margin, not by SKU count, and work down the list. New SKUs should go live at the minimum standard rather than join the backlog.

Product data as the source of truth

Generated content should be assembled from the PIM record: attributes, materials, dimensions, care instructions and variant colours. If the record says a jug holds 1.5 litres, no description says two, and no lifestyle image places it among props that make it look bigger. When a record changes, the assets built on it should be flagged for regeneration. In Synthetic White, a product feed or PIM export can drive a whole batch at once, and product-lock holds each item's shape, label and colours exactly while the scene around it changes. Fix bad data at the source, because generation multiplies errors as efficiently as it multiplies good content.

Syndication without drift

Most catalogues live in several places at once: your own site, marketplaces, retailer sites and shopping feeds, each with its own image slots, title rules and copy lengths. Keep one master per SKU and generate the derivatives for each destination, so a change in the PIM updates every version instead of one. Expect outside edits too. On Amazon, a detail page is shared by every seller of that ASIN, and Brand Registry gives the brand owner more control over it, so compare live listings with your masters regularly. Retailers that receive syndicated content often run their own approval queues; allow for them in launch dates.

Seasonal refresh without reshoots

Catalogues change faster than photography budgets. Seasonal hero images, gift-guide scenes and banners for trading events such as Black Friday, Prime Day or Singles' Day on 11 November can be regenerated around the same locked products instead of reshot. Promotions carry a rule of their own in the EU: an announced price reduction must show the lowest price charged in the 30 days before the reduction (Price Indication Directive, as amended by Directive 2019/2161). Pull that figure and the sale price from pricing data rather than typing them into generated banners. Archive last season's assets with their dates, because disputes about what a listing showed need an answer.

Updated 25 September 2026

Questions

E-commerce teams, answered.

Which SKUs should get AI-generated content first?

Start where traffic meets thin content: products that get visits but carry one supplier image and copied text, and new SKUs that would otherwise launch half-dressed. Hero ranges usually have photography already. The long tail is where generated content changes the most listings for the least effort, provided the product data behind it is clean.

How do we avoid duplicate product descriptions across retailers?

Write from your own attributes and your customers' questions rather than rewording the manufacturer's text. Give buyers what they need to decide, such as fit, use, compatibility and care, in the brand's voice. Keep one master per SKU and adapt its length and structure per marketplace instead of spinning several versions of the same paragraph.

Will AI product content increase our returns?

It can, if images flatter the product or show the wrong shade, which is why accuracy has to be enforced rather than hoped for. Track return reasons such as 'not as pictured' or 'colour different' per SKU before and after new content goes live, and treat any rise as a content defect to fix at the source.

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