Apparel discovery has moved into the assistant, and the questions are unusually specific: "What's a good work dress for someone tall, under £120, that isn't dry-clean only?" An assistant answering that is filtering on length, price, fabric care and fit — attributes most fashion stores never publish in a form a machine can read. The brands with complete data get named; the rest are excluded before style ever enters the conversation.
Fashion has a second problem no other retail vertical carries so heavily: returns. The majority are sizing-related, and they quietly erase the margin the sale created. The same structured measurement and fit data that gets you recommended by AI also gets shoppers into the right size first time — which is why this work pays back twice.
What actually moves the needle for fashion
- Complete variant and size schema — actual garment measurements, fit notes, fabric and care — so assistants can match a shopper's stated requirements to a specific SKU.
- Fit and sizing guidance that machines can extract, cutting the returns that sizing uncertainty causes.
- Style, occasion and body-type content answering how people really describe what they want, rather than how your catalogue is organised.
- Healthy product feeds keeping price, stock and variants accurate everywhere AI reads them.
- Durable category authority so each new collection inherits visibility instead of starting from nothing.
What winning looks like
Your pieces get recommended when shoppers describe an occasion, a fit or a budget to an assistant; they arrive pre-sold and confident about size; and fewer of them come back. That is organic revenue you own, at a materially better margin than the returns-heavy paid traffic most fashion brands are hooked on.