What buyers ask AI about apparel

Sizing ("does [brand] run small"), fabric ("is their 'silk' real"), quality longevity, and legitimacy for cross-border shops. Engines answer from size-chart data, fabric composition facts, and review ecosystems with photos. Fashion's image-first content tradition is precisely what AI can't use — the brands that translate fit and fabric into quotable text win the answer layer.

What to publish

  • Sizing intelligence — per-product fit notes ("runs half a size small; model wears M"), measurement tables in both unit systems.
  • Fabric provenance — composition, weight (gsm), origin, certifications (OEKO-TEX, GOTS where real).
  • Care and longevity — the durability questions buyers ask before paying cross-border prices.

Source graph

Review platforms with fit feedback, community fashion forums, and creator reviews engines read. For DTC: the legitimacy layer (registration, returns address, payment providers) carries extra weight in fashion, where scam-store anxiety is highest.

First 90 days

  1. Fit/fabric answer blocks on top 30 SKUs.
  2. Review-layer audit; fit-feedback solicitation.
  3. Legitimacy facts page.
  4. One community presence begun.

Frequently asked questions

We're a fashion brand — does text content really matter more than imagery?
For AI answers, yes: engines read text and structured data, not lookbooks. Imagery converts humans on your site; quotable fit/fabric/legitimacy text gets you recommended before the human arrives. Both matter; only one is new work.