Fashion GEO & AI SEO. Because AI shops by attributes, not aesthetics.
Your brand is built on imagery and feel. Models answer in text, from attributes: fit, fabric, sizing, price, provenance, returns. Brands that make those explicit get recommended. Brands that rely on the lookbook do not.
Style questions are attribute questions underneath
Shoppers ask in the language of occasions and taste. Models resolve those into filters. If your product data cannot answer the filter, taste never gets a look in.
What should I wear to a winter wedding?An occasion prompt resolved into fabric, formality, colour and season attributes before any brand is named.
Best sustainable trainers under £100Price plus a claim that needs evidence. Unsupported sustainability language is increasingly discounted by models.
Which brands actually fit tall women properly?Fit and sizing queries are high-intent and poorly served. Explicit, structured fit data wins them outright.
Affordable alternatives to [designer item]Comparison prompts where models need attribute parity to justify naming you as the alternative.
We build your prompt set from your own category and run it against live models - these are illustrative.
The category where the data gap is widest
Fashion has the richest visual merchandising and the thinnest machine-readable attribute data of any retail category. That gap is an opportunity: the work is unglamorous, so few competitors have done it.
Text is the only channel
Fit, fabric, cut, care, provenance and sizing must exist as data, not just as photography and copy.
Claims need backing
Sustainability and quality claims without evidence get discounted. Certifications and specifics get cited.
Trends move faster than models
Seasonal and trend-led prompts need a source strategy that keeps pace with a category that turns over constantly.
What fashion GEO involves
Style prompt & attribute audit
We test the occasion, fit and comparison prompts in your category and map the attribute gaps keeping you out of answers.
Product attribute enrichment
Structured fit, sizing, fabric, care and provenance data across the catalogue, built for machine extraction.
Sustainability & claim evidencing
Turning marketing claims into specific, sourced, citable statements models will actually repeat.
Review & editorial source strategy
Improving how the review sites, editorial sources and comparison content models rely on describe your brand.
Fit, returns and honesty are the trust signals
Fashion's biggest commercial problem - returns driven by fit uncertainty - is also its biggest AI visibility lever. The data that reduces returns is the data that gets you recommended.
Explicit sizing, width, model measurements and fit notes are the single highest-value data you can publish for AI visibility.
Clear, structured returns terms read as reduced risk to a model weighing which brand to name.
Vague sustainability language is a liability under advertising rules and gets discounted by models. Specifics win both ways.
How we measure fashion GEO
- Citation share across occasion and style prompts
- Product attribute coverage vs competing brands
- Inclusion in fit and sizing recommendation answers
- Comparison and alternative prompt outcomes
- Review and editorial source sentiment
- AI referral traffic and assisted revenue in GA4
- Before/after prompt testing on live models
- Reduced fit-related ambiguity in model answers
We work across regulated and high-consideration categories
Fashion GEO questions
What is fashion GEO and AI SEO?
How can AI recommend fashion when it can't see the clothes?
Does this replace our visual and social marketing?
What about sustainability claims?
Should fashion brands also look at agentic commerce?
See what AI says when shoppers describe your category
We'll run your occasion, fit and comparison prompts against live models and show you exactly which attributes are keeping you out.