What is GEO, and how can fashion brands be found in AI search?

GEO, or generative engine optimisation, is the practice of making a brand's products and content more likely to be cited or recommended in AI-generated answers. For fashion brands, it rests on the same foundations as good SEO, plus complete product data feeds and clear, factual content that AI systems can extract and trust. There is no shortcut or special markup that guarantees inclusion.

What is generative engine optimisation (GEO)?

The term was introduced in a research paper by Pranjal Aggarwal and colleagues, published at the KDD 2024 conference. It describes methods content creators can use to improve their visibility in responses from generative engines, meaning AI systems that read many sources and compose a single answer. The authors built a benchmark of queries across domains and reported that GEO methods can boost visibility by up to 40 percent in generative engine responses, while noting that the efficacy of strategies varies across domains.

In practice, GEO is used as a label for everything that helps a brand appear in answers from ChatGPT, Gemini, Perplexity, Microsoft Copilot and Google's AI Overviews and AI Mode. McKinsey's State of Fashion 2026 report describes AI chatbot responses as "the new SEO" for fashion retail, as customers turn to large language models to search for products, compare offerings and receive tailored recommendations.

How is GEO different from SEO?

The overlap is large. Google's own guidance on AI features is explicit: "There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary," and the best practices for SEO remain relevant. Google also states that site owners do not need to create new machine-readable files, AI text files or special schema.org markup to appear in these features. That should make brands cautious about services that sell secret GEO techniques.

What differs is the shape of the outcome. A traditional results page shows ten links, and a brand in position six still gets some traffic. An AI answer may name three products or brands and explain why. Being included is more valuable, and being left out more costly, which is why the quality and consistency of information about a brand across the web matters more than individual page tweaks.

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How do AI assistants choose which fashion products to show?

Each platform works differently and changes often, but published documentation gives some indications. OpenAI says that ChatGPT selects products based on relevance to the user's intent, considering price, reviews, availability and user preferences, and that product results use structured metadata from first-party and third-party providers. It says these results are not ads, and that merchants can provide product feeds directly and apply for direct feed access. Ranking factors it lists include availability, price, quality and whether the merchant is the maker or primary seller.

The same logic applies across platforms. Where accurate, structured product data is available, AI systems can use it; where it is missing or inconsistent, they fall back on whatever they can find, including outdated retailer pages or third-party descriptions. Brands that leave gaps effectively let others describe their products.

For fashion, the attributes that matter most are often the ones brands treat as secondary: precise fit descriptions, fabric weight and composition, care requirements, size ranges and how sizing compares to other brands. These are exactly the details shoppers ask AI assistants about, and exactly the details missing from many product pages.

What can fashion brands do to improve GEO?

1. Complete product data: fill every relevant attribute (material composition, fit, rise, length, occasion, care, size range, colour names) consistently on product pages and in feeds.
2. Keep feeds current: wrong prices or out-of-stock items in feeds reduce the chance of being recommended and frustrate shoppers who are.
3. Write answerable content: size guides, fit notes, care instructions and material explanations that state facts clearly in text, not only in images.
4. Be consistent everywhere: product names, descriptions and specifications should match across your site, marketplaces and retail partners.
5. Earn credible mentions: AI systems summarise what reputable sources say; reviews, press coverage and expert content shape that picture.
6. Check access settings: make sure your robots rules do not unintentionally block the crawlers of AI search services you want to appear in, and use preview controls where you want to limit snippets.

How can brands measure visibility in AI search?

• Prompt testing: regularly ask major assistants the questions customers ask ("best linen shirts for hot weather", "brands like yours") and record whether and how your brand appears.
• Referral analytics: track traffic from AI assistants and AI search features where analytics tools can identify it.
• Accuracy checks: note where assistants state wrong facts about your products or policies and fix the underlying sources.
• Trend, not snapshot: answers vary by user and model version, so look at patterns over time.

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What are the limits and risks of GEO?

Visibility in AI answers can be volatile, and some answers satisfy the shopper without a click, which may reduce site traffic even when the brand is mentioned. Attempts to manipulate AI systems with keyword stuffing or misleading claims risk breaching platform policies and consumer protection law. The durable approach is to be the clearest, most accurate source of information about your own products, in every place AI systems look.