What is GEO? An AI-visibility guide for beauty brands

GEO stands for Generative Engine Optimization: optimizing for generative AI engines. The goal is simple to state — when a user asks an AI a question in your category, such as 'recommend a good vegan skincare brand', your brand appears in that answer with correct information.

The term isn't a marketing coinage; it comes out of the research literature. Aggarwal et al. (2024) defined GEO in work presented at KDD and built GEO-bench, a benchmark of 10,000 queries, to measure systematically which methods improve a source's visibility in generative engine responses.

AI engines build their answers largely from content they have learned from and crawled on the open web. Google says so plainly for its own AI features: your content has to be crawlable, because its generative models ground their responses in publicly accessible, crawlable content (Google Search Central). The clearer, more consistent and more machine-readable the information about your brand is, the better the chance these engines recognize you accurately.

They're not rivals; they're two layers of the same foundation. SEO targets ranking on a results page: the user sees a list, clicks, and lands on your site. GEO targets being inside the answer: the user often clicks nothing at all and simply reads what the AI wrote.

• SEO's unit is the page and the keyword; GEO's unit is the brand and the claim — what does the AI 'know' about you?
• SEO earns you clicks; GEO often earns you mentions without a click. That's why the information has to be correct at the source.
• Good SEO is a prerequisite for GEO: a site that can't be crawled or parsed is invisible to AI engines too.

The practical takeaway: you don't need to build a separate world for GEO. Solid technical SEO, clear content and consistent brand information feed both layers at once. Google puts it more bluntly from its own side: optimizing for generative AI search is optimizing for the search experience, and thus still SEO (Google Search Central). That verdict covers Google's own surfaces (AI Overviews, AI Mode); non-Google engines like ChatGPT and Perplexity run their own crawlers and make their own sourcing decisions.

Nobody has the exact formula — these systems aren't public and they change often. Still, common principles emerge from their observable behavior and published documentation:

1. Crawlable, readable content

Answer engines are fed by web crawlers, and those crawlers are publicly documented: OpenAI publishes OAI-SearchBot and GPTBot, Anthropic publishes ClaudeBot, Claude-User and Claude-SearchBot, and Perplexity publishes PerplexityBot and Perplexity-User.

On the visibility side Google's condition is concrete: to be eligible as a supporting link in AI Overviews or AI Mode, a page must be indexed and eligible to appear in Search with a snippet (Google Search Central). In practice that means content served as plain HTML rather than hidden behind JavaScript, a sensible heading hierarchy (H1/H2/H3), and paragraphs that answer a question directly.

2. Structured data (schema markup) — without overselling it

Schema.org markup such as Organization, Service, FAQPage and Article is the machine-readable answer to 'whose page is this, and what is it about'. Google states that it uses structured data found on the web to understand page content, and recommends JSON-LD as the easiest format to implement at scale (Google Search Central).

One common exaggeration needs correcting here: schema is not the key to the AI answer box. Google explicitly says structured data isn't required for generative AI search and that there's no special schema.org markup you need to add for these features — the same document notes that llms.txt files are ignored by Google Search (Google Search Central). Google still recommends keeping structured data as part of overall SEO, because it makes you eligible for rich results. In short: schema is useful hygiene, not a magic GEO lever.

3. Consistency and third-party signals

Your brand's name, what it does and its contact details should be consistent across your site, your social profiles and pages written about you. Mentions on other sites — press, lists, directories, genuine user reviews — reinforce the picture. When AI engines meet contradictory information, they either misrepresent the brand or skip it entirely.

4. Content shaped like questions

People ask AI full-sentence questions. Content that uses real questions as headings — 'How do I choose a foundation for my skin type?' — followed immediately by a clear answer is the format these engines most like to cite. That's why FAQ blocks and guide articles are the load-bearing columns of GEO.

How the content is written has a measurable effect. Aggarwal et al. (2024) measured relative improvements in the 30–40% range on their visibility metric from methods such as adding quotations and adding statistics. The same study found that keyword stuffing — the classic SEO reflex — offered little to no improvement in generative engine responses. These results were measured in the study's own test setup and don't guarantee identical behaviour from live commercial engines.

Beauty runs on recommendations. 'Which serum?', 'Is this ingredient safe during pregnancy?', 'Which sunscreen for acne-prone skin?' — personal, context-sensitive questions that AI assistants happen to be good at. As user behavior shifts toward these tools, a share of the category's discovery moments moves with it.

The category is also information-sensitive: ingredient lists, skin-type compatibility, animal testing and vegan certifications are areas where wrong information hurts a brand. Making sure AI engines can reach accurate information about you isn't just visibility — it's reputation.

• Make sure all important site content (especially FAQ answers) is visible in the source HTML without depending on JavaScript.
• Add Organization, Service/Product, FAQPage and Article schema markup; keep your brand name and contact details identical across every channel.
• Collect real customer questions (DMs, comments, questions your sales side hears) and build a clear, honest answer for each on your site.
• Treat robots.txt bot by bot: visibility is driven by the search-side crawlers (OAI-SearchBot, Claude-SearchBot, PerplexityBot). GPTBot and ClaudeBot, per their providers' own documentation, collect content for model training — a brand that wants to appear in answers without contributing to training can decide on those two separately.
• Build a testing routine: once a month, ask ChatGPT and Perplexity 10–15 questions about your category and brand; note how you're mentioned (or not) and update your content plan accordingly.

None of this is a one-and-done task; content and consistency compound over time. But once the technical base (crawlability + schema) is set up correctly, every piece of content you produce starts working on both layers at once: classic search and AI answers.