The AI Search Glossary

GEO sits next to (not against) traditional search engine optimization. Where SEO targets the ranking of a URL on a Google results page, GEO targets the probability that a brand is mentioned, recommended, or quoted inside an AI-generated answer. The two disciplines share many tactics — clear titles, structured data, third-party citations — but the optimization surface is different: instead of ten blue links, you are optimizing for the model's output distribution.

In practice, brands working on GEO focus on three levers: (1) being present in high-authority third-party sources that the models retrieve from, (2) publishing canonical, machine-readable summaries of their own products and brand (the role Citorial's Brand Hubs play), and (3) maintaining consistent factual signals — name, address, claim, category — across the open web.

AI search visibility is the leading metric in GEO work. It generalizes "do I appear?" into a triplet of dimensions: presence (am I mentioned at all?), prominence (am I in the first sentence, last sentence, or buried in a list?), and accuracy (is what the model says about me correct?).

A useful visibility audit samples 100–1,000 buyer-intent prompts per category, executes them across multiple LLMs in parallel, and reports per-prompt presence/prominence/accuracy plus an aggregated share-of-voice score. Citorial audits are structured this way.

AEO and GEO are often used interchangeably, but AEO usually emphasizes the user-facing answer surface, while GEO emphasizes the underlying generative model. In practice, work that wins one usually wins the other: structured data, clear summaries, strong third-party citations.

A key AEO tactic is to make sure that a brand's most-citable claims appear in indexable HTML — not only inside a PDF or behind a login — because answer engines tend to retrieve from open, crawlable HTML before they reach for anything else.

LLMO is broader than GEO and AEO. It includes work that is not strictly "search" — for example, making sure a model that is asked to "draft a comparison table" includes a specific brand, or making sure a code-assistant model cites a specific library in code suggestions.

For e-commerce, the most common LLMO surface is buyer-intent recommendation: a model is asked "what's the best running shoe under $150 for flat feet?" — the brand wants to appear in the model's answer with correct framing.

In an audit that probes 100 prompts and finds your brand mentioned in 23 of them, your share of voice is 23%. The metric is most useful when it is computed alongside the SoV of the top 3–5 competitors, because the absolute number is hard to interpret without that baseline.

Share of voice should always be reported per LLM, not only as a single aggregate — most brands have noticeably different SoV in Perplexity than they do in ChatGPT, and the corrective actions are not always the same.

Examples: "best running shoes for flat feet under $150", "is brand X worth it for sensitive skin?", "alternatives to brand Y for European shipping". These are the prompts that matter commercially — they are the ones where a citation directly maps to a potential sale.

A good GEO audit explicitly distinguishes buyer-intent prompts from informational prompts ("what causes plantar fasciitis?") because the optimization actions differ: buyer-intent visibility comes from review sites, structured product data, and brand-comparison content, while informational visibility comes from editorial content depth.

Brand Hubs (the term Citorial uses) are similar in spirit to a Wikipedia entry or a Crunchbase profile: a single URL that bundles a brand's name, description, structured data, references, FAQ, and sameAs links into a clean HTML page LLMs can read without JavaScript.

Maintaining a Brand Hub on a domain that LLMs already trust (and that has an explicit `robots.txt` opt-in for AI crawlers) materially improves the brand's chance of being surfaced when a user asks a category question.

Most consumer-facing AI search products (Perplexity, ChatGPT Search, Google AI Overviews, Gemini) use a retrieval-augmented pipeline. They issue web queries, fetch a handful of pages, and pass them to the language model as context for the final answer.

GEO work largely targets this retrieval stage. If your content is the highest-quality source the retriever can find for a query, your brand ends up grounded into the answer — even if the underlying model has never seen your brand in its training data.

The most common format for structured data on the web is JSON-LD embedded in a `<script type="application/ld+json">` tag inside the page's HTML. Schema types relevant to e-commerce include `Organization`, `Product`, `Offer`, `Review`, `FAQPage`, and `BreadcrumbList`.

AI search engines lean on structured data heavily when they need to extract a fact under tight latency budgets. A well-marked-up page is easier to cite than a visually-equivalent page that buries the same information in unmarked prose.

sameAs is one of the cheapest disambiguation wins in GEO. When the model encounters a brand name that could be a person, a product, and a software library, the sameAs graph tells it "the brand on this page is the same entity as @brand on X, the LinkedIn page at this URL, and the Wikipedia article at this URL". That removes ambiguity at retrieval time.

Most LLM crawlers either do not execute JavaScript at all, or execute it under strict time/cost budgets. A single-page app that only renders content after a client-side fetch is, in practice, invisible to those crawlers.

Two effective fixes: pre-render the relevant pages to static HTML at build time (the approach the Citorial landing site uses), or run server-side rendering on every request. Either way, the goal is to have the brand-relevant text exist in the raw HTML response.

Without hreflang, search engines often serve the wrong-language version of a page or, worse, treat the two versions as duplicate content. The link tag — `<link rel="alternate" hreflang="pt-BR" href="https://example.com/pt-br/page" />` — is small but materially important for any brand operating in more than one language.

On Citorial's Brand Hubs, every page that has a sibling translation emits hreflang links pointing at each sibling and an `x-default` pointing at the canonical English version.

Where `robots.txt` controls what crawlers may fetch, `llms.txt` is a positive signal: "here is what is worth fetching, summarized for you". The proposal is still emerging in 2026, but several large AI labs have signaled support, and the cost of adding the file is low.

Citorial's `/llms.txt` lists the homepage, brand directory, glossary, how-it-works, and blog, plus the Brand Hub URL pattern and a pointer to the sitemap.

Most brands either ignore AI user-agents (leaving the default Allow:/ behavior) or block them across the board. Citorial recommends the opposite: explicitly opt in to AI crawlers for any content the brand actively wants cited.

The opt-in is the easiest first GEO action a brand can take. It costs nothing, ships in a single commit, and changes which crawlers are willing to fetch the brand's pages.

Coverage of 100 prompts is the practical minimum for a small-category audit — enough to detect a meaningful presence gap but vulnerable to per-prompt noise. 300 prompts is a stable mid-tier, and 1,000 prompts is the gold standard for multi-brand benchmarking.

Citorial's three audit tiers correspond to these three coverage levels.