LLM SEO is the practice of getting a business named and cited when a large language model such as ChatGPT, Claude or Gemini answers a buyer's question. It focuses on the signals answer engines weigh before recommending anyone: independent mentions, entity clarity, question-shaped content and machine-readable pages.

What is the difference between traditional SEO and LLM SEO?

Traditional SEO earns a position on a search results page. LLM SEO earns a place inside the AI's answer, which typically names only two or three businesses in prose. The levers differ too: measured across thousands of businesses, off-page brand signals separate the named from the invisible, while technical SEO scores are nearly identical between the two groups.

How do I check my brand's LLM visibility?

Ask the answer engines the questions your buyers actually type and read who gets named. A free scan at areyoufoundbyai.com automates this: it queries the answer engines with buyer-phrased questions, keeps every answer verbatim, and shows which questions name you and who is named instead.

Does schema markup help with LLM SEO?

Schema helps AI read you correctly, and it only works if it is present in the raw HTML, because most AI crawlers never execute JavaScript. On its own it is necessary rather than sufficient: measured across real businesses, schema scores barely differ between businesses AI names and businesses it ignores, while off-page mention signals differ sharply.

Guide · measured, updated continuously

LLM SEO: what actually gets a brand named in AI answers

LLM SEO is the practice of getting your business named and cited when a large language model such as ChatGPT, Claude or Gemini answers a buyer's question. Traditional SEO earns you a position on a results page. LLM SEO earns you a place inside the answer itself, and the answer usually names two or three businesses in prose with no page two beneath it. Everything on this page comes from measurement: we have run the same buyer questions across all 7 answer engines for 2,558 real businesses and kept every answer verbatim.

LLM SEO vs traditional SEO

Traditional SEO optimises a document so a crawler ranks it. An answer engine works differently: before it answers, it quietly runs its own web searches (the fan-out), reads what independent sources say about you, and then decides whether you are a safe recommendation. So the levers move. Your page speed matters less than whether anyone else on the web vouches for you. Your keyword density matters less than whether the engine can resolve your business as one unambiguous entity. And a ranking tracker tells you nothing, because the thing to track is the answer.

What moves AI naming, measured across 2,558 businesses

We score every scanned business on the same pillars and then compare the ones AI names against the ones AI never mentions. The gap is not where most agencies point.

Off-page brand recognition is the whole story. Businesses that AI names average 87/100 on brand signals (independent mentions, entity verification, real-world footprint). Businesses AI never mentions average 34/100. Meanwhile technical SEO is nearly flat between the two groups (81 vs 78): the invisible businesses have fast, crawlable, well-built sites. Speed does not get you named. Being known does. The full breakdown, updated as the dataset grows, is on our research page.

A working LLM SEO checklist

1. Earn independent mentions. The single strongest measured signal. Engines trust what third parties say about you far more than what you say about yourself. Industry directories, local press, supplier pages and genuine reviews all count; a mention on a page the engine's fan-out search retrieves is worth more than any tag on your own site.

2. Become one unambiguous entity. Engines cross-check names against registries and knowledge bases. If your trading name collides with a dictionary word, another company or a defunct registration, the engine hedges and names someone else. Consistent naming everywhere, a government business registration the engine can find, and a Wikidata or sameAs trail all reduce that hedge.

3. Publish pages shaped like buyer questions. The fan-out searches are phrased the way buyers type. A page that answers "best emergency plumber in Austin" in its first sentence gets retrieved for that search; a page titled "Our Services" does not.

4. Make your site readable without JavaScript. Most AI crawlers read raw HTML and never execute scripts. If your content or your schema is injected by JavaScript, the engine sees a blank page. This is one of the most common fatal findings in our scans, and one of the fastest to fix.

5. Measure the answers, then re-measure. Engine answers drift week to week even when your site is unchanged. One lucky answer proves nothing. Ask the same questions across every engine on a schedule, keep the answers verbatim, and judge changes against the trend rather than a single sample.

How to check your LLM visibility right now

Run our free scan: it asks the questions your buyers type across the answer engines, keeps every answer word for word, and shows who the engines name instead of you. It takes about 60 seconds and needs no signup. If you want the answers tracked week over week with every change logged, that is what monitoring does. The whole method is open source, so you can read exactly how every number on this page is measured.

Method. Found by AI asks ChatGPT, Claude, Perplexity and Google's AI the questions real buyers ask for each category, then measures which businesses the engines actually name. Scores are 0–100 point-in-time measurements; the index re-scans and grows automatically. Businesses listed here were selected and measured by us from public directories — scan your own site free to see where you'd rank. Queries and methodology are open: github.com/techhorizonlabs.