What is generative engine optimization (GEO)?

GEO and SEO overlap heavily — both reward crawlable, fast, well-structured, authoritative content — but they optimize for different consumers. SEO optimizes for a ranking algorithm that returns links. GEO optimizes for a language model that reads your page, decides whether it's a reliable source, and either quotes it or ignores it.

The targets diverge: SEO aims to rank in a list of links, GEO aims to be cited inside a generated answer. The signals diverge too — SEO leans on backlinks, keywords, page speed, and intent match, while GEO leans on answer-first clarity, JSON-LD, source citations, llms.txt, and factual density. The failure modes differ as well: an SEO page ranks too low to click, whereas a GEO page fails when the model can't extract a clean claim and cites a competitor instead. SEO is mostly off-page (links); GEO is mostly on-page (structure and clarity).

An LLM rewards pages that make its job easy. That means leading with the answer instead of burying it, marking up entities and FAQs with JSON-LD so the model doesn't have to guess at structure, publishing an llms.txt so AI crawlers find a clean map of your best content, and keeping the substance in real HTML rather than client-rendered JavaScript a crawler may never execute.

seo·check scores exactly these signals. Paste a URL and it reports whether your page leads with a clear answer, ships structured data, exposes an llms.txt, and renders crawlable content — the structural foundation every AI answer engine relies on.