LLM SEO
Also called Generative engine optimisation, GEO, AI search optimisation, AEO
LLM SEO is the practice of getting a page cited inside the answers large language models give, rather than only ranked in a list of links. It covers how a page is written, how it is structured, whether AI crawlers can reach it, and whether the brand exists as a recognised entity elsewhere on the web.
Written and maintained by the SEO Engico team
Two mechanical engineers turned marketers, and the people who do the work. Meet the team
Last reviewed 4 August 2026
How is LLM SEO different from ordinary SEO?
Classic SEO competes for a position in a list. LLM SEO competes to be one of the three or four sources an answer is built from, and those are not the same contest. A page can rank tenth and still be quoted, or rank second and be ignored.
The practical difference is what gets selected. Search engines rank documents. Language models select passages. So the unit of work moves from the page down to the paragraph, and a clear, self-contained answer near the top of a page matters more than total word count.
What actually makes a page citable?
A direct answer in the opening paragraph, written so it still makes sense lifted out of context. Headings phrased as the questions people actually ask. Lists and tables rather than long unbroken prose. A named author with a real byline. A visible date. And crawler access, because a page a model cannot fetch cannot be cited.
Structured data helps less than most agencies claim. It is worth adding because it is cheap and it disambiguates entities, but the published evidence for schema driving AI citations is weak. Treat it as hygiene rather than as the lever.
Can anyone guarantee AI citations?
No, and be wary of anyone selling that. AI answers are non-deterministic: ask the same model the same question twice and the sources move. Independent measurement suggests that resampling variance alone accounts for a large share of what a single-run visibility check reports, which means one measurement of your brand is mostly noise.
The honest approach is to measure the same prompt set several times, report a detection rate with a confidence interval rather than a score, and track it over months. That is what we do, and it is why we guarantee the measurement rather than the outcome.