AI retrieval · 2026
LLM optimization: be the source a model quotes
LLM optimization makes your pages the ones a language model retrieves and quotes when it answers a question in your category. It is a different target from classic search: there is no list to climb, only a short set of passages the model pulled before writing. Being in that set depends on how cleanly your answer stands alone, how clearly your facts identify you, and whether independent sources say the same thing — all of which a newer site can fix long before it can out-rank an established one.
How LLM SEO differs from ranking
Four things about retrieval that change what you should actually work on.
It retrieves, it does not rank
A model answering a live question fetches a handful of candidate passages and writes from them. There is no page two. You are either in the small set it pulled or you are absent from the answer entirely.
It quotes passages, not pages
Retrieval works on chunks. A single self-contained paragraph that answers one question can be lifted cleanly; the same fact spread across an intro, a table and a footnote usually cannot.
It needs to know who you are
Before naming you to a stranger, a model needs consistent machine-readable facts about your organisation — the same name, the same claims, across your site and the sources it trusts.
It corroborates before it cites
Claims that appear in only one place tend to be summarised without attribution. Claims echoed on independent sources get named. That is why citation building is part of the work, not an add-on.
What it takes to rank in AI search
- 1. Find out where you stand. Ask the engines the questions your buyers ask and record what they say and who they name. Without that baseline you cannot tell whether anything you change worked.
- 2. Make your identity machine-readable. Organisation and service schema, one consistent name, claims that match across every page. This comes first — restructuring content before an engine knows who you are produces answers that borrow your wording without your name.
- 3. Rewrite answers to stand alone. One question per passage, the claim first, the support after. If a paragraph only makes sense with the two above it, it will not survive being lifted.
- 4. Get corroborated elsewhere. Facts echoed on independent sources get attributed; facts that exist only on your own site tend to get summarised anonymously.
Where to start
Start here
AI Visibility Audit
Your baseline: what the engines say about you today, which questions you are absent from, and who they cite instead. A fixed one-off, no commitment.
See the audit →The ongoing work
Generative Engine Optimization
The programme that closes the gap: entity schema, answer-shaped rewrites and citation building, run continuously rather than as a one-off fix.
See GEO →Related reading: AI SEO services (the umbrella), answer engine optimization (every answer surface, not just AI), and ChatGPT SEO (one engine, specifically).
LLM optimization, answered honestly
What is LLM optimization?
LLM optimization is the practice of structuring your content and your published facts so that large language models — ChatGPT, Claude, Gemini, Perplexity and Google's AI Overviews — retrieve your pages and quote them when answering a relevant question. It differs from classic SEO in what it targets: traditional SEO competes for a position in a ranked list, while LLM optimization competes to be inside the generated answer itself. In practice it means answer-shaped passages a model can lift without editing, entity and schema markup that states plainly who you are, and consistent corroborating facts on third-party sources the model already trusts.
Is LLM SEO different from normal SEO?
They overlap but they are not the same job. Normal SEO optimises a page to out-rank competitors for a query; LLM SEO optimises a passage to be retrieved and quoted inside an answer. A page can rank fifth on Google and still be the source an assistant quotes, because retrieval rewards clarity and self-containment rather than link authority alone. That is genuinely useful if your domain is newer: the parts a model cares about are things you can fix in weeks, while out-ranking an established competitor on classic signals takes far longer.
How do I rank in AI search?
Start by finding out where you stand — what the engines say about you today and who they cite instead. Then work in this order: make your entity facts consistent and machine-readable, restructure the pages that answer real buyer questions so each answer is self-contained, and get those same facts corroborated on independent sources. The order matters. Restructuring content before an engine can tell who you are tends to produce answers that use your wording without naming you.
How long does it take?
The markup and content work is usually visible to engines within a few weeks, because they re-crawl and re-embed far more often than classic rankings move. Citation building is slower and never finishes — it is the part that compounds. We would rather set that expectation now than have you measure us against a timeline nobody can hold.
Can you promise ChatGPT will cite me?
No, and nobody honestly can. Model providers change what they retrieve and cite without notice, and no vendor controls their output. What we commit to is the work that measurably improves your odds — the markup, the passage structure and the corroborating citations — plus an audit that shows your standing before and after, so you can judge the change rather than take our word for it.