Generative engine optimisation: being cited, not just ranked

For twenty five years the shape of discovery was stable. A person typed a query, received a page of links, and chose one. Everything in search marketing followed from that: position on the page, click through rate, the long tail, the whole apparatus.

The new behaviour is different in a way that matters more than its size. Somebody types a question into an assistant and receives a paragraph that names two or three companies, with citations underneath that most people do not open. There is no page ten. There is barely a page one. If you are not in the paragraph, you were not considered, and you will never know it happened, because no impression was logged anywhere you can see.

This does not replace search. Traditional search volume remains enormous and Google's own results now carry generated summaries above the links, which is the same dynamic wearing a familiar interface. What has changed is the number of slots. Ten links became three named sources, and the competition for those three is not decided by the signals that decided the ten.

Why this lands hardest on considered purchases

The queries assistants are used for skew towards research: comparison, shortlisting, understanding a regulation, working out which kind of supplier one needs. That is precisely the top of a business to business funnel. A consultancy, a software vendor or a manufacturer with a long sales cycle is more exposed to this shift than a shop selling a commodity, because the assistant is doing the shortlisting that a buyer used to do across six browser tabs.

The uncomfortable part

Most of the sites we audit are not losing this on content quality. They are losing it because a machine could not fetch the page, or fetched it and could not tell which organisation the page belonged to. The work that moves the needle first is technical, cheap and slightly boring, which is why so few agencies lead with it.

An assistant does not cite pages. It lifts passages. That single fact changes how a page should be built more than any other consideration in this discipline.

Answer first, in forty to seventy words

Every page should open with a short, self-contained answer to the question the page exists for, written so that it survives being cut out and pasted into someone else's paragraph. No throat-clearing introduction, no company history, no scene setting. The first sentence states the answer; the next two or three qualify it. If that block cannot stand alone, it will not be lifted. There is one at the top of this page and you can judge for yourself whether it works.

Headings shaped like questions

Assistants generate their own internal queries, and those queries are more literal and more conversational than the phrases humans type into a search box. Headings that match that shape are easier to retrieve against: what it costs, how long it takes, what happens if you block it, when it is the wrong tool. Mix them with statement headings so the page still reads like prose written by a person.

Lists and comparison tables are quoted out of proportion

Structured formats are disproportionately represented in generated answers, because they are easy to parse, easy to attribute and easy to summarise without distortion. A comparison table with clear column headers does work that four paragraphs of careful prose will not. The crawler table further up this page exists for that reason as well as for yours.

Definitions, plainly stated

Write the sentence that starts X is. Assistants answer a great many definitional questions, and a clean definition block, marked up and placed near the top of the relevant section, is among the most reliably extracted units on the web. Glossaries earn their keep here in a way they did not five years ago.

Freshness, and why stale pages quietly lose

Engines favour recency when a question is time-sensitive, and a great deal more of the buying process is time-sensitive than people assume. Keep dateModified accurate in your schema and visible on the page, and make it honest: bumping the date without editing the content is a habit engines are getting better at seeing through. Real reviews on a real cycle, with the change reflected in the text, is the version that works. Pages that have not moved in three years lose to pages that were revised last month, even when the old page is better.

llms.txt: worth doing, not a strategy

llms.txt is a proposed convention: a markdown file at the root of your domain listing your important pages with a line of context each, so a model reading it gets a curated map rather than a scrape of your navigation. It costs an afternoon. Adoption is growing, particularly among documentation-heavy sites. The honest position is that public evidence it directly lifts citations remains thin, and no major engine has committed to consuming it. Publish one, because it is cheap and genuinely useful to anyone reading your site with a machine. Do not let anyone charge you for it as though it were the programme. Ours is at /llms.txt.

This discipline is roughly two years old. The vocabulary is not settled, the same practice is sold as generative engine optimisation, answer engine optimisation and AI search optimisation depending on who is writing, and the underlying systems change without release notes. Anyone speaking about it with total confidence is telling you something about themselves.

So here is what is well established. Crawler access is causal and provable: a blocked agent cannot cite you, and removing the block restores the possibility. Entity resolution is well understood from a decade of knowledge graph work and the mechanics carry over. Extractable structure genuinely helps, which is observable in the passages engines actually quote.

And here is what is not. The relative weight of on-page changes against everything an engine already believes is not publicly known. Whether llms.txt influences any major engine today is unproven. How much third-party mentions matter compared with your own site is contested. Nobody outside the labs can separate retrieval effects from training effects with any rigour, and the published studies in this area are small, quickly outdated and often produced by firms selling the remedy.

Which leads to the only promise worth making. We can make you reachable, resolvable and quotable, and we can measure whether citations move. We cannot guarantee an engine will recommend you, because no such guarantee exists to sell. If a supplier offers you one, ask them to put the mechanism in writing.

Check our work on us

This site is built to the standard described on this page, which felt like the minimum before charging for it. View source and read the JSON-LD graph. Open /llms.txt. Note the extractable answer block at the top of every page, the question-shaped headings, the breadcrumbs, the markdown alternate on each URL and the reviewed date at the foot of the article. Then look at how our crawler policy separates training from search. Five minutes, and you will know whether we practise this or only sell it. The wider technical programme sits alongside ethical analytics and applied AI, because measurement and machine readability are the same problem viewed from two ends.