A growing share of product research now happens inside AI assistants and AI-generated search summaries, where there are no ten blue links to place in — either your brand is named in the answer or it isn't. Generative engine optimization is the practice of making your content easy for those systems to find, extract, cite and trust. It does not replace SEO; it rides on the same structured foundations and adds a few habits on top. Here is what we do, and how we check whether it is working.
What GEO is, and how it differs from SEO
SEO optimises for a ranked list a human scans; GEO optimises for a synthesised answer a machine composes by pulling facts from sources it deems trustworthy. The unit of success changes accordingly. In SEO you want position; in GEO you want to be the sentence the model quotes and the brand it attributes. That rewards different things: unambiguous facts a model can lift without hallucinating, content structured so the answer to a question sits right next to the question, and an entity the model can recognise as the same brand across the web. Crucially, GEO is additive — the crawlable, well-structured HTML that ranks in search is also what AI crawlers ingest, so you are hardening one foundation, not maintaining two.
llms.txt and machine-readable summaries
An llms.txt file is a plain-text map at your domain root that points AI crawlers at your best, most quotable pages with a one-line, fact-dense description of each — the machine-readable equivalent of a good table of contents. It will not single-handedly get you cited, but it lowers the cost for a model to find the page that actually answers a query, and it lets you state, in your own words, what each page is authoritative about. Pair it with fact-dense standfirsts and summaries on the pages themselves: a crisp opening sentence that states the answer, concrete numbers and definitions, and clean HTML a parser can lift without tripping over decoration.
Fact-dense, extractable content
Models quote what is easy to quote correctly. That means answering the question in the first sentence rather than burying it under three paragraphs of throat-clearing, stating specifics (a number, a rule, a date, a directive by name) instead of vague claims, and using tables for genuine comparisons because a table is a gift to an extractor. Hedged, padded prose is not just bad for humans; it gives a model nothing clean to lift, so it reaches for a competitor who wrote the plain sentence.
Two more habits raise your odds of being the quoted source. First, make each fact self-contained — a sentence that survives being lifted out of context, so a model can quote it without dragging in a pronoun that no longer resolves. Second, be consistent about your own facts across pages: if one article says a rule is 30 days and another implies something else, you have handed the model a reason to distrust both and cite someone steadier. The same evidence-first discipline that keeps AI-written content rank-worthy — grounding claims in real sources — is what makes content citable by AI engines, which is why GEO and helpful, ranking content are the same investment viewed from two angles.
Question-shaped headings mirrored in JSON-LD
People ask assistants full questions, so write headings and FAQ entries as the questions people actually type, and place the answer immediately beneath. Then mirror those question-and-answer pairs in structured data — FAQPage markup whose text agrees with the visible text on the page. The discipline is that the heading, the visible answer, and the JSON-LD all say the same thing: a model reading either the rendered page or the structured data gets one consistent, extractable Q&A. Divergence between what a human sees and what the markup claims is both a spam signal and a missed citation. Done well, one page serves a human reader, a search snippet, and an AI answer from the same source.
Measuring brand presence in AI answers
You cannot optimise a channel you do not measure, and most teams are not measuring this one yet. Once a month we probe the assistants directly: we ask them the queries that matter for the business — category questions, "best X for Y", brand comparisons — and record whether our brand appears, whether it is cited with a link, and how that shifts over time. It is deliberately low-tech: a fixed question set, run on a schedule, logged so the trend is visible. The output is not a vanity number; it is a list of queries where we are absent and should be, which feeds back into which pages to sharpen next.
Treat that probe like any other monitoring signal — a repeatable check with an owned follow-up list, in the spirit of the weekly sweeps we describe in building an analytics stack you own. The metric that matters is share of answers where you are named and cited, tracked over months, not a single snapshot.
How GEO complements SEO — and points at agentic commerce
GEO is not a pivot away from search; it is the same technical hygiene aimed at a new consumer. Dynamic sitemaps, canonical discipline and clean structured data — the whole of technical SEO for marketplaces — are what let an AI crawler ingest you accurately in the first place. The next step beyond being cited is being usable: exposing your catalog as tools an assistant can call to search, compare and add to cart, so the AI does not just mention you but transacts with you. That is agentic commerce, and it inherits every GEO habit here — fact-dense data, honest structure, machine-readable everything — as its foundation.
| Dimension | Classic SEO | GEO |
|---|---|---|
| Surface | Ranked list of links | Synthesised answer |
| Goal | Position on the page | Named and cited in the answer |
| Rewards | Relevance, authority, links | Extractable facts, clean structure, entity clarity |
| Measured by | Rankings, clicks, impressions | Share of AI answers citing you |
Key takeaways
- In AI answers there is no list to rank in — you are either named and cited or invisible, so optimise to be the quotable sentence.
- Publish an llms.txt and fact-dense summaries to lower the cost for AI crawlers to find and correctly quote your best pages.
- Answer the question in the first sentence and use concrete numbers and tables — padded prose gives a model nothing clean to lift.
- Mirror question-shaped headings in FAQPage JSON-LD so the visible text and the structured data say exactly the same thing.
- Probe the assistants monthly with a fixed question set and track share of answers that name and cite you over time.
- GEO is additive to SEO — the same clean structure and honest data feed both, and set up agentic commerce next.
Frequently asked questions
What is generative engine optimization (GEO)?
What is an llms.txt file?
How do I get my brand cited by ChatGPT and AI answers?
Does GEO replace SEO?
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