What is GEO? Generative engine optimisation explained

What does generative engine optimisation mean?

Generative engine optimisation is the practice of shaping how AI assistants talk about you. When someone asks ChatGPT for the best payroll software for a small firm, or asks Perplexity which agencies handle Shopify migrations, the answer is written by a model. GEO is the work that makes your brand part of that answer, described accurately.

The term is new and the field is young. The tools change how they search, rank and cite sources often, and much of what happens inside them is not published. Treat any confident claim about exactly how a given assistant chooses sources with some caution, including the ones in this article.

What is stable is the general shape. AI assistants build answers from two places: what the model learned during training, and what it retrieves from the web at the moment you ask. GEO tries to improve your position in both.

How do AI assistants choose what to cite?

When an assistant searches the web before answering, it usually runs one or more queries through a search index, reads a handful of the pages that come back, and writes an answer from them. The pages it reads are the pages it can cite. If your site is not crawlable, not indexed or not near the top for the queries the assistant runs, you are unlikely to appear.

From the retrieved pages, models tend to favour text that answers the question directly, states facts plainly and agrees with other sources. A clear definition, a specific number with a source, or a comparison laid out in a table is easier to lift than a vague paragraph. Pages that contradict the consensus, or that bury the point, are harder to use.

For answers drawn from training data rather than live search, what matters is how often and how consistently your brand appeared across the web before the model was trained. A business that is described the same way on its own site, in directories, in reviews and in press coverage leaves a clearer impression than one with five different descriptions.

• ChatGPT and Copilot can search the web and cite links, depending on the mode and the question.
• Perplexity searches by default and shows its sources alongside the answer.
• Gemini and Google AI Overviews draw on Google Search, so ordinary ranking signals carry over.
• All of them can also answer from training data alone, with no visible source.

How is GEO different from SEO?

SEO aims to rank your pages in a list of search results, where the reader chooses what to click. GEO aims to have your brand mentioned and cited inside a written answer, where the model has already done the choosing. The unit of success moves from a ranked page to a mention in a sentence.

They overlap more than the new name suggests. A crawlable site, fast pages, clear structure and genuinely useful content help with both. Many assistants retrieve pages through a search index, so strong SEO is often the entry ticket for GEO.

Where GEO asks for more is in what happens off your site. Consistent entity data, third-party mentions in places the assistants trust, and prompt-level tracking matter more here than in classic SEO. A brand can rank well on Google and still be missing from AI answers if the wider web says little about it.

What does GEO work actually involve?

Most GEO work is unglamorous and specific. It is about making your facts clear, consistent and easy to reach, then giving assistants a reason to quote you rather than someone else. The list below is where we would start for most businesses.

None of these steps is a trick. Each one makes your business easier for a machine to understand and verify, which is the same thing that makes it easier for a person to trust.

• Entity consistency: state your name, one-line description, founders, services and locations the same way on your site, social profiles, directories and Wikidata where you qualify.
• Organization schema with sameAs: structured data on your site that names your official profiles, so systems can tie the brand to the right facts.
• Authoritative third-party mentions: listings, reviews, comparisons and coverage on the sites assistants already cite in your category, where you have a real case to be included.
• Citation-worthy original content: your own data, methods and clear explanations, written in sentences that can stand on their own.
• AI crawler access: check robots.txt, firewall and CDN bot settings so answer crawlers such as OAI-SearchBot, PerplexityBot and Claude-SearchBot can read your pages.
• llms.txt: a plain-text file at your site root that summarises who you are and links your key pages. It is an emerging convention, not a standard, so treat it as low cost and low certainty.

What makes content worth citing?

An assistant cites a page when it needs a source for a specific claim. If your page says the same thing as twenty others, there is no particular reason to pick yours. If it is the only place with a given number, method or explanation, it becomes the natural source.

Original substance does not have to mean a large research report. It can be your own pricing patterns, what you see across projects, a clear definition of a term in your field, or a step-by-step method you actually use. What matters is that it is real, specific and stated plainly.

Write so a single sentence can be lifted without losing its meaning. Put the claim first, add the number or the condition, and cite where it came from. Keep dates on anything that changes, and update pages when the facts move.

How do you measure AI visibility?

There is no Search Console for AI assistants, so you build your own view. Start with a fixed set of prompts your buyers would plausibly ask, such as category questions, comparisons and problem descriptions. Run them across the main assistants and record what comes back.

For each answer, note whether you are mentioned, where in the answer, how you are described, which competitors appear and which sources are cited. Answers vary between runs, so look at the pattern across many prompts and repeat runs rather than reading too much into one result.

Rerun the same set on a schedule, monthly is usually enough, and track the trend. Where your analytics allow it, also watch for visits referred from AI tools. Together these tell you whether the work is moving anything and where the gaps are.

• Mention rate across the prompt set, compared with named competitors.
• Accuracy of how you are described, including old or wrong facts.
• Which sources are cited, so you know where third-party work should go.
• Referral visits from AI assistants, where they can be identified.

Where to start with GEO

Begin with a baseline. Run twenty or so buyer prompts across the assistants, read the answers and list the sources they cite. That one exercise usually shows whether the problem is access, consistency, authority or content.

Then fix the basics in order: make sure crawlers can reach you, make your facts consistent everywhere, add Organization schema with sameAs, and only then invest in third-party mentions and original content. If you want a second pair of hands, WhiteThread runs this as part of its search and AI visibility work, but the steps are the same whoever does them.