What is generative engine optimization (GEO)?

When someone asks ChatGPT for "the best project management tool for a small agency", they get a short list of named products - not ten blue links. If your product is on that list, you win a customer you never paid to acquire. If it isn't, you were never in the running. Generative engine optimization (GEO) is the practice of understanding and improving whether AI answer engines name your brand in moments like that.

Why this is different from SEO

Search engines rank pages; answer engines synthesize an answer and cite a handful of sources. A page ranking third on Google still gets clicks. A brand mentioned nowhere in an AI answer gets nothing - there is no page two. That makes AI visibility closer to a binary outcome than a gradient, and it makes knowing where you stand the first job.

The second difference is that answers are non-deterministic. Ask the same question twice and the list can change. Any serious measurement has to sample repeatedly and report a rate - "you appear in 6 of 10 runs" - rather than pretend there is a fixed rank.

What actually influences AI answers

Nobody outside the model providers knows the full picture, and you should distrust anyone selling certainty. But the observable inputs are fairly clear:

• Web search grounding. Modern assistants search the live web before answering commercial questions. The sources they retrieve - review sites, comparison articles, documentation, forums - heavily shape who gets named.
• Training data. Brands widely discussed across the public web before a model's cutoff are more likely to be recalled even without search.
• Entity clarity. Models name brands they can describe. If your site never states plainly what you do, for whom, and how you compare, there is nothing to synthesize.
• Third-party corroboration. A claim that exists only on your own site is weak evidence. The same claim echoed on independent sites is strong evidence.

Of these, grounding is the one that changes week to week and the one you can influence fastest - it's a function of what's currently indexed and currently ranking, which is normal SEO territory. Training data moves on a model's release cycle, measured in months, and no amount of publishing this quarter reaches a model that already shipped. Entity clarity and third-party corroboration are the slow-compounding ones: they're what turns a single grounded citation into something the next training run remembers on its own.

Why a single check is worthless

Ask an AI assistant the same buyer question ten times in a row and you won't get the same answer ten times. Providers sample from a distribution, not a lookup table, and web search grounding pulls slightly different sources run to run. A brand that's genuinely well-positioned might show up in 7 of 10 runs; a brand that's marginal might show up in 2. Both numbers look identical in a single screenshot - "ChatGPT recommended us!" - and only the repeated sample tells them apart.

The three numbers a real score is built from

Once you're sampling instead of spot-checking, the results collapse into a small set of numbers worth knowing by name, because every serious GEO report is some version of these three:

• Mention rate - the share of sampled answers that named you at all. This is the headline number and the closest thing GEO has to "are we visible."
• Position - where you land in the list on the runs where you were named. Being named third or fourth of five is a materially weaker outcome than being named first, even though both count toward mention rate.
• Share of voice - your mention rate measured against competitors sampled in the exact same runs, which is the only way the number means anything on its own.

The practical GEO loop

GEO in practice is a measurement loop, not a bag of tricks. Write down the questions your buyers actually ask. Sample multiple AI providers with those exact questions, repeatedly. Record who gets named, in what position, with what framing, and which sources the answers cite. Then work on the gap: earn presence on the sources being cited, fix the pages that misdescribe you, and re-measure.

The cited sources are the actionable part. If Perplexity keeps citing a comparison article that omits you, that article is your roadmap. If answers cite your own docs but describe you incorrectly, your docs are the roadmap.

"Fix the pages that misdescribe you" usually means rewriting a sentence, not a whole site. A model can only synthesize what's stated plainly - copy that's technically true but built for a human skimming a hero section gives it nothing solid to extract.

What GEO is not

It is not prompt injection, not keyword-stuffing pages with "best X" lists about yourself, and not a guarantee. AI answers move as models and their retrieval change. The durable strategy is the boring one: be genuinely well-documented, well-reviewed, and clearly described across the web - and measure often enough to notice when things shift.