When a buyer asks ChatGPT which vendor to use, a name comes back. Generative engine optimization is the work of making that name yours — by fixing what the web says about you, not just what your site says about itself.
Three patterns account for most of the generative engine optimization work we take on. If one of them is yours, the audit will confirm it in about two weeks.
ChatGPT recommends competitors, not us.
Models draw on what is said about you across the web, not what you say about yourself. Without third-party corroboration you simply don't come to mind when the model assembles a shortlist.
Corroboration buildAI describes our product wrongly.
Ambiguous entity signals. When your own site never states plainly what you are, who you serve and what you don't do, models infer it from whoever mentions you — including reviewers who got it wrong in 2023.
Entity correctionWe're invisible in a category we helped build.
Being early is not the same as being cited. If the sources models trust in your category don't reference you, your history counts for nothing in a generated answer.
Share of answerGenerative engine optimization (GEO) is the practice of getting a brand named, cited and recommended inside answers produced by generative AI systems — ChatGPT, Perplexity, Gemini and Google AI Overviews. Because those systems synthesize an answer rather than list ten links, GEO works on how your brand is described across the whole web, not only on where your own pages rank.
The practical consequence is that a lot of GEO work happens off your website. A model deciding which three vendors to name is drawing on review sites, forum threads, comparison articles, documentation, podcasts and news coverage. Your homepage is one input among hundreds. That is why the levers here are entity clarity, corroboration and quotability rather than title tags and internal links.
It is also why GEO is measured differently. There is no position one. There is a rate: across a fixed set of buyer questions, run repeatedly, how often does your brand appear — and how often does each competitor?
The three overlap more than the acronyms suggest. The differences that matter are what you optimize and what you measure.
SEO
Earning a ranked position for a query. Optimizes whole-page relevance, technical health and earned links. Measured in positions, clicks and conversions.
PositionAEO
Owning the answer slot for a specific question — AI Overviews, featured snippets, People Also Ask. Optimizes passage extractability and structure. Measured in slots held.
SlotGEO
Being the brand a model names when someone asks for options. Optimizes entity clarity and third-party corroboration. Measured in citation and mention rate.
MentionIf your problem is that nobody clicks through from an answer you're already inside, start with answer engine optimization instead. Most engagements end up running both.
Six workstreams. Not every engagement needs all six — the baseline decides which ones carry weight for you.
Prompt set and baseline
A fixed set of questions built from how your buyers actually ask, run across four engines, with your citation rate and every competitor's recorded before anything changes.
MeasurementEntity graph
Organization and Person schema, a definitive About page, and consistent descriptions across every profile that models read. The goal is one unambiguous answer to "what is this company".
FoundationCorroboration
Placements on the sources models already trust in your category — review platforms, comparison articles, industry publications, credible communities. Earned, never bought.
Off-siteQuotable content
Definitional, comparison and criteria content written so a model can lift a clean passage and attribute it. Specific claims, plain sentences, no throat-clearing.
On-siteCrawler policy
A deliberate decision on GPTBot, ClaudeBot, PerplexityBot and the rest, plus llms.txt where it earns its place. Most sites have this set by accident.
TechnicalAI referral reporting
GA4 segmented by engine so you can see what AI traffic actually does on site — because a citation that sends nobody is not a result.
ReportingSequenced deliberately — each phase depends on the one before it. Numbering here means order, not decoration.
Benchmarks, not promises. Your starting point decides how far and how fast — the audit gives you both numbers before you commit to anything.
Generated answers change constantly. A one-off snapshot tells you nothing about direction.
ChatGPT, Perplexity, Gemini and Google AI Overviews, on one shared prompt set.
Appearances across repeated runs, not a position. Answers vary by run, so a single check proves nothing.
Benchmarks reflect the standards we work to, not guaranteed outcomes. Nobody can guarantee a citation in a system that regenerates its answer every time.
Generative engine optimization (GEO) is the practice of getting a brand named, cited and recommended inside answers produced by generative AI systems such as ChatGPT, Perplexity, Gemini and Google AI Overviews. Because those systems synthesize an answer rather than list pages, GEO focuses on how a brand is described across the web, not only on where its own pages rank.
GEO is about being the brand the model recommends when someone asks for options. AEO is about owning the answer slot itself for a specific question, including AI Overviews, featured snippets and People Also Ask. GEO is a brand and corroboration problem; AEO is a content structure and extractability problem. Most engagements need both, but they are measured differently.
Yes, by running a fixed prompt set on a schedule and recording whether you are named or cited in each response. It is not a rank tracker, because answers vary between runs, accounts and regions. We measure a citation rate across repeated runs rather than a position, and report the trend.
It removes you from the answers those systems generate. Blocking GPTBot means ChatGPT cannot cite your pages, and if competitors allow it they take the mention instead. It is a genuine trade-off between content protection and visibility, and it should be a deliberate decision rather than a default left in robots.txt.
Entity and structural fixes can change how models describe you within weeks, because the systems re-crawl and re-index continuously. Building third-party corroboration is slower and usually shows in citation rate over three to six months. We baseline in the first three weeks so the change is measurable rather than assumed.
Yes. Generative systems overwhelmingly draw on pages that already rank and sources that already carry authority. GEO adds a layer on top of technical health, content quality and earned links rather than replacing them, which is why we run the two together rather than selling GEO as a standalone fix.
Generative engine optimization rarely runs alone. These are the three we most often run alongside it.
Winning the answer slot itself — AI Overviews, featured snippets and People Also Ask.
Explore →GrowthDigital PR, HARO and editorial placements in US, UK and EU media — the corroboration layer GEO depends on.
Explore →FoundationStructured data that defines your entity clearly enough for a model to describe you correctly.
Explore →No obligation, no sales sequence. We'll run a sample prompt set against your brand and show you where you currently stand against the competitors models are naming instead.