AEO is how a brand appears inside ChatGPT, Perplexity and Claude. GEO is how it appears inside Google's AI Overviews. Different surfaces, largely the same work — and most agencies have not built the muscle for either.
A growing share of searches now end inside a generated answer. Most brands are not cited there — not because their content is weak, but because the signals the models trust were never built.
Incomplete or wrong schema, gaps in the Knowledge Graph, silence on Wikidata. ChatGPT and Perplexity assemble their understanding of a brand from these sources. Without a coherent entity you are not cited; you are confused with someone else.
Models pull short, definitive answers from long pages. Without clear answer blocks, scannable headers and comparison tables, a page is skipped even when it holds the best answer available.
Language models cite a small, stable set of source types: Reddit threads, Wikipedia, established publications, Pinterest for visual queries. A brand absent from those is outside the pool, however well it ranks on Google.
About 40% of this work improves classic search as a side effect. The other 60% is genuinely new: entity, extraction, citation seeding and tracking.
Make the models understand who you are.
Make every page liftable as an answer.
Appear where the models look.
Know where you are cited, and where you are not.
A six-to-twelve-month compounding game, like classic search on a different surface. Citations appear when entity, extraction and signal work compound — not on day three.
Where do the models surface you today, and where do they surface competitors instead? Visibility mapped across ChatGPT, Perplexity, Gemini, Claude and AI Overviews; entity and schema coverage audited. Week one ends with a complete citation map.
Schema deployed, Wikidata corrected, Knowledge Graph aligned. Existing pages restructured for extraction. First content commissioned around the prompts your buyers actually ask.
Reddit presence built, Pinterest citations where the category supports it, publication mentions placed. Expect the first citations on long-tail prompts by the end of month three. Early, but real.
Citation rate grows and new prompts surface you. By month six, brand-adjacent prompts across several surfaces. By month twelve, a citation default in your category — and a later starter has to fight uphill to displace you.
AI citation is new. Most agencies showing AEO results are showing search results relabelled. We would rather be specific about what compounds, when, and what sits outside anyone's control.
We do not promise +700% traffic from AEO; the discipline is too young for numbers like that to be honest. What we promise is that by month six you are cited where you were not, and by month twelve you are cited consistently.ScaleArk operating principle
Schema, entity and restructuring shipped. No citations yet, and that is normal.
Long-tail prompts start surfacing the brand. AI Overviews begin to include you.
The brand surfaces across several AI surfaces for core category prompts.
The reference in your category, and hard to displace.
Younger tooling than search. Some are platforms, some are APIs, some are workflows we built. All of them touch your account every week.
A younger discipline, so the fit list is shorter and stricter than for classic search. That is where the compounding lives.
New enough that most people arrive with these. The answers we give on the call, laid out.
AEO — answer engine optimisation — is how you appear inside assistants like ChatGPT, Perplexity and Claude. GEO — generative engine optimisation — is how you appear inside Google's AI Overviews and similar surfaces. Mostly the same work; most practitioners use the terms interchangeably and we do not argue about labels.
No, though the overlap is real. Roughly 40% — schema, entity, content quality — also helps classic search. The other 60% is different: structuring for extraction rather than ranking, seeding the sources the models trust, and tracking mentions across surfaces. None of that exists in classic search.
The opposite. Schema helps both, and content restructured for extraction tends to perform better in classic search too: clear answer blocks reduce bounce and increase dwell time.
Weekly tracking across ChatGPT, Perplexity, Gemini, Claude and AI Overviews. We log the prompts your buyers might ask, run them at intervals, and record which surface you, which surface competitors, and which surface nobody. The map moves month over month on a live dashboard.
Because the discipline is young enough that anyone showing one is presenting search results relabelled, or month-three wins they hope you will not probe. Our search case studies include citation work as a contributing factor; isolated AEO cases are being built now.
Together, ideally — the foundation overlaps and running them apart costs more. If you must choose, start with search: the entity layer is built once and both use it.
Voice collapsed into assistant queries. Optimising for AEO optimises for voice as a side effect, so there is nothing separate to sell.
No, and anyone who does is misleading you. Citation depends on model training cycles, retrieval changes and query routing — none of which an agency controls. We promise the work that compounds; the outcome follows.
A thirty-minute look at where AI engines surface your brand against your closest competitors, and the highest-leverage gap to close first. You keep the citation map.