Answer engine optimization
Answer engine optimization (AEO)
AEO is the work of getting a brand named, cited and recommended inside the answer an AI assistant gives — rather than ranked in a list of links the buyer then chooses from.
Why the distinction is not just vocabulary
A ranked list gives a buyer ten choices. An answer gives them one, or three. The vendors that were not named did not rank eleventh — they were not part of the conversation at all. That changes what "improving" means: the target is inclusion in a synthesised answer, not position on a page.
What AEO work actually consists of
Being a citable source
Assistants ground answers in retrievable pages. If nothing of yours is retrievable for a question, you can only be included from model memory — which you do not control.
Third-party presence
Comparison pages, review sites and community threads are cited heavily. Much of what decides an answer is not on your domain.
Answering the actual question
Pages that answer a buying question directly are easier to ground on than pages that describe a product in the abstract.
Being an unambiguous entity
If a model cannot tell your brand from a common word or another company, it will hedge or omit you. This is why brands with ordinary-word names measure worse.
The part most AEO advice skips
Almost all of it is about what to change. Very little is about how you would know it worked. Without a frozen question set, a stored baseline and per-engine reporting, "our AEO improved" is a claim with nothing behind it — and AI answers drift on their own, so something will always appear to have changed.
Minimum bar for an honest before/after:
- The same questions, unchanged and versioned, on both sides of the comparison
- The same engines, with the model that actually answered recorded per run
- Failed calls excluded from denominators on both sides
- Enough runs that you are comparing states, not single samples
- An explicit statement that a measured change is correlation, not proof of cause
Our reports do the first four and say the fifth out loud.
Common questions
What is answer engine optimization (AEO)?
AEO is the work of getting a brand named, cited and recommended inside answers produced by AI assistants and answer engines, rather than ranked in a list of links.
Is AEO different from GEO?
In practice the two terms are used for overlapping work. AEO is usually framed around being the answer; GEO around being present in generated output. Neither has a settled standards-body definition, and anyone quoting one is inventing it.
How do you know whether AEO work is doing anything?
By measuring the same frozen buyer questions before and after, per engine, with failed calls excluded from denominators. Without a stored baseline there is nothing to compare against and no way to separate your work from drift.
What we actually measure
Every result comes from the official OpenAI, Google Gemini
and Perplexity APIs with web search or grounding enabled. In our data those
surfaces are recorded as openai_api_web_search,
gemini_api_google_search and perplexity_api_sonar, and reports name
them explicitly.
That is not the same thing as a logged-in person using the ChatGPT app. These are official interfaces with web search on, but they are measurement surfaces rather than consumer sessions: no personalisation, no memory, no app-only features. Results can differ from what any individual user sees, and we do not claim the two are identical. We would rather put that on the pricing page than in the small print.
Generative engine optimization (GEO) · What is AI visibility? · Measuring change over time
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