Skip to main content
This page is background reading for anyone new to AEO/GEO.

Search is changing

Instead of searching Google for “used car prices”, people have started asking ChatGPT questions like this.
“Recommend a used SUV in the 20 million won range”
The AI does not give you ten links. It gives you one answer. It recommends specific models, mentions specific platforms, and cites specific web pages as evidence. The shift shows up in the data too. On Naver, search volume for “used car” dropped by more than a third over the past year. (Example of measured values from Naver trend data)

What AEO/GEO is

AEO (Answer Engine Optimization), or GEO (Generative Engine Optimization), is the work of getting your brand into that “one answer”. There are two goals.
  1. Mention: the AI names your brand or product directly in its answer
  2. Citation: the AI links to your web page as evidence for its answer
If traditional SEO was about “getting onto page one of the search results”, AEO is about “getting into the sentences of the AI’s answer”.

How AI builds an answer

To measure anything, you first need to know how the AI generates an answer. Looking at real collected data:
  1. The user asks in conversational language: “Tell me what to watch out for when buying a used EV”
  2. The AI internally creates and issues fan-out queries: “used EV purchase precautions battery health check” (these are the AI’s search terms, not a human’s)
  3. It reads a few of the web pages it found and builds an answer while citing them
  4. Specific brands and products are mentioned in the answer
Each point in this pipeline is a measurement point, and each maps 1:1 to the structure of this API.

What you measure: questions mapped to the API

Why repeated measurement matters

AI answers are non-deterministic. Ask the same question twice and you can get different answers.
So “is our brand mentioned?” is not a yes or no, it is a probability. This is why a run in this API can repeat the same prompt and model combination up to 100 times, and why visibility is measured as “how many mentions out of 100”. Models also behave differently. In one measured example, Perplexity cited YouTube content 38 times while ChatGPT cited it once. Your strategy depends on which model you want to be visible in. See Supported models.

Terminology

Get started

Quickstart

Your first API call in a minute

Running prompts

Run your first monitoring collection