What is Generative Engine Optimization (GEO)?

What is Generative Engine Optimization?

Generative Engine Optimization (GEO) is the practice of improving how often, and how accurately, AI answer engines such as ChatGPT, Google AI Mode, Gemini, Claude and Perplexity mention, recommend and cite a brand. Traditional SEO aims for a ranked link on a results page. GEO aims to be part of the answer itself. AI engines build answers by retrieving pages that match the searches they run behind the scenes, then summarizing the sources they judge most relevant and trustworthy. GEO therefore works on three levels: publishing clear, self-contained answers a model can quote; earning mentions on the third-party pages models already cite, such as review sites, community threads and industry lists; and keeping brand facts consistent everywhere so models don't contradict themselves. GEO is measured by how often a brand appears across a set of buyer prompts, which sources are cited, and what traffic and pipeline those answers produce.

The term is often used interchangeably with Answer Engine Optimization (AEO) and AI search optimization. The labels differ; the work is the same.

How is GEO different from SEO?

SEO and GEO share foundations: crawlable pages, clear structure, and content that answers a real question. They differ in what counts as winning. In SEO the unit is a ranked URL and the metric is position and clicks. In GEO the unit is a mention inside a generated answer, and the metric is whether you are named, how you are described, and which source the engine used to say it.

That shift moves effort off your own site. A search engine ranks your page; an answer engine often summarizes someone else's page about you. Review sites, community discussions, comparison articles and documentation carry as much weight as your homepage, so GEO includes earning and correcting those third-party mentions.

How do AI engines choose which brands to mention?

When a model answers a question that needs current information, it rewrites the question into one or more search queries, retrieves pages for those queries, and composes an answer from the passages it finds most relevant. Brands that appear clearly and consistently in those retrieved passages get named. Brands that are absent, vague, or described inconsistently across sources get skipped or misdescribed.

Answers also vary between runs. The same prompt can name different vendors on different attempts, which is why a single check is not a reliable measurement. Asking each prompt several times and reporting how consistently a brand appears gives a more honest picture.

How do you measure GEO?

Start with a fixed set of prompts your buyers actually ask, grouped by stage: problem research, category exploration, vendor shortlisting and comparison. Run them regularly across the engines your buyers use and record three things for each answer: whether your brand is mentioned, where it appears, and which URLs the engine cited.

• Share of Model: the percentage of answers in your prompt set that mention your brand.
• Citation sources: the domains and pages each engine relied on, and which of them mention competitors but not you.
• Accuracy: claims about your pricing, features or leadership that contradict your own published facts.
• Outcome: visits that AI answers send to your site, and the accounts and pipeline those visits become.

What does a GEO tool do?

A GEO tool automates the measurement loop above. It runs your prompts across AI engines on a schedule, stores every answer and citation, and tracks how your share of answers changes over time. Better tools go beyond monitoring: they point to the specific pages to rewrite, the content to publish, and the third-party sources worth pitching, then re-measure after each change.

Targetlytics is an AI visibility and GEO platform built for B2B revenue teams. Alongside measurement and recommendations, it identifies the companies behind visits that AI answers send to your website and writes them into your CRM, so the work can be tied to pipeline rather than reported as impressions.