Generative Engine Optimization (GEO): What It Is and How It Works

Generative Engine Optimization covers everything a business does to make generative AI systems absorb its information correctly and use it in their answers. When someone asks ChatGPT "who offers independent pension advice near me" or asks Perplexity for "the best bookkeeping tool for startups," the AI decides — based on the information it can access, understand and verify — which businesses to name.

The term was coined in a 2023 paper by researchers at Princeton University ("GEO: Generative Engine Optimization") and has since become standard vocabulary. Classic SEO optimizes a page for position one in Google's results. GEO optimizes for the AI mentioning and linking a business inside its generated answer — often without the user ever seeing a traditional results page.

More and more research starts not on Google but in ChatGPT, Perplexity or Google AI Overviews. These systems return an answer instead of a list of links. If you're not in that answer, you effectively don't exist for a growing share of users — regardless of your classic search ranking.

The category is young. That's the opportunity: because reliable, well-structured sources are still scarce, AI systems lean heavily on the ones that are clear, verifiable and machine-readable. Building that early earns disproportionate citation share.

First, write answer-first. Every key question should be answered in the opening one or two sentences, self-contained and without requiring the context before it. AI systems preferentially extract passages that stand on their own.

Second, back facts with evidence. Concrete numbers, regions, services and source links reduce ambiguity and hallucination. Third, structure. Schema.org data, clear headings, FAQ blocks and lists help the machine parse content cleanly.

Fourth — where classic GEO usually stops — make it machine-readable. Files like llms.txt, agent.md and agent.json summarize what a business does, for whom, where, and which requests are allowed. They are the direct entry point an AI system can read without wading through marketing pages.

A relocation firm wants to appear in AI answers for "relocation services in Zurich." With GEO in the narrow sense, it describes its services clearly, backs them with sources and publishes structured data. Result: ChatGPT and Perplexity name it accurately.

The decisive second step: through an agent-readable profile on anewera, "gets mentioned" becomes "can be contacted." An AI agent — say Claude Code or a ChatGPT agent — can request a quote or a meeting through defined functions, without ever seeing a private email address. GEO makes the business citable; the agent-ready profile makes it actionable for agents.

The most common mistakes: describing services only as marketing slogans instead of concrete terms; burying key facts in images or sliders that machines can't read; missing sources; and accidentally blocking AI crawlers like GPTBot, ClaudeBot or Google-Extended in robots.txt.

Also widespread: treating GEO as pure tracking. Knowing whether an AI mentions you is useful — but it changes nothing while the content isn't understandable and the contact paths aren't machine-readable. GEO is a supply-side discipline, not just measurement.