Generative Engine Optimization (GEO) is the practice of getting generative AI engines — ChatGPT, Claude, Perplexity, Google Gemini, Google AI Overviews and Microsoft Copilot — to recommend and cite your brand in their answers. Where traditional SEO optimizes for a ranked link, GEO optimizes for being named inside the answer itself.
This matters because buyers increasingly ask an AI before they ask you. When someone asks "what's the best tool for X?", the engine returns a short answer that names a few brands. If it names your competitors and not you, you have lost the moment of decision, and there is no second page to rank on.
How GEO is different from SEO
SEO is about links on a results page; GEO is about mentions in a synthesized answer. The two share fundamentals — good content, crawlability, authority — but the winning signals differ. AI answers lean heavily on third-party sources the model trusts (Reddit, review sites, industry roundups), on structured data that makes your facts easy to lift, and on clear, answer-first writing.
You also cannot see your GEO performance in a rank tracker. It only shows up when you actually ask the engines the questions your buyers ask, across engines, and measure who they name.
How to start with GEO
Begin by measuring: run your real buyer questions across the major engines and record who gets named, how positively, and which sources are cited. That baseline tells you exactly where you are losing.
Then fix the highest-impact gaps: earn mentions on the sources those engines already cite, publish answer-first content with FAQ and Organization schema, keep a clean llms.txt, and make sure your robots.txt actually lets AI crawlers in. Re-measure to prove the fixes moved the answer.
Parlax does this end to end — it measures your visibility across six engines and generates the fixes that change what the AI says next.