GEO was named by Aggarwal et al. (KDD 2024)1. It covers the content and structural techniques aimed at increasing a brand's visibility inside generative engines — ChatGPT, Gemini, Perplexity, Google's AI surfaces — where answers are synthesized rather than ranked as a list of links.
Rhinegold's position: GEO is the tactic, not the goal. It is the lever you pull once Semantic Intelligence has shown what to optimize for — optimizing without measurement is guesswork. In practice GEO works on three handles: retrievability (can the engine fetch you into its source pool), citability (are you a quotable, source-worthy reference), and recommendability (are you named when the engine shortlists providers).
GEO is the right frame when the question shifts from "are we in the answer?" to "how do we get into the answer?" — and when a measured visibility gap points to a specific handle to pull.
GEO has no single KPI. Its effect shows distributed across Mention Rate, Citation Rate, Brand Recommendation Share and Share of Voice — and it is provider-specific and volatile. A technique that lifts citation on one engine may do nothing on another, and behaviour shifts when providers update.
Against SEO, which ranks pages in classic search results. Against the vendor synonyms AEO ("Answer Engine Optimization") and LLMO, which describe the same activity. And against Semantic Intelligence, which is the measurement frame GEO serves — GEO acts, Semantic Intelligence judges whether the action worked.
The field is young: "GEO" competes with AEO and LLMO, and the efficacy of most techniques is largely unvalidated and shifts with provider updates. Treat vendor "GEO checklists" with scepticism.