Generative Engine Optimization (GEO) is an emerging practice for making owned content accurate, explicit, crawlable, and easier for generative retrieval systems to parse. Common work includes direct answers, clear entities, consistent product facts, source attribution, appropriate schema, and visible limitations. The term does not describe one accepted industry standard or a formula that reliably controls provider output. Each provider decides what to crawl, retrieve, transform, and cite through systems EntityMesh cannot inspect.
GEO and AEO overlap
GEO and Answer Engine Optimization are often used for overlapping content and technical practices. The useful distinction is operational: publish high-quality answer material on the surface you control, then observe search and generated-answer outcomes separately. Do not infer a citation from structure alone.
EntityMesh supports the controllable publishing workflow
EntityMesh can turn reviewed sources into approval-gated drafts and an immutable hosted answer snapshot through a separate explicit publish action. The free diagnostic assesses bounded structural signals. LLM Presence or EchoScan evidence requires separately configured providers or analyst capture and completed runs; it does not start automatically when content is published.
Run a free diagnostic for a confidence-labelled structural baseline. Completion time and coverage vary.