GEO (Generative Engine Optimization)
Generative Engine Optimization (GEO) is the practice of making content visible and citable in the outputs of generative engines — systems that synthesize answers with AI rather than returning ranked links. The term emerged from research into how content characteristics affect inclusion in generated answers, and in practice it overlaps heavily with Answer Engine Optimization; the two are often used interchangeably.
A generative engine retrieves sources, then composes an answer from them, so visibility is gated first on being retrieved at all, and then on being useful enough while the answer is composed to be drawn on and cited. GEO addresses both — the retrievability of content and its legibility to the model doing the composing.
The techniques echo the AEO playbook: clear and quotable structure, direct answers placed early, consistent terminology, and evidence a model can attribute. The distinction from classic SEO is the target — a generative engine's answer, with its handful of citations, replaces the ranked results page as the surface where visibility is won.
Discry works the API-side analogue of the same problem. Generative engines read websites to compose answers; agents read API documentation to build integrations. Discry measures that second surface — whether an agent can find a machine-legible entry point on a plain fetch and whether models can operate the API from the docs, graded mechanically against citation-verified ground truth.
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