A Critical Insight: AI Search Is a Multistage Process
Many people understand GEO as SEO ranking in AI, but this analogy is imprecise. A 2026 survey paper, Optimizing Visibility in Generative Engines: A Critical Survey (Martinez, arXiv:2607.14035), proposes a more accurate framework: AI search is a system of multiple independent stages, each with its own mechanisms and influencing factors.
Understanding this multistage framework is the key to avoiding the confusion of optimizing content without seeing any visibility improvement.
Five Stages: From User Question to Brand Mention
Stage 1, Search Activation: not all user questions trigger a web search—AI models judge whether a question requires retrieving current information or can be answered from training data. If your target queries are primarily treated as known information, even excellent content may never reach the retrieval stage.
Stage 2, Retrieval: when search is activated, the retrieval system finds relevant web pages, articles, and sources from its index, influenced by technical accessibility, relevance matching, and domain authority.
Stage 3, Reranking: retrieved content is reranked to select the most relevant and authoritative sources for answer generation, where content quality, trust signals, and entity clarity matter.
Stage 4, Citation Generation: AI decides during answer generation whether to cite a source and how to cite it, shaped by content structure, citability, and match to the question.
Stage 5, Content Absorption: whether users notice the citation and click through to the source is the final effect layer.
Why a Single Rank Cannot Describe GEO
Traditional SEO can use keyword ranking to concisely describe a site's position in search results. But GEO visibility exists across all five stages above, and performance at each stage is an independent variable.
A site may perform well at retrieval (found) but be filtered at reranking (insufficient authority); or pass reranking but be overlooked at citation generation (insufficient citability). This is why GEO analysis must be multidimensional rather than a single number.
Broccoli AI GEO is built on this insight, providing analysis across visibility, citation quality, and content credibility dimensions rather than offering a single AI ranking number.
Methods to Improve Visibility at Each Stage
Improve search activation: ensure your content covers question types that genuinely require real-time information (latest recommendations, best in current year), signaling to AI that retrieval is warranted.
Improve retrieval performance: strengthen technical site health (fast, crawlable, with sitemap); increase high-quality backlinks; ensure content has topical depth and breadth.
Improve reranking performance: build brand authority (cited by industry media, expert endorsement); structure content (FAQ, How-to, lists) to aid AI comprehension; maintain entity clarity (brand name, product, industry labels consistent).
Improve citation generation: citability (clear data, quotations, conclusions); avoid vague statements; use precise language to describe product features and differentiation.
Replacing Single-Rank Promises with Multidimensional Evidence
The most important practical implication of this research: GEO effectiveness should be evaluated using multidimensional evidence, not sought through a simple ranking promise.
Truly valuable GEO analysis should tell you: in which question types does your brand appear? When mentioned, is it recommended or merely listed? Which page is cited? At which stage is the gap with competitors largest? Is the sample size sufficient to be trustworthy?
This is the principle behind Broccoli AI GEO's report design: retaining raw answers, labeling sample size, providing confidence intervals, and ensuring every conclusion has traceable evidence supporting it.