Glossary
The vocabulary of AI search visibility.
Plain definitions for the terms we use in audits and reports - so the numbers are never a black box. These are the near-synonyms for AI Search Optimization (AEO/GEO) - we own the umbrella and every synonym. See the full method on the methodology page.
- AEO - Answer Engine Optimization
- Making your brand the one an AI engine names and cites when a buyer asks for the best tool in a category. The successor discipline to SEO for an answer-first world. See the full explainer on the what-is-AEO page.
- GEO - Generative Engine Optimization
- Often used interchangeably with AEO; emphasises optimising for generative answers across assistants rather than ranked link lists. A near-synonym under the AI Search Optimization (AEO/GEO) umbrella.
- AI SEO
- SEO adapted for AI answers - the classic SEO foundation plus the AI-answer (AEO/GEO) layer, done together. A near-synonym for AEO/GEO, emphasising that AI citations ride on a real SEO base.
- LLMO - Large Language Model Optimization
- Optimizing to be surfaced by large language models - a near-synonym for AEO/GEO / AI SEO. Less common than AEO or GEO, but the same discipline.
- AI visibility
- How present, recommended and cited a brand is across AI answers - the outcome AEO/GEO improves, measured as share-of-model. The category we help you win.
- Answer engine
- An AI system that answers a question directly - ChatGPT, Perplexity, Claude, Gemini, and Grok - instead of returning a page of links. The surfaces AEO/GEO optimizes for, distinct from a classic search engine.
- Citation
- A link in an AI answer that attributes a claim to a specific source page. Distinct from a mention: a citation links a page, a mention only names the brand - citations are the durable, verifiable form of AI visibility.
- Mention
- An AI answer naming a brand without linking to a page it owns. Tracked separately from citations: a mention puts you in the conversation; a citation makes you the source the answer stands on.
- % citing your URL
- KPI 1 of 7 - how often an answer links a claim to a page you own (the most durable, defensible form of visibility).
- % naming your brand
- KPI 2 of 7 - how often the answer names you at all in the category, your floor for being in the conversation.
- Sentiment
- KPI 4 of 7 - whether you're recommended, listed neutrally, or cautioned against in the answer.
- Presence quality
- KPI 5 of 7 - mention depth + source quality + data richness behind a mention. A cited, detailed recommendation beats a passing name-drop.
- Brand recognition
- KPI 6 of 7 - whether the engine describes you accurately and consistently when asked about your product or category.
- Market position
- KPI 7 of 7 - where you rank in the answer's shortlist against the competitors the engine names alongside you.
- Adaptive sampling
- We don't run every engine a fixed number of times. We run each as many times as it takes to reach a stable estimate - more where answers vary, fewer where they're stable, never below a 5-run floor.
- Non-determinism
- AI answers vary run to run for the same prompt. Why we report a median across adaptive per-engine runs and a confidence interval - never a single screenshot.
- E-E-A-T
- Experience, Expertise, Authoritativeness, Trustworthiness - the signals engines weigh when deciding whether to trust and cite a source.
- Wilson 95% confidence interval
- A statistically sound interval for a proportion (well-behaved at small samples and at the extremes, near the 0 and 1 bounds). We report it on every presence figure - do you appear, do you get cited - in both the paid audit and the public AI Visibility Index. The Index also uses it for share of model: a Wilson 95% confidence interval.
- Confidence interval
- The range the true value plausibly sits in, given how many answers we sampled. We match the method to the metric and to the product. Every presence figure carries a Wilson 95% confidence interval. Share of model carries a percentile bootstrap 95% confidence interval in the paid audit, and a Wilson 95% confidence interval in the public AI Visibility Index — the method matched to the metric and named per product, both computed from the actual runs. Share of model is a ratio over a pooled mention denominator whose observations are clustered — one answer may name several products, and repeated runs of a prompt are correlated. The paid audit already resamples at the answer level with a percentile bootstrap. The public AI Visibility Index still reports a Wilson interval, and Wilson assumes an independence that a clustered denominator does not have, so the Index's published share intervals are NARROWER than a cluster-corrected estimate would give. Bringing the Index onto the same clustered bootstrap the audit already uses is planned for a future measurement cycle; we have not restated the released figures, because a DOI points at them. We state this because it works against us: wider intervals would make MORE of our published rankings statistically indistinguishable, not fewer.
See these measured for your brand.
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