AI Score
AIRRNK's 0–100 grade for how likely a site is to be cited by a language model, calculated from 47 weighted checks across four pillars.
The AI Score compresses a site's AI readiness into a single number. It's computed from 47 individual checks across four pillars — technical readiness, content extractability, schema coverage, and authority & freshness — each weighted by empirical regression against real citation-rate data.
Weights are re-fit quarterly against a cohort of roughly 3,000 tracked sites. The score is calibrated such that 85+ represents the top decile of sites in our dataset; 70–84 is solid; 50–69 is average; below 50 indicates significant gaps.
The score is a proxy, not a rank. There is no public ranking against which to measure AI visibility, because LLM retrieval is probabilistic and platform-specific. The score captures 'readiness to be cited' in a way that correlates with 30-day forward citation rate at roughly 0.72 in our data.
In AIRRNK
The AI Score is the single headline metric in every AIRRNK report, dashboard, and email. It's also the target that our copilot optimizes for — every suggested fix is ranked by its expected AI Score delta, so you always know which lever moves the number most.
- Citation Density
A measure of how many distinct, quotable 100–400 token chunks appear per 500 words of content on a page.
- Generative Engine Optimization
The practice of making a website more likely to be cited by AI answer engines (ChatGPT, Claude, Perplexity, Google AI Mode) rather than simply ranked on a traditional search results page.
- Schema Markup
Structured data embedded in a page (usually as JSON-LD) that describes what the page is about in a machine-readable vocabulary defined at schema.org.
What is AI Score in the context of AI SEO?
AI Score describes one piece of the larger Generative Engine Optimization (GEO) problem — measuring and fixing how ChatGPT, Claude, Perplexity, and Gemini talk about a business. GEO differs from classical SEO because LLM answers do not return a list of links; they return a paraphrase, and the signals that get you inside that paraphrase are different.
How does AIRank measure ai score?
AIRank's Observer agent queries ChatGPT, Claude, Perplexity, and Gemini daily with the prompts your customers actually use and logs every mention. The Scanner agent then walks your site the way an LLM does — 47 signals across headings, schema, entity mesh, and source trust — and flags the specific gaps driving the result.
Why does ai score matter for AI visibility?
Roughly 42% of B2B buyer research now starts inside an LLM (Forrester 2026). Pages that do not satisfy the GEO signal set get paraphrased without attribution or omitted from answers entirely — a situation Aggarwal et al. (Princeton, 2023) measured as a 30-40% citation gap against pages that do.
What is the fastest way to improve ai score?
Start by running a free AIRank scan to surface the three highest-leverage fixes for your domain, then ship them through the Injector agent in a single click. Most teams see their first fix land within 12 minutes of install; citation lift typically shows up in weeks two and three once assistants re-crawl the edge-rewritten HTML.
Written by
The AIRank Editorial Team
Research & editorial, AIRank
The AIRank editorial team runs the 47-point scanner, the Observer pings, and the GEO research programme every week. Writing is reviewed by the core engineers who build the Injector, Blaster, and Surgeon agents.
About the team →