The AI Visibility Score is the only standardised measure of how well a website can be found, understood, and cited by AI-powered search engines. Developed by SearchScore. Check any website in 10 seconds.
An AI Visibility Score is a composite metric from 0 to 100 that measures how discoverable, comprehensible, and citable a website is to AI-powered search engines. The higher your score, the more likely AI systems are to find your content, understand its context, trust your authority, and surface your website as a source when users ask relevant questions.
The AI Visibility Score was created by SearchScore in 2026 as a response to a critical gap in the SEO measurement landscape. While traditional metrics like Domain Authority or Page Authority measure a website's potential to rank in keyword-based search engines, they say nothing about how visible a site is to AI systems that answer questions directly, draw on trusted sources, and bypass the traditional ten-blue-links model entirely.
SearchScore audits any publicly accessible website across eight weighted categories, evaluating over 250 individual signals to produce a single, actionable number. Just as Moz's Domain Authority became the shorthand for link-based trust, the AI Visibility Score is the shorthand for AI search readiness.
The score is calculated in real time and updates automatically as websites are improved. It provides a common language for SEO professionals, marketing teams, and business owners to discuss AI search readiness without ambiguity.
Two SearchScore metrics share this family name. This page covers the audit-side score. For the full breakdown of how the site-audit metric is calculated and improved, see the AI Visibility Readiness Score guide; for live citation tracking across six engines, see the SearchScore Tracker.
The way people find information online is changing fundamentally. AI-powered answers are replacing ranked lists of links. If your website is not optimised for AI citation, you are invisible to a growing share of your audience.
Platforms like Perplexity and ChatGPT answer questions directly, citing two to five sources. There are no page two results, no sponsored listings below your citation – just your brand or a competitor's. Visibility requires a fundamentally different strategy than traditional ranking.
Language models weigh signals that traditional search engines largely ignore: whether you have an llms.txt file, whether AI bots are explicitly permitted in your robots.txt, whether your content is clearly attributed to a named expert, and whether your brand appears across multiple authoritative sources.
AI search is not a passing trend. Businesses that establish AI visibility now, while competitors are still focused exclusively on traditional SEO, build citation habits into AI systems early. These associations are difficult to dislodge once formed, giving early movers a durable advantage.
Without a standardised metric, AI search optimisation is guesswork. The AI Visibility Score gives teams a baseline, a benchmark against competitors, and a clear set of actions to improve. What gets measured gets managed – and the AI Visibility Score makes AI readiness measurable for the first time.
ChatGPT, Perplexity, Gemini, and Claude each use different mechanisms to retrieve and cite sources, but all share common requirements. Understanding what each looks for is the foundation of improving your AI Visibility Score.
Every AI Visibility Score is built from eight weighted categories. Together they capture the full picture of AI search readiness – from the technical signals that AI bots detect on first crawl to the brand authority signals they cross-reference across the web.
The highest-weighted category. Experience, Expertise, Authoritativeness, and Trustworthiness are the framework Google introduced and that AI systems have adopted as a core trust signal. This category checks for named authors with credentials, publication dates, transparent editorial standards, external links to authoritative sources, and content that demonstrates genuine subject-matter expertise.
Measures whether your content is structured so that AI systems can confidently extract, quote, and cite it. This includes content clarity, direct question-and-answer formatting, scannable lists and tables, concise quotable passages, and citation-friendly attribution.
Checks whether AI systems are explicitly permitted and equipped to read your content. This includes the presence of an llms.txt file, explicit AI bot permissions in robots.txt for GPTBot, PerplexityBot, and ClaudeBot, and readiness across AI platforms such as Perplexity and AI Overviews.
JSON-LD schema markup is one of the most direct signals you can send to AI systems about what your content means. This category checks for the presence and quality of key schema types: Organisation, WebSite, Article, FAQPage, HowTo, BreadcrumbList, and Person. Comprehensive structured data dramatically reduces ambiguity for AI content extraction.
AI crawlers behave differently to traditional search bots, but they still need clean, fast, accessible HTML to extract content reliably. This category evaluates page speed, mobile responsiveness, crawlability, semantic HTML structure, internal linking, and the absence of technical barriers that might block or confuse AI content extraction.
AI systems cross-reference what they know about a brand from across the web. This category evaluates your brand's presence on Wikipedia, LinkedIn, Crunchbase, industry directories, and other high-authority platforms. A brand that exists in multiple trusted sources is far more likely to be cited than one that only appears on its own website.
Generative engines favour sources that cover a subject comprehensively. This category evaluates the depth and breadth of your topic coverage, content hub and cluster structure, internal linking architecture, and the interconnected, in-depth content that signals genuine authority on a subject.
AI systems draw information from across the web, including social platforms and professional networks. This category checks for active presence on platforms that AI systems query regularly: LinkedIn company page quality, X (Twitter) activity, GitHub presence for technology companies, and YouTube for brands in video-heavy industries.
AI Visibility Scores fall into five tiers. Each tier reflects the current state of AI search readiness and the priority and effort required to move to the next level.
| Score Range | Tier | What It Means | Priority |
|---|---|---|---|
| 0 – 30 | 🚫 Invisible | AI search engines cannot reliably find or understand your content. Critical foundations are missing – AI bots may be blocked, content is unstructured, and there is little or no brand authority signal across the web. | Critical |
| 31 – 50 | ⚠️ Low Visibility | Basic signals are present but incomplete. You may occasionally appear in AI responses, but citations are unreliable. Key categories – typically Brand Authority or Structured Data – have significant gaps that prevent consistent citation. | High |
| 51 - 70 | 📈 Emerging | Solid foundations with meaningful room for improvement. AI systems can find and understand your content but may not consistently prioritise you over better-optimised competitors. Targeted improvements to your weakest categories will have a measurable impact. | Medium |
| 71 – 89 | ✅ Strong | Well-optimised for AI search. Your site is regularly cited by AI systems and you have a competitive advantage over most websites. Focus at this stage is on strengthening your weakest category and maintaining consistency across all signals. | Maintenance |
| 90 – 100 | 🤖 AI-Ready | Exceptional AI search visibility. Every category is comprehensively optimised, brand authority is strong across the web, and AI systems consistently recognise you as a trusted, authoritative source. Reserved for sites that have excelled across all eight categories. | Sustain |
Each category of the AI Visibility Score responds to specific, actionable improvements. Below are the highest-impact actions for each category, ordered by weight.
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