The Factors That Determine Your AI Visibility Score
A breakdown of the factors we assess in every audit and why each one matters.
February 20, 2026 · 16 min read
Ben Nawin
TLDR — Most AI visibility tools give you a number and don't explain it. We publish our methodology. This breaks down all 12 factors we check, how much each one matters, and where the research comes from.
Most AI visibility services hide how they calculate scores. You get a number, maybe a letter grade, and a vague list of suggestions. You have no idea what actually matters or why your score went up or down.
We think that defeats the purpose. So here is every factor we look at and why each one matters. This methodology is based on published research: the Princeton GEO study (showing citations boost AI visibility by up to 40%), Ahrefs' analysis of 17M AI citations, and SE Ranking's study of 129K domains.
1. Entity Clarity
AI models need to understand who you are before they can recommend you. Pages with clear entity definitions are 2.3x more likely to be recommended (SE Ranking, 129K domains). This is the single most important factor we look at.
What we look at:
- Value proposition in the first 300 words
- Business name and services clearly stated
- Target audience identified
- H1 heading present and descriptive
- Meta description with entity information
- About statement or "who we are" section
If your homepage doesn't say what you do in the first few paragraphs, that's the first thing we fix.
2. Schema Markup
Structured data makes your content machine-readable. Pages with schema markup have 40% higher citation rates in AI Overviews.
What we look at:
- JSON-LD structured data present
- Schema types used (Organization, Article, FAQPage, HowTo, Product, etc.)
- Organization schema completeness (name, description, logo, contact)
- FAQPage schema validation
- Schema nesting and relationship depth
No schema at all means AI has to guess what your page is about. Even a single well-formed Organization schema makes a difference.
3. Content Structure
AI models parse content hierarchically. Well-structured content with clear headings, logical flow, and readable prose is easier for models to extract and recommend.
What we look at:
- Heading hierarchy (H1 through H6, proper nesting)
- Word count and content depth
- Flesch Reading Ease score (target: 60-70, "Standard")
- AI-generated content detection
- Paragraph length and scannability
Thin content (under 150 words) and poor readability are the most common issues we see. Skipped heading levels also hurt.
4. Trust Signals
AI models prioritize trustworthy sources. Trust signals tell models your content is credible and your business is real.
What we look at:
- HTTPS (SSL certificate)
- Contact information (email, phone, address)
- Author credentials
- Privacy policy and terms of service
- Cross-channel presence (social profiles linked from your site)
Missing HTTPS or no contact information are the biggest trust red flags. Author credentials with verifiable expertise score highest.
5. Citation Worthiness
AI recommends content it can quote directly. Pages with original statistics are 4x more likely to be recommended. Data tables make content 4.1x more quotable.
What we look at:
- Statistics and numerical data points
- Original research or proprietary data
- Data tables with structured information
- Passage quotability (self-contained paragraphs of 40-60 words)
- Source attribution for claims
Pages with no statistics or data rarely get recommended. Three or more attributed statistics significantly improve your odds.
6. Content Freshness
76.4% of most-cited pages updated within 30 days (Ahrefs, 17M AI citations). AI models heavily favor recent content.
What we look at:
- Date parsing from multiple sources: meta tags, HTTP headers, visible dates, schema dateModified
article:modified_timeandLast-Modifiedheaders- Visible "Last Updated" dates on the page
Content updated within the last 30 days performs best. Over 180 days without an update and AI largely ignores it.
7. FAQ Content
FAQPage schema has a 41% citation rate vs 15% without it (Relixir study via Frase.io). FAQ sections give AI ready-made answers to recommend directly.
What we look at:
- FAQ sections detected on the page
- FAQPage schema markup present and valid
- Number and quality of Q&A pairs
- Answer completeness (self-contained, fact-rich answers)
Having FAQ content without the FAQPage schema is a missed opportunity. Five or more well-structured Q&A pairs with schema performs best.
8. AI Crawler Access
23% of websites accidentally block AI crawlers (Ahrefs). If bots cannot access your content, AI cannot recommend you.
What we look at:
- robots.txt rules for 13 AI bots: GPTBot, ChatGPT-User, Google-Extended, PerplexityBot, ClaudeBot, anthropic-ai, Bytespider, cohere-ai, Diffbot, FacebookBot, OAI-SearchBot, YouBot, and CCBot
- llms.txt file presence (emerging standard for AI-specific crawling instructions)
- Per-bot allow/block status
All bots allowed plus an llms.txt file is the ideal setup. Blocking even a few bots means those platforms will never recommend you.
9. External Validation
Businesses in the top 25% for web mentions are 10x more likely to be recommended by AI. External validation tells models you are a recognized authority.
What we look at:
- Awards and recognition mentions on the page
- Wikipedia references
- Media mentions and press coverage
- Brand citations from third-party sources
- Customer testimonials with named sources
10. Image Optimization
AI models use alt text and image context to understand page content. Well-described images add semantic meaning to your page.
What we look at:
- Alt text quality (descriptive, 5-125 characters, not stuffed with keywords)
- Responsive images (srcset, sizes attributes)
- Modern image formats (WebP, AVIF)
- Image count relative to content length
Text-only pages are fine. But if you have images, they need descriptive alt text.
11. Page Speed
Fast pages get 3x more recommendations than slow ones. AI crawlers have time budgets. If your page takes too long to load, it may not get fully indexed.
What we look at:
- Time to First Byte (TTFB)
- Total content size
- Cache headers (Cache-Control, ETag)
- Mobile viewport meta tag
12. Sitemap Validation
A valid sitemap helps AI crawlers discover all your pages efficiently. It also signals which pages you consider important and when they were last updated.
What we look at:
- XML sitemap present and well-formed
- lastmod dates on URLs (signals freshness to crawlers)
- Sitemap referenced in robots.txt
- URL count and structure
Frequently Asked Questions
Why do you publish your methodology?
Most AI visibility services treat their scoring as a black box. We think that defeats the purpose. If you cannot see how your score is calculated, you cannot act on it. Publishing our methodology lets you verify our logic, prioritize your own fixes, and hold us accountable when something is off.
How often does the methodology change?
We review our approach quarterly based on new research. When a major study shifts the evidence (for example, the Ahrefs 17M citations study that confirmed the importance of freshness), we adjust. Changes are documented and communicated.
What score should I aim for?
Most websites score between 30 and 50 on their first audit. A score above 70 means you are well-positioned for AI recommendations. Above 85 puts you in the top tier. Focus on the highest-impact factors first: entity clarity, schema markup, and content structure.
Can I get a perfect 100?
It is possible but rare. A perfect score requires strong performance across all factors, including signals like Wikipedia mentions and author credentials that take time to build. Most highly optimized sites score between 80 and 95.
Why does entity clarity matter the most?
AI models need to understand who you are before they can recommend you. If a model cannot identify your business, services, or audience, none of the other factors matter. Research from SE Ranking (129K domains) shows pages with clear entity definitions are 2.3x more likely to be recommended.
How are per-platform scores different from the overall score?
The overall score uses all the factors listed in this article. Per-platform scores (ChatGPT, Perplexity, Google AI, Claude, Gemini, Grok, DeepSeek, Copilot) use different formulas tuned to what each platform actually prioritizes. For example, ChatGPT favors schema markup heavily, while Perplexity prioritizes content depth and citations.
Conclusion
Transparency builds trust. We publish our methodology because we believe you should know exactly how your score is calculated. If you disagree with something, we want to hear about it. That feedback makes the system better for everyone.
Get in touch to see where your site stands across all these factors. We'll show you a score for each one, an overall AI visibility score, per-platform breakdowns for 8 AI platforms, and prioritized recommendations for what to fix first.