How the AI Visibility Score works
AudFlo is an AI Visibility Audit Platform for founders. The AI Visibility Score is how it measures whether AI will recommend you: a number from 0 to 100, built from four weighted pillars and 32 Checks.
The short version
The AI Visibility Score is one number from 0 to 100 that reflects how easily AI engines like ChatGPT, Claude, Gemini, and Perplexity can understand, classify, and recommend your product. It comes from 32 diagnostic layers grouped into four weighted pillars that add up to 100%. Technical Visibility, worth 20%, asks whether AI engines can reach and read your site. Structural Understanding, worth 30%, asks whether they can tell what your product is and who it is for. Answer Selection, worth 30%, asks whether they will pick you when a buyer asks a category question. Authority Signals, worth 20%, asks whether your site looks trustworthy to people and machines. A higher score means a higher chance of being recommended when it counts.
The four pillars
Each pillar measures a different dimension of AI engine readability. The pillars are weighted because not all signals matter equally, together they make up 100% of your AI Visibility Score.
Technical Visibility
What it measures
Whether AI engines can crawl, parse, and trust your site. The eight layers cover indexability, robots.txt configuration, sitemaps, schema markup, llms.txt, meta tags, page speed, and social meta.
Why it matters
If AI engines cannot reach your site or read its structure, none of the other signals matter. A site that is blocked from crawling or missing structural cues scores zero on this pillar regardless of content quality.
How it is scored
We run eight checks. Each one passes, partly passes, or fails. Added up, they form the score for this pillar, which counts for 20% of your total.
What fixes improve it
Generating an llms.txt file, adding JSON-LD schema to key pages, submitting a sitemap, and confirming robots.txt allows AI crawlers are the four highest-leverage fixes. See the technical layers checked on a real site in the sample audit.
Structural Understanding
What it measures
Whether your content states clearly what your product is, who it serves, and what category it belongs to. The eight layers evaluate your primary headline, category statements, word count, reading level, jargon density, and paragraph structure.
Why it matters
AI engines classify products in one or two sentences before deciding whether to recommend them. Vague or jargon-heavy positioning means an AI engine cannot confidently describe your product to a buyer, so it recommends a competitor it can describe.
How it is scored
Each check looks at one thing and measures it against a clear standard. Added up, they count for 30% of your total, which ties this for the most important pillar.
What fixes improve it
Rewriting your homepage headline to name your category, your buyer, and your outcome in one sentence is the fastest win. A clear category statement like the definition block on the AudFlo homepage is the target pattern. Adding a product description in plain English and removing unexplained acronyms also raises this pillar quickly.
Answer Selection
What it measures
Whether an AI engine will pick you when a buyer asks a category question. The eight layers measure FAQ presence and FAQPage schema, comparison content, use case pages, question-shaped headings, and topic coverage breadth.
Why it matters
AI engines answer questions by assembling the most complete, structured response they can find. Sites with published answers to common questions, and clear comparison content showing how they differ from alternatives, are far more likely to be cited or recommended.
How it is scored
Each check looks for clear answers to the questions buyers actually ask. A proper FAQ that AI can read and quote scores higher than the same answers buried in paragraphs. Added up, they count for 30% of your total.
What fixes improve it
Adding a FAQ block with FAQPage schema markup and publishing a comparison page naming your category alternatives are the two highest-leverage moves. The AudFlo comparison page is an example of comparison content structured to score well on this pillar.
Authority Signals
What it measures
Whether your site signals trustworthiness to both people and AI engines. The eight layers cover testimonials, customer logos, founder presence, customer stories, Review schema markup, Person schema markup, and external authority signals.
Why it matters
AI engines weight trust signals when choosing between two sites with similar structural scores. A site with named testimonials, a credible founder page, and Review schema consistently outperforms one with generic "loved by thousands" copy.
How it is scored
Each check looks for real proof that people trust you. Testimonials with real names, a founder page, and reviews AI can actually read score higher than vague claims. Added up, they count for 20% of your total. We weight this lower than Structural Understanding and Answer Selection because trust takes longer to build than the other fixes.
What fixes improve it
Adding a founder page with credibility signals, wrapping existing testimonials in Review schema, and publishing at least one named customer story are the three fastest wins. The AudFlo founder page is an example of a founder credibility page built to score on this pillar.
The five recommendation factors
The four pillars are how AudFlo measures your score. They all serve one question from the AI Visibility Playground: when an AI engine decides whether to recommend you, how confident is it?
Recommendation Confidence = Accessibility × Classification × Evidence × Entity Confidence × Contextual Fit
These five factors multiply. If any one is near zero, the whole result collapses. Each factor is measured by one or more of the four pillars, and read from signals AudFlo already collects during a scan:
Accessibility. Can AI crawlers reach and read your site?
Measured by Technical Visibility. Read from robots.txt access, llms.txt, sitemap, schema presence, meta tags, and load speed.
Classification. Can the AI tell what category you are in and who you serve?
Measured by Structural Understanding. Read from the primary headline, title tag, meta description, and category statement.
Evidence. Can your claims be verified?
Measured by Authority Signals. Read from testimonial count and naming, customer stories, founder presence, and Review schema, graded against the Evidence Ladder.
Entity Confidence. Does the AI recognize your brand as a real, distinct entity?
Measured by Structural Understanding + Authority Signals. Read from name and category consistency across the headline, title, and metadata, plus Organization and SoftwareApplication schema and external mentions.
Contextual Fit. Are you the best match for the exact prompt?
Measured by Answer Selection. Read from FAQ coverage and FAQPage schema, comparison and use-case pages, and question-shaped headings that match real prompts.
Two factors worth naming
Entity Confidence is whether an AI can tell your brand apart from similarly named products. AudFlo reads it from how consistently your name and category appear across your headline, title, and metadata, plus your Organization and SoftwareApplication schema, and it lives inside Structural Understanding and Authority Signals. Contextual Fit is whether you are the best match for the exact prompt a buyer types. AudFlo reads it from your FAQ, comparison, and use-case coverage, and it lives inside Answer Selection.
Entity Confidence
The full breakdown is in the Entity Confidence framework.
The Evidence Ladder
The Evidence factor is not simply pass or fail. AudFlo grades the proof on your site against five tiers inside the Authority Signals pillar, because AI engines do not trust every signal equally. A vague claim is weak. A named customer story is stronger. Original data is strongest.
Stronger, harder to fake
Weaker, easy to fake
The hardest evidence to copy and the easiest to cite. A figure only you have is one an AI must attribute to you.
AudFlo check: Original data or research present?
Independent sources carry more weight than anything you say about yourself, because an AI can corroborate them.
AudFlo check: Independent third-party mentions present?
Proof from a real, named customer, with a result an AI can repeat back to a user.
AudFlo check: Named case studies present?
A named person vouching for you helps verify trust, but it is still something you publish about yourself.
AudFlo check: Named testimonials present?
The weakest signal, because nothing here can be checked against an independent source.
AudFlo check: Unsupported claims detected?
Want to see this applied? The sample audit shows all four pillars scored on a real site, with the blockers found and the fixes generated.
View sample audit →The four ranks
Your score maps to one of four ranks.
Recommended
80 and above
AI engines will reliably surface you when relevant queries come in. You are doing the work other founders skip.
Approaching recommendable
65 to 79
Close. A handful of targeted fixes (usually FAQ schema, comparison content, or testimonial markup) move you into Recommended territory.
Below recommendable
45 to 64
Real gaps. AI engines can find you but struggle to classify you or to confidently recommend you over competitors with stronger signals.
Invisible
below 45
AI engines either cannot reach your site or cannot understand what it offers. This is where most founders start. It is also where the biggest wins are.
What the score does not tell you
AudFlo measures structural and discoverability signals. It cannot measure brand reputation, product quality, network effects, or word of mouth. A great product with a poorly structured website still scores low. A mediocre product with a perfectly optimized website still scores high. The score reflects how discoverable your product is to AI engines, not how good your product is. Discoverability is necessary, not sufficient.
How we built this
We built AudFlo by studying how ChatGPT, Claude, Gemini, and Perplexity actually decide what to recommend when users ask category questions. Some signals match traditional SEO best practices. Many are new and specific to AI engines: FAQ schema, Person markup, the llms.txt specification, structured comparison content. The weights are based on observed AI engine behavior in 2026 and informed by published research on AI search ranking.
Methodology updates
AI engines change. The methodology updates when major shifts occur, for example, the llms.txt specification became part of the Technical Visibility pillar when major AI engines began reading it. Significant changes are versioned and announced.
Current version
1.0
Last updated
June 2026
Related
Evidence Ladder
How AI engines grade the proof on your site, from marketing claims to original research.
Entity Confidence
Whether an AI can confidently identify who you are and tell you apart from similar names.
Glossary
Plain definitions of the AI visibility vocabulary.
AI Visibility Guide
The complete guide to AI visibility and answer engine optimization.
Sample audit
See the methodology applied to a real site.
Compare
See how AudFlo's methodology differs from competitor approaches.
Run a scan
See your own score across the four pillars.
Pricing
Free scan includes your score. Pro unlocks all six Fix Pack assets.
About
Who built AudFlo and the thinking behind it.
Data handling
Exactly what we store when we run your scan.
FAQ
Common questions
How are the four pillars weighted?+
Where do the weights come from?+
Will the methodology change over time?+
What is the difference between Recommended and Approaching recommendable?+
Why is Authority Signals only 20 percent?+
Does the AI Visibility Score predict revenue?+
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