Discovery Score: How AI Finds You
Your Discovery score measures how easily AI systems can find, understand, and index your content. It is the foundation of AI visibility -- if AI cannot discover you, it cannot cite you.
Score Range
What We Check
Your Discovery score is built from six key signals that AI systems look for when deciding whether to crawl, index, and understand your site.
llms.txt
+20 pointsA machine-readable file at your domain root that tells AI systems who you are, what your site offers, and how to interpret your content. Think of it as a cover letter for AI models -- compact, structured, and instantly parsable within their context windows.
Read implementation guiderobots.txt AI Crawler Rules
+10 pointsExplicit permissions in your robots.txt for AI crawlers like GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. Without these rules, AI systems may skip your site entirely or rely on outdated cached versions of your content.
Read implementation guideSchema.org JSON-LD Markup
+20 pointsStructured data embedded in your pages that provides AI with precise, machine-readable context about your content: organization details, articles, products, FAQs, and more. JSON-LD is the format preferred by both search engines and AI systems.
Read implementation guideai-discovery.json
+15 pointsAn AI Discovery Protocol manifest that declares your site's capabilities, preferred interaction modes, and content taxonomy. It goes beyond llms.txt by providing a structured JSON specification for more sophisticated AI integrations.
Read implementation guideknowledge-graph.json
+15 pointsA structured export of your site's entity relationships: people, products, concepts, and how they connect. AI systems use knowledge graphs to build richer, more accurate representations of your domain expertise and authority.
Read implementation guideai-sitemap.xml
+20 pointsAn AI-optimized sitemap with purpose annotations that tells AI crawlers not just where your pages are, but what each page is for. Includes content type labels, priority signals, and update frequency hints tailored for AI consumption.
Read implementation guideWhy Discovery Matters
AI systems like ChatGPT, Claude, Perplexity, and Google AI Overviews are rapidly becoming the primary way people find information. But unlike traditional search engines that crawl everything and rank it later, AI models are selective. They have finite context windows and limited crawl budgets. If your site does not provide clear, machine-readable signals about what it contains, AI will simply skip over you.
Discovery is the prerequisite for everything else. You cannot be cited if you are not found. You cannot be recommended if you are not understood. And you cannot compete in AI-driven search if your content is invisible to the models generating the answers.
The sites that proactively make themselves AI-discoverable are already seeing 2-4x more citations compared to similar domains that rely on traditional SEO alone. As AI-driven search continues to grow -- with Perplexity processing millions of queries daily and ChatGPT Browse becoming mainstream -- the gap between discoverable and invisible sites will only widen.
A strong Discovery score means AI systems can find your site, parse its structure, identify its authority, and route relevant queries to your content. It is the foundation on which your Agentic and Citation scores are built.
How to Improve Your Discovery Score
Start with llms.txt
Create a plain-text file at your domain root that describes your site, its purpose, and its key pages. This takes 10 minutes and is the single highest-impact action you can take.
Configure robots.txt for AI crawlers
Add explicit User-agent rules for GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. Most sites block AI crawlers by default without realizing it.
Add Schema.org JSON-LD markup
Embed structured data in your page headers: Organization, WebSite, Article, Product, or FAQ schemas depending on your content type. AI systems parse JSON-LD natively.
Create an ai-discovery.json manifest
Declare your site's capabilities, content taxonomy, and preferred interaction modes in a structured JSON manifest for advanced AI integrations.
Build a knowledge-graph.json
Export your entity relationships so AI systems can understand how your content connects. This is especially valuable for complex sites with many interrelated concepts.
Generate an AI-optimized sitemap
Go beyond a standard sitemap by adding content type annotations and purpose labels. Tell AI crawlers not just where your pages are, but what they are for.
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