Scanning 2,500+ websites taught me what makes sites visible to AI search

I've spent the last 6 months building GEOScore AI (geoscoreai.com) -- a free tool that scans websites for AI search readiness. After scanning 2,500+ sites, here are the patterns I didn't expect.

The average GEO score is 45/100. Most websites are barely optimized for AI search engines like ChatGPT, Perplexity, and Claude. Even sites that rank well on Google.

~30% of sites block AI crawlers without knowing it. This was the biggest surprise. robots.txt files written years ago for Googlebot often accidentally block GPTBot, ClaudeBot, and PerplexityBot. These sites are literally invisible to AI search.

Schema markup is the #1 missing signal. Most sites have zero structured data. AI models use schema to understand what a page is about -- product, FAQ, how-to, organization. Without it, AI has to guess.

Sites scoring 70+ share common traits: They have llms.txt files, allow all AI crawlers, use comprehensive schema markup, and structure content with clear headings and direct answers. Basically, they make it easy for machines to extract and cite information.

The biggest quick win is always robots.txt. Fixing AI crawler access takes 5 minutes and has immediate impact. Everything else is optimization on top of access.

What surprised me most: Traditional SEO and GEO (Generative Engine Optimization) overlap about 60%. Good SEO gives you a head start, but there are specific AI signals that traditional SEO doesn't cover -- llms.txt, AI-specific crawler management, citation-ready content structure.

I'm sharing this because I think most founders aren't aware this is a thing yet. AI search is still a small percentage of total search traffic, but it's growing fast. The founders who optimize now will have a significant advantage.

The scanner is free at geoscoreai.com -- takes 30 seconds, no signup. Happy to answer questions about what we've learned from the data.