Methodology

Two things power this site: leaderboards of who AI recommends, and a free checker that scores how ready a website is to be read and cited by AI. Both are built to be transparent and repeatable.

How the leaderboards are built

1. 1. Each category is asked as a natural question, e.g. “What is the best CRM?”
2. 2. Every question is sampled five times per engine across ChatGPT, Gemini and Perplexity — a single run would be noise.
3. 3. The businesses named are fuzzy-matched and canonicalized (so “HubSpot” and “HubSpot CRM” count once).
4. 4. Each one is scored by how often it appears across runs and engines, with average rank as a tie-break.
5. 5. The citation each engine points to is kept and shown, so every ranking is traceable.
6. 6. Ledgers refresh on a cadence earned by volatility — hot categories more often, stable ones less.

How the AI-readiness score works

The checker fetches your page the way an AI crawler does — raw HTML, no JavaScript — plus robots.txt, sitemap.xml and llms.txt. It then scores seven weighted pillars:

• • AI crawlability & access — can answer engines reach the page at all?
• • Rendering & content availability — is the content in the HTML, not hidden behind JS?
• • Structured data — Schema.org that AI can parse.
• • Answer-engine optimization — direct answers, FAQs, stats, citations.
• • Authority, trust & E-E-A-T — who you are and why you’re credible.
• • Performance and SEO fundamentals.

The result is an overall score, an AI-specific sub-score (only the pillars that decide whether an answer engine can reach, read and lift the page), a tier, and a prioritized list of what to fix. The audit is deterministic — no AI is used to grade you.

Independence

Placement on any leaderboard is never for sale. Rankings are observations of public AI output and nothing else. See about for more.

Last updated June 2026.