How AI-readable
is the open web?
Lyrenth crawls and audits the open web for the signals that matter to AI agents reading it: structured data coverage, render-mode mix, heading hygiene, content density, and access friction. The numbers below are aggregates across our public index, refreshed every 10 minutes.
Total pages currently in the canonical index
85.8% of indexed pages audited
Weighted mean across seven content signals
What the audit measures
Each audited page is scored on seven content-derived signals. Below is the corpus-wide mean for each one: how the open web performs against AI-agent-friendly hygiene, aggregated across every page Lyrenth has audited.
Whether the page carries JSON-LD blocks (Article, Product, FAQPage, etc.). Structured data lets agents extract facts without parsing prose.
Single H1, monotonic descent through H2 / H3. Predictable structure makes a page easier to skim and section.
Share of pages served without a headless-Chromium escalation. Static-renderable pages cost less for crawlers and never arrive empty to first-pass scrapers.
Ratio of meaningful markdown to raw HTML. High density means most of the page is content, not nav / chrome / ads.
Non-empty, sensible-length title and description that are not generic placeholders.
Whether content reaches readers (and agents) without hitting a paywall or login wall. Higher means the page is open.
Whether the page has enough words to be substantive on its own. Sub-stub pages get partial credit; pages with no body fail outright.
What these numbers mean
A high site-wide structured-data percentage means agents can extract facts cheaply. Low static-renderability or low content density means more of your input tokens are paying for headless rendering and chrome, not content. Lyrenth normalizes these for you in a single AIDocument shape regardless of how the source page is built.
Read the quickstartThe signals above are exactly what Lyrenth measures per page on your verified domains. Lyrenth handles AI agent traffic to your site: clean, structured pages served to every agent reading you. Verify a domain to see your own AI Readiness score on the dashboard.
Verify a domainMethodology: every audited page is scored on seven content signals, each 0.0 to 1.0. Per-page scores roll up to a domain average and a corpus-wide mean. Raw page content stays in our private index; only aggregate counts and means are public. Refreshed every 10 minutes. Full methodology in /llms-full.txt.