From URL to action plan in five stages
No install, no tag. Give us a domain; we rebuild the way a generative engine actually reads it.
A robots-respecting crawler with JS rendering. It follows canonicals and records status codes, redirects and every internal link it meets. The free audit covers the first 10 pages.
Every page is read the way an LLM would ingest it: entities, heading hierarchy, schema.org coverage and how answer-shaped the copy is.
Pages become nodes and internal links become edges, with meaning as a second layer — the graph an AI reader actually follows, not the one in your sitemap. Every node receives one of six roles: pillar, cluster, hub, money, orphan, noindex.
One number from 0 to 100 you can drop into a report, a pitch or a tweet — with the full picture one click away in the app.
Fixes ranked by score impact: orphans to reclaim, clusters to reconnect, money pages to surface. Re-scan and compare the diff to prove the gain. Agents read it all via MCP.
What the score reads
The GraphoRank Score condenses four dimensions of AI visibility into one 0–100 benchmark. The exact methodology is proprietary — deliberately: a score you can't reverse-engineer is a score you can't game. Every site is measured the same way, on every scan, so the number is comparable across domains and consistent over time.
How your pages connect: whether authority actually reaches your pillar and money pages, how deep your content sits, and which pages are cut off entirely.
Whether your content holds together as topics: real clusters, pillars with genuine support, and no gap between what a page promises and what it says.
How legible your site is to a generative engine — how easy your pages are to read, extract and cite inside a generated answer.
The fundamentals that keep pages reachable: indexability, canonicals, redirect chains, broken links and sitemap consistency.