Citerank, the AI citation scanner at citability.dev, built by Chudi Nnorukam as an operator-facing evidence layer for AI-answer visibility, recommendation quality, and citation proof.
Citerank exists because AI answers changed the discovery surface faster than analytics changed with it.
Citerank is the AI citation scanner at citability.dev. Your CiteRank score (0-100) measures how often AI engines cite your site.
Citerank (citability.dev) is built and run by Chudi Nnorukam, an AI-Visible Web Architect and freeCodeCamp author who created the open-source AI Visibility Readiness (AVR) framework that powers the platform.
Traditional SEO tools tell you where a blue link ranks. They do not tell you whether ChatGPT recommends you, whether Claude cites your docs, whether Perplexity links a competitor, or whether Gemini silently omits your brand.
Who runs Citerank, and what it does
A platform that measures whether AI can find, recommend, and cite you, centered on prompt-based audits, signed receipts, model-version pinning, competitor comparison, and AVR scoring.
Because high-intent AI answers are now a distribution channel, and most teams still cannot inspect what those systems say about them.
Design principle: evidence over claims.
The product is intentionally not a bright growth dashboard. It is a trust surface. Metrics summarize risk. Receipts prove it. Every higher-level view must drill down to model output, extraction logic, and sourceable proof.
Where Citerank fits in AI visibility
The broader direction is to build machine-readable trust and attribution surfaces for the AI-visible web, with citability.dev acting as the measurable proof engine.
Methodology, proof model, and data-source policy are public. The machine-readable transparency schema lives at /.well-known/citability.json.
The same operator builds ReviewReplyCopilot, an AI tool that drafts on-brand replies to customer reviews. It is a working example of an AI-visible product, the kind of site whose ChatGPT, Perplexity, and Claude citation rate Citability is designed to measure.