geo-citability

To improve web page content for AI citations, you need to evaluate and rewrite passages so they are self-contained and fact-dense. This process involves scoring answer quality, structural readability, and statistical density to generate targeted rewrite suggestions that models can easily extract.

A passage is easily extractable by AI models when it features an answer-first structure, high statistical density, and self-contained facts. Block-level analysis measures these uniqueness signals and structural hierarchy to determine if the content can be quoted without surrounding context.

You audit landing pages for AI citability by running a block-level analysis that segments content by headings and evaluates per-section strengths. This generates a structured markdown report with an overall 0–100 score, coverage metrics, and prioritized rewrite recommendations.

AI citability scoring applies to standard web page content including landing pages, articles, and technical informational sections. It evaluates existing structural readability hierarchies and suggests adding concrete facts, statistics, lists, or tables to increase citation probability.

Your article scores low on AI citability metrics because it likely lacks self-contained passages, statistical density, or an answer-first structure. Block-level analysis identifies these specific weaknesses and recommends concrete additions like specific facts or tables to improve extraction likelihood.