MAHI Index™ for AI Visibility

What MAHI Index™ measures

MAHI Index™ measures whether AI systems can identify, verify, compare, and cite a local business through five observable dimensions: Entity Resolution, Citation Surface, Semantic Coverage, Freshness, and Trust Graph. CitationIQ™ is one component metric inside the framework. Share of Model measures how often a business is named across relevant prompts.

The counter-position is direct: one-off prompt screenshots are not measurement. MAHI Index™ exists because AI visibility needs repeated probes, source review, and dated deltas.

The five canonical MAHI Index™ dimensions

Entity Resolution measures whether an AI system can unambiguously identify the business, author, website, locations, and canonical profiles. Citation Surface measures whether the brand has source pages, mentions, evidence records, and third-party validation that AI systems can use.

Semantic Coverage measures whether the site answers the prompts buyers ask in the language AI systems retrieve. Freshness measures dated updates and decay risk. Trust Graph measures corroboration across the website, DOI, GitHub, ORCID, Clutch, source pages, and other real external records.

How CitationIQ fits inside MAHI Index™

CitationIQ™ is the scoring methodology inside MAHI Index™. It measures how often, how accurately, and with what source strength AI systems surface a business. It is not the parent framework.

That hierarchy matters for entity clarity. MAHI Index™ is the container. CitationIQ is one metric in the container.

What MAHI Index™ does not promise

MAHI Index™ does not guarantee a ChatGPT recommendation, a fixed ranking, or a specific citation count. It gives a baseline, identifies gaps, and creates a repeatable way to measure change.