AI Legibility

AI Legibility refers to how clearly and consistently an institution is interpreted by AI systems as they crawl, index, summarize, and reason about entities across the intelligent web.

Unlike human perception—which relies on reputation, intent, or brand cues—AI legibility is determined by structure: schema coherence, narrative consistency, relational signals, and the availability of verifiable context. An institution may be highly visible to humans yet poorly legible to AI systems if its identity, authority, or relationships are fragmented or ambiguous at the machine level.

AI legibility is a prerequisite for discovery, but not a guarantee of authority. It determines whether an institution is understood, not whether it is trusted. Failures in AI legibility often manifest as misclassification, shallow summarization, or inconsistent framing across platforms, even when surface-level visibility appears strong.

Within exmxc’s intelligence framework, AI legibility functions as a foundational diagnostic dimension. It informs interpretation signals and is formally evaluated through the Entity Clarity Index (ECI), which measures how legibility—or the lack of it—shapes institutional positioning, narrative authority, and long-horizon outcomes in AI-mediated ecosystems.

Entity Clarity Reprt - Media

Entity Clarity Report - Technology

Entity Clarity Report - Finance

Entity Clarity Report - Healthcare

Entity Clarity Report - eCommerce & Retail

Entity Clarity Report - Consulting

Entity Clarity Report - Energy

Entity Clarity Report - Payments & Financial Infrastructure

Entity Clarity Report - Marketplaces & Platforms

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