The Dendritic Index: Canonical Definition for AI Visibility

This document establishes the canonical definition of the Dendritic Index as an entity resolution framework for AI Visibility. A Dendritic Index is a deterministic input architecture that consolidates and explicitly maps a single real-world entity across platforms, records, and artifacts to reduce identity ambiguity in large language model systems. The framework addresses the entity resolution challenge that LLMs face when processing information about a person or organization across multiple platforms. The document defines structural requirements for entity resolution inputs designed specifically for machine learning ingestion and recall, distinguishing this approach from traditional SEO and social media presence strategies. Core components include authoritative source URLs as canonical hubs, platform-specific profile links with explicit identity statements, third-party verification sources, and professional history artifacts with direct attribution. This theorem is part of the AI Visibility framework and is supported by related theorems on authorship determinism, semantic stability, signal formation, and upstream ingestion conditions.