The ARC framework measures and audits brand visibility across major AI platforms through three interconnected analytical dimensions. It maps multi-directional memory linkages between entities, queries, and brands (E2B, B2E, Q2B) to quantify ungrounded parametric salience, while simultaneously evaluating direct recommendation rates through category-specific relevance prompts and monitoring which web sources are extracted during grounded search responses.