Per-Entity Bias Mapping for AI Visibility: Why Brand Mentions Require Entity-Specific Calibration

AI-mediated answer systems increasingly influence how brands, organizations, experts, and products are represented to users. Existing visibility approaches reduce this representation to mention rate, citation rate, or aggregate platform visibility. This paper argues that such aggregate metrics are insufficient because entities exhibit systematically different AI visibility error profiles that aggregate scores cannot detect. We identify three distinct failure modes. First, small or underrepresented entities may suffer from invisibility due to weak structured data, limited knowledge graph presence, and low co-citation density. Second, large or highly familiar entities may suffer disproportionately from false attribution and hallucinated authority — a pattern we formalize as the Brand Hallucination Paradox, where model familiarity creates a stronger surface for plausible but incorrect completions. Third, entities from underrepresented regions such as Central and Eastern Europe face a structural CEE Entity Infrastructure Gap operating across three technical layers: knowledge graph absence (missing Wikidata identifiers), named entity recognition performance deficits for morphologically complex languages, and entity linking difficulty due to linguistic variation — producing AI visibility deficits that content optimization alone cannot resolve. A fourth dimension cuts across all three: the Parametric-Retrieval Lag Asymmetry. AI answer systems operate through two knowledge mechanisms with fundamentally different update speeds. Retrieval-augmented systems can incorporate new information within days, while parametric memory encoded in model weights updates only at retraining intervals spanning twelve to twenty-four months. Entities undergoing rapid change — rebranding, mergers, market entry, reputation events — may be accurately represented in retrieval-augmented outputs while remaining outdated or absent in parametric outputs, producing measurably divergent representations of the same entity across platforms or query modes. This paper introduces Per-Entity Bias Mapping as a framework for calibrating AI visibility at the level of individual entities. The framework distinguishes raw mentions from verified mentions and proposes a ten-dimensional measurement agenda covering retrieval inclusion, mention probability, hallucination rate, citation fidelity, source authority, and parametric-retrieval lag. We introduce ghost cartography as a unifying mechanism for entity-level AI misrepresentation: when entities occupy sparse latent regions, models produce confident, evidence-like output interpolated from neighboring dense regions. This yields a two-dimensional confabulation space in which the Brand Hallucination Paradox captures fabricated presence (Type 3) and Parametric-Retrieval Lag Asymmetry captures frozen representation (Type 4). We further frame AI visibility as a field phenomenon: entities become machine-recognizable figures through citation density, co-citation patterns, structured data, and knowledge graph anchoring, not through self-description alone. Empirical study: n=100 Hungarian B2B entities, 1,400 probe runs, 2,062 sources, non-AI HTTP+Crossref verification. Platforms: claude-sonnet-4-6 and gpt-4o-2024-08-06. Data collection: 2026-05-22, 07:25-12:16 UTC. Full data released as supplementary materials (CC BY 4.0).