Parametric memory recall evaluates what large language models remember about a brand natively without live web search, utilizing zero-shot prompts and token-level confidence scores to uncover unprompted brand associations. Specialized diagnostic engines analyze logprob distributions across multiple generations, tree-walking alternative token paths above set probability thresholds to expose hidden semantic associations and branch points. These extracted recall assertions are then curated into canonical fact claims to measure model certainty, tracking how consistently specific products, entities, and attributes surface in baseline training data. Setting this parametric baseline directly against the Grounded Validation system allows brands to pinpoint where static model memory diverges from live facts, isolating deep-seated hallucinations and critical knowledge gaps.