Low-relevance gaps are audited by running structured recommendation probes across active providers, querying whether a brand is recommended for specific entities and queries over multiple sample iterations. Responses are recorded as binary outcomes to calculate Query Relevance Scores, isolating topic blind spots where models consistently return negative recommendations or diverge in their assessments.