How a Luxury Jewelry AI Benchmark Kept 96 Data Points Honest With Just 12 Answers

A small AI visibility benchmark was conducted across eight luxury jewelry brands — including Cartier, Tiffany & Co., and Piaget — using three neutral buyer questions across two platform surfaces with two replicates each. Rather than querying the model separately for each brand, the researcher used an answer-once design, collecting 12 raw answers and then evaluating each against all eight brands, yielding 96 answer-brand cells without inflating the number of independent model responses. The approach highlighted a key methodological distinction: the unit of data collection differs from the unit of brand-level analysis, meaning brand mentions and recommendations are judgments derived from a single immutable answer, not separate API outputs. A planned third platform surface was excluded from content analysis after all six requests returned access errors, with those attempts logged as collection failures rather than brand-absence data. The exercise revealed that Piaget appeared in all answers about brands with verified China channels but in none of the wedding jewelry recommendation answers — a discrepancy the researcher attributed to metric gaps rather than a confirmed visibility trend.