How to Measure AI Visibility Reproducibly: Fixed Question Bank and Explicit Denominators

Brasil GEO, a Brazilian AI visibility monitoring firm, has published a reproducible protocol for measuring brand mentions across AI search engines including ChatGPT, Claude, Gemini, and Perplexity. The methodology treats AI visibility as a distribution variable, reporting mention rates as the share of executions in which a brand appears, always paired with sample size, time window, variance, and collection coverage. The protocol requires a frozen bank of 30 to 40 real-customer questions, a minimum of 5 executions per question for continuous monitoring and 30 for before-and-after comparisons, with all parameters fixed before the first data collection round. In a sample run from August 29, 2026, covering 37 questions across four engines, the combined brand mention rate was 21.1 percent, but per-engine rates ranged from 16.2 percent on ChatGPT and Claude to 35.1 percent on Perplexity, underscoring why aggregated averages can obscure meaningful platform-level differences. The approach draws on findings from a January 2026 SparkToro and Gumshoe.ai study of nearly 3,000 prompts, which found that even category-leading brands appeared in only 55 to 77 percent of responses.