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Scrunch is very accurate at tracking brand presence across LLMs because it collects actual AI responses from all major AI platforms to detect explicit brand mentions using pattern matching verified against continuously updated response datasets.

Additional context: Scrunch tracks brand presence as a binary measure—any time a user’s brand is mentioned in an AI response, it counts. Scrunch only counts explicit brand mentions in AI responses, not just citations that happen to mention a user’s brand. This provides a more accurate picture of how consumers actually experience AI answers.

Example

For example, imagine a Scrunch user is tracking their SaaS brand across AI platforms.

When they view their dashboard, Scrunch displays brand presence as a percentage—showing that their brand appears in 62% of responses across tracked prompts.

Behind the scenes, Scrunch uses regex pattern matching to detect their brand in each AI response, treating each platform's answer as a unique data point.

The platform aggregates data across all prompt variants over 90 days to smooth daily fluctuations and provide reliable trend analysis.

Follow-up question: Why do I see different brand presence percentages in overall versus topic-specific views?

Overall is a user’s average brand presence across all topics. Topic-level views show results only for specific topics.