No. This audit focuses on how AI systems generate and phrase answers — not on search rankings or website traffic.

The audit analyses responses generated by large language models such as ChatGPT, using a structured and repeatable methodology. The focus is on identifying consistent patterns in how brands are framed across discovery, comparison and trust-oriented questions, rather than evaluating performance on any single platform or output.

The audit is designed to identify consistent patterns in how AI systems frame brands, not to predict or guarantee outcomes. Accuracy, in this context, means: • using a structured and repeatable set of category-relevant questions • analysing patterns across multiple responses rather than relying on a single output • applying a consistent, programmatic scoring framework While individual AI responses can vary, broader framing patterns tend to be stable enough to provide reliable, actionable insight at a given point in time.

B2B SaaS companies operating in competitive categories where AI-generated answers increasingly influence buyer research.

Yes. For brands where discovery often starts with broad or experiential intent — such as activities, entertainment, destinations, or services — the audit includes questions that reflect those discovery contexts. This ensures the analysis reflects how AI systems surface brands when users are exploring options, not only when they already know what they’re looking for.

To preserve consistency and reliability across audits, the exact question sets are not shared publicly. However, each audit is grounded in a transparent framework that ensures coverage across discovery, comparison and Trust. Questions are selected to be representative of how people research and explore categories in practice, rather than optimised for any single brand. This approach ensures results are comparable, repeatable, and not influenced by bespoke prompt selection.

The audit framework is fixed, but the question sets are adapted to reflect the brand’s category and typical discovery context. This allows the audit to remain consistent and comparable, while still reflecting how AI systems surface brands in different industries, markets, and use cases.