Rose-Brook captures how selected AI models answer a controlled set of hotel-choice prompts, then separates commercial preference from evidence quality. Every result is a dated sample, not a prediction of every answer a traveller may receive.
The user confirms the subject hotel and real competitors before the run.
Every hotel is tested as the candidate with the same prompt pack, enabled providers and scoring version.
Captured answers, provider identity, eligibility decisions and partial failures remain attached to the saved run.
Commercial preference
A 0–100 read of whether the captured answers appear ready to choose the hotel. At least three component types and 65% of the available weight are required; otherwise Rose-Brook reports insufficient evidence rather than forcing a number.
Knowledge and support
A separate 0–100 view of whether eligible answers recognise and describe the hotel accurately with adequate support. Preference-only prompts do not inflate this score.
What the models can access
The core benchmark calls the enabled OpenAI, Google and Anthropic provider APIs directly. It does not enable live browsing or web-search tools during those prompts. The result therefore reflects each model’s available knowledge plus the controlled prompt context at capture time. Provider behaviour can change between runs.
The separate action-plan workflow does use live web search. Its source URLs and search activity are saved so users can distinguish web evidence from benchmark model output.
Limits that matter
Interpretation rule
Treat one run as a decision-quality snapshot. Treat movement as credible only when the hotel field, prompt pack, providers and scoring version remain comparable.