Hi, I'm Bhavik. I built this. All of it.

PeekWise only reads what's already public. Every night, the system collects the new reviews posted on the public Google and Yelp listings of the competitors you track — the same pages anyone can open in a browser. We never touch private accounts or messages, and we never store personal information about the people who wrote the reviews. The analysis works at the level of patterns, not individuals.

An AI language model then reads every one of those reviews and does what a diligent owner would do with unlimited time: it scores the sentiment of each review, groups complaints and praise into named themes — "slow weekend service," "surprise fees," "great front desk" — and counts how often each theme comes up. A separate nightly job compares this week's counts against previous weeks, which is how a quietly growing complaint gets flagged before it becomes obvious.

Every insight in your briefing traces back to real review text with a real date. We deliberately don't speculate: if the data doesn't support a claim, it doesn't go in the briefing. And when the AI is uncertain — small sample sizes, ambiguous language — the briefing says so rather than dressing a guess up as a finding. That's the standard the whole product is built around: you should be able to ask "why is it telling me this?" and always get a concrete answer.