CueLoop is a macOS app for user interviews. It is built around the idea that interviews can miss leading questions, dropped threads, and other signals, and it presents itself as an expert in the interviewer’s corner. The product is described as optimized for macOS 26 (Tahoe), and access is offered through an email-address waitlist for early access.
Its workflow is organized into three steps: listen, coach, and learn. CueLoop captures both sides of the conversation in real time, combining the interviewer’s microphone and the participant’s audio with transcribed dialogue. It also includes speaker identification. During an interview, the app watches for leading or biased questions, unexplored conversation threads, and moments worth exploring further, then surfaces a subtle cue without interrupting the conversation. After the session, it generates a personalized report card that grades the interview across five dimensions and includes a narrative debrief, specific examples, and advice for the next interview.
CueLoop is aimed at people who talk to users as part of building products. The page names founders, product teams, and solo builders, and it specifically mentions PMs, designers, and engineers. Its stated purpose is to help users conduct better interviews and learn from customer conversations before building or shipping the wrong thing. The page also frames the product as AI that coaches the interviewer rather than replacing them.
The available information identifies CueLoop as a macOS application, but does not state a price, license, or other distribution model beyond early access sign-up.
In the Other analytics space, CueLoop takes a focused approach. It focuses on improving the quality of user interviews by providing real-time coaching and transcription. It is built as a B2B product for product managers and UX researchers. CueLoop is available on the web and macOS.
CueLoop first shipped in 2026. Among its 7 catalogued features are real-time transcription, speaker identification, and interview coaching.
Summary written by a language model from the project’s public pages.
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