What problem does it solve?
Synthesizes raw interview notes and transcripts into clarified user struggles and opportunity statements so teams can prioritize discovery findings without confusing feature requests with underlying problems. Focuses on behavioral evidence, workarounds, and desired outcomes to surface product opportunities mapped to OST (Outcomes, Signals, Tests) thinking.
Core Features & Use Cases
- Interview assessment: Detects number of interviews, formats, and missing interviewee context and flags sample gaps.
- Data cleaning & extraction: Pulls context, moments of struggle, workarounds, emotional signals, and reframes feature requests into problems to solve.
- JTBD & switch interview application: Applies the Switch Interview pattern to adoption stories to surface decision triggers and onboarding signals.
- Clustering & OST mapping: Groups similar struggles into opportunities with counts, representative quotes, severity, and persona ties, then maps them into an existing or new OST structure.
- Quality assessment & next steps: Evaluates attitudinal vs behavioral evidence, leading questions, recency, and recommends target follow-up interviews and assumptions to test.
- Use case: Turn a set of 12 user call transcripts into a prioritized opportunity list, OST mapping, and a recommended set of 4 follow-up interviews.
Quick Start
Synthesize these interview transcripts into opportunity clusters, map each opportunity to the OST structure in memory, and produce an executive summary with recommended next interviews.
