Automated Customer Interview Scheduler and Insight Extractor for Product Teams
Product teams know they should talk to customers regularly but rarely do because scheduling is friction-heavy and synthesis is manual. An automated system that schedules customer calls based on user segments, records conversations, extracts product insights using AI, and maintains a searchable insight library would make continuous discovery practical.
Problem Statement
A product team commits to doing 5 customer interviews per week. By week 3, they've done 8 total. Scheduling takes 15 minutes per interview. Recording setup varies. Someone takes notes during the call (and misses key moments). Notes go into a Google Doc that nobody reads after the first week. Insights are lost, patterns go unnoticed, and the team reverts to building based on assumptions. The problem isn't that teams don't value customer research — it's that the operational overhead kills consistency.
The Idea
An automated customer interview pipeline for product teams that schedules calls based on user segments, records and transcribes conversations, extracts product insights using AI, and builds a searchable insight library.
Why Now
Continuous discovery has become standard product management practice, but the operational overhead prevents consistent execution. AI transcription and analysis have become accurate and cheap. Calendar scheduling APIs are mature. The missing piece is connecting these capabilities into a workflow optimized for product insight extraction, not generic meeting notes.
Target User
Product managers doing customer discovery, UX researchers, SaaS founders committed to user research
Target Market
Product teams practicing continuous discovery at companies with 5-100 employees
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