Extract recurring themes from customer interviews with an open-source CLI analyzer

Product managers, UX researchers, and early-stage founders spend hours manually coding customer interviews and synthesizing themes, a process that is error-prone, inconsistent, and hard to scale across hundreds of transcripts. This manual work delays product decisions and inflates research costs for teams that often lack budget or tooling to automate qualitative analysis. Build an open-source, CLI-first analyzer that ingests transcripts, leverages embeddings and automated clustering to surface recurring themes, representative quotes, and structured tags, while enabling local or self-hosted deployment for privacy-conscious teams. Expose scriptable outputs (JSON/CSV), CI integrations, and a minimal web UI for auditability so researchers can validate and refine clusters with a human-in-the-loop. The market is compelling now—estimated TAM of $1.5B (500K product & research teams × $3K ACV), with a high attractiveness score (86/100) supported by mature NLP, growing demand for self-hosting, and a preference among early-stage teams for developer-centric workflows. Differentiation comes from being open-source, privacy-first, and CLI-native—appealing to teams that want scriptable, low-cost tooling rather than cloud-only SaaS—while delivering measurable time savings by reducing manual coding. Key challenges are earning researcher trust in automated clusters, ensuring cross-domain accuracy, and competing with medium-level incumbents, so a transparent validation framework and tight human-in-the-loop workflows will be critical to adoption.