Hearsay

Turn customer interview transcripts into product roadmap recommendations. Point it at your transcripts and get: What should we build next, and why?

Web App

A browser-based interface lives in /app. BYOM (bring your own model) — supports OpenRouter, OpenAI, and Maple AI.

cd app
npm install
npm run dev

All data stays in your browser (IndexedDB). Your API key is stored locally and only used to authenticate with your chosen LLM provider.

What This Does

Analyzes interview transcripts to produce:

• Frequency-ranked problems (X of N transcripts mentioned...)
• Frequency-ranked solution desires
• Verbatim quote evidence with attribution
• Concrete build recommendations

Explicitly avoids:

• Per-interview summaries
• Vague themes ("bad UX")
• Opinion-driven prioritization
• Mention-counting (counts transcripts, not mentions)

Quick Start (CLI)

Analyze the interviews in ./transcripts/ using the methodology in PROMPT_core.md

The agent reads all transcripts, applies the methodology, and produces a report matching TEMPLATE_output.md.

Toolkit Contents

File	Purpose
app/	Web app for browser-based analysis (BYOM)
INTERVIEW_script.md	20-minute interview script optimized for later analysis
PROMPT_core.md	Core methodology: ingest → extract → normalize → count → synthesize
PROMPT_with_quotes.md	Quote-heavy variant for stakeholder presentations
TEMPLATE_output.md	Expected output structure
RUBRIC_scoring.md	7-dimension quality rubric (1-5 scoring)
EXAMPLE_usage.md	Detailed usage guide

Workflow

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Conduct        │     │  Analyze        │     │  Build          │
│  Interviews     │ ──▶ │  Transcripts    │ ──▶ │  Recommendations│
│                 │     │                 │     │                 │
│ INTERVIEW_      │     │ PROMPT_core.md  │     │ "What to build  │
│ script.md       │     │ + transcripts   │     │  next and why"  │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Interview Script

INTERVIEW_script.md provides a tight 20-minute structure:

Phase	Time	Focus
Open	2 min	Setup, purpose frame
Context	5 min	Background, current focus
Stack	5 min	Tech choices, buy vs. build
Problems	6 min	Challenges, gaps, workarounds
Magic Wand	2 min	Aspirational solutions
Close	2 min	Follow-up, referral ask

Consistent interviews yield better cross-transcript pattern detection.

Note: This script is optimized for B2B and developer-facing interviews. The "Stack" phase and technical probing will need adaptation for B2C or non-technical contexts.

Who to Interview

Interview selection matters more than interview volume. Key principles:

• Interview your best customers — those who intuitively see value, are profitable, and recommend you to peers. Not just anyone with a heartbeat.
• Pursue variation — across engagement levels (power users, new users, churned), roles, company sizes, and use cases. Variation beats "representative samples" in qualitative research.
• Match selection to goals — acquisition insights come from prospects; retention insights from current/churned customers.
• Recent switchers are gold — people who just started or stopped using a solution remember their decision context vividly.
• Ask for referrals — warm intros from interviewees have 80-90% success vs. 10% for cold outreach.

Common mistakes:

• Over-optimizing for the "perfect" sample (starting anywhere beats paralysis)
• Interviewing whoever responds instead of deliberately recruiting
• Mixing personas in the same analysis (CTOs and junior devs have different problems)

See: Teresa Torres on selecting customers, Customer Dev Labs B2B scripts

Quality Bar

A good analysis lets someone:

• Write a feature spec from the top problem
• Defend any ranking with "X of N transcripts said..."
• Verify any claim by checking the cited source

Use RUBRIC_scoring.md to score outputs (target: 32+/35).

License

MIT