Measure and optimize your product-market fit using the Superhuman framework from First Round Review.

1. Collect survey responses with the 4 proven PMF questions
2. Calculate your PMF Score (benchmark: 40%)
3. AI analyzes responses to detect personas and themes (optional)
4. Generate actionable insights:
   • High-Expectation Customer (HXC) profile
   • What users love (from "very disappointed" responses)
   • What holds users back (from "somewhat disappointed" responses)
   • Roadmap recommendations (50% double down / 50% address gaps)

1. Click Remix
2. Customize PRODUCT_NAME in main.ts line 9
3. Customize PROMPT.txt with your product context
4. Share /survey with your users
5. Watch your PMF score update at /dashboard

Optional: Add OPENAI_API_KEY for AI-powered persona detection and HXC profile generation. The survey works without it — AI just enhances the insights.

The Superhuman PMF engine uses Sean Ellis's leading indicator: % of users who would be "very disappointed" without your product.

Benchmark: Companies with strong traction exceed 40%. Companies struggling to grow are below 40%.

1. How would you feel if you could no longer use [product]?

   • Very disappointed
   • Somewhat disappointed
   • Not disappointed

2. What type of people do you think would most benefit from [product]?

3. What is the main benefit you receive from [product]?

4. How can we improve [product] for you?

• Survey collection
• PMF Score calculation
• Response breakdown (very/somewhat/not)
• Basic persona detection (keyword-based)
• Basic theme extraction

• Rich persona detection
• Detailed HXC profile generation
• Smart roadmap recommendations
• Nuanced sentiment analysis

After adding your API key, POST to /reprocess to analyze existing responses.

Survey

• Users complete the 4-question survey
• Responses are stored immediately

Analysis

• Basic persona/theme extraction runs automatically
• If OPENAI_API_KEY is set, AI enhances the analysis

Dashboard

• PMF Score: % of "very disappointed" responses
• Segmented PMF: Score filtered to ideal personas
• Response breakdown with visual bars
• HXC Profile (AI-enhanced)
• Top benefits and improvements
• 50/50 roadmap recommendations

"If you only double down on what users love, your product-market fit score won't increase. If you only address what holds users back, your competition will likely overtake you."

The framework teaches you to:

• Ignore "not disappointed" users — they won't convert
• Focus on "very disappointed" users to understand what to double down on
• Convert "somewhat disappointed" users who already value your main benefit