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Founder Sherpa integrates AI throughout the discovery process to help you write better hypotheses, parse transcripts, generate questions, and analyze evidence at scale.

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Problem statement quality check

When creating or editing a project’s problem statement, two layers of quality feedback are available:

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Real-time heuristics

As you type, a quality indicator classifies your statement as weak, moderate, or strong. The heuristic checks for:
  • Statement length and specificity
  • Vague phrases (“improve things”, “better experience”)
  • Missing specifics (numbers, roles, time/money references)
  • Whether you’re describing a problem vs. a solution
  • Whether the statement is phrased as a question

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AI quality check

Click Check with AI for deeper analysis. The AI returns:
  • A quality rating with reasoning
  • Specific improvement suggestions
  • An optional rewrite you can accept with one click
The AI uses your project name as context to ground its suggestions.

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Hypothesis quality check

Similar to the problem statement checker, each hypothesis has an AI quality check button that evaluates:
  • Whether the hypothesis is specific and testable
  • Clarity of the target persona and expected behavior
  • Suggestions for strengthening the statement
The AI receives your project name, problem statement context, and persona name to provide relevant feedback.

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AI-suggested interview questions

While building interview guides, click Suggest Questions to generate targeted interview questions. The AI considers:
  • The persona you’re researching
  • The hypotheses you’ve selected to test
  • Your project’s problem statement
Each suggestion includes:
  • The question text
  • A rationale explaining why it’s relevant
  • A suggested category
You can add suggestions to your Question Library or insert them as custom notes in the guide.

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Transcript parsing

When you import a transcript, AI processes the full text to extract:
  • Evidence items — Key insights, quotes, and observations organized by hypothesis
  • Interviewee identification — Who was being interviewed, with fuzzy matching against existing interviewees
  • Question detection — Questions asked during the interview, matched against your question catalog
For long transcripts, the system automatically chunks the content and processes each section, then merges the results.

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Batch analysis

From the project’s Insights tab, run batch analysis across all collected evidence. The AI:
  • Summarizes patterns across interviews
  • Identifies which hypotheses have strong support or contradiction
  • Highlights gaps where more evidence is needed
  • Generates an audit report of your discovery progress
This is the “Refine & Repeat” step of the Discovery Journey — helping you decide what to investigate next.
All AI features use server-side processing. Your data is sent to the AI provider for analysis and is not stored by the AI service beyond the request.