Built for researchers who need methods they can trust.
Jake Bialer has worked across journalism, data analysis, and data engineering. Across those roles, one issue kept repeating: teams needed better tools to get honest answers and reproducible evidence.
QuestionPunk was built to solve that directly: one workflow for survey design, adaptive interviews, and analysis, with transparent AI setup that researchers can inspect and report.
"I wanted a tool that helps teams move faster without hiding the methodology. If results matter, transparency matters."
Research workflows were fragmented across separate tools for design, recruitment, and analysis.
Static surveys missed key follow-up questions that only appear once participants start answering.
Teams spent too much time manually cleaning and synthesizing responses before making decisions.
To improve research by making quality respondent recruitment effortless and survey engagement meaningful
Our roadmap is driven by researchers and the experiments they need to run.
Prompts, model choices, and logic are visible so studies can be documented and reproduced.
Researchers can adjust prompts, models, and study setup without engineering support.
Academic and lean teams should be able to evaluate AI research methods without inflated costs.
If this approach fits your workflow, try it with a small study.