From messy data to
publication-ready analysis
For researchers who know what they want to find, but not always how to code it.
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Our Philosophy
Research rigor should be the default, not an afterthought
We optimize for correctness. Every model includes diagnostics. Every estimate includes proper uncertainty. Every transformation is documented. Because peer reviewers will ask—and you should have answers.
We didn't invent these standards—we learned from the best: J-PAL's randomization protocols, the World Bank DIME data handbook, BITSS reproducibility guidelines, and 3ie evidence synthesis.
How It Works
From question to publication-ready analysis
Describe your analysis
Tell the AI what you want to analyze—treatment effects, correlations, predictions. It understands research methodology.
Get rigorous code
Receive Python code with proper standard errors, diagnostics, and assumption checks. Every transformation documented.
Run in isolation
Execute in secure containers with Marimo notebooks. Full reproducibility appendix with seeds, versions, and data hashes.
Made with Inquiro
Example analyses
Development Economics
Female Labor & Economic Growth
Fixed effects panel analysis with cluster-robust standard errors
Infrastructure
Electricity Access & Poverty
Cross-country regression with heteroskedasticity diagnostics
Program Evaluation
Baseline Survey Analysis
Descriptive statistics and balance checks for RCT design
Ready to write better
research code?
Join researchers using Inquiro for rigorous, reproducible statistical analysis.