Blue Kiln
Blue Kiln provides infrastructure for reliable science by applying software engineering principles—automated testing, structured data, and traceable provenance—to academic research verification. The platform extracts every table, figure, and statistical result from articles, flags unsupported claims, and computes reproducibility scores without requiring programming skills. It targets researchers, peer reviewers, and journal editors facing a crisis where 50% of published studies cannot be independently reproduced.
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Approximately 50% of published studies cannot be independently reproduced, and over $28 billion in annual research funding is spent on irreproducible preclinical research. The tools researchers use to verify each other's work have not kept pace with the volume, complexity, or stakes of modern science.
Solution
Blue Kiln applies the same principles that power reliable software—automated testing, structured data, and traceable provenance—to the verification of academic research and the quality of published data. The platform systematically extracts every table, figure, and statistical result from the article body, making raw numbers, effect sizes, confidence intervals, and reported parameters machine-readable. Each stated claim is matched against the underlying extracted data, flagging unsupported claims, exaggerated interpretations, and logical inconsistencies between results and conclusions. A rigorous reproducibility index is computed for every analysis step, providing a transparent, quantitative measure of how independently verifiable each piece of research is without requiring programming skills. Supplementary materials are automatically parsed and scored for completeness, surfacing missing datasets, undescribed methods, and inaccessible code. Every verification step is logged with full provenance, allowing reviewers to trace any finding back to its source, mirroring version-control practices from software engineering.
Target Audience
Primary users are academic researchers, peer reviewers, journal editors, and research institutions seeking to verify the reliability and reproducibility of published scientific work.
Features
- Full data extraction of every table, figure, and statistical result from the article body, including raw numbers, effect sizes, confidence intervals, and reported parameters
- Claim–evidence alignment engine that flags unsupported claims, exaggerated interpretations, and logical inconsistencies between results and conclusions
- Reproducibility scoring that computes a quantitative index for each analysis step without requiring programming skills
- Supplement completeness scoring that automatically parses and scores supplementary materials for missing datasets, undescribed methods, and inaccessible code
- Audit trail logging every verification step with full provenance—which data, which method, which conclusion—for traceability
- Actionable warnings that surface specific, evidence-backed flags pointing directly to the data or method in question