EmpiriQal
EmpiriQal evaluates the reliability of scientific findings and forecasts experimental outcomes across all scientific fields, helping researchers, policymakers, and the public identify trustworthy evidence. The platform constructs a space of plausible alternative results and scores each based on existing literature, providing both probability estimates and explanatory views of why certain outcomes are well-supported. Core algorithms are open source, promoting transparency and scrutiny.
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Over 3 million papers are published annually, far beyond any human capacity to evaluate, synthesize, or act upon. No researcher can track what is known, let alone connect the dots to anticipate where the next breakthrough is most likely to emerge. Crucial insights lie buried in the scientific literature, while countless informative findings go unpublished entirely—the file drawer problem—leaving the scientific record systematically incomplete and distorted. Non-replicable papers are cited more often than reliable ones in top journals, amplifying what the system should filter out.
Solution
EmpiriQal evaluates the reliability of scientific findings and forecasts the outcomes of experiments before they are run, across every field of science. Rather than simply summarizing what a study claims, EmpiriQal constructs a space of alternative possibilities—all the outcomes that could plausibly have been reported given what science already knows—and scores the likelihood of each, grounding every score in the evidence and references that support it. The result is not just a prediction but an explanation: users see which possibilities are well-supported, which are surprising, and why, drawing on the broader scientific literature to illuminate the landscape of what is and is not established. Core algorithms are open source, as the company believes scrutiny, debate, and transparency make better science.
Target Audience
Primary users are scientists, policymakers, and the public who need to identify trustworthy evidence and understand what the scientific literature will support.
Features
- Constructs a space of plausible alternative experimental outcomes and scores each based on existing literature
- Provides probability estimates for experimental outcomes before experiments are run
- Grounds every score in evidence and references that support it
- Offers explanatory views showing which possibilities are well-supported, which are surprising, and why
- Core algorithms are open source for transparency and external scrutiny
- Applies across all scientific fields, not limited to a specific domain