Transform weeks of manual paper review into hours. Extract entities, structure events, and build quantitative datasets from qualitative sources—powered by Large Language Models and designed for researchers.
Automate the manual work of extracting data from hundreds of papers. Focus on analysis, not data entry.
Identify dates, locations, actors, and event attributes from any text source with LLM-powered parsing.
Collect data from news articles, reports, academic databases, and policy documents in parallel.
Full methodology transparency and reproducible workflows that meet academic research requirements.
Convert qualitative text into quantitative datasets ready for statistical analysis.
Built-in quality checks and validation ensure data accuracy and consistency.
Interactive demo - no signup required
A machine learning proof-of-concept developed to predict the likelihood of violence at protest demonstrations. This educational project demonstrates the application of supervised learning to social science research questions.
Educational project demonstrating technical proficiency in machine learning workflows, data preprocessing, model training, and deployment.
I'm open to collaborating on research projects involving protest event data, Arabic-language text analysis, or machine learning applications for social science research.