Turn fieldwork into structured databases. FieldBase helps research teams collect and organize qualitative data with AI agents, creating clean datasets ready for analysis in your tools of choice.
Research generates messy data—interviews, field notes, documents, observations. FieldBase transforms this into structured, queryable databases that you can analyze with the tools you already use.
We don't replace your statistical software or analysis tools. We're the layer that comes before: the workbench where raw research inputs become organized data structures. Think of us as your data collection and structuring infrastructure.
Deploy AI agents to handle coding, categorization, and data entry. They learn from your decisions, follow your protocols, and maintain consistency across your entire dataset. Your team stays focused on methodology and interpretation.
Export to PostgreSQL, CSV, or connect directly to Stata, R, SPSS, Tableau—whatever tools your research workflow requires. FieldBase is the foundation, not the final destination.
Configure AI agents to process interviews, code responses, extract entities, and categorize data according to your research protocols. Agents maintain consistency and learn from corrections.
A collaborative space where your team structures incoming data. Review agent work, make refinements, and ensure every data point follows your coding scheme.
Define your data structure once—fields, types, validation rules, relationships. Agents enforce your schema consistently across all data collection.
Built-in validation, duplicate detection, and consistency checks. Track inter-rater reliability when multiple researchers code the same data.
PostgreSQL database export, CSV downloads, or direct connections to your analysis tools. Your structured data works with whatever you use for statistics and visualization.
Complete provenance tracking. Know who coded what, when decisions were made, and how interpretations evolved over time—essential for research transparency.
Process transcripts into coded databases. Extract themes, categorize responses, and structure qualitative data for systematic analysis. Perfect for gender studies, policy research, or ethnographic work.
Catalog research centers, universities, or healthcare systems. Structure organizational data, extract key attributes, and build databases ready for comparative analysis.
Transform open-ended survey responses into structured data. Code comments, categorize feedback, and prepare datasets for statistical analysis alongside closed-ended questions.
Extract structured data from reports, policies, or archival materials. Build databases from unstructured documents with consistent coding and categorization.
Set up your database schema, coding framework, and collection protocols. Configure agents with instructions specific to your research methodology and data types.
Upload your research materials and let agents begin structuring. Review their work, correct inconsistencies, and train them to match your standards through iterative feedback.
Once your data is structured, export to PostgreSQL, CSV, or connect directly to your analysis tools. Your clean, validated dataset is ready for statistical analysis, visualization, or further research.