Your documents become rows, your questions become columns. Every cell linked to evidence.
A platform for research-grade data extraction using privacy-first AI.
Developed by Karl Rohe, Professor of Statistics at UW-Madison
Extracting data · Coding transcripts · Annotating texts · Analyzing content
All of these tasks involve turning a collection of documents into a structured dataset:
The instructions for what to extract—the questions, definitions, edge-case rules—are your codebook. The process of applying that codebook to produce a structured, evidence-linked dataset is data minting.
With datamint.ing:
This is data minting on datamint.ing.
No coding. No cleanup. Quickly iterate. Scale from 10 to 10,000 documents.
PDFs, images, Word docs, and web pages flow into one structured table.
Define fields once. Get consistent output across 10 or 10,000 files.
Every cell links to the exact source text and model reasoning.
Upload once and run full collections without prompt juggling.
Export clean tables to CSV, Sheets, or your warehouse.
A penny per page of text and a penny per cell of data.
Specifies exactly what to extract from each document.
Multiple readers—each a different AI model—extract independently. An arbiter compares their extractions and identifies where your codebook allowed two reasonable interpretations.
Click any cell to view the evidence, quotes, and AI reasoning behind each value.
Inspect → refine → re-mint. Forgot a field? Add it. Found an edge case? Update the codebook. Minutes, not weeks.
Forgot a field? Add it and re-extract.
Discovered new patterns? Refine and extract again.
Minutes, not weeks. Iteration built-in.
Systematic reviews, meta-analyses, scoping reviews across any field. Extract study characteristics, outcomes, and quality assessments at scale.
Code interviews, focus groups, and open-ended data. Extract themes, contradictions, and patterns with speed and precision.
Extract features that were previously unmeasurable at scale. Turn qualitative patterns into quantitative variables. Documents become observations, meanings become features.
Build datasets from published literature in your field. Extract methods, sample sizes, statistical tests, and effect sizes—turn the literature into a structured database.
Clinical: adverse events, patient outcomes. Legal: case precedents, contract terms. Policy: stakeholder positions, implementation barriers. Any field with specialized extraction needs.
When non-academic decisions require academic rigor—systematic analysis with complete audit trails and defensible methods.
Data Minting: like pressing coins from metal. Documents are ore. Codebooks are stamps. Data is currency.
Don't craft the data—craft the codebook with our AI assistant. Test it. Refine it. Share it. Scale it.
Reusable, testable specs for exactly the data you need.
Every cell links to quotes, summaries, and reasoning.
Codebooks evolve as edge cases surface and teams share.
Provenance: every spreadsheet cell retains source evidence and the AI's reasoning process for complete auditability.
PDF, HTML, DOCX, images (JPEG, PNG), and more. If it contains text or visual information, datamint.ing can extract from it.
Most users mint their first dataset in under 10 minutes. Once your codebook is ready, batch processing is automatic and scales to thousands of documents.
Click any cell in the output table to view the exact source text, key quotes, and the model's reasoning. Every extraction includes complete provenance.
Yes. Codebooks are reusable, shareable, and versionable. Create once, apply to any number of documents or share with your team.
Add new fields to your codebook and re-extract in minutes. Iteration is built into the workflow—no need to start over.