Messy data in.
Structured study data out.
Three steps take the exports your client actually sends and turn them into a normalized dataset your team can run a study on.
Drop in whatever your client sent
Start with the raw exports that land in your inbox — payroll runs, wage records, time-tracking exports, project logs. No templates to fill out, no fields to pre-map, no asking the client to reformat anything.
- Upload CSV, XLSX, and PDF files directly
- Mixed formats and layouts from every client, side by side
- Files from Gusto, Rippling, ADP, Harvest, Toggl, Jira, Linear, QuickBooks and more
AI parses and maps — your team reviews
Data Wrangler reads each file, identifies the columns that matter, and maps them into a consistent structure. It reconciles employees across sources, aligns hours to projects, and flags the gaps that need a human decision — so nothing gets silently guessed.
- Automatic field detection and column mapping
- Cross-source matching and de-duplication of employees
- Gaps and low-confidence matches flagged for expert review
Get a study-ready dataset out
The result is a clean, consistent dataset built around the fields an R&D study actually needs: employee wages, project allocation, and time by business component. Export it and drop it straight into your existing study workflow.
- One consistent schema across every client
- CSV and JSON exports
- Wages, allocations, and time mapped to business components
Human expertise, AI assistance
The AI does the tedious first pass — parsing, mapping, and matching thousands of rows in minutes. Your team stays in control of every judgment call, reviewing flagged gaps instead of starting from a blank spreadsheet. That is the difference between automation you can trust and a black box.
Get early access to Data Wrangler
We're onboarding R&D tax firms and CPAs first. Join the waitlist and we'll reach out when a spot opens.