Hiring at scale means processing hundreds — sometimes thousands — of resumes. Pulling candidate data out of PDFs and Word docs by hand is slow and error-prone. I built a pipeline that turns unstructured resume files into clean, structured candidate data in seconds.

It handles multiple file formats automatically, routing each document to the right extraction path. AI agents parse names, emails, phone numbers, skills, education, and work history, then write it all straight to Google Sheets — with error handling and retry logic so it holds up on the messy, unconventional resumes too.

Whether it’s a recruiting agency processing high volume or an HR team unclogging its applicant pipeline, the manual data-entry bottleneck disappears and the team gets back to evaluating candidates instead of copying their contact info.

What it does

Built with

Have a data-entry bottleneck like this?

I build production automation on a layer you own — feeding clean data into the ATS, sheet, or HR system you already run. Let’s look at where it fits.

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