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Editorial illustration of a qualitative research interview transcript being anonymized offline on a laptop

A transcript anonymizer that runs on your laptop

A 60-minute interview transcript takes a research assistant 2 to 4 hours to anonymize by hand. This tool does it in about 5 seconds — and the same person gets the same pseudonym in every transcript she appears in.

If you interview people for a living, you know the redaction tax. A research assistant takes 2 to 4 hours to anonymize a single 60-minute transcript by hand. A qualitative study typically runs 15 to 40 of them. The cloud AI tools you'd reach for first get rejected by ethics boards and corporate NDAs the moment "the data leaves the participant's computer" comes up. So you redact by hand, or you don't run the project. I built a Windows desktop app that does the whole batch in about five seconds per transcript, on your laptop, with nothing uploaded anywhere.

Drop a folder in. Everything it needs to do its job ships inside the app.

The desktop app main window — drop a folder of .docx transcripts in, pick an output folder, click run

Names, organizations, phone numbers, postcodes, national IDs — all replaced in place, with diacritics handled correctly.

A transcript paragraph before and after — Müller, Mueller, and MÜLLER all caught as the same person, mapped to the same pseudonym

Consistency across the whole batch is the whole point. Dr. Sarah Thompson becomes Participant-07 in every transcript she appears in.

The rehydration index — a mapping file stored only on the user's machine, letting them reverse the anonymization later to verify a quote

Five seconds per transcript. Twenty-five transcripts is a coffee break, not a two-week task.

Progress bar racing through a batch of 25 transcripts in just over two minutes total

Filenames get anonymized too. `INTERVIEW SARAH THOMPSON.docx` doesn't sit in your output folder shouting the name you just removed from the body.

Input filenames with participant names replaced by pseudonyms in the output folder, including Word document metadata stripped

The reusable part is the anonymization method — a set of rules for what to strip, what to keep, and how to stay consistent across a whole batch. It's the same rulebook as the CV anonymizer on the other side of this site. Different document, different platform, same method.

If you need a custom version — a different document format, your own entity list baked in, a walkthrough on your own transcripts before you commit — drop me a line.

Let's talk →

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