Understand how your students
write with AI.

Colophon logs every edit, paste, and AI interaction as a verifiable timeline, right inside Google Docs. No cloud, no surveillance, no subscription.

Running research case studies this fall: free for one course section, tool + onboarding + a published report included.

Join the Waitlist TWFF Spec: Open Source
Coming soon on the Chrome Web Store
Typed by you AI-generated Pasted sources AI-paraphrased

It's one file: a verifiable record you control.

col·o·phon /kŏl′ə-fŏn″/ noun

An inscription placed usually at the end of a book, giving facts about its publication.

A colophon is a section of a book that describes the technical and production details of how the book was created, including printing methods, materials, typography, and contributors.

See it in action

A real look at the extension and the TWFF viewer.

Colophon popup showing the writing breakdown and recent activity

At a glance, in the popup

Click the toolbar icon any time to see the current document's own-writing / AI / pasted breakdown and a running activity feed; no need to open a dashboard.

Colophon floating panel open next to a real Google Doc

Lives beside your document

The panel floats right next to the page you're actually writing in; nothing to switch to, no separate app to keep open.

Colophon TWFF viewer showing an annotated writing session report

Open any session in the TWFF viewer

Drop a .twff file into the viewer at colophon.firl.nl/viewer, entirely in your browser with nothing uploaded, and get the full annotated, exportable report. Don't have one? Try our sample essay or download the raw file.

Is Colophon an AI detector?

No. Colophon does not try to guess whether text was written by AI. Instead it gives students and teachers a verifiable, timestamped record of the entire writing process: every keystroke block, paste, and AI interaction. A percentage score can be disputed; a timestamped edit history can be checked against the document itself.

Join the waitlist

How we work

  1. 01

    Never optimize for surveillance. We record process, not verdicts; no keystroke-content logging, no AI-detection score, no feature that flags a student.

  2. 02

    The author owns their process. A student's writing record is theirs to export, share, or delete, not a log kept from them.

  3. 03

    Evidence beats prediction. Charts show what happened (counts, timelines, composition), never a predicted "quality" or "originality" score.

  4. 04

    Publish what we learn. Real case studies end in a findings report anyone can read, not a private result.

  5. 05

    If a feature needs a "trust us" explanation, don't build it. What's collected, and why, should be visible and checkable, not taken on faith.

In conversation with

Institutions and standards groups we've been talking with about Colophon and the TWFF format.

Cosmopolitan University, Abuja
Maryam Abacha American University of Niger
Connect with the DPV Community Group →

More as these conversations become formal partnerships.

Standards alignment: our TWFF process-log schema maps onto the W3C Data Privacy Vocabulary's dpv:AutomatedDecisionMaking concept, a contribution the DPV Community Group has already accepted.

Get early access

Colophon is in beta and not yet published to the Chrome Web Store. We're onboarding a small cohort of educators and students directly; join the waitlist and we'll get you set up with the unpacked extension.

Join the Colophon waitlist

What's next

  • Running research case studies with course sections this fall
  • Publishing the TWFF specification paper on arXiv
  • A proper Chrome Web Store listing, once case-study feedback has settled the details
  • Refining which in-editor writing suggestions are worth surfacing, based on real usage