About Us
We're building AceVoz to bring AI tools, progress tracking, and teaching resources to independent language teachers. One of our goals is to help any native speaker teach effectively toward the specific exams their students are preparing for.
Ryan Dunn
Ryan is an English teacher who just finished a year abroad with the NALCAP program, teaching in Almassora, Spain. He's building the software that powers acevoz.com.
ryansdunn.comNat Dunn
Nat, Ryan's father, is recently retired and the founder of Webucator, a technology training company. He brings decades of experience building education tools and businesses to the team.
webucator.comA father-and-son team, we're combining hands-on classroom experience with a background in educational technology to build the operating system for independent language teachers.
Why We Built AceVoz
Independent language teachers do the work of a whole school — curriculum, scheduling, assignments, progress tracking — usually alone, and usually without the tools a school would give them. We're building AceVoz so that one teacher, working independently, can do that job as well as a team.
How We Built AceVoz
AceVoz is built incrementally and in the open, one small, reviewed change at a time. Where good research and data already exist, we build on it rather than reinvent it: our dictionary and grammar reference content are seeded from published, CEFR-aligned datasets rather than written from scratch.
Resources
With thanks to the researchers who compiled and shared these datasets:
- The CEFR-J Wordlist Version 1.5 and The CEFR-J Grammar Profile Version 20180315. Compiled under Yukio Tono, Tokyo University of Foreign Studies. cefr-j.org/download.html
- The Octanove Vocabulary Profile C1/C2, from Octanove Labs, licensed under CC BY-SA 4.0.
- The CEFR-Annotated WordNet dataset (Kikuchi et al. 2025), licensed under CC BY 4.0 and built on the Princeton WordNet. arXiv:2510.18466
- The Words-CEFR Dataset by Maximax67 (MIT License), which extends the CEFR-J Wordlist with interpolated CEFR levels and word frequencies from the Google Books Ngram corpus. github.com/Maximax67/Words-CEFR-Dataset