TAG Ecosystem is a non-profit AI education community. We don't charge tuition. We rely on grants and donations to train job-ready AI talent across Africa and to build SabiLearn, an offline-first learning platform for students in Nigeria's most under-connected communities.
TAG Ecosystem operates as a non-profit. We don't charge our community anything to join, learn, or build with us. Every part of what we do, the curriculum, the mentorship, the research, exists because we believe quality AI education should not be a privilege.
Millions of learners across West Africa are locked out of the AI conversation, not because they lack ability, but because the tools, the language, and the infrastructure were never built with them in mind. That's the inclusivity gap we exist to close, one learner, one dataset, one deployed model at a time.
Because we don't charge tuition, we rely on donations, grants, and partnerships to keep the community free and to fund the research and engineering work, like SabiLearn, that turns our mission into something learners can actually use.
The product our research is building toward: an educational platform designed for students the internet doesn't reach yet.
SabiLearn is built on TAG Ecosystem's applied research into glossary-augmented machine translation for Nigerian Pidgin, the same Eng-PidginEdu work recognized with a Top 50 Poster Award at Deep Learning Indaba 2026. The translation model at its core is currently being trained, funded by a GPU compute grant we recently won from OpenToken.
The core design decision behind SabiLearn is that it has to work offline. It's built for students in the remote parts of Nigeria where a live internet connection isn't a given, so learners shouldn't need one to access educational content in a language they actually understand.
Text is just the starting point. We're planning to expand SabiLearn to a voice solution for Pidgin speakers as well, so we can curate both voice and text educational content, helping high school students across different regions of Nigeria engage with material in whichever format reaches them best.
Glossary-augmented Pidgin MT research recognized at Deep Learning Indaba 2026 and the WiML Symposium @ ICML 2026.
SabiLearn's translation model is currently training, funded by TAG Ecosystem's OpenToken GPU grant.
Planned next steps are a voice solution for Pidgin speakers and packaging SabiLearn to run fully offline for remote schools.
As a non-profit, we rely on partners who believe in this mission. Here's where your support goes.
GPU compute is the single biggest cost standing between SabiLearn's research and a working product. Donations here go directly toward training and evaluating the translation model.
Help us run our AI Engineering Bootcamp cohorts and keep mentorship free for every learner in the community.
Support the collection and curation of Pidgin voice data that will power SabiLearn's next phase: voice-based learning content.
Unrestricted donations keep the lights on: infrastructure, tools, and the day-to-day work of keeping TAG Ecosystem free to join.
Tell us a bit about how you'd like to support the mission. We read every message.
We've received your message and will be in touch soon. You can also reach us directly at flora.oladipupo@tagecosystemai.com.
Prefer email? Reach us directly at flora.oladipupo@tagecosystemai.com
TAG Ecosystem is proof that community-driven AI research from Africa can compete on the global stage. Help us take it from research to something a student in a village school can actually use.
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