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Breaking Language Barriers with AI
Developing natural language processing models for 50+ African languages, enabling AI accessibility for hundreds of millions of speakers who have been underserved by mainstream NLP research.
50+
Languages Covered
89%
Accuracy on Swahili NLU
10M+
Users Reached
15+
Production Deployments
Africa is home to over 2,000 languages, yet the vast majority of NLP research has focused on high-resource languages like English, Chinese, and Spanish. This creates a fundamental barrier to AI accessibility for hundreds of millions of African speakers.
At VE.KE, we're working to change this. Our African Language NLP research program develops models, datasets, and tools that bring the benefits of language AI to African language speakers. From Swahili chatbots to Amharic sentiment analysis, our work enables new applications that simply weren't possible before.
Our approach combines state-of-the-art multilingual techniques with deep partnerships with local communities, linguists, and organizations to ensure our models truly serve African users.
Understanding the unique obstacles we're working to overcome.
Most African languages have minimal digital text data. Swahili Wikipedia has 75K articles vs. 6.7M for English—a 100x difference.
Africa's languages span multiple language families with vastly different structures, from tonal languages to those with complex morphology.
Real-world African text often mixes multiple languages, requiring models that can handle fluid multilingual communication.
Models must run efficiently on low-resource devices common in African markets, not just high-end cloud infrastructure.
The methods and techniques we've developed to address these challenges.
We build on and extend multilingual models like mBERT and XLM-R, fine-tuning them on African language data to improve performance.
We partner with local organizations, media companies, and governments to collect high-quality training data in target languages.
We leverage cross-lingual transfer from related languages and high-resource languages to bootstrap performance in low-resource settings.
We develop compressed and distilled models that can run on mobile devices and in offline environments.
Measurable outcomes from our research and deployments.
50+
Languages Covered
Our models support over 50 African languages across multiple language families.
89%
Accuracy on Swahili NLU
Our Swahili models achieve 89% accuracy vs. 72% for off-the-shelf multilingual models.
10M+
Users Reached
Our language technology powers applications serving over 10 million users across East Africa.
15+
Production Deployments
Our models are deployed in production systems across banking, healthcare, and government.
Adeyemi, G., Asante, K., et al.
Adeyemi, G., Okonkwo, A., et al.
Banda, M., Adeyemi, G., et al.
Building automatic speech recognition systems for 10 major African languages, enabling voice interfaces in local languages.
Partners:
Mozilla Common Voice, African Voices Foundation
Production-ready chatbot and virtual assistant capabilities for Swahili, deployed with banking partners.
Partners:
Equity Bank, KCB Group
Creating open-source datasets for African language NLP, including parallel corpora and annotated data.
Partners:
Lacuna Fund, Masakhane
Dr. Grace Adeyemi
Research Lead
Dr. Kofi Asante
Chief Research Officer
Michael Banda
Research Engineer
We're always looking for collaborators, partners, and talented researchers to advance this work.