Our AI Research

We research and develop Contextual General Small Models (CGSMA)—efficient, scalable AI systems designed for local languages, cultural knowledge, and real-world environments across multilingual NLP, speech, and vision

Languages

AfriMMT-EA: Multi-domain Machine Translation for Low-Resource East African Languages

Low-resource African languages remain underexplored in multilingual translation. AfriMMT-EA introduces the first highly multilingual MT benchmark and dataset for East African languages.

AI Research Team

March, 10 2026

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Voice

Learning from Scarcity: Building and Benchmarking Speech Technology for Sukuma.

We present the first ASR system for Sukuma, one of the most severely under-resourced Tanzanian languages, and provide an open-source Sukuma speech corpus comprising 7.47 hours of carefully transcribed audio.

AI Research Team

Feb, 15 2026

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Datasets

Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations

We assess state-of-the-art (SOTA) speaker diarization and ASR systems on long-form, accented speech, comparing their performance with native accents and discover a 10%+ performance degradation

AI Research Team

April, 15 2025

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Languages

PAWA: Pan-African Wide Alignment Small Language-Vision Model

PAWA is a large-scale language model designed to understand and generate text in multiple African languages, addressing the lack of resources for these languages in natural language processing.

AI Research Team

Sept, 15 2024

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Vision

Learning Visual Concepts Directly From African Languages Supervision

We present a novel approach to learning visual concepts directly from African languages supervision, addressing the lack of annotated data for these languages in computer vision tasks.

AI Research Team

April, 15 2024

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