If you’re not very familiar with the African AI ecosystem, you might expect to venture in and just find people using AI. What you’ll be certain to find, alongside that, are people building it. Not frontier building, but the more immediate kind of identifying problems that global tools were not designed to solve, and solving them.
When YPIT spent the better part of a year mapping Nigeria’s emerging AI ecosystem, speaking to engineers, researchers, founders, and practitioners, that’s exactly what they found – A cluster of people doing good, hard work in the field of AI. This cluster is small enough that many of them know each other, and their work, specific enough to be worth examining.
Taking a deeper look across the companies that have emerged from this ecosystem, a pattern becomes visible. Companies like Tonative are working at the data layer, working hard at curating African language datasets. Without this raw linguistic material, any model trained for African contexts is operating on low-resource data. It’s unglamorous work, rarely discussed, but it’s foundational.
On top of it, companies like Spitch and Intron are building the model layer. Spitch is building speech recognition and text-to-speech for Yoruba, Hausa, Igbo, and Nigerian-accented English, amongst others. Intron Health is doing similar work in healthcare with Sahara: a speech recognition model covering 20-plus African languages and accents, with offline functionality and live deployments in Nigerian and Kenyan courts and hospitals.
