Mapping the landscape and trajectory of
AI in Africa
Partnered with
The problem
African AI is being shaped from fragments.
There’s no shared map of African AI. So the decisions that shape the field get made from scattered pieces, assumptions, and borrowed blueprints:
Funders back what’s visible — not what’s most needed.
The hardest gaps are the hardest to see, so they stay unfunded.
Researchers rebuild what already exists.
Prior work sits scattered across repos, PDFs, and dead project pages — so it gets done twice.
Builders ship with the wrong data — or none at all.
Local languages and context were invisible, so the product breaks for the people it’s meant to serve.
Policymakers govern from borrowed blueprints.
With no evidence of what’s actually happening on the ground, the assumptions get imported from elsewhere.
These are not isolated friction points. They are foundational gaps that weaken Africa’s digital sovereignty and limit our ability to shape the technologies that increasingly affect us.
At Lanfrica, we are building the evidence layer for African AI
By organizing what exists, building what’s missing, and tracking what’s changing, we help Africa shape its AI future as architects, not consumers.
Connecting the landscape
Connecting the landscape
The African AI Atlas
The most complete map of African AI anywhere — datasets, models, papers, and policies, continuously tracked and connected as the field grows.
Building what’s missing
Building what’s missing
Bespoke datasets for African AI
When the data your AI needs doesn’t exist, we help create it through our unique approach called data farming.
We’ve created notable datasets like NaijaVoices, VoiceAfrica, and NaijaActions.
Understanding what it means
Understanding what it means
Lanfrica Insights
Evidence-based analysis of the forces shaping African AI — trends, gaps, and bottlenecks.