Perspectives and educational content on applied AI and building in Africa.
Most AI companies fall into one of two camps: research labs that publish papers, or consultancies that implement other people's tools. An applied AI laboratory does both - and the gap between them is where the actual value lives.
The most effective AI projects in Africa don't start with a model. They start with a problem that someone has been trying to solve for years and hasn't been able to - usually because the problem requires processing more information than humans can handle manually, or because the existing solutions are too expensive or too rigid.
We wanted an AI assistant on our own website that could answer questions about our work, services, and products - without sending visitor data to third-party services. Here is how we built it, what we learned, and what we would change next time.
Cloud AI services are easy to start using and difficult to leave. The convenience of managed APIs masks long-term costs, data dependencies, and operational risks that become visible only after you are deeply committed to the platform.
In many parts of Africa, internet connectivity is not a given - it is a condition that comes and goes. AI systems that assume constant connectivity will fail in these environments, and they will fail at the moments when they are needed most.
Most AI maturity frameworks were designed for organizations that already have mature data infrastructure, dedicated technology teams, and budgets for experimentation. If you are an African organization that does not have these things, the frameworks tell you what you are missing but not what to do about it.