How a Kenyan Startup Is Rethinking AI for Small Businesses

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From the original article

Lucila Pinto is a journalist from Argentina who writes about AI, trust in science, and economic development.

The first time Mutembei Kariuki thought about how AI models could work in Kenya, his home country, was in 2019. He was attending a conference at the University of Tokyo on the future of AI, and kept hearing about how this technology was going to transform the world. “But nobody mentioned Africa at all,” he says.

Even then—before AI giants like OpenAI and Anthropic had launched their chatbots to the public—Kariuki could see significant obstacles to AI adoption in Africa. Internet access was slow and unequal, computing resources for training models were scarce, and there was little high-quality, structured training data. Most people, meanwhile, reached the internet through outdated, low-memory cellphones rather than computers or high-end phones.

Together with co-founders Jude Mwenda and Stephanie Njerenga, Kariuki created Fastagger, a start-up that builds AI models that can run offline and on low-power cellphones. Its first big product, Auni, is a business intelligence assistant that launched in 2024; it got a big push when it was integrated into the business app of M-Pesa, Kenya’s largest mobile money service.

Since then, about 6,000 businesses have signed up. Auni helps small and medium businesses like barbershops, restaurants, and small manufacturers analyze their transaction data and gives them marketing recommendations.

Peter Chege, owner of a salon and barbershop in Nairobi, started using Auni last year. Before that, he recorded all his sales on paper. “We were not able to translate that information into a graph,” he says. Now, the app helps him identify his busiest days and hours, as well as his loyal customers. If a regular customer stops coming, for example, he can send a personalized message encouraging them to return.

Fastagger is part of an emerging effort to build AI around the technological realities of users in developing economies, rather than adapting products designed for wealthier markets. In Fastagger’s case, that meant finding a way to make their models work despite inexpensive phones, unreliable internet connections, and business records that may consist of little more than mobile-money transactions.