How Kenya’s Fastagger Builds AI for Small Businesses

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 Most people, meanwhile, reached the internet through outdated, low-memory cellphones rather than computers or high-end phones.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.

Designing AI for Kenya’s Small Businesses

Before developing Auni, Kariuki and his co-founders started by tackling the problem of data availability for AI training. They asked themselves: “How do we get Africa to catch up fast? How do we create training data fast enough, and then how do we actually get it into people’s hands fast enough?”

Mutembei Kariuki cofounded Fastagger to develop AI systems that can run offline and on basic phones. Afrikanna Production/Microsoft

They decided to focus on mobile phones, and specifically on mobile money apps. With more than 80 percent of Kenya’s adults using them, they offer a rich source of data.

One of Fastagger’s first steps was to adapt an open-source optical character recognition (OCR) model to read financial statements produced by mobile money apps. Existing models…