Mutembei Kariuki在2019年首次思考AI模型如何在家乡肯尼亚工作。 他当时正在东京大学参加一个关于AI未来的会议，并不断听到这个技术如何改变世界。 “但是根本没有人提到非洲，”
即便如此，在AI巨头如OpenAI和Anthropic将发布的聊天机向公众开放之前，Kariuki在非洲可以看到AI收养的重大障碍。 互联网上网速度缓慢，而且不平等，用于培训模式的计算资源稀缺，而且几乎没有高质量的结构化培训数据。 与此同时，大多数人通过过时的低模手机而不是计算机或高端手机进入互联网。
与联合创始人Jude Mwinda和Stephanie Njerenga一起，Kariuki创建了Fastagger，这个启动程序构建了AI模型，可以运行下线，并使用低功率手机。 它的第一个大产品是Auni，是发布在2024年的商务情报助理；当它被整合到肯尼亚最大的移动货币服务M-Pesa的商业应用中时，它得到了很大的推动。 此后约有6000家企业报名。 奥尼帮助理发店，餐馆，小制造商等中小企业分析交易数据，并给出营销建议。
去年开始使用Auni。 在此之前，他把所有的销售记录在纸上。 称先生说，“我们无法将这些资料翻译成图表”。 现在，这个应用帮助他确定他最繁忙的日子和时间，以及他忠诚的客户。 例如，如果一个普通客户停止前来，他可以发出个性化的信息，鼓励他们返回。
Fastagger是围绕发展中经济体用户的技术现实建设AI的新兴努力的一部分，而不是为更富有的市场改造产品。 在Fastagger案中，这意味着尽管手机价格低廉，互联网连接不可靠，商业记录可能仅包括移动货币交易，但还是想方设法使其模型发挥作用。
为肯尼亚小企业设计AI
在开发Auni之前，Kariuki和他的共同创始人首先着手解决AI培训的数据提供问题。 他们问自己：“我们如何让非洲迅速赶上？我们如何创造足够快的培训数据
Mutembei Kariuki共同创立了Fastagger公司，以开发可以运行在下线和在基本手机上的AI系统。 阿夫里卡纳生产/Microsoft
他们决定专注于手机，特别是手机资金应用。 超过80%的肯尼亚成年人使用这些数据
Fastagger的第一步是调整开源光学字符识别(OCR)模型，以读取由移动货币应用程序制作的财务报表。 现有的模型与PDF的低质量纠缠不休，PDF格式不熟悉，有时还含有邮票和错误。 由于Fastagger的预期用户往往有不可靠的互联网连接，他们确保关键步骤，比如将提取的数据整理成表格，可以运行端侧。
一旦他们有了能够处理这些文件的模型，他们就开始探索这种结构化的财务数据如何能够帮助企业。 他们花了一整天时间观察小企业主的工作，并与他们的客户互动。 一项挑战很快变得明确：业主除了日常业务之外，没有多少时间或资源来思考。
美发店老板Kariuki 称，“你必须思考用户旅程的这种动态，他们如何生活，以及背景中会发生什么事。”
Fastagger应用帮助沙龙老板Peter Chege更好地管理他的时间，并跟踪忠诚的客户。 阿夫里卡纳生产/Microsoft
对Chege来说，奥尼最大的好处之一是帮助他掌握时间。 一张简单的图表显示，上午是他最慢的工作时间，因此他意识到，他当时可以跑腿，或者在店里保留更少的理发师，直到下午1时左右，当生意开始。
创始人还看到企业花在手工流程上的时间以及自动化如何简化这些任务。 例如，在内罗毕的一家鱼餐厅，确定忠诚的客户给他们一个接一个地通过短信销售收据所需的折扣，这是一项烦琐的任务，使保留客户的销售不切实际。 现在，在奥尼的帮助下，餐厅自动识别其顶级的客户并给他们提供折扣。
Auni的一些功能——如阅读销售说明、分析数据、可视化结果——可以在低成本电话上下线，而不进行云查询。 这些功能涉及AI工具和常规软件；例如标准OCR软件提取文本，一个小神经网络确定每个文档的结构。 然而，更详细的分析和回答有关数据的问题需要互联网连接。
研究AI对发展中经济体的影响的哥伦比亚大学经济学家Daniel Björkegren，称撒哈拉以南非洲普遍使用基本电话而不是智能手机和计算机，这可以为不同种类的AI使用打开大门。 称表示：「科技史应该表明，“你会有这样的人，但对于大部分人口来说，我们必须找出不同的使用方式。 ”
本地需求如何塑造AI
尽管奥尼融入了M-Pesa，但Fastagger仍然是个小小的创业者。 它得到了Google,Microsoft和NVIDIA加速器程序的支持，但仍只有3名员工：Kariuki,Mwenda和Njerenga，最初的创始人。 目前他们正致力于将agents加入Auni
Fastagger还为肯尼亚安保公司开发了劳动力管理工具，雇用了数十万人。 保安经常在没有可靠互联网的偏远地点工作，并使用低端电话。 Fastagger的AI agent通过短信与他们通信，以追踪他们的路线，检测异常，并为行动团队生成可操作的警报。
Fastagger并非是针对新兴经济体的使用条件，其他例子包括智利农村学校没有互联网的应用软件；印度农村病人的医疗分门别类聊天机；以及使用基本电话的非洲语言翻译模型。 这种创新是受限制产生的，有助于使获得大赦国际的机会民主化。 但这并不表示限制本身会消失
Björkegren认为为硅谷服务不足的人口建立的系统具有巨大潜力，但他认为，在为新兴经济体设计的小型模型和为较富裕地区设计的日益庞大、强大的模型之间划分过于明确，有风险。 称先生说：“我担心这会导致两边分裂。” “我们需要提供各种选择的大菜单，并真正确保我们能够进入，然后看到什么是可行的。”
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Lucila Pinto是一位来自阿根廷的双语记者，他写道AI，信任科学，经济发展等等主题。 她的作品出现在"自然","科学","世界的其他地方","拉纳西翁"等出版物中。 她拥有哥伦比亚大学的科技新闻学硕士学位。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
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 struggled with the low quality of the PDFs, which had unfamiliar formatting and sometimes contained stamps and errors. And because Fastagger’s prospective users often had unreliable internet connections, they made sure key steps, like organizing the extracted data into tables, could run on-device.
Once they had a model that could process these documents, they began exploring how that structured financial data could help businesses. They spent entire days watching small-business owners work and interact with their customers. One challenge quickly became clear: Owners had little time or resources to think beyond day-to-day operations.
“They are standing up the whole day cutting hair,” Kariuki says of salon owners, who are now among Auni’s main users. “You have to think about that dynamic of the user journey, and how they live, and what will be going on in the background.”
The Fastagger app helps salon owner Peter Chege better manage his time and keep track of loyal customers.Afrikanna Production/Microsoft
For Chege, one of Auni’s biggest benefits was helping him manage his time. A simple chart showed him that mornings were his slowest hours, so he realized that he could run errands then or keep fewer barbers in the shop until around 1 p.m., when business picked up.
The founders also saw how much time businesses spent on manual processes and how automation could simplify those tasks. At a fish restaurant in Nairobi, for example, identifying loyal customers to offer them discounts required going through SMS sales receipts one by one, a tedious task that rendered client-retention marketing impractical. Now, with the help of Auni, the restaurant identifies its top customers automatically and offers them discounts.
Some of Auni’s functions—such as reading sales statements, analyzing data, and visualizing results—can happen offline on low-cost phones, without making cloud queries. These functions involve both AI tools and conventional software; for example, standard OCR software extracts the text and a small neural network identifies how each document is structured. More detailed analyses and answering questions about the data, however, require an internet connection.
Daniel Björkegren, an economist at Columbia University who studies AI’s implications for developing economies, says sub-Saharan Africa’s prevalence of basic phones rather than smartphones and computers could open the door to different kinds of AI use. “The history of technology should suggest that this is not going to be people running Claude Code or Codex on their laptop,” he says. “You will have people like that, but for much of the population, we’re going to have to figure out different usage modalities.”
How Local Needs Are Shaping AI
Despite Auni’s integration into M-Pesa, Fastagger remains a small startup. It has received support from Google, Microsoft, and NVIDIA accelerator programs, but still has just three employees: Kariuki, Mwenda and Njerenga, the original founders. Currently, they’re working on adding agents to Auni that could automatically implement marketing actions for business owners.
Fastagger has also developed a workforce management tool for Kenya’s security companies, which employ hundreds of thousands of people. Security guards often work in remote locations without reliable internet and use low-end phones. Fastagger’s AI agent communicates with them via SMS to track their routes, detects anomalies, and generates actionable alerts for operations teams.
Fastagger isn’t alone in tailoring AI models for the usage conditions in emerging economies. Other examples include an app for rural schools without internet in Chile; a medical triage chatbot for rural patients in India, and translation models for African languages that run on basic phones. Such innovations, which emerge from constraints, could help democratize access to AI. But that doesn’t mean the constraints themselves disappear or stop limiting development.
While Björkegren sees great potential in systems built for populations that have been poorly served by Silicon Valley, he believes there’s a risk in establishing too clear a division between small models designed for emerging economies and increasingly large, powerful models for wealthier regions. “I am worried that that could lead to a bifurcation,” he says. “We need to enable a large menu of different options and really make sure that we have access and then see what works.”
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Lucila Pinto is a bilingual journalist from Argentina who writes about AI, trust in science, and economic development, among other topics. Her work has appeared in Nature, Science, Rest of World, La Nación, and other publications. She holds a master's degree in science and technology journalism from Columbia University.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.