Gebeya, founded by Amadou Daffe in 2016, has rapidly expanded its suite of AI products, reaching 85,000 users in just four months since their launch. Among its four AI offerings, Gebeya Dala, or Dala, stands out as the most popular. Dala empowers users to build applications using natural language, eliminating the need for coding skills. While inspired by the $6.6 billion AI startup Lovable, Dala distinguishes itself by offering more than just vibe coding, a use case Daffe notes is not yet widely embraced in Africa. Instead, Daffe's inspiration came from his nephews, who suggested integrating comic book creation and digital selling, a concept deemed easier to implement than a generic vibe coding platform.

Amadou Daffe's journey in building and iterating products spans a decade, with Gebeya itself undergoing several transformations since its inception. Initially, Gebeya 1.0 functioned as a school, focused on training high-end software engineers in East Africa to facilitate outsourcing opportunities, a model akin to Andela's. It then evolved into a Pan-African talent marketplace, aiming to be an "Upwork for Africa," before pivoting again to a SaaS platform for entrepreneurs to run their own talent marketplaces. More recently, Gebeya shifted into developing agentic AI products, a strategic move Daffe attributes to keeping pace with technological advancements. The catalyst for Dala's creation was a non-technical staff member successfully building a product using Lovable, reinforcing the idea that AI could democratize creation beyond mere "vibe coding."

Despite Ethiopia, Daffe's home country, not typically being among Africa's top contenders for startup funding or presence, Gebeya has successfully secured investments from prominent VCs like Partech Africa and Orange Digital Ventures. This achievement is largely due to the company's inherent Pan-African structure, which made fundraising more viable than if it had remained solely an Ethiopian entity. Gebeya benefits from a highly skilled engineering team, a result of its long history of talent development dating back to 2016. This expertise proved invaluable in developing Dala, which, like Mark Essien's Tripdesk, has shown impressive early traction. Dala not only boasts thousands of users but also a high conversion rate of 8% paying customers, significantly above the industry average of around 3%. Daffe attributes this success to features like local currency payment options and integration with popular mobile money services.

However, Daffe acknowledges that Dala's growth, while impressive for its context, is still dwarfed by global AI platforms such as Lovable, which achieved 300,000 monthly active users in two months and 8 million in a year—a scale that typically demands substantial funding rounds. While Gebeya has secured funding for its previous iterations, it is yet to raise capital specifically for its AI ambitions. Nonetheless, Daffe highlights several competitive advantages unique to Africa. Dala is designed for a mobile-first continent, integrating with platforms like WhatsApp and Telegram, and offering support for multiple African languages, addressing specific needs that larger global companies often overlook. Daffe also believes Dala is well-positioned to be the first point of contact with AI technology for many on the continent who currently lack access.

In its approach to building AI for Africa, Dala leverages existing foundational models from major AI labs but incorporates a unique "orchestrator" system layer. This orchestrator intelligently routes each user prompt to the most suitable underlying AI model, optimizing performance. While currently relying on external models, Gebeya's long-term ambition is to develop its own context-specific or small language models, moving beyond full dependence on external providers. Daffe emphasizes that Gebeya is not aspiring to build a large general-purpose language model but rather highly focused models tailored to specific areas and user behaviors observed across its products like Dala and Jetume. This ambition is supported by a strategic relationship with Cassava Technologies, which operates data centers and fiber infrastructure across Africa. Cassava provides the essential GPU-ready infrastructure, crucial for Gebeya to train and deploy its own contextual models locally, especially in markets with data residency requirements.

Building AI in Africa presents its own set of challenges. Relying on foundational models from companies like OpenAI and Google means Gebeya must constantly contend with issues such as AI hallucinations, shifting APIs, and fluctuating costs per prompt. Daffe notes that these technical bottlenecks can impede development speed. Beyond technical hurdles, the problem of scale looms large. While Dala has grown quickly, Daffe understands that in the fast-paced AI landscape, speed is paramount for survival. The race, he believes, encompasses not just product quality but also distribution, working capital, and effectively communicating AI's potential to an African audience. Despite these challenges, Daffe views the current moment as an inflection point, seeing Africa's young, mobile-first population as an opportunity to leapfrog, not only in coding but in a broader concept of "vibing everything," from music and games to comprehensive digital products.