How One of Africa’s Fastest Growing AI Platforms Was Built Out of Ethiopia

Why It Matters

Dala’s rapid adoption demonstrates the untapped demand for localized, mobile‑first AI tools in Africa, positioning Gebeya as a potential hub for continent‑wide AI services. Its success could accelerate AI democratization and spur investment in African cloud infrastructure.

Key Takeaways

• Dala reached 85k users in four months
• 8% paying conversion, double industry average
• Mobile‑first, WhatsApp/Telegram integration targets African users
• Orchestrator selects optimal foundation model for each prompt
• Plans to train context‑specific language model using local data centers

Pulse Analysis

The African artificial‑intelligence market has long been dominated by overseas giants, but Ethiopia’s Gebeya is rewriting that narrative with Dala, a no‑code platform that lets users create applications through plain language. Launched in late 2025, Dala attracted 85,000 users in just four months, a pace that rivals global entrants such as Lovable. The startup’s journey—from a software‑engineering school to a pan‑African talent marketplace and now an agentic AI suite—illustrates how deep local talent pipelines can accelerate product‑market fit in emerging economies.

Dala’s edge lies in its mobile‑first architecture and an orchestrator that intelligently dispatches each query to the most suitable foundation model, whether from OpenAI, Google, or emerging providers. By embedding WhatsApp and Telegram, the service meets Africans where they already communicate, while multilingual support removes language barriers that larger platforms often ignore. The platform also accepts local currencies and mobile‑money payments, driving an 8% paid‑user conversion—more than double the industry norm. These design choices translate technical sophistication into tangible adoption across Kenya, Nigeria, and Ethiopia.

Scaling remains the biggest hurdle. Dala still relies on external large‑language models, exposing it to API volatility and per‑prompt costs, while the race for indigenous models intensifies. Gebeya’s partnership with Cassava Technologies gives it access to African data centres and GPU clusters, a critical step toward training a context‑specific language model that respects data‑residency rules. If the company secures dedicated AI funding, it could become the continent’s first home‑grown AI platform, catalyzing further venture interest and infrastructure investment across Africa. Such a move would also lower reliance on costly foreign cloud services.

How one of Africa’s fastest growing AI platforms was built out of Ethiopia

Victoria Fakiya · Senior Writer, Techpoint Digest · February 17, 2026

At 85,000 users, Gebeya’s suite of AI products is growing fast, a feat achieved in just four months since launch. Out of the four AI products created, Gebeya Dala, or Dala for short, is the most popular.

Dala helps people build apps with natural language — no coding skills necessary. It is similar to Lovable, the $6.6 billion AI startup that inspired it. But rather than just being “Lovable for Africa,” Dala distinguishes itself by doing more.

Doing more means going beyond vibe‑coding, which may seem like one of the most popular use cases for AI currently, but Amadou Daffe, who founded Gebeya in 2016, says not many Africans are into it yet.

“I interviewed my nephews, who actually told me it would be cool if we integrated comic books. That way, you can create your own comic books and even sell them digitally. Turns out it is actually easier to do that than to create a vibe‑coding platform.”

Daffe is not new to building and iterating products. While Dala might just be four months old, Daffe has been building for 10 years. He founded Gebeya in 2016, and since then the company has taken many forms.

“The first version of Gebeya (Gebeya 1.0) was actually a school, and the purpose was to train many high‑end software engineers on the continent and eventually provide an outsourcing opportunity.”

The model was similar to Andela’s but focused on training talent across East Africa. It later evolved into a pan‑African talent marketplace, aspiring to become an “Upwork for Africa,” before pivoting again into a SaaS platform that allowed other entrepreneurs to run their own talent marketplaces.

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Since then, Gebeya has shifted once more—this time into agentic AI products. According to Daffe, he is running a technology company, so it only made sense that he keep up with the times.

The decision to build Dala came when a non‑technical staff member built a working product with Lovable. While Lovable inspired it, Dala isn’t just another vibe‑coding platform; it is a platform where you can “vibe” anything.

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Gebeya Dala’s growth

When it comes to funding or startup presence in Africa, Ethiopia—Daffe’s home country—is rarely discussed. Africa’s “big four” (Egypt, South Africa, Kenya, and Nigeria) are usually the top contenders.

Despite this, Daffe has raised funding from VCs such as Partech Africa and Orange Digital Ventures. This is possible because of the pan‑African structure he gave the company.

“By default, Gebeya was built in Africa. If I just stayed in Ethiopia and created a company in Ethiopia, it would have been impossible to raise money.”

Building talent since 2016 has its perks. According to Daffe, his team is one of the best engineering teams in the world. Mark Essien, another African tech entrepreneur, has been training tech talent through his HNG internship for years. This talent came in handy while building his new product, Tripdesk, which already crossed $2.3 million in revenue in four months.

Similarly, Dala was launched four months ago, and not only does it have thousands of users, but 8 % of them are paying customers—unusually high for AI products today.

“It is very high if you look at it in comparison to the industry rate, which is around 3 %.”

Daffe says the high conversion rate is due to people being able to pay in their local currency and use popular payment methods like mobile money.

However, Daffe admits that Dala’s growth is still dwarfed by global AI platforms. For example, Lovable grew to 300,000 monthly active users in two months and 8 million users in one year—a kind of growth that requires massive funding (Lovable raised a $500 million round).

Gebeya has secured funding for its previous iterations but has yet to raise money for its AI ambitions. Beyond financing, Daffe points to several competitive advantages unique to Africa:

• Dala is built for a mobile‑first continent, integrated with WhatsApp and Telegram.

• It supports multiple African languages because, in Daffe’s words: “Those big companies don’t have time to solve what I just described.”

• Dala can be the first point of contact with AI for many Africans who still lack access to such tools.

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Building AI for Africa

Like many AI startups, Dala relies on existing foundational models from massive AI labs, but it adds a unique twist through what Daffe calls an orchestrator.

“Gemini may not be the right tool, OpenAI may not be the right tool. The orchestrator picks which one it wants.”

The orchestrator intelligently routes each user prompt to the most suitable underlying model. Currently Dala routes prompts across existing foundational models, but the bigger ambition is to move beyond dependence on external providers and eventually develop a context‑specific language model of its own.

“But we have an appetite right now to build our own models because what we are doing is so specific.”

Gebeya aims for a smaller, focused model trained on its own user behaviour and use cases across products like Dala and Jetume.

“I’m not aspiring to build a large language model. I’m aspiring to either build a context language model — meaning very specific to an area — or a small language model.”

This ambition ties into its relationship with Cassava Technologies, which operates data centres and fibre infrastructure across Africa. As Gebeya gathers more user data and prepares to train its own models, it will need GPU‑ready infrastructure on the continent—especially in markets where governments may require local data residency.

“If I wanted to launch the language model, or even an agent that’s very specific to data control in a given country, I’ll sign a lease with them to run my application and my language model there.”

In other words, Cassava provides the physical infrastructure—data centres and GPUs—that would allow Gebeya to train and deploy its own contextual model within Africa rather than relying entirely on foreign cloud environments.

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Challenges of building AI in Africa

Because Dala relies on foundational models from companies like OpenAI and Google, Gebeya must constantly manage issues such as hallucinations, shifting APIs, and cost per prompt.

“Some of my engineers are stuck because this is something new. We could have moved much faster if I were able to unlock these issues that this agentic thing does.”

Beyond technical bottlenecks lies the harder problem of scale. Dala has grown quickly, but Daffe knows that in AI, speed determines survival. For him, the race is not just about product quality but about distribution, working capital, and becoming the loudest voice explaining AI’s possibilities to an African audience.

Yet Daffe frames the moment less as a threat and more as an inflection point. He believes Africa’s young, mobile‑first population gives it an opportunity to leapfrog—not just in coding, but in what he calls “vibing everything,” from music to games to full digital products.