Four graduates are building an AI video-dubbing tool for African filmmakers

Apotierioluwa Owoade had a problem he could not stop thinking about.

He had spent time working at Aforevo, a local streaming and dubbing firm in Lagos, Nigeria, and his experience stayed with him. During his time at the company, from 2022 to 2023, he saw firsthand how cost-prohibitive the dubbing industry could be.

Translating a film into another language costs upwards of $500,000 for a full production, according to Owoade.

Yet, beyond the cost, something frustrated him even more: the lack of nuance that most translators failed to capture in the local tongue. Voice actors, overstretched and underpaid, flattened the emotional texture of scenes they were recording. The existing software tools were no better.

He had seen Yoruba rendered so poorly that the phrase “I am pregnant” came out flatly as “I have a ball,” he explained, his face visibly grimacing over our video call.

He wanted to fix it. He called his friend David Mac-Asore, who was a Computer Engineering undergraduate at the time and a software developer.

Owoade and Mac-Asore had known each other for years, a friendship anchored partly through shared work at Living Faith Church Worldwide International, one of Nigeria’s largest churches. Since 2022, the two have collaborated on projects to bridge the language divide between the church’s English and French-speaking congregations at its headquarters in Ota, Ogun State, in south-western Nigeria, said Mac-Asore.

When Owoade pitched his idea, Mac-Asore was in, but they agreed they needed someone steeped in machine learning.

Mac-Asore reached out to two of his former schoolmates from Covenant University, a private Christian university in Ota: Maryann Nnaji and Emmanuel Ibiang. Both had graduated in 2024.

The four got on a call. At the time, they did not even have a name for what they were building. According to Owoade, they called it the Hagen Project, a name that made me chuckle.

The ‘Hagen Project’ eventually evolved into Reedapt in 2025.

Nnaji brought the machine learning depth the team needed.

Before joining, she had built a sign language-to-speech and text model as part of her undergraduate thesis, working through the full pipeline from data collection to training, deployment, and testing.

She had noticed the friction the hard-of-hearing (HOH) community faced in everyday interactions and wanted to use technology to address it.

She had to put the work on hold, partly due to a data gap that would later feel very familiar when she began working on Reedapt.

According to Nnaji, most research on sign language recognition was built on Western contexts, not Nigerian or African ones.

“When I started the project, it was just a way to actually see how technology could be a way of bridging this gap,” Nnaji said. “To prove something to myself that this is applicable.”

Ibiang, Reedapt’s product engineer, arrived with a different but equally critical instinct: an obsession with usability. Where the AI engineers on the team reached for accuracy, Ibiang optimised for the user.

“Can the average Joe use your product without having to be walked through?” Ibiang asked rhetorically, almost as if he was expecting a response. “Ease of use of the product—that’s what my role optimises for.”

For a product as technically complex as Reedapt, that perspective has been the team’s internal check against building something impressive that nobody can use.

In a four-man small team where Mac-Asore and Nnaji are the technical engineers, and Ibiang is the product specialist, Owoade was described by his teammates as the person with the ideas.

The four of them, all fresh out of school and under 25, decided to build together.

I first met Owoade and Mac-Asore at the Builders Summit by Founders Connect in May 2025, a networking event for early-stage technology founders held in Lagos, Nigeria.

At the time, they told me they wanted to build a translation tool that allowed users to move between different languages over text or audio, without needing to learn a new language.

I remember joking that their ambition would eventually put Duolingo out of business.

More seriously, I asked them why this needed to exist in a world where we already have DeepL.

They didn’t have a clear answer. I sensed they were still finding the edges of it.

Since that conversation, the vision has sharpened considerably.

Reedapt is now focused on becoming the go-to dubbing and real-time multilingual streaming platform for Nollywood filmmakers, churches, and African content creators who want their work to travel further than the English language will take it.

The startup, Owoade said, has signed two enterprise dubbing contracts with a Nollywood gospel producer, with those projects expected to be completed before the end of 2026.

Reedapt’s paying customers today are a mix of Nollywood producers and churches. It currently serves over 200 active users.

About 94% are individual creators on its consumer tier, while the remaining 6% are enterprise clients who generate the majority of revenue, said Owoade.

Reedapt makes money by charging subscription fees.

Pricing is structured in dollar-denominated tiers: a free plan offering up to 60 minutes of usage, a creator plan at $11 per month, and higher tiers at $39 and $99.

The decision to price in dollars was deliberate, said Owoade.

The team had experimented with Naira pricing early and found that it undercut their credibility with both users and potential investors.

“Most of our costs are not in Naira,” he said. “So it would be significantly disadvantageous to charge in Naira.”

The team is now aiming to reach 50,000 users before the end of 2026.

Building a speech technology product for African languages is not the same as building one for English or French.

According to Owoade, the tools that exist in the market were not built with Africa as a priority.

“Big Tech isn’t really building with Africa for Africa as a priority,” he said.

“Apple released AirPods with a live translation feature, and you see their focus is on languages like English, French, and Spanish.

In fact, they seem to prioritise Spanish even more because they’re catering to Latin American users. So, if I had to say it, Nigeria is out of the conversation.”

Reedapt is trying to change that, but the foundation of any good model is data, and the data problem for African languages is acute.

According to Nnaji, training data for the languages Reedapt needs is either very scarce or too poor in quality to be useful.

The machine learning engineer likened the problem of training a model to raising a child.

If you show a child only one breed of dog, they will struggle to recognise other breeds, said Nnaji, visually describing the challenge.

“If the volume of what you’re feeding it is not enough,” she said. “Your model still performs poorly.”