It began with a speech model that didn’t understand the people I was building for. What I found when I went looking for alternatives, and why I think the distance between African-language research and usable products is worth building a company around.
Sauti Labs started with a practical problem, not a plan to start a company.
In July 2026, I was building Scribe AI, an offline system that listens to a church service and puts the scripture being referenced on screen. Everything in it depends on speech recognition, and for that I used OpenAI’s Whisper.
On the African languages and accents the system needed to handle, it disappointed me.
I went looking for alternatives. As I explain in Coverage Is Not Capability: What Whisper Taught Me About African Speech, Whisper’s training data is heavily concentrated in English. Swahili appears with roughly five hours of transcribed speech, while Dholuo—the language that first drew me into this work—is absent from its supported-language list.
I wanted a model I could access, integrate, and rely on. That search took longer than I expected, and it changed what I was looking for.
Arriving in a Field That Already Existed
The first thing I learned was that I was arriving late to serious, sustained work.
Masakhane, a grassroots community focused on strengthening natural language processing research in African languages, has spent years building open datasets, models, and benchmarks. Its MasakhaNER project, for example, released a large, publicly available named-entity recognition dataset covering ten African languages, alongside code and models.
Closer to home, the Maseno Centre for Applied AI is collecting voice data in five Kenyan languages, including Dholuo, as part of African Next Voices—a multi-country effort to build speech datasets for African languages, funded mainly by the Gates Foundation.
In South Africa, Lelapa AI builds products directly. Its Vulavula platform transcribes and translates contact-centre conversations in languages such as isiZulu and Sesotho. Its researchers have also released InkubaLM, a small language model for five African languages.
Global research labs have expanded their efforts too. Meta’s speech models now claim coverage of more than 1,600 languages.
This is not a field waiting to be discovered. It has years of difficult, often under-resourced work behind it, and whatever I build stands on that work.
The Distance I Kept Running Into
And yet, as a developer with a concrete problem, I kept running into the same distance.
A dataset existed, but not a model trained for my use case.
A model listed my language, but its error rates were far from what a product needed.
A model worked, but its licence ruled out commercial use, or it required hardware my users did not have.
The products that did exist had been built, sensibly, around particular languages and markets. That is how products should start, but it still left the speech I needed uncovered.
Over time, that experience hardened into a conviction. I sometimes put it bluntly:
Africa doesn’t need another conference paper. It needs products that are accessible to the people and work for them in the most efficient and natural way.
I should be clear about what I mean, because it is easy to read that statement as a criticism of research. It is not.
Papers, datasets, and benchmarks are how a field learns what works. Without them, there would be nothing for products to stand on. Research and product development are not opposites.
My point is about where the work tends to stop.
Knowledge has its greatest effect when someone carries it the rest of the way: into a model trained on real speech, served behind an interface a developer can call, packaged so a business can deploy it, and made good enough that the person on the other end stops noticing the technology.
That last stretch is often nobody’s job.
It is the stretch I want to work on.
Why Speech?
I chose speech and voice because I believe they will become some of the main ways people interact with AI and digital services generally. Yet many African languages remain underrepresented in precisely the systems that make those interactions possible.
That underrepresentation is more than a technical inconvenience. It limits what people can build.
A developer who wants to create a voice service for their community—a clinic line, an advisory service for farmers, or a support agent for a local business—cannot build it reliably if there is no capable model underneath.
It also shapes how people access services and how naturally they can speak to the technology around them.
When I listen to AI-generated speech in African languages today, I often hear the gap: pronunciation that is slightly off, an accent that belongs to someone else, or a delivery without the rhythm of a real speaker.
Something I watched recently made the stakes concrete. It was a demonstration of a system that allowed a person who had lost the ability to speak to communicate again in a synthetic version of their own voice.
Programmes such as ElevenLabs’ Impact Program offer voice-restoration support to people living with conditions such as ALS.
What stayed with me was not only the technical achievement, impressive as it was, but what had been returned to that person.
I found myself asking what the equivalent could mean for someone whose language such a system does not yet speak.
What I’m Building Towards
ElevenLabs is one of the clearest examples I know of carrying research the rest of the way into useful products.
Its work spans speech generation, voice cloning, dubbing, and conversational agents. What inspires me is less any single model than the range of things its products allow people to do.
I want Sauti Labs to make that same connection between serious technical work and useful products—for languages, accents, and settings that existing systems were not designed around, and with the linguistic, social, and technical challenges those contexts bring.
Sauti Labs is an AI research and products company. Its research areas span speech, language, language resources, model intelligence, and responsible AI.
The ambition extends beyond any single capability:
Speech recognition
Natural speech synthesis
Voice cloning and dubbing
Conversational systems and voice agents
Datasets and models
The infrastructure needed to develop and deploy these technologies
It also extends beyond any one language.
Dholuo shaped how I came to this work, but the work is meant for African languages generally.
These capabilities represent a direction, not a claim that every product or system is already complete. There is still a great deal to build, test, and learn.
I am learning from the communities and companies that came before me, and from the work itself.
Why Take the Risk?
Speech and language AI is expensive and research-intensive. It requires data, compute, funding, expertise, and time—precisely the resources many young Africans who recognise these problems clearly do not have.
I decided to take the risk anyway.
I believe these problems are important enough to justify starting early, even with limited resources. I also want people like me to be among those who build these systems, not only among those who receive technologies already designed around other languages and contexts.
This is not a claim that the work will be easy, or that conviction alone is enough. Building capable speech systems requires rigorous research, engineering, evaluation, and sustained investment.
But the difficulty is not a reason to ignore the problem.
I want African languages to take a full part in whatever AI becomes.
That is why Sauti Labs exists.
Sources and Further Reading
Masakhane — Research and community work in African-language NLP.
MasakhaNER: Named Entity Recognition for African Languages — Adelani et al. (2021), Transactions of the Association for Computational Linguistics.
African languages for AI: The project that’s gathering a huge new dataset — The Conversation.
Lelapa AI — African-language AI research and products.
InkubaLM: A Small Language Model for Low-Resource African Languages — arXiv.
Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages — arXiv.
Robust Speech Recognition via Large-Scale Weak Supervision — Radford et al., the Whisper paper.
ElevenLabs Impact Program Announcement — ElevenLabs.
About Sauti Labs: Sauti Labs is working towards making African-language speech and language technology more useful, accessible, and deployable. Its long-term ambition spans research, models, datasets, and products across African languages.
Top comments (0)