Real voice, not synthetic
Why we build speech models from real, consented conversations rather than synthetic or scraped data — and why that matters most in growing markets.
The shortcut everyone takes
When a language is underserved by speech technology, the tempting shortcut is to manufacture data: synthesise speech, scrape whatever recordings exist online, or translate text corpora and hope the acoustics follow. It is fast, it is cheap, and it produces impressive-looking dataset sizes.
We don't do this, and this is why.
Synthetic audio learns the wrong distribution
A model trained on synthetic or clean web audio learns the statistics of that audio — studio acoustics, careful pronunciation, broadband frequencies. Real customers call from a market stall, a moving car, a noisy office, over a compressed 8 kHz line, code-switching between languages, interrupting and being interrupted. The gap between the two distributions is not a detail you can fine-tune away cheaply; it is the whole problem. A model that has never heard the real distribution will fail on it in production, no matter how large its synthetic training set was.
Consent is a first principle, not a compliance afterthought
Scraped data carries a second problem beyond quality: no one consented to it. We take the opposite stance. Every hour we train on comes from a real conversation on an account that explicitly opted in, and every segment is de-identified before anyone sees it. Those rules are enforced in our data layer, not just promised in a policy — a segment cannot become training-ready unless it is both consented and redacted.
This is more work. It is also the only version of this that we think is defensible, and the only version that a serious partner — a bank, a telco, an enterprise — can actually build on.
The data is the moat
Model architectures are shared; the open engines we build on are available to everyone. What is not available to everyone is a large, consented, human-verified corpus of real Nigerian telephony speech. That asset is slow to build and cannot be shortcut, which is exactly what makes it durable. It is reusable across every model we train, and it is valuable in its own right.
We start where the need is greatest
We begin with Hausa, Yoruba, Igbo and Pidgin in Nigeria — languages spoken by hundreds of millions of people and served poorly, if at all, by mainstream speech technology. The same approach extends to other growing markets across Africa, the GCC, Asia and Latin America, where the pattern is identical: real voices, real networks, real need, and almost no training data built for them.
An open approach
We publish our methods, our data practices and our progress as we go — including what does not work yet. Growing markets have been an afterthought in speech AI for too long. Building the voice layer they deserve starts with being honest about how it is built.