Voice data for how people actually talk.

Curated conversational audio in code-switched languages — Hinglish, Taglish, Nigerian Pidgin English — for the teams training the next generation of voice AI.

The gap

Two billion people switch languages mid-sentence every day. The audio data to train models on how they actually speak doesn’t exist. Less than 100 hours of natural Hinglish is publicly available — and similar gaps exist for every other code-switched pair.

STT and TTS models trained on monolingual corpora break the moment users speak naturally. We close that gap with consented, native-speaker, mobile-recorded audio.

What we deliver

Talk to us

If you’re building voice AI for emerging-market users — or you’ve hit the multilingual ceiling on your current dataset — we have samples ready.