A voice agent for a contact centre is not “a model that dials people”. It is a pipeline: speech recognition, request understanding, response and synthesis — all of which must fit within a latency where a human feels no pause. In Uzbek the bottleneck is recognition itself: accents, switching to Russian mid-sentence, banking terminology.
What works out of the box
The base model handles clean studio speech and standard phrases well. For FAQ scenarios — “card balance”, “opening hours”, “branch address” — that is nearly enough. The trouble starts on the phone channel: compressed audio, noise, interruptions.
What we had to push
We raised accuracy on domain vocabulary not by swapping the model but with data: labelled real calls, a glossary of terms, handling of “Uzbek ↔ Russian” code-switching. Latency was a separate fight — streaming recognition instead of waiting for the end of a phrase removes the most noticeable pause.
In voice AI it is not the largest model that wins, but the shortest, most predictable path from sound to answer.
Takeaway for anyone choosing a voice vendor
Ask not “which model do you use” but “on what data is it fine-tuned for our language and our industry” and “what is the latency on the phone channel”. For CIS banks those are the two decisive questions. Exact figures for your environment are confirmed on a demo.