Many large enterprises struggle with voice AI that feels slow, sounds robotic, or forces them to sacrifice control over pronunciation. Traditional pipelines run speech recognition first, then feed a text-only model to the LLM, losing tone, hesitation, and uncertainty before the model even sees the input. This cascade adds latency, hurts containment, and makes turn‑taking clumsy. When the model is forced to run continuously, it pins a GPU for the whole call, driving up cost. Finally, most speech‑to‑speech models bake the voice into the network, limiting the ability to swap TTS voices or adjust language without retraining.
PolyAI’s Dialog‑RSN‑1 solves these pains by keeping the audio native at the input side while separating generation from understanding. The model receives raw caller audio, predicts turn‑taking (empty, ongoing, complete) as its first token, and decides in under 300 ms whether to speak, call a function, or generate a response. Because it is probed on demand, a lightweight VAD and timers trigger the model only when needed, so no GPU is tied up for idle periods. The output goes to a separate, promptable TTS system, giving enterprises full control over voice style, language, and pronunciation without retraining the core model.
Early results show sub‑300 ms response times, an 11 % relative lift in containment for a restaurant chain, and a 37 % latency cut for an insurer. The approach works for booking, billing, authentication, call routing, order management, and troubleshooting across restaurants, insurance, finance, healthcare, hotels, retail, telecom, travel, and utilities. Companies already on PolyAI can enable it today; new customers can request early access through the provider’s channel.
For enterprises that need real‑time, controllable voice agents without the overhead of always‑on models or the loss of audio nuance, Dialog‑RSN‑1 offers a practical, deployable path forward.
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