AI agents for concierge customer experiences
San Francisco
Based in United States
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Conversational voice agents need to know when a different person is speaking, but not every background voice should count.
We combined speaker embeddings with a post-trained audio-language model to determine when a speaker change matters.
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Fast text serving ≠ fast speech.
Here's how we got our time to first audio under 30ms with nearly 10x more audio throughput.
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Modern TTS models can sound great — and still fail badly on pacing, pauses, and prosody.
We adapted DPO + GRPO to flow-matching models to tackle the tail end of TTS behavior:
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