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Sub-400ms: Why Latency is the Most Important Metric in Conversational AI

When building an AI voice agent, you can have the smartest Large Language Model (LLM) in the world, the most realistic text-to-speech (TTS) voice, and flawless internal logic.

But if it takes your agent 2 seconds to respond to a user, the illusion shatters immediately.

In human conversation, the average gap between speakers taking turns is around 200 to 300 milliseconds. Sometimes, it's even negative (we anticipate and talk over each other). When a machine takes 1.5 seconds to reply, the human caller assumes the machine didn't hear them, repeats themselves, and causes a catastrophic collision of audio.

This is why latency is the single most important metric in voice AI.

The Anatomy of a Voice Turn

To understand why sub-400ms is so difficult, let's look at what happens when a human finishes speaking:

  1. Voice Activity Detection (VAD): The system must realize the human has stopped speaking. This alone usually requires waiting 300ms to ensure they didn't just pause for breath.
  2. Speech-to-Text (STT): The audio must be transcribed into text.
  3. LLM Inference: The text is sent to an LLM to generate the next response.
  4. Text-to-Speech (TTS): The generated text is synthesized back into audio.
  5. Network Transit: Audio packets must be sent back over the telephony network.

In a naive implementation, these steps happen sequentially, resulting in latencies of 1.5 to 3 seconds.

How We Achieve Sub-400ms at Wirevox

To build a truly conversational agent, we had to rethink the entire pipeline:

The Result

The result is a voice agent that feels alive. It can handle interruptions, say "mhm" while you're thinking, and respond with the rapid-fire cadence of a real human conversation.

In the world of AI voice, speed isn't just a feature. It's the entire product.

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