Voice AI engineering, written by people building it

How to instrument real-time speech recognition so your latency data reflects what callers actually experience, not just what the model reports.

The engineering decisions behind Smallest AI's text-to-speech latency target: streaming chunk size, buffer management, and inference scheduling.

Batch recognition looks cheaper on paper. Here is why streaming is the only viable path for interactive voice agents and where the latency costs actually live.

Six months of early-access feedback from voice product teams showed us which latency problems matter most and which ones you can safely defer.

From mismatched audio sample rates to unbuffered partial transcripts, the integration mistakes that add hundreds of milliseconds without anyone noticing.

The latency budget for a voice agent is the sum of three sequential components. Here is how to evaluate each leg independently and where the budget breaks down.

A practical framework for allocating milliseconds across STT, network, LLM, and TTS so you know exactly where your agent stands before deployment.

Callers interpret silence as system failure. The data on how much latency the human ear tolerates and what teams building phone bots need to target.

Protocol choice affects tail latency more than most teams expect. A comparison of WebSocket and gRPC for bidirectional audio in voice agent deployments.

8kHz narrowband, G.711 codec artifacts, line noise, and DTMF tones: the audio quality issues that degrade transcription accuracy in production phone systems.

Barge-in detection, partial transcript cancellation, and TTS abort: the mechanics of making a voice agent respond naturally when a caller talks over it.

What it takes to replace a DTMF menu system with a conversational voice agent while keeping response latency in the range callers tolerate.

CPU vs GPU inference trade-offs, concurrency limits, network topology, and the operational model that keeps voice agents responsive under real call volume.