Real-time voice AI is less about adding three APIs together and more about protecting the flow of a conversation. The user speaks, the system transcribes, the model reasons, and the voice responds. If any step drifts, the experience stops feeling alive.
The three-part loop
Speech-to-text turns audio into usable text. The LLM decides what should happen next. Text-to-speech turns the response back into a voice. The hard part is orchestration: each part has different latency, payload, retry, and streaming behavior.
Latency is a product feature
For a normal web app, a slow request is annoying. For voice AI, a slow response feels like silence. I think about latency as part of the interface: partial transcription, streaming model output, and early TTS generation all help the product feel more responsive.
What I watch in production
- Audio chunk size and streaming boundaries.
- Error recovery when one provider stalls.
- Prompt size and model response speed.
- The handoff between LLM output and TTS playback.
- Guardrails around private or proprietary data.
The best voice systems feel simple to the user because the complexity is hidden behind disciplined infrastructure.