ModelRefs / Voice AI — Canonical Workflow
Voice AI — Canonical Workflow
Canonical Voice AI workflow: ASR, LLM, TTS, latency budgets and deployment patterns.
Overview
Voice AI combines speech-to-text, an LLM and text-to-speech behind a low-latency turn-taking pipeline. The canonical stack budgets every hop for sub-second response so conversations feel natural over the phone or in real-time apps.
Implementation profile
| Category | audio-models |
|---|---|
| Implementation maturity | production |
| Primary use cases | customer-support |
| Deployment options | managed-api, edge |
| Architectures | serverless-api, edge-runtime |
Candidate models with published references
- Gemini 3 Pro
- Gemini 2.5 Pro
- Gemini 2.5 Flash
- GPT-4o
- Gemini 2.0 Flash
- Gemini 1.5 Pro
- Gemini 1.5 Flash
Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Voice AI — Canonical Workflow.