The complete deployment guide: Docker multi-stage builds, Kubernetes orchestration, CI/CD with GitHub Actions, zero-downtime deploys, go-live checklist, production monitoring with Prometheus/Grafana, and the operational runbook that keeps voice AI running at scale.
Multi-language voice AI for research: language detection, provider routing (Gemini Live for 30+ languages, OpenAI Realtime for English), locale-aware VAD tuning, i18n prompt packs, and cross-language analysis pipelines.
Scaling from 10 sessions/week to 200 concurrent. The enrichment bottleneck (30,000 API calls), session recovery for dropped WebRTC connections, provider failover, and the operational metrics that keep it all visible.
Real-time per-minute cost tracking, provider comparison (OpenAI Realtime ~$0.053/min vs Gemini Live ~$0.029/min), budget enforcement with soft/hard limits, and the self-hosting math that saves 90% on transport.
The 3-stage automatic pipeline that turns raw interview recordings into enriched, queryable research data in 3-7 minutes. Transcription, enrichment, analysis — with the transcript batching trick that cut DB load by 80%.
Research interviews follow structured protocols with distinct phases. How to build an LLM-driven state machine with next_phase() function calling and dynamic instruction swapping via set_chat_ctx().
The production pain points nobody warns you about: zombie agents, metadata latency, pre-warming for 1-2s time-to-first-voice, VAD tuning for research respondents, and provider quirks.
Why research interviews need server-side voice agents, the three-tier architecture, room metadata as configuration transport, and the 100-500ms propagation latency nobody tells you about.
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