Advanced Voice AI

Building Voice AI Agents in Production

A hands-on engineering course on production voice AI as it stands in 2026: streaming ASR, LLM-driven conversational cores, streaming TTS, full-duplex real-time transport, native speech-to-speech models, evaluation and guardrails, and the cost and scaling engineering that separates a demo from a system real users can call. Written for teams shipping voice agents, not for notebook prototypes. Includes real, worked examples against NextNeural's voice agent API alongside Deepgram, ElevenLabs, Cartesia, and the OpenAI and Gemini realtime APIs.

25 Modules
4.0h Duration
Text + Code Format
Free Cost
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Prerequisites
  • Comfortable Python (async, type hints, WebSocket/HTTP clients)
  • Basic familiarity with LLM APIs and streaming responses
  • Working knowledge of audio fundamentals (sample rates, codecs) is helpful but not required

What you'll learn

  • Decide between cascaded (ASR-LLM-TTS) and native speech-to-speech architectures for a given latency and control budget
  • Build streaming ASR pipelines with real-time partial transcripts, noise robustness, and semantic turn detection
  • Design the conversational core: system prompts for spoken language, mid-call function calling, and long-session memory
  • Engineer streaming, chunked TTS with production voice engines, including cloning consent and brand-voice design
  • Build full-duplex, low-latency transport over WebRTC and WebSockets with barge-in and interruption handling
  • Ship native speech-to-speech integrations with the 2026 real-time model APIs and know when they beat a cascaded stack
  • Evaluate voice agent quality end to end: WER, latency percentiles, task success, and voice-specific guardrails
  • Model per-minute cost, scale concurrent sessions, and deploy across telephony, web, and mobile channels
  • Design and present a complete production voice support agent end to end in the capstone project

Course Curriculum

25 modules · 4.0 hours total

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