Build Voice AI Agents
That Actually Works.
Deep-dive into ASR, NLU, Dialog Management, NLG, and TTS. Explore architectures, code examples, and real-world implementations.
The complete guide to building production-ready Voice AI agents — from architecture to deployment.
Voice AI System Architecture
The complete end-to-end pipeline powering modern voice assistants — from speech input to audio response.
Supporting Infrastructure
Context Management • Knowledge & Tools • Personalization • Safety & Security • Evaluation
Voice AI Pipeline Explained
Every voice assistant follows this 5-stage pipeline. Understanding each component is key to building great voice experiences.
Live Pipeline Processing
The 5 Stages of Voice AI
Click each component to learn more about how it works.
ASR / STT
Automatic Speech Recognition converts raw audio into text using transformers like Whisper.
NLU
Natural Language Understanding extracts intent and entities from transcribed text.
Dialog Manager
The brain of the system. Tracks conversation state and decides next actions.
NLG
Natural Language Generation crafts human-like responses using LLMs.
TTS
Text-to-Speech converts text back to natural, human-like voice.
Infrastructure
Context management, APIs, databases, and real-time streaming.
Build voice agents
with clean APIs.
Simple, intuitive interfaces for complex voice AI. Process speech, extract intent, and generate responses with just a few lines of code.
1 from voice_ai import Agent 2 3 # Initialize the voice agent 4 agent = Agent( 5 asr="whisper-large", 6 nlu="gpt-4", 7 tts="elevenlabs" 8 ) 9 10 # Process voice input 11 response = agent.process(audio) 12 13 print(response.text) 14 # → "Found 3 flights to Paris"
Ready to build Voice AI?
Get in touch for collaborations, speaking engagements, or to chat about voice technology.