Voice AI Implementation Guide

For quick deployment: Vapi provides an end-to-end platform with built-in telephony, STT, TTS, and LLM integration.

For maximum control: Combine OpenAI Realtime API with Twilio for custom voice experiences.

For cost optimization: Consider hybrid approaches using cheaper providers for simple queries.

Great voice AI starts with great conversation design. Map user intents, design dialogue flows, and plan for edge cases.

Voice is different from text: users speak naturally, often with 'um's, interruptions, and unclear requests.

Design for recovery: when the AI doesn't understand, provide clear paths forward.

Vapi simplifies voice AI development with pre-built components and easy integration.

Define your assistant's personality, knowledge base, and available actions.

Connect to phone numbers, web widgets, or custom endpoints.

// Vapi assistant configuration
{
  "name": "Property Assistant",
  "voice": {
    "provider": "11labs",
    "voiceId": "rachel"
  },
  "model": {
    "provider": "openai",
    "model": "gpt-4o",
    "systemPrompt": "You are a friendly property management assistant..."
  },
  "functions": [{
    "name": "book_viewing",
    "description": "Schedule a property viewing",
    "parameters": {
      "propertyId": "string",
      "preferredDate": "string",
      "contactPhone": "string"
    }
  }]
}

The Realtime API enables true speech-to-speech conversations with GPT-4o. No separate STT/TTS required.

Supports interruption handling, emotional tone, and natural conversation flow.

Best for high-end experiences where voice quality and naturalness are paramount.

Twilio provides the phone infrastructure: numbers, call routing, and PSTN connectivity.

Handle inbound calls (customer support) or outbound campaigns (reminders, follow-ups).

Consider compliance: call recording consent, Do Not Call lists, and regional regulations.

Voice AI agents need to do things: book appointments, look up information, transfer calls.

Define functions the AI can call during conversations. Keep actions simple and reliable.

Always confirm important actions verbally before executing.

Test with real voices and accents. Synthetic test data doesn't catch all issues.

Monitor conversation quality: completion rates, escalation rates, customer satisfaction.

A/B test conversation designs and voice personas.

Plan for scale: voice AI costs can grow quickly with call volume.

Implement fallbacks: human handoff when AI struggles, graceful degradation during outages.

Monitor and iterate: analyze transcripts, identify failure patterns, continuously improve.