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Introduction

Learn how to build voice-based AI agents in your app. ElevenLabs Conversational AI enables real-time, voice-first AI experiences with natural speech, tool calling, and telephony support. It allows you to create highly realistic voice agents that can interact with users, access data, and perform actions. This is ideal for building assistants, tutors, and phone-based AI systems.

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What You Can Build

With ElevenLabs Conversational AI, your app can support:
  • Voice-Based AI Agents – Create assistants that communicate naturally through speech.
  • AI Tutors and Guides – Build conversational learning experiences.
  • Phone-Ready AI Receptionists – Handle calls, bookings, and customer inquiries.
  • Tool-Connected Voice Agents – Enable agents to fetch data or trigger workflows.
  • Knowledge-Based Assistants – Allow agents to answer questions using structured knowledge.

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How It Works

When ElevenLabs Conversational AI is enabled, your app connects to ElevenLabs’ voice system to handle real-time speech input and output. Your app can:
  • capture user voice input
  • process conversations in real time
  • generate natural-sounding voice responses
  • connect to tools or APIs for dynamic actions
  • integrate with telephony systems for call-based experiences
This allows you to build advanced voice agents without managing complex speech systems.

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Example Prompts

You can use prompts like these to implement features: Add a voice-based AI tutor Add a voice-based AI tutor to my app that can have natural conversations and answer questions using ElevenLabs. Add a phone-ready AI receptionist Add a phone-ready AI receptionist to my app that handles calls and schedules appointments using ElevenLabs Conversational AI.

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Common Use Cases

ElevenLabs Conversational AI is commonly used for:
  • AI voice assistants
  • education and tutoring apps
  • customer support and call handling
  • appointment scheduling systems
  • voice-driven automation tools

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Best Practices

To get the best results:
  • design natural, conversational flows
  • keep responses clear and concise
  • integrate with tools for real functionality
  • optimize latency for smooth interaction
  • provide fallback options for unclear inputs