ElevenLabs Conversational AI: Deploy Production-Ready Voice Agents in Minutes
Executive Summary
ElevenLabs Conversational AI represents a quantum leap in voice agent development, transforming what once required months of specialized engineering into a platform where production-grade conversational agents can be deployed in minutes. Built by ElevenLabs—the company that revolutionized AI voice synthesis—this platform combines sub-100ms latency voice interactions, automatic multilingual language detection across 32+ languages, integrated knowledge base retrieval (RAG), and enterprise-grade security into a unified system that eliminates the complexity barrier that has historically prevented most organizations from deploying voice AI.
The platform's fundamental innovation is architectural: instead of requiring developers to assemble and maintain a complex stack of separate services (speech-to-text, large language model, retrieval system, text-to-speech, telephony integration), ElevenLabs provides a complete, optimized voice agent infrastructure where you simply configure your agent's behavior, connect your knowledge base, and deploy. The system handles the intricate coordination between components, manages latency optimization, and provides built-in monitoring—all while delivering conversational quality that users describe as indistinguishable from human interactions.
With support for leading LLM providers (Claude, GPT-4, Gemini) or custom model integration, a library of 5,000+ voices across 32 languages, automatic language switching mid-conversation, and sophisticated turn-taking models that understand natural conversational rhythm, ElevenLabs Conversational AI addresses the full spectrum of voice agent requirements. Whether you're building customer support agents, phone-based services, virtual assistants, or conversational interfaces for accessibility, the platform provides the infrastructure and intelligence needed for production deployment.
For organizations that have attempted to build voice agents using tools like OpenAI's Realtime API, Deepgram, or combinations of separate services, ElevenLabs offers a cohesive alternative that dramatically reduces engineering complexity while delivering superior voice quality and lower latency. The platform's success is evidenced by real-world deployments handling hundreds of calls daily with 80%+ resolution rates, demonstrating that AI voice agents have crossed the threshold from experimental technology to reliable business infrastructure.
The Voice AI Challenge
Understanding the Problem
Building production-quality conversational voice agents has historically been one of the most complex AI implementation challenges, requiring expertise across multiple domains and integration of numerous specialized services:
The Multi-Service Integration Nightmare: A functional voice agent requires orchestrating at least five distinct services in real-time:
- 1. Speech-to-Text (STT): Convert user speech to text with low latency and high accuracySpeech-to-Text (STT): Convert user speech to text with low latency and high accuracy
- 2. Natural Language Understanding: Process transcribed text to extract intent and entitiesNatural Language Understanding: Process transcribed text to extract intent and entities
- 3. Large Language Model: Generate contextually appropriate responsesLarge Language Model: Generate contextually appropriate responses
- 4. Knowledge Retrieval (RAG): Search knowledge bases for relevant information to inform responsesKnowledge Retrieval (RAG): Search knowledge bases for relevant information to inform responses
- 5. Text-to-Speech (TTS): Convert generated responses back to natural-sounding speechText-to-Speech (TTS): Convert generated responses back to natural-sounding speech
- 6. Telephony Integration: Handle phone calls, manage connections, deal with call quality issuesTelephony Integration: Handle phone calls, manage connections, deal with call quality issues
Each service introduces latency, requires API management, has distinct error modes, and adds cost. Coordinating these services to produce conversations that feel natural (not robotic pauses between components) requires sophisticated engineering:
Traditional Voice Agent Architecture:
User Speech → STT Service (200-400ms)
↓
Transcript
↓
NLU Processing (50-100ms)
↓
Intent/Entities
↓
RAG System (200-500ms)
↓
Context Retrieved
↓
LLM Generation (1-3 seconds)
↓
Response Text
↓
TTS Service (500-1000ms)
↓
Audio Response
Total latency: 2.5-5+ seconds
This latency creates conversations that feel stilted and unnatural. Users expect voice interactions to flow like human conversation with minimal delay—anything over 1 second becomes noticeably robotic.
The Turn-Taking Problem: Human conversation relies on subtle cues for turn-taking: pauses, intonation changes, filler words ("um," "uh"), and breathing patterns signal when someone is finished speaking or just pausing to think. Voice agents must interpret these cues accurately to avoid:
- •Interrupting users: Cutting off mid-sentence when the user pauses briefly
- •Long awkward silences: Waiting too long after the user finishes, creating dead air
- •Talking over users: Starting to respond while the user is still speaking
- •Missing interruptions: Continuing to speak when the user tries to interrupt
Building robust turn-taking models requires extensive training data and sophisticated audio analysis—non-trivial for most development teams.
The Multilingual Complexity: Global applications require multilingual support, but traditional approaches force developers to:
- •Detect language before starting conversation (adds friction)
- •Build separate voice agents for each language
- •Require users to explicitly select their language
- •Lose context when switching languages mid-conversation
True multilingual voice agents should seamlessly handle language switching: a user asks a question in English, continues in Spanish, and the agent responds appropriately in each language without manual language selection.
The Knowledge Grounding Challenge: Voice agents without knowledge grounding produce generic responses or hallucinate information. Integrating retrieval-augmented generation (RAG) requires:
- •Chunking and embedding your knowledge base (documents, FAQs, help articles)
- •Deploying a vector database
- •Implementing semantic search
- •Managing context window limitations (LLMs can't process infinite context)
- •Handling retrieval failures gracefully
- •Optimizing for low latency (retrieval adds 200-500ms)
Many organizations abandon voice agents at this stage due to the operational complexity of maintaining a RAG system alongside the voice pipeline.
The Voice Quality and Diversity Problem: Early TTS systems produced robotic, monotone voices that screamed "I'm a bot!" Modern voice agents require:
- •Natural prosody (rhythm, stress, intonation patterns)
- •Emotional expressiveness appropriate to context
- •Multiple voice options for branding and user preference
- •Consistent quality across languages
- •Low latency without sacrificing quality
Achieving this typically means either compromising on quality for speed or accepting high latency for better voice quality—a lose-lose trade-off.
Why ElevenLabs Conversational AI Matters
ElevenLabs eliminates these challenges through a purpose-built platform that handles the entire voice agent stack as an optimized, integrated system. The platform's key innovations address each historical pain point:
Unified Voice Pipeline with Sub-100ms Latency: Instead of chaining separate services, ElevenLabs orchestrates STT, LLM, RAG, and TTS as a single optimized pipeline. The system uses streaming architectures, predictive processing, and intelligent caching to achieve end-to-end latencies under 100ms—fast enough to feel like natural conversation.
Advanced Turn-Taking Model: ElevenLabs' proprietary turn-taking model uses acoustic features, prosodic cues, and linguistic patterns to accurately determine when users have finished speaking. The model understands that a pause after "I want to..." is likely mid-sentence, while a pause after "That's all I need, thanks" signals completion. This intelligence creates conversations that flow naturally without awkward interruptions or delays.
Automatic Language Detection and Switching: The platform's multilingual engine detects spoken language in real-time and switches seamlessly. A conversation can begin in English, switch to Spanish, then Mandarin, and back to English—the agent adapts dynamically without explicit language selection or configuration. This works across 32 languages simultaneously, enabling truly global deployments from a single agent.
Integrated RAG with Low-Latency Retrieval: Knowledge base integration is built into the platform, not bolted on. Upload documents, configure chunking strategies, and the system handles embedding, indexing, and retrieval optimization. The retrieval system is co-optimized with the voice pipeline to minimize latency while maintaining high retrieval quality.
ElevenLabs Voice Quality at Scale: Leveraging ElevenLabs' industry-leading TTS technology, the platform provides 5,000+ voices across 32 languages with prosody and expressiveness that rivals human speech. The Eleven Flash v2.5 model delivers this quality with ~75ms latency—fast enough for real-time conversation without sacrificing naturalness.
Production-Ready Infrastructure: The platform includes monitoring, analytics, conversation logging, rate limiting, error handling, and scalability infrastructure out-of-the-box. What would typically require months of DevOps work to productionize is provided as managed infrastructure.
The result: developers can focus on defining agent behavior, knowledge base content, and conversational flows rather than wrestling with infrastructure complexity. This abstraction doesn't sacrifice flexibility—the platform supports custom LLMs, sophisticated prompt engineering, tool/function calling, and dynamic agent instantiation for advanced use cases.
Key Features and Capabilities
Sub-100ms Latency Voice Interaction
End-to-End Optimization: ElevenLabs achieves conversational latency through architectural optimizations across the entire pipeline:
Streaming Architecture: Instead of waiting for complete utterances, the system processes speech incrementally:
Traditional: Wait for complete speech → transcribe all → generate → synthesize all
ElevenLabs: Stream speech chunks → transcribe incrementally → start generation
while receiving more speech → stream TTS output while generating
Result: Response audio begins playing while user is still speaking
(when appropriate based on turn-taking model)
Predictive Processing: The system anticipates common response patterns and pre-computes portions of likely responses based on conversation context, dramatically reducing perceived latency for common queries.
Intelligent Caching: Frequently accessed knowledge base chunks, common responses, and voice synthesis for standard phrases are cached at multiple levels, eliminating redundant processing.
Optimized Model Selection: The platform automatically selects the fastest model that meets quality requirements:
- •Simple queries → Fast models (GPT-3.5 Turbo, Claude Haiku)
- •Complex reasoning → Capable models (GPT-4, Claude Sonnet)
- •Voice synthesis → Eleven Flash v2.5 (75ms latency, high quality)
Latency Budgets by Component:
Component Latency Budget ElevenLabs Achievement
Speech-to-Text (streaming) 30-50ms ~40ms
Intent Understanding 10-20ms ~15ms
Knowledge Retrieval 50-100ms ~60ms
LLM Generation (streaming) 200-500ms ~250ms
Text-to-Speech (streaming) 100-200ms ~75ms
Network/Processing 20-50ms ~30ms
--------------------------------
Total (target) <1000ms ~470ms average
This sub-500ms average latency creates conversations that feel instantaneous to users.
Latency Monitoring and Optimization: The platform provides real-time latency dashboards showing p50, p95, and p99 latencies for each component, enabling continuous optimization.
Multilingual Voice Agents with Automatic Language Detection
Real-Time Language Switching: Unlike systems requiring manual language selection, ElevenLabs agents detect and adapt to language changes automatically:
Example Conversation:
User (English): "Hello, I need help with my account"
Agent (English): "Hello! I'd be happy to help with your account. What do you need?"
User (switches to Spanish): "Quiero cambiar mi dirección de correo electrónico"
Agent (detects Spanish, switches): "Claro, puedo ayudarte a cambiar tu dirección
de correo electrónico. ¿Cuál es la nueva dirección que te gustaría usar?"
User (switches to English): "Actually, can you also help me update my phone number?"
Agent (switches back): "Of course! I can help you update both your email and phone
number. What's your new phone number?"
No configuration required—the agent automatically detects language from speech prosody, phonetics, and linguistic patterns.
Supported Languages (32 total):
European: English, Spanish, French, German, Italian, Portuguese, Dutch, Polish,
Swedish, Norwegian, Danish, Finnish, Czech, Romanian, Greek, Ukrainian
Asian: Mandarin Chinese, Japanese, Korean, Hindi, Tamil, Indonesian, Malay, Thai,
Vietnamese, Filipino/Tagalog
Other: Arabic, Turkish, Hebrew, Russian
Language-Specific Optimizations:
- •Accent Handling: Accurately recognizes regional accents (US English, UK English, Australian English, etc.)
- •Code-Switching: Handles mixed-language sentences common in multilingual contexts
- •Cultural Context: Understands culturally-specific phrases and conventions
- •Tone and Formality: Adapts to language-specific formality levels (tu/vous in French, formal/informal in Japanese)
Business Impact: Deploy a single agent that serves global customers in their native languages without building separate agents or requiring language selection menus.
Integrated Knowledge Base (RAG)
Simplified Knowledge Integration: Traditional RAG implementations require managing vector databases, embedding models, chunking strategies, and retrieval optimization. ElevenLabs abstracts this complexity:
Setup Process:
1. Upload Documents:
- PDF documents (manuals, guides)
- Text files (FAQs, policies)
- Web URLs (documentation sites)
- Structured data (JSON, CSV)
- 2. Configure Chunking (optional, intelligent defaults):Configure Chunking (optional, intelligent defaults):
- Chunk size: 512 tokens (configurable)
- Overlap: 50 tokens (prevents context loss at boundaries)
- Metadata: Extract titles, sections, dates
- 3. Automatic Indexing:Automatic Indexing:
- System embeds chunks using optimized embedding model
- Creates vector index for semantic search
- Generates keyword index for exact matching
- Builds metadata filters for scoped retrieval
- 4. Agent Configuration:Agent Configuration:
- Top-K: Retrieve 5 most relevant chunks (configurable)
- Similarity threshold: 0.7 minimum (filters low-quality matches)
- Re-ranking: Enable cross-encoder re-ranking for improved relevance
- Citation style: Include source references in responses
Hybrid Retrieval: The system combines multiple retrieval strategies for optimal results:
Semantic Search: Vector similarity for conceptual matching
- •Query: "How do I reset my password?"
- •Matches: "Password recovery instructions," "Account security settings," "Login troubleshooting"
Keyword Search: BM25-style exact matching for technical terms
- •Query: "What is the API rate limit?"
- •Matches: Exact mentions of "API rate limit" even if semantically distant chunks also discuss "limits"
Metadata Filtering: Scope retrieval to specific document types, dates, or categories
- •Query: "What's our return policy?" + metadata filter:
document_type = "policies" - •Matches: Only chunks from policy documents, ignoring other mentions of "returns"
Retrieval Quality Optimization:
Example knowledge base configuration
{
"knowledge_base": {
"sources": [
"s3://my-kb/documentation/**",
"https://docs.mycompany.com",
"local://kb-files/faqs.pdf"
],
"retrieval_config": {
"top_k": 5,
"similarity_threshold": 0.7,
"enable_reranking": true,
"enable_hyde": false, # Hypothetical Document Embeddings (advanced)
"max_context_length": 4000 # tokens to include from retrieval
},
"update_strategy": "incremental", # or "full_refresh"
"update_schedule": "0 2 * * *" # Daily at 2 AM
}
}
Dynamic Knowledge Updates: Update your knowledge base without redeploying agents:
- •Add new documents: Automatically indexed and available within minutes
- •Update existing content: Version control tracks changes, agents use latest
- •Remove outdated information: Automatic pruning of deleted/expired content
Real-World Application: ElevenLabs deployed their own conversational agent for documentation support. The agent, embedded directly in their documentation site, successfully handles over 80% of user inquiries across 200+ calls per day by retrieving relevant information from their knowledge base and formulating helpful responses.
Flexible LLM Integration
Supported LLM Providers: Choose from leading large language models based on your requirements:
OpenAI:
- •GPT-4 Turbo: Best reasoning and complex task handling
- •GPT-4: Balanced performance and capability
- •GPT-3.5 Turbo: Fast, cost-effective for straightforward queries
Anthropic:
- •Claude 3.5 Sonnet: Excellent instruction following and nuanced responses
- •Claude 3 Opus: Highest capability for complex reasoning
- •Claude 3 Haiku: Fast, efficient for simple interactions
Google:
- •Gemini Pro 1.5: Strong multimodal understanding
- •Gemini Pro: Balanced performance
- •Gemini Flash: Low latency for real-time use
Custom LLM Integration: For specialized requirements or proprietary models:
Custom LLM server integration
{
"llm_config": {
"type": "custom",
"endpoint": "https://my-llm-server.com/v1/chat/completions",
"auth": {
"type": "bearer_token",
"token": "${CUSTOM_LLM_TOKEN}"
},
"model": "my-fine-tuned-model-v3",
"streaming": true,
"timeout_ms": 5000
}
}
This enables:
- •Fine-tuned models for domain-specific language
- •On-premise LLM deployment for data sovereignty
- •Specialized models (medical, legal, technical)
- •Cost optimization through local models
Model Selection Strategy: Optimize cost and performance by routing queries to appropriate models:
{
"model_routing": {
"default": "gpt-3.5-turbo",
"routing_rules": [
{
"condition": "user_query_length > 200 OR conversation_history_length > 5",
"model": "gpt-4-turbo", # Use smarter model for complex conversations
"reason": "Complex query or extended conversation"
},
{
"condition": "requires_knowledge_base = true",
"model": "claude-3.5-sonnet", # Claude excellent at knowledge synthesis
"reason": "Knowledge retrieval query"
},
{
"condition": "language != 'english'",
"model": "gpt-4-turbo", # Better multilingual performance
"reason": "Non-English language"
}
]
}
}
Advanced Voice Library
5,000+ Voices Across 32 Languages: ElevenLabs' extensive voice library provides unprecedented choice for branding and user experience:
Voice Categories:
Professional: Business-appropriate, clear, authoritative
- •Use case: Customer support, corporate communications, IVR
Conversational: Warm, friendly, approachable
- •Use case: Virtual assistants, casual interactions, companion apps
Character: Unique personalities and tones
- •Use case: Gaming NPCs, entertainment, storytelling
Multilingual: Native speakers for each supported language
- •Use case: Global customer support, international services
Voice Customization: Fine-tune voice characteristics to match brand identity:
{
"voice_config": {
"voice_id": "21m00Tcm4TlvDq8ikWAM", # Professional female voice
"stability": 0.75, # 0.0 = maximum expressiveness, 1.0 = maximum stability
"similarity_boost": 0.85, # How closely to match training voice
"style": 0.5, # Exaggeration of style (0 = neutral, 1 = maximum style)
"use_speaker_boost": true # Enhance vocal clarity
}
}
Multi-Voice Agents: Use different voices for different agent roles or moods:
{
"voice_strategy": {
"default_voice": "professional-female",
"voice_switching": [
{
"condition": "greeting OR farewell",
"voice": "warm-friendly",
"reason": "Create welcoming first/last impression"
},
{
"condition": "delivering_bad_news OR error_occurred",
"voice": "empathetic-calm",
"reason": "Soften negative information"
},
{
"condition": "user_sentiment = angry",
"voice": "professional-calm",
"reason": "De-escalate emotional conversations"
}
]
}
}
Custom Voice Cloning (Enterprise): Create brand-specific voices by cloning existing speakers:
- •Clone CEO voice for executive communications
- •Clone brand spokesperson for consistent brand voice
- •Clone multiple team members for personalized interactions
Requires ~10 minutes of clear audio recording from target speaker.
Tool Calling and Function Integration
Server-Side Tools: Agents can call external functions to access data or trigger actions:
Example: Weather information tool
{
"tools": [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather information for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, e.g., San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"default": "fahrenheit"
}
},
"required": ["location"]
}
},
"endpoint": "https://my-api.com/weather"
}
]
}
Conversation example:
User: "What's the weather in San Francisco?"
Agent: [Calls get_weather function with location="San Francisco, CA"]
Agent: "The current weather in San Francisco is 62°F with partly cloudy skies."
Client-Side Tools: Trigger actions in client applications:
// JavaScript client example
const agent = new ElevenLabsAgent({
agentId: 'your-agent-id',
onToolCall: async (toolName, parameters) => {
// Handle tool calls in client application
if (toolName === 'open_settings') {
window.location.href = '/settings';
return { success: true };
} else if (toolName === 'search_products') {
const results = await searchProducts(parameters.query);
return { results };
}
}
});
Common Tool Use Cases:
- •CRM Integration: Look up customer information, create tickets, update records
- •Database Queries: Fetch user data, order history, account status
- •Transaction Processing: Initiate payments, process refunds, update subscriptions
- •Calendar Operations: Schedule appointments, check availability, send reminders
- •Email/SMS: Send notifications, confirmations, follow-ups
- •Third-Party APIs: Weather, maps, stock prices, news, translations
Tool Calling with Knowledge Base: Combine tools with knowledge retrieval for powerful hybrid agents:
User: "What's your return policy for items over $100?"
Agent:
1. Retrieves return policy from knowledge base
2. Extracts relevant information about high-value items
3. Calls get_customer_order() tool to check user's order history
4. Formulates response: "According to our policy, items over $100 can be
returned within 60 days. I see you purchased a $150 jacket last week—
you have until March 15 to return it if needed."
Dynamic Agent Configuration
Runtime Agent Instantiation: Create or modify agents dynamically based on context:
Example: Personalized agent per customer segment
import elevenlabs
def create_agent_for_user(user_profile):
# Configure agent based on user attributes
config = {
"agent_id": f"agent-{user_profile['id']}",
"voice_id": select_voice_for_demographic(user_profile['age'], user_profile['gender']),
"system_prompt": generate_personalized_prompt(user_profile),
"knowledge_base_filters": {
"customer_tier": user_profile['tier'], # VIP vs Standard
"region": user_profile['region'] # Regional-specific information
},
"llm_config": {
"model": "gpt-4-turbo" if user_profile['tier'] == 'VIP' else "gpt-3.5-turbo",
"temperature": 0.7
}
}
return elevenlabs.create_agent(config)
Usage
agent = create_agent_for_user(user_profile)
conversation = agent.start_conversation(phone_number=user_profile['phone'])
Agent Overrides: Modify agent behavior mid-conversation based on context:
Example: Escalation to supervisor mode
if user_sentiment == "angry" and conversation_length > 5:
agent.override({
"system_prompt": "You are a senior support specialist. Prioritize de-escalation...",
"llm_config": {"model": "gpt-4-turbo", "temperature": 0.5}, # More careful responses
"enable_supervisor_tools": true # Access to refund/escalation tools
})
A/B Testing Agents: Compare agent configurations to optimize performance:
{
"experiment": {
"name": "prompt_variation_test",
"variants": [
{
"name": "control",
"weight": 0.5,
"config": {
"system_prompt": "You are a helpful customer support agent...",
"temperature": 0.7
}
},
{
"name": "empathy_focused",
"weight": 0.5,
"config": {
"system_prompt": "You are an empathetic support specialist who...",
"temperature": 0.8
}
}
],
"metrics": ["resolution_rate", "customer_satisfaction", "call_duration"],
"duration_days": 14
}
}
Getting Started with ElevenLabs Conversational AI
Account Setup and API Access
Step 1: Create Account:
- •Visit https://elevenlabs.io
- •Sign up with email or Google/GitHub SSO
- •Verify email address
Step 2: Choose Plan:
Free Tier:
- •10,000 characters/month TTS
- •Basic agent capabilities
- •Testing and development use
Starter Plan ($5-22/month):
- •30,000 - 100,000 characters/month
- •Full agent features
- •Multiple agents
- •Basic analytics
Creator Plan ($22-99/month):
- •100,000 - 500,000 characters/month
- •Voice cloning (custom voices)
- •Advanced analytics
- •Priority support
Professional Plan ($99-330/month):
- •500,000 - 2M characters/month
- •Unlimited agents
- •Phone integration
- •Team collaboration
Enterprise (Custom):
- •Volume pricing
- •Dedicated support
- •Custom SLA
- •On-premise options
Step 3: Get API Key:
- •Navigate to Profile Settings
- •Click "API Key"
- •Generate new key
- •Store securely (treat like a password)
Creating Your First Voice Agent
Quick Start via Web Interface:
Step 1: Navigate to Agents Platform:
- •From dashboard, click "Conversational AI"
- •Click "Create New Agent"
Step 2: Basic Configuration:
Agent Name: "Customer Support Bot"
System Prompt:
"You are a friendly and professional customer support agent for Acme Inc.
Your role is to:
- •Answer questions about products and services
- •Help troubleshoot common issues
- •Route complex problems to human agents
- •Maintain a helpful, patient, and positive tone
Always introduce yourself as 'Alex from Acme Support' at the start
of conversations."
Voice Selection:
- •Browse voice library
- •Filter by: Language (English), Style (Professional), Gender (Female)
- •Preview voices
- •Select: "Sarah - Professional Female"
LLM Selection:
- •Provider: OpenAI
- •Model: GPT-4 Turbo
- •Temperature: 0.7 (balanced creativity and consistency)
Step 3: Add Knowledge Base:
Click "Add Knowledge Base"
Upload sources:
- •Product documentation (PDF)
- •FAQ document (text file)
- •Company policies (PDF)
- •Troubleshooting guides (PDF)
Total: 50 pages of content
Indexing: Automatic (takes 2-5 minutes)
Status: ✓ Knowledge base ready
Step 4: Configure Tools (Optional):
Add Tool: "Check Order Status"
Tool Type: API Call
Endpoint: https://api.acme.com/orders/{order_id}
Method: GET
Authentication: Bearer token
Parameters:
- •order_id (string, required): Customer's order ID
Returns: Order status, tracking number, estimated delivery
Description for LLM:
"Use this tool when a customer asks about their order status.
Ask for their order ID if they haven't provided it."
Step 5: Test Your Agent:
Click "Test Agent"
Test conversation:
You: "Hello, I'd like to check on my order"
Agent: "Hello! I'm Alex from Acme Support. I'd be happy to help you check
on your order. Could you please provide your order ID?"
You: "It's ORDER-12345"
Agent: [Calls check_order_status tool]
"Thank you! I've found your order. It's currently in transit and
scheduled for delivery tomorrow, November 8th. Your tracking
number is 1Z999AA1012345678."
You: "Great, thanks!"
Agent: "You're welcome! Is there anything else I can help you with today?"
Step 6: Deploy:
Deployment Options:
- 1. Web Widget:Web Widget:
- Copy embed code
- Add to website
- Users can click to start voice conversation
- 2. Phone Number:Phone Number:
- Select country and area code
- ElevenLabs provisions number (takes ~5 minutes)
- Incoming calls automatically route to agent
- Cost: $5-10/month per number + per-minute usage
- 3. API Integration:API Integration:
- Get agent ID and API key
- Integrate with custom application
- Full programmatic control
- 4. Webhook:Webhook:
- Provide webhook URL
- Receive call events and transcripts
- Trigger agent from external systems
Code-Based Agent Creation
Python SDK Example:
from elevenlabs import ElevenLabs, ConversationalAgent
Initialize client
client = ElevenLabs(api_key="your_api_key_here")
Create agent
agent = client.create_conversational_agent(
name="Customer Support Agent",
system_prompt="""
You are a helpful customer support agent for TechCorp.
You assist customers with:
- Account questions
- Billing inquiries
- Technical troubleshooting
- Product information
Always be professional, patient, and concise.
If you cannot help, offer to transfer to a human agent.
""",
voice_id="21m00Tcm4TlvDq8ikWAM", # Professional female voice
llm_config={
"provider": "openai",
"model": "gpt-4-turbo",
"temperature": 0.7,
"max_tokens": 500 # Limit response length for voice
},
first_message="Hello! This is Emma from TechCorp support. How can I help you today?",
language="en" # Primary language (auto-detects others)
)
print(f"Agent created with ID: {agent.id}")
Add knowledge base
knowledge_base = client.create_knowledge_base(
name="TechCorp Support KB",
sources=[
"https://docs.techcorp.com",
"/local/path/to/faq.pdf",
"/local/path/to/user_manual.pdf"
]
)
Attach knowledge base to agent
agent.attach_knowledge_base(knowledge_base.id)
Add tools
agent.add_tool({
"type": "function",
"function": {
"name": "get_account_balance",
"description": "Retrieve the current account balance for a customer",
"parameters": {
"type": "object",
"properties": {
"customer_id": {
"type": "string",
"description": "The customer's account ID"
}
},
"required": ["customer_id"]
}
},
"endpoint": "https://api.techcorp.com/balance"
})
Deploy to phone
phone_deployment = agent.deploy_to_phone(
country_code="+1",
area_code="415" # San Francisco area code
)
print(f"Agent deployed! Call: {phone_deployment.phone_number}")
Monitor conversations
for conversation in agent.get_conversations(limit=10):
print(f"Call ID: {conversation.id}")
print(f"Duration: {conversation.duration_seconds}s")
print(f"Resolution: {conversation.metadata.resolved}")
print(f"Transcript: {conversation.transcript}")
JavaScript/TypeScript SDK Example:
import { ElevenLabs, VoiceAgent } from '@elevenlabs/sdk';, voiceId: '21m00Tcm4TlvDq8ikWAM', llmConfig: { provider: 'anthropic', model: 'claude-3-5-sonnet', temperature: 0.8 // More conversational for hospitality }, firstMessage: 'Hello! Welcome to Bella Vista. How may I assist you with your reservation today?' });const client = new ElevenLabs({ apiKey: process.env.ELEVEN_API_KEY });
// Create agent const agent = await client.conversationalAI.createAgent({ name: 'Restaurant Reservation Agent', systemPrompt:
You are a friendly restaurant reservation assistant. Help customers: - Check table availability - Make reservations - Modify existing reservations - Answer questions about the restaurantAlways confirm reservation details before booking.
// Add custom tool for checking availability await agent.addTool({ type: 'function', function: { name: 'check_availability', description: 'Check table availability for a specific date, time, and party size', parameters: { type: 'object', properties: { date: { type: 'string', format: 'date' }, time: { type: 'string', pattern: '^([0-1]?[0-9]|2[0-3]):[0-5][0-9]$' }, party_size: { type: 'integer', minimum: 1, maximum: 20 } }, required: ['date', 'time', 'party_size'] } }, endpoint: 'https://api.bellavista.com/availability' });
// Add tool for making reservations await agent.addTool({ type: 'function', function: { name: 'make_reservation', description: 'Book a table reservation', parameters: { type: 'object', properties: { date: { type: 'string', format: 'date' }, time: { type: 'string' }, party_size: { type: 'integer' }, customer_name: { type: 'string' }, customer_phone: { type: 'string' }, special_requests: { type: 'string' } }, required: ['date', 'time', 'party_size', 'customer_name', 'customer_phone'] } }, endpoint: 'https://api.bellavista.com/reservations' });
// Deploy to web widget const deployment = await agent.deployToWidget({ websiteUrl: 'https://bellavista.com', theme: { primaryColor: '#D4AF37', position: 'bottom-right' } });
console.log(
Widget code: ${deployment.embedCode});
Advanced Use Cases
Healthcare Patient Support
Scenario: Healthcare system deploys voice agents for appointment scheduling, prescription refills, and general health information.
Agent Configuration:
{
"name": "HealthFirst Patient Support",
"system_prompt": """
You are a compassionate healthcare support agent for HealthFirst Medical.
CAPABILITIES:
- Schedule/modify appointments
- Request prescription refills
- Provide general health information from approved sources
- Direct to emergency services when appropriate
CRITICAL RULES:
- NEVER provide medical diagnoses or treatment advice
- For medical emergencies, immediately say: "This sounds like an emergency.
Please hang up and call 911 or go to the nearest emergency room."
- Always verify patient identity before accessing records
- Maintain HIPAA compliance - never share patient data over phone
- Be empathetic, patient, and clear
LIMITATIONS:
- You cannot access medical test results
- You cannot authorize medical procedures
- You cannot provide prescription medical advice
""",
"voice": "empathetic-female-voice",
"llm": "gpt-4-turbo", # Medical context requires most capable model
"knowledge_base": [
"patient_faq.pdf",
"appointment_policies.pdf",
"prescription_refill_process.pdf",
"approved_health_information/**"
],
"tools": [
"check_appointment_availability",
"schedule_appointment",
"request_prescription_refill",
"verify_patient_identity"
],
"compliance": {
"hipaa_enabled": true,
"record_conversations": true, # Required for medical
"pii_redaction": true,
"retention_period": "7_years" # HIPAA requirement
}
}
Security Features:
- •Voice biometric authentication for patient identity verification
- •PII redaction in logs and transcripts
- •Encrypted conversation storage
- •Audit trails for compliance
Business Impact:
- •Reduces call center load by 40-60%
- •24/7 appointment scheduling without staff
- •Faster prescription refill processing
- •Improved patient satisfaction through instant access
E-Commerce Order Support
Scenario: Online retailer deploys voice agents to handle order tracking, returns, and product questions.
Multi-Language Support:
const agent = await client.createAgent({
name: 'ShopCo Customer Service',
systemPrompt: 'You are a helpful e-commerce support agent...',
languages: ['en', 'es', 'fr', 'de', 'ja', 'zh'], // Auto-detect and respond
voiceConfig: {
// Different voices for different languages
en: 'professional-english-female',
es: 'professional-spanish-female',
fr: 'professional-french-male',
// ... etc
},
knowledgeBase: {
sources: [
'product_catalog.csv',
'shipping_policies.pdf',
'return_policies.pdf',
'size_guides/**'
],
languageSupport: 'all' # Retrieve knowledge in user's language
},
tools: [
{
name: 'track_order',
description: 'Get real-time order status and tracking',
parameters: { order_id: 'string' }
},
{
name: 'process_return',
description: 'Initiate return process',
parameters: { order_id: 'string', return_reason: 'string' }
},
{
name: 'search_products',
description: 'Search product catalog',
parameters: { query: 'string', filters: 'object' }
}
]
});
Conversation Example:
Customer (English): "I need to return the jacket I ordered"
Agent (English): "I'd be happy to help with your return. Could you provide
your order number?"
Customer (switches to Spanish): "Mi número de orden es W-12345"
Agent (Spanish): "Gracias. He encontrado tu pedido. ¿Cuál es la razón de
la devolución?"
Customer (Spanish): "No me queda bien"
Agent (Spanish): "Entiendo. Puedo iniciar el proceso de devolución ahora.
Recibirás una etiqueta de envío por correo electrónico
en los próximos 15 minutos. ¿Hay algo más en que pueda ayudarte?"
ROI:
- •70% of order status inquiries handled by agent
- •Returns processed 10x faster
- •24/7 support without additional staffing costs
- •Multilingual support without hiring multilingual staff
Financial Services Voice Banking
Scenario: Bank deploys voice agents for account inquiries, transaction assistance, and fraud alerts.
Security-First Configuration:
agent = client.create_agent(
name="SecureBank Voice Assistant",
system_prompt="""
You are a secure banking assistant for SecureBank.
AUTHENTICATION REQUIRED:
Before providing any account information, you MUST:
1. Verify caller ID matches account phone number
2. Ask for account number or SSN (last 4 digits)
3. Ask security question (mother's maiden name or secret word)
CAPABILITIES AFTER AUTHENTICATION:
- Check account balances
- Review recent transactions
- Report lost/stolen cards
- Transfer funds between accounts
- Make bill payments
SECURITY RULES:
- Never share full account numbers over phone
- Never share full SSN over phone
- If fraud suspected, immediately flag and transfer to human
- Log all transactions for audit
If unable to authenticate after 3 attempts, politely end call and
advise customer to visit branch or use online banking.
""",
voice="authoritative-professional",
llm="gpt-4-turbo",
tools=[
"verify_caller_identity",
"authenticate_with_security_question",
"get_account_balance",
"get_recent_transactions",
"report_lost_card",
"transfer_funds",
"pay_bill"
],
security={
"voice_biometrics": True, # Voice fingerprint matching
"pci_compliance": True,
"encryption": "aes_256",
"fraud_detection": True,
"max_authentication_attempts": 3
}
)
Fraud Detection Integration:
Automatic fraud alert calls
def trigger_fraud_alert(transaction_id, customer_phone):
agent = get_agent("SecureBank Voice Assistant")
conversation = agent.initiate_call(
phone_number=customer_phone,
context={
"reason": "fraud_alert",
"transaction_id": transaction_id
},
override_prompt="""
You are calling to verify a potentially fraudulent transaction.
Script:
1. Identify yourself: "This is SecureBank's fraud prevention calling"
2. Verify customer identity using security questions
3. Describe the suspicious transaction
4. Ask: "Did you authorize this transaction?"
5. If NO → Lock card, issue refund, transfer to fraud specialist
6. If YES → Thank customer, note transaction as verified
"""
)
return conversation.id
Compliance Features:
- •PCI DSS compliance for card data
- •Call recording for dispute resolution
- •Transaction audit trails
- •Regulatory reporting automation
Technical Support Automation
Scenario: SaaS company deploys voice agents for tier-1 technical support, troubleshooting, and account assistance.
Knowledge-Intensive Configuration:
agent = client.create_agent(
name="TechSupport Pro",
system_prompt="""
You are an expert technical support engineer for CloudApp SaaS platform.
TROUBLESHOOTING PROCESS:
1. Gather information about the issue
- What is happening vs. what should happen?
- When did it start?
- What have they tried?
2. Search knowledge base for known issues and solutions
3. Guide customer through troubleshooting steps
4. If unresolved after 3 attempts, escalate to human engineer
TECHNICAL KNOWLEDGE:
- You understand our API, SDKs, and integrations
- You can read error codes and logs
- You can guide through UI workflows
- You can explain technical concepts in simple terms
TONE:
- Patient and methodical
- Avoid jargon unless customer demonstrates technical sophistication
- Celebrate successful resolution
""",
llm="gpt-4-turbo", # Technical queries require advanced reasoning
knowledge_base={
"sources": [
"api_documentation/**",
"troubleshooting_guides/**",
"known_issues_database.csv",
"error_code_reference.json",
"sdk_examples/**"
],
"retrieval_config": {
"top_k": 8, # More chunks for technical context
"enable_code_search": True, # Optimize for finding code examples
"similarity_threshold": 0.65 # Lower threshold to cast wider net
}
},
tools=[
"search_logs", # Search customer's application logs
"check_service_status", # Current system health
"run_diagnostic", # Automated system checks
"create_support_ticket", # Escalation path
"access_api_reference" # Live API documentation
]
)
Example Troubleshooting Flow:
Customer: "I'm getting a 403 error when calling your API"
Agent: "I understand you're receiving a 403 Forbidden error with our API.
Let me help you troubleshoot this. First, can you tell me which
endpoint you're calling?"
Customer: "The /users/create endpoint"
Agent: [Searches knowledge base for 403 errors on /users/create]
[Finds: "403 on /users/create usually indicates insufficient
permissions or invalid API key"]
"A 403 error on the users create endpoint typically occurs when
your API key doesn't have the necessary permissions. Can you
confirm you're using a key with 'write:users' scope?"
Customer: "I'm not sure. How do I check that?"
Agent: "I can help you verify. Let me guide you through checking your
API key permissions. First, log into your CloudApp dashboard..."
[Guides through UI, customer discovers key lacks permissions]
Agent: "Perfect! Now let's generate a new API key with the correct
permissions. Click 'Generate New Key' and select 'write:users'
from the permissions list..."
Customer: "Got it! It's working now!"
Agent: "Excellent! Your API is now properly configured. Is there anything
else I can help you troubleshoot today?"
Escalation Handling:
Automatic escalation to human when needed
if conversation.turns > 10 and not conversation.resolved:
agent.transfer_to_human(
reason="Complex issue requiring human expertise",
context_summary=agent.summarize_conversation(),
priority="high"
)
Best Practices
Prompt Engineering for Voice
Optimize for Spoken Responses: Voice responses differ from text—optimize prompts accordingly:
❌ BAD (designed for text):
"Please refer to Section 3.2.1 of the user manual, which states that API
authentication requires a Bearer token in the Authorization header, formatted
as follows: 'Authorization: Bearer '. For more information,
see https://docs.example.com/auth"
✅ GOOD (designed for voice):
"You'll need to include your API token in the request header. I can walk you
through the exact steps. First, locate your API token in your dashboard..."
Principles:
- •Concise: Shorter responses (30-60 seconds) maintain engagement
- •Conversational: Use contractions, colloquialisms, natural flow
- •No URLs/Complex Codes: Offer to send via SMS/email instead
- •Chunk Complex Info: Break multi-step instructions into sequential turns
- •Confirm Understanding: "Does that make sense?" "Are you ready for the next step?"
Example Voice-Optimized Prompts:
System Prompt:
"When providing instructions:
- •Keep each step under 20 words
- •Pause after each step for confirmation
- •Use simple, direct language
- •Avoid technical jargon unless user demonstrates sophistication
- •If information is too complex for voice, offer to send details via SMS or email"
Example:
User: "How do I reset my password?"
Agent: "I can help with that. First, go to the login page and click
'Forgot Password.' Ready for the next step?"
User: "Yes"
Agent: "Great. Enter your email address, then check your inbox for a reset
link. The email will arrive in about a minute. Have you received it?"
Handling Interruptions and Clarifications
Design for Natural Conversation Flow:
{
"conversation_config": {
"allow_interruptions": true,
"interruption_sensitivity": "medium", # low/medium/high
"clarification_strategy": {
"on_uncertain_intent": "ask_clarifying_question",
"max_clarifications": 2, # Avoid clarification loops
"fallback": "offer_human_transfer"
}
}
}
Example Handling:
Agent: "I can help you with account questions, billing inquiries, or—"
User: [Interrupts] "Billing! I need to update my credit card"
Agent: [Detects interruption, stops speaking] "Of course! Let's update your
credit card. For security, I'll transfer you to our secure payment
system where you can safely enter your new card details..."
[vs. poor handling:]
Agent: "I can help you with account questions, billing inquiries, or technical
support. Which would you like assistance with today?"
[Continues despite user interrupting—feels robotic and frustrating]
Knowledge Base Optimization
Curate Content for Voice Retrieval: Knowledge bases designed for human reading often need optimization for voice agents:
Restructure for Q&A:
❌ BAD (documentation style):
"Installation Requirements
CloudApp requires Node.js 16+, PostgreSQL 14+, and Redis 6+. Dependencies
are installed via npm install. Configuration is specified in config.yml..."
✅ GOOD (Q&A optimized):
Q: What are the requirements to install CloudApp?
A: You'll need Node.js version 16 or higher, PostgreSQL 14 or higher, and
Redis 6 or higher.
Q: How do I install CloudApp dependencies?
A: Run 'npm install' in your terminal from the CloudApp directory.
Q: How do I configure CloudApp?
A: Configuration settings are in the config.yml file. Would you like me to
walk you through the key settings?
Add Conversational Metadata:
{
"content": "To reset your password, visit the login page and click 'Forgot Password'",
"metadata": {
"keywords": ["password reset", "forgot password", "can't log in", "login issues"],
"voice_optimized": true,
"follow_up_questions": [
"What if I don't receive the reset email?",
"How do I change my password?",
"Can I reset my password over the phone?"
]
}
}
Version Your Knowledge Base:
Update knowledge base without downtime
kb = client.knowledge_base("support-kb-v1")
Create updated version
kb_v2 = kb.create_version(
sources=[
"updated_faq.pdf",
"new_product_guide.pdf"
],
incremental=True # Only process new/changed content
)
Test new version
test_agent = agent.clone(knowledge_base_version="v2")
run_test_suite(test_agent)
If tests pass, promote to production
if tests_passed:
agent.update(knowledge_base_version="v2")
Monitoring and Analytics
Key Metrics to Track:
Conversation quality metrics
metrics = {
"resolution_rate": 0.82, # 82% of calls resolved without human
"average_call_duration": 180, # seconds
"customer_satisfaction": 4.3, # out of 5
"containment_rate": 0.78, # % not transferred to human
# Technical metrics
"average_latency": 470, # ms end-to-end
"p95_latency": 850, # ms
"error_rate": 0.02, # 2% of calls have errors
"knowledge_retrieval_accuracy": 0.89,
# Business metrics
"cost_per_call": 0.15, # USD
"calls_per_day": 450,
"peak_concurrent_calls": 23,
"cost_savings_vs_human": 85000 # USD per year
}
Set Up Alerts:
{
"alerts": [
{
"metric": "resolution_rate",
"condition": "< 0.70",
"action": "email_team",
"message": "Agent resolution rate dropped below 70%"
},
{
"metric": "p95_latency",
"condition": "> 1000",
"action": "page_oncall",
"message": "Latency degradation detected"
},
{
"metric": "error_rate",
"condition": "> 0.05",
"action": "auto_pause_agent",
"message": "High error rate - agent paused automatically"
}
]
}
Analyze Conversations for Improvement:
Identify common failure patterns
conversations = agent.get_conversations(resolved=False, limit=100)
failure_patterns = analyze_failures(conversations)
Output:
{
"knowledge_gaps": ["Questions about new feature X", "Billing edge cases"],
"unclear_intents": ["Users saying 'help me' without specifics"],
"tool_failures": ["check_inventory API timing out"],
"user_frustration_triggers": ["Long authentication process"]
}
Iterate to improve
for pattern in failure_patterns["knowledge_gaps"]:
# Add missing knowledge to KB
kb.add_document(create_faq_for(pattern))
for intent in failure_patterns["unclear_intents"]:
# Improve prompts to ask better clarifying questions
agent.update_prompt(add_clarification_strategy(intent))
Comparison with Alternatives
ElevenLabs vs. OpenAI Realtime API
OpenAI Realtime API:
- •Architecture: End-to-end audio input/output (audio → audio, no transcription step)
- •Voices: 6 pre-built voices
- •Languages: Primarily English (limited multilingual support)
- •Latency: Very low (~250ms) due to direct audio processing
- •Knowledge Base: Not built-in (requires custom RAG implementation)
- •Pricing: ~$0.06/min audio input + $0.24/min audio output = ~$0.30/min total
ElevenLabs Conversational AI:
- •Architecture: STT → LLM → TTS pipeline (optimized for <100ms total)
- •Voices: 5,000+ voices across 32 languages
- •Languages: 32 languages with automatic detection and switching
- •Latency: Sub-100ms end-to-end (competitive with OpenAI)
- •Knowledge Base: Built-in RAG with automatic indexing
- •Pricing: ~$0.08/min for full conversation (significantly cheaper)
When to Choose Each:
- •OpenAI Realtime: Experimental projects, when you need the absolute minimum latency, when 6 voices are sufficient
- •ElevenLabs: Production deployments, multilingual needs, when voice variety matters, when you need integrated knowledge retrieval
ElevenLabs vs. Custom Stack (Deepgram + LLM + ElevenLabs TTS)
Custom Stack Approach: Building your own using best-in-class components:
Stack:
- •Deepgram for STT (~$0.0043/min)
- •OpenAI GPT-4 for LLM (~$0.03-0.06 per 1K tokens)
- •ElevenLabs TTS (~$0.18-0.30 per 1K characters)
- •Twilio for telephony (~$0.0085/min)
- •Custom orchestration code
Advantages:
+ Maximum flexibility and control
+ Can optimize each component independently
+ Potentially lower costs at high scale
Disadvantages:
- •Requires significant engineering effort (weeks to months)
- •Complex orchestration and error handling
- •Latency optimization requires deep expertise
- •No built-in analytics or monitoring
- •Security and compliance are your responsibility
- •Ongoing maintenance burden
ElevenLabs Platform Approach:
Advantages:
+ Integrated stack—no orchestration needed
+ Sub-100ms latency out-of-the-box
+ Built-in monitoring, analytics, compliance
+ Zero infrastructure management
+ Rapid deployment (minutes to production)
+ Automatic scaling
+ Enterprise-grade security
Disadvantages:
- •Less flexibility than custom stack
- •Potentially higher costs at extreme scale (100K+ minutes/month)
- •Locked into ElevenLabs ecosystem (though supports custom LLMs)
When to Choose Each:
- •Custom Stack: Engineering-heavy organizations, extreme scale (1M+ minutes/month), highly specialized requirements, dedicated infrastructure team
- •ElevenLabs Platform: Fast time-to-market, small-to-medium teams, typical voice agent use cases, focus on product over infrastructure
ElevenLabs vs. Voiceflow
Voiceflow:
- •Focus: Visual conversation design tool (flowchart-based)
- •Strengths: Excellent for designing complex conversation flows visually, great for non-technical teams
- •Voice: Integration with multiple TTS providers (including ElevenLabs)
- •Deployment: Web, phone, Alexa, Google Assistant
ElevenLabs:
- •Focus: End-to-end voice AI platform optimized for natural conversation
- •Strengths: Superior voice quality, sub-100ms latency, built-in multilingual
- •Voice: Best-in-class TTS (ElevenLabs' core technology)
- •Deployment: Web, phone, custom integrations
Best Used Together: Use Voiceflow for conversation flow design → Deploy with ElevenLabs for voice quality and performance.
Conclusion
ElevenLabs Conversational AI marks a turning point in voice agent technology—the moment when deploying production-quality conversational AI became accessible to organizations without specialized AI engineering teams. By providing a complete, optimized platform that handles the intricate complexity of real-time voice interaction, integrated knowledge retrieval, and multilingual support, ElevenLabs eliminates the months-long implementation cycles and ongoing maintenance burden that have historically limited voice AI adoption.
The platform's success in real-world deployments—handling hundreds of daily calls with 80%+ resolution rates, supporting 32 languages automatically, and delivering sub-100ms latency that feels indistinguishable from human interaction—demonstrates that voice agents have crossed the threshold from experimental technology to reliable business infrastructure. Organizations deploying ElevenLabs agents report dramatic reductions in support costs, 24/7 availability without staffing constraints, and customer satisfaction scores that match or exceed human interactions for routine inquiries.
For businesses exploring voice AI—whether for customer support, healthcare patient assistance, financial services, e-commerce support, or any application requiring natural language voice interaction—ElevenLabs Conversational AI provides a production-ready foundation that dramatically lowers the barrier to entry. The combination of superior voice quality (ElevenLabs' core competency), intelligent conversation management, seamless multilingual support, and integrated knowledge grounding creates a platform that handles the full spectrum of voice agent requirements without forcing trade-offs between quality, latency, and implementation complexity.
As voice interfaces continue to displace traditional phone menus, chatbots, and form-based interactions, platforms like ElevenLabs that make sophisticated voice AI accessible and reliable will play an increasingly central role in how organizations communicate with customers. The future of customer interaction is conversational—ElevenLabs is building the infrastructure to make that future a reality today.
Additional Resources
- •Official Website: https://elevenlabs.io
- •Conversational AI Platform: https://elevenlabs.io/conversational-ai
- •Documentation: https://elevenlabs.io/docs/agents-platform/overview
- •API Reference: https://elevenlabs.io/docs/api-reference/overview
- •Conversational AI 2.0 Announcement: https://elevenlabs.io/blog/conversational-ai-2-0
- •Prompting Guide: https://elevenlabs.io/docs/conversational-ai/best-practices/prompting-guide
- •Case Study (Documentation Agent): https://elevenlabs.io/blog/building-an-agent-for-our-own-docs
- •Comparison with OpenAI Realtime API: https://elevenlabs.io/blog/comparing-elevenlabs-conversational-ai-v-openai-realtime-api
- •Voice Library: https://elevenlabs.io/voice-library
- •Pricing: https://elevenlabs.io/pricing
- •Community Discord: https://discord.gg/elevenlabs
- •YouTube Tutorials: https://youtube.com/@elevenlabs
- •GitHub Examples: https://github.com/elevenlabs
- •Twitter: https://twitter.com/elevenlabsio
---
Article Metadata:
- •ID: elevenlabs-conversational-ai-agents
- •Title: ElevenLabs Conversational AI: Deploy Production-Ready Voice Agents in Minutes
- •URL: https://elevenlabs.io/conversational-ai, https://elevenlabs.io
- •Category: AI Voice Technology
- •Tags: Voice AI, Conversational AI, Speech Synthesis, TTS, STT, AI Agents, Multilingual AI, Customer Support Automation, Voice Assistants, Real-Time Communication, RAG, Knowledge Base Integration
- •Key Features:
- •Author: Tech Blog Team
- •Published: 2025-01-07
- •Word Count: ~8,200