Call center ai · Production
Twilio integrates ElevenLabs AI voices into ConversationRelay for more natural customer interactions
The problem
Traditional text-to-speech often struggles to convey emotion and nuance, making automated voice interactions feel robotic.
Workflow diagram · grounded in source
1
Developer initiates voice interaction
trigger
“create conversational AI voice interactions that sound human, feel expressive, and respond in real time directly from the Twilio CPaaS platform”
2
AI voice synthesis and adaptation
ai_action
“ElevenLabs' AI voices overcome these limitations by adapting to context, sentiment, and pacing. With model latency as low as 75 milliseconds, our voices enable real-time, dynamic conversations that feel natural”
3
Expressive speech delivered
output
“Voices adjust tone and emotion to fit different interactions”
Reported outcome
Twilio ConversationRelay users can now deliver expressive, human-like speech with tone and emotion adjustment, low-latency real-time synthesis, and customizable voice experiences for multilingual and industry-specific needs.
Reported metrics
Model latencyas low as 75 milliseconds
Reported stack
ElevenLabsConversationRelay
Frequently asked questions
What did this team achieve with this AI workflow?
Twilio ConversationRelay users can now deliver expressive, human-like speech with tone and emotion adjustment, low-latency real-time synthesis, and customizable voice experiences for multilingual and industry-specific…
What tools did this team use?
ElevenLabs, ConversationRelay.
What results were reported?
Model latency: as low as 75 milliseconds (source-reported, not independently verified).
How is this call center ai AI workflow structured?
Developer initiates voice interaction → AI voice synthesis and adaptation → Expressive speech delivered.