WebRTC is a foundational technology behind many real-time communication applications, including telehealth platforms handling sensitive health data and enterprise collaboration tools exchanging confidential business information. While WebRTC offers strong security by design, building a secure application requires more than just relying on its built-in protocols. This WebRTC
Voice AI applications are changing how businesses handle customer interactions and how users navigate digital interfaces. These systems process spoken requests, understand natural language, and respond with generated audio in real time. Building a voice AI application requires understanding speech processing, language models, and real-time communication infrastructure.
In a previous post, Reducing Voice Agent Latency with Parallel SLMs and LLMs, we showed how to reduce response times and create more natural conversational experiences using the LiveKit Agents framework. But optimization is only half the equation. Once your voice agents are deployed and handling real
Voice assistants powered by real-time AI are increasingly being used to automate phone-based customer interactions. Whether for contact centers, internal help desks, or voice-driven workflows, a reliable architecture needs to support low-latency audio streaming, accurate speech-to-text (STT), intelligent response generation, and real-time speech synthesis. In this post,
