AI-powered voice agents are transforming communications across industries like telecom, healthcare, and enterprise customer service. But delivering low-latency, natural-sounding AI responses in real time at low costs remains a major challenge. Leveraging the “lightweight AI” of Small Language Models (SLMs) and free open-source stacks can help overcome
WebRTC applications present unique operational challenges that traditional monitoring tools cannot address. Unlike conventional web applications, real-time communication systems operate with complex peer-to-peer connections, dynamic network conditions, and media processing pipelines that can fail silently or degrade gradually. The primary challenge lies in observability. WebRTC applications generate
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,
