Voice AI agents have unique deployment needs. Operational complexity multiplies quickly. You’re not just deploying code; you’re orchestrating real-time audio pipelines that need to maintain call quality under load, coordinate between AI services that each have their own scaling characteristics, and handle the networking complexities of audio
In an era where artificial intelligence is transforming every aspect of customer service, Interactive Voice Response (IVR) systems remain a critical touchpoint for millions of daily interactions across call centers and customer service departments. As explored in my previous article on “Building a Smart IVR Agent System
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.
Large Language Models (LLMs) have dominated conversations about AI integration in WebRTC, particularly when it comes to voice-based features like transcription, summarization, and intent detection. But there’s an emerging layer that many outside of research circles are missing: Vision Language Models (VLMs). Unlike LLMs, which work with
