Human speech is often unstructured and messy. To keep up with real conversation, Voice AI bots must parse partial sentences, filler words, and interruptions, all without the visual cues a human listener relies on. On top of that linguistic challenge, real-world latency budgets and network conditions test
Voice AI tooling broadly falls into two categories: managed platforms that get you a production agent fast, and custom-built frameworks that give you full ownership of your voice pipeline. Within each category, the specific tools differ in ways that are worth understanding before you commit engineering time
AI-powered voice agents continue to expand into telephony, healthcare, and enterprise customer service. Teams building these systems face a shared set of problems: keeping latency low, keeping calls reliable, and keeping the underlying infrastructure fast enough to support real-time audio at scale. Two members of the WebRTC.ventures
How can AI scale clinical roleplay training beyond what live sessions with human trainers can deliver? For CETA Global, a nonprofit that trains frontline mental health providers around the world, the answer was an AI “flight simulator”: a real-time platform where psychologists practice difficult client sessions with an
