As Voice AI becomes more prevalent, applications need a way to store conversation data that supports observability, auditing, and long-term retention. The audio recording or a text transcript alone leaves out critical context. Additional details matter too, such as who was in the conversation, what devices were
WebRTC and SIP both carry live voice to an AI agent. Voice AI platforms list them side by side, so teams building an AI receptionist often expect to pick one or the other for connecting callers to the agent. In practice, each protocol serves a different way
On September 22–23, I was in Chicago for the IIT Real-Time Communications Conference at Illinois Tech, an annual IEEE conference where industry engineers and academic researchers present work on real-time communications. This year’s tracks covered programmable networks, WebRTC applications, emergency communications, and AI in real-time communications. I
A production voice agent has a latency budget of roughly 800 milliseconds before a conversation starts to feel slow. That budget is spent across eight sequential stages, typically split across three to five different vendors depending on how the stack is assembled. This post maps that voice
