Case Study - AI-native unified communications, built on real-time infrastructure

Overview

Most "AI calling" demos work once, on a good network, with one caller. Turning that into unified communications — something an organization can route real calls through — is a different discipline. It's a real-time systems problem first and a model problem second.

This platform makes AI a participant on the call rather than a feature bolted onto it. Audio streams in, gets transcribed and reasoned over, and the system can answer, route, summarize, or trigger downstream actions while the conversation is still happening. The engineering challenge is doing that within a latency budget tight enough that the conversation still feels natural.

What we did

We built the real-time backbone: bidirectional media streaming, the telephony bridge, and an event-driven backend that fans work out to the right handlers without blocking the call. The webhook and signaling flows are instrumented with structured logging and tracing so that when something goes wrong on a live call, it's diagnosable after the fact instead of a mystery.

Reliable under real load

The difference between a prototype and infrastructure shows up under concurrency: many simultaneous calls, degraded upstream services, and the long tail of telephony edge cases. We designed for those conditions explicitly — backpressure, graceful degradation, and clear failure modes — so the system stays predictable when the network doesn't.