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The entire journey links together — transfers, escalations, and platform handoffs, measured against audio, context, duration, and drops.
Disconnect reason, wait time, hold, mute, MOS, network type, and CPU sit on the trace beside the AI turn and the API error. Objective data for the post-incident review.
The journey builds as a flame-graph timeline. Each span is a segment of the interaction, in the order it happened, sized by the time it took. Across telephone, IVR, queue, voice AI, transfers, WebRTC and network, open any span for the detail beneath it.
Across telephony, IVR, queue waits, AI turns, intent metrics, agent handling, transfers, mute and holds, WebRTC, and network — the exact error is clearly identified, providing evidence of what happened.
Every API the AI calls appears as a span, with latency, errors, and timeouts. When a downstream service runs slow, the customer sits in silence. Pinpoint the timeout and the context it dropped.
Every trace comes with an AI Summary automatically, helping teams understand the customer journey, isolate problem areas, and focus investigations where they matter most.
Nicolas De Kouchkovsky
No Jitter
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