Observability, the control layer for CX - Introducing Overture

AI turns and intent.

What the AI understood on each turn, the intent it assigned, and the path it took to get there.

Conversation turns and transcript.

What both parties said, with a summary of the conversation and its outcome.

Guardrails.

Which guardrails triggered, and where in the conversation.

Voice AI media quality.

The audio the AI agent itself received, because an AI agent cannot serve a customer it cannot hear.

Tool calls.

Every call the AI makes to internal and third party APIs, with the latency and result of each one. A timed out tool call can leave the customer listening to dead air for fourteen seconds.

The AI to human handoff.

Audio quality, whether context passed, duration, and drop events, captured at the moment the AI transfers to a person.

Key performance metrics and insights.

Performance surfaced by service view, with the insight that goes with each metric.

Verified containment.

The vendor's self reported figure alongside the evidence underneath it: disconnect reason, intent state, NLU confidence, queue events, agent interaction, hold time.

AI agent optimization.

Slice AI conversations by intent, NLU confidence, and version, then compare cohorts on containment, escalation, resolution, transfer abandons, and repeat calls. Same trace, every cohort. Pick a version, pick a cohort, see the delta.

Transfer analysis.

Transfer rate in the context of intent design, with designed escalations separated from misroutes. Shows which intents, which AI, and which points in the flow are leaking callers to human agents.

Repeat and failed interactions.

Repeat contacts that follow interactions the AI marked complete, with the technical signature shared across them.

Voice AI performance.

Operational metrics per voice agent, including customer effort, with the tool calls, errors, and handoffs that affected the experience.

Unresolved intent analysis.

Where the voice agent could not resolve the intent, the events downstream of it, including the queue the call finally landed in, show what the customer actually wanted. Map that back into the agent design.

Human agent estate.

Network quality trends by ISP, region, country, and network type, which surfaces problems beyond an agent's home setup. Headset and audio device metrics, with non compliant devices flagged to the agent. Browser versions and hardware load during calls.

Live monitoring.

Every production interaction is reported as it happens, so a breach shows while there is still time to act on it.

Tenor, your CX expert.

Operata's knowledge base is built from millions of monitored minutes and thousands of investigations. It holds the definitions, the thresholds, what a failure signature means, and how to escalate it to a carrier. Tenor runs the knowledge lookup and the trace queries in the same step, so a vague, non technical question comes back with a root cause rather than a chart. IT, Ops, and human agents all work from the same answer.

One query, every affected layer.

Packet loss on a carrier trunk can show up as degraded audio, as low bot confidence, and as an AI disclosure the customer never hears. One query returns all of it, carrier, media, AI, and compliance, where each would otherwise be a separate tool and a separate team.

Evidence packs.

The evidence for a carrier or vendor escalation, assembled from the same data.

Build what your process needs.

Operata data works alongside the other connectors your team already runs, so the workflow is something you compose rather than something you wait for. Route it into incident management, collaboration channels, ticketing, or your own services, and trigger whatever the process calls for. One example: a P1 raised and assigned in PagerDuty with the full detail, the same incident posted as a single card in Slack, and the relevant runbooks attached to both, so whoever wakes up to it knows what to do before they open a laptop.