Call Intelligence
Terminal voice-call evidence, operational quality scoring, optional audio analysis, decision traces, and playback timelines.
Call intelligence combines several kinds of evidence produced during and after an eligible voice call. These outputs are related, but they are not one universal score: the terminal operational summary measures runtime behavior, post-call analysis evaluates the recorded interaction, and the Voice Judge evaluates audio quality. Availability depends on the voice runtime, configuration, recording, and downstream processing.
Evidence Paths
Terminal Operational Summary
At call completion, the primary voice runtime derives a terminal artifact from the session state it has observed. Depending on the available signals, that artifact can include:
Emotion
Aggregated emotional state and compound-emotion observations when emotion processing was available
Latency
Engine and audio time-to-first-byte averages and percentiles, navigation and rendering latency, and estimated silence ratio
Conversation shape
Turn count, duration, completion reason, final state, state revisits, and barge-ins
Tool performance
Tool-call totals, failures, failure rate, and per-tool timing
Operator involvement
Whether the call escalated and whether an operator was active
The persisted wire shape still contains risk_summary and safety_summary for compatibility, but the current runtime leaves both empty. Authored agent policy and operator workflows govern safety and escalation; consumers must not infer a live risk scorer or safety-rule engine from those fields.
Some external voice runtimes do not provide the per-turn inputs needed for this summary. They emit a minimal completion envelope with list and lifecycle fields, while leaving analytical summaries empty and the quality score unset.
Optional Post-Call Analysis
Call responses retain an optional call_analysis field, and workspace settings retain post_call_analysis_enabled. Their presence does not establish that every completed call triggers a recording-analysis job. The source reviewed September 27, 2026 does not connect that setting to the former five-dimension scoring or recognition-suggestion pipeline. Treat absent analysis as unavailable evidence.
Use the separately supported Call Trace Analysis, Voice Judge, and Production Evals workflows for their documented outputs and prerequisites. Existing or historical analysis artifacts do not establish current processing coverage.
Voice Judge Overview
The Voice Judge is a separate scheduled pipeline for eligible recorded production calls. It evaluates audio-native characteristics that structured runtime data cannot establish. See Voice Judge for its rubric and eligibility boundaries.
Operational Quality Score
Full terminal operational summaries receive a 0-100 quality score. The deterministic calculation starts at 100 and applies penalties for:
High p95 audio time to first byte
Excessive estimated silence
Repeated caller barge-ins
Revisited conversation states
Operator escalation
Tool-call failures
This score measures observable runtime friction. It is not a task-success score, clinical-risk score, or safety judgment. Post-call rubric scores and Voice Judge scores remain separate.
Analytics group operational scores into four ranges:
90-100
Excellent
70-89
Good
50-69
Fair
0-49
Poor
Use individual scores to inspect a call and distributions to compare services or time periods. A missing score means the runtime did not produce sufficient operational evidence; it should not be treated as zero. See Call and Eval Analytics for aggregate views.
Summary Flags and Transcript Verification
Call detail can derive review flags from aggregate evidence such as latency, silence, barge-ins, loops, tool failures, and operator escalation. These flags explain why the operational score changed, but they are not guaranteed to identify the exact timestamp of an incident. Use the playback timeline or call trace analysis for time-oriented review.
The separate post-call re-transcription and transcript-accuracy comparison have been retired. Current call responses no longer expose verified_transcript, verified_words, transcript_accuracy, or per_turn_accuracy, and voice settings no longer accept transcript_correction_enabled. Live transcripts, available word timings, recordings, terminal intelligence, and supported evaluations remain separate evidence. Review and configure recognition keyterms explicitly; missing accuracy fields are not a failed verification job.
Production and Simulation Evidence
Production voice calls and simulation sessions preserve different evidence boundaries:
Production voice calls can produce terminal operational summaries, recordings, post-call analysis, playback timelines, trace analysis, and Voice Judge results when their prerequisites are met.
Simulation sessions emit source-tagged, run-linked terminal artifacts built from simulated turns, tools, terminal state, and simulation score. The metric pipeline keeps simulation and production scopes separate.
Audio-native analysis does not apply to simulations without recordings. A simulation score is simulation-specific evidence, not a production operational quality score.
This separation lets teams compare like with like without allowing synthetic sessions to blend into production call analytics.
Decision Trace
For supported voice calls, the runtime emits terminal per-turn decision events after the call. Each event can include:
The signal classification that triggered the turn
The effect classification describing what the agent did
Current and transitioned states
Tool names and success state
Emotion observations and latency measurements when available
Counts of configured inner-thought annotations, rather than hidden model reasoning
Decision-trace events support structural analysis such as turns per call, tool invocation rate, and state transition rate. They describe recorded actions and evidence; they do not reconstruct an unobserved chain of thought.
Debug Traces
Administrators and owners can use two sensitive debugging views for a completed voice call:
Execution trace - Persisted per-turn inputs and outputs, including transcripts, actions, tools, state transitions, emotion observations, configured inner-thought annotations, and latency fields.
Prompt trace - Best-effort prompt-log records for turns whose prompt evidence was durably emitted.
Prompt evidence may be unavailable even when the execution trace exists. Access is restricted because these views can contain model internals and protected data.
Call Trace Analysis
For an eligible completed call with accessible audio, asynchronous trace analysis can produce:
A narrative summary and outcome
Key decision moments with audio-grounded factors
An emotional arc and critical emotional shifts
Interaction dynamics and signal-response alignment
Coaching recommendations and missed opportunities
Counterfactual alternatives and predicted impact
A synthesized explanation of why the call unfolded as it did
The API distinguishes ready, pending, and unavailable. A read can trigger best-effort on-demand processing for a qualifying production call, but a result is not guaranteed. Simulation sessions are not eligible for the recording-based trigger.
Call Playback Timeline
The call detail response can include a canonical timeline that places speech and runtime events on one shared coordinate system.
Lanes and Actors
Lanes are derived from the segments present in a call. Each lane has a stable ID, display order, semantic track, and actor when applicable. Supported tracks are caller, agent, operator, tool, and system. Tool and system events can receive distinct lanes based on their actor identity.
Operator speech uses the caller_speech segment type with operator actor metadata and the operator track. Consumers should use the actor and track fields rather than infer the speaker from the segment type alone.
Segment Types
The current timeline vocabulary is:
Speech
caller_speech, agent_speech, greeting, interrupted_speech
Fillers and checks
filler_nav, filler_hesitation, silence_check
Interaction
barge_in
Execution
processing_gap, tool_call, state_transition
Inactivity
silence
Shared Timebase
Segment start and end values are seconds on the response's shared timebase. Where exact audio offsets exist, the timeline aligns segments to the media. Where a legacy producer lacks exact offsets, the platform supplies synthetic positions so the call-detail UI still receives one consistent lane and seek model. Consumers can therefore render the response as provided instead of rebuilding timing independently.
See Audio Pipeline for the voice timing settings that influence the underlying events.
Voice Judge
The Voice Judge scores eligible recorded production calls across ten audio-native dimensions. It reads the caller channel and, when available, the agent channel; an inbound-only recording can be evaluated as mono.
Ten Evaluation Dimensions
Latency and Dead Air
Response timing and silence management
Pronunciation
Speech clarity and correctness, including medical terms
Clarity
Intelligibility of speech
Filler and Silence
Tool-call acknowledgments and dead-air handling
Interruption Handling
Barge-in detection and recovery
Audio Consistency
Volume and pitch stability without cutoffs
Pacing
Conversational tempo and pauses
Warmth and Tone
Tonal appropriateness to the caller's state
Accent Quality
Language match and accent naturalness
Voice Identity
Consistency of the agent voice
Each dimension can produce a score from 0.0 to 1.0, a severity of none, warning, flag, or critical, and audio-grounded evidence for non-none findings.
Eligibility and Delivery
The scheduled job considers completed production calls within its lookback window, skips calls with a known duration of five seconds or less, excludes calls that already have a result, and requires an accessible inbound recording. Missing recordings, processing errors, and warehouse latency can leave an otherwise completed call without a Voice Judge row.
Results are available through two paths:
Recent per-service results - Per-call scores and the bounded evidence payload for investigation
Aggregate metrics - Ten built-in Metric Store metrics for workspace-level trends
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