Overview

• Metrics — your library of everything the project can measure, plus the collectors that run it on your calls
• Studio — where you author new metrics and run evaluation batteries against real calls
• Datasets — named collections of calls or chats for evaluation, comparison, and analysis

​

What’s a Metric?

• An output type — boolean, numeric, scale, text, classification, or count
• A scope — global (one value per call) or per-participant (separate values for agent and customer)
• A context — call-level, segment-level (single utterance), or segment-range (span of conversation)

​

Types of Metrics

​

System Metrics (Built-in)

• Performance — Response time, talk time, silence duration, overlap/interruptions, latency
• Emotion & Sentiment — Sentiment tracking, 64+ emotion detection, vocal cues (raised voice, frustration), stress indicators
• Speech — Interruption detection, pause analysis, repetition detection
• Compliance — Disclosure completeness, prohibited language, PII handling, prompt injection resistance
• Call Quality — Speech quality scoring (DNSMOS), accent detection, voicemail handling

​

Custom Metrics

• “Did the agent verify the caller’s identity?” → Boolean
• “Rate the agent’s empathy on a scale of 1-10” → Scale
• “What was the primary reason for the call?” → Classification
• “How many times did the agent attempt to upsell?” → Count

​

The Metrics Page

• Collectors strip — each collector runs a set of metrics on the calls its segment matches. Compact cards show each collector’s name, segment, and metrics, with a Manage collectors link and a New collector tile.
• Library — everything this project can measure, grouped by package. Filter with the All / System / Custom pills or search; clicking a metric opens it in Studio for editing and testing.
• New metric — jumps straight into Studio’s Author mode.

​

How It All Fits Together

​

Quick Start Examples

const collector = await client.metricPolicy.create({
  name: 'Agent Response Time SLA',
  status: 'ACTIVE',
  conditions: [
    {
      conditions: [
        {
          conditionType: 'AGENT',
          conditionKey: 'your-agent-id',
        },
      ],
    },
  ],
  metrics: [
    {
      id: 'response-time-metric-id',
      threshold: {
        operator: 'LESS_THAN',
        value: 1000,
        aggregationMode: 'P95',
        participantRole: 'AGENT',
      },
    },
  ],
})

// 1. Create the metric definition
const metric = await client.metric.createDefinition({
  name: 'Appointment Booked',
  outputType: 'BOOLEAN',
  analysisPackageId: 'your-package-id',
  llmPrompt: 'Did the agent successfully book an appointment for the caller?',
  booleanTrueLabel: 'Booked',
  booleanFalseLabel: 'Not Booked',
})

// 2. Add it to a collector so it runs on every call
const collector = await client.metricPolicy.create({
  name: 'Appointment Booking Check',
  status: 'ACTIVE',
  metrics: [{ id: metric.data.id }],
})

// Run metrics against simulation calls after they complete
const job = await client.metricCollectionJob.create({
  callIds: ['sim-call-1', 'sim-call-2', 'sim-call-3'],
  metrics: [
    { id: 'task-completion-metric-id' },
    { id: 'compliance-check-metric-id' },
  ],
})

// Check results
const result = await client.metricCollectionJob.getByID(job.data.id)
console.log(`${result.data.completedItems}/${result.data.totalItems} processed`)

​

Sections