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Documentation Index

Fetch the complete documentation index at: https://docs.squawkvoice.ai/llms.txt

Use this file to discover all available pages before exploring further.

The Call Analytics page provides a concise, data-rich overview of how your voice agent is performing during the last 30 days. It combines four headline metrics with two interactive charts so that you can monitor trends at a glance.

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Headline metrics

Total

Absolute number of calls handled by the workspace.

Unique Callers

Count of distinct phone numbers that reached your agent.

Avg Duration

Average conversation length expressed in minutes and seconds.

Resolution Rate

Percentage of calls that were resolved (contained) by the AI agent without requiring a human transfer.

User Satisfaction

Percentage of calls marked Satisfied by the AI-inferred evaluation. See Customer Satisfaction (CSAT) below for how this score is determined.
Each metric also shows a % change compared with the previous 30-day window so you can quickly spot positive or negative trends.

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Charts

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Call volume by day

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Call volume by day
chart
A bar chart that plots the number of calls for every calendar day in the selected period. Hover over a bar to inspect the exact count for that day.

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Call outcomes

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Call outcomes
chart
A donut chart that breaks down the final outcome of each call:
  • Contained – issue resolved entirely by the AI agent
  • Transferred – call was handed to a human agent
  • Caller-Hungup – caller hung up before the agent completed the flow
  • Insufficient-Balance – call was rejected due to insufficient account balance
  • Error – call ended due to a system or bot error
Each slice is labelled with its percentage share and absolute call count.

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Transfer dispositions

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Transfer dispositions
chart
A donut chart that shows the breakdown of transfer reasons when calls are handed to a human agent. Each disposition represents the specific reason why the AI agent transferred the call. You can manage which transfer dispositions count as contained (resolved) via the Manage containment button, allowing you to fine-tune your resolution rate metric.

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Customer Satisfaction (CSAT)

Every completed call automatically receives a Satisfied or Not Satisfied label — no post-call survey is required. The score is inferred by AI the moment the conversation ends and is visible on the call record immediately.

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Evaluation criteria

The satisfaction score measures whether the bot handled the interaction correctly, not whether the caller was happy with the business outcome. This is an important distinction.
The bot is marked Satisfied when any of the following are true:
  • Protocol Accuracy — The bot delivered correct information or enforced a policy (even a restrictive one) clearly and accurately.
  • Procedural Completion — The bot reached a definitive state, provided a valid justification for a refusal, or offered the next available step.
  • Successful Handoff — The bot correctly identified the need for a human agent and transitioned the caller without error.
  • Technical Competence — The bot addressed the specific intent of the user without circular logic or irrelevant tangents.
  • Passive Success — The caller ended the call after receiving a correct (even if unwanted) answer, or there was no further user input following a correct final response.
The bot is marked Not Satisfied only when one of the following occurred:
  • Functional Failure — The bot provided incorrect information, hallucinated a policy, or failed to perform a requested action it is capable of.
  • Logic Loops — The bot repeated scripts or failed to progress the conversation despite user clarity.
  • Navigation Error — The bot refused a request without providing a reason, or its response actively prevented the user from reaching a resolution.
  • Incoherence — The bot’s response was non-sequitur or failed to recognize the user’s primary intent.
Outcome Independence — If a caller is upset because they were denied a request (e.g. a refund or credit), but the bot’s denial was based on correct policy, the call is still marked Satisfied. The system distinguishes between an upset human (due to the situation) and a failed interaction (due to the bot). High caller frustration does not equal a bot failure when the bot was clear, correct, and professional.

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Data source & refresh cadence

The analytics are computed server-side from call records stored in BigQuery. Data is updated in real-time, refreshing immediately after each call is completed to ensure you always have the most current insights.

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Advanced Filtering

To help you perform more granular analysis, the Call Analytics dashboard includes an AI Agent Filter. This allows you to:
  • Analyze Single Agents: View performance metrics for a specific AI agent.
  • Compare Agent Groups: Select multiple AI agents to see aggregated data across a specific set of assistants.
  • Identify Trends: Quickly isolate results to understand which agents are driving your key metrics.

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Where to go next

Call History

Drill into individual calls, listen to recordings and read transcripts.

Analytics Dashboard

View a wider set of operational metrics across your entire workspace.