# Explore language operator results

Because Conversation Intelligence runs in real time, receiving and handling language operator results is as important as generating them. You can use language operator results to:

* Test and validate intelligence configurations
* Observe language operator behavior during or after conversations.
* Trigger downstream business workflows during or after conversations.
* Retrieve execution details (latency, model info, usage metrics for cost analysis etc.) via the system [metadata object](#operator-result-metadata) in the Operator results.

In this guide, you'll learn the different ways to activate and how to view language operator results.

## Activating language operator results

You can activate language operator results in two ways:

* Activate in real time during conversations
* Activate after conversations end (post-conversation)

### Real-time activation

Take immediate action while the conversation is in progress. Common real-time use cases include:

* **Agent assist**: Push live language operator results to an agent's desktop or Customer Relationship Management (CRM) UI.
  * Surface recommendations from the Next Best Response language operator as they're produced
  * Display real-time compliance feedback from the Script Adherence language operator to guide agent behavior
* **Automated decisioning**: Trigger automated actions based on language operator results without human intervention
  * Detect suspected fraud and immediately decline transactions
  * Escalate conversations to a supervisor when risk thresholds are exceeded

### Post-conversation activation

Analyze, report, and activate downstream workflows after a conversation ends. Common post-conversation use cases include the following:

* **Results aggregation**: Aggregate language operator results across conversations
* **Trend analysis**: Measure trends over time (such as quality, sentiment, and outcomes)
* **Data export**: Feed Business Intelligence (BI) tools, data warehouses, and reporting pipelines

These workflows rely on the Insights API and BI connectors. Learn more about [conversation insights](/docs/conversations/intelligence/conversation-insights).

## View results in Twilio Console

In Twilio Console, you can:

* View and filter conversations for an intelligence configuration
* Inspect conversations and their language operator results

### View and filter conversations

1. In [Twilio Console](https://1console.twilio.com), go to **Products & services** > **Conversation Intelligence** > **Intelligence configurations**.
2. Click the intelligence configuration you want to explore.
3. Click the **Conversations** tab.\
   You'll see a list of conversations analyzed by the intelligence configuration.
4. Optionally, filter conversations by date, channel, or other metadata.

![Voice Support Assistant configuration page showing a list of conversations](https://docs-resources.prod.twilio.com/3cd470c8517a63e9170ff7b3aa21983e240946620779f8a8c04fe577c734606b.png)

### Inspect conversations and language operator results

1. On the **Conversations** tab of an intelligence configuration, click a conversation from the list.
2. If the conversation is still active, click **Refresh** to load new transcript segments and language operator results in real time.

On the **Threads** tab, view the full conversation transcript with language operator results. The transcript:

* Groups language operator results by rule
* Inserts language operator results at the exact point in the conversation when the rule executed
* Timestamps and associates language operator results with the triggering event

## Operator result metadata

Every operator result includes a `metadata.system` object automatically populated by Twilio at execution time. You can use it to inspect model usage, performance, and usage metrics (`inputCharacters` and `outputCharacters`) that correlate with billing for each operator run.

**Note**: `metadata.system` is read-only and cannot be modified.

| Field                              | Type    | Description                                                                                      |
| ---------------------------------- | ------- | ------------------------------------------------------------------------------------------------ |
| `metadata.system.resolvedModel`    | string  | The LLM used for this execution (for example, `gpt-5.4-mini`).                                   |
| `metadata.system.latencyMs`        | integer | The operator execution time in milliseconds.                                                     |
| `metadata.system.inputCharacters`  | integer | The number of input characters sent to the model. Corresponds to your billable input unit.       |
| `metadata.system.outputCharacters` | integer | The number of output characters returned by the model. Corresponds to your billable output unit. |
| `metadata.system.inputTruncated`   | boolean | Whether the conversation input was truncated to fit the model's context window.                  |

### Example

```json
{
  "id": "intelligence_operatorresult_98765",
  "result": {
    "score": 5,
    "explanation": "The agent followed the script closely."
  },
  "metadata": {
    "system": {
      "resolvedModel": "gpt-5.4-mini",
      "latencyMs": 245,
      "inputCharacters": 12840,
      "outputCharacters": 356,
      "inputTruncated": false
    }
  }
}
```

## Next steps

* Learn how to [integrate language operator results with your application](/docs/conversations/intelligence/integrate-results-with-application).