***

title: Post-Call Analytics
sidebarTitle: Post-Call Analytics
description: Configure AI summaries and disposition metrics for automated call insights.
----------------------------------------------------------------------------------------

After each call, the platform automatically generates analytics. You can configure what data to extract.

## What Gets Generated

Every completed call includes `postCallAnalytics`:

| Field                | Description                         |
| -------------------- | ----------------------------------- |
| `summary`            | AI-generated call summary           |
| `dispositionMetrics` | Extracted data points you configure |

***

## Accessing Post-Call Data

```python
from smallestai.atoms.call import Call

call = Call()

# Get call details
details = call.get_call("CALL-1768842587790-69eb58")
data = details["data"]

# Access analytics
analytics = data.get("postCallAnalytics", {})

print(f"Summary: {analytics.get('summary')}")

for metric in analytics.get("dispositionMetrics", []):
    print(f"  {metric['identifier']}: {metric['value']}")
    print(f"    Confidence: {metric['confidence']}")
    print(f"    Reasoning: {metric['reasoning']}")
```

### Example Output

```
Summary: The call involved an agent reaching out to discuss AI products. 
The user expressed interest and provided their name.

  user_interested: yes
    Confidence: 1
    Reasoning: The user explicitly stated 'I am interested'.
  user_name: John
    Confidence: 1
    Reasoning: The user provided their name directly.
```

***

## Configuring Disposition Metrics

Use `set_post_call_config()` to define what data to extract:

```python
from smallestai.atoms.call import Call

call = Call()

call.set_post_call_config(
    agent_id="696e655577e1d88ff54b4fbf",
    summary_prompt="Summarize this sales call briefly.",
    disposition_metrics=[
        {
            "identifier": "user_interested",
            "dispositionMetricPrompt": "Was the user interested? yes, no, or unclear",
            "dispositionValues": {"type": "ENUM"},
            "choices": ["yes", "no", "unclear"]
        },
        {
            "identifier": "user_name",
            "dispositionMetricPrompt": "What is the user's name? Return 'unknown' if not mentioned.",
            "dispositionValues": {"type": "STRING"}
        }
    ]
)
```

***

## Disposition Metric Types

| Type       | Description                    | Requires `choices` |
| ---------- | ------------------------------ | ------------------ |
| `STRING`   | Free text (names, notes)       | No                 |
| `BOOLEAN`  | Yes/No values                  | No                 |
| `INTEGER`  | Numeric values (ratings)       | No                 |
| `ENUM`     | Selection from predefined list | Yes                |
| `DATETIME` | Date/time values               | No                 |

***

## Metric Configuration Schema

Each disposition metric requires:

| Field                     | Required | Description                              |
| ------------------------- | -------- | ---------------------------------------- |
| `identifier`              | Yes      | Unique ID (e.g., `customer_status`)      |
| `dispositionMetricPrompt` | Yes      | Question to extract this data            |
| `dispositionValues.type`  | Yes      | STRING, BOOLEAN, INTEGER, ENUM, DATETIME |
| `choices`                 | For ENUM | List of allowed values                   |

***

## Getting Current Configuration

```python
config = call.get_post_call_config("696e655577e1d88ff54b4fbf")

print("Configured metrics:")
for metric in config["data"].get("dispositionMetrics", []):
    print(f"  {metric['identifier']}: {metric['dispositionMetricType']}")
```

***

## Complete Example: Sales Call Analytics

```python
import time
from smallestai.atoms import AtomsClient
from smallestai.atoms.call import Call
from smallestai.atoms.audience import Audience
from smallestai.atoms.campaign import Campaign

client = AtomsClient()
call = Call()
audience = Audience()
campaign = Campaign()

# 1. Create agent
agent = client.new_agent(
    name=f"Sales Agent {int(time.time())}",
    prompt="You are a sales agent. Ask if interested and get their name.",
    description="Testing disposition metrics"
)
agent_id = agent.data

# 2. Configure disposition metrics
call.set_post_call_config(
    agent_id=agent_id,
    summary_prompt="Summarize this sales call briefly.",
    disposition_metrics=[
        {
            "identifier": "user_interested",
            "dispositionMetricPrompt": "Was the user interested? yes, no, or unclear",
            "dispositionValues": {"type": "ENUM"},
            "choices": ["yes", "no", "unclear"]
        },
        {
            "identifier": "user_name",
            "dispositionMetricPrompt": "What is the user's name? Return 'unknown' if not mentioned.",
            "dispositionValues": {"type": "STRING"}
        }
    ]
)

# 3. Create audience and campaign
phones = client.get_phone_numbers()
phone_id = phones["data"][0]["_id"]

aud = audience.create(
    name=f"Test Audience {int(time.time())}",
    phone_numbers=["+916366821717"],
    names=[("Test", "User")]
)
audience_id = aud["data"]["_id"]

camp = campaign.create(
    name=f"Analytics Test {int(time.time())}",
    agent_id=agent_id,
    audience_id=audience_id,
    phone_ids=[phone_id]
)
campaign_id = camp["data"]["_id"]

# 4. Start campaign
campaign.start(campaign_id)
print("Call in progress...")

# 5. Wait for completion
time.sleep(60)

# 6. Get call with analytics
calls = call.get_calls(agent_id=agent_id, limit=1)
call_id = calls["data"]["logs"][0]["callId"]

details = call.get_call(call_id)
data = details["data"]

# 7. Display results
print(f"\nCall Status: {data['status']}")
print(f"Duration: {data['duration']}s")

print("\nTranscript:")
for line in data.get("transcript", []):
    print(f"  [{line['role'].upper()}]: {line['content']}")

analytics = data.get("postCallAnalytics", {})
if analytics:
    print(f"\nSummary: {analytics.get('summary')}")
    print("\nDisposition Metrics:")
    for m in analytics.get("dispositionMetrics", []):
        print(f"  {m['identifier']}: {m['value']}")
        print(f"    Confidence: {m['confidence']}")
        print(f"    Reasoning: {m['reasoning']}")

# 8. Cleanup
campaign.delete(campaign_id)
audience.delete(audience_id)
client.delete_agent(id=agent_id)
```

***

## SDK Reference

| Method                                     | Description                               |
| ------------------------------------------ | ----------------------------------------- |
| `call.get_post_call_config(agent_id)`      | Get agent's analytics config              |
| `call.set_post_call_config(agent_id, ...)` | Configure summary and disposition metrics |
| `call.get_call(call_id)`                   | Get call details with analytics           |
| `call.get_calls(agent_id=..., limit=...)`  | List calls with optional filters          |

***

## Tips

<AccordionGroup>
  <Accordion title="When are metrics populated?">
    Disposition metrics are extracted after the call ends, typically within 10-30 seconds. The AI analyzes the transcript based on your configured prompts.
  </Accordion>

  <Accordion title="What makes a good disposition prompt?">
    Be specific and direct. Instead of "What happened?", use:

    * "Did the customer agree to schedule a follow-up? Answer yes or no."
    * "What is the customer's email? Return 'not provided' if not mentioned."
  </Accordion>

  <Accordion title="Can I change metrics after calls are made?">
    Yes. New calls use the updated config. Existing calls keep their original analytics.
  </Accordion>

  <Accordion title="What if the AI can't extract a value?">
    The metric will have an empty or null value. Specify fallback behavior in your prompts, like "Return 'unknown' if not mentioned."
  </Accordion>
</AccordionGroup>