> ## Documentation Index
> Fetch the complete documentation index at: https://docs.famulor.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Post-call analysis
> Automatically score sentiment, success, and extract structured data from every call
After a call ends, **AI analysis** can evaluate the transcript. It can rate caller sentiment, decide whether the call met a success criterion, and extract structured fields such as a callback number, order ID, or yes/no answer. The result is available in [History](/monitoring/history), the public API, and MCP.
## What analysis produces
For each analyzed call the result can include:
* **Sentiment** — `positive`, `neutral`, or `negative` (overall caller sentiment).
* **Success** — `true` / `false` (or `null` when success evaluation is off), plus a short **reason** explaining the verdict.
* **Data** — a map of the structured fields you defined, keyed by field name.
* **Analysis time** — when the result was produced.
Use sentiment and success filters in History or `GET /api/v1/calls?sentiment=&success=` to find matching calls quickly.
Analysis runs **after** the call and never affects the live conversation. If analysis is unavailable, the call and its transcript remain unchanged and no result is shown.
## Configuring analysis
Open the assistant's **Analysis** card and turn on the parts you need. Everything is optional; an empty configuration means no analysis runs.
Enabled by default. Turn it off if you don't need per-call sentiment.
Enable **Success** and describe, in plain language, what a successful call looks like — e.g. *"The caller booked an appointment"* or *"The caller confirmed their delivery address."* The result contains a boolean plus a reason.
Add fields to extract. Each field has a `name` (snake\_case, unique), a `type` (`string`, `number`, `boolean`, or `enum`), a `description` explaining what to extract (max. 500 characters), and — for `enum` — a list of `choices`. Every field is always attempted — there's no editor control to make one optional, though the API's `required` flag still exists in the schema.
### Example configuration
```json theme={null}
{
"sentiment": true,
"success": {
"enabled": true,
"criteria": "The caller booked an appointment"
},
"fields": [
{ "name": "callback_number", "type": "string", "description": "Phone number the caller wants a callback on" },
{ "name": "appointment_day", "type": "enum", "description": "Requested weekday", "choices": ["mon", "tue", "wed", "thu", "fri"] },
{ "name": "is_existing_customer", "type": "boolean", "description": "Whether the caller is already a customer" }
]
}
```
A resulting `calls.analysis` looks like:
```json theme={null}
{
"sentiment": "positive",
"success": true,
"success_reason": "Caller agreed to a Tuesday appointment and gave a callback number.",
"data": {
"callback_number": "+493012345678",
"appointment_day": "tue",
"is_existing_customer": false
},
"analyzed_at": "2026-07-05T09:12:44Z"
}
```
## Using analysis results
* **History filters** — filter the call list by sentiment and success to find, say, all negative calls that did *not* succeed.
* **Public API** — every call in `GET /api/v1/calls` and `GET /api/v1/calls/{id}` carries `analysis`, `sentiment`, and `success`. Filter the list with `?sentiment=negative` and `?success=false`.
* **MCP** — the same call fields are exposed through the MCP `list_calls` / `get_call` tools.
Re-running analysis on a past call — **History → Re-evaluate** — overwrites the stored sentiment, success, and extracted fields with a fresh result. It costs extra credits at the workspace's **History re-evaluate** rate; current rates are on the [Usage page](https://app.famulor.io/usage).
## AI QA scorecards
**AI QA scorecards** (Beta) score every finished call against your own quality checklist — a separate, complementary result from the sentiment/success/fields analysis above. Configure it in the same **Analysis & QA** tab as post-call analysis.
Off by default. The card is only shown once **Beta features** are switched on for the workspace under [Settings → Workspace](/settings/workspaces), and enabling it needs a plan that includes AI QA scorecards.
An overall score from 0–100; a call passes once it meets or exceeds this value.
Each criterion has a name, a weight (how much it counts toward the overall score), and a source:
* **LLM judge** — the AI reviewer reads the transcript and scores the criterion from a free-text description you write. Costs an extra evaluation per criterion.
* **Reuse success** — reuses this call's Analysis success flag (yes = 1, no = 0), at no extra cost.
* **Reuse sentiment** — reuses this call's Analysis sentiment (positive = 1, neutral = 0.5, negative = 0), at no extra cost.
Results appear in **History**, on the call's detail view. Configure scorecards the same way as analysis — through the assistant editor, `PATCH /api/v1/assistants/{id}`, or the MCP `update_assistant` tool.
This is different from the cohort-level [AI Quality Assurance](/assistants/ai-quality-assurance) tool, which runs a QA pack you choose against a batch of past calls on demand. Scorecards run automatically, per call, as it finishes.
## Configuring via the API
Use `PATCH /api/v1/assistants/{id}` or the MCP `update_assistant` tool to update the same analysis options programmatically. An empty configuration means analysis does not run. See the [API reference](/api-reference/introduction) for the request schema.
## Turn a real call into a simulation test
On a finished call in **History**, use **Create Simulation Test**. Famulor prepares a persona, script, and success criteria from the transcript and analysis, then opens the assistant's [Simulations](/assistants/simulations) panel for review.
The same action is available as:
* `POST /api/v1/assistants/{id}/tests/from-call` with `{ "call_id": "…" }` (`assistants:write`; Simulations must be included in the plan)
* MCP tool `create_assistant_test_from_call`
Clicking a transcript line in History jumps the recording to that moment.