> ## 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.

# AI Quality Assurance

> Run cohort QA over call transcripts — packs, credits, dashboard.

Analyze completed calls with structured insights across language and performance. Pick a **pack**, choose a **date range**, and review average score, resolution rate, trends, and top user questions.

Audio-level metrics (WER, overlapping speech) are not included in v1 — analysis is transcript-based.

## QA packs

| Pack                      | Focus                                                               |
| ------------------------- | ------------------------------------------------------------------- |
| Full QA                   | Score, resolution, hallucinations, sentiment, trends, top questions |
| Language & Hallucinations | Hallucinations, language quality, score                             |
| Resolution & Sentiment    | Resolution, sentiment, top questions                                |
| Performance Trends        | Score and resolution trends                                         |

## QA credits

QA runs consume workspace credits based on LLM usage for each analysed call. The Usage page shows charges as **QA usage**. Cost breakdowns and markup are not exposed in the product UI.

## Availability

Requires **AI Quality Assurance** in your plan. This is distinct from per-assistant **[AI QA scorecards](/assistants/analysis#ai-qa-scorecards)**.

## UI

Open **AI QA** in the sidebar (`/qa`). Create a run, wait for the analysis progress, then open the Call QA Overview dashboard.

## API

* `GET/POST /api/v1/qa/runs`
* `GET /api/v1/qa/runs/{id}?include_results=true`

Scopes: `calls:read` / `calls:write`.

## MCP

* `list_qa_packs`
* `list_qa_runs`
* `get_qa_run`
* `create_qa_run`