> ## Documentation Index
> Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Quality AI

Automated quality management and conversation analysis.

***

## Overview

Quality AI enables you to:

* Evaluate 100% of customer interactions.
* Identify coaching opportunities automatically.
* Monitor compliance and adherence.
* Drive continuous improvement with data.

***

## How It Works

<div class="ascii-art">
  ┌───────────────────────────────────────────────────────┐
  │                  Customer Interactions                │
  │                     (Voice, Chat)                     │
  └───────────────────────────┬───────────────────────────┘
  │                           ▼                           │
  ┌───────────────────────────────────────────────────────┐
  │                      Quality AI Engine                │
  │                                                       │
  │   ┌─────────────┐  ┌─────────────┐  ┌─────────────┐   │
  │   │  Speech/    │  │  Evaluation │  │  Insight    │   │
  │   │  Text       │  │  Scoring    │  │  Generation │   │
  │   │  Analysis   │  │             │  │             │   │
  │   └─────────────┘  └─────────────┘  └─────────────┘   │
  └───────────────────────────┬───────────────────────────┘
  │        ┌──────────────────┼──────────────────┐        │
  │        ▼                  ▼                  ▼        │
  ┌────────────────┐  ┌───────────────┐  ┌────────────────┐
  │  Auto-Scores   │  │  Coaching     │  │   Compliance   │
  │  & Evaluations │  │  Assignments  │  │   Monitoring   │
  └────────────────┘  └───────────────┘  └────────────────┘
</div>

***

## Evaluation Criteria

### Standard Criteria

Pre-built evaluation criteria available out of the box:

| Criteria                | Description                            |
| ----------------------- | -------------------------------------- |
| **Greeting**            | Proper introduction and identification |
| **Empathy**             | Acknowledging customer emotions        |
| **Issue understanding** | Correctly identifying the problem      |
| **Resolution**          | Providing an accurate solution         |
| **Closing**             | Proper wrap-up and next steps          |
| **Compliance**          | Following required disclosures         |

### Evaluation Forms

Group criteria into evaluation forms and apply them to queues:

```yaml theme={null}
Form: Customer Service Standard
Criteria:
  - greeting (10%)
  - empathy (15%)
  - issue_understanding (20%)
  - resolution (30%)
  - product_knowledge (15%)
  - closing (10%)
Pass threshold: 80%
Apply to:
  - queue: support
  - channel: all
```

## Auto-Scoring

### How It Works

AI evaluates interactions in four steps:

1. **Speech/text analysis** — Transcribe and analyze the conversation.
2. **Criteria matching** — Map conversation content to evaluation criteria.
3. **Scoring** — Assign scores based on evidence found.
4. **Confidence flagging** — Flag low-confidence scores for human review.

***

## Conversation Mining

### Topic Analysis

Automatically identify and track conversation topics:

| Analysis               | Description                    |
| ---------------------- | ------------------------------ |
| **Topic clustering**   | Group conversations by theme   |
| **Sentiment by topic** | Track sentiment for each topic |
| **Volume tracking**    | Monitor topic frequency        |

## ​Taxonomy Builder​

### Create Taxonomies

Define and manage a structured topic hierarchy for consistent, business-relevant conversation analysis:

```yaml theme={null}
Taxonomy: Customer Interaction Analysis
├── Communication (L1 - Strategic)
│   ├── Call Handling (L2 - Product/Service Category)
│   │   ├── Clarity (L3 - Customer Contact Reason)
│   │   ├── Tone (L3 - Customer Contact Reason)
│   │   └── Active Listening (L3 - Customer Contact Reason)
├── Knowledge (L1 - Strategic)
│   ├── Product (L2 - Product/Service Category)
│   │   ├── Features (L3 - Customer Contact Reason)
│   │   └── Pricing (L3 - Customer Contact Reason)
├── Problem Solving (L1 - Strategic)
│   ├── Issue Diagnosis (L2 - Product/Service Category)
│   │   ├── Solution Accuracy (L3 - Customer Contact Reason)
│   │   └── Efficiency (L3 - Customer Contact Reason)
└── Compliance (L1 - Strategic)
    ├── Legal & Regulatory (L2 - Product/Service Category)
    │   ├── Disclosures (L3 - Customer Contact Reason)
    │   └── Data Handling (L3 - Customer Contact Reason)
```

## Analytics

### Reports

| View                                    | Description                                                                  |
| --------------------------------------- | ---------------------------------------------------------------------------- |
| My Dashboard (Agent)                    | Assigned evaluations, sentiment trends, and resolution effectiveness         |
| Supervisor View (Agent-Specific)        | Per-agent insights with sentiment, resolution quality, and L3 topic analysis |
| Agent Dashboard (Supervisor Evaluation) | Language-specific, recent evaluation data for coaching                       |

## Dashboards

### Quality Dashboards

* Agent Performance Report: Scores, pass/fail, metric adherence, coaching assignments.
* Interaction Evaluation & Conversation Analytics Report: Sentiment, topics (L3), agent metrics, evaluation outcomes.
* Evaluation Form Summary Report: Metric adherence, compliance stats, fatal violations, overall trends.

## Setup Quality AI

Complete these steps to get Quality AI running.

### 1. Configure Permissions

* Go to **User Management** > **Role Management** > **New Role** > **Other Modules**. [Learn more](/ai-for-service/user-management/users-and-role#other-modules).
* Assign the Supervisor role or create custom roles with QM permissions. [Learn more](/ai-for-service/user-management/users-and-role#custom-roles).

### 2. Set Up Contact Center

* Assign Supervisors and Auditors to the relevant queues so they can access the right interactions. [Learn more](/ai-for-service/contact-center#contact-center-ai).

### 3. Enable Features

* Enable **Conversation Intelligence**, **Auto QA**, and **Bookmarks** in Quality AI **Settings**. [Learn more](/ai-for-service/quality-ai/configure/quality-ai-general-settings).
* Enable **Answer** and **Utterance** suggestions in **GenAI Settings**. [Learn more](/ai-for-service/quality-ai/configure/evaluation-criteria/metrics-measurement-types/by-question).

### 4. Create Evaluation Metrics

* Choose a measurement type: By Question, Question Answer Pair, or Adherence (Static or Dynamic). [Learn more](/ai-for-service/quality-ai/configure/evaluation-criteria/metrics-measurement-types/by-question).
* Create evaluation metrics. [Learn more](/ai-for-service/quality-ai/configure/evaluation-metrics#create-new-evaluation-metric).
* Set the count type: Entire Conversation or Time Bound. [Learn more](/ai-for-service/quality-ai/configure/evaluation-criteria/metrics-measurement-types/by-question).

### 5. Create Evaluation Forms

* Assign a name, description, channel, and pass score.
* Select metrics, assign weights, and link the form to queues. [Learn more](/ai-for-service/quality-ai/configure/evaluation-forms#create-a-new-evaluation-form).

### ​6. Configure Agent Scorecards

* Enable Agent Scorecards in Quality AI Settings.
* Create agent attributes using evaluation metrics.
* Create scorecards, assign attributes and weights.
* Assign scorecards to agents or groups and set a default scorecard. [Learn more](/ai-for-service/quality-ai/configure/agent-scorecards).

### 7. Configure Taxonomy

* Create L1, L2, and L3 topics for conversation classification.
* Enable or adjust resolution tracking for L3 topics.
* Save and version taxonomy changes. [Learn more](/ai-for-service/quality-ai/configure/taxonomy-builder/overview).

### 8. Analyze Interactions in Conversation Mining

* Use filters to review scored interactions. [Learn more](/ai-for-service/quality-ai/analyze/conversation-mining-interactions).
* Save filters to reuse them in audit assignments. [Learn more](/ai-for-service/quality-ai/analyze/conversation-mining-audit-allocations).

### 9. Create Audit Allocations

* Assign interactions to auditors for manual evaluation.
* Create allocations with filters and sampling.
* Assign interactions to auditors.
* Evaluate interactions using assigned forms.
* Use AI-assisted audits and navigation. [Learn more](/ai-for-service/quality-ai/analyze/conversation-mining-audit-allocations).

### 10. Run AI-Assisted Manual Audits

* Use AI-assisted audits for faster, more consistent scoring.
* Navigate interactions using adherence moments and violations. [Learn more](/ai-for-service/quality-ai/analyze/ai-assisted-manual-audit).

### 11. Monitor Performance

* Use the **Dashboard** to track individual QA progress and queue statistics. [Learn more](/ai-for-service/quality-ai/analyze/dashboard).
* Use the **Conversation Intelligence Dashboard** for contact center-wide performance trends. [Learn more](/ai-for-service/quality-ai/analyze/conversation-intelligence).

***