Quality AI

Automated quality management and conversation analysis.

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Overview

Quality AI enables you to:

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

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How It Works

┌─────────────────────────────────────────────────────────────────┐
│                   Customer Interactions                          │
│                 (Voice, Chat, Email, Social)                     │
└─────────────────────────────────┬───────────────────────────────┘
                                  │
                                  ▼
┌─────────────────────────────────────────────────────────────────┐
│                      Quality AI Engine                           │
│                                                                  │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐              │
│  │  Speech/    │  │  Evaluation │  │  Insight    │              │
│  │  Text       │  │  Scoring    │  │  Generation │              │
│  │  Analysis   │  │             │  │             │              │
│  └─────────────┘  └─────────────┘  └─────────────┘              │
└─────────────────────────────────┬───────────────────────────────┘
                                  │
           ┌──────────────────────┼──────────────────────┐
           ▼                      ▼                      ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Auto-Scores   │    │  Coaching       │    │   Compliance    │
│   & Evaluations │    │  Assignments    │    │   Monitoring    │
└─────────────────┘    └─────────────────┘    └─────────────────┘

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Evaluation Criteria

Standard Criteria

Pre-built evaluation criteria:

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

Custom Criteria

Create custom evaluation criteria:

Criteria: Product Knowledge
Description: Agent demonstrates accurate product knowledge
Weight: 15%
Scoring:
  - 5: Excellent - Comprehensive, accurate information
  - 4: Good - Mostly accurate with minor gaps
  - 3: Acceptable - Basic knowledge demonstrated
  - 2: Below expectations - Significant gaps
  - 1: Unacceptable - Incorrect information provided
Auto-evaluate: true
Keywords:
  positive: ["correct", "accurate", "helpful"]
  negative: ["wrong", "incorrect", "misinformation"]

Evaluation Forms

Group criteria into evaluation forms:

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

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Auto-Scoring

How Auto-Scoring Works

AI automatically evaluates interactions:

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

Configuration

Auto-Scoring:
  enabled: true
  evaluation_rate: 100%  # Evaluate all interactions
  confidence_threshold: 0.8
  human_review:
    - low_confidence_scores
    - failed_evaluations
    - random_sample: 5%

Calibration

Ensure consistent scoring:

1. Select calibration sample
2. Have multiple evaluators score
3. Compare scores and discuss differences
4. Adjust criteria definitions
5. Re-train auto-scoring model

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Conversation Mining

Topic Analysis

Automatically identify conversation topics:

Analysis	Description
Topic clustering	Group conversations by theme
Trend detection	Identify emerging topics
Sentiment by topic	Track sentiment for each topic
Volume tracking	Monitor topic frequency

Root Cause Analysis

Identify drivers of quality issues:

Quality Issue: Low resolution scores in billing queue

Root Causes Identified:
├── 45% - Complex billing system navigation
├── 30% - Outdated knowledge articles
├── 15% - Missing escalation paths
└── 10% - Training gaps on new features

Insights Dashboard

Automatic insights:

• Top coaching opportunities
• Emerging quality trends
• Best performing agents/teams
• Areas needing attention

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Coaching

Auto-Assignment

Automatically assign coaching based on scores:

Coaching Rule: Resolution Improvement
Trigger:
  criteria: resolution
  score: < 3
  count: 3 consecutive
Action:
  assign_coaching: resolution_training
  notify: supervisor
  priority: high

Coaching Workflow

[Quality Issue Detected]
         │
         ▼
[Coaching Assigned to Supervisor]
         │
         ▼
[Supervisor Reviews Evidence]
         │
         ▼
[Coaching Session Scheduled]
         │
         ▼
[Session Completed]
         │
         ▼
[Follow-up Evaluation]

Evidence Attachment

Coaching includes relevant evidence:

• Conversation transcript
• Audio recording
• Evaluation scorecard
• Specific timestamps/sections
• Comparison to best practices

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Compliance Monitoring

Compliance Rules

Define compliance requirements:

Compliance: PCI-DSS Card Handling
Rules:
  - must_not_say: ["full card number", "CVV"]
  - must_say: ["secure", "encrypted"]
  - action_required: mask_card_data
Alert:
  severity: critical
  notify: compliance_team

Required Disclosures

Track required script elements:

Disclosure	Required For
Recording notice	All calls
Rate disclosure	Financial products
Terms and conditions	New accounts
Privacy policy	Data collection

Compliance Dashboard

Monitor compliance metrics:

• Compliance rate by disclosure type
• Violations by agent/team
• Trend analysis
• Alert history

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Taxonomy Builder

Create Taxonomies

Organize quality categories:

Taxonomy: Quality Categories
├── Communication
│   ├── Clarity
│   ├── Tone
│   └── Active listening
├── Knowledge
│   ├── Product
│   ├── Process
│   └── Policy
├── Problem Solving
│   ├── Issue identification
│   ├── Solution accuracy
│   └── Efficiency
└── Compliance
    ├── Disclosures
    ├── Data handling
    └── Regulatory

Apply Taxonomies

Use taxonomies for:

• Structured evaluation forms
• Analytics categorization
• Coaching focus areas
• Reporting dimensions

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Analytics

Quality Dashboards

Dashboard	Metrics
Overview	Quality score trends, pass rates
Agent performance	Individual scores, improvement
Team comparison	Team-level benchmarking
Criteria analysis	Performance by criteria
Compliance	Compliance rates, violations

Reports

Automated reports:

• Daily quality summary
• Weekly team performance
• Monthly trend analysis
• Compliance audit reports

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Related

• Contact Center AI — Agent desktop and operations
• Queue Management — Queue configuration
• Skills Management — Agent skills for routing
• Agent AI — Real-time agent assistance
• Administration — Analytics and settings