#Ticketing Solution Detailed Design

Version: v2.0-beta

Updated: 2026-01-05

Status: Preview

#1. System Overview and Design Philosophy

#1.1 System Positioning

This system is an AI-driven intelligent ticket management platform built on the NocoBase low-code platform. The core goal is:

Let customer service focus on solving problems, not tedious process operations

#1.2 Design Philosophy

#Philosophy One: T-Shaped Data Architecture

What is T-Shaped Architecture?

Inspired by the "T-shaped talent" concept — horizontal breadth + vertical depth:

• Horizontal (Main Table): Universal capabilities covering all business types — ticket number, status, assignee, SLA and other core fields
• Vertical (Extension Tables): Specialized fields for specific business types — equipment repair has serial numbers, complaints have compensation plans

Why This Design?

#Philosophy Two: AI Employee Team

Not "AI features", but "AI employees". Each AI has a clear role, personality, and responsibilities:

Why the "AI Employee" Model?

• Clear Responsibilities: Sam handles routing, Grace handles replies, no confusion
• Easy to Understand: Saying "Let Sam analyze this" is friendlier than "Call the classification API"
• Extensible: Adding new AI capabilities = hiring new employees

#Philosophy Three: Knowledge Self-Circulation

This forms a Knowledge Accumulation - Knowledge Application closed loop.

#2. Core Entities and Data Model

#2.1 Entity Relationship Overview

#2.2 Core Table Details

#2.2.1 Ticket Main Table (nb_tts_tickets)

This is the core of the system, using a "wide table" design with all commonly used fields in the main table.

Basic Information

Source Tracking

Contact Information

Assignee Information

Time Nodes

SLA Related

AI Analysis Results

Multi-Language Support

#2.2.2 Business Extension Tables

Equipment Repair (nb_tts_biz_repair)

IT Support (nb_tts_biz_it_support)

Customer Complaint (nb_tts_biz_complaint)

#2.2.3 Comments Table (nb_tts_ticket_comments)

Core Fields

AI Review Fields (for outbound)

#2.2.4 Ratings Table (nb_tts_ratings)

#2.2.5 Knowledge Articles Table (nb_tts_qa_articles)

#2.3 Data Table List

#3. Ticket Lifecycle

#3.1 Status Definitions

#3.2 Status Flow Diagram

Main Flow (Left to Right)

Branch Flows

Complete State Machine

#3.3 Key Status Transition Rules

#4. SLA Service Level Management

#4.1 Priority and SLA Configuration

#4.2 SLA Calculation Logic

#On Ticket Creation

sla_response_due = submitted_at + response_time_minutes
sla_resolve_due = submitted_at + resolve_time_minutes

#On Pause (pending)

-- Record pause start time
sla_paused_at = NOW()

#On Resume (from pending to processing)

-- Calculate pause duration
pause_duration = NOW() - sla_paused_at

-- Add to total pause duration
sla_paused_duration = sla_paused_duration + pause_duration

-- Extend deadlines
sla_response_due = sla_response_due + pause_duration
sla_resolve_due = sla_resolve_due + pause_duration

-- Clear pause time
sla_paused_at = NULL

#SLA Breach Determination

-- Response breach
is_sla_response_breached = (first_response_at IS NULL AND NOW() > sla_response_due)
                        OR (first_response_at > sla_response_due)

-- Resolution breach
is_sla_resolve_breached = (resolved_at IS NULL AND NOW() > sla_resolve_due)
                       OR (resolved_at > sla_resolve_due)

#4.3 SLA Alert Mechanism

#4.4 SLA Dashboard Metrics

#5. AI Capabilities and Employee System

#5.1 AI Employee Team

The system configures 8 AI employees in two categories:

New Employees (Ticketing System Specific)

Reused Employees (General Capabilities)

#5.2 AI Task List

Each AI employee is configured with 4 specific tasks:

#Sam's Tasks

#Grace's Tasks

#Max's Tasks

#Lexi's Tasks

#5.3 AI Employees and Ticket Lifecycle

#5.4 AI Response Examples

#SAM-01 Ticket Analysis Response

{
  "category_code": "COMPUTER",
  "sentiment": "NEGATIVE",
  "urgency": "HIGH",
  "keywords": ["ERP", "login failure", "timeout", "month-end closing"],
  "confidence": 0.92,
  "reasoning": "This ticket describes an ERP system login issue affecting finance department month-end closing, high urgency",
  "suggested_reply": "Dear Customer, thank you for reporting this issue...",
  "source_language_code": "zh",
  "is_translated": true,
  "description_translated": "Hello, our ERP system cannot login..."
}

#GRACE-01 Reply Generation Response

Dear Mr. Zhang,

Thank you for contacting us about the ERP login issue. I fully understand this issue is
affecting your company's month-end closing work, and we have prioritized this as high priority.

Current status:
- Technical team is investigating server connection issues
- Expected to provide an update within 30 minutes

In the meantime, you can try:
1. Access via backup address: https://erp-backup.company.com
2. For urgent report needs, contact us for export assistance

Please feel free to contact me if you have any other questions.

Best regards,
Technical Support Team

#5.5 AI EQ Firewall

Grace's reply quality review blocks the following issues:

#6. Knowledge Base System

#6.1 Knowledge Sources

#6.2 Ticket-to-Knowledge Flow

Evaluation Dimensions:

• Generality: Is this a common problem?
• Completeness: Is the solution clear and complete?
• Reproducibility: Are the steps reusable?

#6.3 Knowledge Recommendation Mechanism

When an agent opens ticket details, Max automatically recommends related knowledge:

┌────────────────────────────────────────────────────────────┐
│ Recommended Knowledge                       [Expand/Collapse]│
│ ┌────────────────────────────────────────────────────────┐ │
│ │ KB-T0042 CNC Servo System Fault Diagnosis Guide  Match: 94% │
│ │ Includes: Alarm code interpretation, servo drive check steps │
│ │ [View] [Apply to Reply] [Mark Helpful]                   │
│ ├────────────────────────────────────────────────────────┤ │
│ │ KB-T0038 XYZ-CNC3000 Series Maintenance Manual   Match: 87% │
│ │ Includes: Common faults, preventive maintenance plan      │
│ │ [View] [Apply to Reply] [Mark Helpful]                   │
│ └────────────────────────────────────────────────────────┘ │
└────────────────────────────────────────────────────────────┘

#6.4 Knowledge Base Health Metrics

#7. Workflow Engine

#7.1 Workflow Categories

#7.2 Core Workflows

#WF-T01: Ticket Creation Flow

#WF-AI01: Ticket AI Analysis

#WF-AI04: Comment Translation & Review

#WF-AI03: Knowledge Generation

#7.3 Scheduled Tasks

#8. Menu and Interface Design

#8.1 Backend Admin

#8.2 Customer Portal

#8.3 Dashboard Design

#Executive View

#Supervisor View

#Agent View

#Appendix

#A. Business Type Configuration

#B. Category Codes

Document Version: 2.0 | Last Updated: 2026-01-05