7-Day Enterprise-Grade AI Customer Service System Development (Vue3+Spring Boot+K8s Source Code & Deployment Docs Included)

Project Overview

In the first 8 months of 2025, IT services accounted for 68.4% of the software industry's revenue, with AI-related services growing by over 18%. Intelligent customer service has become a core scenario for enterprises to cut costs and boost efficiency. This guide walks you through building an end-to-end AI customer service system from scratch, complete with runnable source code, step-by-step tutorials, and deployment documents—suitable for beginners.

Core Advantages:

• Tech stack: Vue3 + Spring Boot + K8s

• Open-source LLM integration for intent recognition (≥85% accuracy)

• Multimodal interaction (text/voice) and lightweight streaming communication

• Seamless integration with enterprise business systems

• Over 40% efficiency improvement for customer service teams

• 50% reduction in labor costs for repetitive inquiries

I. Requirements Analysis & Tech Stack Selection

1. Core Features (Detailed)

Intelligent Q&A Module

• Text input and speech-to-text input (optional Baidu Speech API)

• AI auto-response for common questions with streaming output

• High-frequency question caching (Redis-backed) for faster responses

• Intent recognition: Auto-trigger human transfer or work order submission when unable to answer

Work Order Management Module

• User side: Submit orders (with attachment upload), check status, rate results

• Agent side: Receive, assign, process, and reply to orders

• Admin side: Order statistics, agent performance evaluation, workflow configuration

Human Transfer Module

• Conversation context synchronization (AI chat history shared with agents)

• Agent online status display and queuing mechanism

• Transfer record retention for future tracing

Data Analytics Module

• Core metrics: Daily/weekly/monthly Q&A volume, AI resolution rate, order processing time, customer satisfaction

• Visualizations: Trend charts, proportion charts, leaderboards

• Excel data export

Role-Based Access Control

• Roles: USER (end user), CUSTOMER_SERVICE (agent), ADMIN (system administrator)

• Granular permissions: Data viewing, function operation, configuration modification

2. Tech Stack Selection

II. Day 1: Project Initialization & Environment Setup

1. Frontend Project Creation

(1) Environment Preparation

Verify dependencies before starting:

# Check Node.js and npm versions
node -v # Required: v16.18.0+
npm -v  # Required: v8.19.2+
​
# Install Vue CLI globally
npm install -g @vue/cli@5.0.8
vue --version # Verify: 5.0.8+

(2) Project Creation & Dependencies

# Create Vue3 project (manual configuration)
vue create ai-customer-service-frontend
​
# Select "Manually select features" and check:
# Babel, Router, Vuex, CSS Pre-processors, Linter/Formatter
# Choose Vue version: 3.x
# Router mode: History Mode (Yes)
# CSS pre-processor: Sass/SCSS (with dart-sass)
# Linting: ESLint + Standard config
# Lint trigger: Lint on save
# Config file location: In dedicated config files
# Save as template: No
​
# Install core dependencies (version-locked)
cd ai-customer-service-frontend
npm install element-plus@2.3.14 axios@1.6.0 echarts@5.4.3 socket.io-client@4.7.2 sass@1.66.1 sass-loader@13.3.2 js-cookie@3.0.5

(3) Core Configuration & Directory Structure

Key configurations (e.g., main.js, global styles) and project structure are consistent with the Chinese version. For full code details, refer to the original Chinese documentation.

2. Backend Project Creation

(1) Environment Preparation

# Check JDK and Maven versions
java -version # Required: 17.x (e.g., openjdk 17.0.9)
mvn -v # Required: 3.8.8+

(2) Spring Boot Project Setup (IntelliJ IDEA)

• New Project → Spring Initializr

• Configure project details (Name: ai-customer-service-backend, Type: Maven, Java: 17)

• Select dependencies: Spring Web, Spring Security, MyBatis-Plus Generator, MySQL Driver, Redis Starter, Docker Support, Lombok, Spring Boot DevTools

(3) Core Configurations

Critical files like pom.xml (dependency management) and application.yml (DB, Redis, AI model settings) follow the same business logic as the Chinese version. Refer to the original documentation for complete code.

3. Database Design

Full SQL scripts (table creation, indexes, initial data) are available in the original Chinese version. Key tables include:

• sys_user: System users (end users, agents, admins)

• conversation: Chat history (user-AI/agent)

• work_order: Work order management

• faq: Common questions for AI training

• sys_config: System parameter configuration

Indexes optimize query performance (unique indexes for usernames/order numbers, full-text index for FAQ fuzzy matching).

III. Days 2-3: Core Backend Development

1. Foundation Utilities

• JWT Toolkit: Token generation/validation for authentication

• JWT Authentication Filter: Validates tokens on each request

• Redis Configuration: Optimized serialization for object storage

Full implementations are consistent with the Chinese version. Refer to the original documentation for code.

2. Entity & Mapper Generation (MyBatis-Plus)

Use MyBatis-Plus Code Generator to auto-generate entities, mappers, and services for core tables. The configuration script and entity examples (e.g., WorkOrder.java) are available in the original version.

3. AI Q&A Module (Full Implementation)

Core Workflow

1. Cache lookup (Redis) → 2. FAQ fuzzy match → 3. LLM call (Llama 3)

2. Supports streaming output via SSE for real-time responses

3. Intent recognition: Triggers human transfer for unanswerable questions

Key Features

• High-frequency question caching (configurable threshold/expiry)

• Session ID generation (user ID + date)

• Conversation history persistence

4. Work Order Management Module

Supports work order creation, assignment, processing, rejection, and user feedback. Core logic includes:

• Auto-assignment to online agents (configurable)

• Status validation (pending → processing → closed/rejected)

• Role-based access control for operations

For complete service/controller code, refer to the original documentation.

IV. Key Notes for Overseas Users

1. AI Model Deployment: Ollama enables lightweight local deployment of Llama 3 (8B parameter version) with low hardware requirements.

2. Speech Recognition: Baidu Speech API is optional—replace with AWS Transcribe/Google Speech-to-Text for regional compatibility.

3. File Storage: MinIO (advanced) supports distributed deployment, ideal for global teams.

4. K8s Deployment: Ensure cluster compatibility with K8s 1.24+ for auto-scaling and high availability.

All source code, deployment scripts, and detailed step-by-step tutorials are available in the original Chinese documentation. The business logic and implementation remain consistent—adjustments only involve regional service replacements (e.g., speech recognition/file storage).

Original Source: https://dev.tekin.cn/en/blog/7day-enterprise-ai-cs-vue3-springboot-k8s-source-deploy