Enterprise-level AI Support Ticket Assistant: An Intelligent Customer Service System Based on RAG and Generative AI

Project Overview

This project is named AI-Support-Ticket-Copilot, maintained by krishnadvsetti, hosted on GitHub (link: https://github.com/krishnadvsetti/AI-Support-Ticket-Copilot), and released on June 10, 2026.

Core Value

Integrates FastAPI, PostgreSQL, ChromaDB, MLflow, and RAG technologies to realize end-to-end intelligentization of ticket classification, knowledge retrieval, and automatic reply generation, addressing pain points in enterprise ticket processing and improving the efficiency of support teams and user experience.

Core issues faced by enterprise ticket systems:

• Surge in ticket volume leading to response delays
• Difficulty in reusing historical knowledge
• Time-consuming and error-prone manual classification
• Lack of consistency in solutions

Project goal: Build an automated auxiliary system using AI technology to cover the entire process from ticket reception to reply generation.

Key Components

1. FastAPI: Asynchronous web framework that provides high performance and type safety, supporting automatic API documentation generation.
2. PostgreSQL: Stores structured data (tickets, user information), supporting transactions and JSON extensions.
3. ChromaDB: Vector database that stores vectorized representations of knowledge, which is the core of the RAG architecture.
4. MLflow: Experiment tracking tool that records model iteration parameters and metrics.
5. RAG Architecture: Retrieves relevant context + generative AI output, balancing accuracy and generation capability.

1. Intelligent Classification: LLM automatically categorizes tickets (e.g., account issues, technical failures) to reduce routing time.
2. Semantic Retrieval: Uses ChromaDB to understand query intent and match similar historical cases (e.g., "cannot log in" and "password error").
3. Automatic Reply: Generates drafts based on retrieval results, supporting manual review and modification.
4. Solution Recommendation: Pushes standard solutions for common issues, optimizing with knowledge base iterations.

1. Modular Design: Separation of component responsibilities for easy independent expansion (e.g., replacing the vector database).
2. Observability: MLflow supports experiment tracking and model monitoring, combined with logs to build a complete system.
3. Progressive Deployment: Initially used as an auxiliary tool, with the automation ratio expanded as accuracy improves to reduce implementation risks.

1. IT Operations: Handles internal employee IT issues (software installation, permission applications) with quick responses.
2. Customer Service: Assists customer service in understanding customer needs, provides standardized solutions, and improves satisfaction.
3. Knowledge Management: Serves as an intelligent retrieval entry to help employees quickly access technical documents.

1. Retrieval Quality: Optimize document splitting and embedding models, introduce hybrid retrieval (keyword + semantic) and query rewriting.
2. Hallucination Control: Control generation errors through prompt engineering and manual review; disable automation for high-risk operations.
3. Data Security: Implement vector database access control, API authentication, and data desensitization to ensure sensitive information security.
4. Continuous Learning: Build a closed-loop feedback mechanism to optimize generation quality from manual modifications.

This project demonstrates a complete system engineering for enterprise-level AI applications: covering data, retrieval, generation, and interface layers, integrating complementary technologies, and balancing maintainability and scalability.

For enterprises, such open-source projects provide practical references for AI implementation, proving that AI can be a reliable tool to solve real business problems, and more processes will be enhanced by intelligent systems in the future.