Agentic AI Customer Service System: Full-Stack Implementation of Voice Ticket Automation

This article introduces an Agentic AI customer service automation system based on React and FastAPI, which achieves end-to-end automated processing from voice input to structured tickets using Whisper speech recognition and Groq LLM. The system adopts a front-end and back-end separation architecture, covering core links such as voice collection, recognition, intent analysis, and ticket generation. It is suitable for scenarios like call center automation and multi-channel customer service integration, and is open-source and extensible.

Traditional customer service systems rely on rule engines and are rigid when dealing with complex problems; Agentic AI can understand context and infer intent. This project was released on GitHub by Tejasre-2504 (May 30, 2026), demonstrating the latest direction of end-to-end voice ticket automation.

Adopts front-end and back-end separation architecture:

• Front-end: React18 + Vite + Tailwind CSS + React Router
• Back-end: FastAPI + Uvicorn + Whisper + Groq API (Llama3.3 70B)
• Dependencies: Node18+, Python3.10+, FFmpeg The tech combination balances development efficiency and performance, complying with AI application best practices.

1. Voice collection and upload: Users upload voice files (max 25MB); 2. Speech recognition: Whisper model converts to text (supports 99 languages, model downloaded on first use); 3. Intent analysis: Groq LLM performs classification, sentiment analysis, routing, and priority evaluation; 4. Ticket generation: Structured storage into JSON files.

Functional Modules:

1. Dashboard: Overview of key metrics; 2. Voice upload: File upload + transcription visualization; 3. Ticket list: Management and filtering; 4. Routing status: Monitor routing accuracy. Technical Highlights: Environment variable configuration (model/size/LLM service), JWT authentication, fault-tolerant design (model download prompts, FFmpeg checks), automatic API documentation.

Applicable to:

1. Call center automation: Improve processing efficiency; 2. Multi-channel integration: Unified handling of voice requests; 3. Quality monitoring: Batch quality inspection of customer service calls; 4. Knowledge base construction: Form a data loop to train robots.

Production Optimization: Use larger Whisper models, load balancing, replace JSON storage with databases, Redis caching. Expansion Directions: Integrate more LLM providers, real-time voice processing, multi-turn conversations, connect to CRM/ERP systems.

This open-source project combines speech recognition and LLM to build an intelligent system that can understand customers and automatically handle problems. With the popularization of voice interaction, such automated systems will become important tools for enterprises to improve service efficiency and experience, and developers can freely expand and adapt to business scenarios.