Machine Learning
NeuroDesk: AI Helpdesk Automation
An intelligent helpdesk ticketing system featuring automated issue classification using LSTM and FastText models to streamline support workflows.
NLP PyTorch Django React LSTM
Overview
NeuroDesk is a comprehensive helpdesk solution designed to reduce the manual overhead of ticket triage. By integrating a Deep Learning classification engine, it automatically analyzes ticket content and routes it to the appropriate department (e.g., Network, Hardware, Software) with high accuracy.
System Architecture
The system operates as a decoupled full-stack application:
1. The AI Engine (Backend)
Built with Django REST Framework and PyTorch, the backend serves as the brain of the operation.
- Hybrid Models: Utilizes both LSTM (Long Short-Term Memory) for deep semantic understanding and FastText for rapid baseline classification.
- API Wrapper: Exposes endpoints for real-time inference, allowing the frontend to request predictions on-the-fly.
2. The Interactive Dashboard (Frontend)
A modern SPA built with React and Material UI.
- Real-time Analytics: Visualizes ticket trends and classification confidence using ApexCharts.
- Role-Based Access: Specialized views for Admins, Agents, and Users.
- State Management: Robust data handling with Redux Toolkit.
Key Features
- Automated Triage: Tickets are tagged and routed instantly upon creation.
- Confidence Scoring: The AI provides a confidence score (e.g., 98%), allowing human agents to intervene on ambiguous cases.
- Performance Metrics: Dashboard tracks resolution times and classification accuracy.
Technical Stack
| Component | Technology |
|---|---|
| Frontend | React, Redux, Material UI, ApexCharts |
| Backend | Python, Django REST Framework |
| ML Core | PyTorch, FastText, Pandas, NLTK |
| Database | SQLite / PostgreSQL |