NetworkAI — AI-Powered Campus Network Monitoring System
A real-time network monitoring dashboard designed to help campus IT teams observe network health, monitor interfaces, and identify potential issues from a single interface.
What I built
Real-time network monitoring with live interface and system status
Network interface analytics showing traffic and connection activity
Historical monitoring data for analyzing network behavior over time
Live WebSocket communication for continuously updated monitoring data
Network controls to start and stop monitoring directly from the dashboard
Interactive charts and visualizations for easier interpretation of
network metrics
Responsive dashboard UI designed for clear monitoring rather than unnecessary complexity
FastAPI backend providing REST APIs and WebSocket communication
React frontend with a modern monitoring-focused interface
Why I built it
The goal was to create a practical campus network monitoring system that demonstrates how real-time networking data can be collected, processed, and presented through a modern web interface.
Live Demo: https://netwrok-ai-a-campus-monitoring-syst.vercel.app/
6
15
934
I build custom AI-powered contract analysis web applications that help users review contracts, identify risks, and generate clear recommendations in minutes.
Using LangChain, RAG, Llama models, Hugging Face embeddings, Flask, and Docker, I create production-ready solutions that can analyze PDFs, DOCX files, and text documents, detect risky clauses, generate plain-English summaries, and provide structured risk scores.
Perfect for LegalTech startups, law firms, procurement teams, HR departments, and founders looking to automate contract review workflows.
Includes source code, deployment, customization, and post-launch support.
0
81
AthletiX Hub — Conversational AI E-Commerce Platform
Built a full-stack fitness supplement e-commerce platform with an integrated conversational AI shopping assistant.
AthletiX Hub combines a Flask backend, MySQL database, responsive web interface, and Dialogflow-powered chatbot to create a conversational shopping experience. Users can browse products and interact with the AI assistant using natural language to add or remove supplements, complete orders, calculate totals, generate order IDs, and track existing orders.
I developed the backend business logic, database integration, conversational workflow, session-based order management, webhook handling, and interactive frontend components.
The project demonstrates how conversational AI can be integrated directly into real e-commerce workflows rather than being limited to simple question-answering.:
0
85
VendorBridge ERP is a full-stack enterprise resource planning (ERP) platform designed to help businesses manage vendors, inventory, procurement workflows, and day-to-day operations from a centralized dashboard.
Key Features:
• Vendor Management
• Inventory Tracking
• Purchase Order Management
• Role-Based Access Control
• Analytics & Reporting Dashboard
• Secure Authentication System
• Real-Time Data Management
My Contributions:
• Developed the backend using Python, Flask, and MySQL
• Designed RESTful APIs for frontend integration
• Implemented authentication and authorization
• Built database architecture and business logic
• Integrated frontend and backend systems
• Optimized application performance and database queries
Technology Stack:
Frontend: TypeScript, Modern JavaScript, HTML5, CSS3
Backend: Python, Flask
Database: MySQL
Impact:
VendorBridge ERP streamlines business operations by reducing manual processes, improving data visibility, and providing a scalable foundation for managing vendors, inventory, and procurement activities.
1
112
NLP Studio — Interactive Natural Language Processing Platform
Built an interactive NLP platform that brings multiple natural language processing capabilities into a single application. The system performs sentiment analysis, Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and text processing on real-world text data.
I developed the complete application, including the NLP processing pipeline, model integration, backend APIs, and user interface. The project uses Python, spaCy, Scikit-learn, Pandas, and Flask to transform raw text into structured and actionable insights.
The project demonstrates my ability to take NLP models and techniques beyond experimentation and turn them into an interactive, usable AI application.
0
75
