Projects & Case Studies

PostNitro is an AI-powered social media carousel generator designed for content creators, marketers, and teams who need to produce high-quality visual content at scale. The platform uses artificial intelligence to automatically generate carousel slides, complete with customizable templates, branding options, and content suggestions. Users can schedule posts, manage publishing workflows, and collaborate with team members through a unified dashboard. The application handles complex state management for real-time carousel editing, supports multiple social media platforms, and provides analytics on content performance.

Bin Saeed Bakers is a comprehensive e-commerce platform built specifically for the bakery industry. The system features a full admin portal for managing categories, products, users, orders, deals, and newsletters, alongside a polished customer-facing storefront with authentication, product browsing, cart management, and order tracking. The platform supports product variants (sizes, flavors), discount codes, and real-time order status updates. The admin side includes automated receipt generation, order notification systems, and a newsletter management module for marketing email campaigns. Firebase notifications for real-time order alerts are currently being integrated to improve response times.

ReadySteadyPlug is a smart electric vehicle charging management platform designed to streamline the EV charging experience for both consumers and station operators. The system provides real-time session monitoring, energy consumption analytics, station availability tracking, and comprehensive admin tools for managing charging stations, pricing tiers, and user accounts. The admin dashboard features detailed analytics on station utilization, revenue reporting, and maintenance scheduling. The platform integrates with Firebase for real-time data synchronization and push notifications, and uses Redux for complex state management across the application.

Ataleeq is a high-performance quiz and assessment platform built to serve educational institutions and corporate training programs at scale. The system supports dynamic quiz creation with multiple question types, timed assessments, automatic grading, and detailed performance analytics. The extensible architecture allows institutions to customize quiz flows, add branding, and integrate with existing learning management systems. The analytics dashboard provides educators with insights into student performance trends, question difficulty analysis, and completion rates. Built with a modular NestJS backend and Next.js frontend, the platform handles concurrent assessment sessions while maintaining data integrity and response accuracy.

HSMP (Housing Society Management Portal) is a comprehensive property management platform designed for housing society administrators and residents. The portal provides operational analytics, automated billing workflows, payment tracking, maintenance request management, and resident communication tools. Administrators can manage plots, track dues and payments, generate financial reports, and handle maintenance scheduling. Residents access a self-service portal for viewing bills, submitting maintenance requests, and communicating with management. The system handles complex billing logic including installment plans, late payment calculations, and receipt generation across multiple housing blocks and plot types.

CellScope is an AI-powered medical diagnostic tool that uses deep learning and computer vision to detect blood cell diseases, specifically Leukemia and Malaria, from microscopic blood smear images. The system processes uploaded blood cell images through trained convolutional neural network (CNN) models and returns diagnostic predictions with confidence scores. The React-based frontend provides an intuitive interface for lab technicians to upload images, view analysis results, and generate diagnostic reports. The backend, built with Java Spring Boot, handles model inference, image preprocessing, and result storage in MySQL. The deep learning models were trained on curated medical image datasets using Python and TensorFlow, achieving high accuracy on validation sets.