An enterprise-grade Full Stack Performance Management System built during my internship at VDart GCC, designed to streamline employee performance tracking, reporting, and communication across CX, CSM, RD, and RM teams. This project serves as a centralized performance tracking and communication dashboard for multiple departments within VDart GCC.

The Employee Exit Interview System is a CRUD-based application that collects, analyzes, and manages employee exit data. It includes an employee form interface, secure admin dashboard, and graphical data analytics module — providing HR and management teams with real-time insights into exit reasons and trends.

A complete Employee Shoutout Management System built using PHP, MySQL, HTML5, CSS3, and JavaScript. This system allows employees to submit shoutouts for their best-performing teammates and lets admins manage, track, and export shoutout data — all within a responsive, interactive dashboard.

The Text to QR Code android application is a basic level image or text converted into qr code. The application is developed using HTML, CSS and JS languages.

A fully customizable Mail Template Builder that allows users to design, generate, and download branded email templates for Outlook or any email client — complete with banners, messages, images, and footers. Built using Core PHP, JavaScript, HTML5, and CSS3, this project combines form-based inputs, rich text editing, carousel image integration.

A modern, responsive personal portfolio website showcasing my projects, skills, and professional experience as a Full Stack Developer. Built with clean design principles and smooth user experience in mind.

Full Stack Developer | Building real-world web apps with PHP, JS & Gen AI | Passionate about scalable backend & automation tools | Turning ideas into web apps 🚀

SignSync is a sign-language translation system I built to support both Indian Sign Language (ISL) and American Sign Language (ASL). I used MediaPipe for gesture tracking and integrated NLP and deep learning models to convert signs into speech and text. The system achieved around 82% accuracy in translation.