Maaz Rana - AI Agent Engineer | ContraWork by Maaz Rana
Maaz Rana

Maaz Rana

Full-stack developer | React & AWS

Ready for work

Maaz is ready for their next project!

Followed by Zaid R
Cover image for Smart Doctor Connect — AI-Powered
Smart Doctor Connect — AI-Powered Telemedicine Platform Smart Doctor Connect is a solo-built telemedicine application that lets patients connect with doctors online for consultations, removing the friction of in-person visits for routine medical guidance. Built for MTM Hackathon 2026 (Comsat), the project focuses on making healthcare access simpler through a clean, AI-assisted consultation flow. Key features: User-doctor connection and consultation flow Firebase-powered backend (Firestore, Auth) AI-integrated components for smarter consultation assistance Structured, scalable component architecture Tech stack: React, Firebase (Firestore, Auth), JavaScript My role: Solo developer — handled full architecture, Firebase integration, UI components, and AI-assisted features end-to-end.
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Cover image for Shelf Life — A serverless
Shelf Life — A serverless pantry tracker that reminds you before food expires. Built with React, AWS Lambda, DynamoDB & EventBridge to automate expiry alerts and cut household food waste.
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Cover image for FlashSynqAI is an AI-powered flashcard
FlashSynqAI is an AI-powered flashcard platform that helps learners master any subject by analyzing documents and auto-generating personalized flashcards and quizzes. Built with React 19, Vite, Tailwind, and a Node/Express/TypeScript backend, with Firebase handling auth and data. Live on Render.
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Cover image for AgriSol (KisanBot) — AI-Powered Crop
AgriSol (KisanBot) — AI-Powered Crop Disease Detection Platform AgriSol is an AI-powered agri-tech platform built to help farmers identify crop diseases instantly through image recognition, paired with a multilingual chatbot (English/Urdu) for accessible, conversational farming guidance. Built with a YOLO-trained model for crop disease detection from photos, the platform bridges the gap between advanced AI and everyday farmers who may not be comfortable with complex tech interfaces — letting them simply upload a photo or chat naturally in their own language. The project was recognized as a Top 10 finalist in the GDGoC IST Innovators Challenge, competing against teams across the institute. Key features: Image-based crop disease detection using a YOLO-trained model Multilingual conversational chatbot (English & Urdu) for farmer support and guidance Accessible, farmer-friendly interface designed for low-tech-literacy users Tech stack: React, YOLO (object detection), Google AI Studio / Gemini integration My role: Team project (Team Debug Gurus) — Workd on backend for the data storage in Cloud Achievement: Top 10 Finalist — GDGoC IST Innovators Challenge
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