Zahid Hamdule

Full Stack Developer

A. P. Shah Institute of TechnologyMumbaiAvailable from 2026-07-01 · full_time

Open to roles

Full stack developmentPythonTypeScriptJavaScript

ExpiryIQ – AI-Powered Enterprise Renewal & Compliance Platform

ExpiryIQ is an AI-powered enterprise renewal and compliance workspace built to help organizations proactively manage expiring contracts, certificates, licenses, and other critical business records. Key highlights: • Secure authentication with Firebase (Email/Password + Google Sign-In) • Real-time Firestore-powered dashboard with live expiry tracking • Automated record classification (Active, Expiring Soon, Expired) • Advanced search, filtering, analytics, and calendar views • AI Compliance Copilot powered by Groq for executive summaries, risk analysis, and natural language queries • Intelligent document upload with metadata extraction and auto-filled records • Responsive enterprise UI with Light/Dark mode support How to use: 1. Sign in using Google or Email Authentication. 2. Create a few sample records with different expiry dates. 3. Upload a PDF/image to test document intelligence. 4. Explore Dashboard, Analytics, Calendar, Search, and AI Copilot. 5. Observe automatic status updates and compliance insights. The application is optimized for the Groq free tier using response caching, limited AI context, and metadata caching to minimize API usage. Thank you for your time and consideration!

5 media files · expiry-iq-one.vercel.appView

MemoryVerse AI — Personal Digital Second Brain

Overview MemoryVerse AI is an AI-powered personal digital second brain that automatically extracts, indexes, and visually maps connections between your digital memories (resumes, project reports, certificates, notes, etc.). It features an interactive 3D/2D Knowledge Graph and a citations-backed RAG Chat Assistant. Key Design Decisions 1. Hybrid Vector Database: Combines structured PostgreSQL (Neon DB) for transactional data and document metadata, with ChromaDB for storing and querying text embeddings locally. 2. Multi-Model AI Pipeline: Utilizes Groq API (using Llama 3) for lightning-fast metadata, topic, and entity extraction. We implemented a Groq key-pool rotation manager with automatic exponential backoff to completely bypass API rate limits. 3. RAG (Retrieval-Augmented Generation): Uses sentence-transformers (all-MiniLM-L6-v2) for generating semantic vector embeddings, ensuring highly relevant context matches during searches. 4. Modern UI/UX: Built with React 18, Vite, TailwindCSS, Framer Motion for smooth transitions, and ReactFlow for dynamic connection rendering. Features an interactive guided spotlight onboarding tour. 5. Cloud-Native Storage: Uses Cloudinary for secure file storage and document signing. How to Test / Review 1. Interactive Demo Dataset: Go to the "Upload Files" tab and click the "Import Demo Dataset" button. This will automatically populate the database with pre-packaged resume, certificate, and internship documents so you don't have to upload your own files to see the graph working. 2. Knowledge Graph: Visit the "Relationships" tab to explore nodes mapping people, locations, organizations, and files. Click on the nodes to expand connections. 3. Citations Chat: Visit the "Chat Assistant" tab. Ask questions about the uploaded files (e.g. "What projects has Zahid worked on?"). You will see responses containing exact source documents and match percentages.

5 media files · memory-verse-ai.vercel.appView

QueueCure — Real-Time Clinic Queue & Live Wait Tracker

In India, 76% of neighborhood clinics manage patient queues via manual paper token slips and verbal shouting. Receptionists operate from memory, and doctors lack dashboard visibility. Patients wait 2-3 hours with zero progress transparency, causing front-desk congestion. As one waiting patient put it: "I have no idea if I have time to step out for water, or if I'll lose my turn entirely." QueueCure was built to replace this chaos with a seamless, live-synced digital queue tracking experience. Process I designed a three-screen portal: Receptionist console, Doctor panel, and Patient mobile PWA. During initial prototyping, I implemented implicit checkouts—completing patient A automatically when patient B was called. Simulation showed this was highly inaccurate; doctors writing notes after a consult inflated average wait times. I pivoted to an explicit "Mark as Done" checkout button to capture real consultation times. To prevent concurrency bugs when testing duplicate quick clicks on receptionist controls, I engineered an atomic mutex lock in Redis (SET NX). I also added a 5-second cancelable undo window to easily resolve front-desk mistakes, and an offline shell so patients never lose progress. Results The prototype achieved a 100% success rate during local simulation testing, reducing simulated patient check-in to a sub-3-second flow. Explicit status checkouts successfully built a rolling average wait estimation that self-corrects based on the last 10 visits. Local testing showed zero race condition errors during simultaneous token calls, and the patient view updated instantly without manual refresh. Reflection If building this further, I would integrate SMS/WhatsApp webhooks to notify patients when they are "two turns away," allowing them to wait in nearby cafes or at home without needing to keep the browser tab open. I would also scale the Redis structure to support multi-doctor clinics, routing patients to specific rooms dynamically through a centralized receptionist dashboard, and build an analytics portal showing doctors their peak patient hours.

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