Eshan Roy

AI Engineer

Presidency Universityinternship

Open to roles

Python

MemoryVerse AI – AI-Powered Digital Identity Platform

MemoryVerse AI is an AI-powered digital identity platform that transforms resumes, certificates, internships and project documents into an intelligent, searchable memory vault. Features: • Upload PDF documents • AI automatically extracts and summarizes achievements • Semantic search using vector embeddings • Interactive timeline of memories • AURA AI assistant for natural language querying • Responsive modern UI Tech Stack: Frontend: React, Vite, Tailwind CSS Backend: FastAPI AI: Groq LLM Embeddings: FastEmbed Vector Database: ChromaDB Database: SQLite Deployment: • Frontend: Vercel • Backend: Render Note: OCR for scanned/image-based PDFs is not implemented yet, so text-based PDFs provide the best experience.

5 media files · memoryverse-ai-eight.vercel.appView

Queue Cure – Digital Clinic Queue Management System

Many clinics still use paper tokens and manual queue management, making it difficult for patients to know their waiting status and estimated wait time. Queue Cure solves this by providing a digital queue system that improves visibility for patients and simplifies queue management for clinic staff. Process I designed the system around three queue states: Waiting, In Progress, and Completed. First, I built the Receptionist Dashboard for patient registration and token generation. Then I added patient tracking, wait-time estimation, and a Doctor Dashboard. Finally, I improved the user experience with a responsive Bootstrap interface and handled edge cases such as empty queues and invalid token lookups. Results Queue Cure successfully digitizes clinic queue management by replacing paper tokens with an automated system. Receptionists can register patients quickly, patients can track their queue position and estimated waiting time, and doctors can monitor queue status through a dedicated dashboard. The solution improves transparency, reduces manual effort, and provides a scalable foundation for future enhancements such as real-time updates and QR-based access. Reflection Given more time, I would implement true real-time updates using WebSockets, add QR code access for patients, and support multi-doctor clinics. I would also enhance analytics and notification features to further improve patient experience and clinic efficiency.

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