Yuvanesh RS

Full-Stack Developer

Chennai Institute of Technologyfull_time, internship

Open to roles

CommunicationProblem SolvingLeadershipGIT

MemoryVerse AI

MemoryVerse AI is an AI-powered career intelligence platform designed to transform scattered career documents into a structured, searchable, and intelligent digital knowledge base. Instead of simply storing resumes, certificates, internship letters, and project reports, the platform understands their content using Google Gemini AI. It automatically extracts meaningful entities such as skills, projects, organizations, certifications, internships, technologies, and achievements, then connects them through an interactive Knowledge Graph and Career Timeline. Key capabilities implemented: • Intelligent document processing using OCR and Google Gemini AI • Automatic entity and relationship extraction • Interactive Knowledge Graph visualization • Chronological Career Timeline generation • AI-generated Career Summary • Semantic Search across uploaded documents • Conversational AI Assistant powered by Retrieval-Augmented Generation (RAG) Technology Stack: Frontend: React + TypeScript + Vite + Tailwind CSS Backend: FastAPI (Python) AI: Google Gemini API, OCR, Embeddings, RAG Database & Storage: Supabase PostgreSQL + Supabase Storage Evaluation Notes: • Upload one or more career documents (Resume, Certificate, Internship Letter, or Project Report). • Allow a few moments for AI processing. • Explore the generated dashboard, career summary, timeline, semantic search, and AI chat features. • Use the Knowledge Graph to understand how entities and achievements are connected. MemoryVerse AI demonstrates how modern AI can move beyond document storage and create an intelligent career ecosystem that helps students and professionals organize, understand, and showcase their complete career journey. Demo Video: https://drive.google.com/file/d/1dIq9zH7KEnBl3qN2w3KwKoa3ueAqxWAw/view?usp=drivesdk GitHub: https://github.com/yuvanesh2356/MemoryVerse-AI Project Description: https://docs.google.com/document/d/1-11ghjvlALfCNEA_Oi0j3GHCOJgzr34s/edit?usp=drivesdk&ouid=117267765384851539211&rtpof=true&sd=true Project Presentation: https://drive.google.com/file/d/1uIHD-gwdvb_vejB4aoz-UJU5I8zM8SZq/view?usp=drivesdk

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QueueCure Pro - Smart Healthcare Queue Management System

Many clinics still manage patients using paper tokens and manual queues. Patients often wait for hours without knowing when they will be called, while receptionists manually track tokens and doctors lack real-time queue visibility. This creates confusion, overcrowding, and poor patient experience. QueueCure Pro solves this by providing digital token management, QR-based check-in, estimated waiting times, and real-time queue tracking through a centralized dashboard powered by Firebase. We analyzed common clinic queue problems and designed a simple digital solution. The frontend was built using React, Vite, and Tailwind CSS. Firebase Authentication was used for user access, while Cloud Firestore served as the real-time database. Firestore collections include clinics, doctors, staff, sessions, and tokens. Clinics store clinic details, doctors store consultation data, staff manages roles, sessions track queue status, and tokens manage patient flow. We implemented clinic setup, receptionist dashboard, QR check-in, token generation, live queue updates, and TV display functionality. Firestore synchronization ensures all screens update instantly in real time. QueueCure Pro successfully digitized clinic queue management through real-time synchronization. Patients can join queues through QR check-in, view estimated waiting times, and track their position live. Receptionists can manage tokens efficiently while doctors receive updated queue information instantly. The system reduced patient uncertainty, improved queue transparency, and demonstrated seamless live updates across dashboard, patient, and TV display screens using Cloud Firestore. Given more time, I would add SMS and WhatsApp notifications, appointment scheduling, multi-clinic support, analytics dashboards, and AI-based wait-time prediction. I would also improve mobile responsiveness, strengthen Firestore security rules, and conduct testing with real clinics to gather feedback and further optimize the patient experience.

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