Neshandra G

Neshandra G

B.Tech IT Student | Passionate About AI | Full-Stack Developer

Chennai Institute of TechnologyChennai, TamilNadufull_time, internshipOpen to roles

2 projects1 challenge entry1 reviewed1 top-3 placement

GITProject ManagementProblem SolvingLeadership
Helix — AI Digital Identity System
Challenge entry · MemoryVerse AI '26 by Wooble

Helix — AI Digital Identity System

HELIX is an AI-powered Digital Identity & Knowledge Graph that transforms scattered academic and professional documents into a structured, searchable, and evidence-backed representation of a student's growth. Instead of simply storing files, HELIX understands uploaded content, automatically categorizes information, discovers relationships across experiences, visualizes growth through a digital journey timeline, and enables natural-language retrieval using Retrieval-Augmented Generation (RAG). PROJECT RESOURCES Live Application https://h-e-l-i-x-peach.vercel.app Demo Video https://youtu.be/5ONEuIMZQLc Presentation https://drive.google.com/file/d/1lZKq4jQd_KRlNsp7IG7yQ0Dzpa9iz_Vp/view?usp=sharing GitHub Repository https://github.com/neshandrag/h.e.l.i.x EXPLORING - HELIX • Register a new account (no test credentials are required). • Upload certificates, resumes, project reports, internship letters, or images. • Optionally connect a GitHub repository to import project information automatically. • Explore the Documents Dashboard to review uploaded files, AI classifications, and evidence scores. • Visualize relationships between skills, projects, certifications, internships, and achievements using the Knowledge Graph. • View your milestones in the Digital Journey Timeline, automatically generated from uploaded evidence. • Use Ask AI to query your digital identity in natural language and receive evidence-backed responses powered by Retrieval-Augmented Generation (RAG). • Visit the Public Profile to view a shareable, read-only representation of your digital identity. KEY DESIGN DECISIONS • AI is responsible for information extraction and categorization, while verifiability, relationship depth, and path coherence are computed using deterministic algorithms for transparency and consistency. • Classification, Verifiability, Relationship Depth, and Coherence are evaluated independently instead of being combined into a single opaque score. • Semantic search powered by vector embeddings and Retrieval-Augmented Generation (RAG) ensures responses are grounded in uploaded evidence rather than generated assumptions. • Original documents remain preserved in their native format and are always accessible. • The modular architecture allows document uploads to function independently, while GitHub and Telegram integrations remain optional extensions. DEPLOYMENT INFORMATION Frontend: Vercel Backend: Render Database: Supabase (PostgreSQL + pgvector) Note: The backend is deployed on Render's free tier. After periods of inactivity, the first API request may take approximately 30–60 seconds while the service resumes. Subsequent requests respond normally.

2nd placeReviewed
5 media files · h-e-l-i-x-peach.vercel.appView

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