Digital Twin

System Architecture

This Digital Twin leverages a Retrieval-Augmented Generation (RAG) architecture to provide context-aware, professional responses to recruiter-style queries. The system combines cutting-edge AI technologies to deliver accurate and personalized information about professional experience, skills, and career goals.

Technology Stack

• Upstash Vector: Vector database for semantic search and efficient retrieval
• Groq AI: Ultra-fast LLM inference for generating responses
• Python Backend: RAG system implementation with embedding pipeline
• Next.js Frontend: Modern, responsive web interface
• STAR Methodology: Professional profile data structured for optimal retrieval

How It Works

1. Professional data is structured using the STAR methodology
2. Content is converted into vector embeddings and stored in Upstash
3. When a query is received, semantic search retrieves relevant information
4. Groq AI generates a personalized, context-aware response
5. The system is optimized for recruiter and hiring team interactions