demo_prep.md

Hackathon Demo - Automated Resume Relevance Check System

30-Second Elevator Pitch

"I built an AI-powered resume screening system that goes beyond simple keyword matching. It uses semantic embeddings, fuzzy matching, and NLP to provide intelligent analysis and actionable recommendations."

Key Demo Points (2 minutes)

1. Problem Statement

• Current ATS systems miss qualified candidates
• Only basic keyword matching
• No actionable feedback for improvement

2. Our Solution - Advanced AI Stack

• Semantic Matching: Understanding context, not just keywords
• Fuzzy Matching: Catches variations (JS vs JavaScript)
• NLP Entity Extraction: Extracts experience, education, skills
• LLM Analysis: Provides human-like insights
• Comprehensive Scoring: Multi-factor weighted algorithm

3. Live Demo Flow

1. Upload sample resume (show file upload)
2. Upload job description
3. Click analyze (show progress bar)
4. Results breakdown:
   • Final Score: 78/100
   • Hard Match: 65% (TF-IDF + keywords)
   • Semantic Match: 8/10 (AI understanding)
   • Missing Skills: Docker, Kubernetes
   • AI Recommendations: Specific next steps

4. Business Value

• For Companies: Better candidate screening, reduce false negatives
• For Students: Clear improvement roadmap, skill gap analysis
• For Placement Teams: Data-driven decisions, automated screening

5. Technical Highlights

• Modern tech stack (FastAPI, Streamlit, AI/ML)
• Scalable architecture (API-first design)
• Real-time analysis with progress tracking
• Exportable reports