HireMatch
An AI-powered hiring platform that maps resumes to job descriptions using vector databases and generates automated skill gap analyses.
Project Overview
HireMatch is an intelligent applicant tracking and matching platform developed as a Software Engineering semester project. It serves both recruiters and job seekers by utilizing vector databases to semantically search and map candidate resumes to specific job requirements. The system calculates a deterministic relevance score for each application and uses an LLM pipeline to generate personalized skill gap analyses, streamlining the hiring process and providing actionable feedback.
Problem It Solves
Recruiters spend countless hours manually filtering through resumes that often lack exact keyword matches, while job seekers lack actionable feedback on why they were rejected. Traditional keyword-based ATS systems fail to understand semantic context, filtering out qualified candidates who use different terminology.
Tools & Technologies Used
Skills Involved
Challenges
Integrating PGVector with Supabase to perform accurate semantic similarity searches between highly unstructured resume data and rigid job descriptions. Another major challenge was designing the AI pipeline to reliably output structured, unbiased gap analyses and scoring metrics without hallucinating candidate skills or overlooking implicit experience.
Learnings
Gained hands-on engineering experience working with embeddings and vector databases in a production-like environment. I learned how to build robust semantic search functionality and architect an AI system that processes complex, real-world documents to extract structured, actionable insights rather than just generating conversational text.