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AI Resume Matcher

A smart resume analysis tool that ranks CVs based on job descriptions using Gemini AI with built-in ZIP parsing and real-time scoring.

AI Resume Matcher

Project Overview

AI Resume Matcher is a front-end-first assessment tool that streamlines initial candidate triage by combining robust file parsing with generative AI-driven evaluation. Users upload a ZIP of resumes in PDF/DOCX/TXT formats, provide a job description, and the tool performs extraction, parsing and normalization of resume content (text extraction, basic entity detection). A generative scoring engine then compares candidate attributes to the JD and emits a match score with structured reasoning and highlights for strengths and gaps. The app includes safeguards around file validation, rate-limited usage for free tiers, and optional Supabase authentication for usage tracking. The UI focuses on clarity—side-by-side comparison of candidate scores, key skill matches, and exportable shortlists. The product lowers the time-to-shortlist substantially, surfaces objective reasons for matches, and can be integrated into downstream ATS workflows by exporting CSV shortlists and annotated candidate reports.

Highlights

Key Features

Bulk resume parsing

ZIP upload support with robust extraction for PDF, DOCX and plain text files, plus basic entity recognition.

AI-driven scoring

Generative comparison of candidate profiles against job descriptions with explainable match reasoning.

Exportable shortlists

Download CSV shortlists and annotated candidate reports for ATS imports and hiring workflows.

Privacy and rate controls

Client-side processing with configurable usage limits and optional authenticated sessions.

Rapid candidate screening

Fast bulk triage of resumes reducing time-to-shortlist dramatically.

Transparent AI evaluation

Explainable AI match scores with clear reasoning for hiring decisions.

ATS integration ready

Exports for ATS and downstream review compatible with major hiring platforms.

Privacy-focused architecture

Front-end-first, privacy-aware design keeping candidate data secure and local.

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