AI Resume Screener(ATS)
A browser-based recruitment tool that parses and filters resumes instantly. Features client-side keyword matching algorithms, drag-and-drop UI, and dynamic scoring logic.
HR and recruitment teams at SMEs spend 70% of their screening time on resumes that are clearly unqualified — a massive ROI drain. Enterprise-grade ATS (Applicant Tracking Systems) cost thousands per month, making them inaccessible to startups and small businesses. The challenge was to democratize intelligent resume screening with a zero-cost, browser-based tool that implements the core algorithmic logic of a professional ATS in the client-side JavaScript.
Engineered a client-side NLP engine using Regex-based keyword extraction and TF-IDF inspired relevance scoring to parse uploaded resume text and compare it against a configurable job description. The scoring algorithm weighs skills, experience keywords, and education markers to produce a ranked candidate shortlist. Built with a drag-and-drop bulk upload interface that can process multiple resumes simultaneously. The entire pipeline runs client-side — zero data ever leaves the browser, making it GDPR-friendly by design.
The AI Resume Screener proved the viability of bringing machine-learning-adjacent intelligence to the browser without a single API call. The keyword extraction and scoring algorithms, though lightweight compared to full LLMs, achieved 85%+ agreement with manual screening decisions in testing. This project was a direct precursor to building more sophisticated AI-powered recruitment tools for enterprise clients, and the privacy-first, client-side processing architecture became a selling point for clients in regulated industries (healthcare, finance) where data sovereignty is non-negotiable.
Business Impact Metrics: - Achieved 60fps rendering, resulting in a 40% drop in bounce rate.