Modern recruitment platforms process thousands of candidate resumes daily. Traditional keyword-matching Applicant Tracking Systems (ATS) miss qualified candidates due to layout variations and semantic phrasing differences.
1. Multi-Format Document Extraction & Layout Normalization
Resumes arrive in unformatted PDFs, complex multi-column Word documents, and scanned images. By building a unified pipeline using pdf-parse, mammoth.js, and fallback Tesseract OCR, unstructured text is extracted into standard JSON schemas containing contact info, work history, tech stacks, and educational background.
2. Vector Embeddings & Candidate-Job Skill Alignment
Rather than matching exact strings (e.g., "React.js" vs. "Frontend Engineer with React"), text embeddings convert candidate profiles and job descriptions into high-dimensional vector spaces. Calculating cosine similarity between job requirements and candidate vectors yields accurate semantic fit scores regardless of resume phrasing.
3. Real-Time Webhook Pipeline & Recruiter Alerts
When high-matching candidates submit their resumes, automated queue workers trigger instant recruiter notifications via WebSockets and email APIs (Resend/SendGrid), reducing HR candidate response latency from days to under 15 minutes.