The problem
High-volume roles generate hundreds of resumes, and manually opening each one to check for basic fit — required skills, years of experience, education — doesn't scale. Recruiters were spending most of their time screening out clearly unqualified candidates instead of evaluating the ones worth a closer look.
The approach
Used AI Builder's document processing to extract structured fields from resumes in varied formats (PDF, DOCX, scanned) — no fixed template required — and scored each candidate against role-specific criteria before a human ever opens the file.
What it does
- Extracts structured data — skills, years of experience, education, prior roles — from unstructured resume documents
- Scores each candidate against configurable role criteria rather than a rigid keyword match, reducing false rejects for resumes phrased differently than the job description
- Routes a ranked shortlist to the recruiter, with the extracted data attached so the score is explainable, not a black-box number
- Flags borderline cases for human review instead of silently auto-rejecting them
Outcome
Reduced the manual first-pass screening load significantly, letting recruiters spend their time on borderline and shortlisted candidates instead of reading every submission. Screening consistency also improved — every resume gets evaluated against the same criteria, removing reviewer-to-reviewer variance.