Faster resume screening for recruiters

Recruiters know the bottleneck isn't sourcing — it's the first read. Two hundred resumes for a single role, half of them in different formats, half of them not matching the role. You shouldn't be the human regex.

The problem

ATS keyword matching is brittle: "React.js" and "ReactJS" miss each other. PDF resumes with multi-column layouts confuse parsers. Designers send stylish PDFs that text-based search just can't read. So the human skim happens — and burns three hours per role.

How SeekFiles AI helps

• Hybrid retrieval matches meaning, not just exact strings. "React" finds candidates who wrote "ReactJS" or "React.js."
• OCR + layout parsing handles multi-column resumes and creative-PDF designers.
• Per-candidate citations — the answer says "Maria has 6 years of React (Resume_Maria.pdf, p.1)."
• Cross-resume Q&A — "who has the most fintech experience?"

A workflow

1. Make a "Engineering Pipeline Q3" assistant.
2. Upload all 200 resumes (drag-and-drop the folder).
3. Ask filter questions:
   • "List candidates with 5+ years React, willing to relocate or remote."
   • "Who has both backend and DevOps experience?"
   • "Sort by years of full-time SaaS experience."
4. Each answer cites the resume PDF — open it for the deep read, skip the rest.

What to upload

• All resumes (PDF, DOCX, scanned, or photographed)
• The job description for the role (so you can ask alignment questions)
• Past hire profiles (if you want a similarity baseline)

A first-pass screen that took an afternoon now takes twenty minutes — without the keyword-matching false negatives.