How It Works

From resume stack to
ranked shortlist

Three steps. No manual screening. No spreadsheets. Just structured candidate intelligence you can act on.

95%+Accuracy

~5sPer Resume

GDPRCompliant

01Step 01

Upload your CV stack

Drag and drop resumes in any format — PDF, DOCX, DOC, or TXT. Upload one at a time or hundreds in bulk. Every file is encrypted on arrival and processed securely. Duplicate files are detected automatically so you never burn quota twice. Data is auto-deleted after 30 days.

All major resume formats
Bulk upload supported
Duplicate detection — no wasted parses
Encrypted on arrival · 30-day auto-delete

Step 02

02Step 02

Define your role

Create a job profile with your requirements — required skills, seniority level, industry focus, and must-haves. CVault scores every candidate against your specific criteria, not a generic benchmark.

Custom skill requirements
Seniority level targeting
Industry and domain focus
Weighted scoring criteria

Step 03

03Step 03

Get your ranked shortlist

Every candidate comes back as a structured dossier with a fit score (0-100), matched and missing skills, experience analysis, red flag detection, and a hiring recommendation. Works in 17 languages — multi-column and table-heavy layouts extracted correctly. Review the Strong Yes candidates first.

Fit score 0-100 per candidate
Skill gap analysis · Red flag detection
17 languages · Any resume layout
Strong Yes / Yes / Maybe / No

What you get

PDF in. Structured JSON out.

A real example of what CVault extracts from a single resume page.

Input: resume.pdf

SARAH CHEN
Amsterdam, NL · [email protected]

Experienced backend eng with 5+ yrs
in fintech. Python, Go, TypeScript.

EXPERIENCE
Stripe — Sr. Backend Engineer
Jan 2021 – Present
Led platform team (6 engineers).
Payment processing pipelines.
Improved throughput by 40%.

Monzo — Backend Engineer
Mar 2019 – Dec 2020
Core banking API. Go & Kubernetes.
Handled 1M+ daily transactions.

SKILLS
Python, Go, TypeScript, FastAPI, gRPC,
Docker, Kubernetes, AWS, PostgreSQL

EDUCATION
State University — BSc Comp Sci, 2018

Output: intelligence.json

{
  "name": "Sarah Chen",
  "location": "Amsterdam, NL",
  "email": "[email protected]",
  "seniority": "Senior",
  "total_experience_months": 70,
  "skills": [
    "Python", "Go", "TypeScript",
    "FastAPI", "gRPC", "Docker",
    "Kubernetes", "AWS", "PostgreSQL"
  ],
  "experience": [{
    "company": "Stripe",
    "role": "Sr. Backend Engineer",
    "start": "2021-01",
    "end": null,
    "months": 48,
    "team_size": 6,
    "impact": ["40% throughput improvement"]
  }, {
    "company": "Monzo",
    "role": "Backend Engineer",
    "start": "2019-03",
    "end": "2020-12",
    "months": 22,
    "impact": ["1M+ daily transactions"]
  }],
  "education": {
    "institution": "State University",
    "degree": "BSc Computer Science",
    "year": 2018
  },
  "red_flags": []
}

Why CVault

Built for precision

95%+ accuracy

Tested against hand-made ground truths. Gets more accurate with every parse.

17 languages

Language-specific extraction for 17 languages including Japanese, Korean, Chinese, and all major European languages.

Any layout

Two-column, sidebar, and table-heavy resumes are extracted correctly — columns read in order, not interleaved.

30-day auto-delete

Candidate data is encrypted and automatically deleted. One-click deletion available anytime.

Get started

Ready to stop reading
resumes manually?

Run the live demo first, then create a workspace for your own candidate pipeline.

See the demo →View pricing

From resume stack toranked shortlist