Parse hundreds of resumes in minutes.

ParseFlow extracts candidate names, contact details, skills, work experience, and education from PDF resumes — perfect for recruiters processing high volumes of applications.

The old way vs. the ParseFlow way

❌ The Old Way

Recruiters manually review each resume, copying candidate details into an ATS or spreadsheet. With 200 applications for a single role, it takes days just to create a structured shortlist.

✅ The ParseFlow Way

Drop all 200 resumes into ParseFlow. In minutes, you have a structured spreadsheet with every candidate's name, skills, experience, and education — ready to filter and shortlist.

Example extraction schema

Define this schema once in ParseFlow, then reuse it across all your resume / cv parsing documents.

{
  "name": "string",
  "email": "string",
  "phone": "string",
  "location": "string",
  "summary": "string",
  "skills": ["string"],
  "experience": [{
    "company": "string",
    "title": "string",
    "start_date": "string",
    "end_date": "string",
    "description": "string"
  }],
  "education": [{
    "institution": "string",
    "degree": "string",
    "year": "number"
  }]
}

Input → Output

Input (PDF)

A two-page PDF resume with contact info, professional summary, skills section, work history, and education.

Output (Structured Data)

Name: Thomas Müller, Email: t.mueller@email.de, Location: Berlin, Skills: Python, SQL, Machine Learning, TensorFlow, 3 positions extracted (Senior Data Scientist at TechCo, 2022-present), Education: M.Sc. Computer Science, TU Munich, 2019.

Automate resume / cv parsing today

Stop copying data by hand. ParseFlow extracts structured data from your PDFs in minutes — entirely on your Mac.

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