Resume / CV Parsing
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.
Before & after
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.
Schema
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"
}]
}Example
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.