ExtractFoxAI resume parser software for CVs and PDFs
AI resume parser software that turns CV PDFs into structured candidate data — contact info, work history, education, and skills — without per-template rules. Free to try in the browser.
What should we pull from this resume / cv?
Or pick specific fields
Or describe it yourself
Developers
Need the API?
Call the same extraction from your backend — curl examples, JSON schema, and error codes on the dedicated docs page. Session-auth beta: sign in through the app and use your session cookie; bearer API keys are not available yet.
Why this matters
Resume parsing has traditionally needed brittle rule-based parsers per layout. With a multimodal LLM, ExtractFox handles wildly different CV designs — including infographic-style layouts and two-column templates — without per-template tuning. Recruiters screening 50+ applicants per role spend three to five minutes per CV just copying contact info, tenure, and skills into a comparison spreadsheet — and a single missed role on a Canva-designed resume means a qualified candidate never gets a call. Legacy ATS parsers choke on two-column layouts and drop entire experience sections, leaving hiring managers with incomplete profiles when they need consistent date formats and role ordering for side-by-side comparison.
How it works
- Step 1Upload the resume
PDF or image. Multi-page CVs are fine.
- Step 2Get structured profile data
Name, contact, headline, summary, skills, and full work and education history.
- Step 3Send to your ATS
JSON for direct API ingest, or Excel for a candidate spreadsheet.
Fields extracted
full_nameemailphonelocationheadlinesummaryskills[]experience[].companyexperience[].titleexperience[].start_dateexperience[].end_dateexperience[].descriptioneducation[].institutioneducation[].degreeeducation[].fieldeducation[].start_dateeducation[].end_dateCommon use cases
Sample output
Example output from a software engineer resume
| full_name | Jane Doe |
| jane@example.com | |
| phone | +1 415-555-0182 |
| location | San Francisco, CA |
| headline | Senior Software Engineer |
- TypeScript
- React
- Node.js
- PostgreSQL
- AWS
| company | title | start_date | end_date | description |
|---|---|---|---|---|
| Acme Corp. | Senior Engineer | 2023-06 | — | Led migration to event-driven architecture; mentored 4 junior engineers. |
| Beta Industries | Software Engineer | 2020-01 | 2023-05 | Built customer-facing analytics dashboards in React. |
| institution | degree | field | start_date | end_date |
|---|---|---|---|---|
| UC Berkeley | B.S. | Computer Science | 2016-09 | 2020-05 |
Frequently asked questions
How do I parse a resume PDF into structured data?+
Drop the PDF here, click Extract, and you'll get a JSON object with name, contact, headline, skills, full work history, and education. Download as JSON for an ATS import or as Excel for a recruiter spreadsheet.
Does it work on infographic-style or two-column resumes?+
Yes. The model reads visual layout, not just text flow, so designer CVs and two-column templates parse as well as plain ones.
Can I integrate this with my ATS?+
Yes — the JSON output is stable and easy to map into Greenhouse, Lever, Workable, or a custom ATS. The REST API is on the paid plan.
Does it extract dates and durations consistently?+
Dates come back in YYYY-MM format where the resume specifies a month, or YYYY when only the year is shown. Current roles return null for end_date so you can compute tenure.
Can I extract data from a LinkedIn profile?+
Yes — see the dedicated LinkedIn profile extractor. Save the LinkedIn profile to PDF (Profile → More → Save to PDF), drop it there, and you'll get a structured candidate object including headline, current company, and tenure that the resume schema doesn't surface.
What about non-English resumes?+
Multi-language resumes work. Names, companies, and dates extract reliably; long-form descriptions come back in the original language.
Does it handle resumes with graphics, icons, or skill bars?+
Yes. The model reads visual layout — skill bars, icon grids, and infographic timelines parse alongside plain-text CVs. Decorative graphics that aren't data are ignored.
Can I get one row per job role instead of one object per candidate?+
Use the Work history sub-extraction. Each role comes back as its own row with company, title, dates, and summary — ready to paste into a flat spreadsheet or pivot by company.
What is resume data extraction?+
Resume data extraction turns a CV PDF into structured candidate fields — name, contact, work history, education, skills — instead of raw text. ExtractFox reads layout visually, so two-column and infographic CVs parse without per-template rules.
How does this compare to a resume extractor in an ATS?+
Built-in ATS parsers often choke on non-standard layouts. ExtractFox returns the same JSON schema for any CV design, which you can import into Greenhouse, Lever, Workable, or a comparison spreadsheet before the ATS ever sees the file.
Is this free resume parser software?+
Yes — upload a CV PDF and download structured JSON or Excel with no signup for your first extraction. Higher volume and API access are on paid plans.
Is there a resume parser API?+
Yes — use the Need the API? section above for the resume parser API docs. Same JSON schema as this page; session-cookie auth beta on paid plans.
What is the best AI resume parser for designer CVs?+
Layout-aware AI parsers beat rule-based tools on two-column, Canva, and infographic resumes. ExtractFox reads visual structure — skill bars, icon grids, and timeline graphics parse alongside plain-text CVs without per-template setup.
How is an AI resume parser different from keyword scanning?+
Keyword scanners count string matches. An AI resume parser extracts structured fields — company, title, dates, skills — from whatever layout the candidate used, so you get comparable rows across 100 different CV designs.