ExtractFoxResume parsing for recruiters and HR teams
When 80 applicants drop a CV for a role, the first hour is spent just reading them. ExtractFox parses every CV — including infographic-style designer resumes and two-column layouts — into the same structured shape: name, contact, headline, work history, education, and skills. Sort, filter, and shortlist in Excel before you read a single PDF. The bottleneck isn't finding talent — it's copying contact details into your ATS, comparing tenure across inconsistent formats, and losing track of who already got a callback because the data lives in separate PDFs instead of one searchable sheet.
Common workflows
Drop a folder of 80 PDFs, get one Excel sheet with one row per candidate. Sort by total years experience, filter by skill keyword, shortlist before reading.
Have candidates export their LinkedIn profile to PDF (Profile → More → Save to PDF), drop the batch in, and get the same structured candidate data without any LinkedIn API access.
Process new-hire IDs (passport, driver's license) and signed offer letters in bulk during onboarding. Each document type has a focused extractor that hits the fields HR systems need.
Run your current team's CVs through the resume extractor (with consent) and get a skills matrix you can use for project staffing, gap analysis, or training planning.
Candidates submit employment verification letters, degree certificates, and professional licenses as PDFs or scans. Extract employer name, dates, degree, institution, and license number into rows you can cross-check against the CV before you make an offer.
Time savings
Reading and tagging 80 CVs by hand takes a recruiter ~3 hours. Bulk-extracting plus a 20-minute spreadsheet sort cuts that to ~30 minutes — and the data persists in a sortable form afterward, which manual reading doesn't.
Frequently asked questions
Does the resume parser handle infographic and designer CVs?+
Yes. The multimodal model reads visual layout, not just text flow, so two-column designs and graphical CVs parse as cleanly as plain ones.
Can I push extracted candidates into Greenhouse, Lever, or Workable?+
Yes. The JSON output is stable and easy to map into any major ATS. The Pro REST API lets you wire ExtractFox as a step in your candidate-intake pipeline.
What about non-English CVs?+
Multi-language CVs work. Names, companies, and dates extract reliably; long-form descriptions come back in the original language.
How do I handle GDPR / candidate data privacy?+
Files are processed and not stored long-term by ExtractFox. For an EU-only data path or full self-hosted deployment, contact us about an enterprise plan.
Can I extract data from a LinkedIn profile URL directly?+
Direct URL fetching is on the roadmap. Today, candidates export their profile to PDF (one click in LinkedIn) and you upload that.
How do I deduplicate candidates who applied twice with different CV versions?+
Extract both versions and compare email or phone in the spreadsheet — duplicates surface in seconds. You can also sort by last-modified filename to see which upload is newer before merging in your ATS.
Does it extract salary expectations or notice periods from cover letters?+
Use free-text mode on cover letters bundled with CVs: type 'extract expected salary and notice period' and the model returns those fields as separate columns alongside the resume data.