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Resume Parser

AI
parse a resume into structured JSON (ATS / spreadsheet ready).
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What you'll get: structured JSON of your resume — name, contact, work history, education, skills, certifications. Ready to push into your ATS. For another use, try AI · ATS Resume Optimizer.

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Private & secure
AI tools process your PDF in memory, send only the extracted text to the model, and discard everything immediately. No file is persisted on our servers.
No watermarks
Clean output, no branding, no daily limits.
Pay only for what you use
Purchased credits never expire. Cancel anytime.

Why people use AI Resume Parser

Resumes are notoriously inconsistent — every applicant has a different format, layout, and section ordering. AI Resume Parser extracts the structured data you actually want (work history, education, skills, certifications, contact info) into a clean JSON format. Useful when you have many resumes and need consistent fields.

HR / recruiter data entry
Recruiter receives 50 resumes via email — manually transcribing each one into the ATS is a 2-hour job. Parse them all, get JSON for bulk-import. Saves hours per requisition.
ATS pre-fill
Job-application portals that ask candidates to RE-ENTER their resume content into 30 form fields. Parse, get the JSON, paste field-by-field (or drive a browser extension if you build one). Better candidate experience.
Recruiting database population
Building or seeding a candidate-search database. Each parsed resume becomes a row with normalized fields (years of experience, top skills, education level). Search/filter on structured data, not full-text grep.
Resume comparison / scoring
Side-by-side comparison of 5 candidates for a senior role. Parse all 5; see structured fields aligned. Easier to spot “all 5 are full-stack but only 2 have ML production experience” than reading 5 PDFs.
Recruiting agency intake
Agency takes a candidate’s resume, parses it, then enriches the structured data with their recruiter notes. Sends a uniform “profile” format to client companies regardless of how the original resume was structured.

How AI Resume Parser works

Three steps. Sign in to run, zero retention — files never persisted on our servers.

  1. STEP 1
    Drop the resume PDF
    Up to 100 MB. Works on standard resume PDFs (Word-export, Google Docs, Canva, etc.). Plain-text resumes also accepted.
  2. STEP 2
    We extract + normalize fields
    Server-side extraction → field-by-field parsing tuned for resumes. Output schema: contact (name, email, phone, location, links), workHistory[] (company, title, dates, bullets[]), education[] (school, degree, dates), skills[] (categorized), certifications[], languages[], summary.
  3. STEP 3
    Get structured JSON
    Standard JSON output. Import into your ATS, candidate database, or spreadsheet. Schema is documented + stable; if a field is missing in the resume, that JSON key is set to null (not omitted).

What makes pdfcraft ai different

  • Citations on every claim
    Every AI summary, answer, and translation links back to the page it came from. You can verify the source before relying on the output. Hallucination is bounded, not blind.
  • Pay only for what you use
    No subscription. Purchased credits never expire. Cancel anytime. Cost shown before you run — exact credit amount appears after upload.
  • Multi-provider routing, no vendor lock-in
    We balance multiple AI providers per call to optimise cost and quality. If one has an outage, the next call automatically tries another. You're not bound to a single vendor's pricing or downtime.
  • Zero retention by design
    Your PDF lives in memory only as long as the AI call needs it, then it's discarded — nothing is persisted to our servers. We don't fine-tune models on your data. Verifiable in your dashboard's audit log.
  • Honest about model limits
    When a doc exceeds the model's context window we surface that the output was truncated (we render result.wasTruncated in every AI tool's UI). When a number can't be verified from the source we flag it. No silent failures.

Frequently asked questions

How accurate is the parsing?
Contact info: 95%+ for well-formatted resumes. Work history: 85–90% (date ranges occasionally drop a month-resolution; bullet extraction occasionally misses one bullet on dense layouts). Skills: 90%+ for explicitly-listed skills sections; lower for skills inferred from job descriptions. Always spot-check before bulk-importing.
What if the resume has a non-standard layout?
Handles most reasonable layouts — single-column, two-column, sidebar-style. Highly designed Canva/InDesign resumes with unconventional flow can degrade accuracy; for those, supplement with manual review.
Can it handle multi-page resumes?
Yes — most candidates with 5+ years of experience have 2-page resumes. Parser handles multi-page natively. The extracted work history will include all roles, not just the first page.
What about non-English resumes?
Best results in English. Other Latin-script languages (Spanish, French, Portuguese, German) work reasonably. CJK / Arabic / Hindi-script resumes will return parsed JSON but accuracy on date formats and field detection drops. For multilingual hiring funnels, consider running through AI Translate first.
What's the credit cost?
3 credits per resume.
Privacy?
Resume sent to inference provider, not stored. For confidential job searches (e.g. headhunter who shouldn’t leak candidate identity to the model provider), redact the candidate’s name + employer first via Redact PDF, then parse the redacted version.

Need to score against a job description?

AI ATS Resume scores how an ATS would rank your resume against a JD — keyword matches, format compatibility, optimization suggestions. Pairs naturally with parser for end-to-end ATS workflows.

Try AI ATS Resume

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