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Resume ↔ JD Matcher

AI
fit score + per-requirement alignment table + missing-keyword ATS audit. Paste the JD, drop the resume.
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What you'll get: a fit score (0–100) + per-requirement alignment table + strengths + gaps + missing keywords (ATS blockers) + concrete next steps.

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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 Resume ↔ JD Matcher

Most Indian and US ATS systems (Workday, Greenhouse, Lever, iCIMS) score resumes against the JD before a human ever reads them. The matcher previews that score for you — per-requirement alignment, missing-keyword audit, and a weighted fit percentage — so you can tune the resume before applying.

Pre-application self-assessment
Drop your resume + paste the JD, see whether you’re a strong fit before spending an hour on a custom application. Saves time on roles where you’re structurally mismatched (e.g. JD requires 5 years X, you have 2).
Resume tuning iteration
Run the match, see which JD keywords are missing from your resume, edit the resume, re-run. The keyword gap typically closes from ~40% to ~80% in 2–3 iterations without lying — you’re surfacing experience you already have but didn’t describe in the JD’s vocabulary.
Recruiter pre-screen at scale
Recruiters with 100+ inbound resumes per role run each one through the matcher to triage. Top-quartile matches go to the hiring manager; bottom-quartile get a polite decline. Faster than skimming each resume manually.
Career coach client advisory
Career coaches use the per-requirement alignment table to walk clients through “here’s why you’re not getting interviews” with concrete evidence. More productive than abstract resume feedback.
Internal mobility assessment
Considering applying for a role at your own company? The matcher gives you an honest read on whether your trajectory positions you for the role — useful before the awkward conversation with your current manager.

How Resume ↔ JD Matcher works

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

  1. STEP 1
    Drop the resume + paste the JD
    Resume as PDF (up to 25 MB). JD pasted as plain text. The JD doesn’t need to be in any specific format — we parse the requirements out of free-form prose.
  2. STEP 2
    We extract + align
    Resume parsed for skills, experience, education, certifications. JD parsed for must-haves, nice-to-haves, and keywords. Alignment scored per requirement, with the missing items surfaced.
  3. STEP 3
    Get a fit score + alignment table
    Output: weighted fit percentage (0–100), per-requirement alignment table (matched / partial / missing), keyword-gap audit. Page references trace each match back to the resume passage.

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 is the fit score calculated?
Weighted by JD signal — must-have requirements count more than nice-to-haves, and exact keyword matches score higher than synonyms. Score is heuristic; real ATS scoring varies by system. Treat the score as a directional signal, not a guarantee.
Will it work for Indian ATS systems specifically?
Most Indian companies use Workday, Greenhouse, Lever, or iCIMS — same systems as global. Indian-specific keyword conventions (CTC, notice period, primary skills, secondary skills) are recognized. For Naukri / Monster ATS, results are slightly less calibrated but still useful.
Should I stuff missing keywords into my resume?
No — lying gets caught at interview. Use the missing-keyword list as a prompt: do you actually have that experience but described it differently? Re-word truthfully. If you genuinely don’t have the experience, the role isn’t a fit and the score is correct.
What if my resume is in a non-standard format?
Highly designed Canva/InDesign resumes degrade parsing accuracy — the matcher may miss skills described in sidebars or non-text-extractable graphics. For ATS-readable single-column resumes, accuracy is high. Our AI ATS Resume tool flags this format issue specifically.
What’s the credit cost?
3 credits per match. Cost is fixed regardless of resume length or JD length.
Privacy?
Resume + JD sent to inference provider, not stored. For confidential job searches (especially internal-mobility within a company), the model never sees your name — we redact PII before inference. Match results are returned to your account only.

Want the full ATS audit?

AI ATS Resume scores ATS-friendliness directly — format compatibility, keyword density, section ordering. Use it after the matcher to tune a specific resume for a specific JD.

Try AI ATS Resume

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Use cases for this tool

Step-by-step workflows that put this tool to work.

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