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UpSkillZone ATS
Free to try · No account required

Beat the applicant tracking system — without faking your resume.

Upload your resume and the job description. Get a transparent ATS score, the exact keywords you're missing, and an AI rewrite that injects them only where they truthfully apply.

Screen my resumeSee how it works
  • Resume isn't stored in plaintext logs
  • Score returned in ~25 seconds
  • Deterministic — same input, same score

Sample scorecard · Senior Full-Stack Engineer

ATS Match

68

Strong, but bleeding
Keyword match62 · 40% weight
Semantic match35 · 10% weight
Experience fit76 · 20% weight
Impact100 · 15% weight

Missing keywords

GraphQL, Microservices, Distributed tracing, OpenTelemetry

Our optimizer fills these only where they truthfully fit your stack — no fabrication.

  • 75%

    of resumes are rejected by ATS filters before a human reads them.

  • 8 sec

    is the median time a recruiter spends on a resume that does pass.

  • 6 layers

    of deterministic scoring — not a black-box model verdict.

How it works

From upload to PDF in under a minute

Built for job seekers who'd rather understand the rubric than guess at it.

  1. 01

    Upload + paste

    Drop in your resume (PDF or DOCX). Paste the job description from the posting. No login required.

  2. 02

    Transparent scoring

    Six deterministic layers (keyword, semantic, experience, impact, format, soft signals) each contribute a visible sub-score. No black-box verdicts.

  3. 03

    Optimize and download

    One click rewrites bullets with quantification and inserts missing keywords only where they truthfully apply. Download the formatted PDF.

Anatomy of a score

No verdict you can't audit

Most ATS tools give you a number. We show you the six contributing components and their weights, so you know exactly where to focus.

Keyword match

40%

Exact + fuzzy overlap with the JD's named skills, with implicit synonyms (EC2 → AWS) credited.

Semantic match

10%

Sentence-embedding similarity between your bullets and the JD prose.

Experience fit

20%

Total years from extracted date ranges, progression, recency of skills.

Impact

15%

Proportion of bullets with metrics (%, $, X×), strength of action verbs.

Format

5%

ATS-readable headers, standard sections, sentence-length sanity.

Soft signals

10%

Leadership, communication, collaboration cues — without buzzword padding.

For hiring teams

For recruiters who want signal, not theatre.

The same engine that scores a single resume against a JD can rank a stack of them. Built-in BS detection flags unverifiable claims; auto-generated probes give your screening calls structure.

  • Rank candidates with a transparent rubric reviewers can defend.
  • Generate interview probes targeted at gaps and exaggerations.
  • Bulk evaluator and shortlist export — on the roadmap.
Talk to us about bulk access

Sample probe

“You list ‘Architected the platform that scaled to 10M users’. Walk me through the decision tree: what would have broken first at 100M, and what actually broke first in production?”

Generated automatically from a flagged claim in the resume.

What it won't do

The optimizer has a hard rule: never fabricate.

If a keyword has no honest place in your resume, the optimizer leaves it out and tells you so. We'd rather lose a point than lose your trust.

No fake metrics injected into bullets
No invented companies, roles, or dates
No keyword stuffing — it gets detected and flagged
PII is redacted before the LLM ever sees it

Questions

Things people ask before they upload.

Is my resume stored?+
We store the parsed evaluation so you can re-open the URL later. Original files are deleted right after parsing, and the resume text held for re-scoring is never logged in plaintext.
Does the optimizer make things up?+
No. The system prompt enforces no fabrication of metrics, companies, dates, or technologies. Keywords are injected only where they truthfully apply to your experience.
Why 6 weighted layers instead of one model verdict?+
Black-box scores aren't actionable. By splitting the score into 40% keyword + 10% semantic + 20% experience + 15% impact + 5% format + 10% soft signals, you can see exactly what to fix.
How long does scoring take?+
About 20–30 seconds for the initial score (it includes one LLM call for reviewer insights). Optimization runs the LLM twice and takes 25–45 seconds.
Can I run multiple JDs against the same resume?+
Yes — every evaluation is versioned. PUT to /api/evaluations/{id} updates the score and snapshots the previous one. UI for switching JDs is on the roadmap.

Find out where your resume actually stands — in 25 seconds.

No account, no credit card. Just an honest score and a path to a better one.

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