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About ZoeVera

A tool that shows you how a specific job posting's vocabulary compares to your resume — and publishes exactly how it reaches that number.

Who built this

ZoeVera is an independently built and run tool. It started in March 2026 as a small tool to answer one question that job seekers have no way to check for themselves: when a recruiter searches their applicant tracking system for the terms in a job posting, does this resume come back?

There is no team of recruiters behind it and no proprietary hiring dataset. It is a piece of software that does one narrow thing carefully. What follows is exactly how it works, so you can judge the number it gives you.

The company is ZoeVera on LinkedIn. That is a company page, not a personal one: the founder's name was taken off this site deliberately, and a verifiable company profile is the part that can be checked without putting it back.

@makelifeeazy

About the research

The keyword guides on this site are not opinion, and they are not a list copied from other resume sites. They come from counting words in job postings, and the counting is published in full so it can be checked or repeated.

What the work actually is. Postings are read from the Greenhouse job board API, the endpoint companies publish so their own careers pages can list their roles. Nothing is scraped and nothing is private. A collector fetches them, a deduplication rule decides when one requisition posted in several cities is one posting, and an analyzer counts document frequency: the share of postings naming a term at least once. Both are in the repository, so the numbers can be reproduced against the same endpoint.

What the expertise is, and what it is not. No recruiting credential is claimed here, and none is implied. There is no hiring background behind these pages and no access to any applicant tracking system's internals. What is claimed is narrower and checkable: collecting a public corpus, counting it consistently, reporting the margins that follow from the sample size, and stating plainly what the sample does not cover.

What the data cannot support. It is one ATS vendor's public boards, weighted toward US technology companies. Every study is one day's snapshot of open roles, not a trend. A term absent from a role's list was never counted, so nothing there says it does not matter. Those limits are restated on every study page rather than buried here.

The full study, its method and its limits, with the dataset as CSV →

How the match score is calculated

Most resume tools give you a score without saying what it measures. Here is the whole method.

  1. The job description is read once and split into three lists: skills, tools, and qualifications, ordered most important first. Responsibilities are extracted too, but they are never scored.
  2. Every one of those items is then classified against your resume as either matched or missing — each item lands in exactly one list, so nothing is quietly dropped from the denominator.
  3. The base score is matched ÷ total. A match needs direct evidence or clearly equivalent experience; ambiguous cases count as missing rather than matched.
  4. Five points are deducted for each missing item that ranked in the job's top five skills or top three tools, because not all gaps cost the same.
  5. The result is capped at 94 and floored at 8. Nothing scores 100 — a perfect score would imply a certainty the method does not have.

Extraction and scoring run at temperature 0, so the same resume and job description produce the same score rather than drifting between runs.

What this score is not

There is no threshold you have to beat. No applicant tracking system publishes a required score, and any site quoting one — 60%, 75%, 80% — is inventing it. This score is our measurement of vocabulary overlap, not a number Workday or Greenhouse computes.

Applicant tracking systems do not auto-reject resumes on a keyword score. They are databases. What actually happens is that recruiters search and filter within them, and a resume worded differently from the posting is less likely to surface in those searches. That is the real problem this tool addresses, and it is narrower than the one the industry usually sells.

A high score does not mean you will get an interview, and a low score on a role you are genuinely qualified for usually means your resume describes the work in different words than the posting does — which is worth fixing regardless of any software.

What the tool does

Paste your resume and a job description and you get your match score, the keywords the posting uses that your resume does not, and the weak phrasing that reads poorly to a human reader — in about 30 seconds, with no signup.

A paid plan unlocks the full rewrite, which works the missing keywords into your existing bullet points, plus DOCX and PDF downloads. Both plans are charged once, not as a subscription: $12 for up to 20 analyses in 24 hours, or $19 for up to 100 over 30 days. Full pricing.

There is also a cover letter checker that scores a letter against the same posting.

Role-specific keyword guides

Alongside the checker we publish ATS keyword guides for specific roles, from software engineers and data analysts to nurses, accountants and architects. Each one lists the terms that recur across real job postings for that role, with before and after bullet examples.

Browse all role keyword guides →

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Get in touch

Questions, feedback, or a result that looks wrong? Email goes straight to the person who built it, and is usually answered within one business day.

support@zoevera.com

Check Your Resume Score

Paste your resume and any job description — get your match score and the exact keywords you are missing in under 30 seconds.

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About ZoeVera — How We Calculate Your Match Score