AI resume tailor
Every saved job gets its own resume, rewritten to mirror the posting. Validator catches dropped roles. Keyword matches verified server-side. Zero fabrication.
Read onMost resume scoring tools hand you a single number and a cheerful "improve your score" button. We give you the list. Here are the keywords that came out of the JD. Here are the ones your resume already matches. Here are the ones it does not. What you do next is up to you.
3 steps. Each one runs in seconds. Scroll in order.
The parser already pulls key_skills and ats_keywords from the posting. Both lists feed the match step.
The tailored resume (or your master, if you just want a quick read) is tokenized and compared against the JD keyword set. Variants are normalized so "React" and "ReactJS" count as the same match.
The breakdown lists every keyword by name, bucketed matched or missing. You decide whether a missing skill is a real gap to learn, something to surface on your resume because you have used it, or noise to leave off.
One real example, same input reshaped two ways.
Your resume matches this job 82%. Improve your score: add more keywords.
Matched: React · Next.js · TypeScript · Jest · Playwright Figma · Core Web Vitals · WCAG 2.1 AA · design system Missing: GraphQL Storybook micro-frontends A/B testing
You should be able to tell how a feature works before you trust it with your job search.
We do literal string matching because that is how most ATS parsers work. On top of that we normalize a small controlled set of variants (React/ReactJS, JavaScript/JS, k8s/Kubernetes) so you do not get dinged for casing or abbreviation.
The JD parser prioritises required skills and ATS keywords from the body of the posting. The match list focuses on what the role actually needs, not on incidental phrasing that happens to appear in the surrounding copy.
Free accounts see the match percentage, the full matched list, and the top five missing keywords with a "+ N more (Pro)" indicator showing how many more exist. Pro Weekly, Pro, Pro Quarterly, and Founders see the full missing list. The trim happens server-side so opening devtools cannot bypass it.
Every time you regenerate a tailored resume, the breakdown recomputes. Watch the number climb as you add real skills. If it does not move, the gap is real.
Honest limits read as trust signals. Hiding them does the opposite.
If your question isn't here, email support and you'll hear back from the founder.
We can report a match percent if you want it. The list is what actually tells you what to do, and a percent alone does not. The point is fixing the resume, not improving the score.
Jobscan scores a resume against a JD. We do that plus generate a tailored resume, track the application, and analyze rejections when they come in. See the full comparison at /compare/jobscan.
No. Keyword match gets your resume past the first filter. After that a human reads it. The breakdown handles the part of the funnel you can measure. The rest is story, timing, and luck.
Yes. You can run a keyword match on a master resume against any pasted JD. You will see what is already there and what is missing before you decide whether to tailor.
Every feature writes to the same record. These three are the closest neighbours.
Every saved job gets its own resume, rewritten to mirror the posting. Validator catches dropped roles. Keyword matches verified server-side. Zero fabrication.
Read onMark a job as denied. Get an AI read on the likely gap, what the resume missed, and what to do differently next time.
Read onDrop a raw job posting. Get structured company, role, skills, and ATS keywords back in under two seconds.
Read onCreate a free account in under a minute. First job tracked, first tailored resume, and first keyword breakdown all happen inside the onboarding flow.
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