Entrovix AI

Resume Job Matcher

Scores your resume against one job description with every keyword weighted by how often the posting repeats it, so the things the role is actually about count for more than the things listed once.

Runs in your browser — nothing is uploaded

Your resume and the posting

Drop your resume here

A PDF, as you would send it. Nothing is uploaded.

Why the file, not pasted text

The faults that break applicant tracking systems are structural — columns, headers, text inside images. Pasting your resume as plain text destroys exactly that evidence, because pasting is the parsing step that failed.

Your file never leaves this device. The whole operation runs in your browser — nothing is uploaded, queued on a server, or deleted later, because nothing was ever sent.

Match against this posting

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Add your resume and the posting

Weighted by how often the posting repeats a term

Something mentioned six times is what the role is about; something mentioned twice is a nice-to-have. An unweighted count treats them identically, which is why other matchers move when you add trivia.
Why use it

Built to be genuinely useful

Weighted, not counted

A term the posting repeats six times moves the score far more than one it mentions in passing.

Nothing is uploaded

Your resume and the job description are read in the browser and never reach a server.

Gaps ranked by weight

Missing terms come back in the order that matters, so you rewrite the load-bearing lines first.

Shows wasted space

Lists the skills your resume leans on that this particular posting never asks for.

How it works

Three steps

  1. 1

    Paste the whole job description, including the requirements block at the end.

  2. 2

    Paste your resume text — the version you were about to send for this role.

  3. 3

    Work down the missing terms in weight order, then re-run to see the score move.

Why repetition is the signal

A job description is written by someone circling the same few things from different angles. If reconciliation turns up six times — in the summary, the responsibilities and again in the requirements — that is the job. If Jira turns up once in a list of tools, it is a nice-to-have. A matcher that scores both as one point each throws that difference away.

Say a posting yields 40 terms and your resume covers 24 of them. An unweighted matcher calls that 60%. Paste in three passing mentions — a certification named once, a tool listed in brackets — and it reads 67%, though nothing about your candidacy changed. Here those three carry about two per cent of the total weight, so the score moves by two.

The score is matched weight divided by total weight, and the total is fixed by the posting rather than by how much you paste. A longer resume cannot inflate it. The only way up is covering the terms the employer kept returning to.

Reading the two lists

Missing terms come back ranked by weight, not alphabetically. The top three usually account for more of the gap than the next fifteen combined, and they are the ones worth rewriting a bullet point around. Adding the tail is cosmetic — the heavy terms decide whether a recruiter's search finds you at all.

The second list, “Not in the posting”, is the inverse: terms your resume leans on that this employer never mentions. A CV that says WordPress nine times, sent for a Django role, is spending its best lines on something nobody is scoring. Not always wrong, since some of it is your actual history, but it shows where the space is going.

Close the gaps by rewriting real experience in the posting's vocabulary, not by appending a keyword block. If you shipped dashboards in Metabase and the posting says business intelligence, say both. A line you cannot defend in the first interview costs more than the two points it earned.

What the score is and is not

There is no model here and no server call. The tool counts term frequencies in the posting, weights them, checks each one against your resume and divides. The same two documents produce the same number every time, which is what makes it useful for comparing drafts — a score that drifts tells you nothing.

It is a coverage measure of one posting's vocabulary, not a probability of being called. Indian recruiters on Naukri and LinkedIn filter with boolean searches on exact terms, and most mid-size employers run an applicant tracking system before a person reads anything. Paid resume-scan services charge ₹500 to ₹2,000 to report roughly this.

Run it once per application. A resume tuned for a data engineering posting will score poorly against an analytics one, and that is the tool working correctly. The point is a tailored version per role, not one document sent to forty companies.

FAQ

Questions people ask

Related tools

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Available tools are linked; the rest are in development.

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