How ATS actually works, and how we score for it

An Applicant Tracking System parses, stores and searches every application, and most mid-size and large employers run one. It is not the gatekeeper the folklore describes. Resumes rarely die at the parser. They die when a recruiter never finds them.

So what actually goes wrong?

Two quieter failures:

Those two are what a score should measure.

• A skills list is cheap talk until you prove it

  In a randomised field experiment published through the NBER, listing more technical skills, with nothing in the work history to back them, moved employer interest by zero. Recruiters discount unproven claims. That is why one rule checks whether each skill you list shows up in a project or a bullet, and flags the ones that do not.

  Source · , NBER field experiment

• Parsing breaks at text extraction, not at meaning

  Textkernel, one of the largest commercial resume parsers, says parsing fails at the document-to-text step: graphics used as text, vertical text, and multi-column layouts that scramble reading order. Benchmarks find roughly one in five real resumes uses a layout that trips naive extraction. Modern parsers often cope; older ones do not. So we warn about a risky layout and never treat it as an automatic fail.

  Source · , Textkernel parsing guidance, RealResume parsing benchmark

• Length follows experience, not a one-page rule

  A ResumeGo simulation with 482 recruiters found two-page resumes were preferred 2.3 times for experienced candidates. The honest version is a band: one page for a fresher with a short history, more room once the experience is there. We score length against your career stage.

  Source · , ResumeGo recruiter simulation

• Keyword matching is semantic, and callbacks are not ours to promise

  LinkedIn's engineering describes recruiter search as learned, semantic ranking, not literal string matching, which is why our match credits “ML” as “machine learning”. And the audit studies in PNAS show identity effects dominate callbacks. So we never claim the score predicts a callback. It measures what your resume does.

  Source · , LinkedIn engineering, PNAS callback audit studies

A rule that punishes you for a myth makes your score dishonest. These are popular beliefs the evidence does not support, so no rule of ours touches them.

• Myth: The ATS auto-rejects your resume

  92% of recruiters say it does not. We score readability and findability, and no rule fails you for a rejection that is not happening.

• Myth: A two-column layout is an automatic fail

  It is not. It can make a Skills section parse wrong, which we flag as a warning, but we never fail a resume for having two columns.

• Myth: Every resume must be exactly one page

  Not past the fresher stage. We score length against your experience, so a strong two-page resume is not penalised for existing.

• Myth: 75% of resumes are rejected by software

  A figure with no credible source. You will not see it in our product, our marketing, or our score.

Your score is 28 fixed rules with fixed weights. No language model sits in the formula, so the same resume always scores the same.

Every point has a name, a fix and a reason you can check. The tiers, the weights and how not-scored rules move their weight are on how we score.

AI is used only where you ask for it: rewriting a weak bullet, drafting a cover letter. Never to grade you.

We never keep your file: we read it in memory, take the text and discard the original. Personal details are replaced before any AI provider sees text.

28 rules you can audit, a keyword match, and no myths. Free, no card, and your file is never stored.