Methodology · Evidence
Most resume tools sell you a scary number and a myth. We took the opposite bet: every one of our 28 rules maps to published evidence. What the evidence does not support, we refuse to score.
No myths. No flattery. 28 checks you can audit.
An Applicant Tracking System parses, stores, and searches every application, and almost every mid-to-large employer runs one. It is not the robot gatekeeper the folklore describes. Resumes rarely die at the parser. They die when a recruiter never finds them.
92%
of recruiters say their ATS does not auto-reject resumes for formatting, content, or design.
Enhancv recruiter survey, 2025. The automated rejection that does exist comes from knockout questions: work authorization, location, a required licence. Not from a parser scoring your bullet points.
The “75% get auto-rejected” stat has no credible source. It is a sales tactic, and we do not repeat it.
Two quieter failures, and both are where applications really die:
Readability
Your resume parses badly, so the text a recruiter searches never contains what you actually wrote.
Findability
It parses perfectly but misses the words the recruiter searches for, so no human ever sees it.
Those two are exactly what a score should measure.
In a randomized field experiment published through the NBER, simply listing more technical skills, with nothing in the work history to back them, moved employer interest by zero. Recruiters discount unproven claims. That single finding is why our flagship rule checks whether each skill you list actually shows up in a project or a bullet, and flags the ones that do not. Prove it, or drop it.
Textkernel, one of the largest commercial resume parsers, is blunt about where parsing fails: the document-to-text conversion, long before any semantic step. Graphics used as text, vertical or diagonal text, and multi-column layouts that scramble reading order are the usual culprits. Parsing benchmarks find roughly one in five real resumes uses a layout that trips naive extraction. Modern parsers often cope; legacy ones genuinely do not. So we warn about a risky layout, we never pretend it is an instant death sentence.
The universal “one page or you are done” rule is not real. A ResumeGo simulation with 482 recruiters found two-page resumes were preferred 2.3x for experienced candidates. The honest version is a band, not a commandment: one page for a fresher with limited history, room to breathe once you have the experience to fill it. We score length against your career stage, not a myth.
LinkedIn's own engineering describes recruiter search as learned, semantic ranking, not literal string matching, which is why our match layer credits “ML” as “machine learning”. And on the biggest question, does a resume score predict a callback, the audit literature in PNAS is clear that identity effects dominate that outcome. So we never claim our score predicts callbacks. It measures what your resume does, not what a biased world does back.
A rule that punishes you for a myth makes your score dishonest. These are the popular beliefs the evidence does not support, so no rule of ours touches them.
The ATS auto-rejects your resume
92% of recruiters say it does not. We score readability and findability, and let no rule quietly fail you for a rejection that is not happening.
A two-column layout is instant death
It is not. It can make a Skills section parse wrong, which we flag as a warning so you can fix it, but we never auto-fail a resume for having two columns.
Every resume must be exactly one page
False past the fresher stage. We score length against your experience, so a strong two-page mid-career resume is not penalized for existing.
75% of resumes are killed by the robots
A scare stat with no credible source. You will never see it in our product, our marketing, or our score.
Your score is 28 deterministic rules with fixed weights, run locally. No language model sits in the formula, so the same resume always scores the same and every point has a name, a fix, and a reason you can check.
Machine readability
Whether a parser can extract your resume cleanly.
Recruiter findability
Whether the words a recruiter searches for are present and match semantically.
Human-skim quality
Whether your bullets are quantified, active, and scannable in a few seconds.
Those are the three things the evidence supports. The folklore is left out on purpose.
AI is used only where it belongs: rewriting a weak bullet, drafting a cover letter. Never to grade you behind a curtain.
And we never keep your resume. We parse it in memory, extract the text, and discard the original file. Personal details are redacted before any AI sees it.
Named sources, in plain text. Every rule in the engine cites the evidence behind it right next to the fix, inside the product.
28 rules you can audit, a keyword match you can trust, and not a single myth. Free, no credit card, and your file is never stored.