July 17, 2026
"ATS resume checker" gets searched a lot, but the tools behind that search term vary wildly — some just count keywords, others actually simulate how a parser reads your document. Here's what a checker should actually be looking at, and how ATSBuddy approaches it.
Does the file have a real text layer? Is it structured as a single readable column, or will a parser scramble a multi-column layout? This is the foundation — everything else is irrelevant if the ATS can't read the document at all.
Are section headings standard? Are dates in a parseable format? Is the resume a reasonable length? These affect whether the ATS extracts your work history and education correctly.
Is your name, email, and phone number somewhere a parser will actually find them — not buried in a header/footer, not glued to icons that break extraction?
Beyond parsing, do your bullets show quantified, specific impact instead of vague responsibilities? This is where AI feedback is genuinely useful — a deterministic check can catch "weak verb openers," but nuanced writing feedback benefits from a model reading it in context.
A separate but related question: even if your resume is perfectly parseable, does it actually fit this specific job? ATSBuddy's job-match report compares your resume against a pasted job description and gives an honest verdict, missing must-have keywords, and rewrite suggestions — not just a generic score.
The core ATS score is computed in code from objective checks — the same resume always scores the same, it isn't guessed by an AI each time. AI is used specifically for the parts that need judgment: writing feedback and job-match analysis. You get 10 free credits on signup, enough to try both a scan and a job match before deciding if you need more.