Most mid-size and large employers run applications through an applicant tracking system before a recruiter opens anything. The system extracts your text into structured fields — name, contact, employment history, skills — and if that extraction goes wrong, the version a human eventually sees is not the document you wrote.

The failures are rarely about writing quality. They are structural: text stored inside an image, a two-column layout that interleaves a sidebar into your job history, headings the parser does not recognise, dates in a format it cannot read, or critical information in a header or footer that gets dropped. A strong candidate can be filtered out by a formatting decision.

That is what this tool measures. It is not scoring how persuasive your resume is — it is checking whether the machine between you and the recruiter can read it correctly, and telling you specifically where it cannot.

The score combines parseability (can each section be extracted cleanly), keyword coverage against the role you are targeting, and format safety. A high score does not guarantee an interview — it means you are being judged on your content rather than eliminated by your file.

Add the job description when you run the check. Without it, keyword analysis is generic; with it, the tool can tell you which specific requirements from that posting are missing from your resume. That single input changes the usefulness of the result more than anything else you can do.