It extracts your resume the way a parser would, then shows you the fields it managed to identify: name, contact details, employment entries with their dates, education, and skills. The useful signal is not a score — it is the gap between what you wrote and what came out the other side.

Most resumes that fail parsing fail loudly and obviously once you can see the extraction. Contact details missing entirely usually means they were placed in the document header region. Job history arriving in a jumbled order usually means a two-column layout was linearised wrongly. Roles showing no dates usually means an unconventional date format. Each of these is a five-minute fix once you know it is happening.

Treat a clean extraction as clearing a floor, not as a prediction of success. A resume that parses perfectly and says nothing specific will still be rejected by the human who reads it next. Parsing is a hygiene check — it stops you being screened out for a technical reason before anyone assesses the substance.

Be sceptical of any tool, including this one, that returns a single percentage and implies it predicts your chances. Employers weight criteria differently for every role, and none of that weighting is visible from outside. A number is easy to display and hard to justify; the field-by-field extraction is the part that is genuinely actionable.

In practice a small number of changes account for most parsing failures. Move contact details out of the page header and into the first line of the body. Use one column rather than two. Write dates as 'January 2022 – Present' rather than abbreviated or parenthetical forms. Keep section headings conventional — Experience, Education, Skills — however tempting a more distinctive label is.

You can sanity-check any resume without a tool at all: open the PDF, select all, copy, and paste into a plain text editor. That is approximately what a parser sees. If the order is wrong or fields are missing there, they will be missing in the applicant tracking system too.