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See the text and fields an applicant tracking system extracts from your resume, and what it would get wrong, with a fix for each.
Upload your resumeDrop your resume here
PDF or Word (.docx), up to 5 MB. It's analyzed in memory and never stored on our servers; your browser keeps your last check until you check another.
No resume handy? with a few typical problems.
About the checker
How an ATS reads a resume, what we check, and the questions people ask.
No AI guesswork
Fixed rules you can read, same result every time
Private
Read in memory, never stored on our servers
Free
No account, no email, no limit on re-checks
PDF, Word or text
Up to 5 MB
How an ATS works
The popular claim that most resumes are thrown out by software before a person sees them isn't how the major systems work: they keep every application for review. The damage a bad layout does is quieter, and it happens in three steps.
The system pulls the words out of your file in the order the file stores them. Columns, tables and text boxes are where this goes wrong.
A parser sorts the text into fields: name, email, jobs with a title, employer and dates, education, skills. Unusual headings and dates are where fields end up empty.
Recruiters filter the database by title, skill, location and years of experience. If a field is wrong, you don't appear, however good the resume is.
What we check
Every check either passes or removes a stated number of points, and tells you how to fix it. Nothing is hidden in a formula: add up the minus signs and you have the score.
Can the text be extracted cleanly, in the right order?
Do the fields a recruiter filters on come through?
Are sections and jobs filed where a parser expects them?
Will a person reading it be convinced? Guidance, not parsing.
How the score works
Common mistakes
And, as important, what doesn't: bullets, bold text, links, a second page and LaTeX-made PDFs all read fine.
Many parsers read straight across the page, so a line from your sidebar gets joined to a line from your main column and the sentences come out scrambled.
FixUse one column. Put skills and contact details above or below the experience, not beside it.
Several systems skip page headers and footers, so your email or phone never reaches the candidate record.
FixPut your name and contact line in the body of the first page.
A heading like “My Journey” isn't a name a parser knows, so the jobs beneath it aren't filed as work history.
FixUse plain names: Experience, Education, Skills, Projects.
“Summer 2021” can't be turned into a month, so your tenure and total years of experience can't be worked out.
FixWrite month and year for both ends: Jun 2021 – Aug 2021.
Icon fonts store symbols as private characters that come out as boxes or junk when the text is extracted.
FixWrite the address or number in plain text. A small label is fine, a glyph is not.
There is no text layer to read, so the system holds an empty resume.
FixExport the PDF again from the original document instead of printing or scanning it.
Questions
No, and no tool can give you that. Applicant tracking systems such as Greenhouse, Lever, Workday and iCIMS don't publish how they rank or filter, and they differ from each other. What can be measured is whether a parser can read your file and put your details in the right fields. That is what the ATS readiness score shows, and every point it removes is listed with the reason.
Rarely because of its layout. Most systems store every application for a recruiter to review, and automatic rejection usually comes from knockout questions you answer on the form, such as work authorization or a required licence. The real cost of a bad parse is quieter: your job title ends up as your employer, a date is dropped, or your email is missed, so your profile doesn't appear when a recruiter searches the database.
The resume checker uses no AI. Your file is read in memory on our server with fixed rules, the report is sent back, and the file is discarded. Nothing is saved to a database or an account. The optional job match is different: it sends your resume text and the job description to an AI model to pull out the requirements, which is why it is in early access.
Either, as long as the text is real text. A PDF exported from Word, Google Docs or LaTeX parses well, and so does a .docx. If you can't select the words in your PDF with the cursor, it is an image, and a parser sees an empty page. We tell you when that is the case.
No. Ordinary bullets, bold headings, links and a two-page resume are all read correctly by modern parsers, and the checker doesn't penalize them. What does cause trouble is a multi-column layout, text inside tables or text boxes, contact details in the page header or footer, icons used in place of words, and unusual section names.
Parsing is a matter of fact: either the system filed your job title correctly or it didn't. Advice like putting numbers in bullets or opening with action verbs is a widely shared guideline, not a rule any parser enforces. Mixing the two would make your readiness score look more certain than it is, so the content signals get their own score and are never counted as parsing failures.
Recruiters search their database for literal words and phrases, and a synonym does not match: "developer" will not find "engineer". The job match pulls the requirements out of a job posting, checks each one against your resume, and shows the wording the posting and recruiters use for the ones that are missing, so you can use it where it is true.
No. Searches match words that are really on the page, and the extracted text is exactly what a recruiter's system holds, so hidden or repeated keywords are visible there and read as a warning sign. Add a skill or phrase only when it is true, and put it where a person would expect to find it: in your skills section and in the job where you used it.
darji rewrites your resume for each job description you paste, in parallel, and hands back a clean single-column PDF or Word file.