Run your resume through the same parsing techniques used by Workday, Greenhouse, and iCIMS. Section-by-section status, layout blockers, technical-keyword scan, in 30 seconds.
PDF or DOCX, under 5MB. We extract the text, run it through a Natural Language Parser, and show you exactly what an ATS would see.
This usually takes 15 to 30 seconds. We run four steps: extract text, detect blockers, parse fields, scan keywords.
Files stay in memory, never stored. Parsed by a third-party AI provider. Terms
An ATS-clean resume gets you past the mechanical filter, nothing more. From there, the recruiter screen takes seconds: keyword fit, level signal, and whether your profile summary makes them want to know more.
Free, personally reviewed within 12 hours by a former Google recruiter.
Background
An ATS (Applicant Tracking System) is the database recruiters use to sort, search, and rank applicants. It does three things to your resume in this order: parse, index, and score. Knowing what each step does tells you exactly what to fix.
The ATS extracts text and uses Natural Language Processing to fit it into structured fields: name, email, jobs, dates, skills. If parsing fails, the ATS stores broken or blank data, or rejects you outright. This is what our checker simulates.
Every keyword in your resume is indexed for search. Recruiters type queries like "Python AWS Seattle" and get a ranked list. If your skills are inside an image, a table cell, or a non-standard section header, they don't make the index, and you don't show up in the search.
Many ATS rank candidates by relevance to the job description. The higher your score, the earlier recruiters see you in the queue. Matching the JD's wording (not just synonyms) is the most reliable lever for this.
Myth busting
There is a ton of misinformation about ATS, mostly to sell you tools. The truth is simpler. Three myths worth dropping:
Templates aren't the problem. What ATS care about is whether they can parse your text and preserve structure. Tables and pictures cause parsing issues. Single-column plain text in standard sections (Experience, Education, Skills) parses correctly in every modern ATS.
The simple test: copy-paste your resume into a blank document. If content, order, and structure are preserved, the ATS can read it.
Modern ATS like Workday, Greenhouse, and iCIMS parse text-based PDFs without issues. The actual problem is image-based PDFs (commonly exported by design tools like Canva) which flatten text into a picture. The ATS sees a blank page.
The simple test: if you can highlight and select the text in your PDF, the ATS can read it. If selecting grabs the whole document as one image, you've got an invisible resume.
Keyword density matters, but stuffing kills you at the recruiter screen. The right play: match the wording in the job description for the skills you actually have, in context (within a bullet point, not a list at the end). One in-context use of "Kubernetes" beats five tag-cloud mentions.
The simple test: can a human read each bullet and learn something? If yes, the keyword is in context. If no, it's stuffing.
Methodology
We replicate the parsing pipeline used by production ATS, end-to-end. Honest about what we do and don't do:
We extract the raw text from your PDF or DOCX using the same library family production parsers use, plus a structural pass to count images, tables, and detect image-based files. Same blocker detection as a real ATS.
The extracted text is sent to a Natural Language Parser (Claude Sonnet 4.6) that splits your resume into structured fields: contact info, profile, education, work experience with bullets, skills, certifications. This is the same fitting step real ATS perform.
Every line is scanned against a curated dictionary of 3,000+ tech keywords (languages, frameworks, cloud, databases, devops, data, ML, tools). The result tells you which technical signals an ATS will index from your resume.
What we don't do: score your resume against a specific job description (those vendor match algorithms are proprietary), or judge whether your content is strong enough to win the recruiter screen. If your resume parses cleanly but calls still aren't coming, the Application Funnel Calculator pinpoints where you're losing in the funnel.
Frequently asked
The most common questions from candidates when their resume isn't getting traction.
An ATS uses Natural Language Processing to convert your resume into structured data: name, email, work history, skills, education. The text is then indexed by keyword for recruiter search and ranked by relevance to each open job. Read the full ATS guide.
PDF is the gold standard, but only text-based PDFs. Image-based PDFs (commonly exported by Canva and other design tools) flatten text into a picture, making it invisible to ATS. DOCX works on most modern systems and is editable by recruiters or agencies, which is sometimes a downside. Full file format guide.
Yes. Most ATS read top-to-bottom, left-to-right. Tables and multi-column layouts disrupt that flow, so job titles get separated from companies, skills end up under the wrong section, and entire blocks of text can disappear. Our checker flags both. Stick to a clean single-column layout.
Open your PDF. Try to highlight and select a sentence. If you can copy-paste it into a notepad and it remains legible, the ATS can read it. If selecting grabs the entire page as one image block, your PDF is image-based and invisible to ATS. Re-export from Word or Google Docs to fix it.
It uses the same parsing techniques production ATS use: PDF text extraction, Natural Language Processing for structured-data extraction, and keyword indexing. The verdict reflects what a typical Workday, Greenhouse, or iCIMS deployment would store from your resume. We're not affiliated with any ATS vendor and don't reproduce vendor-specific match-score algorithms.
No. The file is processed in memory only, parsed once, then discarded. Nothing is written to disk, no resume content is stored, nothing is shared. The text is sent once to the Natural Language Parser to extract structured fields and is not retained. For full sub-processor details, see our Terms.
A clean ATS parse means you passed the mechanical filter. From there, callback rate depends on content quality: keyword fit for the JD, level signal in your bullets, profile summary clarity. The Application Funnel Calculator diagnoses where you're losing in the funnel. A free human review covers the same ground in 12 hours.
Disclaimer. This tool simulates the parsing techniques used by production ATS but doesn't reproduce any specific vendor's match-score algorithm. Treat the verdict as directional, not deterministic. Files are processed in memory only and never stored.