Free ATS diagnostic · No signup
See your resume the way hiring software does
Upload your PDF. Legible runs the same five-stage pipeline every major ATS uses — and shows you the exact text it extracted, the sections it found, and the fields it missed.
What the report shows
Six sections. No filler.
Parse readout
The raw text the parser extracted — not your formatting, the linear character stream. Side by side with your original.
Structure map
Which sections were detected, with what confidence, and which ones were missed or misclassified.
Mode scores
Two scores: Strict mode (Taleo, iCIMS, older Workday) and Lenient mode (Greenhouse, Lever). Different systems, different results.
Issue list
Every detected problem ranked Critical → Warning → Info. Each includes a specific fix, not a generic suggestion.
Field extraction
Name, email, phone, LinkedIn, current title, current company, years of experience, education — exactly what the NER layer found.
3 AI recommendations
Three specific actions, each referencing your actual resume content. Not 'use more keywords' — something you can act on today.
Five things that silently break your resume
Legible checks for all of them.
Multi-column layout
ATS text extractors read PDFs top-to-bottom, left-to-right. A two-column layout interleaves both columns line by line — your skills and job titles collapse into a single garbled string.
Affects: Workday · Taleo · iCIMS
Content in tables
Most parsers strip table structure entirely. If your skills or work history are inside a table, those fields go missing or get merged into surrounding text.
Affects: Taleo · iCIMS · older Workday
Image-based PDF
Design tools like Canva, some Adobe exports, and scanned documents save as flat images. The ATS sees a blank page — zero text extracted. Your resume is invisible.
Affects: All ATS platforms
Contact info in header region
Name, email, and phone placed in the PDF document header — not the visual top section, the actual header region — are silently ignored by most parsers.
Affects: Greenhouse · Workday · Lever
Non-standard section headers
ATS section detection is trained on "Experience", "Education", "Skills". Creative labels like "Where I've Worked" or "My Toolbox" cause the entire section to be misclassified or dropped.
Affects: Workday · Taleo
How Legible works
Upload your resume
PDF (preferred) or DOCX. Up to 10 MB. No account required.
We run the full parsing pipeline
Text extraction, layout analysis, section detection, named entity recognition — the same five stages every major ATS uses, implemented directly.
You see exactly what the parser sees
Raw extracted text, section confidence map, strict vs. lenient mode scores, field-by-field extraction, and three specific recommendations.
What we can and cannot claim
Legible shows you what a PDF text extractor reads from your file — the same underlying technology used by all major ATS platforms for initial text extraction. We simulate how strict vs. lenient ATS systems interpret that text.
We do not have access to Workday's, Taleo's, or Greenhouse's internal parsers or ranked outputs. Every ATS uses a different configuration of the same five-stage pipeline. We run your resume through the full pipeline, show you what was extracted, and flag problems known to break the systems engineers are most likely to encounter.
Read the full methodology →Find out what your resume actually says
Free. No account. Results in under 10 seconds.
Check my resume — free