What this page can support
This article previously described a benchmark without publishing the files, parser versions, or per-document results needed to reproduce it. Those numerical claims have been withdrawn. Do not use the earlier article as evidence of a vendor's failure rate or Guul's accuracy.
The guidance below distinguishes observed extraction from modeled risks. Guul's ATS-family previews are simulations. They are not tests against an employer's live Workday, Greenhouse, Lever, Taleo, iCIMS, or SAP SuccessFactors installation.
Same story. A little more breathing room.
Switch the layout. Watch how spacing and hierarchy change the way this sample reads.
Alex Morgan
Product designerProfile
Product designer focused on making complex workflows easier to understand.
Experience
Product Designer · Example Studio
- Mapped the onboarding journey with the support team.
- Designed and tested a clearer account setup flow.
- Documented reusable components for the engineering team.
Skills
Interaction design · Prototyping · Design systems
Everything is asking for attention.
Closely packed headings and text leave fewer visual signposts. Try the other treatment.
Three different kinds of evidence
- Extracted text shows what the parser used for that check recovered from a particular file. Compare it with the visible document, including contact details, employers, dates, and section order.
- Modeled risks describe patterns that may cause trouble based on Guul's family profiles and document structure. A predicted risk is not proof that an employer lost a field.
- An actual application preview shows what a particular employer's application form captured. Review and correct its fields before submitting. A successful import at one employer does not establish universal compatibility.
See the scoring methodology for the boundaries of Guul's checks and the template risk predictions for layout-specific guidance.
Your experience. A fresh canvas.
Try a different look. These are actual Guul template previews, with illustrative sample content.
A clean slate. Literally.
One column, distinct sections, and room for your experience to lead. A simple place to start.
Make this layout yours ↗The builder opens with your selected template.Replace all sample details with your own experience.Explore the resume lookbook →
Check the fields that matter
- Confirm your name, email address, and phone number are separate, complete values.
- Keep the employer, job title, and dates associated with the right role.
- Read the recovered text in order. A multi-column layout is worth checking, but columns alone do not establish extraction failure.
- Use consistent dates. Review any application that converts a month or an ongoing role incorrectly.
- Check both the file you upload and the application form. A visually correct PDF does not prove that every parser will recover it in the same way.
The Workday field-preview guide explains how to review a modeled result without confusing it with a live Workday test.
What a reproducible benchmark should publish
A useful benchmark includes synthetic source files, the exact exported documents, the extraction tool and version, the expected fields, the observed output, and the rules used to compare them. It should include a plain control document and report cases where a predicted problem did not occur.
A control failure limits what can be concluded about the other documents. A percentage without its denominator, scoring rules, and per-file results cannot establish reliability. Text extraction also does not measure recruiter preference, interview probability, or an employer's hiring decision.
Use the result to review your resume
Check your resume, compare the recovered information with your source document, and correct only the issues you can verify. Keep all career facts accurate. No parsing score guarantees an interview or a job.