How the report works.
A recruiter wrote the criteria. AI applies them to your resume, line by line, and quotes the line behind every note.
The write-up.
What your resume says first, in plain words, judged against the criteria.
A customer success manager with a defined account portfolio, but few details about renewals or what changed.
Fictional example
Where your resume stands.
One of five bands, with a line on what it means, and the number beside it. The top one, Ready to send, is earned: it means no line on the page needs work. The band is what a recruiter would do with the resume after one pass.
Mostly clear
- Not ready yet
- Needs more context
- Mostly clear
- Clear and specific
Most lines show what you did. A few leave your part or the result unclear.
- 1The results you show.
- 2How clearly each line reads.
- 3How your experience fits together.
- 4How easy the text is to follow.
Lines that need work.
Every line a recruiter trips on gets a card: the line as written, what they can’t tell from it, an example with the blanks for you to fill, and the question that fills them. Nothing in an example is invented; the facts are your resume’s or yours.
What did you review, and what did you do with the findings?
The line: Introduced monthly renewal-risk reviews across the portfolio
Fictional example
Only from your facts.
Every example keeps your facts and leaves blanks for the rest. Your answers go into the blanks word for word, and the line can’t be copied until every blank is yours. A recognisable recent employer can lift the resume one band; the write-up says so when it does.
What it can’t tell you.
- Whether you’ll get an interview or an offer.
- How a specific employer, team, or hiring process weighs things.
- How your file looks. The report reads the text we pull from it, not the layout, fonts, or columns.
- Anything about referrals, the job market, or bias in hiring.
A recruiter with fifteen years in hiring wrote these criteria. AI applies them, the same way to every resume, and nobody reads yours but you. Check every suggested edit against what you actually did.
How we write the research.
Every page opens with the question you asked and a straight answer, with the study behind it linked right there. We link the full study when it’s public and say so when we could only read the abstract. Each finding travels with its limits: who was studied, where, how many, and what the study can’t tell you. When something is our advice rather than a finding, we label it. We’d rather print a small, honest number than a big, round one. Each page shows when it last changed, and that date moves for a corrected claim or a new section, never for a redesign.
- 1The Importance of Representative Design in Judgment Tasks: The Case of Resume Screening. Journal of Occupational and Organizational Psychology, 2002. (opens in a new tab)
- 2Using Machine Learning with Eye-Tracking Data to Predict if a Recruiter Will Approve a Resume. Machine Learning and Knowledge Extraction, 2023. (opens in a new tab)