How your resume score
is actually calculated
Not a keyword counter. A multi-step pipeline that reads your resume the same way a modern ATS and a senior recruiter would — text first, then layout, then role fit.
Score my resumeWhat your resume actually says
We extract the full text of your resume and run a structured analysis to understand its content quality, not just its length. This step evaluates the strength of your writing, the evidence behind your claims, and how clearly your skills map to your target role.
- ✓Section completeness — contact, summary, experience, education, skills, certifications
- ✓Impact and quantification — are achievements backed by numbers and outcomes?
- ✓Action verb strength — weak passive phrases vs. strong outcome-first bullets
- ✓Role signal clarity — does the resume signal the right seniority and function?
- ✓ATS parse readiness — will structured content survive automated extraction?
- ✓Career consistency — unexplained gaps, conflicting dates, unclear progressions
How your resume looks to a parser
ATS systems parse your PDF before a human ever reads it. Formatting choices that look good on screen — multi-column tables, icons, progress bars, embedded text boxes, unusual fonts — can silently strip out your experience before your name reaches a recruiter.
We render your PDF pages to images and run a vision analysis to flag layout risks that text extraction alone can't catch.
- ✓Multi-column and table layouts that break linear ATS parsing
- ✓Icon and progress-bar skills sections that strip to nothing in text extraction
- ✓Font, density, and white space — readability for human reviewers
- ✓Header and footer risks — contact info that parsers may discard
- ✓Photo and graphic elements — neutral flag for markets where photo resumes are discouraged
What we never evaluate: Age, gender, ethnicity, nationality, religion, health, disability, or any other protected attribute. If a resume photo is present, the system notes it as a neutral formatting consideration only.
How well your resume fits a specific role
When you paste a job description, scoring switches from general readiness to targeted role match. The model reads both documents together and evaluates the semantic overlap between what the JD requires and what your resume demonstrates.
- ✓Semantic match — do your experience descriptions convey the same competencies the JD wants?
- ✓Keyword coverage — exact and near-synonym matches for priority JD terms
- ✓Core qualification alignment — required qualifications, years of experience, domain depth
- ✓Seniority alignment — does your experience level match the role's expectations?
- ✓Missing priority evidence — what the JD emphasises that your resume doesn't address
Your overall score is deterministic
The overall score is not what the AI "feels" about your resume. It is a weighted average of dimension scores — computed with fixed weights based on your scoring mode. The AI evaluates each dimension independently; the final number is math.
When layout analysis is available, vision-derived ATS and readability scores are blended into the relevant text-derived dimensions (40% vision weight, 60% text weight), giving a more accurate picture than text alone.
Fixes tied to what we actually found
Every suggested fix is generated from the specific weaknesses identified in your resume — not from a generic checklist. When we say "add a quantified outcome to your lead generation bullet," it is because we identified that bullet as weak and confirmed your role likely produces measurable results.
- ✓Priority fixes ordered by impact on your score
- ✓Weak bullets flagged with specific improvement direction
- ✓Missing JD keywords identified with context, not just listed
- ✓Layout fixes specific to detected risks, not generic advice
What this score can and cannot tell you
Resume scoring is an estimate, not a guarantee. Here is what to know before acting on it:
ATS behaviour varies. There are hundreds of ATS systems (Workday, Greenhouse, Lever, iCIMS, Taleo, etc.) and each parses PDFs differently. Our layout analysis targets common failure patterns — it does not simulate a specific system.
Scores do not predict hiring outcomes. A high score improves the odds your resume clears automated screening. It does not account for budget freezes, referral hires, internal candidates, or a hundred other factors outside your resume.
Context you provided shapes the result. If you did not paste a job description, Mode A scores against general expectations for your target role. Adding a JD often changes your score — this is expected, not a bug.
The model can miss things. AI analysis is not perfect. Use the score as a structured signal, not an authority. A human career coach remains the gold standard for tailored advice.
Ready to see your score?
Upload your resume and optionally paste a job description. Your score is ready in under 60 seconds.
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