How ClariCV's ATS Scoring Works

Can ATS software actually read your CV? Parsability checks catch issues before they cause silent rejections.

• File format safety — PDF/DOCX formats that ATS systems can reliably parse.
• Character encoding — UTF-8 compliance — special characters and symbols that break parsers.
• Section heading recognition — Standard heading labels ATS software looks for to categorise content.
• Column layout detection — Multi-column CVs often parse incorrectly — sections get merged or scrambled.
• Font embedding — Non-standard fonts that aren't embedded can become unreadable.
• Table and text-box detection — Content inside tables or text boxes is frequently skipped by ATS parsers.
• Header/footer content — Important details in headers/footers are often missed entirely.
• Image and graphic detection — ATS systems cannot read text embedded in images.

Is your CV complete and correctly organised? Structural checks verify the sections recruiters and ATS systems expect.

• Contact information completeness — Name, email, phone, and location — all four should be present and parseable.
• Work experience section — Presence, correct labelling, and reverse-chronological ordering.
• Education section — Presence with institution, degree, and graduation year.
• Skills section — Explicit skills section improves keyword match scores significantly.
• Section ordering — Contact → Summary → Experience → Education → Skills is the ATS-safe order.
• CV length check — Under 2 pages for junior/mid, 2–3 pages for senior roles.
• Summary/objective section — Presence of a concise professional summary above the fold.
• Certifications section — Separate certifications section improves visibility for technical roles.

Does your CV contain the keywords the role requires? Keyword checks measure alignment with ATS filters.

• JD keyword coverage — Percentage of job description keywords present in your CV.
• Keyword density — Are key terms spread naturally across sections, or missing from critical areas?
• Keyword placement — Keywords in summary and experience score higher than keywords only in skills.
• Synonym recognition — Common industry synonyms — e.g., 'machine learning' vs 'ML' — both counted.
• Required vs preferred keyword split — JD keywords classified as must-have vs nice-to-have.
• Technical skills coverage — Hard skills, tools, and software named in the JD vs present in CV.
• Seniority keyword alignment — Leadership and strategic language for senior roles; execution language for junior.
• Industry terminology — Industry-specific vocabulary checked against the relevant sector dictionary.

Do your bullets demonstrate impact or just describe duties? Achievement checks measure the quality of your content.

• Action verb usage — Bullets should start with strong action verbs: Led, Delivered, Built, Reduced.
• Quantified results — Percentage of bullets containing measurable outcomes (numbers, percentages, currency).
• Passive voice detection — Passive constructions weaken bullets and lower recruiter engagement scores.
• Bullet length — 15–30 words per bullet is the ATS-safe and recruiter-readable range.
• Duty vs achievement ratio — Too many 'Responsible for' statements vs outcome-framed bullets.
• STAR method signals — Situation → Task → Action → Result structure in high-scoring bullets.
• Filler phrase detection — Phrases like 'hard worker', 'team player', 'results-driven' add no signal.
• Recency weighting — Recent roles should have more bullets and higher keyword density than older ones.