AI resume tailoring that still sounds like you — built for Naukri & ATS India
AI resume tailoring means adapting your resume to a specific role using job-description signals while preserving facts and outcomes from your real work. Built for the Indian job market — Naukri, LinkedIn and corporate ATS parsers.
ATS optimization
Role targeting
Outcome-focused bullets
Naukri-ready
What changes on a tailored resume
- Headline and summary aligned to role and seniority.
- Bullets reordered around role-critical requirements.
- Keywords added naturally where experience supports them.
What should never change
- Employment dates, company names, and titles.
- Unverified claims or fabricated achievements.
- Metrics without real ownership or evidence.
For Naukri & Indian ATS
- Naukri's parser prefers simple headings: Summary, Experience, Skills, Education — avoid creative titles.
- Length rule: 1-page for freshers and 1–2 years experience; 2-page max for 5+ years — recruiters skim fast.
- keywords must match JD verbatim (e.g., if JD says “Node.js” don’t write only “Node”) — exact match filters pass you through.
- avoid tables/images — Naukri and corporate ATS often drop or garble them; use plain bullets.
Before / After example (ATS-first)
| Before (vague) | After (ATS keyword + metric) |
|---|---|
| Worked on backend | Built Node.js checkout service handling 1.2M req/day, cut p95 410→210ms (ATS keywords: Node.js, microservices, performance) |
Same experience, rewritten to surface verifiable stack, scale, and outcome — what Naukri filters actually match.
3-step process
- Paste the target job description and identify required skills.
- Map each requirement to existing outcomes from your experience.
- Regenerate bullets and review for truthfulness and specificity.
Use Civi workflow
Import your resume, run tailoring against a real job description, review ATS insights, and export only when you are satisfied with final language and evidence.
Free to tailor, ₹49 only when you download PDF