AI drafts, you decide
The best workflow treats AI output as a draft with source evidence and review controls, not an automatic truth layer.
CV su misura
Evidence-backed AI CV workflow
AI CV Tailoring
Use AI to reframe your real experience for a target role without silently adding employers, skills, dates, metrics, or certifications.
Step 1
Import or paste your current CV.
Step 2
Paste the job description and confirm the target market.
Step 3
Review the evidence-backed changes before export.
AI CV tailoring
Built for truthful job-specific CVs
Market-aware conventions
Target-market wording can guide structure and tone while preserving user-provided facts and privacy boundaries.
No fabricated metrics
Numbers should be preserved exactly from the source CV or added only after the user provides evidence.
Outcome loop
Export is not the end of the workflow: tracked applications make later tailoring more practical and measurable.
Recruiter-reviewed examples
Stronger wording without fake evidence
Product Manager
B2B onboarding evidence
Before: Worked on signup flows and coordinated roadmap planning.
After: Led B2B onboarding discovery and roadmap planning, using signup drop-off analysis to prioritize account setup improvements.
The rewrite surfaces role-relevant product evidence without adding a new metric or seniority claim.
Frontend Engineer
Performance and architecture
Before: Built React pages and fixed bugs across the dashboard.
After: Built React dashboard flows and improved frontend reliability through reusable components, state cleanup, and performance-focused bug fixes.
The change improves scan clarity while keeping the achievement qualitative because no source metric was provided.
Operations Coordinator
CRM process ownership
Before: Helped the sales team keep records updated in the CRM.
After: Maintained CRM records for sales handoffs and clarified follow-up processes so account updates stayed consistent across the team.
The tailored version translates support work into operational ownership without inventing revenue impact.
Missing requirements stay visible
If the job asks for a skill your base CV does not evidence, the product should flag it as a gap instead of quietly adding it.
Every AI change needs a reason
Tailoring is useful only when the user can see what changed, why it changed, and which source CV evidence supports it.
Canonical topic: /ai-cv-tailoring. CV su misura helps with drafting and review; users remain responsible for final factual accuracy.