Using the Application Workspace

Leading practice: tailor with evidence, not just keywords

• Start from a strong Master CV. Then tailor a copy per job so you don't "break" your baseline.
• Reorder for the role. Put the most relevant evidence higher (summary → best experience → strongest skills).
• Every keyword needs proof. Only add a tool/skill/requirement if you can point to real experience, a project, or a measurable result.

Profile (AI-assisted)

A strong profile is short and specific: target role + specialty + proof + direction.

• Length: 3 sentences for most candidates. Add a 4th only if you hold a relevant postgraduate degree (Master's, PhD) or a named professional certification (CPA, PMP, CFA, etc.).
• Proof: include scale (e.g., users, revenue, budget, timelines) when true.
• Avoid: generic adjectives without evidence ("highly motivated", "hard-working").

Achievements (AI-assisted)

The AI generates achievement bullets using Action + Scope + Result as the default structure, shifting to Result + Action + Scope only when the metric is strong enough to stand alone without context. Bullet count adapts to career level (3-4 entry, 4-5 mid, 5-6 executive).

Why this works: Based on Harvard Career Services, TopResume, and UC Davis guidelines. Short, metric-rich bullets are more likely to be read fully by recruiters scanning under time pressure.

Skills (AI-assisted)

The AI scans your entire CV (profile, achievements, work experience, education, certifications) and suggests 8-15 relevant skills to add to your Skills section. Suggestions are fact-based only - the AI extracts skills explicitly mentioned in your CV text as terms, tools, technologies, methods, or named competencies.

What the AI is doing

• Match Result: A directional indicator of how well your CV covers the most important requirements. It's meant to guide iteration, not predict outcomes.
• General Requirements: Key basics like job title, education, years of experience, and required languages (when present in the job description). These fundamentals have the highest impact on your match result.
• Keywords & gaps: A prioritized list of requirements with clear statuses: matched (exact), covered (related but not exact), or missing.

How ATS systems work (and why we built Match Result differently)

Modern ATS platforms (Workday, Greenhouse, Lever) have shifted from simple keyword counting to semantic and contextual matching. As of 2025, 78% of major platforms use AI-powered screening that evaluates context, relevance, and how qualifications relate to job requirements - not just keyword density.

However, there's no universal ATS standard. Older systems (Taleo, SuccessFactors) still use Boolean keyword searches, while newer ones use semantic ranking. Some companies use hybrid approaches.

Our approach: Match Result blends both worlds. We prioritize contextual evidence (skills used in achievement bullets with metrics) over keyword repetition, while ensuring exact matches for critical terms are still visible. This aligns with the industry trend toward semantic matching and helps your CV succeed with human recruiters who read beyond keywords.

Key principle: Adding more keywords has diminishing returns. Quality of evidence > keyword stuffing.

How to improve your match

• Start with "covered" items (Quick Wins): Your CV likely has the evidence, but the wording is different. Swap in the JD term or make the evidence more explicit.
• Evaluate "missing" items: Only add them if you genuinely have the experience. It's better to have a lower result than a dishonest CV.
• Context matters: Don't just list keywords. Recruiters look for skills used in context (e.g., inside an achievement bullet with measurable outcomes).

How the AI drafts your cover letter

250-350 words max, 3 paragraphs, identifying the top 3-5 critical JD requirements and ensuring 80% are addressed with 2-3 specific achievements from your CV (brief context, action, and measurable outcome per achievement).

Why this works: 66% of recruiters spend <30 seconds on initial review, 70% prefer shorter letters, 72% of hiring managers require customization, and MIT/Harvard recommend specific examples with metrics over exhaustive summaries.

What you should do: Review the draft

• Verify claims: Confirm each achievement and metric reflects your actual experience
• Adjust voice: Rewrite anything that doesn't sound like you
• Check truthfulness: Remove or modify any connections the AI made that aren't accurate

Why this beats generic AI

• Enforces research-backed length limits (not endless)
• Maps to actual JD requirements (not generic templates)
• Selects only your strongest evidence (not everything)
• Professional and humble tone (avoids presumptuous language)