How Evalo Match Works: CV → Job → Optimization

TL;DR

The Three-Step Pipeline

Evalo Match follows a linear pipeline: you provide your CV and a job description, and the system produces an optimized CV targeted at that specific role. Each step builds on the previous one — parsing informs matching, matching informs tailoring.

1. 1

   Upload Your CV

   Submit your CV as PDF, DOCX, or TXT. AI extracts and structures your experience, education, skills, and tech stack into a searchable profile.

2. 2

   Paste the Job Description

   Submit a job title and description. AI extracts required skills, tech stack, keywords, and seniority — then scores your CV against them.

3. 3

   Get Your Tailored CV

   AI rewrites your CV to align with the job requirements, improving match score and keyword coverage. Your original content is preserved — only targeting and phrasing are adjusted.

Step 1 — CV Parsing

When you upload your CV, the system reads the document and extracts structured data from it. It does not store raw text — it identifies and categorizes each part of your background into a structured profile that can be meaningfully compared against job descriptions.

What Gets Extracted

• General info: name, title, location, contact details
• Employment items: role, company, dates, responsibilities, achievements
• Education items: degree, institution, dates
• Skills: transferable competencies (e.g. team leadership, system design, CI/CD pipeline design)
• Technologies: specific tools and platforms (e.g. Kubernetes, .NET, Terraform, Azure)

Why structured extraction matters

Step 2 — Match Analysis

Once your CV is stored, you submit a job title and description. The system extracts requirements from the JD and runs a multi-dimensional comparison against your profile. The result is a structured analysis: a match score plus a breakdown of exactly where you align and where you fall short.

What the Analysis Covers

• Match score (0–100) — overall alignment between your CV and the role
• Tech stack score — how well your stack aligns with the JD’s stack (e.g. .NET vs SvelteKit)
• Matched skills — skills in your CV that satisfy job requirements
• Missing skills — required skills absent from your profile
• Matched keywords — exact JD terms present in your CV text (ATS readiness)
• Missing keywords — JD terms absent from your CV text
• Title alignment — how your past job titles compare to the target role
• Suggestions — specific, actionable CV improvement recommendations

Semantic Matching vs Keyword Matching

How Tech Stack Alignment Works

Tech stack alignment is the most significant factor in the match score. If your CV reflects a fundamentally different stack from the job — for example, you’re a .NET backend engineer applying for a SvelteKit role — the match score will be noticeably low regardless of other skill overlaps. This prevents false confidence from inflated scores when the core stack is wrong.

Example

Skill Confidence and the Skill Graph

Evalo uses a skill graph to infer related skills from your explicitly listed technologies. If you list Terraform, the system infers Infrastructure as Code (IaC) at high confidence, and CI/CD at medium confidence — because those relationships exist in the skill catalog. Your effective skill profile is richer than what you explicitly wrote.

How Confidence Is Assigned

• Confidence 1.0 — technology you explicitly listed in your CV
• Confidence 0.95 — prerequisite skill inferred via a ‘requires’ relationship
• Confidence 0.45 — related domain inferred via ‘is part of’ or ‘related to’ relationship

Pro Tip

Step 3 — CV Tailoring

After reviewing the match analysis, you can send your CV for tailoring. The AI takes your original CV and the generated improvement recommendations, then produces a new version optimized for that specific job. The tailored CV is a separate document — your master CV is never modified.

What Tailoring Does

• Incorporates missing keywords into relevant experience bullet points
• Adjusts phrasing to match job description terminology exactly
• Highlights skills and technologies that align with the role requirements
• Surfaces implied skills — where your experience demonstrates a requirement not yet stated
• Preserves factual accuracy — rewrites phrasing, never fabricates experience

The 20–30% rule

Before vs After (Tailoring Example)

The Output: Your Tailored CV

The tailored CV is available to review in the app and export as PDF. It’s linked to the specific job it was created for, so you always know which version was targeted at which role. You can create multiple tailored CVs from the same master — one per job application.

Why This Matters for IT Roles

IT job descriptions are dense with specific technology requirements. Both ATS and hiring managers scan for exact tool names — not broad skills. Generic CVs fail because they lack the stack terminology and keyword density that IT roles demand. Evalo addresses this precisely: it maps your existing experience to the exact language of the job description, without you having to rewrite your CV manually for every application.

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