Recruiter AI Screening Assistant

A portfolio prototype for structured, evidence-based CV screening.

This static demo helps recruiters compare a CV against role requirements using transparent scoring, evidence highlights and interview follow-up questions. It supports human review rather than replacing hiring judgement.

Prototype Mode Local Logic Human Review AI-ready GitHub Pages Ready

Step 1Select role template

Step 2Review criteria

Step 3Load demo CV

ActionRun screening

Step 4Review screening result

Step 5Save or export report

Main Workflow

Step 1: Select role template

Role template

Step 2: Review editable criteria

Edit criteria

Must-have requirements Nice-to-have requirements

Step 3: Load demo CV

Candidate

This prototype uses built-in demo CVs only, keeping the workflow local, repeatable and easy to review.

Step 4: Review Screening Result

1. Overall Match Snapshot

This tool does not make hiring decisions. It supports recruiter review by showing structured evidence, gaps and follow-up questions.

2. Requirement Evidence Table

Requirement	Type	Status	Evidence found in CV	Confidence	Recruiter note

3. Missing or Weak Areas

4. Risk Flags

5. Interview Questions

6. Save / Export Report

Saved Candidates

Saved candidate reviews will appear here after screening.

Compare Evidence

Review differences side by side. The app does not rank candidates as best or worst.

What Live AI Would Add

The current version uses local rule-based logic: role templates, editable criteria, keyword matching, evidence extraction and template-based feedback. A future API integration could improve the workflow without changing the human review principle.

Prototype mode

Works now without an API key and avoids claiming live AI where none is used.

Local rule-based logic

Uses transparent keyword matching, structured criteria and repeatable scoring.

AI-ready workflow

The interface already separates inputs, evidence review, gaps, risks and reporting.

Future API integration

Could add richer parsing, nuanced matching, reasoning summaries and stronger reports.

Case Study

Original idea

The original idea was to create an AI recruitment screening assistant that helps recruiters review CVs against role requirements in a more structured way.

Why no API in this version

This portfolio version does not use an external AI API. Instead of pretending to use live AI, it uses local rule-based logic to demonstrate the workflow clearly and honestly.

Functional workaround

The prototype uses role templates, editable criteria, keyword matching, evidence extraction and scoring rules. This allows the app to produce useful screening feedback without needing an API key.

Three-layer prototype approach

The functional demo layer shows what works now. The AI concept layer explains what live AI would improve. The human review layer makes clear that the tool supports recruiter judgement.

Human review principle

The app does not make hiring decisions. It shows evidence, gaps, risks and follow-up questions so a recruiter can review candidates more consistently and transparently.

Future AI integration

A future version could connect to an AI API to improve CV parsing, evidence extraction, skill matching, reasoning summaries and report writing.

What this demonstrates

This project shows practical AI workflow design, product thinking, structured analysis, UX design, responsible AI awareness and basic JavaScript logic.