Overview
This project explores an AI-assisted screening workflow that compares resumes against job descriptions and helps recruiters review candidates more consistently at the top of the funnel.
Problem
Hiring teams often spend time on repetitive early-stage screening work that could be made more consistent and efficient with structured text analysis and workflow logic.
Planned MVP
- Parse resumes and job descriptions.
- Extract skills, tools, experience, and role-specific keywords.
- Score candidate-job fit using text similarity and rule-based logic.
- Build a simple interface for reviewing candidate matches.
Architecture
- Resume and job-description ingestion layer
- Skill and experience extraction workflow
- Similarity scoring and rule-based screening logic
- Reviewer interface for candidate comparison
Build Plan
- Python
- NLP
- text processing
- similarity scoring
- resume analytics
- recruitment automation
Expected Output
The initial release is intended to produce structured fit scores, readable match explanations, and a lightweight recruiter workflow for shortlist review.
Limitations
This is still a planned MVP. Public proof points, interface artifacts, and benchmarking outputs will be added after the first working version is implemented.