A multi-agent AI system that automates resume tailoring, cover letter generation, job matching and application strategy using LLM driven workflows
Problem
Applying to jobs is repetitive and inefficient. Candidates spend hours tailoring resumes, writing cover letters, and trying to understand job requirements, often without clear feedback on fit or strategy.
Solution
This project builds a multi-agent AI system that transforms a resume and job description into a structured application workflow. It evaluates candidate-job fit, generates tailored resumes and cover letters, and produces a strategic application report to improve outcomes.
How it works
1. Resume Parsing
Extracts structured candidate data from PDF/DOCX/TXT resumes
2. Job Description Analysis
Breaks down job postings into skills, requirements, and experience signals
3. Agentic Workflow Execution
Runs a multi-step pipeline including:
- Fit analysis & Resume tailoring
- Strategy generation & Review
4. Output Generation
- Tailored resume & Cover letter
- Application strategy report
5. Assistant Layer
Allows users to ask grounded questions about outputs and decisions
Tech Stack
- Streamlit (UI)
- OpenAI API (LLM generation)
- Supabase (authentication + persistence)
- Python (core logic)
- WeasyPrint (PDF generation)