ATS Resume Checker & Optimizer

A premium, AI-powered Applicant Tracking System (ATS) scoring engine designed to help candidates optimize their resumes for specific job descriptions and roles.

Technical Architecture

The platform is built with a modern, high-performance stack:

Frontend (Next.js)

• Framework: Next.js 16 (App Router)
• Language: TypeScript
• Styling: Tailwind CSS with a "Premium Enterprise" aesthetic.
• Animations: Framer Motion for smooth transitions and interactive elements.
• Icons: Lucide React.
• State Management: React Hooks + Local Storage (for 24h history).

Backend (FastAPI)

• Framework: FastAPI (Python)
• LLM Engine: LangChain + Groq (Llama 3.3 70B) for near-instant analysis.
• Parsing:
  • PyMuPDF for high-fidelity PDF text extraction.
  • python-docx for Word document processing.
• Scoring Heuristics: Custom Python engine that extracts signals like keyword density, formatting quality, and quantified achievements.

Core Features

• JD & Role Alignment: Tailors the ATS score based on a specific job description and target role.
• Keyword Gap Analysis: Identifies critical missing skills and tools that recruiters look for.
• Immediate Fixes: Provides actionable, high-impact recommendations to improve parsing.
• Formatting Validation: Checks for ATS-unfriendly structures (tables, multi-column layouts, etc.).
• Privacy First: No signup required. All data is processed in-session with local history.

How it Works

1. Extraction: The system extracts raw text from PDF or DOCX files.
2. Heuristic Analysis: A preliminary scan calculates objective metrics (word count, bullet points, contact info presence).
3. AI Comparison: The LLM compares the extracted text against the provided Job Description and Role.
4. Structured Feedback: The AI generates a validated JSON response containing the score, strengths, and specific missing keywords.

Advanced Scoring Engine

We use a Strict AI Auditor model (Llama 3.3 70B) with a rigorous 100-point rubric:

• JD Match (40%): Intense scrutiny of skill alignment. Missing core skills = heavy deductions.
• Quantified Impact (25%): Penalizes "fluff" and rewards measurable metrics (%, $, numbers).
• Formatting (20%): Validates section headers and ATS-readability.
• Contact (15%): Ensures professional links (LinkedIn/GitHub) and contact info are present.

The system is designed to be a harsh critic, ensuring that only truly optimized resumes achieve scores above 80.

Monetization & Ad Integration

The platform now includes dedicated Google AdSense integration areas designed to support sustainable monetization while maintaining a clean user experience.

Current Ad Placement Areas

• Sidebar recommendation blocks
• Analysis result sections
• Mobile-responsive inline ad containers

Design Philosophy

Ads are intentionally placed to:

• Avoid disrupting resume analysis workflows
• Preserve premium UI aesthetics
• Maintain fast loading performance
• Ensure responsive behavior across devices

AdSense Readiness

• Semantic HTML structure
• Responsive layout support
• Optimized content spacing for ad rendering
• SEO-friendly architecture for improved discoverability

Upcoming Features

• Downloadable ATS analysis reports (PDF export)
• AI-generated cover letter suggestions
• Resume keyword heatmap visualization
• Shareable public ATS report links
• Resume score tracking history
• AI-powered interview question generation
• Dark mode support

Getting Started

Prerequisites

• Python 3.10+
• Node.js 18+
• Groq API Key

Installation

1. Backend:

   cd backend
   pip install -r requirements.txt
   # Create .env with GROQ_API_KEY
   python -m uvicorn app.main:app --reload

2. Frontend:

   cd frontend
   npm install
   npm run dev

License

MIT