ResumeTailor AI
Project Overview
ResumeTailor AI is an AI-powered resume customization platform that transforms a master resume into an ATS-optimized, recruiter-ready version tailored to any job description. The application combines semantic job analysis, intelligent keyword prioritization, AI-assisted content refinement, and secure PDF generation while enforcing strict factual accuracy through validation mechanisms that prevent unsupported information from being introduced. The result is a professional, ATS-safe resume that preserves the candidate's original experience while improving relevance for the target role.
Key Highlights
- Semantic keyword analysis
- AI-powered resume rewriting
- ATS optimization
- Interactive keyword selection
- Professional PDF rendering
- Resume scoring dashboard
- Resume completeness validation
- Hallucination prevention
- Secure authentication
- Production deployment
Problem Statement
Most AI resume tools rewrite resumes by introducing unsupported skills, generic recruiter buzzwords, or fabricated experience. This reduces trustworthiness and may negatively impact hiring outcomes. The goal of ResumeTailor AI was to build a resume tailoring engine that improves ATS compatibility while ensuring every generated statement remains traceable to the user's original resume.
Solution Approach
Developed a complete AI-powered resume customization workflow where users upload a master resume and a target job description. The application performs semantic analysis, extracts relevant keywords, allows users to prioritize keyword integration, generates recruiter-quality resume content using Google Gemini, validates outputs against the original resume to prevent hallucinations, and exports a professional ATS-friendly PDF.
Architecture Overview
ResumeTailor AI follows a modular FastAPI architecture designed for performance and reliability.
Tech Stack
Frontend
- HTML
- CSS
- JavaScript
Backend
- Python
- FastAPI
Artificial Intelligence
- Google Gemini API
- Semantic Job Description Matching
- Jinja2
- WeasyPrint
Infrastructure
- Docker
- Render
Authentication
- Starlette Session Middleware
- Rate Limiting
Key Features
- AI Resume Tailoring
- ATS Optimization
- Semantic Job Description Analysis
- Keyword Selection Interface
- Resume Match Scoring
- Recruiter Readability Analysis
- Capability Match Analysis
- Resume Completeness Validation
- PDF Resume Generation
- Password Protected Access
- Session Authentication
- Rate Limiting
- Hallucination Prevention
- Responsive Interface
System Modules
- Upload Resume
- Paste Job Description
- Analyze Keywords
- Select Priority Keywords
- Generate Tailored Resume
- Validate Resume
- Generate ATS PDF
- Download Final Resume
System Screenshots & Wireframes









Lessons Learned
Retrospective
"Building ResumeTailor AI highlighted the complexities of balancing generative AI capabilities with strict factual accuracy. Implementing hallucination prevention mechanisms required careful prompt engineering and post-generation validation. The integration of WeasyPrint and Jinja2 proved highly effective for programmatically generating pixel-perfect PDFs. Overall, the project reinforced best practices in building secure, user-centric AI applications optimized for real-world hiring constraints."
Project Details
- TIMELINEJUN 2026 – JUL 2026
- PROJECT TYPEIndependent Project
- ROLEFull Stack Developer
- STATUSCompleted
- DEPLOYMENTRender
- REPOSITORYGitHub