Ghosting Analyzer is an application that helps job seekers understand possible reasons behind job application rejections by analyzing CVs against job descriptions using AI-based and rule-based approaches.
The project is designed not only as a functional product but also as a production-oriented backend system with a strong focus on reliability and supportability.
- CV and job description analysis
- Match score and ATS readability evaluation
- Probable rejection / ghosting reasons
- Actionable CV improvement suggestions
- Analysis history per user
This project is built with real-world application support and production operations in mind:
- External dependency handling (AI provider availability)
- Automatic fallback to a rule-based engine when AI services fail
- Structured logging for incident investigation
- Secure authentication and protected endpoints
- Containerized deployment using Docker
- Configuration management via environment variables
The system is designed to remain operational and debuggable even under partial failures, simulating realistic production scenarios.
- Java 17
- Spring Boot
- Spring Security (JWT)
- PostgreSQL
- Docker
- External AI service integration
- React
- TypeScript
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MVP completed
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Production-ready backend structure
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Error handling and fallback mechanisms implemented
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Mustafa Kadak