An Integrated AI Platform for Resume Analysis, Document Generation, and Intelligent Job Matching

The increasing reliance on automated recruitment tools has

introduced challenges such as inaccurate filtering, loss of qualified candi-

dates, and lack of transparency in resume evaluation. This study presents

CVision, an AI-powered web-based platform designed to enhance the re-

cruitment process through intelligent resume analysis, automated docu-

ment generation, and optimized job matching. By integrating traditional

rule-based evaluation with advanced natural language processing (NLP)

and machine learning models, CVision provides a holistic assessment that

goes beyond keyword matching to interpret context, structure, and skill

relationships. The platform operates through a dual-layer architecture —

a baseline rule-based scoring engine and an AI-driven analytical layer —

both connected via a FastAPI backend and React-based frontend inter-

face. The system allows candidates to upload resumes, receive personal-

ized improvement feedback, and explore job recommendations tailored to

their profiles. In controlled evaluations on real-world datasets, CVision

demonstrated a 24% improvement in accuracy, reduced false positives

by 67%, and achieved a 19% faster processing rate compared to con-

ventional Applicant Tracking Systems (ATS). Additionally, the platform

maintained 94% compatibility across irregular document formats, high-

lighting its adaptability in real-world hiring scenarios. The outcomes of

this study showcase CVision’s ability to redefine automated recruitment

through contextual understanding and performance-driven intelligence,

paving the way for fairer and more efficient hiring ecosystems.