CareerCraft AI: An Intelligent Employment Support System Integrating Resume Building, Applicant Tracking System (ATS) Analysis, and GeoBased Job Matching

Abstract: Job seekers often rely on separate tools for resume analysis, resume enhancement, job-description matching, interview practice, and job search. This paper presents CareerCraft AI, an integrated, candidate-facing web prototype that combines these functions in one workflow. The system uses rule-based extraction for PDF and DOCX resumes and a hosted large language model (LLM), with rule-based fallbacks for selected tasks. The paper describes the architecture, data model, processing workflow, and exploratory observations from a deployed instance. Illustrative outputs demonstrate the available interfaces, while consistency checks identify unresolved differences between displayed scores and simple count-based calculations. Exploratory testing also identified parsing limitations, variation in generated content, and unsupported achievement metrics in some resume rewrites. These observations motivate factual review, transparent score provenance, privacy safeguards, and systematic evaluation. The study documents an integrated prototype; its accuracy, reliability, fairness, and effectiveness have not been established.

Keywords: CareerCraft AI; Applicant Tracking System; Large Language Models; Resume Analysis; Job Matching; Interview Preparation.