Cost-Optimized Recruitment Automation: A 7-Stage Framework for Small-Scale Enterprises

Abstract: Traditional Applicant Tracking Systems (ATS) present a high financial barrier for small and medium-sized enterprises (SMEs), often costing between $79 and $200+ per month. This paper proposes a cost-effective HR Recruitment Automation Platform that replaces manual Excel based workflows with a structured 7-stage automation pipeline. By leveraging rule-based screening, open-source Natural Language Processing (NLP), and free-tier Google Cloud APIs, the platform reduces hiring cycles from 45 days to 15 days. We demonstrate a 97% reduction in manual labour while maintaining high accuracy without premium model costs. This study details the architecture, stages, and cost-benefit analysis of the proposed system. Keywords: Recruitment Automation, ATS, NLP, SME, HR Technology, Cost Optimization.