75% Reduction in Recruitment Effort Through Digital Health Transformation with GenAI

About Client:

A leading healthcare organization focused on modernizing recruitment operations and improving efficiency across high-volume hiring roles. The organization has a strong track record of workforce excellence and continuous innovation, making recruitment modernization a core pillar of its broader health digital transformation strategy.

Background:

As part of its ongoing digital health transformation, the client sought to address growing inefficiencies in recruitment. Thousands of applications per role made it difficult for recruiters to identify and engage suitable candidates at scale. Manual, time-intensive screening resulted in delayed hiring cycles, inconsistent evaluations, and missed qualified talent, directly affecting workforce planning and operational readiness.

Challenge:

The recruitment process faced several structural limitations:

  • High resume volumes made manual shortlisting slow and inconsistent
  • Unstructured resume formats and incomplete data limited effective comparison
  • Keyword-based screening tools lacked contextual understanding, failing to identify transferable or synonymous skills
  • Limited recruiter capacity constrained personalized engagement and assessment

These issues created friction in hiring workflows and limited the impact of digital health transformation initiatives focused on workforce efficiency.

Solution:

Recruitment operations were modernized using AROMA, a GenAI-powered, cloud-native recruitment automation platform designed to digitize and optimize the end-to-end hiring lifecycle.

Phase 1: Secure Resume and Job Description Ingestion

  • Recruiters upload resumes and job descriptions via the AROMA web application or a designated Google Drive folder
  • Automated synchronization through Google Drive API
  • Secure storage in AWS S3 with metadata logged in AWS RDS (PostgreSQL + PGVector)
  • Backend services built on FastAPI manage structured job–candidate mappings
  • Dockerized workloads hosted on AWS EC2 with CI/CD pipelines deployed via GitHub Actions

Phase 2: Resume Parsing and Data Structuring

  • Python-based pipelines standardize PDF, DOCX, and TXT formats
  • Entity extraction using regex, spaCy NLP, and custom parsing logic
  • Candidate data normalized into structured profiles for downstream analysis

Phase 3: AI-Powered Matching and Ranking

  • Large Language Models accessed via Amazon Bedrock enable semantic matching between resumes and job descriptions
  • Context-aware scoring using semantic similarity and zero-shot classification
  • Vector-based RAG architecture enhances role-fit and experience alignment assessment

Phase 4: Chatbot and Interview Automation

  • GenAI chatbot powered by Bedrock Agents supports candidate Q&A, pre-screening, and interview preparation
  • Google Calendar integration enables automated interview scheduling
  • Candidate interactions and summaries logged in AWS CloudWatch for visibility and compliance

Phase 5: Monitoring and Continuous Optimization

  • AWS CloudWatch tracks uploads, parsing, matching workflows, and chatbot activity
  • Recruiter feedback loops refine ranking logic and LLM prompts
  • Modular architecture supports scalable adoption across departments and hiring pipelines

This solution aligned recruitment modernization with the organization’s broader digital health transformation goals.

Outcome:

  • 75% reduction in manual resume screening time
  • Higher candidate-to-role matching accuracy compared to traditional keyword-based systems
  • End-to-end hiring cycle reduced to 1–2 weeks, from intake to onboarding
  • Increased recruiter productivity supported by centralized analytics and AI-driven insights
  • Scalable, cloud-native platform capable of supporting large hiring volumes with minimal manual intervention

The initiative positioned recruitment as a digitally optimized function within the client’s health digital transformation roadmap, improving both speed and quality of hiring outcomes.

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