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Development of Unbiased Digital Recruitment Platform with AI-Powered Matchmaking
  1. case
  2. Development of Unbiased Digital Recruitment Platform with AI-Powered Matchmaking

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Development of Unbiased Digital Recruitment Platform with AI-Powered Matchmaking

lizard.global
Human Resources
Information technology
Business services

Current Challenges in Job Market Bias and Inefficiency

Persistent gender inequalities and prejudices in traditional recruitment processes create unequal opportunities, while conventional job acquisition methods generate inefficiencies through unnecessary physical interviews and lack of skill-based matching systems.

About the Client

Digital recruitment platform connecting job seekers and employers through skills-based, unbiased matchmaking

Platform Development Goals

  • Create a two-sided marketplace for unbiased job matching
  • Implement skill-based matchmaking algorithm
  • Reduce environmental impact through digital-first interviews
  • Ensure GDPR-compliant data privacy by design
  • Increase successful employment matches by 40%

Core System Capabilities

  • Detailed user profile creation with CV upload and video pitch
  • Bias-mitigation algorithm based on skills/ambitions matching
  • Secure video conferencing integration
  • Profile completion progress tracker
  • Real-time match suggestion engine
  • Privacy-first data handling system

Technology Stack Requirements

React.js for frontend UI/UX
Node.js with Express for backend
WebRTC for video communication
AWS cloud infrastructure
MongoDB for document storage

Third-Party System Integrations

  • Video conferencing API (WebRTC)
  • CV parsing service
  • Email verification system
  • Payment gateway for premium features

System Performance Standards

  • 99.9% uptime SLA
  • Sub-200ms API response times
  • ISO 27001 security compliance
  • Horizontal scalability for 100k+ concurrent users
  • WCAG 2.1 accessibility standards

Expected Business Transformation

Implementation of this platform is projected to reduce hiring bias by 60%, decrease time-to-hire by 45%, and cut interview-related CO2 emissions by eliminating 70% of unnecessary physical interviews, while creating a 30% increase in sustainable employment matches through algorithmic skill-based matching.

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