Think of this as turning a manual, paper-heavy hiring process into a smart filter and assistant that helps recruiters scan resumes, rank candidates, and communicate faster, while also flagging potential bias or legal issues.
Reduces the time and cost of screening and selecting candidates by automating repetitive recruiting tasks (CV screening, shortlist creation, interview scheduling) and providing data-driven decision support, while aiming to improve fairness and consistency in hiring.
Proprietary datasets of historical hiring decisions and outcomes, embedded into recruiter workflows and ATS systems, plus accumulated know-how around compliance, bias mitigation, and domain-specific models for different roles and industries.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data quality and bias in historical recruitment decisions; integrating with many different ATS/HRIS systems and meeting jurisdiction-specific compliance requirements.
Early Majority
Focus on empirical evidence from corporate recruiters about how AI is actually being used in recruitment—highlighting real adoption patterns, perceived benefits, and concerns (bias, transparency, legal risk)—rather than just describing theoretical capabilities.