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Intelligent Resume Screening with Semantic Understanding
"Most companies reject 95% of applicants, but the rejection happens so fast that qualified people get filtered out by the same keyword logic that rejects the unqualified."
The Problem
Recruiters spend 20-30 minutes per application on initial screening, yet critical candidates are missed because traditional ATS systems rely on keyword matching. A qualified 'Project Coordinator' is rejected because the posting requires 'Project Management.' Time-to-hire averages 65+ days, and top candidates accept competing offers while applications sit in queues.
Current State
HR uses keyword filters in applicant tracking systems. Sequential resume review creates bottlenecks. Subjective evaluation leads to inconsistent scoring across hiring managers. Template-based screening questions miss nuanced qualifications.
GenAI Solution
AI reads complete resume text, job requirements, and company-specific hiring criteria via RAG. It generates quantitative assessments (Boolean checks against requirements), qualitative evaluations (project difficulty, leadership signals), skills mapping (validated skills, likely skills, skills to validate), and structured candidate summaries. The AI decides which candidates advance, assigns priority scores, and routes qualified candidates forward—parking edge cases for human review.
Key Differentiator
Keyword matching rejects qualified candidates who use different terminology. GenAI understands semantic equivalence ('managed P&L' = financial responsibility), infers skills from context, and evaluates soft skills from narrative descriptions. It also adapts to company-specific culture fit criteria that rules-based systems cannot encode.
Example Workflow
- 1 Job requisition analyzed and parsed into structured requirements with must-have vs nice-to-have distinctions
- 2 Resume received and semantically parsed — understands 'managed a team of 12' implies leadership even without 'management' keyword
- 3 AI generates match score with explanation: 'Strong match (87%) — 5 of 6 must-haves met, leadership experience inferred from project descriptions'
- 4 Candidates ranked and shortlisted; edge cases flagged with specific questions for recruiter review
- 5 Recruiter reviews AI-generated summaries for top candidates, validates or overrides recommendations
- 6 Approved candidates automatically moved to interview scheduling pipeline
Prerequisites
- ATS integration (Workday, SAP SuccessFactors, Greenhouse, or similar)
- Structured job requisitions with clear requirements
- 12+ months hiring history for calibration
- GDPR-compliant consent framework for AI processing of personal data
Red Flags
- Fewer than 50 hires per year — volume too low for ROI
- Highly specialized technical roles where human judgment is irreplaceable
- Strong union involvement in hiring processes
- Recent investment in ATS with built-in screening they haven't fully deployed
- 'We prefer to keep hiring very personal and high-touch'
Complexity Drivers
ATS integration depth, GDPR compliance setup, bias testing requirements, EU AI Act high-risk classification
Risk Factors
EU AI Act classifies HR AI as high-risk — conformity assessment required by Aug 2026. Bias in training data. Works Council approval in Germany/Austria/Netherlands. Candidate perception of AI screening.
Value Metrics
Time per application screen, time-to-hire, quality of hire
20-30 min per application, 65+ days time-to-hire
2-3 min per application, 30-40% reduction in time-to-hire
Industry Perspectives
Specific pain points, solutions, and regulatory factors for 6 industries.
C-Suite Relevance
Key Metrics
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