Candidate Matching

Candidate resumes and job data are analyzed to rank and recommend best-fit candidates, reducing screening time.

Business impact

• Time-to-hire — AI reduces screening and sourcing time, accelerating overall hiring speed
• Candidate matching accuracy — Improved algorithms increase relevance of candidate-job fit and selection
• Recruiter productivity — Automation of repetitive tasks frees recruiters to focus on strategic work

Data requirements

• Resumes and CVs (Text) — Extract candidate skills, experience, and qualifications for matching
• Job descriptions (Text) — Define role requirements and criteria for candidate matching
• Candidate interaction data (Text) — Analyze engagement patterns and responses to improve recommendations
• HRIS and ATS databases (Structured) — Provide structured candidate and job data for AI processing

AI methods and techniques

• Predictive AI — Forecast candidate success and fit based on historical hiring data
• Generative AI — Generate personalized job recommendations and candidate ranking summaries

AI models and model families

GPT-4o, Claude, Llama

Industries

Real-world evidence

7 documented case studies on record.

Companies using this: Allegis Group, Obra Jobs, Persol Career, Provenbase, TechRecruit, Upwork, Vanderlande.