Talent Sourcing & Screening

Automate candidate sourcing and screening using AI for faster, fairer hiring decisions

Business impact

• Time to hire — AI accelerates candidate identification and screening, shortening hiring cycles
• Candidate sourcing efficiency — Semantic search and intent-based matching improve sourcing precision
• Quality of hire — Better matching algorithms increase fit and reduce turnover risk
• Recruiter productivity — Automation frees recruiters to focus on high-value tasks
• Candidate engagement — Conversational AI maintains timely communication and improves experience

Data requirements

• Structured candidate profiles (Structured) — Used for semantic matching and skills inference
• Resumes and job descriptions (Text) — Parsed for skills, experience, and intent extraction
• Recruiter interaction logs (Text) — Capture recruiter intent and feedback for model training
• Candidate communication data (Text) — Supports conversational AI and engagement tracking
• Historical hiring outcomes (Structured) — Used to train predictive models for candidate success

AI methods and techniques

• Predictive AI — Forecast candidate fit and hiring success based on historical data
• Generative AI — Enable conversational interfaces and natural language candidate search
• Agentic AI — Automate scheduling and candidate engagement workflows

AI models and model families

GPT-4, Claude, Proprietary domain-specific LLMs, Custom machine learning models

Industries

Real-world evidence

7 documented case studies on record.

Companies using this: Amazon, EZ, Indeed, Juicebox, Linked In, Paradox, Seek Out.