Find candidates others miss
Recruiters search by keywords and miss qualified candidates. Silver medalists and past applicants sit forgotten in the ATS while teams struggle to fill roles.
Semantic search that understands capabilities, not just keywords. Finds the best-fit candidates across internal talent, past applicants, and overlooked silver medalists.
2.4M skill nodes and 18.7M relationships mapping people, jobs, and skills across your organization.
Vector-based semantic search finds the right people through meaning, not keywords.
14 specialized tools for matching, predicting, and acting on workforce data.
Connects people to roles, learning, and mentors based on skills and goals.
Policy enforcement, approval workflows, and audit trails for every AI action.
Measurable Impact
Keywords match exact terms – Python matches Python. Semantic search understands meaning – it knows a data engineer with Spark experience is relevant for a machine learning pipeline role even if those words never appear together.
Yes. The agent automatically searches past applicants who made it to final rounds but were not selected. It re-evaluates them against current roles, factoring in any new skills or experience they have gained since.
Yes. The agent searches across internal employees, past applicants in your ATS, silver medalists, and referral networks – presenting the best candidates regardless of source.
Your ATS ranks on keyword overlap. The agent ranks on capability fit, trajectory alignment, cultural signals, and likelihood to succeed – using models trained on millions of career outcomes.
Yes. Managers ask natural language questions in Teams or Slack – describe the person they need and get a ranked slate with fit analysis. No ATS training required.
AI-powered candidate discovery that surfaces the talent your keyword searches miss.