candidate-match

Candidate matching uses a talent graph to score and rank profiles against role requirements and constraints. It evaluates experience, sector fit, seniority, availability, and prior contact history to build explainable shortlists.

Finding warm introduction paths is handled during the matching process by analyzing prior contact history within the talent graph. The skill maps these connections to help consultants discover viable warm intros to potential candidates.

Yes, candidate matching detects conflict risks by flagging off-limits statuses, recent placements, and client overlaps. It applies these constraint checks alongside fit rationale generation to safeguard candidate recommendations.

Yes, candidate ranking requires human review safeguards before finalizing recommendations. The skill generates fit rationales and flags conflicts, but a human consultant must review and approve the shortlist for the new role.

The best way to explain candidate fit is by generating match explanations alongside the ranking scores. The skill provides fit rationales based on evaluated profile data, sector alignment, and seniority constraints for each candidate.

A limitation of automated candidate matching is that it cannot finalize placements without human oversight. The system relies strictly on profile data and constraints to rank candidates, requiring a consultant to review and approve all recommendations.