The algorithmic core of a talent platform that analyzes job requirements and candidate or employee profiles to calculate compatibility scores and surface the most relevant matches for a role or opportunity.
A talent matching engine's accuracy is a function of three interdependent inputs: the quality of job descriptions (clear requirements, properly weighted), the richness of candidate profiles (verified skills, structured experience, career preferences), and the quality of training data calibrating the matching model. Organizations investing in improving job description quality before deploying a matching engine see matching accuracy improvements of 30 to 40 percent with no changes to the engine itself — confirming that input quality determines output quality as much as model sophistication. Regular calibration of matching weights against actual quality-of-hire outcomes is the ongoing maintenance that keeps the engine accurate as roles and markets evolve over time.
What the research says about employee engagement.
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Common questions about employee engagement.