Traditional CV screening is manual, slow, and vocabulary-dependent. Recruiters spend hours reading applications to identify candidates that match job requirements, and the matching is based on whether the right words appear in the right places, not whether a candidate actually fits the role. At scale, this process does not hold up.
Keyword-based ATS systems make this worse in a specific way. They filter out qualified candidates who describe the same skills in different terms, and surface unqualified candidates who have learned to optimise their CVs for the system. The shortlist reflects vocabulary matching more than genuine fit, and the hiring team still carries most of the evaluation burden.
The platform replaces keyword scanning with semantic understanding. CVs are parsed into structured profiles, embedded into vector representations, and matched against job descriptions using contextual similarity. Candidates are ranked by how well their actual experience aligns with what the role requires, not by whether they happened to use the same words as the job specification.