Job Skills Matching

An enterprise staffing platform was struggling with matching: connecting candidates to jobs at scale. Their system relied on keyword matching, if a resume and listing shared exact terms, it was a match. Everything else was missed.

Qualified candidates were overlooked because they described skills differently. A candidate with "Agile methodology, sprint planning, stakeholder coordination" wouldn't match "project management." Recruiters spent hours manually reviewing candidate pools.

The platform needed a system that understood skills semantically, recognizing related competencies, transferable experience, and adjacent skills.

We built an AI matching agent that maps both job requirements and candidate skills into a shared embedding space where similarity is measured by meaning, not string matching.

The agent works in three moves: it reads both resumes and job descriptions to extract the real skills behind the wording; it reasons over relevance across multiple dimensions (direct skill match, adjacent skill coverage, experience level, and domain relevance); and it ranks the field, with weights recruiters can tune per role. The output is a shortlist with the reasoning attached, not a black-box score.

We grounded the agent in historical placement data, so it learned from actual successful placements: what candidate profiles really succeeded in what types of roles.

Recruiters now spend their time evaluating genuinely qualified candidates instead of sifting through irrelevant matches. The platform is surfacing candidates that would have been invisible under keyword search.