Spotly: semantic AI matching for recruitment, beyond keywords

Challenge

Traditional job matching relies on keyword search and manual filtering, which misses qualified candidates who describe their skills differently and cannot capture transferable skills or nuanced relationships. Manual screening is slow and subjective. Spotly needed a way to surface the best candidates beyond exact keyword overlap, while keeping precision high.

Approach

We built an AI vectorization and matching engine for Spotly that converts job postings and candidate profiles into semantic embeddings, then matches them by measuring proximity between vectors rather than overlapping keywords. The engine captures relationships between skills and transferable competencies, matches in real time as new candidates and jobs enter the system, and produces explainable scores that show why a candidate was recommended.

Outcomes

• Surfaces qualified candidates that keyword search overlooks
• Semantic matching that captures transferable skills and nuanced fit
• Real-time matching as new candidates and jobs enter the system
• Explainable match scores, with a foundation for continuous learning