How accurately can rediscovery software match candidates in our database to new client job orders?

Accuracy depends on how detailed the job order's requirements are and whether the matching engine reads the full context of candidates or just keyword text. Well-built rediscovery software scores matches based on verified skills, experience, and placement history, rather than guessing from a job title alone. A vague job order produces broader, less reliable matches than one with specific, detailed requirements.

Semantic matching reads a candidate's full profile in context instead of just scanning for exact keyword matches. This approach catches qualified candidates that keyword searches might miss. For example, a candidate whose resume lists "supply chain coordination" can still match a job order requiring "logistics operations experience," since the matching engine understands meaning rather than exact phrasing.

Rediscovery software like Skima AI reflects this directly through its match score. It scores each candidate from 0 to 100, providing reason bullets tied to specific skills, experience, and placement history instead of an unexplained ranking. Every candidate card shows which requirements a candidate meets and which they miss, based on evidence found directly on their resume. This allows recruiters to judge accuracy directly instead of relying on an opaque score.

You can improve accuracy on your end by making job order requirements as specific as possible. A job order stating "3 years of warehouse management experience with WMS software and forklift certification" produces sharper matches than one simply stating "looking for a warehouse supervisor." If early results seem off, refining the job order's stated requirements and repeating the scan typically tightens results without starting the search over from scratch.