How do other companies get more value out of their existing candidate database?

Most companies get more value from their candidate database by scanning it automatically against every new job opening instead of relying on recruiters to remember past applicants. This turns years of stored resumes into an active sourcing channel rather than a passive archive.

The pattern shows up across companies of different sizes and ATS platforms. Teams check their existing database against a new job's requirements before writing a job ad or briefing a sourcing partner, and the scan triggers automatically the moment a new job opens rather than waiting for a recruiter to remember. Results come back as a ranked list with an explained score, replacing a raw pile of resumes pulled from a keyword search.

Companies that do this well also keep the database clean by matching duplicate candidates through email, phone, or LinkedIn URL, instead of scattering one person's history across three separate profiles.

Documented pilots across companies using Bullhorn, Lever, and Workday show consistent results from this approach: time-to-screen dropping by 58% or more, interviews from the top shortlist doubling, and hiring manager satisfaction rising by 24 points on NPS.

The database itself does not create this value on its own. Companies that benefit treat rediscovery as a standing process attached to every job opening, not a one-time cleanup project. Skima AI is one example of a tool built around this exact workflow, connecting directly to an existing ATS and scanning stored candidates automatically whenever a new role opens.