AI sourcing: from 500 résumés to 5 you'll actually love
How natural-language search across a 50M+ talent pool changed the way we shortlist senior talent.
There's a quiet truth in recruiting: every senior role attracts 300–500 inbound résumés in week one, and the recruiter ends up reading maybe 40 of them. The other 460 are noise — or worse, they're hidden gems buried under noise.
"The recruiter's job doesn't go away — it just shifts. Less time sorting, more time building rapport with five great candidates instead of skim-rejecting 495 mediocre ones."
Natural-language AI sourcing flips this. Instead of skim-reading résumés, you describe the candidate you actually want: "senior React engineer in Lisbon with design-system experience, open to USD contracts." Olamee AI ranks the entire pool — inbound plus passive — by fit against that brief. You go from 500 applicants to a shortlist of 5 in seconds.
What's surprising in practice isn't that the AI is fast. It's that it surfaces candidates you'd never have found manually: the staff engineer who isn't actively looking but is open to the right thing, the senior PM who's culturally a perfect fit but whose résumé buries that signal under unrelated experience.
The recruiter's job doesn't go away — it just shifts. Less time sorting, more time doing the actual human work of building rapport with the 5 great candidates instead of skim-rejecting 495 mediocre ones.
Jordan runs talent at Northbound and has been beta-testing AI sourcing tools since 2023.
