We're saving 3 hours a day by flagging risky candidates before recruiters waste time

Recruiters were screening candidates who couldn't be hired

Alex Ngundji's team hires exclusively within the US. But at high volume, applicants were claiming US residency while applying from overseas — often behind VPNs and proxies — and it was nearly impossible to catch early.

Recruiters would read resumes, schedule first and sometimes second interviews, only to discover a location mismatch near offer stage. All that time: wasted.

"At our volume, recruiters were spending real time reading resumes and scheduling interviews — sometimes multiple rounds — before realizing the location didn't check out."

With spikes of up to 14,000 applicants in a single month and ~900 new applications in 24-48 hours from a single job posting, the problem wasn't theoretical. It was bleeding hours every day.

Why Abstract

One platform — instead of stitching vendors together

Alex's team initially looked at IPQS and similar tools via Google search, but pricing at their expected volume was a barrier. They expected their ATS — Greenhouse — to expose geolocation via API. It didn't.

When they found Abstract, they came for IP geolocation. What they got was more.

"We came for geolocation, but then realized Abstract also gave us phone and email — everything in one."

Accuracy at correctly identifying risky candidates

Value for money at high application volumes

Hands-on support — demos, explanations, onboarding help

Bundled coverage: IP + phone + email in a single integration

The Result

Recruiters focus on candidates who can actually be hired

With Abstract running in their Workato pipeline, risk scores surface before a recruiter ever opens a resume. The signal is immediate — visible in Greenhouse and in Slack — and it's changed how the team operates.

~3 hrs/day recovered from manual investigation

20-30% flagged automatically before review

‍Phase 2

routing clean candidates to resume screening

The recruiting manager, Trey, was immediately excited. The team is now planning to build resume keyword matching on top of the risk layer — routing only "No Risk" candidates into a clean shortlist pipeline, and exploring selective auto-rejection for strictly US-only roles.