The Front Page
The reject pile was never a graveyard. It was a warehouse.
It is 9:14 on a Tuesday and a recruiter at a growth-stage company opens an inbox with 2,000 applicants in it. Somewhere in that pile is the person they will hire. Also in that pile: a few hundred keyword-perfect resumes that mean nothing, a handful of fabricated ones, and dozens of quietly excellent candidates whose resumes happen not to say the magic words. The old move is to skim, to filter, to trust the keyword search and hope. SquarePeg's move is different - it reads all 2,000 before the coffee cools, scores each one on genuine fit, and hands back a shortlist with the reasons attached.
That is the whole pitch, and it is refreshingly small. SquarePeg does not want to replace your applicant tracking system. It wants to sit on top of it and make it smarter overnight. Recruiters already live inside Greenhouse, Ashby, Lever, Workday. SquarePeg meets them there, as a single agent you can talk to, and quietly does the screening work that no human should have to do by hand.
Turn noisy, unstructured applicant data into recruiting intelligence that saves time and improves hiring outcomes.
SquarePeg - Company Mission
Founder and CEO Claire McTaggart did not arrive at this idea by accident. She spent years watching good candidates fall through the cracks of keyword filters - the square pegs that never found their square holes because a matching algorithm was looking for the wrong shape. SquarePeg began life as a two-sided job-matching marketplace measuring hundreds of signals beyond the resume: soft skills, behavior, preferences, incentives. The market taught a lesson, the product pivoted, and what survived is sharper: an intelligence layer that recruiters adopt because it lives where they already work.
What It Actually Does
Six jobs. One agent. Zero migration.
Ask it in plain language. It answers with scored candidates and the reasoning behind them.
01
Applicant Screening
Score unlimited applicants with confidence - surfacing top talent that keyword matching misses entirely.
02
Talent Rediscovery
Mine the candidates already sitting in your ATS to hire faster and cheaper, with no new sourcing spend.
03
Passive Sourcing
Reach passive candidates across 500M+ profiles using adaptable, role-specific criteria.
04
Fraud Detection
Filter fraudulent and ineligible applicants before they ever reach a recruiter's desk.
05
Data Enrichment
Infer skills and enrich resumes with company research for scalable, contextual evaluations.
06
Outcome Intelligence
Refine job requirements and predict hiring timelines using real data from your funnel.
The Glass Box
A score you can't explain is a score nobody should trust.
Most AI in hiring is a black box: it says "yes" or "no" and dares you to argue. SquarePeg took the opposite bet. Every score arrives with the reasoning that produced it - what the model saw, what it inferred, why it ranked the way it did. The company calls it a glass box, and it backs the claim with continuous, independent assurance from Warden AI, which audits the models for bias on an ongoing basis rather than once at launch.
This is not a compliance footnote. In hiring, the whole game is trust - a recruiter has to defend a shortlist to a hiring manager, and a company has to defend its process to a regulator. Transparency is the feature that makes all the other features usable.
SquarePeg always provides strong candidates.
Brenda Vujanic • Benchworks
