She decided the resume was lying to everyone. Then she built the machine that reads people instead.
Claire McTaggart runs SquarePeg, an AI platform that sits on top of the software companies already use to track job applicants and quietly does the part humans are worst at: paying close attention to every single resume in the pile. It screens unlimited applicants, filters out the fakes, enriches thin profiles, and hands recruiters a score with the reasoning attached. The promise is small and enormous at once - look at everyone, not just the first forty.
The company calls its approach "Glassbox AI." Not a black box that spits out a name, but a system that shows its work - why this candidate, why that ranking. For an industry that has spent a decade nervously asking whether the algorithm is fair, an algorithm you can argue with is a real position to take.
SquarePeg plugs into more than 95 applicant tracking systems - Greenhouse, Ashby, Lever, Workday, the usual suspects - with what the company likes to call "no rip-and-replace." It will surface a candidate you rejected eighteen months ago for a role that didn't exist back then. It will comb a pool of roughly 500 million profiles for the passive people who never applied. The pitch is less "throw out your tools" and more "your tools were not paying attention, and now they are."
Stop matching keywords. Start measuring whether someone actually fits.- The SquarePeg thesis, in one line
Before any of this, there was a consulting career going exactly to plan, which was the problem. McTaggart spent about four years at Monitor Deloitte doing strategy work across industries as unglamorous and varied as oil and gas and as bright as entertainment. Somewhere in there she ran the firm's hiring - campus recruiting, sourcing strategy, the case-study interviews that decide who gets in. She was, in other words, a professional judge of fit long before she sold software for it.
She was on track for promotion. She also could not find, inside herself, the appetite to become a partner. The three-question test she now repeats - are you learning enough, are you satisfied, are you motivated - kept returning the wrong answers. So she did the thing consultants are trained to advise against and clients rarely do: she walked, with no parachute, after a few visits to WeWork and a few conversations with founders who looked like they were having more fun.
Are you learning enough, are you satisfied, and are you motivated?- Her three-question test for a career
The path there was not a straight line through Silicon Valley. McTaggart started out poking at journalism, foreign policy at a think tank, and government work. She earned a master's in foreign service from Georgetown after an undergraduate degree from the University of Michigan, and spent more than four years working in the Middle East before New York City pulled her in. It is an unusual resume for a hiring-tech founder, which is a little bit the point. The whole company is an argument that the obvious credential is not the same as the right person.
SquarePeg's first incarnation leaned on psychometrics - measuring 19 workplace personality traits like detail orientation, adaptability and perseverance, then handing recruiters a batch of candidates who might never have applied or might have been filtered out by a careless first pass. The bet was that personality and context predict success better than a list of former employers. By 2022 the company had built an all-in-one recruiting platform aimed at startups. Then it spent something like eighteen months interviewing recruiters about what was actually broken, and rebuilt itself as the intelligence layer it is today.
Here is the detail that tells you who she is. McTaggart took SquarePeg's own assessment. It scored her high on being proactive and innovative - the sort who acts before anyone hands her permission. It also flagged her as weaker on logical reasoning and organizational structure. She shares this freely. A founder who publishes her own unflattering data is making a product argument and a personal one at the same time: the score is not a verdict, it is a starting point for a conversation. That is the entire company in one anecdote.
The money has followed. An early pre-seed of roughly $780,000 came out of a pipeline of 300 funds - the kind of brute-force arithmetic that founders rarely brag about because it is mostly rejection. In February 2025 SquarePeg closed a $3.5 million seed round led by Next Frontier Capital, with Acadian Ventures, Bread & Butter Ventures, Silicon Road Ventures and a roster of earlier backers, bringing total funding to around $6.4 million. The company now runs out of Salt Lake City - not the coast, which suits a founder whose whole story is about looking where other people don't.
Profile compiled from public sources including LinkedIn, Crunchbase, The Org, SquarePeg's own announcements, and published interviews. Figures reflect the most recent public reporting and may change.
Six jobs, one idea: make the software you already own pay attention to every applicant - and tell you why.
Read every resume in the pile, not just the first handful, and score each with confidence.
Resurrect the silver-medalist candidates you already rejected for roles that didn't exist yet.
Comb ~500M profiles and adapt the criteria as availability changes.
Filter the fabricated and ineligible before a human wastes an interview slot.
Infer missing skills and fill thin profiles so evaluation scales.
Refine requirements, dodge bad hires, predict time-to-fill.
"Traditional resume-based matching overlooks the personality and cultural-fit factors that actually predict success."
The name is a wink at "square peg, round hole." The company exists to find the right-shaped hole instead of forcing the peg.
She joined Twitter in January 2012 - years before there was a SquarePeg to tweet about.
Her first round took a pipeline of 300 funds to land 3 checks. Founding is mostly arithmetic on rejection.
She spent more than four years working in the Middle East before tech ever entered the picture.