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Back in 2014, Pete Fader, professor of marketing at Wharton and a pioneer of customer lifetime value analytics, was grading every paper in his course by hand and posting the strongest ones so students could see what excellent looked like. Over lunch one day, a curious student asked whether reading each other's work should be part of the grade, and whether students could even tell good work from bad. Nobody in the room thought much of it. But the question stayed with Pete, and weeks later, on a flight home from South Africa, he sketched out how it might actually work.
He brought the sketch to Dan McCarthy, then a statistics PhD student at Wharton who would go on to co-found Zodiac (acquired by Nike) with Pete and become a marketing professor himself. Pete's sketch posed the right problem. Dan's answer went further than either of them expected. The obvious approach would have been to simply minimize disagreement between scores. Instead, Dan built a full statistical model of the raters themselves, one that learns how reliably each person assesses and weighs their judgment accordingly. Refined over a decade with thousands of students, it worked. That model is still the engine at the heart of Incompass.
In 2017, Asuka Nakahara saw Pete present the system at a Wharton teaching-technology session and brought it into his own course: different students, different work, different standards. A real estate lecturer who had been a partner and CFO at Trammell Crow Company, Asuka had spent a career in business, and he saw something bigger than a grading tool. He kept stretching the vision of what it could become.
At a conference in London, Pete described the system to Gary Morrison, CEO of Hostelworld, and Gary's reaction was immediate: his company needed this for its people. He was onto something. A grade and a performance review are the same kind of scenario, a consequential decision built from subjective assessments, made by raters who are biased, inconsistent, and calibrated to no one. Promotions, compensation, succession: all of it rests on judgment that everyone acts on and nobody entirely trusts. A method that made a grade defensible could do the same for a talent decision.
Getting that method into companies took far more than the algorithm. Deniz Beser, an AI researcher, joined in 2022 to lead the way, building the engineering team and turning the method into a modern software product. The result is a review platform designed around this unique use case: thoughtful UI/UX carries people through giving real feedback, the calibration runs quietly underneath, and what comes out is something leaders can actually act on.
With the product built, Jessica DeVlieger came aboard as CEO in 2024 to scale the business. She'd grown a global company from its first sales to 500 people, and she built Incompass's commercial engine around the audiences the product serves, from executives who need a defensible view of talent to HR leaders who run the process and managers who live it, turning a platform customers loved into a repeatable business.
Trust in traditional performance reviews keeps falling. People expect genuine feedback, not a rating handed down once a year. And AI has dissolved the old tradeoff between statistical rigor and a process people will actually complete.
Combine that with the fact that we're now in the "human skills economy," where empathy, judgment, and leadership are becoming core to business performance rather than nice-to-haves. Our ability to reliably judge and reward these "softer skills" has never been more critical to business success.
That's what Incompass delivers: performance insights leaders can actually trust, so the highest-stakes talent decisions get made with confidence, not guesswork. Human input, machine-calibrated. Ask the people who actually work with someone, then correct for the bias in their answers.
When work moves continuously but insight into impact lags behind, organizations make high-stakes decisions without shared clarity. Leaders are forced to rely on partial views, employees lose trust when contribution feels misunderstood, and momentum suffers when the signals aren’t clear.
