London-based startup Prolific has raised $32 million to further develop its unique system that uses a network of 120,000 human participants to train and stress-test AI models.

Prolific, a company that was founded to source verified data from human participants, said today that it has raised $32 million in a Series A funding round co-led by Partech and Oxford Science …

AI, when it works well, can feel like magic, but all too often AI-based systems don’t work as they should: If the data used to train models is not deep, wide and reliable enough, any kind of curveball can send that AI in the wrong direction. A London startup called Prolific has built a system it believes can help head off that issue, by tapping a network of 120,000 human participants to inform and stress test AI models. And in a sign of demand for its services, Prolific has now raised some funding — £25 million ($32 million) — to expand its operations.The round was co-led by Partech and Oxford Science Enterprises (OSE).Prolific was founded in 2014 and already counts organizations like Google, Stanford University, the University of Oxford, King’s College London and the European Commission among its customers, using its network of participants to test new products, train AI systems in areas like eye tracking and determine whether their human-facing AI applications are working as their creators want them to. Up to now, it’s been revenue from users like these that have helped Prolific grow. In fact, the only money Prolific had raised prior to this round was a seed round of $1.4 million it got after going through YC. (Yes, it was profitable; no longer now that it’s taking VC money and investing in growth.)“We’ve seen incredible traction recently, and have a huge opportunity in front of us so are taking on this new funding to supercharge our efforts and expand our product, and range of participants, much faster than we could have without it,” Phelim Bradley, the founder and CEO, said over email to TechCrunch.The company was conceived initially not out of a specific need in the world of AI, but out of a general problem that researchers often face with panels for anything, something Bradley identified in his own academic work (his background, before Prolific, was in computational biology and physics).In short, it’s a challenge to find comprehensive cross sections of people to respond to questions, and nearly impossible to do so in a timely manner