AI data platform iMerit believes the next step toward integrating AI tools at the enterprise level is not more data, but better data. And better data doesn’t come from hordes of gig workers, but from experts across mathematics, medicine, healthcare, finance, autonomy, and other cognitive fields, the company says.
“What’s become exceedingly important is the ability to attract and retain the best cognitive experts, because we have to take these large models and make them very customized towards solving enterprise AI problems,” Radha Basu, CEO and founder of iMerit, told TechCrunch.
The California- and India-based startup has for the past nine years quietly built itself into a trusted data annotation partner for companies working in computer vision, medical imaging, autonomous mobility, and other AI applications that require high-accuracy, human-in-the-loop labeling.
Now, iMerit is bringing its Scholars program out of beta, the company exclusively told TechCrunch. The goal of the program is to build a growing workforce of experts to fine-tune generative AI models for enterprise applications and, increasingly, foundational models.
iMerit already calls some of the top AI firms customers, including three of the big seven generative AI companies, eight of the top autonomous vehicle companies, three large U.S. government agencies, and two of the top three cloud providers, according to the company.
iMerit Scholars workflow example focused on mathematics Image Credits:iMerit
The news comes as Scale AI, arguably the biggest name in AI data annotation, has lost its founder and CEO Alexandr Wang to Meta, which also acquired a 49% in the company. In the wake of Meta’s investment, many of Scale’s major clients pulled back, including Google, OpenAI, Microsoft, and xAI, out of concerns that Meta could gain access to their product roadmaps.
iMerit doesn’t claim to replace Scale AI’s core offering of high-throughput, developer-focused “blitz data.” Instead, it’s betting that now is the right moment to double down on expert-led, high-quality data, the kind that requires deep human judgment and domain-specific oversight.
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“We’re the adults in the room,” Rob Laing, iMerit’s VP of global specialist workforce, told TechCrunch. “A lot of money is being spent on AI right now. There are some very intelligent people building large platforms of human workforces. The output that they’re getting from that mass approach and that very quick speed to market approach is not at the level of quality that enterprises need.”
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