Mercor, the AI data company helping organizations train and evaluate AI using human expertise, today announced it raised a $750 million Series D at a $20 billion valuation. The funding will accelerate Mercor’s evaluation (evals) and post-training business, giving companies in any industry a way to measure and set standards for how reliably AI agents complete tasks and at what cost.

The investors participating in this round include Benchmark, Felicis, Fundomo, funds managed by affiliates of Apollo, G Squared, General Catalyst, Glade Brook Capital, NVIDIA, Salesforce, Sapphire Ventures, StepStone Group, XTX Ventures, and Zoom.

AI is only as useful as the human expertise behind it, and as models grow more capable, the demand for specialized, domain-specific knowledge compounds.

“Every job in the economy will need its own AI agents, and every company will need a way to train and evaluate them on its own work,” said Brendan Foody, Co-Founder and Co-CEO of Mercor. “We spent the last two years building that infrastructure with the frontier labs. Now we’re bringing it to enterprises across the economy.”

Building for every profession

Over the next five years, more jobs will be created and more money will be spent applying existing models to real work than building new ones. Every new capability opens more workflows that companies need to evaluate, train, and operate.

“Every company deploying AI needs to know whether it works, and the answer is different for each one,” said Adarsh Hiremath, Co-Founder and Co-CEO of Mercor. “A model can score well on a public benchmark and still fail on the systems, policies, and edge cases that define how a specific business runs. We build custom benchmarks with each enterprise to measure models against its own standards, and that benchmark becomes the starting point for post-training.”

“For the last few years, frontier labs have been the primary buyers of expert-driven training data, which is about to change. As open models become good enough to build on, every enterprise will want to evaluate and train agents on its own work. Mercor has built something hard to replicate—an expert network, task design, as well as training and evaluation environments shaped by several years of working with the frontier labs,” said Jai Das, President, Partner and Co-Founder at Sapphire Ventures. “We’re thrilled to partner with Brendan, Adarsh, and team to help them scale in the Enterprise market.”

Agents need to learn how a job gets done and how a specific company does it, through its own systems, policies, and exceptions. Mercor acquired Deeptune earlier this year, adding a team that spent two years recreating hundreds of enterprise applications as training environments.

The approach builds on APEX, Mercor’s public benchmark series for measuring how AI performs on economically valuable work.

The Series D will accelerate Mercor’s investment in research, evaluation, and post-training infrastructure, including realistic environments where agents can learn complex professional workflows.

For more information on Mercor, its expert network, and evals, please visit https://www.mercor.com. To learn more about open positions and career opportunities, please visit https://www.mercor.com/careers/.

About Mercor

Mercor is the AI data company for building AI systems from human expertise. Its network of more than five million experts helps frontier AI labs, big tech, AI startups, and Fortune 500 companies evaluate and post-train models on real professional work. Founded in 2023 and headquartered in San Francisco, Mercor is backed by Benchmark, Felicis, General Catalyst, Menlo Ventures, NVIDIA, and Salesforce. Learn more at mercor.com.

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