US-based Mercor has announced the completion of a $750m Series D funding round, bringing the AI data company’s valuation to $20bn.
The new capital is expected to support the expansion of Mercor’s evaluation and post-training services. These services aim to help organisations measure and standardise the performance and reliability of AI agents across a range of business functions.
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Participants in the financing round include investment companies such as Benchmark, Fundomo, Felicis, and funds managed by affiliates of G Squared, Apollo, General Catalyst, and Glade Brook Capital.
The funding round also saw the participation of Nvidia, Sapphire Ventures, Salesforce, StepStone Group, XTX Ventures, and Zoom.
Mercor provides tools for companies to train and validate AI with human expertise, tailoring models to meet specific business needs.
Mercor co-founder and co-CEO Brendan Foody said: “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.
“We spent the last two years building that infrastructure with the frontier labs. Now we’re bringing it to enterprises across the economy.”
According to Mercor, the demand for domain-specific AI systems is increasing as model capabilities advance.
Mercor co-founder and co-CEO Adarsh Hiremath said: “Every company deploying AI needs to know whether it works, and the answer is different for each one. 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.”
Earlier this year, Mercor acquired Deeptune, a company known for reconstructing hundreds of enterprise applications into training environments over a two-year period.
This move is intended to enhance Mercor’s ability to offer realistic environments where AI agents can be trained on complex professional tasks.
The company’s efforts build on APEX, its public benchmark series designed to assess how AI performs in economically relevant scenarios.
The additional funding is set to further Mercor’s research and investment in infrastructure dedicated to the evaluation and post-training of AI models.
