Mercor acquires Deeptune to build AI training environments

Mercor is acquiring Deeptune, a company that builds reinforcement learning environments for AI training, to combine Deeptune's software platform with Mercor's expert network for creating realistic training environments. The acquisition aims to address the bottleneck of building environments where AI agents can practice real-world tasks, enabling faster and better training for frontier AI models and enterprise deployments.

Company /blog/?category=company Mercor to acquire Deeptune Today's frontier AI models can pass nearly any exam you give them, but put an agent inside a real workflow and it stalls on the first task an entry-level employee could handle. This is because the model lacks experience. Experience comes from doing real work: navigating messy documents, legacy enterprise software, and sprawling company context while making hundreds of small decisions along the way. It means knowing what to do when something breaks or when the next step isn't obvious. A model learns to perform well only by practicing repeatedly in the environment where the work takes place. Building environments rigorous enough for that kind of training is incredibly hard. That's where Deeptune http://deeptune.ai/ excels. Today we're announcing that Mercor will acquire Deeptune, a leading company building environments for reinforcement learning. Reinforcement learning has reached the point where a model can learn almost any task that can be clearly defined and scored. The constraint has shifted to the environments themselves: the places where models practice the work and get measured on whether they did it well. Building environments like these is exceptionally difficult. Each one has to faithfully recreate the software people use, translate expert judgment into realistic tasks, and measure performance clearly enough for a model to improve. Mercor's experts are already doing much of this work by writing evals. Deeptune provides the missing piece: the software platform where that expertise becomes realistic training environments at scale. Building the place agents learn to work Every environment has three parts: the software where the work takes place, the tasks that define what the agent must accomplish, and the verifiers that determine whether it succeeded. Mercor has built the expert layer behind that system. Our network of more than five million domain experts creates the tasks and verifiers that translate human judgment into learning signals for frontier models. Through APEX, we measure model performance on economically valuable tasks across thousands of real-world workflows. Deeptune builds the software layer. Over the past two years, the team has recreated hundreds of enterprise applications — from spreadsheets to Salesforce — and built some of the most sophisticated training environments used by frontier AI labs. We've seen the quality of their work firsthand because Mercor is already a Deeptune customer. Together, Mercor's expert network and Deeptune's software platform make it possible to build realistic training environments across far more industries, roles, and workflows than either company could alone. The team None of that happens without the people building it. I first met Tim, founder and CEO of Deeptune, two years ago, long before we started talking about working together. I was impressed by the technical ambition of Deeptune and Tim's conviction that environments would become the defining bottleneck for AI, long before most of the industry saw it. He and his team executed on that vision with remarkable speed. That's why I became an early investor, and ultimately why it felt obvious to bring our companies together. Deeptune is a small, focused team of engineers and operators with deep ties to frontier research. Earlier this year, Andreessen Horowitz led Deeptune's $43 million Series A. Tim and the entire Deeptune team are joining Mercor as we expand our team in NYC. Organizing human intelligence together For the labs we work with, this means environments that ship faster and train better. For enterprises deploying agents into their own workflows, it means one partner that can build the environment, staff the experts, and tell you whether the agent is ready. Every frontier lab is scaling environment production, and every enterprise will need to do the same. Training agents is becoming one of the largest categories of work in the economy, and the demand cuts across every profession. That is what we are building toward, and it is how we organize human intelligence to power the AI economy. Welcome to Mercor, Deeptune.