We are a research accelerator that develops RL datasets, benchmarks, and environments that replicate real-world, economically valuable work scenarios—and we sell these to leading AI labs. Models and agents are placed into these environments and asked to accomplish practical objectives, whether that's designing new features, refactoring existing codebases, or tracking down bugs in an application. Their outputs are evaluated by a carefully selected group of domain experts, whose assessments become the reward signals that teach models how to perform real tasks well.

Today, our work is focused on helping AI labs and agents develop dependable coding capability and agentic skills. Over the long term, our ambition is to support the automation of all high-value work across the economy. Below are several case studies illustrating the kinds of tasks we typically handle: