Simulation providers

Reconstructs factory stations as digital twins to test process changes and robotic interventions. The current offering is industrial simulation rather than a general-purpose AI dataset marketplace.

Studies autonomous AI through real-world business deployments and simulation benchmarks, including retail operations and spatial or robotics tasks.

Develops software and simulation tools for teams building autonomous vehicles and other physical AI systems.

Offers resettable API sandboxes that model business services, allowing integration testing and agent rollouts against persistent but isolated state.

Creates stateful service twins accessed through APIs, command-line tools, or MCP for training agents and testing integrations outside production.

Builds long-horizon simulated tasks and benchmarks for agent security, reliability, scientific work, and operational incident handling.

Builds agent-learning environments and benchmarks, including workplace simulations, robotics tasks, and a runtime for executing and verifying agent work.

Builds simulated work settings with users, tools, persistent context, and tasks for training and evaluating agents under changing conditions.

Builds agent-training gyms that imitate workplace software and include tasks, datasets, and runtime infrastructure. Its website announces that Deeptune is joining Mercor.

Builds simulated worlds and challenges to study and improve AI behavior. Its public homepage gives limited detail about individual commercial deliverables.

Turns selected games into interactive learning environments designed to exercise planning, memory, tool use, and collaboration.

Develops simulation-ready assets, first-person human data, and evaluation infrastructure for robotics and physical AI.

Provides first-person activity datasets and gripper demonstrations alongside simulation infrastructure. Offerings combine video, inertial signals, pose information, and annotations with tools for testing and training robot policies.

Creates simulation assets and digital twins to evaluate robot policies and generate targeted training experience. Its workflow connects failure analysis, domain variation, and reinforcement learning with physical deployments.

Designs multi-agent simulations for training and assessing AI systems. Environments explore negotiation, coordination, enterprise tasks, and emergent behavior under explicitly chosen incentives and participant roles.

Transforms company documents, communications, and tool records into privacy-processed digital twins. These support multi-step agent tasks and evaluations that preserve relationships across organizational systems.

Researches model failures and converts them into training tasks, synthetic experiences, and simulated environments. Published work spans software engineering, computer use, knowledge work, alignment, and scientific reasoning.

Converts real-world data into simulated software environments for training and evaluating agents. Its public example combines multiple business tools with loadable tasks and scored agent rollouts.

Builds simulated work environments in which autonomous agents can practice extended tasks. Its data-lab approach combines applications, task data, and verifiers to improve agent reliability through experience.

Develops reinforcement-learning environments with an emphasis on robust reward functions and hardened graders. Public task examples include coding kernels, strategic games, mathematical reasoning, and image-data curation.

Builds simulated software environments and verifiable tasks for agent training and evaluation. Its published coverage includes connected workplace applications, coding tasks, and Blender-based design and animation workflows.

Transforms business workspaces and expert input into simulated environments for agents learning multi-step professional tasks.

Creates simulated versions of systems, APIs, data, and users for agent testing, training, and software development before production use.