Infrastructure providers

Builds wearable touch sensors and synchronized first-person cameras for capturing robotic-learning signals. Its current homepage emphasizes capture hardware rather than a ready-made dataset catalog.

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

Builds production-style RL environments and infrastructure, with agent evaluation and optimization tools and open research datasets.

Provides web-data collection infrastructure and datasets for organizations building data products and AI applications.

Provides shared cloud computers and tooling for teams and AI agents that interact with desktop software.

Develops Cerebro, a platform for turning operational records into fine-tuning data, reinforcement-learning material, and outcome-graded environments.

Provides a platform for managing, curating, versioning, and annotating unstructured datasets. Teams can connect human feedback, model operations, and automated pipelines across image, video, audio, and other data.

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

Supplies pre-training and post-training datasets, with a catalog spanning web text, documents, and video. Its published offering also includes large-scale collection infrastructure and sourcing of specialized training material.

Researches reinforcement learning, environment design, and multi-agent coordination over long tasks. Its current site emphasizes research and open releases.

Provides a data platform for organizing and managing unstructured data used in machine-learning development.

Provides adversarial assessments and security controls for AI systems, combining vulnerability testing with filtering and behavioral monitoring.

Runs automated experiment loops against a customer-defined metric to improve models or algorithms, including generated training environments.

Offers an SDK and platform to build, run, inspect, and grade training environments; its site also connects environment creators to DataVendor.

Develops physical-AI data and deployment infrastructure, including human video and sensor collections and an off-the-shelf catalog.

Builds infrastructure for training autonomous cyber capabilities, including verifiable environments based on realistic security challenges.

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

Processes robot and first-person recordings into timestamped action segments, reconstructed hand motion, and automated quality checks. Its pipelines assess visual quality and task completion in training and deployment footage.

Combines human-activity datasets, task-specific collection, evaluation tools, and deployment support for embodied systems. Its Egoverse platform provides first-person interaction data and tools for inspecting robot-learning datasets.

Its Atlas service records manual industrial work, trains models on the captured tasks, and deploys suitable robots into customer operations. The program links on-site collection with task-specific model development.

Builds industry-specific benchmarks, develops training data, and runs model post-training and deployment. Projects begin with company workflows, expert practice, and historical cases to define measurable task performance.

Turns organizational workflows, outcomes, and expert judgments into model-training signals. Its platform supports evaluations, fine-tuning, reinforcement learning, and ongoing improvement from production interactions.

Provides expert annotation and evaluation services alongside its Syncronus collaboration platform. Work covers difficult reasoning, tool use, agent behavior, safety testing, and peer-reviewed specialist workflows.

Offers an integrated stack for model training, evaluation, inference, and compute. Its environment hub and open-source tooling help teams create tasks, execute rollouts, and run reinforcement-learning workflows.

Researches data and engineering systems for technically capable AI agents. Public work includes long-horizon coding benchmarks, cybersecurity evaluation analysis, and infrastructure for running agentic reinforcement learning.

Provides coding-agent usage and cost analysis, with public benchmark and evaluation work. Its site also documents prior development of long-duration training environments and detailed analysis of agent trajectories.

Provides code sandboxes and evaluation infrastructure for developing and deploying AI agents, including options within a customer’s cloud account.

Develops a cloud-based factory for producing and standardizing agent training and evaluation data, according to its company profile.

Combines multimodal annotation, data orchestration, human review, and evaluation tooling, including support for agent trajectories and RL environments.

Builds research tools, evaluations, and environments for models working on empirical science and automated discovery.

Offers an API that extracts brand systems, retrieves style references, and checks whether agent output follows a brand’s visual direction.

Captures production agent traces, recreates hard cases as training environments, and feeds resulting evaluation signals into custom-model improvement.

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

Develops Campaign, a self-play system that turns proprietary data and evaluations into new tasks and environments. Access is described as early-stage.

Builds robotics data infrastructure, teleoperation-related systems, and production-scale datasets for teams developing embodied AI.