Cortex AI — Real-World Data for Embodied AI

What

Cortex AI provides real-world data services for embodied AI, with a focus on egocentric and robot data collected in workplaces and industry settings. The site positions the company as a data partner for frontier labs building robotics and physical intelligence systems.

Its core workflow appears to center on collecting and structuring real-world training data, including egocentric video, robot trajectories, and human-in-the-loop rollout data. The product is likely positioned as specialized infrastructure for teams that need high-quality, embodiment-specific datasets to train, fine-tune, and evaluate robotics models in practical environments.

Features

• Egocentric data collection for robotics world models — Captures workplace egocentric video designed to be directly usable for robotics training rather than general video analysis.
• Rich frame-level annotations — Includes hand pose, body pose, depth, and subtask annotations, which can improve data utility for perception and action modeling.
• Robot trajectory collection in real workplaces — Gathers embodiment-specific robot data through industry partners, supporting fine-tuning on top of foundation models.
• Human-in-the-loop rollouts and evaluations — Adds human oversight during real-world deployments, which can help teams monitor behavior and gather feedback safely.
• Recovery data capture for edge cases — Records failure and recovery scenarios during rollouts, supporting iterative improvement on difficult real-world tasks.
• Continuous data flywheel support — Frames deployment data as feedback into training, enabling an ongoing loop between operation, evaluation, and model improvement.

Helpful Tips

• For products in this category, verify how annotation quality is managed across pose, depth, and subtask labels, since downstream robotics performance depends heavily on consistency.
• Confirm whether the collected data matches the target embodiment, environment, and task distribution, because robotics models often transfer poorly across mismatched settings.
• Evaluate how rollout oversight is operationalized in practice, including escalation and recovery procedures, even if the site does not provide those implementation details.
• If comparing vendors, distinguish between raw data supply and end-to-end data operations support; this page suggests both collection and evaluation support, but does not fully specify delivery format or tooling.
• Ask for clarity on dataset access patterns, schema, and update cadence, since the site emphasizes a data flywheel but does not describe technical interfaces.