Cortex AI
Cortex AI builds the world’s most diverse and large-scale real-world workplace robot & egocentric dataset — where the physical world becomes the next training and evaluation set for embodied AI. We power frontier labs developing robotics foundation models and general-purpose robots by providing the data they need: 1️⃣ Egocentric Data — real-workplace human video with hand/body pose, depth, and subtask labels. 2️⃣ Robot Data — trajectories collected from manipulators and humanoids in real industr
Y Combinator
yc-company-directory #157Cortex AI builds the world’s most diverse and large-scale real-world workplace robot & egocentric dataset — where the physical world becomes the next training and evaluation set for embodied AI. We power frontier labs developing robotics foundation models and general-purpose robots by providing the data they need: 1️⃣ Egocentric Data — real-workplace human video with hand/body pose, depth, and subtask labels. 2️⃣ Robot Data — trajectories collected from manipulators and humanoids in real industr
sourceCortex AI is classified as AI infrastructure from source descriptions and fund-directory context. Similar companies below are ranked from offline product facets and meaningful description overlap; category and source affiliation can only strengthen an existing product match. For lookup-created rows, the profile starts as pending enrichment until deeper source collection runs.
Local clusters rerank extracted product concepts and description terms. The generated graph remains the fallback for sparse descriptions: offline reciprocal product-similarity graph: extracted concepts + description terms + bounded category/affiliation boosts. Minimum fallback score: 0; max competitors: 6.
Companies similar to Cortex AI
A deterministic local cluster built from offline product facets and meaningful description terms. Category and selected-institution affiliation provide bounded tie-breaks. Open any peer to continue exploring its cluster.
One Robot
match 78One Robot builds simulation environments that are realistic to see and realistic to interact with, so robotics teams can train and evaluate robot policies without being bottlenecked by robot time. Today, improving a VLA often means more real-world hours: setting up the scene, running trials, resetting, and repeating. This loop is slow, expensive, and hard to scale. For example, material handling and manufacturing assembly tasks, models need far more training and evaluation data than teams can co
shared extracted concept terms: robot; shared product terms: scale, real, robot, train; same category: AI infrastructure; shared affiliation: Y Combinator
Piggy Robotics
match 67We build humanoid robots that do your chores at iPhone prices. Just like the millions of cars generating data for self-driving, we need millions of humanoids generating real-world data for general-purpose robotics. Humanoid companies today can only manufacture hundreds a year because motors are expensive and slow to assemble. We use artificial muscles. Each muscle is just a tube wrapped in braided fibre, and the whole robot is powered by a single pump — which means we can mass-produce humanoids
shared extracted concept terms: real; shared product terms: real, robot, robotic, general; shared affiliation: Y Combinator
Cerulion
match 56Cerulion is an open source operating system for robots built by 2 MIT robotics PhDs. Engineers at Amazon Robotics and Boston Dynamics AI Institute are using us to develop faster and ship more reliable robots than with ROS. With up to 1000x improvements in communications performance over state of the art, Cerulion is enabling the next generation of embodied AI.
shared extracted concepts: embodied ai; shared product terms: robot, robotic, develop, next; same category: AI infrastructure; shared affiliation: Y Combinator
Philon
match 55Building open-source general-purpose robots capable of performing unsafe, repetitive or boring tasks.
shared extracted concept terms: general, purpose, robot; shared product terms: general, purpose, robot; shared affiliation: Y Combinator
Liva AI
match 54Speech models trained on internet data still lack realistic results. We solve this by collecting targeted training data for model labs. We hope to create a world where AI feels more human.
shared extracted concept terms: model; shared product terms: model, train, collect, labs; same category: AI infrastructure; shared affiliation: Y Combinator
Lucky Robots
match 44Automating real-world robotics development in virtual environments.
shared extracted concept terms: real, robotic; shared product terms: real, robotic, robot