Cortex AI Analysis: $50M Raised
What is Cortex AI?
Cortex AI collects egocentric and robot data from real workplaces for embodied AI systems. The company says it captures hand and body pose, depth, and subtask annotations, and it supports human-in-the-loop rollouts and evaluations. Cortex AI was backed by Y Combinator and contributed to the MolmoAct2 dataset curation and third-party benchmarking work.
Location
2261 Market St, STE 85831, San Francisco, California 94114, United States
Employees
1-10
Founded
2025
Industry
Robotics, AI/ML
AI Category
AI-native
YC Batch
F25
YC Partner
Garry Tan
Product Features & Capabilities
- Egocentric data collection from real workplaces
- Robot trajectory data for fine-tuning
- Human-in-the-loop rollouts and evaluations
Use Cases
Capture diverse egocentric data for robotics; Fine-tune AI models with real-world robot data; Support real-world deployments with human oversight; Create continuous data feedback loops from deployments; Collect recovery data for edge cases
How much Cortex AI raised
Funding - $50,000,000
Who are the founders of Cortex AI
HQ Full Address
2261 Market St, STE 85831, San Francisco, California 94114, United States
Product Description
Cortex AI specializes in providing comprehensive data solutions for robotics and embodied AI. Their main product offerings include:
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Egocentric Data for Robotics World Models: This service captures diverse egocentric data from real workplaces, including hand and body pose, depth, and subtask annotations. This data is crucial for training AI models in robotics, allowing for more accurate and context-aware interactions in real-world environments.
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Robot Data for Fine-Tuning: Cortex AI collects robot trajectory data from real workplaces through partnerships with industry leaders. This high-quality data is essential for fine-tuning AI models, ensuring they perform optimally in practical applications.
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Human-in-the-Loop Rollouts and Evaluations: This offering supports real-world rollouts with human oversight, capturing recovery data for edge cases. This approach creates a continuous data flywheel, enhancing the training process for AI systems by incorporating real-world feedback.
Key Features and Benefits:
- Real-World Relevance: The data collected is from actual workplaces, ensuring that AI models are trained on relevant and practical scenarios.
- Comprehensive Data Types: The inclusion of various data types (e.g., hand and body poses, depth information) allows for a more holistic understanding of interactions in robotic applications.
- Industry Collaboration: By partnering with industry leaders, Cortex AI ensures the data's quality and applicability, making it a trusted source for robotics researchers and developers.
Overall, Cortex AI's offerings are designed to advance the field of embodied AI by providing essential data that enhances the performance and reliability of robotic systems.
Other Considerations
Backed by Y Combinator; Collaborates with industry partners for data collection; Focused on advancing physical intelligence in AI
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