- 01
Real-World Data Collection
Multimodal, diverse, and high-quality data collection tailored for generative foundation models.
- 02
Domain-Expert Annotation
Specialized human-in-the-loop labeling, RLHF, and custom data curation.
- 03
End-to-End Evaluation
Robust benchmarking, bias mitigation, and model safety testing.
GROUND TRUTH SYSTEMS
End-to-end, high-quality data services for foundation models, embodied AI, and world models — collected in the real world, verified and annotated by domain experts, delivered ready for large-model training.
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MILLION FRAMES ANNOTATED
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MEDIAN QA ACCEPTANCE
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DELIVERY CENTERS
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MODALITIES SUPPORTED
Embodied data has no internet-scale corpus to scrape — it has to be manufactured, one interaction at a time, through the right collection paradigms and rigorous quality control.
- 01
Ego-Centric Capture
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UMI Gripper Capture
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Real-Robot Teleoperation
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Dexterous Hand
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Simulation
Off-the-shelf, high-quality datasets for Generative AI, Physical AI, and World Models — collected in the real world, verified and annotated by domain experts, delivery-ready for large-model training.
OrphLux goes beyond labeling — every benchmark dataset carries the insight of PhDs and industry veterans behind it, the foundation we're building toward a longer-term goal: designing evaluation methodology and defining the boundaries of model capability.
R.01
Physical-plausibility validation methodology for embodied AI
COMING SOONR.02
Standardized methods for cross-modal annotation consistency
COMING SOONR.03