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We believe high-quality real data is the foundation of embodied AI. Centered on the EditReal high-fidelity synthesis paradigm, Qianzhen Tech connects Ego device capture, data synthesis and model training, linking data supply and demand.
Data infrastructure platform for embodied AI data synthesis and circulation.
To become the core data infrastructure of the embodied AI era, accelerating intelligent adoption across robotics, autonomous driving, industrial intelligence, medical robotics, and agricultural robotics.
Within two years, build scalable high-fidelity synthetic-data delivery and become a leading player in embodied-AI data synthesis.
Progress in embodied AI depends on how deeply we understand the real world. Starting from the first-person view, we weave video, IMU, audio and spatial cues into a data language AI can learn from.
We never fabricate data for models. Every sample comes from real capture and real operations, with traceable labels.
Millisecond-level timestamp alignment, resumable uploads that never lose a frame — reliability of the data infrastructure is our lifeline.
High-quality data shouldn't live behind walls. We open up selected datasets and capture specs, and build the ecosystem with academia and industry.
Synthesized from real data with distributions close to the real world; physics-consistent editing preserves lighting, materials and collision, greatly reducing the Sim2Real gap.
"Collect once, refine a thousand times" — quickly generate multi-view, multi-lighting, multi-object and multi-task variants from a single real sample for a steady supply of quality training data.
Pixel-level annotations generated automatically during synthesis: instance segmentation, hand pose, depth, optical flow, object trajectories and more.
No complex simulation environments required — edit and expand real scene data directly, shortening the path from capture to model-ready data.
Designed for robot manipulation, embodied AI and imitation learning, with native support for VLA, RT series, Octo and other mainstream architectures.
EditReal uses real video as the source, combining multiple techniques for high-fidelity lighting transforms and fast physics-model inference.
Training data for humanoid, service and industrial robot manipulation, covering grasping, assembly, transport and cleaning.
Data augmentation for complex and long-tail scenarios with multi-sensor synthesis and annotation across cameras and LiDAR.
Transform existing collected data into reusable, sellable assets and maximize data value through the synthesis engine.
Synthetic training data for inspection and assembly in smart manufacturing, cutting data collection costs on the production line.
Synthetic training data for surgical and rehabilitation robots, addressing data scarcity and privacy in healthcare.
Synthetic data for complex field environments to accelerate harvesting, inspection and agricultural automation.
Beijing Qianzhen Technology Co., Ltd. is a data-synthesis company serving frontier AI scenarios such as embodied intelligence, robotics and autonomous driving. Together with the Institute of Computing Technology, Chinese Academy of Sciences (ICT-CAS), we are building an "embodied brain" world model and, on top of it, an integrated embodied data platform. The platform spans the full chain — EGO device manufacturing, data collection, high-fidelity data synthesis and model training — and accumulates vertical high-quality datasets, offering vertical intelligent-application training services and forming a verifiable data loop. This addresses core problems such as data scarcity, high collection cost, low annotation efficiency and the Sim2Real gap, making us an indispensable data infrastructure and industry hub for the embodied-AI era. Our core business is high-fidelity synthetic data generation and multimodal training-data services, focused on three capabilities: world models, vertical high-quality datasets and vertical intelligent-application training services — backed by the original EditReal paradigm, the ICT-CAS technical heritage, a full multimodal data chain, pixel-level automatic annotation and national-level project capability.
Data needs, joint research, or commercial partnership — we'd love to talk.