Our work focuses on building reliable datasets from real-world environments to support robotics and embodied AI. By collecting extensive video and sensor data from industrial settings, we enable the training of models capable of understanding and operating within complex physical spaces. These datasets are carefully curated to ensure high accuracy, consistency, and scalability for machine learning applications. By bridging the gap between digital intelligence and real-world interaction, we help accelerate the development of AI systems that can perceive, adapt, and perform tasks safely in dynamic environments.
How We Annotate Data
Video Segmentation
We divide each video into precise time segments or frames to accurately capture individual actions and events occurring within the scene.
Frame-by-Frame Analysis
Our annotators carefully review each frame to identify movements, object interactions, and human actions, ensuring precise and context-aware annotations.
Structured Time Stamping
Every annotation includes detailed metadata such as serial number, start time, stop time, and action description, allowing models to understand temporal sequences effectively.
Fine-Grained Annotation Granularity
We use fine-grained annotation, breaking complex actions into smaller steps (e.g., hand reaches, grabs, lifts) to improve AI learning and model accuracy.
Professional Annotation Tools
We use industry-standard tools such as CVAT to perform scalable video annotation, enabling efficient frame navigation, object tracking, and structured labeling.
Multi-Level Quality Assurance
All annotations go through multiple review stages to ensure accuracy, consistency, and high-quality datasets before delivery to the client.
Example Video
Annotation Example
