Our pipeline watches video the way an expert would. It identifies objects, tracks hands, understands interactions, and outputs the structured procedural data that embodied AI needs to learn from the real world.
For AI to operate in the physical world, it needs to understand how humans do things. Our pipeline extracts structured procedural data from video. The kind of data that teaches robots, copilots, and autonomous systems to act with intent.
No prompts. No labels. No configuration. Upload a video and get structured procedural data, ready for training embodied AI.
Our detection engine identifies and classifies every visible object (tools, containers, food, body parts) without text prompts or predefined categories.
Our segmentation model produces precise masks for every tracked entity. Even overlapping objects get clean, separate masks across the entire video.
Our vision-language model analyzes keyframes in context, generating specific entity names, action descriptions, and interaction labels through reasoning.
Raw detections become temporal graphs: which objects interact, what state changes occur, and how individual actions compose into higher-level tasks.
From raw video to structured procedural data, ready for physical AI training pipelines.
Our detection engine identifies every object in every frame. Pose estimation tracks hand landmarks. A spatial map of the scene is built automatically.
Advanced segmentation generates pixel-perfect masks and tracks entities across the full video. A vision-language model reasons about what it sees.
A language model writes a natural-language overview and organizes the full procedure. Download segmentation video, JSON, or a PDF report.
A preview of the structured output: procedural timelines, detected entities, and exportable data.
Upload a video and get structured procedural data. The building blocks for robots, copilots, and autonomous systems that act in the real world.