OpenVector

Operations across robotics, manufacturing, logistics, home care, fitness, restaurants, retail and other physical settings depend on visual monitoring that today requires manual watching or review, such as checking staging bays for empty materials, confirming a robot placed a bottle in a cup, documenting care visits, or noticing when a restaurant table is ready. Without automated interpretation of camera feeds, these checks are either done by people watching screens or not done consistently, and the resulting follow-up actions, like restocking, notifying a supervisor, or logging a completed task, depend on someone noticing and manually updating business software.

OpenVector connects any video stream to frontier vision-language models to deliver real-time video intelligence, with inference as low as 50ms. Users describe in plain English what should happen, and OpenVector builds an editable workflow that defines what to check in the camera feed and what action to take next, such as requesting a restock or notifying a supervisor. The platform demonstrates this across camera examples including a wrist camera confirming a robot has released a bottle into a cup, with confirmed matches logged as task completion and uncertain evidence routed to a human operator for review. The company organizes its offering around Vision, Language, and Action components, alongside platform features for deployment and integrations.