A delivery robot cannot navigate a building using a tourist video. It needs to know where the doors are, how wide the corridors are, and which paths are actually passable. That’s where @vangrid_io gets interesting. Imagine someone walking through a shopping mall, a warehouse, or an office building with a smartphone. Multiple angles of that environment can be reconstructed into dense point clouds and Gaussian splats, giving robotics teams structured 3D data to work with. For example, a lab developing warehouse robots could use spatial data to study aisle layouts, turning clearance, and obstacles. A team building delivery robots could map entrances, corridors, and ramps before testing navigation. An autonomous systems developer could use 3D reconstructions to help build and evaluate perception models. Instead of collecting hours of video and building everything from scratch, teams get a structured representation of the environment they want to study. There’s also a verification layer: faces and license plates are removed before encoding, clips are hashed at capture, and batch roots are recorded on Base. The reconstructed mesh is the actual deliverable. Satellites might show a building’s roof, but not the layout of its corridors. Street-view imagery might show an entrance, but not provide the 3D geometry a robot needs to reason about clearance and movement. Random internet footage rarely comes with the structure or provenance a robotics team needs. That’s the bigger idea behind @vangrid_io. A simple walk through a real environment could become reusable spatial data for training and testing physical AI. The opportunity isn’t just collecting more footage. It’s turning real places into geometry that machines can learn from.