Great question. We are building our entire labeling and data management system in house. Early on we tried existing platforms but they did not fit our workflow. We have a lot of video data and need custom labeling for things like keypoints, body outlines, and deformity classification that off the shelf tools do not handle well. Building it ourselves is cheaper at our scale, gives us tighter integration between labeling, training pipelines, and deployment, and lets us iterate faster. We can assign tasks to annotators, version datasets, and push models to edge devices from one system. When you are trying to close the loop between data collection on farm and deployment you cannot afford fragmented tooling.