Edge-Case Miner is a tool for robotics and self-driving teams that have hours of camera footage but no time to label it.
You type a request like "forklift passing close to a person" and pick which cameras to look at. The tool searches the whole video archive for likely matches, then plays each candidate clip to a video-understanding model that answers yes or no and fills in labels such as who is in the shot, what they are doing, how close they are, and the lighting and weather. Clips that fail the check are rejected with a reason, so nothing gets into the dataset on a caption match alone. Every confirmed clip also gets a quality score based on object detection, blur, and whether the whole event is visible.
At the end you get a zipped dataset with the clips, a labels file, and a dataset card, plus a coverage report that shows which situations you have plenty of and which you are missing, with a plan for what to film next. If too few clips pass, the tool proposes a better indexing prompt and, once you approve, re-indexes the closest chunks and searches again.
