video1.0<iframe src="https://www.loom.com/embed/fdd4587794bd4a56afce6051f57c739b" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/fdd4587794bd4a56afce6051f57c739b-904422d8def70954.gif282.3In this Loom, I show our intelligence failure prevention approach. Every major failure mode has data on the body that existed, and our solution attaches one detector per failure mode using new ontologies like Phantom, Cassandra, and Deadstart. We do vessel intelligence, fire dimension threat scoring, live DIS auto analysis, warning mens scores, and 48 hour escalation using a graded prior score core engine. The prototype includes a 3D view with activity monitoring, and example outputs include Quasite 87 out of 100 and Confidence 0.19. I do not see a specific action requested from viewers.