<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/b6fb5cd87e5a458385541d9e994bba04&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/b6fb5cd87e5a458385541d9e994bba04-7d0a9014e471afae.gif</thumbnail_url><duration>118.869</duration><title>AI Reconstructs Evidence From Depositions</title><description>This Loom demonstrates using Gemini Fog and Argon to extract evidence from a deposition and reconstruct events accurately. The author describes analyzing a 212-page, nearly three-hour deposition video in one call, pulling out vehicles, timing, and where each witness stated they stood, then building a dynamic 3D world as code and checking it against the evidence to fix its mistakes. They review findings showing a witness claimed her view was unobstructed, but video analysis identified the exact moment when a bench shelter and a turning truck blocked the call completely using a plane geometry check reproduced by 23 agents. The workflow is extended with a new witness to confirm that new information feeds back into the model, and the lawyer can export a clean courtroom-ready report.</description></oembed>