<?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/eccf0a9d23fd48919071e31d19767e87&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/eccf0a9d23fd48919071e31d19767e87-bc6471a9a212ca34.gif</thumbnail_url><duration>107.531</duration><title>Using AI signals to support diligence</title><description>This Loom outlines an AI account intelligence connector assessment by uploading a prospect’s PowerPoint and receipts to estimate IRR. It explains that the solution targets customer success workflows by tracking losses of accounts, contacts, CRM notes, and support tickets, with “Zoom ranks” described as AI in the middle. The presenter reviews early indicators such as Meridian’s Labs and Harvard data restructuring, plus comparables including Turn Zero and Gauge Sign, clean hat. They also reference product council-style analysis based on input from top product managers and discuss evidence ledger and diligence reading at a high level.</description></oembed>