<?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/7414f52464b74e57b27582197adfe8c8&quot; frameborder=&quot;0&quot; width=&quot;1110&quot; height=&quot;832&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>832</height><width>1110</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>832</thumbnail_height><thumbnail_width>1110</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/7414f52464b74e57b27582197adfe8c8-bc1daabdda2fa93a.gif</thumbnail_url><duration>326.101</duration><title>Dialshark | Voicemail Detection Good vs Bad Examples</title><description>Hi, I&apos;m Carlos from Dialshark, and in this video, I demonstrate the differences between a poor voicemail detection system and a successful one. I played a typical UK answering machine message to highlight how the AI mishandles the situation by failing to recognize whether it&apos;s interacting with a machine or a human. This is a critical aspect of effective voicemail detection, which is a key feature of Dialshark&apos;s semantic detection technology. I encourage you to consider how our system can improve your voicemail handling. Please let me know if you have any questions or need further information.</description></oembed>