[0:06] All right. So, let's start from the very
[0:08] beginning so you can see how this works
[0:10] in a real scenario. This is the LinkedIn
[0:13] post and let's say a user named Chesa
[0:15] comes in and reads a comment something
[0:17] like this looks interesting. Can you
[0:18] explain how this works? And instead of
[0:20] manually checking notifications or
[0:22] figuring out how to respond, the system
[0:24] immediately detects the comment,
[0:26] analyzes it, and classifies the intent.
[0:29] The moment that happens, I receive this
[0:31] email. And as you can see, it's not just
[0:33] a notification. It gives me the author,
[0:36] their profile, the exact comment, and a
[0:38] fully generated reply that's already
[0:39] tailored to what they ask. So instead of
[0:41] thinking about what to say, I just take
[0:43] this response, go to the post, and drop
[0:45] it indirectly.
[0:47] Now, once I posted the reply, I simply
[0:49] confirm it by replying to the email like
[0:52] this.
[0:58] Once I confirm that the reply has been
[0:59] posted for engagement only comments, the
[1:02] workflow stops there. But for inquiry
[1:05] and high intent conversation, I get
[1:07] another email asking me to approve
[1:09] whether its interaction should be
[1:11] treated as a qualified lead. So once I
[1:13] approve it, the system creates the
[1:14] contact, opens an opportunity and logs
[1:17] all the engagement data into go high
[1:19] level. By this point, the person has
[1:21] already been invited in the public reply
[1:23] to reach out. And when they do, I follow
[1:25] up by sharing a chat link along with a
[1:28] unique reference tied to their
[1:29] interaction. From the users's
[1:31] perspective, it feels very simple. They
[1:33] open the link, drop in their reference
[1:35] code, and instantly the system knows who
[1:37] they are, what they commented, and what
[1:40] they are interested in. From there, the
[1:41] AI takes over the conversation. They can
[1:44] ask questions, explore more, and can
[1:46] request available time slots. The system
[1:48] fetches realtime availability, shows it
[1:51] to them and they can just pick a slot
[1:53] that works and the booking is handled
[1:55] automatically in the background. And
[1:57] once that booking is completed,
[1:58] everything updates again. The
[2:00] opportunity move to the next stage.
[2:03] The appointment is created in the
[2:04] calendar and I receive a final email
[2:06] with all the call details so I can
[2:08] prepare and follow up properly. Now
[2:10] before I wrap this up, I want to quickly
[2:12] show you something important because in
[2:14] real world usage things don't always go
[2:16] perfectly and the system is designed
[2:18] with that in mind. So for example, when
[2:20] a user enters the chat, the first thing
[2:21] we ask for is the reference code. That's
[2:24] what allows the system to pull in all
[2:25] the context, their comment, their
[2:27] intent, everything. Now if the user
[2:29] enters an incorrect code or maybe make a
[2:31] typo, the system doesn't break or return
[2:34] something confusing. It simply
[2:36] recognizes that the code doesn't match
[2:37] anything and responds in a clean user
[2:39] friendly way asking them to verify and
[2:41] try again. And the same logic applies
[2:44] throughout the flow. Now on the booking
[2:46] side, this is where edge cases really
[2:48] matter. If they pick a slot that was
[2:50] just taken by someone else, it doesn't
[2:52] fail silently. It tells them that the
[2:54] slot is no longer available and suggests
[2:55] choosing another one. And even if they
[2:58] paste something incorrectly, maybe the
[2:59] format is off or it doesn't match the
[3:01] available options, the system handles
[3:03] that gracefully. and ask them to copy
[3:06] the exact slot format.