Automate LinkedIn Lead Gen: AI Agents & GoHighLevel Workflow (Step-by-Step)

[00:06] All right. So, let's start from the very

[00:08] beginning so you can see how this works

[00:10] in a real scenario. This is the LinkedIn

[00:13] post and let's say a user named Chesa

[00:15] comes in and reads a comment something

[00:17] like this looks interesting. Can you

[00:18] explain how this works? And instead of

[00:20] manually checking notifications or

[00:22] figuring out how to respond, the system

[00:24] immediately detects the comment,

[00:26] analyzes it, and classifies the intent.

[00:29] The moment that happens, I receive this

[00:31] email. And as you can see, it's not just

[00:33] a notification. It gives me the author,

[00:36] their profile, the exact comment, and a

[00:38] fully generated reply that's already

[00:39] tailored to what they ask. So instead of

[00:41] thinking about what to say, I just take

[00:43] this response, go to the post, and drop

[00:45] it indirectly.

[00:47] Now, once I posted the reply, I simply

[00:49] confirm it by replying to the email like

[00:52] this.

[00:58] Once I confirm that the reply has been

[00:59] posted for engagement only comments, the

[01:02] workflow stops there. But for inquiry

[01:05] and high intent conversation, I get

[01:07] another email asking me to approve

[01:09] whether its interaction should be

[01:11] treated as a qualified lead. So once I

[01:13] approve it, the system creates the

[01:14] contact, opens an opportunity and logs

[01:17] all the engagement data into go high

[01:19] level. By this point, the person has

[01:21] already been invited in the public reply

[01:23] to reach out. And when they do, I follow

[01:25] up by sharing a chat link along with a

[01:28] unique reference tied to their

[01:29] interaction. From the users's

[01:31] perspective, it feels very simple. They

[01:33] open the link, drop in their reference

[01:35] code, and instantly the system knows who

[01:37] they are, what they commented, and what

[01:40] they are interested in. From there, the

[01:41] AI takes over the conversation. They can

[01:44] ask questions, explore more, and can

[01:46] request available time slots. The system

[01:48] fetches realtime availability, shows it

[01:51] to them and they can just pick a slot

[01:53] that works and the booking is handled

[01:55] automatically in the background. And

[01:57] once that booking is completed,

[01:58] everything updates again. The

[02:00] opportunity move to the next stage.

[02:03] The appointment is created in the

[02:04] calendar and I receive a final email

[02:06] with all the call details so I can

[02:08] prepare and follow up properly. Now

[02:10] before I wrap this up, I want to quickly

[02:12] show you something important because in

[02:14] real world usage things don't always go

[02:16] perfectly and the system is designed

[02:18] with that in mind. So for example, when

[02:20] a user enters the chat, the first thing

[02:21] we ask for is the reference code. That's

[02:24] what allows the system to pull in all

[02:25] the context, their comment, their

[02:27] intent, everything. Now if the user

[02:29] enters an incorrect code or maybe make a

[02:31] typo, the system doesn't break or return

[02:34] something confusing. It simply

[02:36] recognizes that the code doesn't match

[02:37] anything and responds in a clean user

[02:39] friendly way asking them to verify and

[02:41] try again. And the same logic applies

[02:44] throughout the flow. Now on the booking

[02:46] side, this is where edge cases really

[02:48] matter. If they pick a slot that was

[02:50] just taken by someone else, it doesn't

[02:52] fail silently. It tells them that the

[02:54] slot is no longer available and suggests

[02:55] choosing another one. And even if they

[02:58] paste something incorrectly, maybe the

[02:59] format is off or it doesn't match the

[03:01] available options, the system handles

[03:03] that gracefully. and ask them to copy

[03:06] the exact slot format.