Meeting Title: AI Ticket Automation Case Study Date: 2025-07-24 Meeting participants: Mustafa Raja
WEBVTT
1 00:00:17.420 ⇒ 00:00:19.610 Mustafa Raja: Yeah, I hope you’re doing good. So this
2 00:00:20.265 ⇒ 00:00:28.650 Mustafa Raja: this video is is about how we created this create linear tickets thing.
3 00:00:28.950 ⇒ 00:00:47.589 Mustafa Raja: So the context and challenge behind this would was that the project managers? Right after the meeting they had, they had to spend a lot of time, on on the meeting to create tickets and assign tickets and all.
4 00:00:48.820 ⇒ 00:00:55.249 Mustafa Raja: So we wanted to reduce the workload. And one way is that we have an agent.
5 00:00:56.079 ⇒ 00:01:12.779 Mustafa Raja: That would create those tickets for them, and Pm’s would then be able to review those tickets and edit those tickets if needed, and then they can have them directly on linear if they are happy with it.
6 00:01:13.100 ⇒ 00:01:21.593 Mustafa Raja: Okay, so the 1st version that we made was actually
7 00:01:23.029 ⇒ 00:01:31.370 Mustafa Raja: so we see that a meeting is assigned to a specific team. Right? So what what would happen?
8 00:01:31.960 ⇒ 00:01:41.560 Mustafa Raja: is, we would see, okay, which team is. It is assigned to and then, it would create tickets only for that team.
9 00:01:42.054 ⇒ 00:02:04.130 Mustafa Raja: And sometimes in A AI standups. We would be talking about multiple teams. And that would be tricky. It wouldn’t capture multiple teams. It would capture only a single team. So that is where our client hubs that we have previously developed came in came in handy.
10 00:02:05.630 ⇒ 00:02:31.749 Mustafa Raja: So we added some more context. Based on the participants of the meeting. We we would fetch their teams that they belong to in linear and based on that, the AI agent would know. Okay, so these are the participants in the meeting. And these teams they belong to. Okay. So if I’m going to assign a ticket to a particular participant.
11 00:02:31.800 ⇒ 00:02:56.409 Mustafa Raja: They are in such such teams. And which team does this? context, likely goes to. So this is how they would. The the next version would assign the tickets. And then, furthermore, we saw that for the for the clients that we have client hubs for the the tickets could actually be more
12 00:02:57.017 ⇒ 00:03:05.280 Mustafa Raja: groomed. Because let’s let’s actually quickly do generate.
13 00:03:05.940 ⇒ 00:03:13.152 Mustafa Raja: So this is a this is this meeting is for a client that we do not have a client hub for right now. So we would see that.
14 00:03:15.460 ⇒ 00:03:20.439 Mustafa Raja: the meet the descriptions are going to be one liners or so. Yeah. So
15 00:03:21.050 ⇒ 00:03:35.059 Mustafa Raja: for the for the meetings that that. For the sorry for the clients that have client hubs we actually use their client hubs to groom the tickets a little bit more to add more context to the tickets.
16 00:03:35.780 ⇒ 00:03:41.729 Mustafa Raja: So they are more descriptive. That that helps the Pm’s even more.
17 00:03:42.010 ⇒ 00:03:47.823 Mustafa Raja: Okay? So so yeah, so the
18 00:03:50.690 ⇒ 00:03:56.650 Mustafa Raja: yeah. So so this was the solution for our challenge, and the and in result.
19 00:03:59.048 ⇒ 00:04:10.571 Mustafa Raja: and in result. I I recall with them saying that this, this actually helped Pm’s save at least
20 00:04:11.210 ⇒ 00:04:31.606 Mustafa Raja: 30 min to an hour per meeting. Because right after meeting, they’d have to go in there and create those tickets. Now they they’d come over here. Okay, we have. We see that over here we have the team assigned. We have all the teams. If we want to edit, we have all the assignees over here. We can edit the
21 00:04:32.270 ⇒ 00:04:41.199 Mustafa Raja: title. And we can change the body. Let’s actually create an example ticket. I’ll I’ll create one in the AI teams.
22 00:04:42.247 ⇒ 00:04:47.020 Mustafa Raja: Yeah, let’s just leave it to me. Let’s let’s say it’s a test.
23 00:04:51.780 ⇒ 00:04:54.449 Mustafa Raja: and you’ll see that we can actually.
24 00:04:58.520 ⇒ 00:05:04.719 Mustafa Raja: yeah, we’ll see that from here. From here we can see see it over in the linear.
25 00:05:05.170 ⇒ 00:05:16.879 Mustafa Raja: So yeah, this this automation helped help the organization a lot. A lot more people would would now be engaging with our platform because it it serves more.
26 00:05:18.927 ⇒ 00:05:22.869 Mustafa Raja: Yeah. So I feel that’s
27 00:05:23.020 ⇒ 00:05:25.500 Mustafa Raja: that’s pretty much it for the impact and results.
28 00:05:26.590 ⇒ 00:05:30.289 Mustafa Raja: Yeah, I feel I think this is pretty good.
29 00:05:32.570 ⇒ 00:05:34.449 Mustafa Raja: Yeah, thank you.