Meeting Title: Mustafa - Hannah - Omni <> Default Case Study Date: 2025-11-04 Meeting participants: Mustafa Raja, Hannah Wang
WEBVTT
1 00:00:55.230 ⇒ 00:00:56.410 Hannah Wang: Hello.
2 00:00:57.610 ⇒ 00:00:58.689 Mustafa Raja: Hey, how are you?
3 00:00:59.630 ⇒ 00:01:00.750 Hannah Wang: Good, how are you?
4 00:01:01.040 ⇒ 00:01:02.120 Mustafa Raja: Yeah, doing good.
5 00:01:03.620 ⇒ 00:01:04.269 Hannah Wang: Great.
6 00:01:04.590 ⇒ 00:01:17.410 Hannah Wang: Okay, so… Another case study, this one is for, I believe, Omni, right? Let me see…
7 00:01:17.960 ⇒ 00:01:18.640 Mustafa Raja: Yes.
8 00:01:19.380 ⇒ 00:01:20.730 Hannah Wang: Omni and Default.
9 00:01:21.240 ⇒ 00:01:23.340 Mustafa Raja: I think that’s what it is. Yeah, yeah. Okay.
10 00:01:23.560 ⇒ 00:01:24.190 Mustafa Raja: That’s correct.
11 00:01:24.190 ⇒ 00:01:25.060 Hannah Wang: Okay.
12 00:01:25.310 ⇒ 00:01:37.970 Hannah Wang: Cool. Alright, so I’m gonna ask you questions, so we can start off with…
13 00:01:38.680 ⇒ 00:01:43.290 Hannah Wang: Hold on, give me one second.
14 00:01:43.290 ⇒ 00:01:43.630 Mustafa Raja: Yeah.
15 00:01:43.630 ⇒ 00:01:52.099 Hannah Wang: my notes. Okay, so… Cool, thanks for sharing your screen. Just a high level, like, what…
16 00:01:52.750 ⇒ 00:02:00.280 Hannah Wang: is the project type? Is it, like, a migration? Is it a deprecation? Like, what… what is it?
17 00:02:03.170 ⇒ 00:02:14.649 Mustafa Raja: So, I don’t think it’s, migration or anything. default did not have,
18 00:02:15.260 ⇒ 00:02:26.220 Mustafa Raja: a BI tool at all. So, by BI tool, I mean they couldn’t visualize, any of their data. I don’t know what’s happening here.
19 00:02:26.350 ⇒ 00:02:30.840 Mustafa Raja: I think, calm these down or something, but…
20 00:02:30.840 ⇒ 00:02:31.810 Hannah Wang: Oh, no.
21 00:02:33.080 ⇒ 00:02:37.629 Mustafa Raja: Anyways, so, we can take a look.
22 00:02:37.910 ⇒ 00:02:39.539 Mustafa Raja: Here, though.
23 00:02:40.430 ⇒ 00:02:53.979 Mustafa Raja: Yeah, this is something that we have for them. They did not have any form of visualization for their data. They just had their raw data, and we were doing analysis and stuff.
24 00:02:53.990 ⇒ 00:02:59.510 Mustafa Raja: And, and I guess we had a partnership with…
25 00:02:59.510 ⇒ 00:03:17.969 Mustafa Raja: Omni, and the product itself is really good. If the data is modeled, really good, they have Omni AI, and that AI is able to generate these, very informative graphs on, sorry, informative graphs on its own.
26 00:03:18.070 ⇒ 00:03:21.240 Mustafa Raja: So yeah, this is what it is.
27 00:03:21.580 ⇒ 00:03:27.019 Mustafa Raja: So didn’t have… they didn’t have anything, and we just went with this.
28 00:03:27.310 ⇒ 00:03:28.859 Hannah Wang: Let’s see if it breaks. Okay.
29 00:03:30.250 ⇒ 00:03:31.060 Mustafa Raja: Let’s triangle.
30 00:03:31.060 ⇒ 00:03:35.009 Hannah Wang: And why did we choose Omni over any other BI tool?
31 00:03:35.440 ⇒ 00:03:37.749 Hannah Wang: Is it just because we have a partnership with them?
32 00:03:37.750 ⇒ 00:03:53.709 Mustafa Raja: We do have a partnership with them, and apart from that, their AI stuff is really good. Prior to this, an alternative for this would be REL, and Rel, we only can build VR code.
33 00:03:53.900 ⇒ 00:04:04.310 Mustafa Raja: But over here, we do not need to have much coding knowledge. If the model is built, we can just lay the graphs out.
34 00:04:04.440 ⇒ 00:04:05.959 Mustafa Raja: Very easily.
35 00:04:06.670 ⇒ 00:04:11.880 Hannah Wang: Okay. And then, how long did it take for us to build the dashboard for them?
36 00:04:12.660 ⇒ 00:04:17.459 Mustafa Raja: So, this could help us timeline it…
37 00:04:19.560 ⇒ 00:04:36.439 Mustafa Raja: I’d say the version 1, might have took us a week, and then Robert, that included… that includes Robert’s review and revisions on it. So, September 17th we started, September 23rd, we had a version.
38 00:04:36.520 ⇒ 00:04:43.230 Mustafa Raja: And then we have been just kind of, adding more stuff on their requests.
39 00:04:43.230 ⇒ 00:04:44.100 Hannah Wang: Okay.
40 00:04:44.280 ⇒ 00:04:44.830 Mustafa Raja: Yeah.
41 00:04:44.830 ⇒ 00:04:48.480 Hannah Wang: And so you’re the one… Oh, cool. Yeah. Okay.
42 00:04:48.890 ⇒ 00:04:50.969 Hannah Wang: Oh, it’s very colorful.
43 00:04:50.970 ⇒ 00:04:51.989 Mustafa Raja: Like, this globe.
44 00:04:52.880 ⇒ 00:04:54.390 Hannah Wang: Yeah, oh wow.
45 00:04:55.340 ⇒ 00:04:57.999 Mustafa Raja: Shows their customers where they are from.
46 00:04:59.070 ⇒ 00:05:00.260 Hannah Wang: Oh, cool.
47 00:05:00.260 ⇒ 00:05:00.850 Mustafa Raja: Yep.
48 00:05:01.360 ⇒ 00:05:03.339 Mustafa Raja: Yeah, so.
49 00:05:03.890 ⇒ 00:05:07.690 Hannah Wang: So you’re the one who built the dashboard, I’m assuming? Yes.
50 00:05:08.210 ⇒ 00:05:10.940 Hannah Wang: Who was PMing?
51 00:05:10.940 ⇒ 00:05:22.049 Mustafa Raja: Yeah, yeah, UTM, Utam PM’d, and V0, kind of a V0, was made by, Utam, and then, and then I took over this.
52 00:05:22.200 ⇒ 00:05:22.950 Mustafa Raja: Okay.
53 00:05:22.950 ⇒ 00:05:23.710 Hannah Wang: Cool.
54 00:05:24.060 ⇒ 00:05:29.330 Hannah Wang: So, just to understand… Like, as you mentioned.
55 00:05:29.500 ⇒ 00:05:34.869 Hannah Wang: They just didn’t have a data visualization tool at all before this, correct?
56 00:05:35.400 ⇒ 00:05:45.710 Mustafa Raja: Yeah, this is my understanding, I’m not 100% sure, because I didn’t see, any, any other visualization stuff on there, and…
57 00:05:46.450 ⇒ 00:05:53.110 Hannah Wang: Okay, and… This is for their customers, right? Just, like, seeing where the customer is from?
58 00:05:53.290 ⇒ 00:05:58.969 Mustafa Raja: And, this actually, not only for,
59 00:05:59.250 ⇒ 00:06:17.070 Mustafa Raja: Not only to see where their customers are from, but, we’re kind of segmenting the usage. We are seeing, the trends of usage, among customers, and, you’d see here, how their customers are segmented by their… by fundings.
60 00:06:17.140 ⇒ 00:06:21.500 Mustafa Raja: How they are segmented by, sectors.
61 00:06:21.700 ⇒ 00:06:27.300 Mustafa Raja: So… Yeah… Multiple stuff.
62 00:06:27.620 ⇒ 00:06:34.140 Mustafa Raja: I guess I can share this link with you, so you can take a look at what kind of stuff we have.
63 00:06:34.260 ⇒ 00:06:35.059 Mustafa Raja: Over here.
64 00:06:35.060 ⇒ 00:06:40.559 Hannah Wang: It’s just, like, everything related to the customer, and the customer journey, and all that stuff, right? Okay.
65 00:06:40.560 ⇒ 00:06:48.289 Mustafa Raja: Yeah. Also, we have annual revenue by customer, how much each customer is paying them and all.
66 00:06:49.540 ⇒ 00:07:03.009 Hannah Wang: Got it. And do you know… oh, I guess you’ve… maybe the answer is no, but you… do you happen to know, like, what they were doing before they visualized all this stuff? I’m assuming it’s just, like, they didn’t even look at the data.
67 00:07:03.010 ⇒ 00:07:03.609 Mustafa Raja: yep.
68 00:07:03.610 ⇒ 00:07:04.120 Hannah Wang: Yeah.
69 00:07:04.120 ⇒ 00:07:07.229 Mustafa Raja: It’s going to be that, yeah.
70 00:07:07.970 ⇒ 00:07:08.690 Hannah Wang: Okay.
71 00:07:08.690 ⇒ 00:07:10.279 Mustafa Raja: Yeah. And so…
72 00:07:10.710 ⇒ 00:07:21.309 Hannah Wang: I feel like this is very similar to all the other data case studies I’ve done, but I’m guessing, like, to understand the challenge of our client, the problem that
73 00:07:21.660 ⇒ 00:07:29.690 Hannah Wang: I guess, default experiences when trying to understand all of this is that they just can’t, because all the data is everywhere.
74 00:07:30.300 ⇒ 00:07:33.510 Hannah Wang: And there’s no one source of truth.
75 00:07:34.050 ⇒ 00:07:34.690 Mustafa Raja: Yeah, yeah.
76 00:07:34.690 ⇒ 00:07:38.870 Hannah Wang: I think it’s… I guess it’s hard to make decisions, based on
77 00:07:39.150 ⇒ 00:07:43.809 Hannah Wang: Nothing, like, not having any data available that they can rely on.
78 00:07:43.960 ⇒ 00:07:44.950 Mustafa Raja: Okay.
79 00:07:45.430 ⇒ 00:07:50.450 Hannah Wang: That was a challenge,
80 00:07:51.960 ⇒ 00:08:08.820 Hannah Wang: Okay, and then… yeah, so I kind of want to learn more about Omni and how it’s set up, so you can walk me through, I guess, from when you picked up the work from Utom, like, what you did and how you developed the dashboard.
81 00:08:09.230 ⇒ 00:08:09.920 Mustafa Raja: Yes.
82 00:08:10.540 ⇒ 00:08:19.169 Mustafa Raja: So, so, we’re visualizing the data here, but the data, is coming from this mother duck
83 00:08:19.320 ⇒ 00:08:20.020 Mustafa Raja: Okay.
84 00:08:20.020 ⇒ 00:08:20.410 Hannah Wang: Okay.
85 00:08:23.860 ⇒ 00:08:27.710 Mustafa Raja: I think there’s some sort of outage going on or something?
86 00:08:28.690 ⇒ 00:08:31.010 Hannah Wang: Oh, no.
87 00:08:32.150 ⇒ 00:08:32.780 Mustafa Raja: Okay.
88 00:08:32.880 ⇒ 00:08:35.919 Mustafa Raja: So this isn’t Pokemon. Okay,
89 00:08:36.400 ⇒ 00:08:51.039 Mustafa Raja: Yeah, so… so we have a Mother Doc instance where all of this data lives. All of this data is coming from there, and then we have… we have a… we have a connection, then let’s… let’s look into that connection.
90 00:08:52.520 ⇒ 00:08:56.690 Mustafa Raja: Salute 20, yeah… Yeah.
91 00:08:57.070 ⇒ 00:09:04.560 Mustafa Raja: Oh… Ancient… And then, default mother dog.
92 00:09:06.050 ⇒ 00:09:21.720 Mustafa Raja: And then here we have the connection with their database. Not actually their database, but we asked them to give us a snapshot of their database so we could build out this dashboard.
93 00:09:21.720 ⇒ 00:09:34.949 Mustafa Raja: we stored that snapshot in Mother Duck, and then connected our Omni instance with that, and then if we go to develop, here we have all of the
94 00:09:35.110 ⇒ 00:09:36.200 Mustafa Raja: Modeling.
95 00:09:37.910 ⇒ 00:09:40.180 Mustafa Raja: So, topic would have all of the tables.
96 00:09:40.650 ⇒ 00:09:51.410 Mustafa Raja: Such form submission queues, queue members, and members meetings and all. And then, we do have to lay out some relationships.
97 00:09:51.460 ⇒ 00:10:03.180 Mustafa Raja: So, once this part is done, we can now, lay out the dashboard, either using AI or, by ourselves.
98 00:10:03.490 ⇒ 00:10:04.730 Mustafa Raja: Yeah, that’s pretty much it.
99 00:10:06.090 ⇒ 00:10:09.490 Hannah Wang: And did you use… I’m assuming you used AI? Yes.
100 00:10:09.490 ⇒ 00:10:12.560 Mustafa Raja: Yeah, yeah, it’s a combination of both.
101 00:10:13.380 ⇒ 00:10:16.459 Hannah Wang: Okay, can you show me how to do it with AI?
102 00:10:17.530 ⇒ 00:10:18.689 Mustafa Raja: Let’s… let’s do that.
103 00:10:27.500 ⇒ 00:10:28.980 Mustafa Raja: Oh, we can just explore.
104 00:10:38.230 ⇒ 00:10:39.650 Mustafa Raja: Parliamentism.
105 00:10:40.850 ⇒ 00:10:42.089 Mustafa Raja: Isn’t been good.
106 00:11:21.610 ⇒ 00:11:31.699 Mustafa Raja: So, this is the topic that we built, for… for them, and then here we have all of the tables. So, we could just go over here.
107 00:11:33.080 ⇒ 00:11:36.870 Mustafa Raja: And let’s think of something… Oh…
108 00:11:39.500 ⇒ 00:11:42.800 Mustafa Raja: Let’s… let’s ask it to give us Teams.
109 00:11:44.020 ⇒ 00:11:45.780 Mustafa Raja: that have…
110 00:11:47.160 ⇒ 00:11:54.810 Mustafa Raja: the most amount of forms. But do you have, do you have any, anything in mind you’d want to, you know, test it out with?
111 00:11:55.380 ⇒ 00:11:56.249 Mustafa Raja: Maybe we could be…
112 00:11:56.250 ⇒ 00:11:57.090 Hannah Wang: No.
113 00:11:57.240 ⇒ 00:11:58.320 Mustafa Raja: Yeah, let’s, let’s.
114 00:11:58.320 ⇒ 00:11:59.970 Hannah Wang: I just want to see.
115 00:11:59.970 ⇒ 00:12:18.640 Mustafa Raja: Yeah, let’s then… let’s just say then, I want to see the… Dings… Sorted by… Sweet things.
116 00:12:19.220 ⇒ 00:12:22.209 Mustafa Raja: Yeah, let’s… let’s see how… how this turns out.
117 00:12:24.590 ⇒ 00:12:30.560 Hannah Wang: So, and is team, like, each of their users? Yes. Is that what Teams means? Yes. Okay.
118 00:12:32.110 ⇒ 00:12:39.369 Mustafa Raja: Yeah, so we see that it’s doing… if I… if I click on over here, you see… you see it’s actually writing SQL.
119 00:12:39.810 ⇒ 00:12:40.380 Hannah Wang: Wow, that’s crazy.
120 00:12:40.380 ⇒ 00:12:47.770 Mustafa Raja: And it knows all of these definitions from the model that we have built, that I showed you earlier.
121 00:12:50.410 ⇒ 00:12:53.560 Mustafa Raja: Let’s, let’s take a look at that again.
122 00:12:55.140 ⇒ 00:13:04.889 Mustafa Raja: So, from all these relationships, we could add more… more context to it. The Omni… Omni allows us to,
123 00:13:05.210 ⇒ 00:13:19.210 Mustafa Raja: have some, custom text linked to a view, so we can… we can actually tell AI, okay, what… what… what this table actually is. But, over here, with this data, it works pretty good, so we didn’t need that. But we could.
124 00:13:20.080 ⇒ 00:13:28.239 Mustafa Raja: And, it, the AI, using this model just understands the data, and that is how…
125 00:13:28.260 ⇒ 00:13:31.659 Hannah Wang: It’s able to lay this out, and we can see that.
126 00:13:31.660 ⇒ 00:13:33.659 Mustafa Raja: It’ll graph it up also.
127 00:13:35.280 ⇒ 00:13:39.339 Hannah Wang: Wow. And so you just insert that chart, or you can tweak it, and then you insert it?
128 00:13:39.340 ⇒ 00:13:54.869 Mustafa Raja: Yeah, we can take it. You see that, by default, it says limit 1000? We can say 1000 is a little too much. Maybe 100 is a better figure. We do that and just paste it, or just publish it.
129 00:13:56.670 ⇒ 00:13:57.400 Hannah Wang: Got it.
130 00:13:59.940 ⇒ 00:14:12.890 Hannah Wang: Okay, let’s see… any other question I have? So, the tools involved, obviously, Mother Duck, Omni, is that basically it?
131 00:14:17.940 ⇒ 00:14:18.650 Mustafa Raja: Hey.
132 00:14:24.610 ⇒ 00:14:26.950 Hannah Wang: Hello, were you able to hear my question?
133 00:14:26.950 ⇒ 00:14:28.799 Mustafa Raja: No, no, no, can you say that again?
134 00:14:29.270 ⇒ 00:14:33.810 Hannah Wang: Yeah, besides Mother Duck, Oh, cool, it’s working, I think.
135 00:14:34.220 ⇒ 00:14:38.549 Hannah Wang: Besides Mother Duck and Omni, were there any other tools that you used?
136 00:14:38.940 ⇒ 00:14:40.470 Mustafa Raja: I guess D…
137 00:14:40.470 ⇒ 00:14:45.060 Hannah Wang: Default… well, it’s… yeah, nevermind. Yeah, Mother Doug, Omni…
138 00:14:45.060 ⇒ 00:14:49.480 Mustafa Raja: Hmm. So, yes,
139 00:14:50.420 ⇒ 00:15:01.479 Mustafa Raja: Omni AI can only do so much for us, so, for some complex, graphs, I did use
140 00:15:01.630 ⇒ 00:15:04.540 Mustafa Raja: What’s it called?
141 00:15:04.680 ⇒ 00:15:07.309 Mustafa Raja: ChatGPT to help me craft queries.
142 00:15:07.670 ⇒ 00:15:08.220 Hannah Wang: Okay.
143 00:15:08.220 ⇒ 00:15:09.839 Mustafa Raja: But that is pretty much it.
144 00:15:11.140 ⇒ 00:15:14.219 Hannah Wang: So what was, like, an example of a complex query?
145 00:15:15.080 ⇒ 00:15:19.739 Mustafa Raja: Let’s, let’s, let’s actually go to the dashboard.
146 00:15:23.450 ⇒ 00:15:30.420 Mustafa Raja: so, so, it actually works good if, if we are linking only two tables.
147 00:15:30.750 ⇒ 00:15:42.370 Mustafa Raja: But as we, as we, want to include more and more, more, more and more views in it, it starts to, you know, hallucinate.
148 00:15:43.000 ⇒ 00:15:44.659 Hannah Wang: Is what happened.
149 00:15:44.980 ⇒ 00:15:45.550 Hannah Wang: I see.
150 00:15:45.550 ⇒ 00:15:49.779 Mustafa Raja: So that is when I would ask ChatGPT to help me out.
151 00:15:53.060 ⇒ 00:15:56.460 Mustafa Raja: Let’s see if I can find some example from here…
152 00:16:22.540 ⇒ 00:16:28.580 Mustafa Raja: I’ll have to drill them down to actually identify… One version.
153 00:16:33.550 ⇒ 00:16:34.820 Mustafa Raja: I think it’s…
154 00:16:41.670 ⇒ 00:16:45.489 Mustafa Raja: Yep, moving down to questions.
155 00:16:46.000 ⇒ 00:16:51.490 Mustafa Raja: But this is something… Yeah, this could be one example.
156 00:16:51.740 ⇒ 00:16:53.980 Mustafa Raja: Andrew…
157 00:17:02.830 ⇒ 00:17:08.950 Mustafa Raja: So here we wanted to see, that’s absolutely…
158 00:17:19.599 ⇒ 00:17:29.060 Mustafa Raja: So, hey, here we want, I’m just… I’ll move things… Give me a moment, please.
159 00:17:29.060 ⇒ 00:17:29.680 Hannah Wang: Sure.
160 00:17:43.560 ⇒ 00:17:44.340 Mustafa Raja: Okay.
161 00:17:48.020 ⇒ 00:17:59.220 Mustafa Raja: Okay, so, so this is about, average, average days, a team would take to reach a certain milestone in terms.
162 00:17:59.220 ⇒ 00:17:59.570 Hannah Wang: Okay.
163 00:17:59.570 ⇒ 00:18:19.509 Mustafa Raja: So, 400 meetings average would be about between 55 to 60, and in 500, this is going to be 120 and 125, and so on. So this is something that, what’s it called? Omni AI would struggle with.
164 00:18:19.560 ⇒ 00:18:24.340 Mustafa Raja: And just ChatGPT would, help us curate the query.
165 00:18:24.550 ⇒ 00:18:27.050 Mustafa Raja: A lot easier, yeah.
166 00:18:29.290 ⇒ 00:18:35.290 Hannah Wang: Okay. Got it.
167 00:18:35.910 ⇒ 00:18:49.870 Hannah Wang: And… Okay, I think that’s… Pretty… Clear, let’s see…
168 00:18:52.940 ⇒ 00:18:56.219 Hannah Wang: Any other, like, notable thing you want to mention about
169 00:18:56.330 ⇒ 00:19:00.230 Hannah Wang: The dashboard that we set up with for default.
170 00:19:03.540 ⇒ 00:19:06.730 Mustafa Raja: Yeah, I think this is, this is pretty, pretty much it.
171 00:19:07.560 ⇒ 00:19:08.350 Hannah Wang: Okay.
172 00:19:08.690 ⇒ 00:19:13.950 Hannah Wang: And so I know we’re continuing to build on this dashboard, right? Is that correct?
173 00:19:14.390 ⇒ 00:19:23.999 Mustafa Raja: Yes, so this is currently in our own instance, and I believe we’ll be transferring this to their instance, they just got it.
174 00:19:24.130 ⇒ 00:19:28.620 Mustafa Raja: So… we’ll set this up over there.
175 00:19:30.550 ⇒ 00:19:37.519 Hannah Wang: Okay, and did we get any feedback from the default team, or since they don’t have the instance?
176 00:19:37.690 ⇒ 00:19:41.840 Hannah Wang: Okay, sure, yeah, what did they… What did they say?
177 00:19:42.350 ⇒ 00:19:44.640 Mustafa Raja: Let me send it… sent it over here.
178 00:19:54.370 ⇒ 00:19:55.640 Mustafa Raja: Yeah, yeah.
179 00:19:56.910 ⇒ 00:19:57.630 Hannah Wang: Oh, yeah.
180 00:20:01.160 ⇒ 00:20:02.140 Hannah Wang: Oh, okay.
181 00:20:02.140 ⇒ 00:20:08.970 Mustafa Raja: So, by this time, we were working heavily on dashboard, and some analysis stuff.
182 00:20:10.580 ⇒ 00:20:16.920 Hannah Wang: Is that the only thing that we’ve done for them so far? Because I know we renewed our contract with them.
183 00:20:16.920 ⇒ 00:20:21.850 Mustafa Raja: We have done some other stuff, too.
184 00:20:21.850 ⇒ 00:20:22.560 Hannah Wang: Okay.
185 00:20:22.560 ⇒ 00:20:28.949 Mustafa Raja: So, earlier we did a, we did a case study for browser base, right?
186 00:20:29.530 ⇒ 00:20:30.180 Hannah Wang: Yep.
187 00:20:30.410 ⇒ 00:20:32.509 Mustafa Raja: Yeah, that is something that we did.
188 00:20:32.510 ⇒ 00:20:33.250 Hannah Wang: Oh, yep.
189 00:20:33.250 ⇒ 00:20:40.280 Mustafa Raja: And after the renewal, we have been pretty much work… working on analysis and dashboard.
190 00:20:42.040 ⇒ 00:20:45.640 Hannah Wang: Oh, right, yeah, the browser-based… I remember that, okay.
191 00:20:46.640 ⇒ 00:20:51.630 Hannah Wang: Okay, that… Sounds…
192 00:20:51.980 ⇒ 00:21:00.749 Hannah Wang: Good. I think… I think this is good for now, and then obviously I’m gonna ask you to look over it, just to make sure everything is okay.
193 00:21:01.050 ⇒ 00:21:06.160 Hannah Wang: And then… Yeah, if you want to log into Mother Duck, and I can also see.
194 00:21:43.780 ⇒ 00:21:45.559 Mustafa Raja: Yeah, exactly the evening.
195 00:21:55.910 ⇒ 00:21:58.350 Hannah Wang: Oh, maybe it’s… something’s down.
196 00:21:58.790 ⇒ 00:21:59.430 Mustafa Raja: Whoa.
197 00:22:00.240 ⇒ 00:22:01.609 Mustafa Raja: Yeah, it looks like it.
198 00:22:02.080 ⇒ 00:22:03.319 Hannah Wang: That’s okay.
199 00:22:04.450 ⇒ 00:22:10.150 Mustafa Raja: Yeah, I guess that’s pretty much it then.
200 00:22:10.840 ⇒ 00:22:20.159 Hannah Wang: Okay, yeah. If you could send me… a screenshot…
201 00:22:20.390 ⇒ 00:22:24.399 Hannah Wang: Let me kind of go… yeah, for the dashboard.
202 00:22:24.670 ⇒ 00:22:25.150 Mustafa Raja: Oh, yeah.
203 00:22:25.150 ⇒ 00:22:35.940 Hannah Wang: For Omni, let me just… I’m gonna cover up all the sensitive data, but if you scroll down, I just want to see, like, what’s the most eye-catching… eye-catching one, and then I’ll…
204 00:22:35.940 ⇒ 00:22:36.870 Mustafa Raja: Yeah, yeah.
205 00:22:36.870 ⇒ 00:22:40.580 Hannah Wang: ask you to screenshot it for me. Keep scrolling…
206 00:22:45.820 ⇒ 00:22:53.500 Hannah Wang: I mean, the globe… this right here is pretty cool. If you scroll a little bit up, yeah, like, right there, I feel like would be a good…
207 00:22:54.150 ⇒ 00:22:55.560 Hannah Wang: Maybe screenshot.
208 00:22:56.200 ⇒ 00:22:56.790 Mustafa Raja: Okay.
209 00:22:57.330 ⇒ 00:23:02.800 Hannah Wang: Yeah, so you can just DM me, that screenshot, and I can put it in.
210 00:23:03.140 ⇒ 00:23:06.480 Hannah Wang: Okay, I think that’s all.
211 00:23:06.590 ⇒ 00:23:08.760 Hannah Wang: Or.
212 00:23:10.860 ⇒ 00:23:13.980 Mustafa Raja: Yeah, I was just wondering one thing.
213 00:23:13.980 ⇒ 00:23:14.549 Hannah Wang: Yeah, sure.
214 00:23:15.910 ⇒ 00:23:26.000 Mustafa Raja: So, Utam said that we should do a… we should do a AI tool, for this sort of stuff. So I’m just wondering,
215 00:23:26.320 ⇒ 00:23:30.999 Mustafa Raja: What, which steps would you, would you think, yeah, would be able to help you?
216 00:23:32.840 ⇒ 00:23:35.570 Hannah Wang: For an AI tool, we can build in the platform to collect these.
217 00:23:36.580 ⇒ 00:23:47.449 Hannah Wang: Yeah, well, do you mind if I stop recording, just because I don’t want my answers.
218 00:23:47.450 ⇒ 00:23:47.860 Mustafa Raja: Yeah, yeah.
219 00:23:47.860 ⇒ 00:23:49.320 Hannah Wang: tamper with… okay.
220 00:23:49.700 ⇒ 00:23:50.970 Hannah Wang: Amazing.