Meeting Title: CTA Delivery Sync Date: 2026-02-23 Meeting participants: Brylle Girang, Uttam Kumaran, Ashwini Sharma, Awaish Kumar
WEBVTT
1 00:00:11.550 ⇒ 00:00:12.610 Brylle Girang: Hello!
2 00:00:13.090 ⇒ 00:00:13.890 Uttam Kumaran: Hello.
3 00:00:25.480 ⇒ 00:00:30.899 Brylle Girang: Putum, do we have an Instagram project for CTA? I asked Rico. Yes.
4 00:00:31.270 ⇒ 00:00:32.080 Brylle Girang: It doesn’t have.
5 00:00:32.080 ⇒ 00:00:33.130 Uttam Kumaran: We do.
6 00:00:33.810 ⇒ 00:00:35.130 Uttam Kumaran: Yeah, let me get it for you.
7 00:00:35.400 ⇒ 00:00:36.010 Brylle Girang: Okay.
8 00:01:50.300 ⇒ 00:01:51.360 Brylle Girang: Hi, Ashwini.
9 00:02:52.060 ⇒ 00:02:52.700 Awaish Kumar: Right?
10 00:02:53.800 ⇒ 00:02:55.339 Brylle Girang: Hello? Okay.
11 00:02:55.340 ⇒ 00:02:56.180 Uttam Kumaran: Hello.
12 00:02:59.140 ⇒ 00:03:00.899 Uttam Kumaran: Okay, let’s get started.
13 00:03:00.900 ⇒ 00:03:01.850 Brylle Girang: Gotcha.
14 00:03:02.060 ⇒ 00:03:20.119 Brylle Girang: Just a moment, my linear’s not working. Okay, so, I just wanted to check in on our progress for CTA, align our linear board with Instagant, our Gantt chart, and then at the same time, prioritize the tickets that we need to work on for CTA for this cycle, because we’re currently
15 00:03:20.570 ⇒ 00:03:23.470 Brylle Girang: I don’t have one, or don’t have too much.
16 00:03:23.760 ⇒ 00:03:29.540 Brylle Girang: I think, Putam, let’s start with you prioritizing what we need to work on.
17 00:03:29.900 ⇒ 00:03:30.660 Uttam Kumaran: Okay.
18 00:03:30.960 ⇒ 00:03:34.660 Brylle Girang: Oh, I think I’m… oh. Can you see my screen? Okay.
19 00:03:34.890 ⇒ 00:03:35.470 Uttam Kumaran: Yes.
20 00:03:36.440 ⇒ 00:03:42.549 Brylle Girang: Yeah, so we have a bunch of tickets here about Cortex, but I don’t think we’re going to proceed yet, because
21 00:03:42.670 ⇒ 00:03:45.800 Brylle Girang: We’re just proposing this for now, right?
22 00:03:46.870 ⇒ 00:03:52.580 Uttam Kumaran: Yeah, so, is there a ticket on the… Like, memo, basically?
23 00:03:53.770 ⇒ 00:04:01.350 Brylle Girang: them all. There is one under delivery assigned to me, but I can create one.
24 00:04:01.770 ⇒ 00:04:02.980 Brylle Girang: For that memo.
25 00:04:04.350 ⇒ 00:04:08.050 Uttam Kumaran: Because, yeah, so we still need to create one just for CTA, right?
26 00:04:09.670 ⇒ 00:04:13.039 Brylle Girang: Oh, I have already created that one, so.
27 00:04:13.040 ⇒ 00:04:19.150 Uttam Kumaran: Okay, so let’s… let’s create that ticket here, and create it as blocked, because me, the three of us have to go review.
28 00:04:19.510 ⇒ 00:04:20.180 Brylle Girang: Okay.
29 00:04:21.070 ⇒ 00:04:31.320 Uttam Kumaran: And then, basically, that memo is going to be what we send to them for, basically, like.
30 00:04:31.740 ⇒ 00:04:33.890 Uttam Kumaran: the next timeline, so…
31 00:04:34.150 ⇒ 00:04:39.970 Uttam Kumaran: One thing that I want to do is, like, some of these I’m just gonna move back…
32 00:04:42.320 ⇒ 00:04:43.890 Uttam Kumaran: Like, this…
33 00:04:51.460 ⇒ 00:04:56.070 Uttam Kumaran: Yeah, so, like, basically everything on the, Cortex project
34 00:04:56.210 ⇒ 00:05:01.079 Uttam Kumaran: Let’s move to… let’s move to, ready for work, because we don’t know yet.
35 00:05:02.160 ⇒ 00:05:02.820 Brylle Girang: Okay.
36 00:05:03.240 ⇒ 00:05:03.840 Uttam Kumaran: Yeah.
37 00:05:05.200 ⇒ 00:05:07.069 Uttam Kumaran: We just don’t know yet on the timeline.
38 00:05:09.000 ⇒ 00:05:16.509 Uttam Kumaran: So right now, the things that we are working on is, 82 and 62.
39 00:05:21.720 ⇒ 00:05:22.420 Brylle Girang: Okay.
40 00:05:29.660 ⇒ 00:05:30.280 Uttam Kumaran: Right?
41 00:05:31.190 ⇒ 00:05:38.350 Brylle Girang: Yep. So the scanner data, this is identity switching, yeah, that’s the priorities that Awash mentioned earlier.
42 00:05:38.350 ⇒ 00:05:41.610 Awaish Kumar: Identity switching, or it was identity switching?
43 00:05:46.270 ⇒ 00:05:47.650 Uttam Kumaran: Identity stitching.
44 00:05:47.650 ⇒ 00:05:48.870 Brylle Girang: Oh, stitching, okay.
45 00:05:48.870 ⇒ 00:05:49.780 Uttam Kumaran: Should be stitching.
46 00:05:52.430 ⇒ 00:05:53.600 Awaish Kumar: Okay.
47 00:05:54.190 ⇒ 00:05:56.940 Awaish Kumar: So, we have… yeah, this is kind of…
48 00:05:57.640 ⇒ 00:06:08.419 Awaish Kumar: half-bagged… we already have half-backed solution, we just need to finalize it, but according to Catherine, the scanner data part is the number one priority.
49 00:06:19.670 ⇒ 00:06:31.110 Brylle Girang: Can you provide more context about the scanner data? Because I don’t see… in my vantage point, I don’t know if I don’t understand, but I don’t see any of their tickets here.
50 00:06:31.270 ⇒ 00:06:32.110 Brylle Girang: About Skylar.
51 00:06:32.110 ⇒ 00:06:32.500 Awaish Kumar: jeez.
52 00:06:32.500 ⇒ 00:06:33.110 Brylle Girang: water.
53 00:06:34.090 ⇒ 00:06:45.950 Awaish Kumar: There’s a communication on this in Slack, like, in the internal channel as well. We, like, we are dealing with some flat files, which… and there’s a Postgres involved, so we… files…
54 00:06:46.360 ⇒ 00:06:57.929 Awaish Kumar: She’s loading into Postgres and running her carries, and then building a model out of it, and we need to just get those flat files directly to Snowflake, run some carries, and build those similar models.
55 00:06:58.050 ⇒ 00:07:00.940 Awaish Kumar: And move out Postgres from the scene.
56 00:07:01.680 ⇒ 00:07:10.850 Ashwini Sharma: That was an ad hoc solution that she was looking for general purpose things, right? Where she had to extract. Scanner data is… is something different.
57 00:07:11.470 ⇒ 00:07:13.949 Ashwini Sharma: That’s my understanding, I don’t know, I may be wrong.
58 00:07:17.030 ⇒ 00:07:19.260 Uttam Kumaran: What did she say in the meeting last week?
59 00:07:21.150 ⇒ 00:07:27.759 Ashwini Sharma: I think, yeah, regarding the scanner data, it was more about cleaning the data before we surface.
60 00:07:27.760 ⇒ 00:07:29.759 Awaish Kumar: Yeah, like, I think it is a…
61 00:07:30.870 ⇒ 00:07:40.669 Awaish Kumar: Like, for the two or three things, I saw the same flow. For SMTP, like, Salesforce Marketing Cloud, also, they were talking a little bit similar.
62 00:07:40.880 ⇒ 00:07:49.019 Awaish Kumar: That we should, get some… read some files and do cleanup and add back to S3.
63 00:07:49.160 ⇒ 00:07:52.579 Awaish Kumar: So that she can load it to Salesforce Marketing Cloud.
64 00:07:53.120 ⇒ 00:08:00.430 Awaish Kumar: And she was… she’s using Postgres currently to… for the cleanup, and we need to adopt.
65 00:08:01.420 ⇒ 00:08:05.200 Awaish Kumar: like, decommission Postgres and do that in Snowflake instead.
66 00:08:09.490 ⇒ 00:08:22.130 Ashwini Sharma: Yeah, there are two different work items, right? One was the Postgres decommissioning, which is basically providing a doc solution for her, where she can load CSV files, do some data transformation, and move them over to SFTP.
67 00:08:22.660 ⇒ 00:08:31.649 Ashwini Sharma: Right? The other part is this one, scanner data. What she wants, is basically clean this scanner data before we surface it on the MART.
68 00:08:31.650 ⇒ 00:08:32.320 Awaish Kumar: Yeah.
69 00:08:32.669 ⇒ 00:08:38.240 Awaish Kumar: to work items, but I’m just trying to say that the flow is the same.
70 00:08:38.360 ⇒ 00:08:45.810 Awaish Kumar: She is using one, maybe one Postgres instance for that. She loads some files from somewhere to Postgres.
71 00:08:45.990 ⇒ 00:08:50.319 Awaish Kumar: And then runs some cleanup for both the tasks, and then loads
72 00:08:50.450 ⇒ 00:08:54.910 Awaish Kumar: Into the relevant, like, the destinations.
73 00:08:55.580 ⇒ 00:09:01.080 Awaish Kumar: And, we have to… Work on that, so that…
74 00:09:02.220 ⇒ 00:09:06.300 Awaish Kumar: We need to understand where she’s getting those files from.
75 00:09:06.520 ⇒ 00:09:08.650 Awaish Kumar: Number one, for both the tasks.
76 00:09:08.820 ⇒ 00:09:21.139 Awaish Kumar: bring it to Snowflake to clean up, and hand it over back in S3 or somewhere, so she can use it. And so that Postgres will be decommissioned in that process.
77 00:09:24.250 ⇒ 00:09:25.929 Brylle Girang: Okay, gotcha.
78 00:09:29.940 ⇒ 00:09:34.300 Brylle Girang: Alright, so that’s for scanner data, the highest priority. Okay.
79 00:09:35.060 ⇒ 00:09:38.410 Uttam Kumaran: Is that clear, or is it, Ashwini, you’re gonna be working on that?
80 00:09:38.720 ⇒ 00:09:44.820 Ashwini Sharma: I’ll… so, basically, I just want to reiterate, right, these are two different work items, right?
81 00:09:44.960 ⇒ 00:09:55.389 Ashwini Sharma: One is cleaning the scanner data we have already ingested. Maybe two weeks back, we are done with ingestion of scanner data, right? Now it’s time to surface that scanner data to MartLayer. And…
82 00:09:55.540 ⇒ 00:10:00.580 Ashwini Sharma: What she wants is us to ensure that we clean this data properly before we
83 00:10:00.700 ⇒ 00:10:19.560 Ashwini Sharma: make it available in the MartLayer. That is one work item. The other work item was she has, you know, multiple, this ad hoc task where she gets some data in a CSV file, she uses Postgres to do some initial cleanup or data processing, and then she exports that somewhere in SFTP,
84 00:10:20.070 ⇒ 00:10:22.169 Ashwini Sharma: Available to Marketing Cloud, right?
85 00:10:22.460 ⇒ 00:10:28.270 Ashwini Sharma: Yeah, two different work items, right? Let’s not mix them up together, please. And…
86 00:10:28.390 ⇒ 00:10:35.059 Ashwini Sharma: So, the first work item, the ad hoc processing, I have a demo for that.
87 00:10:35.320 ⇒ 00:10:38.130 Ashwini Sharma: Whenever they say, we’re ready, I can show it to them.
88 00:10:38.380 ⇒ 00:10:41.619 Ashwini Sharma: And this other scanner data, I’ll take care of it.
89 00:10:45.390 ⇒ 00:10:46.170 Brylle Girang: Okay.
90 00:10:50.730 ⇒ 00:10:55.320 Uttam Kumaran: So, maybe, Braille, you want to take this… Transcript, and then make those…
91 00:10:55.320 ⇒ 00:10:56.979 Brylle Girang: Separate work streams? Okay.
92 00:10:57.190 ⇒ 00:10:58.480 Brylle Girang: Yeah, definitely.
93 00:10:58.970 ⇒ 00:10:59.450 Awaish Kumar: Yep.
94 00:10:59.450 ⇒ 00:11:12.320 Brylle Girang: Okay, but aside from those two work items, and aside from identity stitching, so right now we have three main work streams that we need to focus on, but do we have any other tickets that we want to move forward?
95 00:11:12.320 ⇒ 00:11:16.589 Uttam Kumaran: Yeah, so there is one ticket Ashwini is gonna be working on, like, basically…
96 00:11:17.010 ⇒ 00:11:21.469 Uttam Kumaran: how do we, like, move data in? So, like, a new process for data ingestion?
97 00:11:22.550 ⇒ 00:11:27.240 Uttam Kumaran: Outside of, like, using ETL tools, right, Ashwin? Is that, like, a good thing, or what should we call that ticket?
98 00:11:27.240 ⇒ 00:11:32.309 Ashwini Sharma: Yeah, it’s, it’s, it’s, the, the user experience is quite complex,
99 00:11:32.800 ⇒ 00:11:38.020 Ashwini Sharma: Like, for example, if we have to ingest Google Sheets via OpenFlow, right?
100 00:11:38.320 ⇒ 00:11:41.780 Ashwini Sharma: We need to create one connection each for this thing.
101 00:11:41.890 ⇒ 00:11:47.210 Ashwini Sharma: And it’s a, you know, tiring process. That’s what I feel.
102 00:11:47.450 ⇒ 00:11:52.219 Ashwini Sharma: Okay. Not as easy as using Polytomic or Fivetran. Okay.
103 00:11:52.220 ⇒ 00:11:52.790 Uttam Kumaran: Yeah.
104 00:11:53.840 ⇒ 00:12:02.409 Ashwini Sharma: you need to create a deployment, and then you create a runtime, deploy a connector into that runtime, and then you configure that connector to extract from a Google Sheet, and then write to Snowflake.
105 00:12:06.220 ⇒ 00:12:12.410 Ashwini Sharma: Yeah, that is how OpenFlow works, and then it only allows, like, one deployment per,
106 00:12:12.660 ⇒ 00:12:16.020 Ashwini Sharma: one deployment within the Snowflake managed infrastructure.
107 00:12:16.120 ⇒ 00:12:20.670 Ashwini Sharma: If we need other deployments, other connections, we’ll have to do it on AWS,
108 00:12:20.870 ⇒ 00:12:25.569 Ashwini Sharma: Which means that, you know, some kind of VPC configuration, and
109 00:12:25.980 ⇒ 00:12:28.620 Ashwini Sharma: EC2 cluster, spin up, things like that.
110 00:12:28.740 ⇒ 00:12:33.719 Ashwini Sharma: So, some help might be required from DevOps side, from their team.
111 00:12:36.200 ⇒ 00:12:40.660 Brylle Girang: Is it different from this ticket, the SFMC pipeline?
112 00:12:40.660 ⇒ 00:12:41.770 Ashwini Sharma: It is different.
113 00:12:41.770 ⇒ 00:12:43.099 Brylle Girang: Okay, gotcha.
114 00:12:44.980 ⇒ 00:12:45.800 Brylle Girang: Alright.
115 00:12:46.000 ⇒ 00:12:49.550 Brylle Girang: Aside from that, Utam, we have 25 other
116 00:12:49.820 ⇒ 00:12:53.719 Brylle Girang: Tickets in backlog here? Are we not going to move?
117 00:12:53.970 ⇒ 00:12:54.559 Brylle Girang: some of the.
118 00:12:54.560 ⇒ 00:13:00.179 Uttam Kumaran: Yeah, so a lot of this, like, I guess one thing I want to talk about is, like.
119 00:13:09.560 ⇒ 00:13:20.239 Uttam Kumaran: Yeah, some… the trouble is, is, like, we’re… right now, we’re blocked by ETL, so really, Ashwini, what we need an answer on is, like, how we’re gonna move all of this data in there, right? Like…
120 00:13:20.920 ⇒ 00:13:22.880 Uttam Kumaran: She wanted to move EventPoint.
121 00:13:23.100 ⇒ 00:13:24.880 Uttam Kumaran: Like, better exhibitor.
122 00:13:25.330 ⇒ 00:13:29.710 Uttam Kumaran: like… random other stuff, right? So…
123 00:13:31.310 ⇒ 00:13:42.630 Uttam Kumaran: we sort of… my… I’m trying to tell her that, like, we can’t do anything until you decide on an ETL tool. She wants to wait on that, so maybe that’s also what we try to get some clarity on, because all these tickets are really related to that.
124 00:13:44.800 ⇒ 00:13:50.189 Ashwini Sharma: Yeah, yeah, all we have done till now is only adjusted whatever’s there in S3, right?
125 00:13:50.820 ⇒ 00:13:55.539 Ashwini Sharma: So, OpenFlow supports some ingestion, but it’s not, you know,
126 00:13:55.900 ⇒ 00:13:59.360 Ashwini Sharma: Like, it does not have an exhaustive list of connectors.
127 00:14:02.150 ⇒ 00:14:05.160 Awaish Kumar: I mean, like, why we are…
128 00:14:05.680 ⇒ 00:14:13.090 Awaish Kumar: like, trying to use some other tools than this one, like, because she hasn’t decided we’re looking for free solution, or…
129 00:14:13.180 ⇒ 00:14:14.070 Brylle Girang: Or what?
130 00:14:15.810 ⇒ 00:14:21.119 Ashwini Sharma: We’re looking for a solution that’s available within the current set of infrastructure that she has.
131 00:14:21.940 ⇒ 00:14:23.620 Ashwini Sharma: With current set of tools.
132 00:14:25.540 ⇒ 00:14:26.080 Awaish Kumar: Okay.
133 00:14:26.520 ⇒ 00:14:28.610 Awaish Kumar: She has, like, open flow.
134 00:14:29.120 ⇒ 00:14:32.429 Ashwini Sharma: It’s already deployed in, this one, Snowflake.
135 00:14:33.100 ⇒ 00:14:34.000 Awaish Kumar: Oh, okay.
136 00:14:39.480 ⇒ 00:14:51.599 Brylle Girang: Okay, gotcha. So, just to summarize, four main items, that’s identity stitching, the cleaning the scanner data, replacing the…
137 00:14:51.800 ⇒ 00:14:54.420 Brylle Girang: Postgres adopt CSV,
138 00:14:54.830 ⇒ 00:15:02.620 Brylle Girang: and then the ETL blocker, which, at the same time, you’re trying to find an alternative for Ashwini, is that right?
139 00:15:07.220 ⇒ 00:15:08.630 Ashwini Sharma: Siri, what is the last line?
140 00:15:08.840 ⇒ 00:15:10.790 Brylle Girang: the ETL blocker.
141 00:15:12.410 ⇒ 00:15:15.420 Ashwini Sharma: Yeah, ETL blocker, we’re trying to find an alternative, yes.
142 00:15:15.640 ⇒ 00:15:24.240 Brylle Girang: Okay, gotcha. And then once the ATL blocker is cleared, then we’ll be able to move most of these tickets to the next cycles, right?
143 00:15:24.560 ⇒ 00:15:29.330 Ashwini Sharma: Some of… yeah, some of these tickets, I would say, are not most of them.
144 00:15:29.720 ⇒ 00:15:37.999 Uttam Kumaran: No, so all… none of… so I would say there has to be a discussion on, like, which ones are gonna move. So this is something, I think, Bryle, today.
145 00:15:38.180 ⇒ 00:15:48.810 Uttam Kumaran: Well, we’re gonna try to… I’m gonna… I messaged Catherine, she said she’s a little bit busy, she’s gonna get back to us. So as soon as she’s available, she… I need… we need her to dictate, like, what’s gonna be next.
146 00:15:48.810 ⇒ 00:15:49.290 Brylle Girang: Okay.
147 00:15:49.290 ⇒ 00:16:04.079 Uttam Kumaran: Ideally, though, I would love to get that memo reviewed. So, Ashwini and Awish, like, maybe that’s something that we can get done today, is review the internal cortex thing and the one for CTA, so we can get that sent over. What do you guys think?
148 00:16:05.100 ⇒ 00:16:07.000 Ashwini Sharma: Which memo is that, Utam?
149 00:16:07.000 ⇒ 00:16:08.810 Awaish Kumar: Oh, yeah, I can…
150 00:16:10.830 ⇒ 00:16:13.420 Brylle Girang: Ryle, do you want to link again? Yeah, sure.
151 00:16:13.420 ⇒ 00:16:14.050 Uttam Kumaran: Yeah.
152 00:16:16.500 ⇒ 00:16:17.190 Brylle Girang: Love that.
153 00:16:18.510 ⇒ 00:16:26.360 Awaish Kumar: we… But what we are trying, like, are we trying to say we are going to build these
154 00:16:27.260 ⇒ 00:16:29.470 Awaish Kumar: AI analysts, right?
155 00:16:29.690 ⇒ 00:16:36.409 Awaish Kumar: For you, in the… In the snowflake, so you can, like, talk to the data.
156 00:16:39.130 ⇒ 00:16:45.989 Uttam Kumaran: Basically. So what I asked Ryle to do is, one, make one for, like, generic one, and then one specific for CTA.
157 00:16:47.430 ⇒ 00:16:57.280 Uttam Kumaran: So, I mean, you both are, like, along with me, or, like, we’re… three of us are, like, kind of experts on Snowflake stuff, so I want to get our three feedback on both docs.
158 00:16:57.690 ⇒ 00:17:00.879 Uttam Kumaran: So that Brile can make changes, and then we can…
159 00:17:01.120 ⇒ 00:17:03.969 Uttam Kumaran: Basically say, like, hey, we can, we can, we can do this work.
160 00:17:04.770 ⇒ 00:17:05.630 Awaish Kumar: Okay.
161 00:17:05.839 ⇒ 00:17:12.529 Awaish Kumar: Yeah, I can review that. The most important thing is, like, these AI analysts need to be very, very specific.
162 00:17:13.109 ⇒ 00:17:19.849 Awaish Kumar: So, like, if we know… for example, for CES, we have member’s team, right? And then…
163 00:17:20.300 ⇒ 00:17:25.160 Awaish Kumar: Maybe one analyst for… remembers data only, so things like that.
164 00:17:29.470 ⇒ 00:17:32.480 Uttam Kumaran: Can I share my screen for a few minutes?
165 00:17:33.540 ⇒ 00:17:34.310 Brylle Girang: Sure.
166 00:17:37.490 ⇒ 00:17:41.860 Ashwini Sharma: So this is the list of, connectors that’s available through OpenFlow.
167 00:17:43.430 ⇒ 00:17:50.500 Ashwini Sharma: We have Amazon ads and this thing, this thing, but I don’t see any connectors out of the box that’s there for…
168 00:17:51.040 ⇒ 00:17:53.000 Ashwini Sharma: sources used in CTE.
169 00:17:55.850 ⇒ 00:17:57.240 Uttam Kumaran: Hmm, okay.
170 00:18:01.050 ⇒ 00:18:02.039 Ashwini Sharma: Yeah, that’s it.
171 00:18:05.130 ⇒ 00:18:06.060 Uttam Kumaran: Okay, okay.
172 00:18:09.520 ⇒ 00:18:11.929 Ashwini Sharma: Yeah, that’s… that’s what I wanted to share, that’s it.
173 00:18:11.930 ⇒ 00:18:12.720 Uttam Kumaran: Okay, okay.
174 00:18:17.090 ⇒ 00:18:20.249 Uttam Kumaran: Okay, so are we good on next steps, then?
175 00:18:21.040 ⇒ 00:18:22.570 Brylle Girang: Yep, I’ll go for me.
176 00:18:24.890 ⇒ 00:18:25.830 Awaish Kumar: Yes.
177 00:18:25.830 ⇒ 00:18:29.340 Uttam Kumaran: You guys… if you guys want to take 10-15 minutes, review those.
178 00:18:29.970 ⇒ 00:18:33.890 Uttam Kumaran: If you can send it again in the channel.
179 00:18:34.100 ⇒ 00:18:39.589 Uttam Kumaran: trial, just, like, what those two docs are, I’m just gonna try to spend the next 10 minutes and just put comments in.
180 00:18:43.480 ⇒ 00:18:44.270 Brylle Girang: Okay.
181 00:18:44.270 ⇒ 00:18:48.660 Uttam Kumaran: I’m going to add you, Bryle, to, to the Gantt.
182 00:18:49.550 ⇒ 00:18:50.410 Brylle Girang: Gotcha.
183 00:18:52.330 ⇒ 00:19:00.459 Brylle Girang: Okay, I’m also in the external channel. Okay, I’m good. I’m going to take this transcript and then convert the remaining action items to tasks.
184 00:19:00.720 ⇒ 00:19:05.710 Brylle Girang: Most of them are for you, Ashwini, so I’m… let’s just work together here.
185 00:19:05.710 ⇒ 00:19:06.390 Ashwini Sharma: Sure.
186 00:19:07.590 ⇒ 00:19:08.790 Brylle Girang: Thank you, everyone!
187 00:19:09.490 ⇒ 00:19:10.210 Uttam Kumaran: Thank you.
188 00:19:10.210 ⇒ 00:19:10.989 Brylle Girang: Bye-bye. Excellent.
189 00:19:11.330 ⇒ 00:19:12.190 Awaish Kumar: Thank you.