Meeting Title: US x BF | Standup Date: 2025-07-30 Meeting participants: Uttam Kumaran, Amber Lin, Caio Velasco
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1 00:00:43.070 ⇒ 00:00:44.390 Amber Lin: Hello! There!
2 00:00:45.550 ⇒ 00:00:46.480 Uttam Kumaran: Hello!
3 00:00:48.010 ⇒ 00:00:55.089 Amber Lin: Oh, I had a question later. So today we have a grooming session. Do you think the
4 00:00:55.520 ⇒ 00:01:02.340 Amber Lin: revenue roadmap is ready to groom? What’s next for revenue?
5 00:01:02.340 ⇒ 00:01:03.740 Uttam Kumaran: Yeah, let’s do that today.
6 00:01:03.980 ⇒ 00:01:06.193 Amber Lin: Okay, sounds good. I’ll keep that session. Then.
7 00:01:07.810 ⇒ 00:01:10.110 Uttam Kumaran: Yeah, we could probably make it like halfway through
8 00:01:10.480 ⇒ 00:01:13.980 Uttam Kumaran: the revenue related tickets. But at least we can create some stuff.
9 00:01:14.330 ⇒ 00:01:16.171 Amber Lin: Okay, yeah. So that’s great.
10 00:01:17.210 ⇒ 00:01:19.910 Amber Lin: Wait.
11 00:01:25.800 ⇒ 00:01:28.420 Amber Lin: Kyle, do you know, if Emily is joining.
12 00:01:30.068 ⇒ 00:01:35.820 Caio Velasco: She did not mention anything to me just regarding the working session earlier, but not about standard.
13 00:01:36.330 ⇒ 00:01:39.791 Amber Lin: Okay, I see. So she probably would be here a bit later.
14 00:01:40.220 ⇒ 00:01:40.875 Amber Lin: And
15 00:01:46.400 ⇒ 00:01:50.740 Amber Lin: yeah, I’m, working on today, i’m working on this issue for perry,
16 00:01:51.840 ⇒ 00:01:54.158 Uttam Kumaran: One of the things that I’m doing,
17 00:01:55.443 ⇒ 00:02:02.780 Uttam Kumaran: and and this, I think, is going to be helpful for everybody’s, I’m starting to add, like last run at columns everywhere.
18 00:02:03.470 ⇒ 00:02:06.060 Uttam Kumaran: Hmm, basically
19 00:02:09.740 ⇒ 00:02:15.239 Uttam Kumaran: like, when a model runs in dbt, like, I want there to be a column that indicates
20 00:02:17.720 ⇒ 00:02:29.000 Uttam Kumaran: like when it was last. Run. A lot of the models don’t have that. So when I go to debug like, it’s something when I go to debug a dbt model. There’s like 4 or 5 typical things I look at.
21 00:02:29.484 ⇒ 00:02:34.699 Uttam Kumaran: One of which is like, when was this run? And I can’t see that. So I’ve just pushed a
22 00:02:34.860 ⇒ 00:02:38.289 Uttam Kumaran: Pr. That adds that.
23 00:02:38.530 ⇒ 00:02:43.740 Uttam Kumaran: So I’ll be continuing to kind of diagnose whatever is going on with this like Kpi table.
24 00:02:43.940 ⇒ 00:02:47.800 Amber Lin: Okay, wait. It’s not.
25 00:02:48.020 ⇒ 00:02:50.610 Amber Lin: Is this, is it? This ticket?
26 00:02:51.470 ⇒ 00:02:52.070 Uttam Kumaran: Yeah.
27 00:02:52.400 ⇒ 00:02:52.720 Amber Lin: Oh!
28 00:02:52.978 ⇒ 00:02:54.010 Uttam Kumaran: Yeah, yeah, this one.
29 00:02:54.010 ⇒ 00:03:00.050 Amber Lin: Okay. But it’s is this the same ticket.
30 00:03:01.630 ⇒ 00:03:07.759 Uttam Kumaran: This is the same one. Wait. Yeah, I think it’s the same one.
31 00:03:12.810 ⇒ 00:03:14.370 Uttam Kumaran: Oh, never mind.
32 00:03:15.260 ⇒ 00:03:15.920 Amber Lin: Hmm.
33 00:03:16.898 ⇒ 00:03:18.839 Uttam Kumaran: Wait, hold on, can I?
34 00:03:22.320 ⇒ 00:03:28.990 Uttam Kumaran: Okay, this is not, yeah. This is a different one. Yeah.
35 00:03:53.150 ⇒ 00:04:00.900 Amber Lin: Okay, do we stop? Is this a necessary ticket?
36 00:04:02.450 ⇒ 00:04:07.230 Amber Lin: This is from one of the meetings, oh, actually, this is from.
37 00:04:09.530 ⇒ 00:04:15.030 Amber Lin: Probably from Emily, so.
38 00:04:15.350 ⇒ 00:04:19.450 Uttam Kumaran: Yeah, I guess I I mean, I wasn’t given any contacts. So.
39 00:04:19.450 ⇒ 00:04:22.750 Amber Lin: Yeah, it’s okay. I’ll I’ll ask her.
40 00:04:22.750 ⇒ 00:04:27.250 Uttam Kumaran: Just ask her to give me some. Put some context in the ticket. Yeah, I can do it today.
41 00:04:42.273 ⇒ 00:04:44.690 Amber Lin: There’s a deprecation stuff left.
42 00:04:45.410 ⇒ 00:04:49.509 Uttam Kumaran: Yeah, that’s also on my plate. I have for my afternoon. Yeah.
43 00:04:49.510 ⇒ 00:04:51.017 Amber Lin: Okay, sounds good.
44 00:04:54.940 ⇒ 00:04:56.100 Amber Lin: I see.
45 00:04:59.891 ⇒ 00:05:04.249 Amber Lin: Kyle, are you able to finish this one today? Fill out the section 4.
46 00:05:16.110 ⇒ 00:05:17.060 Uttam Kumaran: You’re on mute.
47 00:05:17.210 ⇒ 00:05:17.800 Amber Lin: No.
48 00:05:20.119 ⇒ 00:05:29.979 Caio Velasco: Okay, I didn’t realize I was on mute. So I said that this one will be for tomorrow. End of my day beginning of your day.
49 00:05:30.860 ⇒ 00:05:40.539 Caio Velasco: And the other one I already did. I designed it on draw I/O because I didn’t have edit access to Figma.
50 00:05:40.690 ⇒ 00:05:45.800 Caio Velasco: So I did it there. If it’s super important to also do in Sigma.
51 00:05:45.950 ⇒ 00:05:54.899 Caio Velasco: I can. I try to even import the 2 types of files they had. But it didn’t work, it was everything was messy
52 00:05:56.320 ⇒ 00:06:06.169 Caio Velasco: So then I don’t know if we want also me to put in in Sigma. I can also do that. Otherwise it’s already there in the ticket, the files and the.
53 00:06:06.170 ⇒ 00:06:06.860 Amber Lin: Oh, okay.
54 00:06:06.860 ⇒ 00:06:12.930 Caio Velasco: The share. URL, as well, yeah.
55 00:06:13.810 ⇒ 00:06:19.160 Amber Lin: Oh, okay, hello! Is this good for the lineage?
56 00:06:21.430 ⇒ 00:06:45.150 Caio Velasco: This one I did, based on the the sheet, the the discovery. So it basically the focus is on the let’s say, on the blue models more or less, and we have the sources. And then, whatever upstream models they are pointing to in their 1st layer, not the whole lineage, but then it would be just redesigning what you understand already have.
57 00:06:45.400 ⇒ 00:06:49.510 Caio Velasco: And then you have also the unique keys for each of them.
58 00:06:49.750 ⇒ 00:06:53.529 Caio Velasco: and I tried to put in the most, the best organized way I could.
59 00:06:53.990 ⇒ 00:06:55.600 Caio Velasco: Oh, yeah.
60 00:06:56.570 ⇒ 00:07:03.340 Amber Lin: Okay? Well done. I guess this is for your review. Is this, is this good to go.
61 00:07:05.088 ⇒ 00:07:07.591 Uttam Kumaran: I haven’t reviewed it so not good to go.
62 00:07:07.870 ⇒ 00:07:10.030 Amber Lin: Okay. So I’m gonna put it.
63 00:07:10.110 ⇒ 00:07:11.519 Uttam Kumaran: Keep it in review, and then you can.
64 00:07:11.520 ⇒ 00:07:12.689 Amber Lin: Just a review.
65 00:07:12.860 ⇒ 00:07:16.030 Amber Lin: Okay, that’s good.
66 00:07:26.490 ⇒ 00:07:27.460 Amber Lin: Okay.
67 00:07:27.790 ⇒ 00:07:32.670 Amber Lin: Oh, I wanted to see. I wanted to ask Emily, when can we meet with Perry?
68 00:07:33.320 ⇒ 00:07:42.299 Amber Lin: That’s the last thing before we finish off the document. When do we want to book? The internal review of that, Doc?
69 00:07:42.960 ⇒ 00:07:45.989 Amber Lin: Is, does it? Do what you want it to be a meeting.
70 00:07:50.250 ⇒ 00:07:59.080 Uttam Kumaran: yeah, I would like it to be a meeting. Maybe we can either do it in like one of the stand ups or
71 00:07:59.857 ⇒ 00:08:06.399 Uttam Kumaran: like, I mean, I would. It would be best if, like we can. All people can add comments. And we can go
72 00:08:07.040 ⇒ 00:08:14.940 Uttam Kumaran: do a meeting like, basically do do. That meeting is more focused on like addressing comments.
73 00:08:16.890 ⇒ 00:08:19.959 Uttam Kumaran: So my goal is like, we have this Doc ready by Friday.
74 00:08:21.960 ⇒ 00:08:27.959 Amber Lin: Okay, I’ll see if we can get the sign off on Friday, or, Monday.
75 00:08:28.070 ⇒ 00:08:28.760 Uttam Kumaran: Okay.
76 00:08:29.000 ⇒ 00:08:30.399 Amber Lin: Yeah. And then I’ll see.
77 00:08:30.400 ⇒ 00:08:34.319 Uttam Kumaran: Yeah, but also be helpful for for you to take a look like I don’t. I? I’m not.
78 00:08:34.320 ⇒ 00:08:34.799 Amber Lin: Totally.
79 00:08:35.399 ⇒ 00:08:37.229 Uttam Kumaran: I’m not really interested in like.
80 00:08:38.059 ⇒ 00:08:45.929 Uttam Kumaran: okay, we said Friday. It’s gonna get done like this. Doc has to be good, and it has to have all the details so like, I don’t want to just like
81 00:08:46.889 ⇒ 00:08:48.819 Uttam Kumaran: I’m not trying to speed. Run this
82 00:08:49.169 ⇒ 00:09:01.159 Uttam Kumaran: like. It’s not an AI doc like it has to have all the details. It has to be like something that’s understandable by all the stakeholders. So if it’s not, we can’t do it. We can’t do it. But this is where, like.
83 00:09:01.919 ⇒ 00:09:06.369 Uttam Kumaran: I mean, like again, there’s so we’ll be yeah, like, that’s.
84 00:09:06.370 ⇒ 00:09:06.690 Amber Lin: Okay.
85 00:09:06.690 ⇒ 00:09:07.910 Uttam Kumaran: Probably what I will say.
86 00:09:08.060 ⇒ 00:09:09.820 Amber Lin: I see you know.
87 00:09:09.820 ⇒ 00:09:14.660 Caio Velasco: Just just on top of that. So since I have the section 4,
88 00:09:14.840 ⇒ 00:09:26.259 Caio Velasco: I’m assuming that, for my part, this is what needs to be done, because I’m I’m hearing all the the whole document, but I’m not sure if I also have to touch all the other parts, or those things are more.
89 00:09:26.260 ⇒ 00:09:38.629 Uttam Kumaran: I guess, like what point. What point I’ll say is, this is on our entire team. So it’s not like a you own this section, and you toss it over the fence like our whole team is putting this doc together.
90 00:09:38.880 ⇒ 00:09:40.149 Uttam Kumaran: Of course, like.
91 00:09:40.620 ⇒ 00:09:58.990 Uttam Kumaran: like, I’m I’m here on the project, but I’m like, sort of overseeing. So like everybody on our team has to have an understanding of the entire document, especially the folks on engineering. So that’s me you and them allotted. So it’s not. It’s not enough for us to just say, like I finished my part.
92 00:09:59.240 ⇒ 00:10:01.569 Uttam Kumaran: I’m like washing my hands of it. So
93 00:10:01.970 ⇒ 00:10:07.279 Uttam Kumaran: my requirement is gonna be that all of us, and including as much amber as you can.
94 00:10:07.930 ⇒ 00:10:13.459 Uttam Kumaran: confident in the document before we deploy. So until we have that confidence I’m not
95 00:10:13.860 ⇒ 00:10:16.509 Uttam Kumaran: like, I don’t wanna move forward so.
96 00:10:16.510 ⇒ 00:10:18.450 Amber Lin: Let’s wait until Friday.
97 00:10:18.620 ⇒ 00:10:22.460 Uttam Kumaran: The only reason. The only reason I’m splitting it up is because
98 00:10:22.780 ⇒ 00:10:36.289 Uttam Kumaran: I’m I don’t like, if I approve it, and I’m the only one that understands it. Then it’s dead. It’s dead on arrival. So like I, I really need, we need sign off from demalade. We need sign up from Kyle. We need sign off from everybody
99 00:10:36.390 ⇒ 00:10:38.040 Uttam Kumaran: that we’re building this for.
100 00:10:39.015 ⇒ 00:10:50.609 Uttam Kumaran: And if this is like a legit document, this isn’t like a doing documentation just for show. So until, like, we’re all really clear on what we’re gonna be building like. We can’t. We can’t pass this phase.
101 00:10:52.520 ⇒ 00:10:57.970 Amber Lin: Okay, yeah. Noted. Let’s do the internal review. Friday, when damala is back.
102 00:10:58.320 ⇒ 00:10:58.675 Uttam Kumaran: Okay.
103 00:10:59.260 ⇒ 00:10:59.860 Amber Lin: Yeah.
104 00:11:01.180 ⇒ 00:11:13.609 Caio Velasco: Yeah, on on that note. Just just another question. So that I cause I know that we talked yesterday, and I understood more or less like the points in the document. But if I look back now at Number 6, for example, target architecture and design.
105 00:11:13.969 ⇒ 00:11:34.079 Caio Velasco: and I read it, I’m trying to understand. If we are already in the future, where we know what those those things will be, and they will become tasks, and then we will come and work on the task, because I’m trying to think somehow, I’m seeing that I’m being asked to do something that I just want to know when the tasks are done.
106 00:11:34.720 ⇒ 00:11:37.930 Caio Velasco: Know what I mean like as if because I don’t.
107 00:11:37.930 ⇒ 00:11:39.370 Uttam Kumaran: I guess it’s just like.
108 00:11:39.490 ⇒ 00:11:47.920 Uttam Kumaran: I guess I just don’t understand, because, like the document is not finished right? There are sections that are empty. So
109 00:11:48.150 ⇒ 00:11:53.079 Uttam Kumaran: until those sections are filled and that we review the doc. And there’s a plan.
110 00:11:53.330 ⇒ 00:11:56.070 Uttam Kumaran: It’s not done right like I don’t.
111 00:11:56.070 ⇒ 00:11:56.650 Caio Velasco: Yeah.
112 00:11:56.800 ⇒ 00:11:58.600 Uttam Kumaran: I don’t know what else to say.
113 00:12:04.070 ⇒ 00:12:10.830 Caio Velasco: Because, for example, like in that one, the high level architecture, diagram, ingestion, storage modeling and serving action layers.
114 00:12:11.110 ⇒ 00:12:13.569 Caio Velasco: I mean, okay, that’s a standard
115 00:12:13.700 ⇒ 00:12:19.059 Caio Velasco: and then data model. Erd, I don’t know what is their Erd before we complete all the tests right?
116 00:12:19.470 ⇒ 00:12:22.649 Uttam Kumaran: Well, can you scroll to that section like, what section is this.
117 00:12:22.940 ⇒ 00:12:23.700 Caio Velasco: 6,
118 00:12:28.410 ⇒ 00:12:32.820 Caio Velasco: because it’s it seems to me that if we are trying to build well, not to build, but to outline.
119 00:12:32.820 ⇒ 00:12:36.250 Uttam Kumaran: Can you scroll to section 6? Can you scroll to section 6? Please.
120 00:12:36.600 ⇒ 00:12:39.820 Amber Lin: I think I’m already in 6. This is 6, right?
121 00:12:40.000 ⇒ 00:12:41.520 Uttam Kumaran: This is a.
122 00:12:41.520 ⇒ 00:12:44.400 Caio Velasco: Pull up what we’re talking about.
123 00:12:44.400 ⇒ 00:12:46.689 Amber Lin: Can you pull up where where it is?
124 00:12:47.200 ⇒ 00:12:48.869 Caio Velasco: It was right there. Number 6.
125 00:12:49.310 ⇒ 00:12:52.970 Amber Lin: Oh, okay, give me a sec.
126 00:13:00.640 ⇒ 00:13:04.759 Uttam Kumaran: Kyle, can you pull it up or I’m gonna pull it up? It’s like, I just don’t know.
127 00:13:04.760 ⇒ 00:13:07.080 Caio Velasco: Yeah, I’m I’m using my.
128 00:13:07.680 ⇒ 00:13:10.339 Uttam Kumaran: So so here we are. So it’s 6.
129 00:13:13.530 ⇒ 00:13:18.559 Uttam Kumaran: So yeah, like, there’s nothing here. Right? So I don’t know what to say. Like, we’re not finished with this.
130 00:13:25.005 ⇒ 00:13:33.399 Caio Velasco: Yeah. So when I look at it, it seems that for me is like we are defining what how things should be with urban steps
131 00:13:33.530 ⇒ 00:13:43.109 Caio Velasco: or how we want. I know all the data models. And but this is the work we are gonna do with them. So we were just gonna know this when everything is done right.
132 00:13:43.380 ⇒ 00:13:48.449 Caio Velasco: Are we trying to have a 1st idea of how things should be.
133 00:13:49.120 ⇒ 00:14:00.649 Uttam Kumaran: Yeah, no, we’re we’re we’re going to. We have to plan before we execute like, we’re not going to just run into data modeling without having this. So there’s gonna be a plan for all of the models that we’re building.
134 00:14:01.900 ⇒ 00:14:04.290 Uttam Kumaran: At least we’re gonna get 80% right?
135 00:14:06.090 ⇒ 00:14:06.680 Caio Velasco: Who cares?
136 00:14:06.680 ⇒ 00:14:14.879 Uttam Kumaran: We already have all we already have, all the questions we need to answer, meaning, if you take those questions, we already know all of the
137 00:14:15.770 ⇒ 00:14:17.800 Uttam Kumaran: columns that we need to support.
138 00:14:19.850 ⇒ 00:14:26.289 Uttam Kumaran: So you know, like, we can definitely say, we need these 10 or 15 data models.
139 00:14:28.810 ⇒ 00:14:32.789 Caio Velasco: Yeah. Well, at least for me, I think for this I would definitely need to consult with the Milan.
140 00:14:32.940 ⇒ 00:14:43.620 Caio Velasco: because I have like an idea of some things, but definitely not the whole picture of like, if it should be an orders behind and behind something else.
141 00:14:44.500 ⇒ 00:14:45.380 Caio Velasco: Yeah.
142 00:14:45.380 ⇒ 00:14:49.719 Uttam Kumaran: But like, that’s the work that has to go into this doc right like this one, I’m saying is like
143 00:14:50.460 ⇒ 00:15:03.659 Uttam Kumaran: we have to do that as part of this document because it has to get approved. Otherwise, we’re gonna build something that that has that’s not gonna have a clear understanding of, like what it’s being built towards, so that the work you’re doing is part of this document.
144 00:15:03.820 ⇒ 00:15:08.930 Uttam Kumaran: So yes, like you have to consult whoever. But it has to end up here, and it has to get approved.
145 00:15:12.470 ⇒ 00:15:14.490 Caio Velasco: Yeah. Well, I don’t know why I’m confused.
146 00:15:15.760 ⇒ 00:15:19.329 Uttam Kumaran: I guess that’s what I’m that’s what I’m having a hard time understanding. Because.
147 00:15:19.490 ⇒ 00:15:28.550 Uttam Kumaran: for example, like, we need to support pulling subscription data. Okay, great. So then, what subscription tables do we need to solve that?
148 00:15:28.840 ⇒ 00:15:37.870 Uttam Kumaran: And like again, I don’t want people to hear to get nervous that your name is on this, and then you get tense. That’s not. The point is like, we’re a team developing this document
149 00:15:38.230 ⇒ 00:15:45.590 Uttam Kumaran: right? And the goal of the document is to give everyone understanding of, like, what is the technical architecture for the revenue mark?
150 00:15:45.970 ⇒ 00:15:50.699 Uttam Kumaran: So we’re all here to develop that. I think my point is that
151 00:15:50.980 ⇒ 00:16:05.430 Uttam Kumaran: I I understand that you may have never done this sort of higher level data model architecture before. I don’t care about that, though, like we’re gonna be doing it now. And what we’re gonna find is that we already have all the questions listed.
152 00:16:05.660 ⇒ 00:16:15.910 Uttam Kumaran: It would I? I would bet you, if you literally just copy, paste this into AI, and had it take a 1st pass, it would give you a pretty good understanding of, like the types of models that we would need
153 00:16:16.080 ⇒ 00:16:27.670 Uttam Kumaran: right. So these are all the things that I’m gonna do right? So most likely if I was to tell you how I would tackle this one. I would immediately take the transcripts from our meetings and this entire document.
154 00:16:27.730 ⇒ 00:16:47.159 Uttam Kumaran: And I would say, Okay, help me design, like what the higher level architecture could be for the revenue data mark. And then I would start to break those data models down. Okay, we need orders. We need transactions. We need sub orders. We need subscriptions, we need, you know. And then I would start to write this. This is what I’m gonna do today. But like.
155 00:16:48.010 ⇒ 00:16:50.969 Uttam Kumaran: I don’t see how I I’m like the only one that can do that.
156 00:16:51.770 ⇒ 00:17:01.790 Caio Velasco: So. But then my question is, you are assuming that whatever you’re putting in the AI is from ubern stems current
157 00:17:02.577 ⇒ 00:17:14.009 Caio Velasco: whole architecture, and then they I will consume that, and give you an idea from what they have as an assumption, or like a general idea, of how to model any commerce.
158 00:17:14.010 ⇒ 00:17:24.790 Uttam Kumaran: Yeah, like, what they have modeled is actually not much of my concern. I want the best model for this type of problem set regardless of
159 00:17:24.920 ⇒ 00:17:26.190 Uttam Kumaran: urban Sams
160 00:17:26.500 ⇒ 00:17:32.510 Uttam Kumaran: right like. And to give you a sense, nobody on their team has Mo modeled their work with any planning
161 00:17:32.970 ⇒ 00:17:35.889 Uttam Kumaran: which is obvious. They just built stuff as they went.
162 00:17:36.280 ⇒ 00:17:37.210 Uttam Kumaran: So
163 00:17:37.440 ⇒ 00:17:45.070 Uttam Kumaran: for us, it’s at this point. It’s agnostic of the fact that they even sell flowers or anything. We’re just trying to solve these data problems
164 00:17:45.240 ⇒ 00:17:48.930 Uttam Kumaran: with the best model architecture.
165 00:17:49.480 ⇒ 00:18:09.319 Uttam Kumaran: But but again, like a lot of these types of models, like subscriptions, orders inventory. These are common patterns in many companies. Right? So there’s nothing unique here. I just wanna make sure that we have addressed all of the edge cases, and that we know that at the when, if we go to develop.
166 00:18:09.680 ⇒ 00:18:17.229 Uttam Kumaran: you know, like, let’s say we have 30 tickets for models. At the end of that we’re going to be able to answer all the questions that are above
167 00:18:17.650 ⇒ 00:18:21.999 Uttam Kumaran: right, and then I particularly want to know which model will go to answer which question.
168 00:18:23.830 ⇒ 00:18:45.919 Caio Velasco: Okay, okay. So I was, I was thinking, something different. Indeed, I was thinking that when I started this number 6, for example, I would have to go into urban stems. Repo is, study the whole thing. And then, based on what they have, what would be the best data model for revenue. What would the best data model for event? This for me, is like 6 months of work. So that’s why I wasn’t understanding. How could we do this document in 2 days.
169 00:18:47.030 ⇒ 00:18:54.689 Uttam Kumaran: Yeah, like. But I guess, like, my point is that like it does Rev, like measuring revenue in a business is something every business does
170 00:18:54.870 ⇒ 00:19:04.559 Uttam Kumaran: like going from transactions to revenue something every business does. It doesn’t really matter what urban stems. There’s something unique about urban stems like
171 00:19:04.990 ⇒ 00:19:10.410 Uttam Kumaran: there are a lot of companies with these same types of problems. Right? So we’re just modeling revenue.
172 00:19:11.270 ⇒ 00:19:12.130 Caio Velasco: Okay.
173 00:19:12.130 ⇒ 00:19:19.059 Uttam Kumaran: Like. Why, I don’t see why we would go learn from what they have done. There’s nothing there except that some pieces of logic.
174 00:19:19.490 ⇒ 00:19:23.489 Uttam Kumaran: We’re the ones from. We’re the ones trying to
175 00:19:23.640 ⇒ 00:19:32.710 Uttam Kumaran: give them like, hey, here’s what you should do. Here’s how the best teams, model subscriptions, revenue orders, sub orders.
176 00:19:33.250 ⇒ 00:19:34.110 Uttam Kumaran: right.
177 00:19:34.660 ⇒ 00:19:35.340 Caio Velasco: Okay.
178 00:19:36.990 ⇒ 00:19:54.320 Caio Velasco: okay, okay, okay, got it. I think. Starting with that is, I think it’s easier for sure, like I can start with just high level. AI teaching me the basic demon facts. And like, Yeah, that’s definitely doable. If that leads somewhere, then I think it’s more up to you. And then I did to tell me, because for me this is
179 00:19:54.400 ⇒ 00:20:08.240 Caio Velasco: I mean, you could be a student in a university being asked to do this thing, which I think is doable. But that’s why I’m missing the connection with the real world stuff. Then you guys have to help me with that because you have more experience. But I can definitely do this first.st
180 00:20:08.560 ⇒ 00:20:28.229 Uttam Kumaran: No, totally totally. Look I I this is where I’m I’m gonna I’m gonna tell you that this is totally possible in 2 days. So start with that constraint. It may not seem real. You may be like yo, what the fuck you’re you’re you’re lying, I’m telling you it’s possible. But where I’m where I’m gonna push is that? Yes, there’s a lot of unknowns like.
181 00:20:28.570 ⇒ 00:20:47.530 Uttam Kumaran: I think it would be very helpful to use AI to research. Okay, how are given all these contexts in this document? How should we model revenue? How should we handle this like use? AI use the AI to answer these questions before AI. These would all be meetings like where I would, we would sit as a team and answer.
182 00:20:47.530 ⇒ 00:21:08.220 Uttam Kumaran: okay, which model would answer this. We would whiteboard something. Now that we have the AI, you can actually ask it, hey? Like, what are, How do the best teams model revenue? And Dbt, how do the best teams model subscriptions. What are the core pieces that we need? Right like? For me? It’s it’s much easier for me to give you feedback
183 00:21:08.920 ⇒ 00:21:16.889 Uttam Kumaran: at that level. But you need to the 20, the first, st as you know, the 1st 20% of getting into that is going to be the hardest part
184 00:21:17.100 ⇒ 00:21:20.589 Uttam Kumaran: right? So one thing that I think would be very helpful is
185 00:21:20.960 ⇒ 00:21:29.209 Uttam Kumaran: 1st is to just take this into use, AI, and honestly use it as a learning guide like, hey, what are the core business models
186 00:21:29.360 ⇒ 00:21:48.059 Uttam Kumaran: that we’re starting to model here? As as we mentioned, we have revenue. We talked about sort of sub items. We talked about subscriptions. We also talked about like some financial metrics and then walk within, say, like, Hey cool, what are the core components of this, and learn about, like
187 00:21:48.570 ⇒ 00:21:55.840 Uttam Kumaran: W. How, how, how to, how, what’s the best practices for modeling revenue? What are the best practices? I can meet you halfway.
188 00:21:56.444 ⇒ 00:22:07.019 Uttam Kumaran: And I’ll meet you halfway with like everything that I’ve learned, because I’ve done this a bunch of times. But I want to meet you there right. If I meet you at this level, I’m gonna do all the work.
189 00:22:09.230 ⇒ 00:22:12.600 Uttam Kumaran: No, got it. Got it? Okay? So that’s gonna answer.
190 00:22:12.600 ⇒ 00:22:15.470 Uttam Kumaran: It’s a lot. It’s a lot. But I think, like
191 00:22:15.620 ⇒ 00:22:28.510 Uttam Kumaran: I, I really want to push you to, to give it a go first, st because then I can meet you and and and I can I can pick you up for the next to the to the next base camp. From there.
192 00:22:29.070 ⇒ 00:22:38.829 Caio Velasco: No, no problem, no problem, no, I had a different, completely different assumption. So yeah, for this, I can. I can spend a few hours. And like, definitely build something. Yeah, yeah, because I have.
193 00:22:38.830 ⇒ 00:22:39.939 Uttam Kumaran: There will be some pieces.
194 00:22:39.940 ⇒ 00:22:41.670 Caio Velasco: Starting point is always better.
195 00:22:41.670 ⇒ 00:22:50.750 Uttam Kumaran: Yeah, there will be some pieces they got right, and there will be some pieces they got wrong. But overall we don’t know right. And that’s the problem is that nobody knows
196 00:22:50.860 ⇒ 00:22:57.000 Uttam Kumaran: how the current thing was architected. And you’re exactly right to go through every model and figure that out would take a long time.
197 00:22:57.350 ⇒ 00:23:04.840 Uttam Kumaran: So instead, it’s like, okay, let’s start with the what we what the source models are. And then where do we want to go ourselves.
198 00:23:05.350 ⇒ 00:23:13.030 Uttam Kumaran: and then right revenue. These things are all a lot of companies do so it it’s not unique to them, you know, is what I mean to say.
199 00:23:13.400 ⇒ 00:23:14.010 Caio Velasco: Okay.
200 00:23:14.440 ⇒ 00:23:15.370 Caio Velasco: Cool. Cool. Got it?
201 00:23:15.970 ⇒ 00:23:16.640 Uttam Kumaran: Okay.
202 00:23:19.820 ⇒ 00:23:29.130 Uttam Kumaran: okay? So I still think we’re on track. Yeah, that that would be really helpful. And I’m gonna I’m continuing to do my own sort of research on that. And so, yeah, just.
203 00:23:29.130 ⇒ 00:23:36.620 Amber Lin: Okay, are we more suited to groom tomorrow? Cause I do want to know how we’re gonna develop the revenue model. And
204 00:23:36.907 ⇒ 00:23:39.299 Amber Lin: I don’t have time tomorrow. So I have to do today.
205 00:23:39.300 ⇒ 00:23:42.100 Amber Lin: I see. I see not even Friday.
206 00:23:42.510 ⇒ 00:23:42.890 Uttam Kumaran: No.
207 00:23:43.522 ⇒ 00:23:45.419 Uttam Kumaran: Okay, that’s okay.
208 00:23:45.816 ⇒ 00:23:48.990 Amber Lin: Let’s do today, I’ll I’ll get you enough.
209 00:23:49.210 ⇒ 00:23:50.310 Uttam Kumaran: Today, I promise you.
210 00:23:50.310 ⇒ 00:23:55.660 Amber Lin: Okay, okay, sounds good. Then, alrighty. I’ll see you guys later.
211 00:23:56.440 ⇒ 00:23:56.790 Uttam Kumaran: Everywhere.
212 00:23:56.810 ⇒ 00:23:57.610 Caio Velasco: Okay.
213 00:23:57.810 ⇒ 00:23:58.570 Amber Lin: Bye.