Meeting Title: Stackblitz x Branforge | Follow-up Sync Date: 2025-03-27 Meeting participants: Luke Daque, Kj Krause, Uttam Kumaran, Amber Lin, Stephane Sol, Mitchell Wright, Alejandro Rojas
WEBVTT
1 00:01:10.900 ⇒ 00:01:12.250 Stephane Sol: Everybody.
2 00:01:15.630 ⇒ 00:01:16.660 Alejandro Rojas: Hello!
3 00:01:16.660 ⇒ 00:01:17.980 Amber Lin: Hi
4 00:01:17.980 ⇒ 00:01:18.760 Uttam Kumaran: Good morning!
5 00:01:19.470 ⇒ 00:01:20.289 Alejandro Rojas: Good morning!
6 00:01:22.880 ⇒ 00:01:23.600 Luke Daque: You guys
7 00:01:24.550 ⇒ 00:01:25.580 Alejandro Rojas: Hello!
8 00:01:25.580 ⇒ 00:01:26.479 Luke Daque: How’s it going
9 00:01:30.510 ⇒ 00:01:32.240 Alejandro Rojas: Going? Well, how’s it going for you
10 00:01:34.610 ⇒ 00:01:36.200 Luke Daque: Doing well, doing well.
11 00:01:40.980 ⇒ 00:01:43.909 Amber Lin: How’s your guys? 1st week at the company
12 00:01:46.040 ⇒ 00:01:50.020 Stephane Sol: It’s smooth smoother than other onboardings right
13 00:01:50.020 ⇒ 00:01:51.369 Amber Lin: Like you guys have, I mean.
14 00:01:51.370 ⇒ 00:01:52.809 Stephane Sol: Oh, yeah, yeah. Oh, yeah.
15 00:01:53.730 ⇒ 00:01:58.590 Stephane Sol: You guys have set up some good stuff. There’s actual documentation, like, yeah.
16 00:01:58.730 ⇒ 00:02:00.040 Amber Lin: In an onboarding.
17 00:02:00.040 ⇒ 00:02:00.820 Amber Lin: No! Oh.
18 00:02:00.820 ⇒ 00:02:01.410 Stephane Sol: Oh, yeah. Yeah.
19 00:02:01.850 ⇒ 00:02:18.099 Uttam Kumaran: That’s great to hear. I mean, I’ve I also, you know, I’ve been part of a lot of startups. And yeah, I I tried to pay it forward on on all the ones that we take on. Because, yeah, it’s an absolute nightmare getting on boarded to data every place I’ve been. So.
20 00:02:18.300 ⇒ 00:02:20.760 Uttam Kumaran: Oh, yeah, that’s great to hear
21 00:02:22.040 ⇒ 00:02:25.880 Stephane Sol: Yeah, I’d say, this is a pretty fact, like we are. We’re ending this week.
22 00:02:26.400 ⇒ 00:02:33.079 Stephane Sol: I think, at our last place it probably took a month to get where we are now, just because there was no documentation
23 00:02:39.920 ⇒ 00:02:46.400 Uttam Kumaran: Cool, I guess, for this meeting. What’s the best use of time? I guess we have some of the questions
24 00:02:47.251 ⇒ 00:02:56.569 Uttam Kumaran: you know. We also sent over some documentation. But happy to use this, however, we’d like to. We have a couple of decisions to make or to continue to, you know.
25 00:02:56.750 ⇒ 00:03:00.659 Uttam Kumaran: Push them off a bit, but happy to use this time, however.
26 00:03:02.920 ⇒ 00:03:15.240 Stephane Sol: Yeah, I was thinking, 1. 1 of the things that we want to know is, you know what what is. I’ve I’ve been looking at some of the docs and some of the linear boards. It’s like, what what can we do to
27 00:03:15.480 ⇒ 00:03:24.699 Stephane Sol: finish what you already started right like, what’s in progress? How can we get that over the finish line? What’s needed? Just kind of.
28 00:03:25.080 ⇒ 00:03:26.100 Stephane Sol: I think.
29 00:03:26.210 ⇒ 00:03:32.610 Stephane Sol: Kind of metrics. Initiative is probably the one that’s kind of in progress. And the one that’s in flux is that about right?
30 00:03:34.110 ⇒ 00:03:39.890 Uttam Kumaran: Yeah, I think all the work around defining the core metrics is certainly
31 00:03:40.100 ⇒ 00:03:43.180 Uttam Kumaran: the number one priority. It’s sort of where we
32 00:03:43.465 ⇒ 00:03:55.239 Uttam Kumaran: if you on the slack channel. It’s where the last thing we needed from Mitch was basically like sign off so the the kind of the couple of pieces of one, making sure that things match up to their metrics, and if they don’t, why.
33 00:03:55.240 ⇒ 00:04:15.680 Uttam Kumaran: that was a that’s 1 thing on our plate that we haven’t closed out. I think we have some findings there. The second piece is, yes, basically, probably downstream effect of that is, we need to define some of these metrics, and then the 3rd piece is just adoption. We. We haven’t met with any of the internal team. I think we just have one
34 00:04:15.680 ⇒ 00:04:34.580 Uttam Kumaran: through line into sales. But we haven’t met with any of the team and sort of built out a plan to bring people into rail start using data and meetings and sort of everything around there. Certainly, I think you guys are really well equipped to to take on that. And then we can continue building up a backlog of
35 00:04:34.861 ⇒ 00:04:40.020 Uttam Kumaran: the next models or data sources we want to do. And that’s probably where we can be most helpful
36 00:04:42.150 ⇒ 00:04:52.400 Stephane Sol: Yeah. And I think a lot of the questions like Alejandro has are kind of around, like, you know. Let’s let’s what were those findings and kind of what are those blockers? The way I’m seeing it is that if
37 00:04:52.480 ⇒ 00:05:20.660 Stephane Sol: if the initiative is, people are comfortable looking at bare metrics, and we needed to align to that. Let’s get that, you know. Let’s let’s, you know, go all in on reconciliation, because from my experience nobody’s good. Gonna want to go to real if if they’re not seeing what they’re seeing in bare metrics, why are they even in real? In the 1st place, unless there’s other dashboards or initiatives you all have started there or like other stakeholders that have asked for something. So in my mind, it’s like.
38 00:05:20.870 ⇒ 00:05:30.082 Stephane Sol: let’s let’s reconcile. Let’s get one or 2 core metrics looking the same way in bare metrics, and then we can start to like entice people to come to real
39 00:05:30.750 ⇒ 00:05:33.490 Stephane Sol: does that align with what you all are thinking
40 00:05:33.780 ⇒ 00:05:36.079 Uttam Kumaran: Yeah, that’s that’s basically it.
41 00:05:40.010 ⇒ 00:05:52.340 Stephane Sol: So, Alejandra, I know you had some questions about, I mean. So in in your, in your kind of like dealings with these metrics, right has. Was there any perceived priority? Obviously we want them all all at once. But like
42 00:05:52.500 ⇒ 00:05:56.819 Stephane Sol: ha! Have there been any kind of initial ones, is it, Mrr. First.st It’s like the most
43 00:05:56.820 ⇒ 00:05:57.800 Uttam Kumaran: Yes.
44 00:05:57.840 ⇒ 00:06:08.950 Uttam Kumaran: yeah, it’s it’s Mrr arr, total customers, active subscription. So anything around the core financials, the the nice to have next stuff is usage
45 00:06:09.337 ⇒ 00:06:26.339 Uttam Kumaran: especially like token usage, token usage, and then segmented by the various plans. So that’s like the that’s like the next level stuff that we already have modeled, but was like one of the reasons we were gonna try to get people to move over to rail is
46 00:06:26.370 ⇒ 00:06:42.470 Uttam Kumaran: now we have access to that data modeled. I don’t believe that’s in bare metrics. But yes, the the core thing to get right is the subscriptions. So subscription revenue as well as the active customer numbers and I think, Luke, you have all the information on
47 00:06:42.660 ⇒ 00:06:54.179 Uttam Kumaran: the reconciliation process that we started. So I think it’s probably best for you to lead and sort of explain what’s different and like, probably where we need to to hand that off
48 00:06:56.840 ⇒ 00:06:57.570 Luke Daque: Yeah.
49 00:06:57.870 ⇒ 00:06:58.570 KJ Krause: Hey? Can I
50 00:06:58.570 ⇒ 00:06:58.940 Luke Daque: Yeah.
51 00:06:58.940 ⇒ 00:07:00.620 KJ Krause: And for a second, here.
52 00:07:01.150 ⇒ 00:07:02.030 Luke Daque: Yeah, sure.
53 00:07:02.030 ⇒ 00:07:17.069 KJ Krause: So, yeah, I’m new to a lot of these tools. And I just want to make sure I understand the system. So the idea here is rail can do in theory anything that bare metrics can do, but not the other way around. Baremetrics is kind of limited for customization
54 00:07:19.290 ⇒ 00:07:24.220 Uttam Kumaran: Bear metrics is basically like a black box. So I don’t know what
55 00:07:24.390 ⇒ 00:07:53.319 Uttam Kumaran: sort of like, how they’re actually what’s in between them calling like the stripe Api, and getting to the number right? And so that’s so. The benefit of this system is that we sort of can go all the way to the individual roles and ladder it up. Additionally, the benefit is we can start to join this data with the segment data with the postgres data. But otherwise you’re you’re right in that real should be able to take over real as in
56 00:07:53.440 ⇒ 00:08:02.810 Uttam Kumaran: real, is just a is just the the window into all the core data models, the core data models, architecture should be able to do everything that’s in barometrics, correct
57 00:08:03.640 ⇒ 00:08:13.980 KJ Krause: Okay, Gotcha. So like long term, is there any reason why we wouldn’t want to just be in rail completely and discourage people from getting into bare metrics?
58 00:08:14.630 ⇒ 00:08:20.657 Uttam Kumaran: There’s no reason, no, that I think the the reason the company was in there is because they they just needed something quick
59 00:08:21.270 ⇒ 00:08:21.670 KJ Krause: Yeah.
60 00:08:21.670 ⇒ 00:08:26.609 Mitchell Wright: Long term. The plan is to not use bare metrics and to to just use real
61 00:08:27.550 ⇒ 00:08:28.700 KJ Krause: Okay. Awesome.
62 00:08:29.770 ⇒ 00:08:35.670 Alejandro Rojas: I mean real real or whatever bi tool we pick like, because that that’s not. That’s not yet
63 00:08:35.799 ⇒ 00:08:45.289 Alejandro Rojas: something we we’ve decided. But yeah, Bi, like, show them those metrics somewhere, basically other than real other than barometrics.
64 00:08:46.950 ⇒ 00:08:58.943 Stephane Sol: Just out of curiosity. Do we have do we have all the capabilities? I think I’m an org admin and real. Are there any other capabilities that we don’t have.
65 00:08:59.480 ⇒ 00:09:08.000 Stephane Sol: I’ve noticed that just it seems like, it’s kind of like the I think it’s like a code based flow. I didn’t see anything about canvases or stuff like that. Is there any like
66 00:09:08.120 ⇒ 00:09:11.970 Stephane Sol: features that we’re limited on in real
67 00:09:13.938 ⇒ 00:09:15.610 Uttam Kumaran: Luke, can you speak to that
68 00:09:16.590 ⇒ 00:09:28.279 Luke Daque: I don’t think so. I think you have all the Admin access. So whatever access I have, you also have. So yeah, you should be able to create your own dashboards if you want, in real and and stuff like that.
69 00:09:28.630 ⇒ 00:09:30.859 Uttam Kumaran: So it’s a code based flow to publish
70 00:09:31.458 ⇒ 00:09:37.589 Uttam Kumaran: metrics, new sources. But then you can build dashboards directly from the Ui. Now.
71 00:09:38.410 ⇒ 00:09:49.360 Stephane Sol: Okay, I will definitely take a look at that. That was one of our concerns. Is that like, you know, once you know, it’s it’s amazing for exploration. But we and I mean, maybe that’s something you all can show us is that once we explore.
72 00:09:49.590 ⇒ 00:09:57.270 Stephane Sol: But what do I do with that exploration. Alejandro was giving it a try and slicing and dicing. He could share me a link, but he couldn’t figure out where to. Then
73 00:09:57.590 ⇒ 00:10:01.070 Uttam Kumaran: Materialize that on any type of canvas or something like that.
74 00:10:01.850 ⇒ 00:10:05.480 Uttam Kumaran: Yeah. So they just released the canvas feature. Luke, is that available
75 00:10:06.360 ⇒ 00:10:12.620 Luke Daque: Yeah, it should be available. But we didn’t create any canvas visualizations at the moment. Yet
76 00:10:12.970 ⇒ 00:10:17.539 Uttam Kumaran: Yeah, so you can, you can go ahead and create create those. And again, that’s there, basically
77 00:10:17.750 ⇒ 00:10:22.746 Uttam Kumaran: their feature that matches sort of creating a fixed dashboard that you can link to
78 00:10:23.410 ⇒ 00:10:26.580 Uttam Kumaran: But again, it’s like, it’s it’s sort of what what’s
79 00:10:26.890 ⇒ 00:10:54.849 Uttam Kumaran: best use case for for the company. So want to leave that to you all to decide. Like, if you pick a looker or an omni or something like that, you’re going to be able to. Yeah, create those like fixed dashboards and link directly here you have the same functionality. But definitely, I think the developer workflow here is a little bit easier. But again, yeah, it’s it’s sort of. There’s a lot of trade offs and subjectivity at the bi layer. So I believe you’re going to have all the functionality
80 00:10:55.000 ⇒ 00:11:00.740 Uttam Kumaran: you need. But some people may be used to more enterprise tools. So we’re sort of open
81 00:11:02.960 ⇒ 00:11:10.629 Alejandro Rojas: Generally some time for for trying it out like it’s just like one or 2 days, like hard, hard to make a decision
82 00:11:10.630 ⇒ 00:11:11.530 Uttam Kumaran: Totally, totally.
83 00:11:11.530 ⇒ 00:11:15.799 Alejandro Rojas: Whether to go all in with with real. So we probably need, like
84 00:11:15.940 ⇒ 00:11:20.999 Alejandro Rojas: probably one or one or 2 weeks, to to be able to try it properly, and
85 00:11:21.000 ⇒ 00:11:21.800 Uttam Kumaran: Yeah.
86 00:11:21.800 ⇒ 00:11:24.469 Alejandro Rojas: You know. It’s also like.
87 00:11:24.880 ⇒ 00:11:30.910 Uttam Kumaran: My, my 2 cents is that it’s pretty cheap, like if you go with like Looker, sigma.
88 00:11:31.010 ⇒ 00:11:40.040 Uttam Kumaran: tableau, or omni, it’s gonna be like a 15 or 20 grand platform fee annually, plus like 40 bucks
89 00:11:40.320 ⇒ 00:11:43.139 Uttam Kumaran: anywhere from 20 to 40 bucks a user.
90 00:11:43.583 ⇒ 00:11:47.840 Uttam Kumaran: I think real quoted us like 2, 50 a month. But it’s an annual deal.
91 00:11:48.070 ⇒ 00:11:52.020 Uttam Kumaran: So yeah, that’s like, yeah, it’s it’s like way cheaper.
92 00:11:52.020 ⇒ 00:12:05.799 Mitchell Wright: Yeah. The the thing that I’ll say is, I don’t want to sign an annual contract with them until we’re like, Oh, yeah, we want to do this, even though it is cheap. So if they’re unwilling to like, give us an extra 2 weeks to.
93 00:12:06.000 ⇒ 00:12:09.310 Mitchell Wright: you know. Look at it, then, honestly.
94 00:12:09.310 ⇒ 00:12:09.960 Uttam Kumaran: No, they’re they’re
95 00:12:10.573 ⇒ 00:12:11.800 Mitchell Wright: So, yeah.
96 00:12:11.800 ⇒ 00:12:29.818 Uttam Kumaran: They’re willing. I just, I guess I’m what I’m saying is that even to try another tool, it’s like, Yeah, I think we’re. I think it’s just gonna take up. It’s gonna take a quite a bit of time. I think we can keep. We can keep trying it out. I guess. My point was that it’s it’s so drastically different on price that
97 00:12:30.220 ⇒ 00:12:42.209 Uttam Kumaran: if that helps or it doesn’t help. Yeah. But they’re they’re totally open to just keep. They’re actually, they actually mentioned that they can help add resources to build more canvas or talk to the team. So whatever we need from them
98 00:12:43.040 ⇒ 00:12:50.000 Alejandro Rojas: Yeah, I would love to be in touch with them to ask questions and things like that. So if you have a Poc that I can, you know.
99 00:12:50.240 ⇒ 00:12:50.620 Uttam Kumaran: Sorry to
100 00:12:50.620 ⇒ 00:12:51.180 Alejandro Rojas: Talk, to
101 00:12:51.180 ⇒ 00:12:53.810 Uttam Kumaran: I’ll start a channel and add us all. Yeah, perfect
102 00:12:54.930 ⇒ 00:12:55.850 Alejandro Rojas: And
103 00:12:56.060 ⇒ 00:13:05.969 Alejandro Rojas: and yeah, it’s like, my concern is not picking a tool like I. I wanna avoid migrations on the line as much as possible, because migrating bi tools is like the worst thing So
104 00:13:05.970 ⇒ 00:13:06.440 Uttam Kumaran: Yeah.
105 00:13:06.440 ⇒ 00:13:14.440 Alejandro Rojas: If we can pick one that works, you know, for the most part. Then I would say, that’ll be good
106 00:13:15.600 ⇒ 00:13:16.220 Uttam Kumaran: Totally.
107 00:13:16.220 ⇒ 00:13:24.161 Alejandro Rojas: But if we pick real, we build like 2030 dashboards on it. And then it turns out it doesn’t do what we need, and we need to migrate all those.
108 00:13:24.800 ⇒ 00:13:27.449 Alejandro Rojas: it’s pretty low, low, low value to do
109 00:13:28.540 ⇒ 00:13:31.479 Uttam Kumaran: Yeah, no, it’s totally up to y’all. Yeah, 100%
110 00:13:35.780 ⇒ 00:13:39.204 Stephane Sol: So aside from. So again, aside from the
111 00:13:40.240 ⇒ 00:13:48.440 Stephane Sol: bare metrics initiative, there’s no other like there’s nothing else I saw there was like some medium and lower priorities, but like essentially like
112 00:13:49.520 ⇒ 00:13:58.720 Stephane Sol: if I so, Luke probably is working on the bare metrics initiative like what’s in flight right now, and who is like allocated to it.
113 00:13:59.220 ⇒ 00:14:19.539 Uttam Kumaran: Yeah, I mean, we, we haven’t been able to move past that until you guys joined to like, basically sign off on those metrics. There’s like, there’s not. I like it would be I. Just. I basically said, it’s not smart for us to keep going until we we sign off on these core metrics and then also getting adoption on the dashboards is A is a large lift.
114 00:14:20.026 ⇒ 00:14:32.399 Uttam Kumaran: So we, I mean, we have backlog of like more analytics, initiatives on understanding token, usage cut by Xyz like a bunch of questions around product analytics, funnels. But again, those are
115 00:14:32.750 ⇒ 00:14:40.970 Uttam Kumaran: those are just analytics questions. But I don’t think anything. I think those are all I would consider blocked by just getting these core things signed off
116 00:14:41.700 ⇒ 00:14:42.460 Stephane Sol: Okay. Cool.
117 00:14:42.460 ⇒ 00:14:44.689 Uttam Kumaran: They’re all gonna build on the core metrics
118 00:14:44.960 ⇒ 00:15:12.149 Stephane Sol: Sweet. We. We were afraid that we were coming in and blocking you all. It sounds like you were blocked. You were intentionally blocked waiting for us. Which is good. So I I imagine what we’ll do now is Alejandro and I, and he’s already started. We’ll go in, and and you know, Cherry, pick some of these metrics, and just I guess we’ll start with Mr. AR. Dive in and then work with Luke to be like, hey, what did you find here? What were your? You know what bombs are in here.
119 00:15:12.170 ⇒ 00:15:15.358 Stephane Sol: and we’ll just start reconciling those
120 00:15:15.890 ⇒ 00:15:26.950 Uttam Kumaran: I think that’s I think that’s perfect. Yeah. And yeah, I think that’s a good. That’s a good path forward again. I think everything is gonna sort of build off of that or a build off of revenue in some way.
121 00:15:27.360 ⇒ 00:15:32.329 Uttam Kumaran: and so ultimately look, all the questions are likely gonna point back to there.
122 00:15:32.730 ⇒ 00:15:35.477 Uttam Kumaran: So you guys know how it goes?
123 00:15:35.970 ⇒ 00:15:39.150 Uttam Kumaran: so I think I’d much rather nail those down
124 00:15:39.430 ⇒ 00:15:47.259 Uttam Kumaran: before being like, okay, what is the next one or 2 larger initiatives. And ideally, y’all sort of help us
125 00:15:47.370 ⇒ 00:15:53.320 Uttam Kumaran: basically dictate what the backlog is and where you need us to to fill in. You know.
126 00:15:55.260 ⇒ 00:15:56.190 Stephane Sol: That sounds good.
127 00:15:56.973 ⇒ 00:16:05.649 Stephane Sol: I know we’ve got everyone here all 100. Did you wanna actually dive into some of the questions you had about metrics, or do we wanna
128 00:16:08.404 ⇒ 00:16:15.639 Alejandro Rojas: I don’t know if it’s gonna be useful for everyone or not. But I can, I can ask my questions. So
129 00:16:16.618 ⇒ 00:16:22.600 Alejandro Rojas: I’ve been checking Mr. For the most part, and
130 00:16:23.160 ⇒ 00:16:30.679 Alejandro Rojas: the 1st thing I found is like there was a message that you did some past reconciliation of January 2025.
131 00:16:31.150 ⇒ 00:16:41.859 Alejandro Rojas: They are on slack. And it said, it was like 1919, something 1 million error on on real.
132 00:16:42.120 ⇒ 00:16:49.810 Alejandro Rojas: I just couldn’t get that number in real I’ve tried like so many cats filters. I don’t know if the that was like
133 00:16:49.910 ⇒ 00:16:54.200 Alejandro Rojas: a few days ago, so I don’t know if something big change that
134 00:16:54.445 ⇒ 00:16:54.690 Luke Daque: Yeah.
135 00:16:54.690 ⇒ 00:16:56.889 Alejandro Rojas: That I know, or
136 00:16:57.540 ⇒ 00:17:12.550 Luke Daque: Yeah, I did some bit of digging, I think. There was a change that happened around 5, maybe last week. We did some updates to like adding the refunds and stuff like that. And I think that changed the numbers now. So
137 00:17:14.240 ⇒ 00:17:25.129 Luke Daque: the screenshots that we shared last week was no longer like what we are getting in in real today today. So I think today, we’re like for January. We were like at 17 million.
138 00:17:25.668 ⇒ 00:17:46.480 Luke Daque: Mr. As. Opposed to the screenshots, which which are like showing 19 million. And I think, one of the things that we removed there was like there were duplicate subscription items, I mean sub active subscriptions for a specific customer. We filtered that out by just choosing the latest
139 00:17:46.730 ⇒ 00:17:51.534 Luke Daque: subscription item, cause the duplicates were happening because of
140 00:17:52.160 ⇒ 00:17:55.010 Luke Daque: customers that were upgrading within the same month.
141 00:17:55.170 ⇒ 00:17:57.409 Luke Daque: and then they were considered as active.
142 00:17:57.520 ⇒ 00:18:01.529 Luke Daque: like those 2 subscriptions were active for that month. Right? So
143 00:18:02.213 ⇒ 00:18:11.560 Luke Daque: yeah, we, we remove the the older one, and we use the the latest one. Just so we can get like one active subscription for that month, and we we would use the
144 00:18:11.810 ⇒ 00:18:13.435 Luke Daque: the Mr. Or like the
145 00:18:14.720 ⇒ 00:18:19.199 Luke Daque: plan price for that higher subscription for the Mrr. Basically.
146 00:18:20.000 ⇒ 00:18:22.089 Luke Daque: And I believe that’s where
147 00:18:22.210 ⇒ 00:18:28.339 Luke Daque: that’s also something that I believe we’re differing. That. There’s some sort of difference in bare metrics, because
148 00:18:29.092 ⇒ 00:18:31.057 Luke Daque: I think they were like,
149 00:18:33.460 ⇒ 00:18:41.169 Luke Daque: what? What do you call that like? They’re like for, like a percent of the month of the month, they’re using the pre the lower rate.
150 00:18:41.640 ⇒ 00:18:45.199 Luke Daque: And then, like, Yeah, the as soon as they changed, or something
151 00:18:45.200 ⇒ 00:18:46.330 Stephane Sol: Prorated Upgrade
152 00:18:46.330 ⇒ 00:18:47.830 Luke Daque: Yeah, something like that.
153 00:18:48.200 ⇒ 00:18:54.160 Luke Daque: So I think that’s what we we Mitch saw before when we had that call.
154 00:18:54.710 ⇒ 00:18:55.600 Luke Daque: Yeah.
155 00:18:56.760 ⇒ 00:19:10.079 Stephane Sol: Would it be fair to say that like when we were looking at some of the dashboards that aren’t real? They seem like they’re in more of like a diagnostic state, where, like, you’ve kind of like, you haven’t encoded any like. You’ve kind of left most of the filters kind of free form to kind of.
156 00:19:10.200 ⇒ 00:19:27.480 Stephane Sol: for instance, like so when when Alejandro is trying to reconcile Mr. It’s kind of like a lot of those filters haven’t been encoded in the metric definition yet, and I and my suspicion is that because you all are still trying to figure out how these all play together. Is that sound about right to you all
157 00:19:27.480 ⇒ 00:19:35.670 Uttam Kumaran: Yeah, like, we, we weren’t comfortable moving forward until basically we get sign off on the numbers. So nothing like we’re the only people in rail
158 00:19:36.990 ⇒ 00:19:56.179 Stephane Sol: Gotcha. Okay, that’s our we, that’s our. That’s what we suspected. And that makes total sense. We we just wanted to. The the one of the assumptions we had was like, Okay, someone’s supposed to go to this and see Mrr. And that’s it. But obviously that’s not it. Like right? They have to do a little work, and that’s intentional, because you all are. Still.
159 00:19:56.330 ⇒ 00:19:57.750 Stephane Sol: We all are still
160 00:19:58.130 ⇒ 00:20:21.380 Uttam Kumaran: Exactly. And that’s like, you know, I you guys know it’ll take. It’ll take 2 seconds to just have a dashboard with like but to to basically start to identify like oh, we saw many customers with several active subscriptions, and all of those were being counted in the number. What should we do? That’s like a core question that like this team needs to answer because
161 00:20:21.510 ⇒ 00:20:41.769 Uttam Kumaran: we were like, Okay, from my experience, there shouldn’t be. And we talked to Mitch. But like, that’s gonna that’s 1 area that’s different. That’s gonna lead to a decrease in revenue, and most likely is going to be the reason why this number is not going to match what’s coming out of bear metrics? Because it’s not clear to me that they’re filtering out.
162 00:20:41.980 ⇒ 00:20:45.639 Uttam Kumaran: They have a way to filter out multiple active subscriptions.
163 00:20:46.030 ⇒ 00:20:58.639 Uttam Kumaran: So this is again, like something where? Okay, if if that’s if our assumption is that people shouldn’t have active subscriptions, then, then, basically, that needs to be communicated to the business. And that’s going to be a reason for a discrepancy
164 00:20:59.170 ⇒ 00:21:19.139 Stephane Sol: Gotcha. And and would you say you all are at the point where, like Alejandro and I can go in, you know. Luke, we? We will. Do. You all agree that, like Luke, now, you are moving into an advisory role, and like we can go in and make some changes. We’ll we’ll obviously have you on Pr review and ask for your input. But it’s okay for us to go in and kind of like mess around
165 00:21:20.060 ⇒ 00:21:29.870 Uttam Kumaran: Totally. Yeah, okay, it’s totally open. Yeah, yeah, we’re not. We’re not touching anything. But also, yeah, everything goes through Pr review, anyway. So whatever questions you guys have for us, like we’re here
166 00:21:30.120 ⇒ 00:21:46.660 Uttam Kumaran: to assist like where I I would say, like the last Pr change we made was earlier this week or last week, and some of we’re just making some changes to upgrade rail. But we want to get you guys up to speed as fast as possible. So that’s where I want all of our resources pointed towards
167 00:21:47.900 ⇒ 00:21:48.949 Stephane Sol: Okay. Sounds good.
168 00:21:50.320 ⇒ 00:21:56.580 Alejandro Rojas: And then I I think that there’s another resource we discovered on baremetrics
169 00:21:57.070 ⇒ 00:22:02.049 Alejandro Rojas: that is, gonna help us tremendously for reconciliation, because now we have
170 00:22:02.600 ⇒ 00:22:07.719 Alejandro Rojas: a Csv. Export of Mr. Each month from each customer.
171 00:22:08.290 ⇒ 00:22:17.020 Alejandro Rojas: So we have the customer Id from the stripe, and then what then? Mr. Number, you show on the dashboard is for each end of month.
172 00:22:17.180 ⇒ 00:22:19.079 Alejandro Rojas: So that’s probably
173 00:22:19.380 ⇒ 00:22:26.029 Alejandro Rojas: when you, if you have some some of those customers that have multiple subscriptions and you have their ids.
174 00:22:26.220 ⇒ 00:22:33.690 Alejandro Rojas: We can go and check on that. Csv, okay, that customer. What? What is the Mmr for them? And then see what they do? That’s kind of like a
175 00:22:34.690 ⇒ 00:22:37.670 Alejandro Rojas: I think that’s a good tool, for it’s not
176 00:22:38.040 ⇒ 00:22:46.323 Alejandro Rojas: as black box anymore. It’s like, okay, we have some. At least, we have the numbers, and we can, you know, double check where it where it differs, and why
177 00:22:46.850 ⇒ 00:22:48.369 Alejandro Rojas: So that could be a good tool
178 00:22:51.320 ⇒ 00:22:52.000 Luke Daque: Nice.
179 00:22:52.380 ⇒ 00:22:54.559 Luke Daque: Yeah, we can definitely like, check
180 00:22:54.680 ⇒ 00:23:01.099 Luke Daque: what numbers they have in the Csv file and then check cross, check it with rail and see where the difference is. And
181 00:23:01.250 ⇒ 00:23:01.940 Luke Daque: yeah.
182 00:23:01.940 ⇒ 00:23:05.309 Alejandro Rojas: Yeah. My my plan was to add it as a seed to Dvt
183 00:23:05.600 ⇒ 00:23:13.560 Alejandro Rojas: and load that into a snowflake, so that we can do some cross validation queries, and see what was what, so
184 00:23:14.450 ⇒ 00:23:16.858 Alejandro Rojas: that will make it easier. Hopefully.
185 00:23:17.900 ⇒ 00:23:28.720 Stephane Sol: There might be a I think there’s like, is it? 4 MB limit on seeds? I don’t know. Back in the day, I remember, there was something where like it would limit the the seed size. So
186 00:23:28.720 ⇒ 00:23:35.830 Luke Daque: I think it’s a hundred now 100 MB. The last thing last try I did, but I don’t know what it is now.
187 00:23:36.140 ⇒ 00:23:39.046 Stephane Sol: Okay, cool either way. We’ll get it in there.
188 00:23:39.370 ⇒ 00:23:45.429 Alejandro Rojas: I have more questions about Mrr, but I don’t know if you want to ask anything else, Stefan, before I jump into that
189 00:23:45.630 ⇒ 00:23:47.280 Stephane Sol: No, I’m good. You go nuts
190 00:23:48.150 ⇒ 00:23:55.400 Alejandro Rojas: So how do? How do we handle discounts and coupons right now on our current? Dvt, mobile
191 00:23:58.140 ⇒ 00:24:03.750 Luke Daque: Yeah, I think I just loaded discounts and coupons as well, coming from stripe. So
192 00:24:04.250 ⇒ 00:24:10.460 Luke Daque: some discounts are for subscriptions related, and some discounts are token reloads, I believe.
193 00:24:11.016 ⇒ 00:24:21.569 Luke Daque: So it it should be able we should be able to split it in real as well. There’s a a discount dimension there and a coupon dimension. So
194 00:24:21.980 ⇒ 00:24:28.680 Luke Daque: yeah, something like that we can. We would be able to see like which ones are for subscriptions which ones are for token reloads
195 00:24:29.700 ⇒ 00:24:30.400 Alejandro Rojas: Now like.
196 00:24:30.400 ⇒ 00:24:31.840 Luke Daque: I missed. Yeah.
197 00:24:32.570 ⇒ 00:24:40.209 Alejandro Rojas: Mrr includes like. If I have a subscription then, and I have a discount
198 00:24:40.210 ⇒ 00:24:40.930 Luke Daque: Hmm.
199 00:24:41.350 ⇒ 00:24:46.259 Alejandro Rojas: Of how to, and it still includes the subscription into them. Right am I right?
200 00:24:46.440 ⇒ 00:24:54.460 Luke Daque: Yeah, it’s not, it’s not. It’s excluding discounts and coupons at the moment. In our Dbt model, the Mrr
201 00:24:55.150 ⇒ 00:24:55.760 Alejandro Rojas: Okay.
202 00:24:55.760 ⇒ 00:24:59.750 Luke Daque: The discount is just in a different measure.
203 00:25:00.020 ⇒ 00:25:09.340 Luke Daque: So if we need to like do some calculation, or like create a different MRI that already incorporates discounts and coupons. Then we, we can add that
204 00:25:09.490 ⇒ 00:25:12.239 Luke Daque: in the model, or even just
205 00:25:12.240 ⇒ 00:25:12.660 Alejandro Rojas: No yes.
206 00:25:12.660 ⇒ 00:25:13.990 Luke Daque: Forward and real. Yes.
207 00:25:14.660 ⇒ 00:25:19.990 Alejandro Rojas: I I think that’s 1 of the difference between barmetrics and and what we have in real right now.
208 00:25:20.390 ⇒ 00:25:24.039 Alejandro Rojas: So I just wanted to double check because they claim they they include
209 00:25:24.320 ⇒ 00:25:25.070 Luke Daque: Hmm.
210 00:25:25.330 ⇒ 00:25:30.889 Alejandro Rojas: So let’s say you have a hundred percent off on like a few months.
211 00:25:31.130 ⇒ 00:25:38.240 Alejandro Rojas: So your Mrr, as a customer is 0 on on barometrics, basically
212 00:25:38.620 ⇒ 00:25:39.060 Luke Daque: Gotcha
213 00:25:39.060 ⇒ 00:25:44.260 Alejandro Rojas: And on real. It won’t be right, because we are not including that yet until the into the calculation
214 00:25:45.315 ⇒ 00:25:49.969 Alejandro Rojas: on a stripe. They only count what you tell them to count right
215 00:25:50.529 ⇒ 00:25:59.520 Alejandro Rojas: if by default, I think they only count forever coupons like the ones that apply forever. But you can also tell it to
216 00:25:59.880 ⇒ 00:26:09.327 Alejandro Rojas: include, you know, fixed duration discounts like recurring discounts and coupons, and you know one offs
217 00:26:09.890 ⇒ 00:26:12.709 Alejandro Rojas: But by default. It doesn’t do it. I mean
218 00:26:13.470 ⇒ 00:26:15.199 Luke Daque: At least at the time I looked.
219 00:26:15.780 ⇒ 00:26:16.520 Luke Daque: Yeah.
220 00:26:18.730 ⇒ 00:26:23.529 Alejandro Rojas: And then the other one that I had is like, if someone cancel mid month.
221 00:26:25.482 ⇒ 00:26:28.900 Alejandro Rojas: Was a current behavior
222 00:26:28.900 ⇒ 00:26:34.869 Luke Daque: We’re ex, we’re extending it to the next month. So basically, it’s still counted as an Mrr.
223 00:26:34.990 ⇒ 00:26:38.520 Luke Daque: despite it being cancelled for that month. Basically
224 00:26:39.610 ⇒ 00:26:52.630 Alejandro Rojas: Okay. So if if I cancel and my end date for my subscription falls in the middle of a month then in our logic. It gets counted on the final month. Error, Mr. Right?
225 00:26:52.630 ⇒ 00:26:53.749 Luke Daque: That’s that’s correct.
226 00:26:54.670 ⇒ 00:26:55.110 Luke Daque: Same
227 00:26:55.110 ⇒ 00:26:56.110 Alejandro Rojas: I think that’s another
228 00:26:56.110 ⇒ 00:26:56.700 Luke Daque: Well.
229 00:26:57.470 ⇒ 00:27:05.150 Luke Daque: yeah, if it gets cancelled mid year, for example, it still gets counted at the end of the year for the yearly subscriptions
230 00:27:05.860 ⇒ 00:27:11.170 Alejandro Rojas: Yeah, that’s another difference between our our logic and their metrics. They
231 00:27:12.250 ⇒ 00:27:14.360 Alejandro Rojas: if you reach the end of the month.
232 00:27:14.680 ⇒ 00:27:22.400 Alejandro Rojas: then you’re counted. If you don’t, then you’re not. So. That’s how they they take the last day of the month as the hey.
233 00:27:22.900 ⇒ 00:27:27.490 Alejandro Rojas: this is how it is, and then same for year. So if you cancel Midgee.
234 00:27:27.760 ⇒ 00:27:32.380 Alejandro Rojas: since they do the calculation like to the day.
235 00:27:32.740 ⇒ 00:27:37.139 Alejandro Rojas: they pick the last day of the year as the as a number So
236 00:27:37.590 ⇒ 00:27:40.949 Alejandro Rojas: I mean, we should probably decide what we want to do in there.
237 00:27:41.550 ⇒ 00:27:44.714 Luke Daque: Yeah, because currently, also, the the model is
238 00:27:45.510 ⇒ 00:27:55.649 Luke Daque: it’s like per month. It’s not enough. We can’t go granular to the daily level. So maybe that’s also something we need to change to. So it matches with their metrics. Basically
239 00:27:56.720 ⇒ 00:28:04.739 Alejandro Rojas: I think that would be great, because that will be. That’ll make it easier to be back, because right now the march, Mr. Is kind of lagging.
240 00:28:05.560 ⇒ 00:28:08.509 Alejandro Rojas: and because of that, so
241 00:28:08.780 ⇒ 00:28:16.160 Alejandro Rojas: moving it to daily will will make it much easier for debugging purposes, and then we can take different days and compare them.
242 00:28:16.350 ⇒ 00:28:17.650 Alejandro Rojas: and things like that
243 00:28:19.480 ⇒ 00:28:46.920 Stephane Sol: So OP operationally. Here’s what I’m thinking right like. I know you said you all have a standing meeting. It feels like right now, Luke, you’re our guy. We’re gonna work with you. So I don’t know if everyone else needs to be involved. I mean, obviously welcome. What I’m thinking is, luke will work with you, maybe like. Well, let’s consume the rest of the documentation this week, Luke. Let’s meet up with you. Let’s either wrap up like
244 00:28:47.000 ⇒ 00:28:54.820 Stephane Sol: either wrap up, reprioritize, or kind of change your existing issues that are in progress or need review. Let’s
245 00:28:54.960 ⇒ 00:29:06.907 Stephane Sol: Alejandra and I will start to create the roadmap for new model additions. Right? And we’ll either do the work ourselves. But always, either you know, we’ll either delegate or ask you for advice on those changes.
246 00:29:07.390 ⇒ 00:29:24.050 Stephane Sol: and and you all can continue to execute unblocked on any of the operational handover like, there’s items here about permissions, and maybe some like additional documentation options. If we wanted to like, assign you all work? Could we create an issue in your linear space
247 00:29:24.870 ⇒ 00:29:40.539 Uttam Kumaran: Yeah, so this so perfect. So I think, yeah, the biggest thing is like amber and Ryan will just be the points of contact. Amber is a Pm on our side. So she’s basically in charge of of who’s working on what? And that’s perfect. I think if you guys can all just work on backlog. And
248 00:29:40.550 ⇒ 00:30:04.539 Uttam Kumaran: we’ve been moving really quickly on stuff with stack list, because it’s it’s like, not too crazy. And there’s not like a ton of people involved. So we can move as fast as you guys need us to. You know. So whether it’s yeah coming in and just advising on how to do things. Again. We’ve modeled subscription revenue and can probably provide a couple of pointers if you guys need it, or if it’s taking on new data modeling work. Yeah, I would just throw it in the backlog. And
249 00:30:04.750 ⇒ 00:30:19.630 Uttam Kumaran: you all set a due date, and we’ll take it on. That’s perfect. And then we have, we do. We’re doing like a we’re doing. I think we’re meeting every other day or so. But we can be as involved as you want. And yeah, if you guys are open to using our linear, that’s also totally fine. So whatever you guys need
250 00:30:20.990 ⇒ 00:30:45.160 Stephane Sol: Cool. I think I think we’ll we’ll put we’ll put linear items in your board that that either correspond to wrapping up items that were in progress or related to Handoff, and then all 100 and myself in our own backlog, you know. Maybe future work will put there and then share as needed. If we need you involved but just kind of wrap up the board to your board to a good state, and then we’ll figure out where to go. From. There
251 00:30:45.320 ⇒ 00:31:04.840 Uttam Kumaran: Okay, cool. And then that last piece is like, you know, we’ve done a lot of subscription modeling. So you guys did a bunch of that out of our cell, too. So if any questions you know, or any way or we can, we can assist on making some of those decisions more than happy to you know. So however, you guys wanna utilize us
252 00:31:04.990 ⇒ 00:31:21.649 Stephane Sol: And and the way we’re thinking about it is for good or worse, we’re gonna replicate exactly what their metrics is doing. And when we find things that seem illogical or maybe not optimal. We’re going to document that. And then that can be maybe where we tie into you guys and be like, Hey, bare metrics is doing some crazy shit
253 00:31:21.650 ⇒ 00:31:22.010 Uttam Kumaran: Yeah.
254 00:31:22.010 ⇒ 00:31:23.319 Stephane Sol: How would you all do this?
255 00:31:23.320 ⇒ 00:31:26.900 Stephane Sol: Yeah, of course, can help us do. V. 2, so to speak.
256 00:31:27.380 ⇒ 00:31:43.649 Uttam Kumaran: Great. That’s perfect. Yeah, I think the number one thing for y’all is just to get people looking at some dashboard somewhere and starting to use it in their meetings. You know, especially around like how people are using the product. I think that’s the one thing that’s probably most opaque right now is, how is
257 00:31:43.860 ⇒ 00:32:09.920 Uttam Kumaran: product usage tying to revenue? One of the questions that we got from someone on the team was like, who are most obvious user customers, and maybe, how do we transition them to higher plans or to enterprise? I feel like that. Really. I told our team that that was like our North Star to try to make sure that the data models are in place to answer those questions. And I don’t think we’re that far. I think we’re like a few weeks from that point, like, not like a few months. So, yeah, we’re here for you all
258 00:32:10.210 ⇒ 00:32:23.430 Stephane Sol: And and you said the usage, you you actually have a model of usage. And it’s the only as as far as I understand. The only missing piece is connecting these 2 models right like the Mrr dimensions and the usage right
259 00:32:23.430 ⇒ 00:32:27.890 Uttam Kumaran: Yeah, I believe we actually have, like they both have the customer ids. And I think we’ve already
260 00:32:27.970 ⇒ 00:32:39.059 Uttam Kumaran: like there’s a real dashboard, or the existing one where we’ve connected them. But again, like I, we didn’t feel comfortable publishing like the value until cause people are immediately gonna say, like.
261 00:32:39.120 ⇒ 00:33:06.519 Uttam Kumaran: Oh, cool like this customer is worth this amount. And so I wanted to sort of wait before we do that. But all the the dimensions are there to join like you can join on customer Id. All the plan types are there between stripe and postgres. And then, of course, we’re getting. We have the actual product analytics events coming in through segment for any sort of funnel work. We haven’t modeled any sort of user journey stuff. And that’s probably what a lot of the stuff you see in our in our backlog, or just open questions about
262 00:33:06.700 ⇒ 00:33:21.719 Uttam Kumaran: like user journey through the product. But really everything from postgres is all the users orgs tokens. And then we have the financials. So that’s I think the data is basically in a place where you can start to join that and start to answer those questions pretty soon.
263 00:33:22.050 ⇒ 00:33:32.250 Stephane Sol: Cool we will. We will look into that. Okay, cool. So we have the usage. We just don’t. We can’t accurately say, you know who the big spenders are yet, and that’s what we’ll get with the Mr. Okay.
264 00:33:32.250 ⇒ 00:33:32.880 Uttam Kumaran: Yeah.
265 00:33:36.970 ⇒ 00:33:40.170 Stephane Sol: Cool anything else you wanted to cover
266 00:33:42.518 ⇒ 00:34:08.779 Amber Lin: I’m just gonna read off what I we had for the agenda. So we wanted to decide on Dbt versus cloud. That’s 1 decide if we wanted to use real I know we had something about that. Maybe we aren’t deciding right now right away. And then the 3rd item is to define when we’re meeting, how frequently we’re meeting. And the 4th is, who’s the point of contact on each team. So what are you guys? Thoughts
267 00:34:09.260 ⇒ 00:34:11.940 Uttam Kumaran: Yeah, I think we answered the 1st 3 questions. So
268 00:34:12.340 ⇒ 00:34:14.540 Uttam Kumaran: I think we’re going to keep using Dvt Core
269 00:34:14.540 ⇒ 00:34:15.070 Amber Lin: Okay.
270 00:34:15.070 ⇒ 00:34:21.949 Uttam Kumaran: We’ll let the team decide on Bi tool. Real real will give us more time like they’re fine. I’ll actually start. I’ll start a thread
271 00:34:22.170 ⇒ 00:34:28.349 Uttam Kumaran: with with us and the team. I think the team schedule again. I think if you can amber if you can just start including
272 00:34:28.590 ⇒ 00:34:35.339 Uttam Kumaran: the team on on whatever. On this the stand ups that we’re doing, and then we can just make sure our backlog is sort of.
273 00:34:35.460 ⇒ 00:34:42.729 Uttam Kumaran: We can just throw throw all of our backlog stuff I guess into won’t do, for now and then the team can pick those off
274 00:34:43.199 ⇒ 00:34:45.760 Uttam Kumaran: And then, yeah, I think that’s that’s basically where we’re at
275 00:34:46.260 ⇒ 00:34:50.320 Uttam Kumaran: any questions we could do, Async, or in those in those sort of stand ups
276 00:34:50.550 ⇒ 00:34:56.920 Amber Lin: Totally, and for those standups I would just be inviting Stefan, Alejandro, and maybe Kj.
277 00:34:58.640 ⇒ 00:34:59.830 Amber Lin: Which is
278 00:35:00.560 ⇒ 00:35:03.660 Uttam Kumaran: Yeah, I guess up to up to y’all. Who who’s there
279 00:35:04.060 ⇒ 00:35:07.619 Stephane Sol: Hey, Jay, it’s whatever you want, man, all 100 and I are. Gonna be there
280 00:35:07.980 ⇒ 00:35:12.910 KJ Krause: No, yeah, I I enjoy staying in the loop, so I’ll definitely make it
281 00:35:13.410 ⇒ 00:35:17.619 Amber Lin: Okay, I’ll put you as optional, so you can come with you when you have time.
282 00:35:20.450 ⇒ 00:35:22.540 Amber Lin: Okay, that’s all. From my end.
283 00:35:23.940 ⇒ 00:35:24.660 Stephane Sol: Cool.
284 00:35:24.660 ⇒ 00:35:25.390 Alejandro Rojas: Cool.
285 00:35:25.720 ⇒ 00:35:36.589 Alejandro Rojas: and I think the last piece is like the the access. So if if we can get like the the highest level of asset access to like the repo, the polytonic unreal
286 00:35:36.750 ⇒ 00:35:38.040 Alejandro Rojas: I think they’ll be.
287 00:35:38.730 ⇒ 00:35:39.859 Alejandro Rojas: That’ll be awesome
288 00:35:39.860 ⇒ 00:35:44.169 Uttam Kumaran: Yeah. So the repo is on your side. So I think
289 00:35:44.170 ⇒ 00:35:46.750 Mitchell Wright: Did, you guys? I don’t remember. I
290 00:35:47.990 ⇒ 00:35:50.169 Mitchell Wright: I thought I gave you. But I’ll look at that
291 00:35:52.980 ⇒ 00:35:58.626 Stephane Sol: In terms of also secrets. So we now have a shared vault.
292 00:35:59.220 ⇒ 00:36:03.139 Stephane Sol: Are there any? Well, I guess. Are there any secrets
293 00:36:03.380 ⇒ 00:36:05.190 Stephane Sol: to be shared right like we
294 00:36:05.190 ⇒ 00:36:12.809 Uttam Kumaran: Yeah, like I, I would love to just hand over, I mean, can we just send you all of our stuff in one password, or you have some stuff in there.
295 00:36:13.790 ⇒ 00:36:18.648 Stephane Sol: Yeah, if you share that with me, I’ll put it into our shared vault, and we can be the owners of those secrets.
296 00:36:19.210 ⇒ 00:36:20.040 Stephane Sol: cool.
297 00:36:22.803 ⇒ 00:36:30.959 Stephane Sol: There was one. I didn’t see it in the docs. I don’t know but there is a the snowflake permission grants. You all are doing
298 00:36:31.560 ⇒ 00:36:40.440 Uttam Kumaran: Yeah, I have a I have a grants worksheet and snowflake. I just, I’ll just share that. I’ll just share that with you guys sorry I didn’t get to it yesterday
299 00:36:40.440 ⇒ 00:36:41.840 Stephane Sol: Sounds good, not a problem
300 00:36:42.080 ⇒ 00:37:10.269 Uttam Kumaran: Perfect. Yeah, it’ll it just has, like everything we’ve ran. Of course, it’s like a little bit. There’s a lot there. But any questions. But that should have everything we’ve we’ve ever ran. Basically grant wise. And then, yeah, I think we’ll we’ll make sure that you’re you guys are account admin sysadmin, or your admin on everything in in snowflake? So in case you can, you guys can run whatever you need to. Just let me know if there’s any other permissions there. That I’m missing. I know that there’s like
301 00:37:10.490 ⇒ 00:37:15.329 Uttam Kumaran: there can be a lot. So just let me know if if you, if you run into any challenges, running grants
302 00:37:16.100 ⇒ 00:37:16.930 Stephane Sol: Sounds good
303 00:37:21.840 ⇒ 00:37:29.530 Uttam Kumaran: Okay, great. I think if that’s it, then, yeah, I think, amber. Maybe we could execute on some of those organizational things today. And then, yeah, we should be able to wrap up
304 00:37:30.050 ⇒ 00:37:34.760 Uttam Kumaran: kind of most of these items, I think, by the end of the week. And then well, we’ll just sort of follow your lead.
305 00:37:36.450 ⇒ 00:37:37.310 Stephane Sol: Sounds good
306 00:37:39.670 ⇒ 00:37:41.120 Amber Lin: Thank you guys for coming
307 00:37:41.470 ⇒ 00:37:42.010 Uttam Kumaran: Thank you.
308 00:37:42.010 ⇒ 00:37:42.670 Stephane Sol: Thank you.
309 00:37:42.670 ⇒ 00:37:43.100 Alejandro Rojas: Thank you.
310 00:37:43.960 ⇒ 00:37:44.580 Luke Daque: Bye, bye.
311 00:37:45.050 ⇒ 00:37:45.760 Alejandro Rojas: Bye.