Meeting Title: [Javy + Brainforge] Roadmap Estimates Date: 2025-03-31 Meeting participants: Aakash Tandel, Aman Nagpal, Robert Tseng
WEBVTT
1 00:00:47.900 ⇒ 00:00:48.800 Aakash Tandel: You’re
2 00:00:49.720 ⇒ 00:00:51.040 Robert Tseng: Hey? Kosh! How’s it going
3 00:00:51.550 ⇒ 00:00:52.800 Aakash Tandel: Not bad.
4 00:01:00.100 ⇒ 00:01:01.410 Aman Nagpal: Hey, guys, how’s it going
5 00:01:03.060 ⇒ 00:01:04.740 Robert Tseng: How are you? How’s your weekend
6 00:01:05.200 ⇒ 00:01:07.659 Aman Nagpal: It was good. How about you guys good to meet you? Akash
7 00:01:07.660 ⇒ 00:01:09.030 Aakash Tandel: Yeah. Good to meet you as well
8 00:01:10.350 ⇒ 00:01:12.949 Robert Tseng: Oh, this is the 1st time you guys have. Okay, cool
9 00:01:12.950 ⇒ 00:01:13.600 Aakash Tandel: Yeah, that’s nice.
10 00:01:15.140 ⇒ 00:01:24.080 Robert Tseng: Yeah, no weekend was good. I don’t know. One of you were in New York weekend, but like Saturday, like, it’s a really hot day, and like
11 00:01:24.080 ⇒ 00:01:28.059 Robert Tseng: Central, the lawns opened up, and it was like.
12 00:01:28.170 ⇒ 00:01:31.219 Robert Tseng: I feel like there were thousands of people there. It’s crazy. So
13 00:01:31.630 ⇒ 00:01:35.150 Aman Nagpal: Gotta stick to that. Those that seventies. What was it? 70? Something
14 00:01:35.460 ⇒ 00:01:42.529 Robert Tseng: Yeah, so 70 something. And then, now it’s dropped back down, I guess. But yeah, 1st glimpse of spring
15 00:01:43.430 ⇒ 00:01:44.560 Aman Nagpal: Hopefully, soon.
16 00:01:44.960 ⇒ 00:01:45.600 Robert Tseng: Yeah.
17 00:01:50.010 ⇒ 00:01:51.299 Robert Tseng: Akash, I think you’re frozen
18 00:01:51.300 ⇒ 00:01:52.329 Aakash Tandel: I think my Internet
19 00:01:52.680 ⇒ 00:01:53.370 Robert Tseng: Yeah.
20 00:01:54.780 ⇒ 00:01:59.940 Robert Tseng: okay, I mean, no worries. I’ll I’ll I can share my, I can share the screen until you reconnect.
21 00:02:02.250 ⇒ 00:02:11.490 Robert Tseng: yeah. So I mean, I guess we just kind of have the 4 week roadmap and all the things that you had listed out and based on our last conversation last week, and then
22 00:02:12.110 ⇒ 00:02:14.970 Robert Tseng: You know, we can sign estimates and then taking some notes, and
23 00:02:16.060 ⇒ 00:02:19.479 Robert Tseng: hey, I guess we’re just gonna go through prioritization with you and then
24 00:02:19.770 ⇒ 00:02:22.420 Robert Tseng: share any other kind of perspectives.
25 00:02:23.930 ⇒ 00:02:24.640 Aman Nagpal: Sounds good
26 00:02:25.240 ⇒ 00:02:25.860 Robert Tseng: Yeah.
27 00:02:29.640 ⇒ 00:02:47.930 Robert Tseng: Yeah. So I mean, I think this is all kind of assuming. Well, this is weird overlap. But I’m we’re just assuming, like today onwards towards like, I don’t know the next 3 weeks. There’s a couple of things that we’re kind of just like closing out stuff from towards the end of the week last week, and like the gross margin stuff, I said on Friday. So
28 00:02:50.240 ⇒ 00:02:58.250 Robert Tseng: I mean that that work is already that work has already happened. So like that estimate would be 0. But before, because, assuming that there’s nothing else to to do on that.
29 00:02:58.410 ⇒ 00:02:59.240 Robert Tseng: yeah.
30 00:03:01.440 ⇒ 00:03:02.760 Robert Tseng: Yeah. But anyway, I’ll look
31 00:03:02.760 ⇒ 00:03:12.069 Aman Nagpal: For actual build hours so that we can compare, you know what was quoted and what we’re actually, you know, spending the time on over the next 3 weeks.
32 00:03:13.970 ⇒ 00:03:15.810 Aman Nagpal: so like there is, you know.
33 00:03:16.120 ⇒ 00:03:18.600 Aman Nagpal: at least 60 h remaining something like that.
34 00:03:19.490 ⇒ 00:03:23.910 Robert Tseng: Yeah, I mean, I don’t know if you want to back the actuals on this one. But I mean, like
35 00:03:24.180 ⇒ 00:03:31.230 Robert Tseng: from between last we checked in, and how much we worked on it. I mean we I mean, I could say we would probably put in 5 h
36 00:03:31.710 ⇒ 00:03:38.509 Aman Nagpal: When I say actuals, I mean more like, what are we getting billed for? Right? So if it’s something like, I’ll give you an example portable. We spent like
37 00:03:38.680 ⇒ 00:03:43.260 Aman Nagpal: 1020, something hours on the last contract.
38 00:03:43.440 ⇒ 00:03:53.019 Aman Nagpal: and with them said, You know, whatever needs to be done on this contract, we’re not gonna take it from our actual hours, right? So the the build hours ended up being 0. So if you know, if the
39 00:03:53.520 ⇒ 00:04:01.419 Aman Nagpal: no, this has been going on for a couple of months. So whatever minor fixes you need to do, I assume we’re not going to pull from our weekly hours for the gross margin right
40 00:04:01.820 ⇒ 00:04:07.240 Robert Tseng: Yeah, no, we don’t. Well, I mean, that’s not necessarily true. But I think like, for example.
41 00:04:07.763 ⇒ 00:04:13.629 Robert Tseng: as we were kind of the the errors that you pointed out on some orders last week. It was just
42 00:04:14.040 ⇒ 00:04:20.330 Robert Tseng: just happened to be that you picked a couple of orders or you picked a couple I mean, whatever we saw
43 00:04:20.910 ⇒ 00:04:36.899 Robert Tseng: in the cogs assumption sheet that we were given like there were. There was a set of duplicate products that we had not previously seen or considered before, and so we had to like, do some additional work to go and exclude those and prevent.
44 00:04:37.090 ⇒ 00:04:44.970 Robert Tseng: so that anytime. We just expect that we get messy data from whoever it is on on the Ops team that they’re always going to give us something.
45 00:04:45.320 ⇒ 00:04:49.920 Robert Tseng: There’s now there’s a list of checks that like this are
46 00:04:50.210 ⇒ 00:04:55.299 Robert Tseng: on on a jet and on ingestion. We we go through now. So
47 00:04:55.490 ⇒ 00:04:59.310 Robert Tseng: if they send us duplicate products, or like
48 00:04:59.850 ⇒ 00:05:05.770 Robert Tseng: same product ids with like different cost assumptions like we have some more
49 00:05:06.570 ⇒ 00:05:16.010 Robert Tseng: like, we have something in place to prevent that from happening yet. But that’s like not something that was really rooted out before. But like that’s that to me is net new work. In order to get
50 00:05:16.250 ⇒ 00:05:18.579 Robert Tseng: to to do more on in the modeling.
51 00:05:18.800 ⇒ 00:05:25.149 Aman Nagpal: Yeah, that’s fair. I mean, I understand it takes you time, and that that’s fine with me. At the same time, you know, I just wanna make sure that
52 00:05:25.290 ⇒ 00:05:34.649 Aman Nagpal: like, obviously, this has been going on for a long time. Right? So we want to make sure it’s accurate. And I know Aisha was working on a lot of it. But even something as simple as you know, we
53 00:05:34.770 ⇒ 00:05:51.650 Aman Nagpal: screen share, and the the shipping costs are coming up as flat rate, 0 0 7. That’s, you know, instantly can be caught very quickly. Right? So I just wanna make sure that you were able to go in and look at a bunch of sample orders and make sure that at least from your side, it looks okay. Before we review it again.
54 00:05:52.060 ⇒ 00:05:54.420 Aman Nagpal: I did. Okay, cool.
55 00:05:55.940 ⇒ 00:06:04.739 Aman Nagpal: So I’ll take a look at that and just see if everything looks good on my side. I saw your message about the margin dropping from 68 to 62
56 00:06:05.153 ⇒ 00:06:08.680 Aman Nagpal: couple of other line items. I’ll take another look at this also.
57 00:06:09.370 ⇒ 00:06:37.509 Robert Tseng: Yeah. So like, there’s the other part that’s like, okay, even as we’re doing it, we cleaned up some tech debt along the way there. There are multiple platform fee definitions, because we have like iterated through it. We’ve consolidated. There’s just one platform fee. There’s 1 processing fee so that that standardizes the cogs in a cleaner way. If we were to do the handoff and teach you and the analysts to be able to pull from this model as well. So like that to me, is like part of part of that work as well.
58 00:06:38.240 ⇒ 00:06:45.229 Robert Tseng: yeah. And then making sure that the definitions are very clear. When we talk about sales revenue, we’re talking about
59 00:06:45.630 ⇒ 00:06:50.775 Robert Tseng: price minus or price, plus shipping minus discount, excluding tax
60 00:06:51.530 ⇒ 00:06:57.329 Robert Tseng: and margin include excludes tax like, you know stuff like that like that’s that’s the you know that that’s
61 00:06:57.460 ⇒ 00:06:59.419 Robert Tseng: but baked into it. So
62 00:06:59.970 ⇒ 00:07:02.970 Aman Nagpal: Got it. The revenue, you said is
63 00:07:04.014 ⇒ 00:07:07.809 Aman Nagpal: like we’re we’re taking discounts into account right
64 00:07:07.810 ⇒ 00:07:14.569 Robert Tseng: Yeah, yeah, it’s just pre- pre tax. So like, yeah, I guess there’s not including tax and revenue
65 00:07:14.570 ⇒ 00:07:16.420 Aman Nagpal: No sounds good.
66 00:07:17.180 ⇒ 00:07:17.800 Robert Tseng: Yeah.
67 00:07:18.070 ⇒ 00:07:23.170 Aman Nagpal: The subscribe and save I actually spoke to Justin about. So I got more clarity and context on what
68 00:07:23.310 ⇒ 00:07:35.090 Aman Nagpal: he was looking for, and let me know. So I guess what you were guys were saying was, subscribe and save data is not available. When you say that data is not available. What do you mean? Specifically.
69 00:07:35.620 ⇒ 00:07:43.369 Robert Tseng: They don’t label, subscribe and safe orders. In the in the data that’s coming in through Amazon. So
70 00:07:44.510 ⇒ 00:07:52.799 Robert Tseng: yeah, unless we have a different way of using existing fields to to label them like we just, we don’t have a filter like that. Yeah.
71 00:07:53.110 ⇒ 00:08:00.999 Aman Nagpal: So I guess they’re not labeling it. But I don’t know if you can pull up the data. The data tables quickly, but the there is a discount that is applied
72 00:08:01.900 ⇒ 00:08:09.700 Aman Nagpal: to every subscribe and save discount. That is an automatic subscribe and save discount. Can we see that discount in the tables
73 00:08:10.110 ⇒ 00:08:10.959 Robert Tseng: Let’s see.
74 00:08:11.240 ⇒ 00:08:14.660 Aman Nagpal: Because if that’s the case, that should, I guess, be the way we look at it.
75 00:08:15.570 ⇒ 00:08:35.070 Aman Nagpal: And there’s 2 discounts. I just want to make sure there’s the regular subscribe and save discount where you get automatic X percent by being a subscriber on Amazon. There’s also a separate coupon that we were providing to some subscribe and save people. But we want to keep that separate right now. It’s just do you get that automatic subscribe and save discount. That means you’re a subscriber
76 00:08:36.659 ⇒ 00:08:46.769 Robert Tseng: Okay? So I mean, obviously, there is a discount here. But so that’s just a number. Unless there’s a discount label. Yeah, maybe go back one more into the ros. But
77 00:08:50.959 ⇒ 00:08:54.919 Robert Tseng: yeah, no way I have. I see a discount number, but I’m not seeing
78 00:08:57.389 ⇒ 00:08:59.049 Robert Tseng: Let’s go.
79 00:08:59.479 ⇒ 00:09:00.199 Robert Tseng: There was
80 00:09:15.799 ⇒ 00:09:16.364 Robert Tseng: no
81 00:09:18.529 ⇒ 00:09:25.419 Robert Tseng: well like to me the best scenario would be if, like a subscribe and save customer, had, like an additional like.
82 00:09:25.719 ⇒ 00:09:35.909 Robert Tseng: you know, string or something attached to their some id or skew, or whatever, and then that’d be an easy way to filter. I mean, I feel like I haven’t seen anything before.
83 00:09:39.550 ⇒ 00:09:43.859 Aman Nagpal: So let’s do this. If you guys can just make a note of this, let’s send Blake.
84 00:09:44.270 ⇒ 00:09:47.240 Aman Nagpal: and let’s send Robbie a message. Robbie, shake
85 00:09:47.470 ⇒ 00:09:52.699 Aman Nagpal: that to give us a couple of examples to you guys of subscribing
86 00:09:52.700 ⇒ 00:09:53.130 Robert Tseng: Yes.
87 00:09:53.130 ⇒ 00:09:55.539 Aman Nagpal: Along with their data. Let’s see.
88 00:09:55.540 ⇒ 00:09:56.039 Robert Tseng: What I wanted.
89 00:09:56.040 ⇒ 00:10:04.549 Aman Nagpal: Here. Yeah, let’s do that. So let’s send a message to Robbie. I’ll I’ll tag him again in the group, and if you guys can. Just
90 00:10:04.800 ⇒ 00:10:06.540 Aman Nagpal: well, I’ll just send it now make it easier
91 00:10:06.540 ⇒ 00:10:07.150 Robert Tseng: Okay.
92 00:10:10.750 ⇒ 00:10:12.020 Aman Nagpal: On the
93 00:10:46.230 ⇒ 00:10:49.110 Aman Nagpal: okay, I will tag you guys.
94 00:10:49.610 ⇒ 00:10:53.730 Aman Nagpal: No, I don’t think you’re in the other Javi Channel.
95 00:10:54.550 ⇒ 00:10:55.270 Aman Nagpal: Robert
96 00:10:56.990 ⇒ 00:11:00.920 Robert Tseng: I might be, I think, my ring for email probably
97 00:11:00.920 ⇒ 00:11:04.529 Aman Nagpal: Your old account. Yeah. Should I tag your old account, I’ll just tag both
98 00:11:04.770 ⇒ 00:11:05.410 Robert Tseng: Okay.
99 00:11:07.590 ⇒ 00:11:11.170 Aman Nagpal: And then, oh, gosh! It’s in there. Okay, I just sent a message.
100 00:11:11.590 ⇒ 00:11:13.489 Aman Nagpal: so we’ll figure out what to do. There
101 00:11:18.290 ⇒ 00:11:27.550 Robert Tseng: Yeah, I mean, this one is not really anything that new. This is, I was supposed to send this on Friday, but I took a extra day to review it. But I mean, this is just
102 00:11:28.090 ⇒ 00:11:34.719 Robert Tseng: in and out, like the canceled Amazon and cancellation analysis. There’s nothing more additional to do here. It’s just proofreading setting it out
103 00:11:37.240 ⇒ 00:11:47.292 Robert Tseng: Yeah. And then I think, you know, if I can just like, take a step back as well like, you know. So I just think about. Okay, we have like these things in flight for the next few weeks.
104 00:11:47.910 ⇒ 00:11:57.732 Robert Tseng: like, yeah. Clay view attentive like. And then north north theme like these are net, new slash, existing data sources that we’re doing some reporting on.
105 00:12:01.290 ⇒ 00:12:05.639 Robert Tseng: yeah, I mean, I I want to see like more.
106 00:12:06.610 ⇒ 00:12:07.700 Robert Tseng: I mean, it’s like
107 00:12:08.940 ⇒ 00:12:13.024 Robert Tseng: I think my I had like a I was reflecting on this over the weekend and
108 00:12:14.450 ⇒ 00:12:23.430 Robert Tseng: like for us to do like a custom modeling, and then build out like we did for Oquendo on like Flavio, and attentive like I. I feel like it’s not
109 00:12:23.690 ⇒ 00:12:48.490 Robert Tseng: not necessary. I can. I feel like we could save us a lot of time. If we just didn’t do that. We just use something out of the box where I mean, they’re they’re just. I’m just saying that there are. I’ve recommended a tool before, like there’s like a there are platforms that work well where they just hook up easily to marketing and Cx data sources. And they have a very simple Ui, where you can just report on like ticket counts and Asian performance and stuff like that
110 00:12:48.510 ⇒ 00:12:54.949 Robert Tseng: to me. That’s like what I would love. I would prefer to leverage stuff like that for non core business reporting
111 00:12:55.000 ⇒ 00:13:00.020 Robert Tseng: until we have, like a very clear like, loop back to.
112 00:13:00.220 ⇒ 00:13:21.989 Robert Tseng: We want to see, like how it’s impact on like core revenue profit margins, or whatever like it, within the core models that we do maintain. What I don’t want to see is like we end up doing all this like custom, modeling for these one off like data sources. And yes, that’s like, I know, that’s what you’ve given us. But like I thinking about like.
113 00:13:22.790 ⇒ 00:13:26.190 Robert Tseng: what’s the most impactful work that we can be doing like.
114 00:13:26.760 ⇒ 00:13:28.300 Robert Tseng: I don’t want to say like.
115 00:13:28.610 ⇒ 00:13:37.379 Robert Tseng: because if we do like, it is, you know, 10 plus hours of time we end up doing that for a single single source. But like we don’t. We don’t need to like we. We could just
116 00:13:37.730 ⇒ 00:13:40.099 Robert Tseng: plug it in. And if you wanted to just
117 00:13:40.320 ⇒ 00:13:45.159 Robert Tseng: have like a tool that plugs into your to your Klavio data
118 00:13:45.610 ⇒ 00:13:57.419 Robert Tseng: that you can ask questions of, have simple reporting on like, that’s very easy to spin up. It’s like just to plug it into like a different, like a like a park. We can plug it into a platform that does that.
119 00:13:58.290 ⇒ 00:14:01.010 Robert Tseng: and and then and then, until like the
120 00:14:01.720 ⇒ 00:14:12.899 Robert Tseng: requests, needs like become more complex than we actually consider. Like, okay, how do we actually bring it to the model like I want to? I guess I know that was like a mouthful. But what I’m trying to say is like
121 00:14:13.120 ⇒ 00:14:29.699 Robert Tseng: moving away from the okay. We’ll just run everything through this modeling process. First, st it introduces like complexity and time in a way that doesn’t actually feel like it’s getting us to like a higher, higher value that I want to see from the work that we’re doing
122 00:14:30.300 ⇒ 00:14:31.119 Aman Nagpal: Yeah, I, I,
123 00:14:31.630 ⇒ 00:14:32.140 Robert Tseng: Yes.
124 00:14:32.140 ⇒ 00:14:35.820 Aman Nagpal: The cost match the benefit that we’re getting out of it right versus. If there’s an
125 00:14:35.820 ⇒ 00:14:36.340 Robert Tseng: Yeah.
126 00:14:36.340 ⇒ 00:14:44.750 Aman Nagpal: Well, I wouldn’t mind that, but is, does it connect to the actual recharge data that we have? That’s the biggest concern. And if it could do that. Then, yeah.
127 00:14:45.750 ⇒ 00:14:53.019 Robert Tseng: Yeah, so recharge data. I consider that part or data, because there’s a clear like Reten, you know that that to me makes sense like
128 00:14:53.230 ⇒ 00:15:02.989 Robert Tseng: retention data that’s directly connected to revenue. We have a model internally. Now, that is, that joins like the core
129 00:15:03.020 ⇒ 00:15:29.099 Robert Tseng: orders, transactions that we have modeled to to recharge data. So that’s there’s a clear use case to me to have that within the custom data models. But something like okendo, I mean, obviously, kind size 2020. But as I’m reflecting on that like, we didn’t need to spend that much time on doing that one, I want to be more critical of like each source that we do do for the Klavio one. Maybe this makes sense because it’s actually like there’s a clear through through line from
130 00:15:29.896 ⇒ 00:15:35.840 Robert Tseng: Klavio like triggers that you want to fire connected to like the recharge data.
131 00:15:36.600 ⇒ 00:15:48.639 Robert Tseng: So that to me maybe makes sense. But maybe attentive, like, I mean this. These are just estimates. I was just talking to a cost like maybe we. We don’t need to do that for attentive yet, because it’s not super clear yet, you just want to know, like
132 00:15:49.220 ⇒ 00:16:02.870 Robert Tseng: like, you know, the requirements weren’t super clear yet. There, and it was just okay. Let’s just see, like some attentive performance metrics. Then it’s like, okay, maybe that’s not really in a place for us to really like model and run through our whole process yet.
133 00:16:03.438 ⇒ 00:16:05.760 Robert Tseng: Anyway. So that’s that’s what I that’s my thought.
134 00:16:06.410 ⇒ 00:16:12.470 Aman Nagpal: So Okendo is kind of separate from everything else. Yeah, that that checks out for these.
135 00:16:13.480 ⇒ 00:16:19.750 Aman Nagpal: like you said with klaviyo, and I think attentive to it makes a little bit more sense because we do need that shopify order data
136 00:16:19.900 ⇒ 00:16:28.999 Aman Nagpal: and or recharge data. So, for example, the klaviyo, one is upcoming order to subscription cancelled on the recharge data side or
137 00:16:29.748 ⇒ 00:16:35.610 Aman Nagpal: you know, Klaviyo could be received a certain flow. Email. Did they
138 00:16:35.710 ⇒ 00:16:53.220 Aman Nagpal: do a renewal with shopify data within 5 days, right? So that it sounds like we do need to do it. And same thing for attending, did they? And they enter this flow? Did they make an order on 1st day, second day, 3rd day, etc, or 5 plus, for example, I’m just throwing out random, you know, charts that we might need
139 00:16:54.037 ⇒ 00:16:58.209 Aman Nagpal: and that’s using shopify data. Right? So I guess those
140 00:16:58.370 ⇒ 00:17:00.620 Aman Nagpal: it probably will need it.
141 00:17:01.760 ⇒ 00:17:04.119 Aman Nagpal: okendo, I get what you’re saying.
142 00:17:04.640 ⇒ 00:17:12.109 Aman Nagpal: Is there what I mean? Would there have been another tool we could use to look at Oquendo data better than what Okendo offers in analytics
143 00:17:12.780 ⇒ 00:17:34.170 Robert Tseng: Yeah. So I mean, I think I’ve I’ve shown you like corral is like one of our partners. So like you can come here and you can type in like they have 500 plus connectors set up. This is basically they’ve they’ve done this enough where, like they have, they use 5 underlying. But like the way that their pricing works a bit simpler, it’s it’s priced by connector.
144 00:17:35.680 ⇒ 00:17:51.789 Robert Tseng: They have 500 plus integrations with tools. You. You plug it in, and then you get to just see like reporting on their platform. And it’s like a very simple Ui. You kind of chat with it like Chat Gpt, and it arranges some basic tiles. So I don’t know if it was on here. But
145 00:17:52.067 ⇒ 00:18:09.519 Robert Tseng: like, you know, Klavio is here attentive is here like any ad source, like all the marketing stuff that you would think of. So if, like one day, you know, yeah, we have this North theme connector. But what we found with other clients is like, hey, north theme is like kind of a stopgap, because it kind of is just like an
146 00:18:09.960 ⇒ 00:18:36.249 Robert Tseng: aggregator of, you know, they’re doing the same thing under the hood. They’re just using 5 fan to connect to these ad platforms. So if their pipeline breaks, it impacts your reporting. It impacts your your spend performance or whatever. And then, you know, at a certain point, like another client we work with was like, Yeah, let’s scrap north theme. We want to do this natively for ourselves, because we we don’t wanna be at the mercy of like north beams, like systems like being up.
147 00:18:36.280 ⇒ 00:18:46.930 Robert Tseng: And so with them, it was like, Okay, well, now, they want to, you know, plug in directly to tick, tock to Google ads to like Facebook ads. And all these things. And it was like.
148 00:18:47.190 ⇒ 00:18:56.460 Robert Tseng: we just, we’re like, well, we’re not gonna build 5 connectors and do all this maintenance. We just plugged it in here, and this was enough for their performance marketing team to run off of
149 00:18:56.904 ⇒ 00:18:59.500 Robert Tseng: so that’s like an example of like where
150 00:18:59.690 ⇒ 00:19:08.340 Robert Tseng: I chose to not push for. Not have our team build 5 more connectors and and do all the modeling for it, and we just plugged it in here
151 00:19:09.210 ⇒ 00:19:28.909 Aman Nagpal: Yeah, I think certain, you know, sources. Something like this will make more sense. So going forward, I think, yeah, you’re right. We can be more critical on what we do pull into here. I think clavier attentive. We do need it, you know, because we need that data and recharge. But for future ones definitely. Let’s think about it some more and see if we actually need it.
152 00:19:29.851 ⇒ 00:19:31.539 Aman Nagpal: And I’ll check out corral also.
153 00:19:32.140 ⇒ 00:19:36.080 Robert Tseng: Okay, sorry. Well, I mean, so yeah, go ahead.
154 00:19:36.080 ⇒ 00:19:39.050 Aman Nagpal: For attentive. I mean, it’s
155 00:19:39.540 ⇒ 00:19:48.889 Aman Nagpal: we don’t really have a ton of reports for Klaviyo either. I think we have the upcoming, and that so is the modeling going to take the bulk of the 10 h
156 00:19:49.450 ⇒ 00:19:56.399 Robert Tseng: Yeah, I feel like, this is probably where I mean to me, this, I bucket these is like, Okay, this is
157 00:19:57.130 ⇒ 00:20:03.960 Robert Tseng: mo, modeling and analysis work for retentions like, kind of in the in the world of retention. And
158 00:20:04.580 ⇒ 00:20:32.650 Robert Tseng: yeah, because this is really net new. And there’s not like, we’re not copy pasting from amplitude or whatever. So it’s making sure that we define what are the what’s the end state of the like. We’re actually doing a full like working backwards, or we’re trying to start with the end in mind. What is a set of reports that we need to see for Klavio and attentive together, or whatever. And then we kind of work backwards on like, okay, these are the models that we need to support it. So that sounds to me like this is the biggest priority, or like, yeah, the the more ambitious chunk of like
159 00:20:32.910 ⇒ 00:20:39.010 Robert Tseng: net new work that we can really get done in the next couple of weeks. Yeah.
160 00:20:39.530 ⇒ 00:20:45.500 Aman Nagpal: That that works. I think we can take out recharge from attentive, because that’ll probably be shopify. Only that last.
161 00:20:46.680 ⇒ 00:20:50.870 Aman Nagpal: But yeah, I think I think that’s fine, and you know we if it.
162 00:20:50.980 ⇒ 00:20:59.080 Aman Nagpal: we’ll figure out what to do in terms of additional reports there. But I think that’s good I know we don’t have a ton of time left, so we can run through everything else really quick.
163 00:20:59.410 ⇒ 00:21:04.669 Robert Tseng: Yeah. Amazon script. There’s nothing really else here. I think we already ready to hand that off. We sent
164 00:21:04.670 ⇒ 00:21:10.913 Aman Nagpal: 3.rd I don’t know if we got a response or not, but the latest, the latest one that
165 00:21:11.940 ⇒ 00:21:13.290 Aman Nagpal: Robbie sent.
166 00:21:14.290 ⇒ 00:21:26.989 Aman Nagpal: That was Friday, so that one he was just asking if we could do it just by address. So it’s a 3rd type of sheet now he’s bringing up, he said. These would be the 3 assuming you can do the 3rd type. Were you guys able to figure out if we can
167 00:21:27.500 ⇒ 00:21:39.299 Robert Tseng: Yeah, we can. But like that, one is not as simple as just. It’s a new format. Right? It’s necessary data point. Different, like the script has. It’s not just like a 1 or 2 line change in the script, so it’s kind of like, well, there’s
168 00:21:39.920 ⇒ 00:21:49.650 Robert Tseng: we can either send it to you as is. And it works for the 2 types of data sources you’ve given us before. Or we need to go back and like rework it so they can actually ingest a 3rd one.
169 00:21:49.800 ⇒ 00:21:55.870 Robert Tseng: And that’s kind of take some time to do that to be testing. And then all that. So
170 00:21:56.070 ⇒ 00:22:03.059 Aman Nagpal: If you can do it. Yeah, let’s go ahead and add that 3rd option, and then once you wrap that up, you can send us the latest script.
171 00:22:03.790 ⇒ 00:22:09.970 Aman Nagpal: And then we can build a ui on our side for that. But yeah, if you can add that 3rd spreadsheet format.
172 00:22:10.786 ⇒ 00:22:11.850 Aman Nagpal: Let’s let’s do that.
173 00:22:12.380 ⇒ 00:22:12.910 Robert Tseng: Okay?
174 00:22:14.410 ⇒ 00:22:17.599 Robert Tseng: Well, yeah, I mean, that’s already there. So we’re you have to add that.
175 00:22:19.690 ⇒ 00:22:22.124 Robert Tseng: Yeah, on the north beam side.
176 00:22:23.100 ⇒ 00:22:31.580 Robert Tseng: I think to emulate the one from amplitude. What is it currently done from the Northeas side, and why the modeling work is still there is like.
177 00:22:32.020 ⇒ 00:22:39.320 Robert Tseng: well, now we have to kind of translate it because you if you basically want like product level, ad spend like metrics
178 00:22:39.830 ⇒ 00:22:40.580 Robert Tseng: and
179 00:22:40.580 ⇒ 00:22:51.829 Aman Nagpal: I don’t need you guys to do the like this to me is I can’t imagine this taking more than 2, 3 h right? So there’s a couple of different charts. All we need to do is ingest, the revenue per spend and
180 00:22:52.380 ⇒ 00:22:54.749 Aman Nagpal: or the revenue spend, and
181 00:22:55.090 ⇒ 00:23:15.070 Aman Nagpal: the grouping is already done on a product and country level by north beam. That’s how we get it into amplitude. North being already takes care of the grouping. So when it sends the data. It’s already broken out by product by, etc. So all we need to do is basically divide that over the number of orders from shopify. So to me this is a super quick
182 00:23:15.270 ⇒ 00:23:18.289 Aman Nagpal: projects. I don’t know how much more modeling that would need if
183 00:23:18.460 ⇒ 00:23:22.019 Aman Nagpal: Northeas already sent it that way, we should have had it modeled already
184 00:23:22.500 ⇒ 00:23:51.139 Robert Tseng: So the mark. The north beam, like product data, is not not the same as the product name named. There’s like another map like it’s north beam product names. And then whatever we have in the current system. And so it’s probably not a 1 to one there. There’s gonna be like some reconciliation work we need to do there. But if you want to, just but yeah, if you if you keep them disconnected and you just look at the north beam stuff, and I guess that’s that’s pretty. That’s pretty easy
185 00:23:51.410 ⇒ 00:24:07.420 Aman Nagpal: Yeah. So the the product quote unquote product level on north theme is, if we go to Facebook ad account, all the money we spent for concentrate ads. Versus all the money we spent for protein, coffee or protein coffee ads. Right? Then I would the concentrate one, and.
186 00:24:07.780 ⇒ 00:24:13.869 Aman Nagpal: you know, divided over the all of the orders that we have already decided our concentrate orders.
187 00:24:15.260 ⇒ 00:24:17.319 Aman Nagpal: On this side. So I think
188 00:24:17.790 ⇒ 00:24:20.210 Aman Nagpal: I guess that would be considered separate right
189 00:24:20.940 ⇒ 00:24:26.174 Robert Tseng: Yeah. So I mean, if you’re talking about product level is just protein concentrate. Sure, that’s fine. I think.
190 00:24:26.450 ⇒ 00:24:27.010 Aman Nagpal: Yeah.
191 00:24:27.010 ⇒ 00:24:30.709 Robert Tseng: A product level. I’m I’m talking about what we have on in like
192 00:24:30.840 ⇒ 00:24:34.209 Robert Tseng: back order lines model where we have it broken out by.
193 00:24:35.290 ⇒ 00:24:39.160 Robert Tseng: I don’t know if you just look at the
194 00:24:39.160 ⇒ 00:24:40.780 Aman Nagpal: I don’t think we would need to do it on northeast
195 00:24:40.780 ⇒ 00:24:41.809 Robert Tseng: All that stuff. Yeah.
196 00:24:41.920 ⇒ 00:24:47.409 Aman Nagpal: Yeah, this, this would come into handy, for which orders are concentrate orders. But we
197 00:24:47.860 ⇒ 00:24:56.209 Aman Nagpal: do the same thing on North Beam Side, north beam because it’s it’s not on the actual order. It’s on the ad spend. So the ad spend concentrate ad spend product
198 00:24:57.030 ⇒ 00:24:58.880 Aman Nagpal: team. So it’ll be separate. Yeah.
199 00:24:59.950 ⇒ 00:25:04.970 Robert Tseng: Okay, yeah. I mean, if that’s if that’s how you want to do it, then, yeah, I think that’s pretty. That’s more straightforward
200 00:25:04.970 ⇒ 00:25:06.590 Aman Nagpal: Cool. Let’s do that.
201 00:25:07.527 ⇒ 00:25:13.970 Aman Nagpal: And then Amazon script. We decide. We talked about Tiktok shop. Is that what’s the status on that
202 00:25:15.514 ⇒ 00:25:22.400 Robert Tseng: Yeah, no. I mean we, the vendor we work with polytomic. They they said that it’s gonna take a month for them to get it in.
203 00:25:23.400 ⇒ 00:25:33.179 Robert Tseng: But I also feel like, if this is a critical date, we should just build it like we should just build a connector from scratch. So, but that that would be more labor intensive. So
204 00:25:33.430 ⇒ 00:25:41.479 Robert Tseng: I I guess it’s just a matter of like, are you willing to wait like a month and like, wait for the vendor to pull it in? Or should we actually go and like
205 00:25:41.650 ⇒ 00:26:00.599 Robert Tseng: like to me, this is a harder engineering problem that’s worth us, our attention, solving rather than you know, building it out for all these other ones. But because Tiktok Shop is just not readily available to most Etl providers at this point. So like there, there! I could see a case for us to go and and actually build it ourselves.
206 00:26:00.780 ⇒ 00:26:02.270 Aman Nagpal: And portable couldn’t build it out
207 00:26:03.117 ⇒ 00:26:09.809 Robert Tseng: Yeah. Portable is gonna take longer. They said it would take months. It’s not in their highest priority, based on all the requests that they’re getting
208 00:26:10.110 ⇒ 00:26:13.079 Aman Nagpal: How much, how many hours do you think it would take for us to build it out ourselves?
209 00:26:14.251 ⇒ 00:26:22.089 Robert Tseng: I think we would need to do like a more formal swag on that. And I don’t really have a I mean, I feel like 10 is underestimating it so
210 00:26:22.550 ⇒ 00:26:27.670 Aman Nagpal: Okay, let me think about that, and then we can come back to that one port over to connectors.
211 00:26:27.980 ⇒ 00:26:32.099 Aman Nagpal: I I thought this was just a matter of. There’s 3 sheets.
212 00:26:32.260 ⇒ 00:26:38.240 Aman Nagpal: Copy all the data from once, you know the extra sheets, the to the main sheet.
213 00:26:38.420 ⇒ 00:26:45.980 Aman Nagpal: and then, you know, rerouted in 5 trans. I guess 2 is fine, but that’s we’re on, you know, in progress, or that’s done
214 00:26:46.520 ⇒ 00:26:49.650 Robert Tseng: Yeah, I mean, I think we’ve already done that consolidation
215 00:26:49.780 ⇒ 00:26:54.256 Aman Nagpal: And the Amazon one again to avoid another spreadsheet. We can just add it to the existing
216 00:26:54.520 ⇒ 00:26:55.210 Robert Tseng: Yeah.
217 00:26:55.210 ⇒ 00:26:57.339 Aman Nagpal: Okay, okay, so I’ll have them do that.
218 00:26:59.910 ⇒ 00:27:05.529 Aman Nagpal: Horrible sync frequency. So I didn’t understand this pricing. We’re paying for X
219 00:27:06.010 ⇒ 00:27:08.440 Aman Nagpal: up to x syncs per day. Already right
220 00:27:09.620 ⇒ 00:27:32.659 Robert Tseng: Yeah, well, I guess it’s like, how many much, how much more syncing you want per source like, they’re not like telling us. There’s an incremental cost to each additional sync like we have to tell them. Well, hey, look, this source needs to be synced hourly, and it’s that’s not included right now, like. And then, then we can actually get it, get a number. But
221 00:27:32.660 ⇒ 00:27:33.320 Aman Nagpal: Yeah, thank you for next.
222 00:27:33.880 ⇒ 00:27:37.479 Aman Nagpal: How much our bill might go up. If we increase the sink
223 00:27:37.810 ⇒ 00:27:48.220 Robert Tseng: It will not go up by much. I think this was just like it’s it’s I mean, the the Snowflake cause is the most stable like that’s probably less the least volatile
224 00:27:50.140 ⇒ 00:27:51.809 Aman Nagpal: It. It definitely costs us sweat.
225 00:27:52.680 ⇒ 00:27:54.790 Robert Tseng: Like it. It will. It will cost us like
226 00:27:55.340 ⇒ 00:28:17.759 Robert Tseng: like up like I don’t know. Like $1010 a day is kind of like what it seems like to to increase it more like to to do, because even if we do free, more frequent syncs, it’s it’s we’re getting charged mostly for the incremental like add ons, and it’s like whether you do 10 syncs in a day or one sync in a day like it from a snowflake perspective is, the cost is not negligible.
227 00:28:18.230 ⇒ 00:28:31.320 Aman Nagpal: Right? Okay? Yeah, that I mean, if that’s the case. And you know, Portal, we already have included to increase the syncs. We may as well bump it up from once a day to, you know, hourly, whatever it is, unless you think there’s any downsides to this
228 00:28:32.825 ⇒ 00:28:41.520 Robert Tseng: I mean, the downside is always just like it it it. It depends on the on the reporting use case. So
229 00:28:41.660 ⇒ 00:28:55.059 Robert Tseng: if somebody’s only checking the data once a day. Then you don’t need to do hourly, you know. Unlike, it kind of depends on if they’re looking at a report multiple times a day. Then maybe that the sync for that that power, that report, need to be adjusted to hourly
230 00:28:55.675 ⇒ 00:28:59.859 Robert Tseng: but generally like, I don’t think I would recommend, like
231 00:29:00.410 ⇒ 00:29:04.300 Robert Tseng: maxing out the number of refreshes, because it’s just
232 00:29:04.900 ⇒ 00:29:09.420 Robert Tseng: like it’s just noise, I guess. And that’s
233 00:29:10.040 ⇒ 00:29:12.699 Robert Tseng: that’s that’s my that’s my take on it.
234 00:29:12.700 ⇒ 00:29:24.999 Aman Nagpal: What do you mean? Noise in that way? Because I figure, you know, if we start, stop looking at amplitude, for, let’s say orders and stuff like that, like even North North Beam or Cac. And you know, we want to look throughout the day multiple times a day.
235 00:29:25.510 ⇒ 00:29:31.180 Aman Nagpal: I you know, I would think hourly would be better. But if you know, is there any other downside like, what do you mean by noise?
236 00:29:32.810 ⇒ 00:29:34.710 Robert Tseng: So like
237 00:29:38.070 ⇒ 00:29:52.136 Robert Tseng: if you were to be looking, I mean, I don’t think I don’t think you’re look. You’re adjusting tax in the middle of the day, or anything like for orders. Let’s say you’re doing like you have an order report. That’s
238 00:29:53.020 ⇒ 00:30:04.889 Robert Tseng: I mean, I don’t. I don’t think we have anything at at that point yet where it’s looking at it on an hourly basis. But if I were to think about like an order Sla performance report where you’re looking at
239 00:30:06.080 ⇒ 00:30:12.619 Robert Tseng: orders like moving through. Kind of your your logistics. Workflow.
240 00:30:13.232 ⇒ 00:30:23.579 Robert Tseng: If you refresh on an hourly basis like you will get to see like which ones are pending orders which ones are being worked on. And see, it’s close to like a real time like update on where things are at
241 00:30:24.447 ⇒ 00:30:33.619 Robert Tseng: but if think, if the order statuses are not changing, frequent that frequently, hour to hour like it’s it’s you. You may set alerts, but it’s just not.
242 00:30:33.800 ⇒ 00:30:46.100 Robert Tseng: It’s it’s the idea of like sometimes when you’re hitting refresh on numbers like too frequently, it doesn’t actually like, give you an accurate perspective. What’s going on. You just become so worried about like
243 00:30:46.250 ⇒ 00:30:50.009 Robert Tseng: some one number that’s like changing hour to hour. But
244 00:30:50.170 ⇒ 00:30:59.860 Robert Tseng: you know, from from like a whole systems perspective like you really only expect things to move like like once a day or twice a day. Kind of kind of situation
245 00:31:00.100 ⇒ 00:31:11.890 Aman Nagpal: Are you talking about updating existing data? Or you know, I mean, for example, if you, if we want to see new orders as they come in every hour, whatever highest frequency we can do, I mean for that. Specifically, let’s say
246 00:31:12.980 ⇒ 00:31:37.319 Robert Tseng: Okay, yeah. I mean, for if for new orders, yeah, I guess you could, you could do that. I think. Generally, I think a daily snapshot is all the executives that we’ve worked with generally wants. But you know, if if they want a if your team wants an hourly snapshot, there’s no, there’s no problem with it. I think it’s like, yes, it’s not free, like there will be a cost. But it’s not. It’s probably insignificant at this point.
247 00:31:37.340 ⇒ 00:31:42.890 Aman Nagpal: Yeah, they all, you know. It’s a negligible cost increase on Snowflake and nothing on portal. Right? Nothing else.
248 00:31:43.210 ⇒ 00:31:43.870 Robert Tseng: Yeah.
249 00:31:44.120 ⇒ 00:31:51.269 Aman Nagpal: Okay, let me think about it. But I yeah, I’ll I’ll think about what you said, and we’ll decide if we want to do that or not.
250 00:31:52.250 ⇒ 00:31:52.880 Robert Tseng: Okay.
251 00:31:54.841 ⇒ 00:31:58.239 Aman Nagpal: What else? We got? Portable sync, north beam. Yeah, we talked about that
252 00:31:58.540 ⇒ 00:32:07.309 Aman Nagpal: training. Yeah, can we get that started tomorrow? I was thinking. I know you guys said with Annie, does she? Does she know the whole build out? Start beginning to end.
253 00:32:08.535 ⇒ 00:32:14.044 Robert Tseng: I mean, she’s more on the analyst side. So she’s been working on like some of the Meta based reports.
254 00:32:14.460 ⇒ 00:32:17.880 Robert Tseng: but yeah. So I think she would be able to help on that side.
255 00:32:19.550 ⇒ 00:32:28.549 Robert Tseng: It’s kind of like how much ground you want us to cover, which up which side for for your new analysts hire, you would just need to start- start from start from there. We don’t need to like
256 00:32:29.180 ⇒ 00:32:35.860 Robert Tseng: go from portable into Snowflake and and like Dbt, and all of that
257 00:32:35.860 ⇒ 00:32:44.940 Aman Nagpal: I actually do want to run through all that, because I’ll be on the calls as well, and then our probably our dev as well, who’s not really gonna touch this much? But again, more eyes on it the better.
258 00:32:45.657 ⇒ 00:33:00.369 Aman Nagpal: But I yeah, I want to start from scratch right from engineering to the now analytics side is, you know, this is portable. This is syncing our data. It’s all going to this snowflake account. And then we can run through it to make sure that, hey? We have ownership portable of snowflake, etc.
259 00:33:00.870 ⇒ 00:33:11.369 Aman Nagpal: Dbt, how that’s set up, and what’s actually happening? All of our tables, you know why we modeled it. Certain the the way we did. I’d like to run through all of that. So
260 00:33:11.680 ⇒ 00:33:12.220 Robert Tseng: Okay.
261 00:33:12.950 ⇒ 00:33:21.330 Aakash Tandel: So I I would say, Annie’s probably not gonna be the right person to only have on that call Robert. And also that sounds like, it’s gonna be a wish
262 00:33:23.350 ⇒ 00:33:28.769 Aakash Tandel: And then, yeah, we’re gonna need to. I have a feeling. It’s gonna take more than 10 h. But we can definitely walk through that
263 00:33:29.494 ⇒ 00:33:30.589 Aakash Tandel: as it comes along
264 00:33:30.590 ⇒ 00:33:40.690 Robert Tseng: Yeah. And we’ll let’s let’s talk through how many calls we want to break it up into definitely a couple of them. I could see either me or Tom doing, and we should just record it. It’d be good just to
265 00:33:40.800 ⇒ 00:33:49.429 Robert Tseng: have that and as like a resource that we can share internally, and also like, if they just had send it to a month’s team. So
266 00:33:49.990 ⇒ 00:33:52.320 Robert Tseng: yeah, like I, I could see
267 00:33:52.470 ⇒ 00:33:55.990 Robert Tseng: this being more useful than just like doing live trainings
268 00:33:56.300 ⇒ 00:34:04.806 Aman Nagpal: That would be super helpful as well. Yeah, I think. And then, whatever live training we do need, you know, if it turns out. We need more hours, then, you know. So be it.
269 00:34:05.410 ⇒ 00:34:09.989 Aman Nagpal: We’ll we’ll figure that out. But yeah, I think that the loans will be great, and
270 00:34:10.620 ⇒ 00:34:13.682 Aman Nagpal: I don’t. I mean it’s Monday now.
271 00:34:14.850 ⇒ 00:34:17.269 Aman Nagpal: maybe we do still have a run through.
272 00:34:17.449 ⇒ 00:34:25.949 Aman Nagpal: I don’t know if it’s with Annie or with whoever else on at least one side of it starting tomorrow, if that’s possible. Maybe we do start off with 2 calls a week
273 00:34:26.320 ⇒ 00:34:29.460 Aman Nagpal: Tuesday, Thursday, and if we need to adjust. We can adjust.
274 00:34:30.420 ⇒ 00:34:35.209 Robert Tseng: Yeah, can you send us like availability? For when you want us to schedule, and so we can try to adjust
275 00:34:36.290 ⇒ 00:34:36.920 Aakash Tandel: I think you have
276 00:34:36.929 ⇒ 00:34:38.469 Robert Tseng: People available for them. Yeah.
277 00:34:38.469 ⇒ 00:34:40.509 Aakash Tandel: Yeah, 1230 to 1, 30,
278 00:34:40.699 ⇒ 00:34:43.089 Robert Tseng: Oh, that’s that’s what we said for Tuesday, Thursday.
279 00:34:43.949 ⇒ 00:34:46.059 Aakash Tandel: I think that’s what you proposed him on right
280 00:34:46.060 ⇒ 00:34:46.760 Robert Tseng: Oh, okay.
281 00:34:47.551 ⇒ 00:34:50.499 Aman Nagpal: I think so. Anyway, we can.
282 00:34:50.830 ⇒ 00:34:56.590 Aman Nagpal: I have a an appointment on tomorrow? And on a meeting on Thursday. Can we do?
283 00:34:59.730 ⇒ 00:35:00.799 Aman Nagpal: Let me see.
284 00:35:01.660 ⇒ 00:35:04.920 Aman Nagpal: So our data analysts will be available
285 00:35:13.810 ⇒ 00:35:15.770 Aman Nagpal: for now, can we just do
286 00:35:15.960 ⇒ 00:35:18.550 Aman Nagpal: Wednesday, Friday, I guess at noon
287 00:35:23.272 ⇒ 00:35:29.663 Aakash Tandel: Friday at noon. We have a company, all hands, so that’s won’t work.
288 00:35:30.320 ⇒ 00:35:36.640 Aakash Tandel: we can do Wednesday at noon. With Annie if we want to start with the Meta based stuff. You start there
289 00:35:37.430 ⇒ 00:35:40.299 Aman Nagpal: Let’s let’s start with that, and then we can figure out the next one
290 00:35:40.460 ⇒ 00:35:41.080 Aakash Tandel: Okay.
291 00:35:41.677 ⇒ 00:35:42.990 Aakash Tandel: Let me. I
292 00:35:42.990 ⇒ 00:35:45.610 Aman Nagpal: I think an hour would be good, or half hour
293 00:35:46.770 ⇒ 00:35:50.499 Aakash Tandel: I think probably an hour for all the all the dashboards.
294 00:35:50.990 ⇒ 00:35:58.370 Aakash Tandel: It would be a quick. It would be very fast if we we did 30 min. But I mean we can try to keep it like 40 min. See what how it goes.
295 00:35:58.960 ⇒ 00:36:02.809 Aman Nagpal: Yeah, that sounds good. I’ll send his email here in the
296 00:36:03.050 ⇒ 00:36:03.780 Aakash Tandel: Perfect.
297 00:36:03.780 ⇒ 00:36:04.880 Aman Nagpal: Zoom chat.
298 00:36:07.040 ⇒ 00:36:14.149 Aman Nagpal: Was there anything else on that list? Or actually, I know we’re low on time. You guys gotta jump
299 00:36:15.060 ⇒ 00:36:16.160 Robert Tseng: Yeah, probably.
300 00:36:16.310 ⇒ 00:36:19.869 Aman Nagpal: Okay. Yeah. I’ll take a look at the list. See if there’s anything else we missed.
301 00:36:20.220 ⇒ 00:36:22.350 Aman Nagpal: and let’s take it from there. Then
302 00:36:22.760 ⇒ 00:36:24.290 Robert Tseng: Okay. Alright!
303 00:36:24.493 ⇒ 00:36:25.920 Aakash Tandel: Nice to meet you. Sorry for meeting
304 00:36:25.920 ⇒ 00:36:26.390 Aman Nagpal: Yes.
305 00:36:26.820 ⇒ 00:36:27.260 Aman Nagpal: Nice meeting.
306 00:36:27.260 ⇒ 00:36:27.670 Robert Tseng: Okay.