Meeting Title: Stackblitz | Weekly Kickof Date: 2025-03-17 Meeting participants: Aakash Tandel, Uttam Kumaran, Amber Lin, Sahanaasokan, Ryan Luke Daque
WEBVTT
1 00:00:41.350 ⇒ 00:00:46.950 Amber Lin: Hello! Just waiting for the team members to come in. Hi, Sahana!
2 00:00:47.580 ⇒ 00:00:49.190 sahanaasokan: Hey? Sorry I’m late.
3 00:00:49.770 ⇒ 00:00:55.259 Amber Lin: Don’t worry. I’m just waiting for just waiting for Luke to be here
4 00:00:55.260 ⇒ 00:00:56.719 sahanaasokan: Okay. Sounds good.
5 00:01:01.610 ⇒ 00:01:03.669 Amber Lin: How was the meeting with the client?
6 00:01:04.930 ⇒ 00:01:06.420 Amber Lin: It was Mitch, right
7 00:01:07.580 ⇒ 00:01:20.189 sahanaasokan: Met with them yet. I just got added to this, I think, like last week. So I I really have no idea what’s going on and what we’re working on. I’ve been primarily on Eden so, and then art helper before that, I just know that
8 00:01:20.470 ⇒ 00:01:23.129 sahanaasokan: they needed some support here. So here I am
9 00:01:23.130 ⇒ 00:01:29.099 Amber Lin: Hmm, cool. Okay, let me try and find if they have any notes on that.
10 00:01:44.670 ⇒ 00:01:50.760 Amber Lin: Okay, Utam has it. He has not yet posted it to the chat.
11 00:01:52.220 ⇒ 00:01:53.239 Ryan Luke Daque: Hello! Hello!
12 00:01:53.240 ⇒ 00:01:53.890 Amber Lin: Hi Luke.
13 00:01:53.890 ⇒ 00:01:55.370 Ryan Luke Daque: Hi, guys, sorry I’m late.
14 00:02:00.140 ⇒ 00:02:01.070 Amber Lin: See.
15 00:02:01.840 ⇒ 00:02:11.909 Amber Lin: Look! Do you know much about the stack list and how we’re at? I know Uta met with the client, but I don’t know what he got from that
16 00:02:13.250 ⇒ 00:02:23.830 Ryan Luke Daque: Yeah, I’m not sure as well, because he did have the meeting with Mitch last Friday. I wasn’t he did not let me join that meeting, so it was only him and Mitch. So I’m not sure
17 00:02:23.830 ⇒ 00:02:24.220 Amber Lin: Yeah.
18 00:02:24.220 ⇒ 00:02:29.039 Ryan Luke Daque: And discuss about maybe the the what do you call this
19 00:02:29.430 ⇒ 00:02:30.339 Amber Lin: Yeah, same.
20 00:02:30.570 ⇒ 00:02:32.329 Ryan Luke Daque: Position. There could be
21 00:02:32.770 ⇒ 00:02:34.539 Amber Lin: Sorry. Go ahead. I’m sorry.
22 00:02:35.410 ⇒ 00:02:36.440 Ryan Luke Daque: No worries.
23 00:02:37.027 ⇒ 00:02:48.790 Ryan Luke Daque: I think they were like trying to figure out maybe, what the next steps would be if the if it would be like the monthly fee or something, right? Because they’re also stack, which is also hiring
24 00:02:49.160 ⇒ 00:02:50.770 Ryan Luke Daque: data engineers
25 00:02:51.330 ⇒ 00:02:52.380 Amber Lin: I remember.
26 00:02:53.049 ⇒ 00:03:01.919 Amber Lin: I pulled this up from our notion. I found it somewhere. I believe this was created March 18.th So this is still pretty relevant
27 00:03:02.320 ⇒ 00:03:08.139 Amber Lin: and product vault current state proposal.
28 00:03:08.780 ⇒ 00:03:10.293 Amber Lin: Our role
29 00:03:13.820 ⇒ 00:03:19.510 Amber Lin: project manager, engineer message, okay?
30 00:03:20.020 ⇒ 00:03:33.160 Amber Lin: The timeline. We wanna okay. So February, that’s done. We have the core platforms, polyatomic foundational marks. Okay, this
31 00:03:33.160 ⇒ 00:03:38.813 Ryan Luke Daque: Yeah. Mostly in February, we were just setting up all the connections into data integrations
32 00:03:39.190 ⇒ 00:03:39.510 Amber Lin: Awesome.
33 00:03:39.510 ⇒ 00:03:49.130 Ryan Luke Daque: Snowflake basically, and then setting up the Dbt project, the Github Repository, the initial rail project and then
34 00:03:49.440 ⇒ 00:03:59.810 Ryan Luke Daque: ingesting the data data from different sources like Stripe, their postgres warehouse database.
35 00:03:59.990 ⇒ 00:04:05.780 Ryan Luke Daque: And we did start working on very basic models, data models like
36 00:04:05.780 ⇒ 00:04:06.350 Amber Lin: Because
37 00:04:06.350 ⇒ 00:04:08.560 Ryan Luke Daque: Customers, Subscriptions and Usage
38 00:04:08.920 ⇒ 00:04:09.460 Amber Lin: Oh!
39 00:04:09.460 ⇒ 00:04:16.040 Ryan Luke Daque: And yeah, for March, you can see here.
40 00:04:16.040 ⇒ 00:04:22.069 Amber Lin: March migrate and verify accuracy. Okay, that’s done. It seems
41 00:04:22.190 ⇒ 00:04:28.709 Amber Lin: onboarding engineers that’s that’s dependent on them. This is
42 00:04:29.100 ⇒ 00:04:37.230 Amber Lin: user or team. I think I can take that if I meet with them develop product funnel.
43 00:04:37.400 ⇒ 00:04:41.069 Amber Lin: Yeah, I think this. Or I think this one.
44 00:04:46.630 ⇒ 00:04:53.249 Amber Lin: What do you guys feel on this? The product, funnel tracking, creating core architecture, documentation.
45 00:04:57.900 ⇒ 00:04:58.529 Ryan Luke Daque: I guess right.
46 00:04:58.530 ⇒ 00:04:59.920 Ryan Luke Daque: I’m not too sure.
47 00:05:01.530 ⇒ 00:05:06.060 Ryan Luke Daque: I’m not too sure what this this is.
48 00:05:07.050 ⇒ 00:05:07.880 Amber Lin: Hmm.
49 00:05:10.050 ⇒ 00:05:13.095 sahanaasokan: I personally have no context like whatsoever.
50 00:05:13.530 ⇒ 00:05:20.489 Amber Lin: Yeah, I I think I am also New Sahana. So I think we have both the same problem
51 00:05:20.490 ⇒ 00:05:20.950 sahanaasokan: Yeah.
52 00:05:20.950 ⇒ 00:05:21.280 Ryan Luke Daque: Yeah, yeah.
53 00:05:21.280 ⇒ 00:05:24.189 Amber Lin: So try and find this
54 00:05:24.870 ⇒ 00:05:30.910 Ryan Luke Daque: I. I have some context on the others. But for the product funnel, I don’t think I
55 00:05:31.220 ⇒ 00:05:34.710 Ryan Luke Daque: heard that before. Let’s talk with Tom on that one. Yeah.
56 00:05:34.710 ⇒ 00:05:35.030 Amber Lin: Oh!
57 00:05:35.030 ⇒ 00:05:35.590 Ryan Luke Daque: So.
58 00:05:36.290 ⇒ 00:05:36.900 Amber Lin: Yes.
59 00:05:36.900 ⇒ 00:05:39.879 Ryan Luke Daque: Maybe that’s something that’s something new, perhaps
60 00:05:39.980 ⇒ 00:05:42.399 Ryan Luke Daque: that. Yeah, we need context on
61 00:05:43.367 ⇒ 00:05:48.372 Amber Lin: Okay, I think this is what we talked about last meeting, where we’re together.
62 00:05:50.690 ⇒ 00:05:52.579 Ryan Luke Daque: Yeah, product funnels there. So
63 00:05:52.580 ⇒ 00:05:57.619 sahanaasokan: Probably is something like maybe acquisition funnel, or some kind of
64 00:05:58.560 ⇒ 00:05:59.160 Amber Lin: Oh!
65 00:05:59.160 ⇒ 00:05:59.810 sahanaasokan: Like sales.
66 00:06:00.030 ⇒ 00:06:00.250 Ryan Luke Daque: Yeah.
67 00:06:00.250 ⇒ 00:06:05.820 sahanaasokan: Product sales funnel. I’m assuming like, I don’t really understand what I I’m pretty sure it’s 1 of those 2
68 00:06:06.250 ⇒ 00:06:11.569 Ryan Luke Daque: Yeah, or marketing, perhaps right? Like which marketing campaign that your customers are coming from something
69 00:06:12.590 ⇒ 00:06:13.170 Amber Lin: Yeah.
70 00:06:13.170 ⇒ 00:06:20.199 sahanaasokan: Actually, yes, that sounds familiar. So I think it actually might be on the marketing side. But I know Utam has more more contact. So let’s just confirm with him
71 00:06:20.870 ⇒ 00:06:25.099 Amber Lin: Yeah, I I’m gonna ping him if he can join this
72 00:06:27.900 ⇒ 00:06:34.429 Aakash Tandel: On the onboard people to real Luke, you’re pretty familiar with real like you. You help set that up right
73 00:06:35.030 ⇒ 00:06:40.900 Ryan Luke Daque: Yeah, I did create a couple of dashboards. I think we have 3 now for stack bits
74 00:06:41.410 ⇒ 00:06:41.790 Aakash Tandel: Okay.
75 00:06:41.790 ⇒ 00:06:45.330 Ryan Luke Daque: And onboarding I don’t know wh. Which.
76 00:06:45.450 ⇒ 00:06:50.920 Ryan Luke Daque: What on this is this for us internally, or is it like for themselves?
77 00:06:50.920 ⇒ 00:07:01.730 Aakash Tandel: I think it was for the stack. Let’s team Amber. If you want to sync up with Luke on that one that you guys compare that one together, or yeah, on board, whoever that is together that makes sense. And then also
78 00:07:01.730 ⇒ 00:07:02.640 Amber Lin: Yeah. Totally.
79 00:07:02.640 ⇒ 00:07:09.420 Aakash Tandel: You can see the output of that like a loom video, or whatever or recording or notes that might be good for you, too.
80 00:07:09.960 ⇒ 00:07:16.159 Amber Lin: Okay? So we would, yeah, let me put that down
81 00:07:21.120 ⇒ 00:07:23.100 Uttam Kumaran: Sorry guys, just running late.
82 00:07:23.480 ⇒ 00:07:24.420 Amber Lin: Hijam!
83 00:07:28.461 ⇒ 00:07:36.000 Aakash Tandel: On the onboarding people to real you had, I think, like a product person on. It’s on their side. You’re talking about right
84 00:07:37.471 ⇒ 00:07:43.439 Uttam Kumaran: Yes, so I think that’s something. So the kind of the interesting part with this client is
85 00:07:43.904 ⇒ 00:07:48.739 Uttam Kumaran: we’re on for another month. They’re onboarding 2 more data people who will kind of become our
86 00:07:48.930 ⇒ 00:07:52.689 Uttam Kumaran: point of contacts at that towards the end of this month.
87 00:07:53.176 ⇒ 00:08:08.189 Uttam Kumaran: But for now we just want to continue to develop sort of everything around product analytics and continue basically, those 2 work streams. I think in terms of onboarding folks. We have one sort of through line to sales.
88 00:08:09.520 ⇒ 00:08:10.869 Uttam Kumaran: But I sort of want.
89 00:08:11.410 ⇒ 00:08:13.860 Uttam Kumaran: like I think, that I’ll leave that sort of to
90 00:08:14.040 ⇒ 00:08:19.650 Uttam Kumaran: to me and amber in terms of like, do we have enough to begin to onboard them? Do we have their their questions really clear?
91 00:08:22.040 ⇒ 00:08:30.130 Uttam Kumaran: yeah, I think that’ll be our our 1st target is onboarding the this. We have one sales stakeholder that’s that wants to sort of start looking at data
92 00:08:32.140 ⇒ 00:08:36.190 Amber Lin: I see? And do we have data on the sales side right now?
93 00:08:38.646 ⇒ 00:08:42.240 Uttam Kumaran: Yes, we have all the, we have basically everything modeled already.
94 00:08:42.360 ⇒ 00:08:47.089 Uttam Kumaran: Okay, that is that is great. So essentially, we just what we need is training materials.
95 00:08:48.770 ⇒ 00:09:07.610 Uttam Kumaran: well, what we need is just to be able to answer their questions. I I don’t know whether we’re gonna onboard, like I think there’s 1 thing about adding them to real, but they have some short term questions that I think we could start to add, to build some trust and build a cadence of like talking to them. I think probably today, when we talk amber, I can add those questions to the backlog
96 00:09:07.610 ⇒ 00:09:08.210 Amber Lin: Okay.
97 00:09:08.360 ⇒ 00:09:09.190 Amber Lin: Okay.
98 00:09:10.190 ⇒ 00:09:15.289 Amber Lin: Alright. What type of questions are there? Are they just how to use real? Or is it
99 00:09:15.290 ⇒ 00:09:18.276 Uttam Kumaran: No, these are just like asking questions about
100 00:09:18.820 ⇒ 00:09:30.710 Uttam Kumaran: just about like their their sales, like, what are our high value? Customers like? What are high usage customers. They have a couple of those. I will let me. Just. I’ll grab those and just throw them into the channel right now.
101 00:09:30.710 ⇒ 00:09:34.820 Amber Lin: Okay, that’s great. So it’s more about insights from the data
102 00:09:35.410 ⇒ 00:09:36.100 Uttam Kumaran: Yes.
103 00:09:36.330 ⇒ 00:09:37.410 Amber Lin: Okay, sounds good.
104 00:09:37.860 ⇒ 00:09:39.040 Amber Lin: And
105 00:09:39.490 ⇒ 00:09:55.949 Amber Lin: while you do that, Sahana, can you? I know you’ve worked extensively with product analytics. So I just want to hear from you? What you think we should progress forward especially for this week. What should we do? What do you plan to do for that
106 00:09:56.450 ⇒ 00:10:21.819 sahanaasokan: Yeah, I think, like, just thinking about product analytics, roadmap, like, the 1st thing we really need to do is like, define, like, what are North Star metrics like, what are our key Kpis like? Not even thinking about funnels or churn, or any of that, just like the basic foundation of product usage right like, how are customers using this product? And where, like, what are our blockers, and where are their opportunity? I think that’s like phase one like before, any of
107 00:10:22.000 ⇒ 00:10:29.210 sahanaasokan: like anything complicated. So I think in my eyes, the 1st step is understanding what kind of product usage data we have.
108 00:10:29.697 ⇒ 00:10:46.950 sahanaasokan: Kind of setting and defining new requirements for what product usage data we need. But all of that comes after setting up some time with these stakeholders and understanding. You know, what are their North Star metrics and kpis like? What are metrics that they want to drive forward right? Like, I don’t really have context there. So
109 00:10:46.950 ⇒ 00:10:53.229 Uttam Kumaran: I have. I have that information. I think if you just if amber, we can list those questions in our session today.
110 00:10:53.230 ⇒ 00:10:54.030 Amber Lin: Yeah, yeah.
111 00:10:54.030 ⇒ 00:11:01.859 Uttam Kumaran: I will, I can answer those. But those are great. And yeah, I think that all there are some nuances to the product beyond, just like
112 00:11:02.120 ⇒ 00:11:06.779 Uttam Kumaran: classic Dau stuff, and then we can get you that today
113 00:11:06.780 ⇒ 00:11:29.690 sahanaasokan: Okay, yeah, perfect. So then that’s good. And then, yeah, based on like what we have like, what questions they want answered, I’ll like, go. I’ll look at it. Probably maybe divvy it up. Based on product area like, have, like one executive kind of dashboard with like top line metrics, North star metrics more executive level, and then one more like granular product usage, metric like usage dashboard. So probably
114 00:11:34.760 ⇒ 00:11:38.070 Aakash Tandel: They use the mix panel or amplitude type of product analytics, tool
115 00:11:38.070 ⇒ 00:11:40.490 Uttam Kumaran: Yeah, they use. They use. They’re using segment
116 00:11:41.110 ⇒ 00:11:41.680 Aakash Tandel: Okay.
117 00:11:42.580 ⇒ 00:11:43.040 sahanaasokan: Segment.
118 00:11:43.040 ⇒ 00:11:44.310 Uttam Kumaran: And we have all that data
119 00:11:44.910 ⇒ 00:11:46.220 Ryan Luke Daque: Baremetrics, right
120 00:11:46.590 ⇒ 00:11:50.539 sahanaasokan: So, and you can model. You can do like visuals and segment
121 00:11:50.540 ⇒ 00:11:55.090 Uttam Kumaran: Oh, no, no, no, they’re they’re well, they’re using segment for product analytics.
122 00:11:55.440 ⇒ 00:11:59.190 Uttam Kumaran: We’re MoD, we’re are. We’re doing all the visualization through. Rel.
123 00:12:00.670 ⇒ 00:12:05.596 Uttam Kumaran: Yeah. So I’ll get you on boarded. Yeah, I’ll get you on boarded to there as well.
124 00:12:06.250 ⇒ 00:12:08.330 sahanaasokan: I’ve never used that. So yeah.
125 00:12:08.330 ⇒ 00:12:09.070 Uttam Kumaran: I think.
126 00:12:09.380 ⇒ 00:12:11.699 Uttam Kumaran: Yeah, I think you’ll like it. It’s pretty simple
127 00:12:11.700 ⇒ 00:12:13.090 sahanaasokan: Okay. Sounds good.
128 00:12:13.680 ⇒ 00:12:17.520 Uttam Kumaran: I think we’ll do a session. Probably this week. I I kinda wanna I will actually
129 00:12:18.030 ⇒ 00:12:33.039 Uttam Kumaran: better yet. I I was emailing the real folks asking them to do a to do an onboarding session like a learning session for our team. Because a lot of folks I want to start to get trained up on it that I think you guys will like that product. It’s and it’s actually really friendly for developers to build on
130 00:12:33.620 ⇒ 00:12:41.439 Uttam Kumaran: and it’s quite cost effective for our customers as well. So I’ll probably schedule. Try to grab some time this week with the the data team to to do that
131 00:12:41.760 ⇒ 00:12:42.350 sahanaasokan: Okay.
132 00:12:42.350 ⇒ 00:12:49.511 sahanaasokan: that sounds good. And then I know we’ve we discussed doing this exercise. But I just wanna know if we’re still trying to document
133 00:12:50.370 ⇒ 00:12:54.799 sahanaasokan: like the schemas like, for example, like what schemas hold, what that’ll be
134 00:12:54.800 ⇒ 00:12:55.240 Uttam Kumaran: Yes.
135 00:12:55.240 ⇒ 00:12:56.330 sahanaasokan: And understood.
136 00:12:56.330 ⇒ 00:13:03.290 Uttam Kumaran: One of the things I’m gonna be. I’m gonna I have a format in mind that we we literally have this same problem across, like every client right now.
137 00:13:03.420 ⇒ 00:13:11.399 Uttam Kumaran: So I’m thinking about like what is like, what is a universal, what is like good documentation? Look like at Brainforge for a client.
138 00:13:11.916 ⇒ 00:13:20.420 Uttam Kumaran: There’s, of course, like the Pm. Documentation of like, who? What? When? Where? But there’s also like what’s in the what’s in
139 00:13:20.690 ⇒ 00:13:35.140 Uttam Kumaran: the database like? What are some of the questions we’ve answered. So I’m gonna propose a format. And I’ll probably get feedback from everyone on the data team on like, is this right. A lot of my time is going to go to working on that across all of our clients to get like the 1st phase done.
140 00:13:35.564 ⇒ 00:13:39.550 Uttam Kumaran: But I will get you something something around that will be confirmed by Wednesday.
141 00:13:39.670 ⇒ 00:13:44.860 Uttam Kumaran: What that looks like for these guys? It’s pretty simple, though. They only have a few sources right now.
142 00:13:45.510 ⇒ 00:13:46.220 Amber Lin: Gates
143 00:13:48.530 ⇒ 00:13:49.880 Amber Lin: So I think.
144 00:13:51.100 ⇒ 00:14:11.040 Amber Lin: do you think? Or and Sahana, do you think it would be helpful if you give us a list of questions of what you tend to think about when you’re thinking about, say, North North Star Metrics, or what type of data where you might need, because I don’t have too much context on this. So I’m just asking you and Uton like, what do you think would be helpful.
145 00:14:11.200 ⇒ 00:14:12.950 Amber Lin: and that we do today
146 00:14:13.840 ⇒ 00:14:23.160 Uttam Kumaran: Yeah, I think it would be helpful to get a list of any of the questions. I will answer everything I know. And then that way. It also explain to me what else I need to go get from
147 00:14:23.340 ⇒ 00:14:24.360 Uttam Kumaran: the client.
148 00:14:24.590 ⇒ 00:14:27.239 Uttam Kumaran: His time is very limited in the moment.
149 00:14:28.270 ⇒ 00:14:50.235 Uttam Kumaran: so until we? They get more data people. Then we’ll probably have someone from their team join this call and sort of be that person. But I’ll act as that for now, if I can just get a list of those questions like already for some of them, I know some of the nuances of like okay, they they most likely want the classic Sas metrics. They also want a couple of specific things related to the way their product is getting used.
150 00:14:50.750 ⇒ 00:14:58.130 Uttam Kumaran: but thinking through like the level one, and like level 2 questions, I’ll try to answer as many of them as possible, and then that’ll give me some questions to go throw back to to Mitch as well
151 00:14:58.920 ⇒ 00:15:11.710 Amber Lin: That’s great. So, Hannah, I’m meeting with Utam in around like an hour or so. Do you think you’ll be able to just spit out some questions in our slack channel. Would that be possible?
152 00:15:12.260 ⇒ 00:15:18.569 sahanaasokan: I’ll try my best. I am part time, so I have, like my my like work work, stuff going on. But
153 00:15:19.250 ⇒ 00:15:27.400 sahanaasokan: like when you say questions, you’re just you basically just want to know, like, you know, what, how do we define like, what a North good North Star metric is right like I’m kind of
154 00:15:27.400 ⇒ 00:15:33.969 Uttam Kumaran: But not really that, like you mentioned, like, what are North Star metrics? What what metrics are? Do we want to define
155 00:15:33.970 ⇒ 00:15:34.540 sahanaasokan: Yeah.
156 00:15:34.540 ⇒ 00:15:35.980 Uttam Kumaran: I just need those questions.
157 00:15:35.980 ⇒ 00:15:38.289 sahanaasokan: Oh, okay, yeah. Yeah. Then, I can send those to you right now.
158 00:15:38.290 ⇒ 00:15:44.699 Uttam Kumaran: Okay, yeah, just like, whatever the 5 or 10 of those questions are, I’ll I’ll I’ll answer as many as I can
159 00:15:44.700 ⇒ 00:15:45.880 sahanaasokan: Yeah, I’ll do that right
160 00:15:47.250 ⇒ 00:15:47.935 Amber Lin: Fantastic.
161 00:15:49.377 ⇒ 00:15:53.940 Amber Lin: Luke, have you been working on this project previously?
162 00:15:54.680 ⇒ 00:16:00.379 Ryan Luke Daque: Yeah, I’ve I’ve worked with mostly the data models and like part of the ingestion
163 00:16:00.680 ⇒ 00:16:03.510 Ryan Luke Daque: piece and the real dashboards as well
164 00:16:03.810 ⇒ 00:16:09.709 Amber Lin: Okay, is that continuing this week as well? How’s the how does that look
165 00:16:10.440 ⇒ 00:16:13.062 Ryan Luke Daque: Yeah, there’s still a couple of
166 00:16:14.350 ⇒ 00:16:24.714 Ryan Luke Daque: metrics coming from their bare metrics like the refunds, or like the reactivated subscriptions that I need to be. I need to add to our current real dashboards, and that’s
167 00:16:25.970 ⇒ 00:16:28.420 Ryan Luke Daque: I can. I can. I’ll be working on those
168 00:16:29.000 ⇒ 00:16:29.369 Amber Lin: All right.
169 00:16:29.370 ⇒ 00:16:32.410 Ryan Luke Daque: The the other thing as well is the
170 00:16:32.820 ⇒ 00:16:37.249 Ryan Luke Daque: usage basically token usage. I haven’t really got into the
171 00:16:37.420 ⇒ 00:16:42.320 Ryan Luke Daque: to the like source where to find that event?
172 00:16:43.890 ⇒ 00:16:52.419 Ryan Luke Daque: because they they do also want to see, token usage aside from like just the subscriptions, or like token reloads as well.
173 00:16:52.580 ⇒ 00:17:00.029 Ryan Luke Daque: So yeah, that’s that’s also something. I did create a couple of tasks or issues in linear
174 00:17:00.030 ⇒ 00:17:00.690 Amber Lin: Oh, great!
175 00:17:01.945 ⇒ 00:17:03.200 Ryan Luke Daque: Yeah.
176 00:17:03.810 ⇒ 00:17:12.900 Ryan Luke Daque: yeah, you can. You can check it out. There’s the create, bare metrics, models. And Dbt, it’s a pretty huge one. Maybe we need to break that down into
177 00:17:12.900 ⇒ 00:17:18.140 Amber Lin: Sure we can always like make sub issues in there. Let me go check our bill.
178 00:17:19.099 ⇒ 00:17:21.860 Amber Lin: Biometrics, models
179 00:17:22.460 ⇒ 00:17:23.230 Ryan Luke Daque: Yeah. That’d be wonderful.
180 00:17:23.230 ⇒ 00:17:26.280 Amber Lin: Oh, you already put so much information in there. Great
181 00:17:27.024 ⇒ 00:17:30.889 Amber Lin: what’s the Hubspot hubspot data modeling
182 00:17:32.090 ⇒ 00:17:33.160 Ryan Luke Daque: Yeah, they have
183 00:17:33.644 ⇒ 00:17:42.590 Ryan Luke Daque: data coming in from help Hubspot. And we would probably need to model those. So we, I don’t have context to that. Yet. I will need
184 00:17:42.880 ⇒ 00:17:51.840 Ryan Luke Daque: to ask, like Mitch what they need from out of Hubspot. Basically, unless somebody knows sure region sucks
185 00:17:53.156 ⇒ 00:17:54.490 Amber Lin: Get contact
186 00:17:54.490 ⇒ 00:18:04.640 Uttam Kumaran: Yeah, I think Amber is probably. But maybe I think we’ll work on creating this backlog in our call, like, I have all the context on what’s coming up next? I don’t. I think it’s probably best to do that. There
187 00:18:04.910 ⇒ 00:18:06.779 Amber Lin: Okay, sounds good. I will put this
188 00:18:06.780 ⇒ 00:18:23.070 Uttam Kumaran: I know I know what the next this next month of roadmap is. I think we just need to spend some time building that today. And then if we can get the questions from Sahana on on what? What questions we have for the product analytics. I’ll answer that by tomorrow. I think you’ll it’ll be really clear like what the next few weeks are.
189 00:18:23.640 ⇒ 00:18:44.559 Amber Lin: Okay, that is great. And see, are we meeting tomorrow? We said, we are meeting on Wednesday, but if we want, we could just move that or I’m free at the same time. At 7 Am. To meet we could move that to Tuesday we can meet at around 7 Am. Pst.
190 00:18:45.140 ⇒ 00:18:47.690 Amber Lin: and then just talk about what we want to do. Forward
191 00:18:47.690 ⇒ 00:18:56.629 Uttam Kumaran: I think it would be effective to talk tomorrow, after we like, get the backlog all set up today, and then we can even do the following meeting on Thursday, or like, push it
192 00:18:57.385 ⇒ 00:18:59.910 Uttam Kumaran: but that way it’s not another 2 days
193 00:19:01.780 ⇒ 00:19:04.939 Amber Lin: Yeah, let me then let me move that.
194 00:19:09.500 ⇒ 00:19:22.819 Amber Lin: Okay. I think that’s all of it from here. To sum it up, I think Sahana will just create a short list of questions. Me and Utam will work on the backlog. We will also work on
195 00:19:22.960 ⇒ 00:19:31.909 Amber Lin: thinking about how to onboard the sales stakeholder to real, and then Luke will continue to work on the real dashboard and the token usage.
196 00:19:33.490 ⇒ 00:19:37.310 Amber Lin: That sounds good, fantastic.
197 00:19:37.310 ⇒ 00:19:41.350 sahanaasokan: Sounds good. I was working on the question, so I’ll send it to you in the next like hour.
198 00:19:41.630 ⇒ 00:19:43.270 Amber Lin: Yeah. Great. Fantastic.
199 00:19:44.490 ⇒ 00:19:45.350 Uttam Kumaran: Thank you.
200 00:19:46.270 ⇒ 00:19:46.620 Aakash Tandel: Thanks. Y’a
201 00:19:46.880 ⇒ 00:19:47.850 Amber Lin: Okay.
202 00:19:47.850 ⇒ 00:19:48.820 Ryan Luke Daque: Guys, alright