Meeting Title: Brainforge Demos & Retros Date: 2025-02-21 Meeting participants: Luke Daque, Anne, Uttam Kumaran, Demilade Agboola, Hannah Wang, Miguel De Veyra, Casie Aviles, Sahana Asokan, Awaish Kumar, Ryan Brosas
WEBVTT
1 00:00:35.290 ⇒ 00:00:36.270 Uttam Kumaran: Hey, guys.
2 00:00:38.520 ⇒ 00:00:39.480 Luke Daque: Hello!
3 00:00:44.100 ⇒ 00:00:45.000 Ryan Brosas: Oh, guys.
4 00:00:45.400 ⇒ 00:00:45.785 Uttam Kumaran: A
5 00:00:53.030 ⇒ 00:00:58.049 Uttam Kumaran: can. Can someone go camera on, please? I’m I’m so lonely like, I feel like everybody’s watching me.
6 00:01:02.960 ⇒ 00:01:05.600 Uttam Kumaran: Hey, guys, hey? Don’t want it.
7 00:01:06.460 ⇒ 00:01:07.240 Uttam Kumaran: How are you.
8 00:01:07.930 ⇒ 00:01:08.730 Demilade Agboola: I’m pretty good.
9 00:01:12.110 ⇒ 00:01:13.019 Uttam Kumaran: There we go!
10 00:01:13.500 ⇒ 00:01:15.629 Miguel de Veyra: Yeah, sorry it’s broken. I’ll buy a new one.
11 00:01:16.890 ⇒ 00:01:18.319 Uttam Kumaran: Your camera’s broken.
12 00:01:19.130 ⇒ 00:01:20.460 Uttam Kumaran: I don’t believe you dude.
13 00:01:20.750 ⇒ 00:01:22.589 Miguel de Veyra: No, my cats, Bro.
14 00:01:23.440 ⇒ 00:01:24.060 Uttam Kumaran: Your cat.
15 00:01:24.060 ⇒ 00:01:24.430 Miguel de Veyra: Yep.
16 00:01:25.680 ⇒ 00:01:27.700 Uttam Kumaran: Yeah. They nipped on the wire.
17 00:01:29.440 ⇒ 00:01:34.180 Miguel de Veyra: So they were like, yeah, so I don’t know if I can just buy the one or something.
18 00:01:35.250 ⇒ 00:01:36.139 Miguel de Veyra: Probably not.
19 00:01:42.270 ⇒ 00:01:44.559 Uttam Kumaran: Yeah, we’ll wait for some more people to trickle in
20 00:02:30.030 ⇒ 00:02:31.910 Uttam Kumaran: busy week this week.
21 00:03:13.901 ⇒ 00:03:19.679 Uttam Kumaran: Okay, cool. I think I may get started. I don’t know where couple of folks are. I will
22 00:03:20.940 ⇒ 00:03:24.238 Uttam Kumaran: see they’re gonna join later. But let’s
23 00:03:26.140 ⇒ 00:03:29.300 Uttam Kumaran: Let’s just run through a few things.
24 00:03:38.836 ⇒ 00:03:40.470 Uttam Kumaran: Great. So
25 00:03:40.916 ⇒ 00:03:47.240 Uttam Kumaran: sort of wanted to continue with this format of how we’ve been doing running this meeting over the last
26 00:03:48.870 ⇒ 00:03:50.930 Uttam Kumaran: sort of few weeks where we’re
27 00:03:51.040 ⇒ 00:03:57.140 Uttam Kumaran: talking about each of our core okrs, and then sort of looking at how we did the previous week.
28 00:03:57.570 ⇒ 00:04:08.169 Uttam Kumaran: and then talking about what we set out for goals this week, and then talking about like how we did. So one thing, I’ll just flash this up again. I think we did a pretty good job on
29 00:04:08.547 ⇒ 00:04:35.580 Uttam Kumaran: each of these, I think, especially on the second one. We’re getting better. I think it’s been really really helpful to have a few more folks on the data side. To sort of take on some additional work, and we’re meeting every day as an Ae team which has been really really cool. And so I’m excited. You know, we’re we’re we’re, of course, meeting every day as an AI team. So I think broader engineering. I’m I’m really happy with. I think I do have a lot of goals
30 00:04:35.850 ⇒ 00:04:45.598 Uttam Kumaran: for how to continue to support each core engineering group. So analytics, engineering analysts. As well as
31 00:04:46.538 ⇒ 00:05:02.570 Uttam Kumaran: I mean, I’m the only kind of sort of doing data engineering work. But the AI team. And then as a whole, how we can all meet like as an engineering group, like once a month, maybe, or something like that, where we can talk about how we do engineering as a whole, maybe like brown bags or something like that.
32 00:05:03.072 ⇒ 00:05:06.690 Uttam Kumaran: And yeah, I guess more on the executive update.
33 00:05:07.253 ⇒ 00:05:12.596 Uttam Kumaran: So yeah, we kicked off this week with urban stems. Urban stems is a large
34 00:05:13.180 ⇒ 00:05:16.739 Uttam Kumaran: flower sort of delivery service.
35 00:05:17.366 ⇒ 00:05:27.429 Uttam Kumaran: And they are. They’re they have a lot of work that they need this 1st month for us is actually just like auditing everything and sort of providing some
36 00:05:27.540 ⇒ 00:05:47.059 Uttam Kumaran: suggestions on how to migrate to new tools. So there’s a lot of work that needs to be done there, so super excited that we got started. I’m currently the only one there, but probably towards the end of the month, or in the next 2 weeks we will sort of start to assemble. What the engineering pod will look like there.
37 00:05:48.480 ⇒ 00:05:57.710 Uttam Kumaran: I guess the only other update is we do have them a lot of here. I just wanted to have him introduce himself. He’s sort of shadowing, thinking about joining us.
38 00:05:57.840 ⇒ 00:06:06.629 Uttam Kumaran: so feel free to talk about how cool for everyone to talk about, how cool we are, and what a cool place this is to work, and don’t talk about any about any of the bad stuff.
39 00:06:06.810 ⇒ 00:06:21.809 Uttam Kumaran: or you should tell me I don’t know what bad stuff is, but there is. You should tell me. But yeah, I don’t know. Never. If you want to give up maybe a brief intro, I’ll I’ll give the work intro, and you could give the more personal intro, so dev, a lot is an analytics engineer
40 00:06:21.840 ⇒ 00:06:41.290 Uttam Kumaran: worked for several companies doing really really great analytics engineering sort of in an agency context as well as as an individual so really familiar with Dbt, how to structure Dbt projects, how to work across multiple clients. All all this stuff that you know we need and then, yeah, I’ve done a lot. If you want to give a brief intro about
41 00:06:41.380 ⇒ 00:06:43.319 Uttam Kumaran: you, yeah, please feel free.
42 00:06:44.187 ⇒ 00:06:56.909 Demilade Agboola: Hi! I’m dimladi I like Tom just said I have worked across multiple industries. So I worked in Agritech Fintech. I’ve worked in like the agency consulting experience as well.
43 00:06:57.448 ⇒ 00:07:07.021 Demilade Agboola: And I currently work with logistics firm. But I am looking to, you know. Join you guys very soon, and I look forward to that
44 00:07:07.530 ⇒ 00:07:30.580 Demilade Agboola: And yeah, I’m great to hear I’m here to help the good stuff, and if there’s any bad stuff as well. I’m here to help them as well. But the idea is, you know, I’m here to, you know, learn from everyone, but also add to like the experience as well. So if there’s good stuff, add to that. If there’s bad stuff, you know, be able to help navigate the water as well, so look forward to working with each and every one of you. Thank you.
45 00:07:31.020 ⇒ 00:07:48.539 Uttam Kumaran: Yeah, it’s been fun, you know, this week we we do a lot of interviewing this week. I feel like I did a lot of interviews. And you know, one of the things that I always tell people is that like when you’re working as an individual engineer and for one in one team and one client like in one company. That’s 1 thing
46 00:07:49.160 ⇒ 00:08:12.630 Uttam Kumaran: in in a data team, you tend to have multiple stakeholders. Right? Maybe you’re supporting sales finance. Okay, that’s another thing. We’re sort of in the position where we’re we’re handling multiple companies and multiple stakeholders for every company. Right? So this is the hardest it basically gets in data. I feel like, I mean, I don’t think in particular. And I’m sure everybody in the data team can agree like any of the work we’re doing is particularly hard.
47 00:08:12.860 ⇒ 00:08:16.600 Uttam Kumaran: It’s actually hard to get clear requirements.
48 00:08:16.750 ⇒ 00:08:19.589 Uttam Kumaran: It’s hard because we have to move very fast.
49 00:08:20.107 ⇒ 00:08:49.290 Uttam Kumaran: And of course, like, we’re also building this company. So it’s like that. It’s like that meme about like building the company as it’s like fly building the plane as it’s flying, you know. And so I think I think about that a lot. But of course, like we, we do what we can. But I’m really excited that we’re able to sort of bring on a lot more people on the data team. And I’m very excited that I think across analytics engineering. Now, we have a very solid crew, so we can make sure that
50 00:08:49.470 ⇒ 00:08:57.430 Uttam Kumaran: you know models come out on time, and that we can allow the analyst team to give us the requirements we need? And so it’s been really, really
51 00:08:57.630 ⇒ 00:08:58.810 Uttam Kumaran: positive. There.
52 00:08:59.606 ⇒ 00:09:11.580 Uttam Kumaran: yeah. So I I want to talk about sort of how are we doing across these 3? Yeah, in terms of like our accelerating our revenue goals? Yeah, we are selling
53 00:09:11.710 ⇒ 00:09:16.879 Uttam Kumaran: well. And I’ll kind of give you what this means, and we’ll talk about it in the next few slides.
54 00:09:18.000 ⇒ 00:09:19.000 Uttam Kumaran: We
55 00:09:19.190 ⇒ 00:09:25.170 Uttam Kumaran: so to date, like all of the sales, has come from me and Robert, we have done all the selling.
56 00:09:25.690 ⇒ 00:09:42.660 Uttam Kumaran: I am not a salesperson like I’m an engineer, and so we did what we could. But now we are sort of focusing on moving from doing what you can to building sustainable sales practices. So we’re starting to build playbooks, icps, positioning documents.
57 00:09:43.039 ⇒ 00:10:12.080 Uttam Kumaran: decks, everything that you would expect a sophisticated sales organization to do so that we can come identify clients that have us make sure that they have the budget. They have the need and then try to close people in 2 meetings or less. And we’re we’re we’re on track to sort of make that happen. Still, while we’re really busy with clients, somehow, people still want to work with us. And so we’re getting a lot of inbound. And we’re processing that. And really, I would say.
58 00:10:12.405 ⇒ 00:10:27.870 Uttam Kumaran: I’ll probably speak for Robert here, and he may not be this honest, but probably his. And he’s really leading a lot of stuff on the sales side is the biggest fear for him and probably for me, is that our data and AI team can’t handle extra load
59 00:10:27.910 ⇒ 00:10:42.640 Uttam Kumaran: right? And that’s the problem that I wake up every day looking to solve one by taking on work myself, but also 2 trying to bring on great people that have seen these problems over and over again, and can build process and then 3.
60 00:10:55.640 ⇒ 00:10:56.250 Luke Daque: Hello!
61 00:10:57.560 ⇒ 00:11:00.619 Luke Daque: You don’t just get get cut off.
62 00:11:00.620 ⇒ 00:11:03.170 Uttam Kumaran: But you know there’s a lot that goes in there.
63 00:11:03.170 ⇒ 00:11:05.489 Miguel de Veyra: We lost you for like an entire minute.
64 00:11:05.700 ⇒ 00:11:07.059 Uttam Kumaran: Oh, really, how about now?
65 00:11:07.280 ⇒ 00:11:09.040 Demilade Agboola: Yeah, yeah. You’re back. Where did I?
66 00:11:09.040 ⇒ 00:11:10.380 Uttam Kumaran: Where did I stop?
67 00:11:10.380 ⇒ 00:11:12.280 Demilade Agboola: We’re all talking about Port number 3.
68 00:11:12.440 ⇒ 00:11:13.150 Uttam Kumaran: Oh, okay.
69 00:11:13.150 ⇒ 00:11:31.850 Uttam Kumaran: yeah, basically, I was saying, it’s like we want to work on process. So one of the things that we’re doing is working on the machine as we’re going and so I think the the key thing, I wanna say, and I’ll kind of stop on this point is for our ability to support sales is giving them confidence that we can take any sort of business that comes in.
70 00:11:31.930 ⇒ 00:11:50.640 Uttam Kumaran: and then for sales, they want to know that anything they go and sell we can execute on we’re getting a lot of luxury in that. We have a lot of people that want to work with us. And so I’m excited to build our engineering function, to take on more and more complicated things on the delightful service delivery. We’re still struggling here.
71 00:11:51.446 ⇒ 00:11:52.860 Uttam Kumaran: I think.
72 00:11:53.430 ⇒ 00:12:07.899 Uttam Kumaran: as I mentioned before, I don’t think any of the data work we’re doing is hard. We do have a requirements problem, and a process problem where the fact, the way we get requirements from clients, the way that gets boiled down into who on the engineering team is executing that.
73 00:12:07.910 ⇒ 00:12:29.350 Uttam Kumaran: and the way that when it gets executed gets handed off to the next person. This is a struggle right now. We are doing better every week, I think, compared to 2 weeks ago. We’re doing better. However, I think part of the getting better is mainly because we have more people. I don’t think like we won’t be able to afford that, you know, in terms of just like
74 00:12:29.480 ⇒ 00:12:32.750 Uttam Kumaran: we can’t have 10 people on every client, so
75 00:12:32.900 ⇒ 00:12:40.359 Uttam Kumaran: this will have to get solved through a lot of process work that I know Kyle is working on. And a team is working on. And we’re working the analyst team on
76 00:12:40.899 ⇒ 00:12:44.590 Uttam Kumaran: and then the driving, attributable revenue from brand content. So
77 00:12:44.820 ⇒ 00:12:56.449 Uttam Kumaran: on this side, we we did get pricing out. We’re basically done with the capabilities deck. I think one thing that I’ll talk to the design team on when we get there is, how do we actually create a tracking plan
78 00:12:56.600 ⇒ 00:13:00.170 Uttam Kumaran: for how our design efforts are
79 00:13:00.370 ⇒ 00:13:19.939 Uttam Kumaran: ramping up to us, bringing in more money. Part of that is gonna be the design team supporting the sales team. Part of that is, gonna be making sure we’re putting up content. And that content is getting impressions and conversions. And driving that. So again, data problem there as well. So
80 00:13:20.060 ⇒ 00:13:24.728 Uttam Kumaran: I’ll just share this we talked about. This is sort of what we did last week.
81 00:13:25.380 ⇒ 00:13:30.760 Uttam Kumaran: so our goal for this week was to bump Eden up by the end of month. But bump Javi up.
82 00:13:31.144 ⇒ 00:13:37.395 Uttam Kumaran: The our helper! Renewal we didn’t end up getting to. I think they just they just didn’t need us at the moment.
83 00:13:38.070 ⇒ 00:14:06.059 Uttam Kumaran: so I would say, we’re pretty good on the increase. Invoice, Mrr. The other piece is, we do have several clients that are in several leads that are in pipeline. Now for another legal firm for another healthcare firm. And I think one other company where we have oh, yeah. And we’re basically in the proposal stage for another telehealth company. So we do have probably another 30 to 40 grand in Mrr that could come in the door like any time.
84 00:14:06.280 ⇒ 00:14:10.719 Uttam Kumaran: So solving the service delivery problem is like.
85 00:14:11.260 ⇒ 00:14:26.619 Uttam Kumaran: there’s like a yesterday’s. Then, you see, it’s all yesterday. So that’s what my day to day focus is on in terms of the 400 K Mrr. And pipeline that I feel pretty good, too, and then expanding into legal services. So we are.
86 00:14:26.720 ⇒ 00:14:31.870 Uttam Kumaran: As I mentioned, we have one legal company and proposal stage.
87 00:14:32.410 ⇒ 00:14:39.249 Uttam Kumaran: We’re having conversations with legal AI platforms like Harvey.
88 00:14:39.729 ⇒ 00:14:45.830 Uttam Kumaran: About how we can start implementing their tools at different legal firms. And we’re having industry conversations.
89 00:14:47.480 ⇒ 00:14:49.464 Uttam Kumaran: with lawyers right now.
90 00:14:51.350 ⇒ 00:15:00.215 Uttam Kumaran: about what their needs are in the legal space across data and AI, and whether there are clients that that fit our our customer profile.
91 00:15:01.030 ⇒ 00:15:06.039 Uttam Kumaran: any questions here on the sales side.
92 00:15:06.740 ⇒ 00:15:13.010 Uttam Kumaran: I know a lot of people probably don’t touch this. It’s probably most of the folks here. See the clients once we sign them. But
93 00:15:13.808 ⇒ 00:15:16.579 Uttam Kumaran: I think it’s important to see like this is.
94 00:15:16.880 ⇒ 00:15:20.610 Uttam Kumaran: This is the the earliest part of the funnel for our customers.
95 00:15:24.730 ⇒ 00:15:28.639 Uttam Kumaran: Cool in terms of delightful service delivery.
96 00:15:29.343 ⇒ 00:15:31.359 Uttam Kumaran: I’ll just use this slide.
97 00:15:31.630 ⇒ 00:15:52.409 Uttam Kumaran: So we’re getting better at the at the measuring. The number and quality of messages. The AI team has worked on a process that actually alerts a few of us about if we’re communicating in slack to each of our clients every day, and we are getting those messages this week. We’ve been doing better now that Javi is is on a little bit of a better track.
98 00:15:52.887 ⇒ 00:16:11.619 Uttam Kumaran: I still think that we need probably a little bit more support on the Eden side. We definitely need a little bit more communication on the stack with side and that shouldn’t fall 100 on the engineers. So I think there’s still some work to do there.
99 00:16:12.296 ⇒ 00:16:22.083 Uttam Kumaran: On this piece. This is getting better, I would say, mainly through the function of us bringing on more people.
100 00:16:22.840 ⇒ 00:16:36.689 Uttam Kumaran: so I feel a lot more confident that I/O can take on the work from Javi. I feel more confident that we’ll bring on an additional ae ideally them a lot to to handle some stuff for
101 00:16:37.056 ⇒ 00:16:56.930 Uttam Kumaran: Eden as well. Some assisted work for Eden, as well as helping me on urban stems. And that’ll bring our a crew up to you know 4 people which which I think we should be able to cover. You know a fair bit of clients there. Some clients are a lot low touch, like pool parts. There’s very little ae work at the moment.
102 00:16:57.386 ⇒ 00:16:59.950 Uttam Kumaran: So we do have some benefit there.
103 00:17:00.330 ⇒ 00:17:15.779 Uttam Kumaran: We still haven’t worked on playbooks at all, so I would say the one thing that we’re we’re starting to work on, and I will talk to. I think I’ll I have a slide about talking to the data team. The folks that are on now, about some of these processes. But basically.
104 00:17:16.130 ⇒ 00:17:26.519 Uttam Kumaran: we’re working on one process by which we can collect requirements from the data team in order to inform the Ae. Team on what models we should be building.
105 00:17:28.089 ⇒ 00:17:34.389 Uttam Kumaran: For example, we’re building 3 or 4 dashboards for Eden. We’re building 4 dashboards for joby
106 00:17:34.850 ⇒ 00:17:40.700 Uttam Kumaran: at the moment. The Ae. Team has sort of built a bunch of models, and then like, go find what you need.
107 00:17:41.151 ⇒ 00:17:50.199 Uttam Kumaran: Instead, we want to sort of flip it. So the analyst team works with us to produce a set of dashboard requirements, and then the Ae team finds out.
108 00:17:50.250 ⇒ 00:17:54.630 Uttam Kumaran: Is this available? What is the mark that we need to best build to support these?
109 00:17:54.946 ⇒ 00:18:21.329 Uttam Kumaran: Right? So again, this is, this is pretty common where we have, like dashboard. Sort of requirements where? Here’s the here’s the metrics I need. Here’s what they solve. And then the Ae team says, Okay, you can go to this table to go find that with the dimensionality you need, or okay, we should go build that table for them. And that way that document sort of lives as the documentation for the dashboard but also allows us that sort of maintain, that interaction.
110 00:18:21.775 ⇒ 00:18:27.140 Uttam Kumaran: What will what the analyst team will also see. I know there’s some questions about like the
111 00:18:27.430 ⇒ 00:18:41.849 Uttam Kumaran: the database organization, the repo organization. So that stuff we’re cleaning up ideally. There shouldn’t be any questions about where to go to find metrics or find tables. So that stuff, you know. We’ll work on cleaning up a bit.
112 00:18:42.325 ⇒ 00:18:49.770 Uttam Kumaran: And then the last piece, I think, before we move on, is on the junior Pm. AI. Agent. I think we’ll show a couple of things in
113 00:18:50.160 ⇒ 00:19:05.909 Uttam Kumaran: in demos but we do. We did improve the tickets here a bit. Which again, I think. It’s just the way that we create tickets and notion using AI so that’s definitely a little bit better. And we’re working on a few other things on the AI
114 00:19:06.588 ⇒ 00:19:12.089 Uttam Kumaran: Pm, side around meeting transcriptions, making the links available. Things like that.
115 00:19:12.661 ⇒ 00:19:17.270 Uttam Kumaran: One exercise I just wanted to go through while we have. I know, Sahana, you’re on Luke
116 00:19:18.149 ⇒ 00:19:31.019 Uttam Kumaran: Aish and demalada. You’re here, too, is I want to just talk about sort of like the processes for the data team, because we have a lot right now. And I wanted to just give you my sense of what I think
117 00:19:31.290 ⇒ 00:19:37.970 Uttam Kumaran: the best set of processes here, and I wanted to get feedback from everybody on like if this works
118 00:19:39.600 ⇒ 00:19:44.600 Uttam Kumaran: so daily, I think we should at least have one touch point where
119 00:19:44.840 ⇒ 00:20:00.760 Uttam Kumaran: the client pod is discussing what needs to happen for the client. I think we be previously did this in 1 30 min meeting for all clients. Now, we basically have 2 groups. I basically divided the group in order to try to find some overlap of engineers that are across both.
120 00:20:01.195 ⇒ 00:20:18.109 Uttam Kumaran: This week has been so chaotic in terms of moving those meetings. So I really apologize. I for the next week I sort of set those in stone. But I want to understand if that’s process works for everybody on the Ae. Team and the analyst team. That’s on the call
121 00:20:18.520 ⇒ 00:20:19.509 Uttam Kumaran: right now.
122 00:20:27.727 ⇒ 00:20:39.759 Demilade Agboola: I I don’t know how you currently do it, but the only objection I will think to a daily meeting is the length of time, because if it takes too much time, I don’t think, and if it’s every day it could be a blocker
123 00:20:40.146 ⇒ 00:21:01.470 Demilade Agboola: but if it’s 1 of those calls where people can Co come in and just go. Hey, this is this is the update, like, literally like, 1 min, 2 min updates on what’s going on. If there’s any blocker that can help identify if there’s any problems but, like ideally, should not be a long call, it should really just be straight to the point. This is what I’m doing. This is the issues I have, like.
124 00:21:01.710 ⇒ 00:21:04.009 Demilade Agboola: you know, straight to points, basically.
125 00:21:04.970 ⇒ 00:21:13.109 Uttam Kumaran: Yeah, at the moment it’s just 30 min typically but I do expect that to get shorter as we get things more clear.
126 00:21:15.670 ⇒ 00:21:18.510 Uttam Kumaran: I don’t know Sahana or Luke. Do you guys have any thoughts.
127 00:21:19.400 ⇒ 00:21:31.750 Luke Daque: Yeah, I’m I’m I think that’s a good way to like, at least for the like, for now, especially since the team is growing, I think that’s a good start. Maybe you can do it daily, but of course we can like
128 00:21:32.010 ⇒ 00:21:36.909 Luke Daque: maybe reconsider in the future if ever it’s like too much of a time to do it daily.
129 00:21:37.250 ⇒ 00:21:44.649 Luke Daque: But yeah, for like for a 1st pass, that would be a great like daily Sync, just to like.
130 00:21:45.050 ⇒ 00:21:48.420 Luke Daque: yeah, like, like, like Demi Ladi mentioned
131 00:21:49.020 ⇒ 00:21:54.070 Luke Daque: like, get to know, like what the people are up to. And if there’s any blockers anybody can help
132 00:21:54.350 ⇒ 00:22:00.039 Luke Daque: and stuff like that like, it’s basically like a daily sync as well. Daily stand up, slash sync.
133 00:22:02.550 ⇒ 00:22:11.199 Sahana Asokan: Yeah, I think just to add on that, I think we also need to come up with maybe an analytics engineering document. For
134 00:22:11.550 ⇒ 00:22:27.010 Sahana Asokan: when we when we’re talking about the requirements for a given dashboard. I think engineering needs to own where that material exists like in what dashboard I think we need to get like a sign off, because I think there’s a lot of confusion right now
135 00:22:27.040 ⇒ 00:22:50.100 Sahana Asokan: with where things exist, and it makes it kind of confusing, like I feel like that process needs to be established. So I don’t know if it’s either like a document, or if it’s a 1 on one sync with like me, and, like the respective analytics engineer, to go over like all the requirements, and where everything exists and what needs to be built out, I think that kind of creates more accountability for both parties.
136 00:22:51.390 ⇒ 00:22:57.949 Uttam Kumaran: Yeah, I agree. I I think we are working on a version of that, and maybe I’ll just I’ll just show
137 00:22:59.520 ⇒ 00:23:02.149 Uttam Kumaran: Oh, what the name of this.
138 00:23:02.750 ⇒ 00:23:05.370 Uttam Kumaran: I think it’s like something semantic.
139 00:23:05.540 ⇒ 00:23:06.230 Uttam Kumaran: Yeah.
140 00:23:08.150 ⇒ 00:23:12.100 Luke Daque: Yeah, it’s a very time question if that’s right, what we what
141 00:23:12.350 ⇒ 00:23:15.439 Luke Daque: we are doing this week, like we just started.
142 00:23:15.660 ⇒ 00:23:20.080 Luke Daque: Cayo was like the one who started this. So yeah.
143 00:23:20.320 ⇒ 00:23:29.859 Uttam Kumaran: Yeah. So so, Sahana, we’re basically working on this thing that’s like for the analyst team. If you can provide us with the dashboard. And the question that’s being answered
144 00:23:30.130 ⇒ 00:23:38.720 Uttam Kumaran: like what your descriptive name would be and like, what a definition is. We were basically gonna start to identify where you can find it in the mark.
145 00:23:39.246 ⇒ 00:23:55.820 Uttam Kumaran: and ideally, like, we have a couple of versions of this that we were sort of iterating on this week. But ideally, it’s it’s basically this, where you can put in like, what information you need, at what dimensionality. And then we supplement where where you can find in which mark if it exists.
146 00:23:55.970 ⇒ 00:24:00.679 Uttam Kumaran: And this becomes sort of like, because the dashboard is iterative process, right? And it’s like a
147 00:24:00.810 ⇒ 00:24:06.269 Uttam Kumaran: it’s sort of like somewhat a moving target as we get closer to the perfect thing. And this
148 00:24:06.650 ⇒ 00:24:12.909 Uttam Kumaran: leaves us with one a great artifact that’s like documentation. But second allows the a team to fill in the gaps.
149 00:24:13.030 ⇒ 00:24:18.669 Uttam Kumaran: and then allows you guys to not really care too much about where it comes from. But like, I just need these.
150 00:24:18.980 ⇒ 00:24:22.630 Uttam Kumaran: you know and I need. These are the validations that I need on them.
151 00:24:24.220 ⇒ 00:24:27.700 Uttam Kumaran: So ideally, this is this is, gonna be our new process for that.
152 00:24:30.880 ⇒ 00:24:36.059 Uttam Kumaran: So what one thing I’ll think we’ll do on monday.
153 00:24:36.630 ⇒ 00:24:44.870 Uttam Kumaran: is one I sort of. I sort of really have been enjoying meeting with the entire a team every day. We just talk about sort of like
154 00:24:45.000 ⇒ 00:24:54.600 Uttam Kumaran: Dbt process stuff. I think we may probably longer term may push that every other day or so. But again, I think roughly.
155 00:24:54.840 ⇒ 00:25:07.380 Uttam Kumaran: most of the people should. Then it’s just really like 2 or 3 meetings. Max per day, which I feel like is okay. The daily group meetings gives us the pods to talk about a client. The daily meetings allow the A team to talk about
156 00:25:07.600 ⇒ 00:25:09.479 Uttam Kumaran: sort of Dbt and structure work.
157 00:25:09.650 ⇒ 00:25:14.580 Uttam Kumaran: I was also thinking about doing one for the analysts. So, Sahana, it would be like you, Jacob
158 00:25:14.960 ⇒ 00:25:25.110 Uttam Kumaran: Pius and Bo. Do you think there’s any like cross functional conversations that would be helpful? We could also do that like once a week. I definitely want to hear more about like
159 00:25:25.600 ⇒ 00:25:30.359 Uttam Kumaran: how you guys are working and like sort of gathering requirements for clients and things like that.
160 00:25:31.860 ⇒ 00:25:36.739 Uttam Kumaran: Do you think like a daily cadence is good there, or like we should do something else.
161 00:25:41.865 ⇒ 00:25:45.550 Uttam Kumaran: Sahana, I don’t know if you’re you might be on mute.
162 00:25:45.840 ⇒ 00:25:47.579 Sahana Asokan: Sorry. Do you mind repeating that.
163 00:25:47.580 ⇒ 00:25:54.350 Uttam Kumaran: Yeah, like, for, like, I wanted to start doing a similar cadence for the analyst team to sort of talk at some cadence every week.
164 00:25:55.860 ⇒ 00:25:59.299 Uttam Kumaran: Just to sort of learn from each other. But also talk about
165 00:25:59.610 ⇒ 00:26:05.550 Uttam Kumaran: developments that we’re doing across clients in terms of analysts work like, do you have an idea for what a good
166 00:26:05.990 ⇒ 00:26:09.730 Uttam Kumaran: like cadence for that is we meet with as an Ae. Team every day. Basically.
167 00:26:10.030 ⇒ 00:26:19.179 Sahana Asokan: Yeah, I think maybe like once a week, because then we could talk about like, okay, this is what we’re trying to build. And do we like? Are we good on
168 00:26:20.190 ⇒ 00:26:24.669 Sahana Asokan: the data for it? I don’t think we really have that sync right now. It’s more of like
169 00:26:24.800 ⇒ 00:26:28.540 Sahana Asokan: Async, like I thing. And I’m like, Hey, like, where does this exist?
170 00:26:28.910 ⇒ 00:26:34.970 Sahana Asokan: And then there’s that’s where the confusion starts, and I think we need to be aligned on it before we even kick off building.
171 00:26:35.530 ⇒ 00:26:36.090 Uttam Kumaran: Okay.
172 00:26:36.350 ⇒ 00:26:44.040 Uttam Kumaran: Okay, so yeah, I’ll probably put some time on for that at the moment. It’ll just be like me, you, bo, Jacob Pius.
173 00:26:44.140 ⇒ 00:26:46.510 Uttam Kumaran: and I’ll try to find time that everybody can join that.
174 00:26:46.750 ⇒ 00:26:49.330 Uttam Kumaran: And then, yeah, I wanted to try to do one
175 00:26:49.670 ⇒ 00:26:51.700 Uttam Kumaran: sync as like a data team.
176 00:26:52.327 ⇒ 00:26:56.802 Uttam Kumaran: Again. Maybe this can just be like 15 min or
177 00:26:57.360 ⇒ 00:27:01.914 Uttam Kumaran: or maybe we can even lump this into the all team, all company meeting
178 00:27:02.390 ⇒ 00:27:12.419 Uttam Kumaran: but I just wanted everybody across the A team and analyst team to sort of talk about larger structural issues that we’re having as like a data crew. I think maybe 2 weeks is fine.
179 00:27:12.989 ⇒ 00:27:21.140 Uttam Kumaran: That way. We could sort of air out process improvements like sort of that aren’t related to specific clients.
180 00:27:21.340 ⇒ 00:27:31.479 Uttam Kumaran: And the last piece is like Async dashboard requirements like, I’m gonna book time on Monday to go through the Eden roadmap, because I don’t know what the roadmap is, and so I can’t
181 00:27:31.720 ⇒ 00:27:37.359 Uttam Kumaran: sort of look forward as an ae crew and sort of determine what needs to be done. So
182 00:27:37.550 ⇒ 00:27:43.070 Uttam Kumaran: we want to start doing probably requirements, meetings around dashboards and around
183 00:27:43.780 ⇒ 00:27:52.409 Uttam Kumaran: just roadmap for every single client. Is there anything else on this list that would be like important? I don’t know a waste you’re also on. If you have any ideas.
184 00:27:55.210 ⇒ 00:28:03.139 Awaish Kumar: No, it looks good. I think. That that.
185 00:28:03.340 ⇒ 00:28:07.200 Awaish Kumar: like the most important one, is that the communication between the analysts
186 00:28:07.900 ⇒ 00:28:10.620 Awaish Kumar: and the Ee team, where we
187 00:28:10.840 ⇒ 00:28:14.090 Awaish Kumar: basically communicate the requirements and the structures.
188 00:28:14.970 ⇒ 00:28:18.260 Awaish Kumar: and, like the weekly meeting between us, is
189 00:28:19.070 ⇒ 00:28:21.550 Awaish Kumar: is much more important for the
190 00:28:21.840 ⇒ 00:28:24.710 Awaish Kumar: a smooth process for building the models.
191 00:28:25.800 ⇒ 00:28:26.360 Uttam Kumaran: Okay.
192 00:28:28.340 ⇒ 00:28:28.780 Demilade Agboola: Oh no!
193 00:28:29.490 ⇒ 00:28:30.300 Uttam Kumaran: Yeah. Go.
194 00:28:30.300 ⇒ 00:28:39.541 Demilade Agboola: I don’t know if it’s if this is necessary, part of the like team processes. But I was wondering about the opportunity to like retros or learnings.
195 00:28:40.390 ⇒ 00:28:42.840 Demilade Agboola: so this isn’t necessarily a thing of like
196 00:28:43.260 ⇒ 00:29:10.190 Demilade Agboola: it’s not like scheduled in the sense of like, it’s just based on projects. So the opportunity to be able to look at the project holistically and go, hey? We had a maybe 5 week, timeline or 3, you know, 3 month, timeline for this project? You really came down to the last, maybe 2 weeks to get things over the line. Is there a reason why that happened like, what was our initial timeline? Or where did we fall short? What caused us to fall short of those timelines? Things like that?
197 00:29:10.628 ⇒ 00:29:22.789 Demilade Agboola: So just that sort of ability to be able to go through projects holistically and kind of see where like things happened. Was he initially access? Did we struggle getting kpis, you know, things like that.
198 00:29:24.280 ⇒ 00:29:29.850 Uttam Kumaran: Yeah, I do think that there’s something around doing like a client. I mean, I sort of see the
199 00:29:30.400 ⇒ 00:29:32.550 Uttam Kumaran: like us meeting as like a
200 00:29:32.740 ⇒ 00:29:36.660 Uttam Kumaran: data team as like, part partly a retro of like.
201 00:29:36.850 ⇒ 00:29:43.640 Uttam Kumaran: how are we all working together as like a full crew? I also think that we probably need some sort of like client specific
202 00:29:43.930 ⇒ 00:29:48.869 Uttam Kumaran: retro or like update. Right now again, we’re we’re not. We’re sort of
203 00:29:48.970 ⇒ 00:29:57.969 Uttam Kumaran: day to day to day for every client. And it’s really a struggle. We wanna start to move so we can see a month ahead, and then we can sort of track
204 00:29:58.080 ⇒ 00:30:01.090 Uttam Kumaran: how we’re going, you know, towards there. So
205 00:30:02.360 ⇒ 00:30:06.909 Uttam Kumaran: yeah, I think we’re we will do something on this ideally, maybe like
206 00:30:07.080 ⇒ 00:30:12.559 Uttam Kumaran: once a month where we’re like, okay, let’s just look at all the work we’ve done here, and if it’s been smooth or not.
207 00:30:13.021 ⇒ 00:30:19.289 Uttam Kumaran: so yeah, I’ll add that as well. So yeah, I mean, I I don’t. I don’t want the goals of this to be like
208 00:30:19.410 ⇒ 00:30:24.289 Uttam Kumaran: meetings on everyone’s calendar but we are starting to get to the point where.
209 00:30:24.870 ⇒ 00:30:29.199 Uttam Kumaran: for me to sort of guarantee it that we execute. We do have to talk every day.
210 00:30:29.330 ⇒ 00:30:40.090 Uttam Kumaran: at least talk in our client pods every day. So the one thing is, I know we have several people on different time zones. So I’m gonna go through and just make sure that everyone can attend
211 00:30:40.523 ⇒ 00:30:46.400 Uttam Kumaran: cause. These are the like, basically the daily group meetings, like, I have to have everybody there in order to
212 00:30:46.710 ⇒ 00:30:52.661 Uttam Kumaran: to understand what we’re doing for clients. So I’ll message everybody and make sure that we can. We can sort of get that
213 00:30:54.090 ⇒ 00:31:01.653 Uttam Kumaran: that booked great. And I think the next piece I just wanted to talk about design work.
214 00:31:03.030 ⇒ 00:31:05.110 Uttam Kumaran: yeah. So we
215 00:31:05.843 ⇒ 00:31:23.479 Uttam Kumaran: we talked about finishing up the capabilities deck. I think we talked with Hannah a little bit about sort of ownership over a couple of different items. We sort of pause some stuff on the newsletter. I don’t know, Hannah, if you want to give an overview of like where the design team is and anything related to.
216 00:31:23.950 ⇒ 00:31:29.669 Uttam Kumaran: I mean, the one thing I messaged this week is like, I want to start working on some sort of tracking plan
217 00:31:29.970 ⇒ 00:31:33.299 Uttam Kumaran: to how to track towards getting these.
218 00:31:34.860 ⇒ 00:31:37.420 Uttam Kumaran: But yeah, if you, if you have anything on there.
219 00:31:39.570 ⇒ 00:31:51.759 Hannah Wang: Yeah, I mean for the tracking tracking plan. I have to think about it more especially cause we talked about yesterday over slack, like kind of reevaluating the content that we’re pushing out and kind of
220 00:31:52.030 ⇒ 00:31:53.230 Hannah Wang: stopping.
221 00:31:54.685 ⇒ 00:32:03.449 Hannah Wang: Yeah, pushing content out on certain platforms. As for the design, I’ll just go over the design team really quick. Yeah, we’re still
222 00:32:03.770 ⇒ 00:32:24.771 Hannah Wang: cranking out the capabilities deck. And then, based on that, just all the one pagers and any other asset that can help the sales team. I think. Yeah, sales is kind of our client now. So in a sense, yeah, just helping them. And whatever assets they need from us will create that with the right branding and content, and everything.
223 00:32:25.350 ⇒ 00:32:34.259 Hannah Wang: and I also feel like there’s a couple of other like ad hoc requests that come in here and there. So just trying to think of a way to fit everything in on top of supporting
224 00:32:34.791 ⇒ 00:32:38.660 Hannah Wang: the content team in terms of like thumbnails and assets that.
225 00:32:38.910 ⇒ 00:32:45.009 Hannah Wang: Yeah, the content team needs as for content, yeah, you and I talked about maybe
226 00:32:45.230 ⇒ 00:33:13.339 Hannah Wang: slowing down on some platforms, maybe Instagram and X and Ryan and and I kind of talked in a meeting earlier today about the strategy that we want to focus on moving forward. Which is to focus on Linkedin posts and the blogs that we have on our website and kind of funneling the audience from Yeah Linkedin to our website. And I also think maybe starting up the newsletter
227 00:33:13.680 ⇒ 00:33:21.400 Hannah Wang: and the lead generation lead magnet would be helpful for that. And kinda going from
228 00:33:21.930 ⇒ 00:33:34.289 Hannah Wang: focusing on like Instagram and Nx. And all these other platforms that we have, and channeling that energy into the Newsletter and the lead generation. Because I think that can link well with Linkedin
229 00:33:35.083 ⇒ 00:33:44.426 Hannah Wang: and just kind of create like a funnel. And then another thing Ryan brought up that was pretty, I think what we want to focus on is
230 00:33:44.870 ⇒ 00:33:50.470 Hannah Wang: kind of working on you. Yours and Robert’s personal accounts and kinda.
231 00:33:50.470 ⇒ 00:33:51.050 Uttam Kumaran: Yeah.
232 00:33:51.050 ⇒ 00:33:57.240 Hannah Wang: Creating content for that, and then that’ll help generate kinda
233 00:33:57.850 ⇒ 00:34:05.519 Hannah Wang: like help funnel into the brain forge company. Linkedin account cause there’s no like organic traffic for that.
234 00:34:06.281 ⇒ 00:34:12.779 Hannah Wang: For the Linkedin, for the brain forge Linkedin. But for your and Robert’s personal accounts, I feel like.
235 00:34:13.040 ⇒ 00:34:17.730 Hannah Wang: yeah, there’s just some thing that we can do there.
236 00:34:18.710 ⇒ 00:34:24.877 Hannah Wang: so that’s kind of the strategy that we’re gonna take moving forward, kinda focusing on these other things.
237 00:34:25.370 ⇒ 00:34:27.750 Hannah Wang: but yeah, I’ll continue to think through
238 00:34:28.810 ⇒ 00:34:32.820 Hannah Wang: how to re prioritize and re-strategize. Moving forward.
239 00:34:32.820 ⇒ 00:34:38.280 Uttam Kumaran: Get if we can even get that into a notion. Because one like I, I definitely think.
240 00:34:38.639 ⇒ 00:35:01.249 Uttam Kumaran: like we’re not seeing these results at all. So I wanna so for me, that’s clear that the content work that we’ve done, although I think it’s been quality, it’s not directed in the right direction. So I want to know that one that sales team is supported with any design assets they need like. I actually think that that’s probably equal importance. For example, I think getting
241 00:35:01.510 ⇒ 00:35:04.440 Uttam Kumaran: the pricing page out and getting the decks out. I think.
242 00:35:04.550 ⇒ 00:35:26.740 Uttam Kumaran: like we got it out. But it took 3 weeks when I want to know sort of why we couldn’t have done that both in in one week, I think part of it was copy. But also we need to move a little bit quicker. There, I want to know. Okay, if if that, if the if the priority is there, what needs to be dropped elsewhere? Right? Because this isn’t we’re not in the mode of like we do both.
243 00:35:26.850 ⇒ 00:35:44.250 Uttam Kumaran: So the content piece, like I, if we need to cut channels, we cut channels, because right now we’re not. I’m not able to see this clearly at all. So that’s also another thing is before we. I’m even honestly more, I would say, pause everything until I’m able, until we’re able to see and measure these
244 00:35:45.800 ⇒ 00:35:50.600 Uttam Kumaran: because I don’t want our time to be spent on things that aren’t driving results.
245 00:35:50.988 ⇒ 00:35:55.360 Uttam Kumaran: And I think we’ve tried a lot of things on the content side. But it’s really not clear to me
246 00:35:55.660 ⇒ 00:35:58.890 Uttam Kumaran: like what the process is for measuring the impact.
247 00:35:59.553 ⇒ 00:36:08.970 Uttam Kumaran: So those would be 2 suggestions is one. I think. There, I think it would be helpful to have some meeting with Robert included every week, so you could gather requirements
248 00:36:09.110 ⇒ 00:36:14.700 Uttam Kumaran: from him and start to directly get requirements on
249 00:36:15.220 ⇒ 00:36:18.700 Uttam Kumaran: prioritization. And what needs to be done on the design side.
250 00:36:18.800 ⇒ 00:36:23.060 Uttam Kumaran: And then, second, I definitely want to to know how we plan to
251 00:36:23.180 ⇒ 00:36:32.091 Uttam Kumaran: some way of measuring these. And I, I would, I would honestly say, stop posting everything until we’re able to do that, because probably not worth it.
252 00:36:32.680 ⇒ 00:36:44.209 Uttam Kumaran: I don’t know Ryan and or Anna like. What are your thoughts on that because I want us to, just I don’t want to take on everything, and then sort of also do this like I’d rather just stop and then figure this out before we move forward.
253 00:36:50.840 ⇒ 00:36:58.499 Hannah Wang: Yeah, yeah, I’m also, yeah, I would need to think about it more like how to
254 00:36:58.660 ⇒ 00:37:01.112 Hannah Wang: measure that tangibly.
255 00:37:05.290 ⇒ 00:37:08.630 Uttam Kumaran: I mean, we have. We have this data coming in post hoc, like.
256 00:37:08.630 ⇒ 00:37:09.390 Hannah Wang: Right.
257 00:37:09.390 ⇒ 00:37:13.650 Uttam Kumaran: Right. How many page views are we getting? All of that? Is there?
258 00:37:14.325 ⇒ 00:37:20.170 Uttam Kumaran: The volume? You can look at the new followers and Linkedin. Right so.
259 00:37:21.180 ⇒ 00:37:25.609 Uttam Kumaran: But like, I’m sort of like, I’m sort of not convinced
260 00:37:26.000 ⇒ 00:37:28.760 Uttam Kumaran: that we have a strategy to affect these at the moment.
261 00:37:29.543 ⇒ 00:37:30.809 Hannah Wang: You know.
262 00:37:34.250 ⇒ 00:37:37.290 Hannah Wang: Yeah, I feel like, maybe even just starting with
263 00:37:37.410 ⇒ 00:37:47.490 Hannah Wang: kind of what I mentioned about your personal Linkedin accounts, or just like reevaluating and re-strategizing like the
264 00:37:47.700 ⇒ 00:37:50.029 Hannah Wang: way we want to tackle this.
265 00:37:50.030 ⇒ 00:37:50.640 Uttam Kumaran: Okay.
266 00:37:52.990 ⇒ 00:38:02.251 Hannah Wang: yeah, I can brainstorm with Ryan and Ann. And kind of think about it. But I don’t know. Ryan. Do you have any. I see you’re unmuting your mic.
267 00:38:03.370 ⇒ 00:38:21.761 Ryan Brosas: Yeah for the for the strategy that we have for Linkedin. It’s more of like making like your profile account as a funnel, and that’s 1 also like, for I think, yeah. Yeah. As Hannah said, that
268 00:38:22.380 ⇒ 00:38:46.537 Ryan Brosas: or a company linking doesn’t really have like a organic traffic. So we want to push more content to personal account as considering as part of my my personal account. Also. I’m as you grow your personal account we can. You know that redirect all the traffic to the company linking. So that’s 1 one way to
269 00:38:47.140 ⇒ 00:39:02.469 Ryan Brosas: one way to see it also, as we grow of our personal account, we can grow also our Prof. Company Lincoln account. And for for that, yeah, engagement across channels.
270 00:39:02.550 ⇒ 00:39:26.890 Ryan Brosas: So for the for the actual other channels. As I mentioned already, that we needed to do engagement, as we want to also connect to other, you know, influencer. people also. And yeah, I think that’s 1 way to, you know, to increase our visibility. And you know, we can start on building that.
271 00:39:27.640 ⇒ 00:39:30.235 Ryan Brosas: yeah, I think that’s all on my side.
272 00:39:30.560 ⇒ 00:39:37.100 Uttam Kumaran: Okay, yeah, i i i’m still like a lot less interested in the execution. I just wanna see a plan.
273 00:39:37.907 ⇒ 00:39:52.930 Uttam Kumaran: Right? So I I hear you on leveraging our linkedins. But I what I’m not hearing is how we speed up the creation of these assets right? And if, if, like 50% of the design team’s time is going to creating
274 00:39:53.060 ⇒ 00:39:55.810 Uttam Kumaran: stuff for content that never gets seen.
275 00:39:56.650 ⇒ 00:40:17.750 Uttam Kumaran: That’s, you know, the answer, right? So that’s what I want to know is like, I want to have a conversation next week about where effort is going and how we can channel that to just the things that we know are gonna win. Like, if if stuff on X Instagram, if that’s taking a 50% of design teams time, but leading to like 10% of the results. We shouldn’t be doing that
276 00:40:17.860 ⇒ 00:40:34.369 Uttam Kumaran: right. So that’s the hard conversation I want to have next week is to look at the strategies. And again. If it’s probably clear that we need to use our linkedin to do stuff, it’s probably clear that we need to cut some other content. I just want to see that in one area before executing on that
277 00:40:34.670 ⇒ 00:40:38.700 Uttam Kumaran: right. So if we can try to book time to do that early next week
278 00:40:38.970 ⇒ 00:40:42.189 Uttam Kumaran: and have that in notion that would be amazing.
279 00:40:42.540 ⇒ 00:40:44.700 Hannah Wang: Yeah, I think, adding on to that sorry.
280 00:40:44.830 ⇒ 00:40:45.610 Uttam Kumaran: Go ahead!
281 00:40:46.190 ⇒ 00:40:47.450 Hannah Wang: Kind of like.
282 00:40:48.510 ⇒ 00:41:07.299 Hannah Wang: yeah, I think. Well, one. Now that I’m kind of in the picture, and I can also co-design with Anne, I think content will be pushed up or assets will be pushed out a lot faster. I think also, because, yeah, we work in different time zones. So it works well, and the collaboration works well. So I feel like
283 00:41:07.820 ⇒ 00:41:15.180 Hannah Wang: things can just move a lot faster now that I can also help with design, too. I think previously things were stuck, because.
284 00:41:15.780 ⇒ 00:41:19.729 Hannah Wang: yeah, maybe a huge part of it was copy. And I think
285 00:41:19.840 ⇒ 00:41:49.479 Hannah Wang: more than that, I think we were just stuck because we were relying on maybe you or other people to get a final approval. But I think you kind of ramping up with other stuff like your time was just stress, really thin. So I think that’s what caused the blocker there. So I feel like now that kind of I can help with the copy and the content. I think that’s kind of like unblocked now. So I think things will be pushed out a lot faster. With like the thumbnails and everything that we need like. I can also help design, too. So that’s like, not a
286 00:41:49.540 ⇒ 00:42:02.320 Hannah Wang: problem, especially if we don’t focus on Linkedin and X and all those other platforms, like all our attention, will be kind of funneled to the things that we’re kind of talking about and want to emphasize right now. So
287 00:42:03.570 ⇒ 00:42:19.087 Hannah Wang: yeah, I think the main blocker I don’t. Yeah. The main blocker was just your time, which makes sense like you’re stretched thin and stuff like that. So I feel like now that we kind of have a better strategy. I think things can go a lot smoother and faster hopefully.
288 00:42:19.420 ⇒ 00:42:25.610 Uttam Kumaran: Yeah, I also just think that even. But I just want to know that, like spending 50% of design time on thumbnails.
289 00:42:26.049 ⇒ 00:42:31.970 Uttam Kumaran: because then I would. Then I would like, I wanna know why we aren’t working on decks and stuff right? Cause then.
290 00:42:31.970 ⇒ 00:42:32.370 Hannah Wang: No.
291 00:42:32.370 ⇒ 00:42:44.060 Uttam Kumaran: So that would be. My question is like, we have several one pagers, several decks, that sort of need to be finalized. We want to work on a couple of it. So I guess that’s what I want to actually want to
292 00:42:44.350 ⇒ 00:42:50.040 Uttam Kumaran: see everything so that I can be the tiebreaker right? And like you could push that decision on to me
293 00:42:50.220 ⇒ 00:43:00.960 Uttam Kumaran: like, I can say, Okay, this is the direction we’re going. But that way I can also save you, because otherwise I’m gonna say, do both. And that’s not gonna work out either. Right? So I
294 00:43:01.570 ⇒ 00:43:07.320 Uttam Kumaran: I want I want us as a design crew to be working on the number one. Most important thing.
295 00:43:08.710 ⇒ 00:43:12.399 Uttam Kumaran: and I want the sales team to feel supported so definitely.
296 00:43:12.520 ⇒ 00:43:16.420 Uttam Kumaran: I need to get moved out of the loop, and there has to be some direct connection between
297 00:43:16.620 ⇒ 00:43:33.679 Uttam Kumaran: the needs on the sales team and the design team. And then also, I wanna make sure that we are putting our content resources into tracking this. So some of Ryan, your time will probably most likely have to go to working on tracking this right? So that’s time that needs to come from somewhere.
298 00:43:33.900 ⇒ 00:43:44.209 Uttam Kumaran: And then also focusing on copy for for our linkedin and stuff. So that’s what that way we can see everything I can sort of say, cool. This is a hundred 50% capacity we need to reduce.
299 00:43:44.590 ⇒ 00:43:48.570 Uttam Kumaran: like the things we’re doing, basically to the one or 2 things that matter.
300 00:43:49.520 ⇒ 00:43:58.160 Hannah Wang: Yeah, that makes sense. I already talked with Robert. And we’re gonna have a weekly sync, so that I can kind of gather requirements from him. I do think supporting the sales
301 00:43:58.500 ⇒ 00:44:02.000 Hannah Wang: team is a priority. Just because you guys have like a lot of.
302 00:44:02.720 ⇒ 00:44:03.050 Uttam Kumaran: Yeah.
303 00:44:03.050 ⇒ 00:44:25.630 Hannah Wang: Clients coming in, and I feel like I think it’s on me then to push the the deck and the one pagers, because that is in progress. But I think I’m just kinda I wasn’t as like pushy about like, Oh, can you review this, please? So I think that’s on me to be like, okay, this is ready for review, like, can I have eyes on it? So I think, moving forward, I’ll be more strategic in that way. Yeah.
304 00:44:26.240 ⇒ 00:44:26.920 Uttam Kumaran: Cool.
305 00:44:27.250 ⇒ 00:44:32.680 Uttam Kumaran: So yeah, maybe we can have some discussion like Tuesday and Wednesday, and look at everything. I think.
306 00:44:32.900 ⇒ 00:44:53.479 Uttam Kumaran: yeah, again, I I really want to see how we can get tracking towards again. We’re a data company. So getting this done won’t be a problem. But I do want. I don’t want to solve this problem for you guys, because then I’ll be the owner. So I kind of want you guys to get a sense for how to go, go into post hog, see the traffic to the site.
307 00:44:53.750 ⇒ 00:44:55.950 Uttam Kumaran: and then the data team. We can help like
308 00:44:56.710 ⇒ 00:45:10.079 Uttam Kumaran: tape together what needs to be taped. But if I if I build the whole thing, then I’m gonna be reporting on our engagement and I’m gonna drop that. So okay, so give it a shot go in there it’s and it’s intimidating. But
309 00:45:10.400 ⇒ 00:45:13.900 Uttam Kumaran: the numbers are good. We’re going. Things are going up. So that’s good.
310 00:45:15.160 ⇒ 00:45:23.369 Uttam Kumaran: Okay, cool. I we’re at 1145. I guess I wanted to just ask a couple of people to do Demos. I think
311 00:45:24.556 ⇒ 00:45:25.203 Uttam Kumaran: one
312 00:45:26.830 ⇒ 00:45:32.539 Uttam Kumaran: I was. Gonna ask a wish, if you want to, demo like the sales mart that we did for
313 00:45:33.870 ⇒ 00:45:36.590 Uttam Kumaran: for Eden, and maybe just share
314 00:45:36.730 ⇒ 00:45:41.449 Uttam Kumaran: sort of, so that so some other folks in the data team can kind of see how we’re structuring that
315 00:45:42.077 ⇒ 00:45:47.540 Uttam Kumaran: and then I think, Casey, if you want to do an update update to on some stuff on the AI side that’d be helpful.
316 00:45:51.640 ⇒ 00:45:52.840 Uttam Kumaran: Oh, wait! She’s still on.
317 00:45:52.840 ⇒ 00:45:53.539 Awaish Kumar: There you go!
318 00:45:53.540 ⇒ 00:45:55.630 Uttam Kumaran: Yeah, do you wanna just do a quick
319 00:45:55.770 ⇒ 00:45:57.860 Uttam Kumaran: like show and tell of, like, the
320 00:45:58.330 ⇒ 00:46:02.410 Uttam Kumaran: the March Pr, about how we’re sort of structuring that for Eden.
321 00:46:04.240 ⇒ 00:46:07.310 Awaish Kumar: Oh, yeah, let me just let me share the
322 00:46:13.540 ⇒ 00:46:14.600 Awaish Kumar: screen.
323 00:46:35.560 ⇒ 00:46:36.840 Awaish Kumar: Oh, wait!
324 00:46:45.100 ⇒ 00:46:47.489 Awaish Kumar: Can you hear me?
325 00:46:47.490 ⇒ 00:46:48.030 Uttam Kumaran: Yeah.
326 00:46:50.990 ⇒ 00:46:56.020 Awaish Kumar: Hey? Sorry I I can share the
327 00:46:59.500 ⇒ 00:47:01.040 Awaish Kumar: screen here.
328 00:47:06.520 ⇒ 00:47:09.230 Awaish Kumar: Okay, so here, basically, we are.
329 00:47:10.480 ⇒ 00:47:16.180 Awaish Kumar: I, I’m trying to show this in the Vs code. Well, it was open here.
330 00:47:16.780 ⇒ 00:47:25.440 Awaish Kumar: Oh, so this is how we are conducting our models. So we have different marks, like marketing sales.
331 00:47:26.070 ⇒ 00:47:28.280 Awaish Kumar: web analytics and
332 00:47:28.860 ⇒ 00:47:34.860 Awaish Kumar: sales. V. 2, which is the second version of the sales part which we are building right now.
333 00:47:35.330 ⇒ 00:47:39.250 Awaish Kumar: and the way we are splitting
334 00:47:40.172 ⇒ 00:47:48.530 Awaish Kumar: our day, our sales data into multiple fact. And the dimension tables is
335 00:47:48.810 ⇒ 00:47:53.436 Awaish Kumar: like this. The dimension tables are are like
336 00:47:54.590 ⇒ 00:48:07.679 Awaish Kumar: named as a with with a dim prefix which shows it’s a dimension, and then the name of the entity. If it is a customer, order on the products or the shipments, whatever it is.
337 00:48:08.040 ⇒ 00:48:17.300 Awaish Kumar: and we have one, the dim calendar which just basically have the kind of values, a kind of
338 00:48:17.570 ⇒ 00:48:22.930 Awaish Kumar: pre-calculated values of of the date, like the
339 00:48:23.160 ⇒ 00:48:28.760 Awaish Kumar: if, if it’s a weekend or day of the week, these kind of things.
340 00:48:30.760 ⇒ 00:48:32.690 Awaish Kumar: So, and the customer
341 00:48:33.560 ⇒ 00:48:42.990 Awaish Kumar: dimension, we we mainly only have the information related to, and a specific customer. Here the primary key would be like a customer. Id.
342 00:48:43.090 ⇒ 00:48:48.050 Awaish Kumar: so we should only have a 1 single roof per per customer.
343 00:48:48.260 ⇒ 00:48:51.790 Awaish Kumar: and it will have all the information related to the
344 00:48:51.960 ⇒ 00:48:56.019 Awaish Kumar: customer itself. Like the contact, information, or
345 00:48:56.480 ⇒ 00:48:59.970 Awaish Kumar: or the like. There can be acquisition, date, or, like
346 00:49:00.647 ⇒ 00:49:04.579 Awaish Kumar: different other parameters like which source it came from.
347 00:49:04.980 ⇒ 00:49:06.289 Awaish Kumar: These kind of things.
348 00:49:07.670 ⇒ 00:49:16.190 Awaish Kumar: Dimension orders is basically the detailed information of an order
349 00:49:16.330 ⇒ 00:49:23.980 Awaish Kumar: like, although so we have fact transaction table, which basically contains the transactions
350 00:49:24.300 ⇒ 00:49:40.850 Awaish Kumar: which are happening which are basically in our case here, transactions are kind of orders. So customer comes in places, an order, and everyone can place multiple orders, or and we are just recording them as our transactions.
351 00:49:41.070 ⇒ 00:49:45.310 Awaish Kumar: So on a specific day, the customer made some orders.
352 00:49:45.500 ⇒ 00:49:48.669 Awaish Kumar: and and then this table. We have some
353 00:49:49.090 ⇒ 00:49:55.299 Awaish Kumar: fields which are kind of metrics, like the quantity or the revenue.
354 00:49:55.520 ⇒ 00:49:58.310 Awaish Kumar: These kind of things, which which can be
355 00:49:59.246 ⇒ 00:50:06.359 Awaish Kumar: which are mayors, basically, and can be aggregated across different dimensions.
356 00:50:06.909 ⇒ 00:50:11.079 Awaish Kumar: So here we have just the basic information about an order.
357 00:50:11.250 ⇒ 00:50:13.820 Awaish Kumar: And if we need more detailed information
358 00:50:13.950 ⇒ 00:50:24.700 Awaish Kumar: for an order like what the what was the payment method used, or what exactly the the
359 00:50:24.930 ⇒ 00:50:39.659 Awaish Kumar: things like that? What was the coupon code or like. And while while placing that order, if there was an SMS consent for for sending the notification updates things like that, these are basically the
360 00:50:40.160 ⇒ 00:50:42.969 Awaish Kumar: information related to this specific order
361 00:50:43.775 ⇒ 00:50:52.479 Awaish Kumar: Which which detailed information goes into order, table dimension orders, table, and the basic transaction is recorded in the fact transaction table.
362 00:50:52.770 ⇒ 00:51:01.359 Awaish Kumar: And similarly, we have to few more like products and shipments where in each order we, we are ordering some product and the
363 00:51:01.700 ⇒ 00:51:04.216 Awaish Kumar: product table will have the
364 00:51:04.980 ⇒ 00:51:15.919 Awaish Kumar: inform detailed information regarding that product? The very. What? What variant of that of the product it is, what is the what is unique? Id or
365 00:51:16.565 ⇒ 00:51:20.950 Awaish Kumar: what could be the cogs related to that product? Things like that?
366 00:51:21.200 ⇒ 00:51:27.430 Awaish Kumar: And similarly, we have shipments which is basically telling for that specific order.
367 00:51:28.014 ⇒ 00:51:34.209 Awaish Kumar: If it is shipped, or what is the status of it when it was shipped in? And and
368 00:51:34.770 ⇒ 00:51:39.630 Awaish Kumar: and more detail, more information regarding shipments.
369 00:51:40.092 ⇒ 00:51:50.010 Awaish Kumar: So this is basically the basic structure as kind of a style schema, where the effect transaction table is in the middle, and it is joined by different keys
370 00:51:50.340 ⇒ 00:52:05.460 Awaish Kumar: for the different dimensions like customer id for customer order id for the orders, table product product id for the product table and the shipments for the shipments. This is also the order Id, because there’s no shipment. Id.
371 00:52:06.950 ⇒ 00:52:10.170 Awaish Kumar: So yeah, this is the. This is what will be the
372 00:52:11.021 ⇒ 00:52:18.180 Awaish Kumar: setting up the ground for this sales mart. Then on top of it we have summary tables
373 00:52:18.570 ⇒ 00:52:22.280 Awaish Kumar: which basically then joins these joins these
374 00:52:22.630 ⇒ 00:52:25.920 Awaish Kumar: effect and dimension tables to generate some summary
375 00:52:27.262 ⇒ 00:52:35.819 Awaish Kumar: which which can be used like plugged in into a bi tool. And we can be used for building some kind of dashboard.
376 00:52:35.940 ⇒ 00:52:39.080 Awaish Kumar: So this order summary table is basically
377 00:52:39.815 ⇒ 00:52:44.050 Awaish Kumar: all the information related to the orders.
378 00:52:44.410 ⇒ 00:52:51.890 Awaish Kumar: So from the transaction table we get some orders. Then we find out, okay, what product were were ordered
379 00:52:52.240 ⇒ 00:52:55.920 Awaish Kumar: in this order, and what was the total price? And the
380 00:52:56.557 ⇒ 00:53:05.000 Awaish Kumar: what is the information about the customer, and then then join them together? To exactly.
381 00:53:05.760 ⇒ 00:53:07.110 Awaish Kumar: Yeah.
382 00:53:08.270 ⇒ 00:53:21.759 Awaish Kumar: grant them together to have kind of somebody of these orders on a on a very granular level. But like, after we gave you more information
383 00:53:21.870 ⇒ 00:53:34.119 Awaish Kumar: about how we want to build the dashboards and the here we give, the granularity can increase. Like, for example, we just, we are just building a dashboard for monthly.
384 00:53:34.300 ⇒ 00:53:43.690 Awaish Kumar: a summary of monthly orders. So we don’t need information regarding individual order. So yeah, we can have multiple summary tables. Then.
385 00:53:43.980 ⇒ 00:53:45.750 Awaish Kumar: yeah, that’s all.
386 00:53:46.840 ⇒ 00:53:52.240 Uttam Kumaran: Cool. Yeah. So one of the exercises we’re working on across clients is basically building these different marts.
387 00:53:52.340 ⇒ 00:54:02.450 Uttam Kumaran: So sales, marts, marketing marts, customer service marts. Basically, it’s a data mart where the analyst team can sort of go source any information they need for analysis.
388 00:54:02.932 ⇒ 00:54:11.130 Uttam Kumaran: I think we’ve sort of worked more ad hoc. And so now we’re sort of going and restructuring and creating more of these Mars models for every client.
389 00:54:11.630 ⇒ 00:54:19.909 Uttam Kumaran: I think I just wanted, Casey, if you wanted to do. I forgot what Demos we talked about this week, but if you want to take on any Demos from the AI team.
390 00:54:20.090 ⇒ 00:54:21.139 Uttam Kumaran: feel free.
391 00:54:22.460 ⇒ 00:54:23.319 Casie Aviles: Yeah, sure.
392 00:54:23.880 ⇒ 00:54:26.880 Casie Aviles: So I just want, let me quickly show.
393 00:54:31.441 ⇒ 00:54:33.929 Casie Aviles: Yeah. So basically, one of the things that
394 00:54:34.600 ⇒ 00:54:39.599 Casie Aviles: we’ve we’ve made some updates over at the for the Zoom Summarizer agent. So
395 00:54:39.850 ⇒ 00:54:44.330 Casie Aviles: one of the features I’ve added, is this meeting recording link? So
396 00:54:44.780 ⇒ 00:54:52.950 Casie Aviles: yeah, if you, if you guys want to check the recordings. You should be able to see them here and click them, although you might
397 00:54:53.280 ⇒ 00:54:57.899 Casie Aviles: be. You might need to request for some permissions first.st But
398 00:54:58.380 ⇒ 00:55:05.355 Casie Aviles: yeah, basically, yeah, you you should be able to see them that that way. So yeah,
399 00:55:06.650 ⇒ 00:55:11.850 Casie Aviles: I guess additionally, some of the things I also added to the Zoom agent is just
400 00:55:12.110 ⇒ 00:55:16.030 Casie Aviles: being able to, you know, getting the logs over here at
401 00:55:16.160 ⇒ 00:55:21.949 Casie Aviles: Snowflake. So yeah, this is also something I talked I showed at the last demo.
402 00:55:22.688 ⇒ 00:55:30.450 Casie Aviles: So yeah, basically, the thing the the thing we want to do is we want to start pulling all the data that we have across different platforms and channels like
403 00:55:31.059 ⇒ 00:55:37.190 Casie Aviles: over at slack. So we have some slack data over here. Some ticketeer data.
404 00:55:37.510 ⇒ 00:55:38.930 Casie Aviles: take it to your logs.
405 00:55:39.370 ⇒ 00:55:43.550 Casie Aviles: So it’s mostly just my tests. But yeah, so we want to
406 00:55:43.930 ⇒ 00:55:48.359 Casie Aviles: start having them in one place. And then eventually, it’s also gonna make it easier for like
407 00:55:48.941 ⇒ 00:55:53.970 Casie Aviles: one of the agents that we’re also going to work on, which is the sales hub agent. So
408 00:55:54.952 ⇒ 00:55:59.147 Casie Aviles: yeah, we want to be able to pull from different data sources. And
409 00:56:00.100 ⇒ 00:56:13.924 Casie Aviles: give that as context to the AI, so that’s also something. That I think the ABC. The team the folks working at working on ABC agent is trying to do like with the Gemini
410 00:56:14.750 ⇒ 00:56:19.529 Casie Aviles: model. So like, I guess the the idea is, what if we could just bypass
411 00:56:21.190 ⇒ 00:56:26.119 Casie Aviles: a rag which is like a mechanism for the AI to kind of for us to feed
412 00:56:26.250 ⇒ 00:56:31.167 Casie Aviles: context to the AI so yeah, basically, that’s what
413 00:56:33.190 ⇒ 00:56:40.179 Casie Aviles: the next thing that we want to do. So we’re putting all of those data here in one place.
414 00:56:40.470 ⇒ 00:56:44.039 Casie Aviles: But yeah, I guess that’s it for me.
415 00:56:44.850 ⇒ 00:56:56.510 Uttam Kumaran: Yeah. So one of the things that we’re working on is as we have, like meetings with clients. We have repos of work, we have emails notion we want to be able to allow anybody in the company to sort of chat with
416 00:56:56.820 ⇒ 00:57:19.410 Uttam Kumaran: like, basically run a chat Gpt on top of all of that. And I’m using that a lot to produce documents for client updates, other sort of planning purposes, ideas. And so that’s 1 of the things that we’re working on is basically being able to put all of that into context for an AI agent and then making that available in slack. So if you have a question about a client you could sort of ask there. So
417 00:57:19.550 ⇒ 00:57:22.809 Uttam Kumaran: that stuff sort of still work in progress, but
418 00:57:22.960 ⇒ 00:57:27.260 Uttam Kumaran: excited because I spent a lot of time sort of in Chat Gbt, doing that sort of work.
419 00:57:29.190 ⇒ 00:57:35.720 Uttam Kumaran: so yeah, I guess that’s all I had for today. Any other questions or things that we wanted to cover.
420 00:57:41.570 ⇒ 00:57:42.420 Uttam Kumaran: Cool.
421 00:57:42.798 ⇒ 00:57:53.060 Uttam Kumaran: If not. Well, I appreciate the time. I’m glad we got to do some Demos. I think next week we have a couple of process changes in the data side. So I’ll be messaging people to make sure everybody can attend everything.
422 00:57:53.300 ⇒ 00:57:59.940 Uttam Kumaran: And then, yeah, I’m looking forward to. We have a bunch of new stuff coming so cool.
423 00:58:00.800 ⇒ 00:58:03.010 Uttam Kumaran: Alright. Thank you. Everybody appreciate it.
424 00:58:03.240 ⇒ 00:58:04.370 Luke Daque: Thanks guys.
425 00:58:04.370 ⇒ 00:58:05.019 Demilade Agboola: Thank you.
426 00:58:05.020 ⇒ 00:58:05.850 Anne: Thanks guys.
427 00:58:06.080 ⇒ 00:58:06.850 Casie Aviles: Thank you.