Meeting Title: Brainforge Internship Project Overview Date: 2025-07-02 Meeting participants: Vishnu, Uttam Kumaran, Abigail Zhao
WEBVTT
1 00:00:19.370 ⇒ 00:00:20.410 Uttam Kumaran: Hello!
2 00:00:37.650 ⇒ 00:00:38.620 Uttam Kumaran: Hey? Guys.
3 00:00:39.130 ⇒ 00:00:40.890 Abigail Zhao: Hey! Hello!
4 00:00:41.900 ⇒ 00:00:42.980 Uttam Kumaran: How’s everything?
5 00:00:43.510 ⇒ 00:00:45.545 Abigail Zhao: I’m doing. Well, how are you guys doing.
6 00:00:46.974 ⇒ 00:00:47.759 Uttam Kumaran: Good.
7 00:00:48.810 ⇒ 00:00:49.130 Vishnu: Hey!
8 00:00:50.400 ⇒ 00:00:51.640 Uttam Kumaran: Hey! How are you?
9 00:00:52.010 ⇒ 00:00:53.390 Vishnu: I’m doing good. How are you?
10 00:00:53.860 ⇒ 00:00:54.610 Uttam Kumaran: Good
11 00:00:55.090 ⇒ 00:01:03.879 Uttam Kumaran: awesome. Well, yeah, I kind of wanted to, probably kick a couple of things off, but I guess. Tell me how the week’s been going. What you guys been reading
12 00:01:04.260 ⇒ 00:01:05.910 Uttam Kumaran: yesterday or today?
13 00:01:07.210 ⇒ 00:01:10.898 Vishnu: It’s been going good so yesterday we
14 00:01:11.690 ⇒ 00:01:29.020 Vishnu: we ex. So I I explored slack notion, and then I did a little bit of revision on Snowflake and Dbt, my old projects that I had built. I looked it up. And then, yeah, for most part of the day I was exploring notion slack and the Github Repos
15 00:01:30.207 ⇒ 00:01:39.660 Vishnu: today we just got access to Snowflake. So again, I was playing around with Snowflake, exporting like data and looking at it. Yeah.
16 00:01:40.340 ⇒ 00:01:40.980 Uttam Kumaran: Cool.
17 00:01:41.400 ⇒ 00:01:42.650 Uttam Kumaran: How about you, Abigail?
18 00:01:43.070 ⇒ 00:02:04.540 Abigail Zhao: Yeah, it’s going. Well, I honestly have just been like taking a look at everything. Like re-familiarizing myself with everything. I’m looking forward to like meeting with my mentor and stuff cause I feel like a lot of this stuff is still like relatively new to me. So I’m like interested in, just like generally learning more like exploring more of it.
19 00:02:04.820 ⇒ 00:02:06.599 Abigail Zhao: That makes sense. Yeah.
20 00:02:07.350 ⇒ 00:02:10.549 Uttam Kumaran: Yeah. So today, I think, project, probably my
21 00:02:10.770 ⇒ 00:02:14.730 Uttam Kumaran: role here is more on just like giving you guys sort of the vision for
22 00:02:14.910 ⇒ 00:02:18.979 Uttam Kumaran: 2 big big projects that I worked with the wish to plan out.
23 00:02:19.341 ⇒ 00:02:23.439 Uttam Kumaran: Did he mention sort of like any of that? I know you guys have probably seen this.
24 00:02:23.947 ⇒ 00:02:30.739 Uttam Kumaran: The sort of internal analytics project that we’re sort of planning. Did you guys take a look at that.
25 00:02:31.394 ⇒ 00:02:42.049 Abigail Zhao: Yeah, I saw it on the notion. And he, he like brought it up like pretty briefly, but didn’t go like super in depth with it, just like mentioned a couple of things about it.
26 00:02:42.620 ⇒ 00:02:43.060 Vishnu: Yes.
27 00:02:43.060 ⇒ 00:02:47.652 Uttam Kumaran: Okay, cool. So I’ll share my screen. If you guys just wanna follow along
28 00:02:49.041 ⇒ 00:02:54.658 Uttam Kumaran: I think this is really probably where I would suggest.
29 00:02:55.520 ⇒ 00:03:02.250 Uttam Kumaran: this is probably going to be the hub, like the thing that I pay the most attention to from my vantage point. Really like.
30 00:03:02.670 ⇒ 00:03:11.580 Uttam Kumaran: I wanted to give you guys some projects that actually would like move the needle for us as a business, but also are not gonna be like
31 00:03:11.960 ⇒ 00:03:23.880 Uttam Kumaran: super Duper challenging or like, there’s things that are like unknown and really, they fall under these 2 buckets. So one is gonna be around like productivity.
32 00:03:24.960 ⇒ 00:03:28.720 Uttam Kumaran: There’s gonna be beyond sort of marketing measurement.
33 00:03:30.680 ⇒ 00:03:36.100 Uttam Kumaran: And so on the productivity side, really like. And and again, I’ll kind of speak from my perspective.
34 00:03:36.240 ⇒ 00:03:44.769 Uttam Kumaran: like mainly the things that I’m looking for are as everyone on the team and able to work and focus. Are we getting more efficient?
35 00:03:45.268 ⇒ 00:03:59.620 Uttam Kumaran: And basically like, who in the team is able to get the most work done and and sort of looking at from our like actual ticket lens like, who’s taking on the most stuff. So those are things like ticket velocity, like, how fast we’re taking tickets on
36 00:03:59.700 ⇒ 00:04:26.316 Uttam Kumaran: the those are the amount of tickets per person. You know. Like if if sprints are getting done in their entirety. I want to look at sort of how many hours are getting are getting used to do certain tickets. So basically, everything around like our company productivity. And so part of like what I labeled here is really the
37 00:04:27.080 ⇒ 00:04:33.819 Uttam Kumaran: the things that are are really like my, I my focus, which is like
38 00:04:35.050 ⇒ 00:04:49.719 Uttam Kumaran: cool to understand where we’re spending time completing tasks. And there are some. There’s some breakdowns here with you know how to go about that. So that’s like sort of the 1st project. The second project, and I don’t entirely sure.
39 00:04:50.200 ⇒ 00:04:50.970 Uttam Kumaran: Sure.
40 00:04:52.335 ⇒ 00:05:04.649 Uttam Kumaran: How a wish is deciding to break these up. So I’ll kind of just walk through the second one. And the second one is around marketing related analytics. So we do a lot of stuff via Linkedin
41 00:05:05.352 ⇒ 00:05:12.910 Uttam Kumaran: and so we want to start to measure how all of our Linkedin work is performing
42 00:05:13.437 ⇒ 00:05:24.019 Uttam Kumaran: like what’s performing better than others, and then we also want to begin to understand like, how our marketing efforts are translating into leads in the door.
43 00:05:24.586 ⇒ 00:05:52.080 Uttam Kumaran: So these are all things that if you work with Hannah and Ryan you’ll sort of get an understanding of like what we’re doing but ideally for me and my spot like, I just wanna know what marketing activities are leading to more engagement. And then, ultimately like, how do we start to measure the impact to site traffic, and then ultimately, like leads booked. So both of these are like probably are really top of mind for me, but are sort of both.
44 00:05:52.450 ⇒ 00:05:59.190 Uttam Kumaran: you know, internally related. And there’s a breakdown of sort of the marketing. Analytics related work down here.
45 00:06:00.900 ⇒ 00:06:02.900 Uttam Kumaran: So I know that they sort of broke.
46 00:06:03.140 ⇒ 00:06:07.124 Uttam Kumaran: I think they did a good job for breaking down each of these sort of
47 00:06:07.750 ⇒ 00:06:13.506 Uttam Kumaran: for sprints. Typically for us. Sprints are 2 week cycles.
48 00:06:14.350 ⇒ 00:06:23.749 Uttam Kumaran: so I do think that not everything here is going to be accurate is sort of built on a plan that we only know now. But ideally like my expectation is like at the end of
49 00:06:24.293 ⇒ 00:06:39.589 Uttam Kumaran: each cycle and each sprint. We have some clear outputs, and that we’re getting closer to you know a report on how we’re performing on the marketing side, and like how our company is doing when it comes to productivity.
50 00:06:41.240 ⇒ 00:06:44.589 Uttam Kumaran: So probably a pause. There, you guys have any questions.
51 00:06:47.420 ⇒ 00:06:49.223 Abigail Zhao: I think I’m good right now.
52 00:06:50.590 ⇒ 00:06:51.729 Vishnu: Yes, me too!
53 00:06:53.880 ⇒ 00:07:14.559 Uttam Kumaran: Yeah. So I think both of these, like, you guys have access. And then, you know, I think, probably Abigail, compared to where, when you were here last. We have a lot more team members. So there’s a lot of people who can assist with these, I think, in particular. If you have questions about extracting data, I would certainly send a note in the
54 00:07:15.153 ⇒ 00:07:31.159 Uttam Kumaran: data channel or send a note in the AI channel. You might just be best setting in engineering overall and and people will definitely reach out to help you extract data like for the Linkedin data. I’m not exactly sure how we’re gonna get that. So.
55 00:07:31.160 ⇒ 00:07:31.550 Abigail Zhao: Okay.
56 00:07:31.550 ⇒ 00:07:52.290 Uttam Kumaran: That’s gonna be a process to work on. Either. The AI probably work on work work with the AI team on, how do we scrape that for the linear tickets we are bringing. We are using a tool to bring that in. I think. I can, I think, for whoever’s taking that work. You can work with a wish. There’s a tool called polytomic that we’re using to bring in linear ticket data.
57 00:07:52.801 ⇒ 00:08:00.660 Uttam Kumaran: And then, yeah, I think it’ll be more heavily like the the bi tool that we use for a lot of our internal stuff is this tool called rail
58 00:08:01.134 ⇒ 00:08:22.129 Uttam Kumaran: where you can write sequel and sort of query data and and and build very simple dashboards on top of. So most likely we will use that and yeah, I see this, both these outputs as a way that you guys can sort of get familiar with the end to end life cycle of data. And also there’s like, hopefully, a presentation component where you guys can present
59 00:08:22.340 ⇒ 00:08:50.919 Uttam Kumaran: sort of questions you find. So my main piece of feedback overall is like, try to drive towards something where you can see the data as fast as possible, like whether it’s just in a spreadsheet or something. So you can validate that everything looks good, and then ideally try to drive towards like of a version of a dashboard that we can have a discussion on. It’s much easier for me to give feedback on. You know a dashboard that I can look at versus
60 00:08:51.090 ⇒ 00:09:02.799 Uttam Kumaran: sort of hypotheticals, because I don’t know yet what the data model is. Gonna look like, for example, for linear tickets. I’m not sure if you’re gonna be able to get like how long
61 00:09:02.860 ⇒ 00:09:25.849 Uttam Kumaran: the ticket was active for, and sort of like, I’m not sure what the data model is gonna look like. But certainly my questions are, gonna be like, who’s taking on the most work? How long on average per team is worth taking to do and I also want to start looking at. Given. We have the clockify hours for folks. We can start to look at what tasks or what type of tasks
62 00:09:25.940 ⇒ 00:09:42.109 Uttam Kumaran: are taking the longest. Additionally, I think, for the productivity work, there is a probably a bigger AI component which is starting to like help? Say, cool with this ticket. Can we estimate like? Can we project? How long it should have taken versus how long it did take
63 00:09:42.593 ⇒ 00:10:06.630 Uttam Kumaran: so really, it’s like, I think there’s there’s 1 other person on the team that’s that’s internal. It’s also helping. Do some analytics around slack usage and calendars. Basically like, how many meetings are we having? Who’s in all those meetings? Also on slack, which is like who’s sending the most messages are the messages productive? So we’re kind of doing a couple of initiatives around this. But I think.
64 00:10:07.010 ⇒ 00:10:10.570 Uttam Kumaran: yeah, I would just drive towards some output as as quick as possible.
65 00:10:12.870 ⇒ 00:10:13.640 Abigail Zhao: Okay. Sounds good.
66 00:10:14.710 ⇒ 00:10:17.790 Uttam Kumaran: And how often you guys meeting with a wish or other folks.
67 00:10:20.199 ⇒ 00:10:29.280 Abigail Zhao: We have, like 15 min daily stand ups like, basically every day or at least that’s just what was like logged into our Google Calendar.
68 00:10:29.710 ⇒ 00:10:30.310 Uttam Kumaran: Cool.
69 00:10:30.710 ⇒ 00:10:33.679 Abigail Zhao: And then I think we’re meeting with Hannah tomorrow as well.
70 00:10:34.220 ⇒ 00:10:39.079 Uttam Kumaran: Okay. Great. Okay. Great. Do you guys know, like how you’re splitting up these projects so far.
71 00:10:40.348 ⇒ 00:10:51.580 Abigail Zhao: I don’t think that was like fully decided yet, but I think we did talk about today how Vishnu wants to like, do more data engineering. And I want to do more like data analysis type of stuff.
72 00:10:52.230 ⇒ 00:10:52.670 Uttam Kumaran: Okay, cool.
73 00:10:52.670 ⇒ 00:10:53.770 Uttam Kumaran: Okay, great.
74 00:10:54.090 ⇒ 00:10:59.380 Uttam Kumaran: So yeah, I think it’s up to you guys. However, I would. However, you guys want to split this up. But
75 00:10:59.540 ⇒ 00:11:06.209 Uttam Kumaran: like, does that give you a little bit of overview of like? Why, these 2 projects are important and sort of like what we’re looking for.
76 00:11:07.520 ⇒ 00:11:07.940 Abigail Zhao: Yeah.
77 00:11:07.940 ⇒ 00:11:08.650 Vishnu: Yes.
78 00:11:08.960 ⇒ 00:11:09.315 Uttam Kumaran: Cool.
79 00:11:09.780 ⇒ 00:11:18.129 Uttam Kumaran: I mean, I guess I didn’t really like. I know both of you have some context into like overall what we’re doing at Brainforge. But are there any questions about like the
80 00:11:18.400 ⇒ 00:11:25.560 Uttam Kumaran: company as a whole, or any sort of process? Questions that I can answer? You know right now.
81 00:11:27.940 ⇒ 00:11:36.289 Abigail Zhao: yeah, I mean, I I think I’m still just a little intrigued on like the whole like ticketing process, and like exactly like what that
82 00:11:36.580 ⇒ 00:11:44.440 Abigail Zhao: like entails. Cause I I feel like I’m I’m like adjusting to this like this fully, like remote environment. So like, how exactly.
83 00:11:44.440 ⇒ 00:11:44.800 Uttam Kumaran: Go ahead!
84 00:11:44.800 ⇒ 00:11:50.229 Abigail Zhao: Like keeping track of, like the work people are doing like through these tickets or whatnot.
85 00:11:50.430 ⇒ 00:11:51.950 Uttam Kumaran: Yeah, let me give you an example.
86 00:11:52.950 ⇒ 00:11:53.600 Uttam Kumaran: So
87 00:11:54.389 ⇒ 00:12:06.940 Uttam Kumaran: you. I don’t know if you met Amber yet, but Amber is our is a project manager. Right now. She’s our only sort of dedicated project manager. But Robert and I are are both managing
88 00:12:07.290 ⇒ 00:12:14.870 Uttam Kumaran: couple of projects ourselves, too. But basically, the way we run this is, every team has a linear board.
89 00:12:15.390 ⇒ 00:12:23.389 Uttam Kumaran: So you can see here there’s teams for every client. There’s even teams like, internally, like operations marketing, recruiting sales content.
90 00:12:23.873 ⇒ 00:12:37.776 Uttam Kumaran: So each of these teams there are people associated with it, and there are people in there that take on tasks. So, for example, this is the work for our internal AI team. Right now. You can see Mustafa is working on
91 00:12:38.420 ⇒ 00:12:41.529 Uttam Kumaran: something around ensuring Hubspot is ready for
92 00:12:41.640 ⇒ 00:12:57.199 Uttam Kumaran: email sequences, making sure. Just hubspot you can. We can send emails outside of that. So these tickets really just have, like what the scope of the work is, who’s gonna take it on. And then we operate in cycles. So for for some teams, they operate for you guys, you’ll be operating in 2 weeks sprints
93 00:12:57.350 ⇒ 00:13:01.939 Uttam Kumaran: for AI team and for the data platform team.
94 00:13:02.350 ⇒ 00:13:07.689 Uttam Kumaran: which and 2 of our clients, all 4 of which I manage. We operate in one week. Cycles.
95 00:13:07.950 ⇒ 00:13:08.460 Abigail Zhao: So I’m.
96 00:13:08.460 ⇒ 00:13:10.980 Uttam Kumaran: Monday I meet, and we plan out the week, and then
97 00:13:11.110 ⇒ 00:13:14.839 Uttam Kumaran: Friday we just talk about how things went, and then the next week is another cycle.
98 00:13:16.650 ⇒ 00:13:34.729 Uttam Kumaran: so all of the work gets logged here. In terms of like what you’re working on, and ideally your ticket. This is sort of a little bit of a lighter ticket. But ideally, your ticket should have like, what’s the goal? What like? What are the steps to accomplish it? And then what is the acceptance criteria like? How do you know this is done?
99 00:13:35.217 ⇒ 00:13:39.850 Uttam Kumaran: And then tickets move through statuses, so you can see here on the right. There’s all these statuses.
100 00:13:40.030 ⇒ 00:13:44.369 Uttam Kumaran: so as soon as a ticket gets picked up, it then moves into in progress, and then moves
101 00:13:44.510 ⇒ 00:13:47.489 Uttam Kumaran: sort of into review, and then gets completed.
102 00:13:49.070 ⇒ 00:13:50.450 Abigail Zhao: Oh, got it? Okay.
103 00:13:50.870 ⇒ 00:13:55.849 Uttam Kumaran: And so, for the most part, our whole company, probably, except for sales.
104 00:13:56.850 ⇒ 00:14:00.649 Uttam Kumaran: Is using this meaning. Everybody’s work is being tracked here.
105 00:14:01.050 ⇒ 00:14:06.220 Uttam Kumaran: So it’s a great opportunity for us to understand, like, what work is getting done, and how fast?
106 00:14:06.410 ⇒ 00:14:12.029 Uttam Kumaran: Right, which is the velocity? But also understand our capacity, like, how much work can we take on at any moment?
107 00:14:13.750 ⇒ 00:14:16.010 Abigail Zhao: Yeah. Alright. Sounds good. Yeah.
108 00:14:17.980 ⇒ 00:14:42.649 Uttam Kumaran: So yeah, just poke around in linear. And then again, anyone who you need ask questions with the other thing I would encourage you guys to do is to just grab time with anybody on the team. I think everybody from folks on marketing or folks in data or AI are all really great people to meet especially the folks on the AI and the data team like Kyle Dumalade, any or or anyone in the AI team. Casey Miguel.
109 00:14:42.780 ⇒ 00:14:54.219 Uttam Kumaran: Oh, Mustafa Luke! I would just try to grab time with any of them, and sort of learn a little bit about their background and and what they do to give you some context about the types of work that we’re doing at the company.
110 00:14:55.430 ⇒ 00:14:55.960 Vishnu: Okay.
111 00:14:55.960 ⇒ 00:15:01.260 Uttam Kumaran: And you know they’ll come in handy when you need help with with getting some stuff done. So
112 00:15:01.706 ⇒ 00:15:18.899 Uttam Kumaran: yeah, that’s probably what I would encourage and if you need help booking any of those, I would just get Rico’s help. He leads up operations. So if you’re like, Hey, I need help like booking this or can you tell me like just any questions about like logistics? I would I would hit him up.
113 00:15:21.420 ⇒ 00:15:25.750 Uttam Kumaran: But yeah, I’m I’m super excited. I think both of these projects are, gonna be pretty impactful. So.
114 00:15:26.700 ⇒ 00:15:29.130 Abigail Zhao: Yeah, sounds good. Thank you.
115 00:15:30.130 ⇒ 00:15:34.410 Uttam Kumaran: Cool anything else. I can answer anything. Vishnu, you have questions on.
116 00:15:35.310 ⇒ 00:15:38.946 Vishnu: So I had one question regarding the ticket creation. So
117 00:15:39.950 ⇒ 00:15:50.280 Vishnu: do we create the tickets ourselves? Or based on like a roadmap that we have? Or is it gonna be created by our managers? And we just pick tasks from them.
118 00:15:51.650 ⇒ 00:15:58.300 Uttam Kumaran: Yeah, this is a good question. I I do believe there is a ticket breakdown in the notion, but I don’t think these are been created yet.
119 00:15:58.858 ⇒ 00:16:03.040 Uttam Kumaran: I believe it’s gonna be sort of a collaboration between you and a wish.
120 00:16:03.200 ⇒ 00:16:16.040 Uttam Kumaran: Are you guys in a wish to create those? I would ask in slack like right after this, because I think he has a plan on what they want to do. I also think Amber is gonna help out a little bit on the project management side. So
121 00:16:16.170 ⇒ 00:16:20.089 Uttam Kumaran: I would just ask in slack what the plan is.
122 00:16:20.580 ⇒ 00:16:22.020 Uttam Kumaran: I feel like it’s
123 00:16:22.180 ⇒ 00:16:31.749 Uttam Kumaran: it may be collaboration. And probably after a while, as you guys plan out your cycles, you can. You guys will be able to create tickets yourselves and and assign them.
124 00:16:32.480 ⇒ 00:16:33.610 Vishnu: Got it? Okay?
125 00:16:37.560 ⇒ 00:16:38.130 Vishnu: Oh.
126 00:16:38.130 ⇒ 00:16:38.820 Uttam Kumaran: Okay.
127 00:16:38.820 ⇒ 00:16:39.389 Vishnu: Yeah, that’s okay.
128 00:16:39.390 ⇒ 00:16:39.960 Uttam Kumaran: Perfect.
129 00:16:41.060 ⇒ 00:16:42.962 Uttam Kumaran: I can’t think of anything as
130 00:16:45.140 ⇒ 00:17:05.589 Uttam Kumaran: yeah. Then we’ll talk weekly or so, and then, yeah, just slack me if you need anything, and then I’m sort of in that channel sort of observing. But yeah, I would rely on Amber and a wish for anything like on the project side, and then both, and then also talk to your mentor and then I would rely on Rico for anything, for operations. Wise.
131 00:17:06.730 ⇒ 00:17:08.259 Abigail Zhao: Okay. Yeah. Sounds good.
132 00:17:08.980 ⇒ 00:17:10.310 Uttam Kumaran: Okay, perfect.
133 00:17:10.910 ⇒ 00:17:13.209 Uttam Kumaran: Alright, thanks. Guys. Appreciate it.
134 00:17:13.210 ⇒ 00:17:16.240 Abigail Zhao: Alright, bye, thank you. Bye.