2025-12-05_brainforge_x_jasmin_multani_recruitment__dc06c416

Meeting Title: Brainforge x Jasmin Multani Recruitment Discussion Date: 2025-12-05 Meeting participants: Uttam Kumaran, Jasmin Multani

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WEBVTT

1 00:03:27.380 ⇒ 00:03:28.550 Uttam Kumaran: Hello!

2 00:03:31.920 ⇒ 00:03:33.560 Jasmin Multani: Hi, Itham, how’s it going?

3 00:03:33.900 ⇒ 00:03:36.390 Uttam Kumaran: Hi, good, how are you? Sorry for the delay.

4 00:03:36.390 ⇒ 00:03:41.270 Jasmin Multani: No, it’s all good. I just have, like, a jam-packed Friday, so I’m like, okay, let me… let me…

5 00:03:41.270 ⇒ 00:03:54.910 Uttam Kumaran: Yeah, do some… well, yeah, so I, like, we were about to end this call, and then it just, like, kept going, and kept going and kept going, but I, like, tried to block out… I mean, it’s good, like, when a client wants to keep talking, like, we keep going, but…

6 00:03:54.910 ⇒ 00:03:55.290 Jasmin Multani: Yeah, I guess.

7 00:03:55.290 ⇒ 00:03:57.510 Uttam Kumaran: Oh, yeah. How’s everything?

8 00:03:57.510 ⇒ 00:04:00.190 Jasmin Multani: You gotta harvest the goodwill while you can.

9 00:04:00.190 ⇒ 00:04:03.420 Uttam Kumaran: No, I know, and it’s… it’s been… I feel really lucky, like.

10 00:04:03.810 ⇒ 00:04:11.589 Uttam Kumaran: I don’t know, maybe 6 months ago, I was… I used to dread calls, because we were just messing stuff up, and it wasn’t perfect, and now…

11 00:04:12.320 ⇒ 00:04:18.279 Uttam Kumaran: Like, we almost have a little bit of the opposite problem, where it’s like, there’s too… like, every client is, like.

12 00:04:18.579 ⇒ 00:04:21.169 Uttam Kumaran: Hey, actually, we need more of your time, like…

13 00:04:21.170 ⇒ 00:04:22.310 Jasmin Multani: Nice, okay.

14 00:04:22.310 ⇒ 00:04:26.669 Uttam Kumaran: more, like, We went on… we went on two calls today, they both were like.

15 00:04:27.310 ⇒ 00:04:38.820 Uttam Kumaran: you’re the only… you’re the only consultants we have that are helping us that are actually decent. I want to, like, move some other resources to you, or, like, have more scope for you, like, what do you think? And I’m like.

16 00:04:39.470 ⇒ 00:04:44.569 Uttam Kumaran: Dude, I haven’t slept, like, I don’t know, but yeah, let’s do it. I’m like, let’s do it.

17 00:04:44.570 ⇒ 00:04:46.070 Jasmin Multani: Yeah, yeah, yeah, yeah.

18 00:04:46.110 ⇒ 00:04:50.620 Uttam Kumaran: I know! You, you and Robert have been working for a minute now, right?

19 00:04:51.020 ⇒ 00:05:06.020 Uttam Kumaran: Yeah, for more than a year, I think. Like, yeah, and so, and then I think a little bit before that, we started doing stuff on Javi, but yeah, we’ve been growing, like, quite a bit. I think we’re up to, like, 14 clients right now.

20 00:05:07.540 ⇒ 00:05:16.309 Uttam Kumaran: And starting to work with a lot of, like, pretty large places, like, I think 3 of our companies are north of $100 million in revenue that we’re supporting.

21 00:05:16.430 ⇒ 00:05:17.220 Uttam Kumaran: Which is…

22 00:05:17.560 ⇒ 00:05:31.309 Uttam Kumaran: amazing. Just, like, a different set of problems, but… but also, I don’t know, I just think the scope of the data is, like, super fun, and, like, the impact that’s… that’s possible is also…

23 00:05:31.430 ⇒ 00:05:34.700 Uttam Kumaran: Just, like, really cool to see, and we haven’t, like.

24 00:05:34.830 ⇒ 00:05:51.759 Uttam Kumaran: we haven’t slowed down our pace, like, we actually just, like, because we win these because we just deliver quickly, and we’re, like, not like most consultancies that are like, yeah, let’s get a deck, and a kickoff meeting, and, like, all this stuff. It’s, like, very… it’s, like, informal in the right places, you know, so…

25 00:05:52.700 ⇒ 00:05:53.420 Uttam Kumaran: Yeah.

26 00:05:53.420 ⇒ 00:05:55.940 Jasmin Multani: Yeah, just impress them with the work. That’s all that.

27 00:05:55.940 ⇒ 00:05:59.730 Uttam Kumaran: Exactly, and then everything else is whatever, you know? Yeah.

28 00:05:59.960 ⇒ 00:06:12.000 Jasmin Multani: Okay, good! Like, congrats, you guys! Like, you should be proud, and I feel like, I… I don’t know Robert, beyond Pungo, but even watching him at Pungo, and watching how…

29 00:06:12.000 ⇒ 00:06:13.829 Uttam Kumaran: How did you guys get originally connected?

30 00:06:14.050 ⇒ 00:06:16.700 Jasmin Multani: Oh my gosh, I was like…

31 00:06:17.240 ⇒ 00:06:34.600 Jasmin Multani: slightly very forward. Irene, I think, I was just looking for a consulting, like, a side gig when I left DoorDash. So I was at DoorDash before, I was on the new verticals program, I was there… I joined, like, right before IPO, and.

32 00:06:34.600 ⇒ 00:06:45.660 Uttam Kumaran: Is that a cool company, by the way? I feel like I’ve heard a lot… I mean, I’ve read their engineering blog, and, like, I don’t know their… I know the guy, the CEO, is, like, really technical. Like, was it a place to work in data?

33 00:06:46.540 ⇒ 00:06:48.400 Jasmin Multani: No.

34 00:06:49.030 ⇒ 00:06:54.200 Jasmin Multani: I think it’s better than TikTok, ironically.

35 00:06:54.200 ⇒ 00:06:54.850 Uttam Kumaran: Okay.

36 00:06:55.510 ⇒ 00:07:04.030 Jasmin Multani: felt like I had more gas… I was just, like, pressing the pedal at DoorDash, but…

37 00:07:05.220 ⇒ 00:07:19.069 Jasmin Multani: Yeah, yeah, and I joined at a really good time, too, right? I joined right before IPO’d, and my manager was someone I had known from high school, but had lost touch with since he graduated high school.

38 00:07:19.070 ⇒ 00:07:32.529 Jasmin Multani: he was hiring, and so I reached out to him, was like, on LinkedIn, was like, hey, remember me! Don’t have your personal style, but I promise I can do a good job for you. So we had to call, and I was able to get

39 00:07:32.530 ⇒ 00:07:44.670 Jasmin Multani: my foot through the door in tech from research, and thankfully, because he was, like, the first 200-300 at DoorDash, and he had been working at the company when he was, like, fresh out of college.

40 00:07:44.670 ⇒ 00:08:00.040 Jasmin Multani: And he and a handful of other people built the LA office out. So he was an early, early story guy, and he’s only a year older, than me. Yeah. So I got someone who was a good Kickstarter.

41 00:08:00.320 ⇒ 00:08:01.410 Uttam Kumaran: Not a lot of clout.

42 00:08:01.570 ⇒ 00:08:03.189 Jasmin Multani: A lot of clout.

43 00:08:03.390 ⇒ 00:08:05.889 Jasmin Multani: And it’s in the same generation, too.

44 00:08:05.890 ⇒ 00:08:06.340 Uttam Kumaran: Yeah, yeah.

45 00:08:06.340 ⇒ 00:08:13.659 Jasmin Multani: grew up in the same hometown, I felt like we had similar values. So he… he and I understood each other, where we were like.

46 00:08:14.420 ⇒ 00:08:15.490 Jasmin Multani: Okay.

47 00:08:15.490 ⇒ 00:08:16.710 Uttam Kumaran: Was this in California?

48 00:08:16.910 ⇒ 00:08:20.640 Jasmin Multani: Yeah, yeah, yeah, California, yeah. We grew up in SoCal. Okay, cool.

49 00:08:20.860 ⇒ 00:08:22.610 Uttam Kumaran: I’m… I grew up in the Bay Area, East Bay.

50 00:08:22.610 ⇒ 00:08:36.729 Jasmin Multani: Oh, okay, okay, so NorCal, okay, okay, slightly different culture, no ribbing. Like, NorCal’s just, like, very laid back, like, if you want to chase something, you get access to those resources. If you don’t want to chase something, you still have a football.

51 00:08:36.730 ⇒ 00:08:37.539 Uttam Kumaran: Just chill.

52 00:08:37.789 ⇒ 00:08:38.659 Jasmin Multani: Yeah. Yeah.

53 00:08:38.699 ⇒ 00:08:54.019 Jasmin Multani: But the thing about Andrew was, like, we had similar values where, he could be my corporate manager, but he would drop the mask and be like, listen, side note, for your career, I need you to focus on these things.

54 00:08:54.019 ⇒ 00:09:05.009 Jasmin Multani: I can’t be helping you with this, you… I will introduce you to people, but you have to present yourself. You have to take… you have to take a lead on certain things. So I had the best intro to tech.

55 00:09:05.010 ⇒ 00:09:05.490 Uttam Kumaran: Yeah.

56 00:09:05.490 ⇒ 00:09:07.670 Jasmin Multani: I did ask for an,

57 00:09:07.930 ⇒ 00:09:26.260 Jasmin Multani: he’s in New York right now, actually. He’s scaling out his own business in restaurant logistics, so I’m just wishing him the best. And I just feel like he and Rob are also very similar, too, in the sense that very kind, very kind leaders, hardworking, but also know when to drop the mask and be like, listen.

58 00:09:27.140 ⇒ 00:09:27.550 Uttam Kumaran: Yeah, yeah.

59 00:09:27.550 ⇒ 00:09:45.380 Jasmin Multani: If you think that’s going on, I need the team to align on these two things. Yeah. You can, if you need to go for a walk at 3pm, go for a walk at 3pm, but, like, we need to get this done. Yeah. And that’s the level of candidness that I really appreciate, and I don’t want to take it for granted.

60 00:09:45.380 ⇒ 00:09:45.710 Uttam Kumaran: Yeah.

61 00:09:46.260 ⇒ 00:10:02.449 Jasmin Multani: And it’s the same way I met… like, I… Jor-El at Pungo had listed a posting, I had left DoorDash, I had my fun at DoorDash, and we can talk about the other things later, but I just slid into his LinkedIn messages and was like.

62 00:10:02.580 ⇒ 00:10:04.979 Jasmin Multani: I know I can deliver XYZ.

63 00:10:04.980 ⇒ 00:10:05.340 Uttam Kumaran: Great.

64 00:10:05.340 ⇒ 00:10:05.840 Jasmin Multani: Yeah.

65 00:10:06.040 ⇒ 00:10:07.360 Jasmin Multani: was hired.

66 00:10:07.780 ⇒ 00:10:13.920 Jasmin Multani: worked with Robert, and now I’m looking to come work again. Yeah.

67 00:10:14.270 ⇒ 00:10:16.690 Jasmin Multani: It’s all a matter of, like, good mutual fit.

68 00:10:17.170 ⇒ 00:10:19.449 Uttam Kumaran: Cool. Yeah, that’s awesome. I mean…

69 00:10:19.480 ⇒ 00:10:28.969 Uttam Kumaran: I would say… I don’t think… so, I think even my background, so I worked in, in data for a number of years. I…

70 00:10:28.970 ⇒ 00:10:40.679 Uttam Kumaran: I grew up in the Bay Area, but went to Bucknell, which is, like, in central PA, and then I lived in New York for about 5 years, so I worked at… I worked at WeWork was my first job. I worked at a data engineer there.

71 00:10:40.690 ⇒ 00:10:44.389 Uttam Kumaran: And then sort of just, like, did everything, worked on the IPO.

72 00:10:44.900 ⇒ 00:10:45.580 Jasmin Multani: Work out.

73 00:10:45.580 ⇒ 00:11:01.459 Uttam Kumaran: And then I worked at another startup where I was, like, the first data person, and then I led product at a data startup, after that. And then I moved here to Austin, where I live now, and, started this business, like, about two and a half years ago.

74 00:11:01.460 ⇒ 00:11:08.220 Uttam Kumaran: Sort of just, like, quit my… where I was leading product, and was like, what am I gonna do? Let’s just see if we can get some clients, and, like…

75 00:11:08.890 ⇒ 00:11:17.090 Uttam Kumaran: Every day, just like… climbed, and just, like, tried as much as possible. It’s so funny because, like.

76 00:11:17.580 ⇒ 00:11:22.719 Uttam Kumaran: where stuff is moving so fast now, and I… it was only a short while ago where I’m like.

77 00:11:23.040 ⇒ 00:11:34.339 Uttam Kumaran: if I… if we got, like, one deal in, like, a quarter, I was, like, really happy, and now it’s, like, we’re probably signing, like, one or two clients, like, a week for the past, like, 6 weeks.

78 00:11:34.340 ⇒ 00:11:36.919 Jasmin Multani: It’s, like, out of this world. Yeah.

79 00:11:36.920 ⇒ 00:11:56.299 Uttam Kumaran: And… and so, I mean, I love what I do in data. I don’t think, in particular, like, any of the work that we’ve done for clients has been more challenging than the work I probably did at WeWork, just because, you know, like, how fast a place with everybody’s, like, mad smart, and there’s a lot of money to be had.

80 00:11:56.300 ⇒ 00:11:57.260 Jasmin Multani: Like…

81 00:11:57.260 ⇒ 00:12:19.470 Uttam Kumaran: you just go, and so we’ve taken that same energy. I think Robert has a lot of the same sort of thought process from his career, and we’ve just, like, helped that with clients, where we move at a pace they’ve never seen from consultants. We’re very, very technical. Like, my background is in data infrastructure, data modeling, data warehousing, and Robert’s is all in analytics, and then we both

82 00:12:19.600 ⇒ 00:12:23.019 Uttam Kumaran: So, I mean, we’ve both worked, like, full stack, like, I’ve…

83 00:12:23.120 ⇒ 00:12:27.249 Uttam Kumaran: I’ve done all of this stuff in analysis, in financial analysis, and so…

84 00:12:27.290 ⇒ 00:12:43.279 Uttam Kumaran: we just now can have a really, like, expansive offering for clients, where we may come in to solve analysis, but immediately we then, like, oh, you have all this infrastructure issues. We come in and do infrastructure work, and we’re like, well, you guys, how do you guys determine pricing? Or, like, how are you guys doing customer segmentation? They’re like.

85 00:12:43.280 ⇒ 00:12:51.100 Uttam Kumaran: we don’t know, and we’re like, well, we just learned about all your data, like, you should have us do that. So our offering is really, really good, and then the other unique part about

86 00:12:51.450 ⇒ 00:12:53.689 Uttam Kumaran: our businesses. I used AI.

87 00:12:53.690 ⇒ 00:13:12.109 Uttam Kumaran: to build, like, the entire thing. Like, I… I started the business right after, like, GPT 3.5 came out, and AI is so intertwined into, like, everything everyone does, and it’s been amazing, because I don’t think we could have scaled this business and kept expenses low, like, without

88 00:13:12.160 ⇒ 00:13:31.610 Uttam Kumaran: that, and it’s just been awesome to, like, lean in more and bring on people that want to build, and, like, we have an internal AI team that sort of builds, like, we have, like, an internal platform that we built for the company and the entire team, and yeah, we’re just trying to continue to build, like, an amazing company.

89 00:13:31.610 ⇒ 00:13:40.480 Uttam Kumaran: Like, I wake up, and I think about our clients, and I just think about the team. And so, for… for me, like, as long as I’m able to…

90 00:13:40.480 ⇒ 00:13:47.849 Uttam Kumaran: Get really tough problems, and then find really smart people, like, we will… the business will make money in pairing those people up.

91 00:13:47.850 ⇒ 00:13:48.230 Jasmin Multani: Like.

92 00:13:48.230 ⇒ 00:14:01.020 Uttam Kumaran: That is… that is at the plateau, like, what this company is. But in the… additionally, though, we’re not, like, a traditional consultancy in that I think we do care a lot about, like, a cohesive team.

93 00:14:01.300 ⇒ 00:14:08.940 Uttam Kumaran: Like, before, like, probably at Pongo and my company, like, when I met Robert, you know, we were, like, seven, eight people, but it was, like.

94 00:14:09.080 ⇒ 00:14:21.289 Uttam Kumaran: Scattered contractors, like, people that were, like, working here part-time, and then, like… but they had, like, their primary thing, and we were always, like, sort of the secondary thing, and so we completely, like, turned all that off.

95 00:14:21.290 ⇒ 00:14:24.329 Jasmin Multani: Like, everybody that works for us now.

96 00:14:24.330 ⇒ 00:14:32.650 Uttam Kumaran: is… is… either works for Brainforge full-time, or is, like, basically part-time, but, like, with the intention that if this works out.

97 00:14:32.910 ⇒ 00:14:33.320 Jasmin Multani: Yeah.

98 00:14:33.320 ⇒ 00:14:40.049 Uttam Kumaran: like, we’re moving to 40 hours. And that… because we couldn’t reliably build culture and, like.

99 00:14:40.050 ⇒ 00:14:40.530 Jasmin Multani: Come on.

100 00:14:40.530 ⇒ 00:14:58.969 Uttam Kumaran: and, like, basically nail the, delivery with, like, people that were, like, just, like, in and out, in and out. But that took, like, a lot of change to do, because we sort of hustled to just find whoever we knew, and now we have… I think there’s almost, like, probably 16 or 17 people at the company now.

101 00:15:00.230 ⇒ 00:15:00.720 Jasmin Multani: Hmm.

102 00:15:00.960 ⇒ 00:15:11.330 Uttam Kumaran: And it’s also super, super smart data people, AI people, and then we have some people helping on internals, like sales, marketing, partnerships, things like that.

103 00:15:11.330 ⇒ 00:15:17.999 Jasmin Multani: So, it’s good. I mean, we’re at… kind of to tell you, like, where we’re at, we’re just basically thinking about, like.

104 00:15:18.140 ⇒ 00:15:21.810 Uttam Kumaran: How to scale this business, and…

105 00:15:21.820 ⇒ 00:15:34.529 Uttam Kumaran: not do it like what I’m used to, which is, like, in product startups. Like, scaling this business is more about, like, how do we continue to deliver a faster service, a faster and better service to clients?

106 00:15:34.530 ⇒ 00:15:44.249 Uttam Kumaran: But do so in a way where we can affect more clients, and that we can also start to have, like, a class of leaders that can either own service lines, or own functional

107 00:15:44.250 ⇒ 00:15:50.310 Uttam Kumaran: parts of the business, and, like, that’s how we’re kind of thinking about things. Like, right now, Robert and I

108 00:15:50.310 ⇒ 00:16:08.889 Uttam Kumaran: both of us are the, like, leads on, like, half the clients, right? So, I have, like, over… I’m, like, over, like, 7 clients, he’s over a bunch, we have a couple where we collaborate, and some of the clients have… we’re doing data engineering or info workforce, some clients we’re doing strategy or analytics workforce, sometimes we’re doing both.

109 00:16:08.900 ⇒ 00:16:14.890 Jasmin Multani: And we’re sort of just thinking about, like, what is the… what is, like, the lieutenant class that we have to bring in?

110 00:16:14.890 ⇒ 00:16:20.839 Uttam Kumaran: So that we can basically take this business from, like, 1 to 2 million in revenue to, like, 10.

111 00:16:20.980 ⇒ 00:16:23.589 Uttam Kumaran: And that can’t happen with us

112 00:16:23.850 ⇒ 00:16:39.400 Uttam Kumaran: saying at this point. Where we need to go, that is, like, the unknown, but I’m okay with being the person in the front of the line, like, with the flashlight. Like, I’m okay with that. Not everybody in the company can feel that way, though, and so what we’re trying to figure out is, like.

113 00:16:39.460 ⇒ 00:16:53.209 Uttam Kumaran: who do we need to put in what position? Give them the right incentives, in order to say, like, okay, we can actually take, like, a service line and grow that. We can take a different part and, like, move some stuff off our plate.

114 00:16:53.210 ⇒ 00:17:03.599 Uttam Kumaran: how do we continue to build… make this a great place for super smart data and AI people? But also continue to kill, like, any sort of politics or, like, BS?

115 00:17:03.650 ⇒ 00:17:22.009 Uttam Kumaran: tech company stuff, which, like, it doesn’t enter our company, because I just, like, kind of alert… I’ve just, like, dealt with that my whole career. And so, there’s no, like, old white people in this company, there’s no, like, gatekeeping, there’s no, like, VCs. There’s no, like, there’s not really, like, games. Like, all we talk about is, like.

116 00:17:22.150 ⇒ 00:17:37.089 Uttam Kumaran: clients and doing work for them, and then, like, we talk about our team. Which, that’s been really good. Like, I have a long career in startups, and, like, how detrimental that type of stuff has been for companies is crazy, and so we’ve done a good job of trying to build, like, a…

117 00:17:37.250 ⇒ 00:17:46.879 Uttam Kumaran: a really cohesive group of people who are all really curious and excited to, like, do data work, and then the AI piece is sort of, like.

118 00:17:47.250 ⇒ 00:18:05.309 Uttam Kumaran: that’s what’s, I think, setting us apart in our pace, but also in the types of work we’re able to do, and people… clients are starting to see us as, like, a unique vehicle for them to accomplish, you know, a lot of their own objectives that maybe they don’t have the talent, or the know-how or the pace internally to do, you know?

119 00:18:06.560 ⇒ 00:18:09.600 Jasmin Multani: Okay, I have a couple follow-up questions, just.

120 00:18:09.600 ⇒ 00:18:09.950 Uttam Kumaran: Place.

121 00:18:09.950 ⇒ 00:18:13.649 Jasmin Multani: Dig deeper on, because it’ll give me clarity about, like.

122 00:18:14.080 ⇒ 00:18:16.119 Uttam Kumaran: Sure. How I can be any help.

123 00:18:16.120 ⇒ 00:18:27.859 Jasmin Multani: So, let’s say in the next 6 months, between January and, like, May or whatever, I know you said that you’re at 14 clients, but you also want to provide faster and better services.

124 00:18:27.860 ⇒ 00:18:39.400 Jasmin Multani: So in the next 6 months, are you focused on… when you… as you show your revenue growth, are you focused on doubling down on these 14 clients, and just…

125 00:18:39.540 ⇒ 00:18:43.440 Jasmin Multani: Proving that your services are fast and efficient, and

126 00:18:43.450 ⇒ 00:19:00.669 Jasmin Multani: Pulling in more money through those existing 14 clients? Or, is the priority, you know, for the next 6 months, keeping the same services, deepening the skill sets, but also expanding the number of clients?

127 00:19:01.690 ⇒ 00:19:15.160 Uttam Kumaran: Yeah, I mean, I think we will start to shed the cheaper client… the cheaper and the clients that are not, like, that we’re not able to deliver for. Like, there are some clients who are still used to us charging $5K a month.

128 00:19:15.160 ⇒ 00:19:31.269 Uttam Kumaran: for stuff we should be charging $15K a month for, but we priced when we were very desperate, so there’s, like, one… there’s some of those. There’s also some clients who don’t appreciate the data work, or don’t have the ability to, and are, like, plundering along because they’re not taking our insights and acting on it.

129 00:19:31.270 ⇒ 00:19:37.750 Uttam Kumaran: like, we will probably end up cutting those. We are starting to sell into, like, true enterprise.

130 00:19:37.750 ⇒ 00:19:48.629 Uttam Kumaran: Where the pie and the prize is a lot larger. And so, for the most part, we will still continue… we don’t want… we want to avoid, like, significant customer concentration.

131 00:19:48.630 ⇒ 00:19:58.629 Uttam Kumaran: In terms of revenue, but we will increase both the number of clients and the ACV, for sure. But it won’t… it won’t be in a way where it’s like.

132 00:19:58.840 ⇒ 00:20:14.400 Uttam Kumaran: it’s not one or the other for us. Like, we can have to continue to expand, like, existing clients where there is more scope available, and then we also have to go after new deals. Like, you can’t stop selling in this business, because we sell, like.

133 00:20:14.740 ⇒ 00:20:28.670 Uttam Kumaran: and then 9 months, it may close. And for enterprise deals that are gonna be, like, quarter million, half million dollar ACV, those will take a long time. And we have to, like, work at those and spend time, and so you can’t…

134 00:20:28.820 ⇒ 00:20:44.790 Uttam Kumaran: we… we have to service the revenue we have. I think for us, it’s figuring out how much of the existing client are distractions, are, like, bad revenue, and moving that aside, but in no way, like, we have to… we have to hire to service that revenue. But I’m not as worried about hiring

135 00:20:44.790 ⇒ 00:20:59.649 Uttam Kumaran: like, mid-level or junior engineers. We have tons of people that want to work at the company, and I’ve never been worried about that necessarily, because we’re a great home to work. You have two people that are data people that run the company, so, like.

136 00:20:59.820 ⇒ 00:21:18.790 Uttam Kumaran: I get how… I could get… know how to make this a fun, cool place for those types of people. When I never got that, like, I worked for people who had no fucking clue, like, what they were doing, and so I… that… I know that we’re a good home for those, but it’s this class of people that, like, can run engagements, or, like, lead…

137 00:21:18.790 ⇒ 00:21:21.069 Uttam Kumaran: How we do analytics.

138 00:21:21.070 ⇒ 00:21:28.449 Uttam Kumaran: Like, how we do strategic analytics or strategic financial analytics as, like, a service, that is, like, really, really tough.

139 00:21:28.450 ⇒ 00:21:43.149 Uttam Kumaran: You know, like, that’s who I think we need, because I can help that person, I can get that person 5 financial analysts, but I… what I can’t get is that… that person is hard to find. So, like, without that person, I can’t support

140 00:21:43.210 ⇒ 00:21:49.849 Uttam Kumaran: To, like, staff them up, to give them leads, to, like, to, like, actually build a book of business, which is, like, what…

141 00:21:49.960 ⇒ 00:22:03.869 Uttam Kumaran: Brainforge, the company, you know, supports that, like, group of leaders with, right? Like, that’s how it’s supporting me and Robert. But we sell all the business, and the company needs us to go do more of that, right? And that’s, like, kind of where we’re at now.

142 00:22:05.120 ⇒ 00:22:08.830 Jasmin Multani: So that’s what you’re considering the delivery lead role.

143 00:22:09.580 ⇒ 00:22:10.529 Uttam Kumaran: Yeah, yeah.

144 00:22:10.530 ⇒ 00:22:14.630 Jasmin Multani: Right. So, you and…

145 00:22:14.780 ⇒ 00:22:31.039 Jasmin Multani: Robert are gonna be front of house, client-facing. It’s also, like, establishing net new clients. Am I getting that right? And then delivery lead would be, deepening the revenue growth for the existing clients, once…

146 00:22:31.220 ⇒ 00:22:37.180 Jasmin Multani: We get a sense that these clients… we know what we want from these clients, and, like, there’s an established relationship.

147 00:22:37.340 ⇒ 00:22:55.569 Uttam Kumaran: Yeah, I think it’s… it’s still up in the air. Like, I won’t shut the door on, like… I don’t think we’ve figured out exactly, like, what the role is. Maybe Robert has a little bit more sense. But for me, everybody has to have some commercial, like, part of their…

148 00:22:55.740 ⇒ 00:23:04.329 Uttam Kumaran: like, goal. Like, even the most junior person, a client, has to spend a little bit of time thinking, hey, where else can I help these folks?

149 00:23:04.330 ⇒ 00:23:23.039 Uttam Kumaran: Like, where else can we be helpful in surfacing that? And at the most senior level, it’s a more significant amount of your time, not only going out and bringing in business, but also expanding existing relationships. So I won’t say that the delivery person doesn’t also contribute to pre-sales or outbound sales and stuff like that.

150 00:23:23.050 ⇒ 00:23:35.339 Uttam Kumaran: But I do think that that’s what allows us to… to give that person way more upside than they would traditionally get as, like, hey, I’m just, like, the… I just lead our Snowflake, like, engagements.

151 00:23:35.340 ⇒ 00:23:35.700 Jasmin Multani: Yeah.

152 00:23:35.700 ⇒ 00:23:47.720 Uttam Kumaran: You’re just like an engineer, you’re just gonna get paid a salary, you have no opportunity to get to sell, which is where the money is, and I also don’t want to hire dedicated salespeople, because they don’t know what it is we do.

153 00:23:48.380 ⇒ 00:23:55.130 Uttam Kumaran: And then what the tough part is, yes, you have to find people that can kind of do both, but that’s something that we both figured out.

154 00:23:55.220 ⇒ 00:24:14.990 Uttam Kumaran: And, like, we can teach that, but what we can’t teach is, like, the interest, or just kind of see, like, why the commercial side is actually what grows our business, but also those same people need to be involved in the delivery, because that’s why our customers like us. They see our face in the intro call, in the first call, all the way.

155 00:24:14.990 ⇒ 00:24:19.740 Jasmin Multani: Right? And that’s why they’re like, oh my god, these guys are, like, awesome, like, they…

156 00:24:19.880 ⇒ 00:24:32.060 Uttam Kumaran: they… and, you know, that’s actually what the sauce is. Like, naturally, the tendency in this business is to be like, yeah, move Robert and these guys out to sales, hire a salesperson, hire a project manager, hire this.

157 00:24:32.060 ⇒ 00:24:41.249 Uttam Kumaran: We tried to do that for a bit there, was horrible, did not work, because you create these, like, bifurcated incentives where nobody truly just cares about the client.

158 00:24:41.250 ⇒ 00:24:44.739 Uttam Kumaran: From start, throughout the engagement, through the renewal.

159 00:24:44.740 ⇒ 00:24:49.120 Uttam Kumaran: And so we’re… that’s… these are the kind of, like, decisions and trade-offs we’re, like, dealing with.

160 00:24:49.360 ⇒ 00:24:56.140 Jasmin Multani: Yeah, yeah. I definitely felt that at DoorDash, where I was part of Strategy Knob, so I was, you know, like.

161 00:24:56.190 ⇒ 00:25:08.920 Jasmin Multani: very much, held responsible for pulling my data, building dashboards, running experiments, doing the pre-post. But the aggravating thing was the EPMs

162 00:25:08.930 ⇒ 00:25:28.700 Jasmin Multani: who are, like, the official enterprise partners, I could be… I could be running a dashboard and experiment for Sprouts and Aldi’s. And, like, I was there when we launched, 2,000, stores with Aldi, and, it was a great launch, but our maintenance was not great because

163 00:25:28.770 ⇒ 00:25:31.079 Jasmin Multani: our EPMs, who are…

164 00:25:32.910 ⇒ 00:25:44.840 Jasmin Multani: You know, they came from McKinsey, and a lot of great places like JP Morgan, very finance-heavy, had Harvard on their education, but could not pull

165 00:25:45.030 ⇒ 00:25:53.350 Jasmin Multani: data, and… or, like, our… someone on our sales team promised, an enterprise company

166 00:25:53.540 ⇒ 00:26:03.599 Jasmin Multani: Like, we promised to get you $10 million by the end of the year, and if we can’t get that to you in revenue, then we’ll pay out of pocket.

167 00:26:04.310 ⇒ 00:26:08.439 Jasmin Multani: We did that with Sprouts, we did that with…

168 00:26:08.440 ⇒ 00:26:09.120 Uttam Kumaran: Damn.

169 00:26:09.120 ⇒ 00:26:19.550 Jasmin Multani: And… we were no… and I pushed back, and I pushed back to the senior managers, because at that point I had, like, been reorged, and I was like, how did you come up with this number?

170 00:26:19.550 ⇒ 00:26:32.980 Jasmin Multani: And this is a senior manager, like, very high up, and his response was, well, someone on sales, steal the deal with them, so they just gave him a quote that…

171 00:26:33.190 ⇒ 00:26:39.459 Jasmin Multani: was attractive. And I’m like, cool, cool, cool, cool. We just got an exclusive… fake exclusive relationship.

172 00:26:39.460 ⇒ 00:26:39.910 Uttam Kumaran: Yeah.

173 00:26:39.910 ⇒ 00:26:41.700 Jasmin Multani: fruit and sprouts locations.

174 00:26:41.890 ⇒ 00:26:45.009 Jasmin Multani: trying to meet a goal that may not work, right?

175 00:26:45.010 ⇒ 00:26:45.620 Uttam Kumaran: Yeah.

176 00:26:45.620 ⇒ 00:26:47.730 Jasmin Multani: Well, we just paid… we paid for it.

177 00:26:47.730 ⇒ 00:26:48.150 Uttam Kumaran: Yeah.

178 00:26:48.150 ⇒ 00:27:01.530 Jasmin Multani: We pay for it, and when we flop, it’s embarrassing, and they still have the option of leaving us. Yeah. So the company loses 10… could potentially lose 10 million. We did that with Grocery Outlet.

179 00:27:01.650 ⇒ 00:27:10.489 Jasmin Multani: Then we did that with Sprouts, because we were desperate for Sprouts. Kind of did that with Aldi. After Aldi, we were like, we’re not doing this, because it got to the point where the Aldi CEO

180 00:27:10.790 ⇒ 00:27:13.920 Jasmin Multani: started setting up meetings with Tony.

181 00:27:14.420 ⇒ 00:27:14.980 Jasmin Multani: It got.

182 00:27:14.980 ⇒ 00:27:15.500 Uttam Kumaran: Yeah.

183 00:27:15.500 ⇒ 00:27:23.500 Jasmin Multani: up, and he’s like, we are having a call to discuss what the sales are. And it would… all hands, it would just trickle down to our vertical, like.

184 00:27:23.500 ⇒ 00:27:24.599 Uttam Kumaran: Yeah, yeah, yeah.

185 00:27:24.770 ⇒ 00:27:28.379 Jasmin Multani: Groceries, allocating a lot of our resources, but…

186 00:27:28.490 ⇒ 00:27:48.399 Jasmin Multani: realistically, I personally buy groceries 4 times a week. There is a hard upper limit in retention for things like groceries because of shelf life, how much space you have in the fridge, so I felt that frustration as a data person, being like, this is not logical.

187 00:27:48.400 ⇒ 00:27:57.509 Uttam Kumaran: Yeah, and I… for me, I’m the biggest fan of data people, because I started, like, really back-of-the-house data, and then…

188 00:27:57.610 ⇒ 00:28:14.529 Uttam Kumaran: really early in my career, I was in rooms deciding on tons of stuff for WeWork, and then throughout my career, I was able to bridge the gap from moving to just, like, let the data team handle it, like, I’m in the room because I happen to just know the most, and I can speak about it.

189 00:28:14.530 ⇒ 00:28:17.329 Jasmin Multani: Like, which is uncommon. And so, for me.

190 00:28:17.470 ⇒ 00:28:23.950 Uttam Kumaran: We’re trying to build a crew of, like, those types of people, and there is no room for, like, your…

191 00:28:24.100 ⇒ 00:28:42.599 Uttam Kumaran: kind of, like, MBA Harvard dude that doesn’t know to pull… like, these people don’t enter, like… they won’t enter… I don’t know, through what door could they get in this company? Like, I don’t know. But, like, I don’t like those people, and neither does Robert. Both of us are core data people. We found a way to do the business side of things, and that’s what we can teach.

192 00:28:42.600 ⇒ 00:28:46.199 Jasmin Multani: how to sell. We can teach how to put together a scope.

193 00:28:46.200 ⇒ 00:28:59.689 Uttam Kumaran: think about the outcome and the value that our type of data analysis is driving, but what we can’t do is, like, what… the reason why sales gets bifurcated and they just start selling whatever it takes to close. I had a meeting with

194 00:28:59.690 ⇒ 00:29:13.729 Uttam Kumaran: a friend of mine who, he was… he… they’re at, like, a much larger consultancy, and he’s like, yeah, they just, like, sold whatever, and then they’re like, you have to deliver, there starts bifurcation. For us, like, there are these natural tendencies in a consultancy that happen as you grow.

195 00:29:13.730 ⇒ 00:29:27.259 Uttam Kumaran: And we’re feeling the pain of, like, it would just be so easy for us to be like, cool, now we need project managers, we need this, this, this. One way is, like, AI has allowed us to actually reduce a lot of the people whose sole job is, like, information brokering.

196 00:29:27.260 ⇒ 00:29:27.610 Jasmin Multani: Yeah.

197 00:29:27.610 ⇒ 00:29:43.940 Uttam Kumaran: EMs, this sort of stuff. So most of our people are all delivery. We instead have, like, a few, like, coordinators, and they use a ton of AI stuff to basically fill in the gaps. But my job is actually just for people to spend time doing data work and presenting data work, and, like, nothing else.

198 00:29:45.120 ⇒ 00:29:47.599 Uttam Kumaran: what else is there to do? You know?

199 00:29:47.600 ⇒ 00:29:48.490 Jasmin Multani: Like…

200 00:29:48.490 ⇒ 00:29:54.430 Uttam Kumaran: I don’t… there’s no, like, meetings to sit in, there’s no, like… don’t… I don’t want people in JIRA tickets, like…

201 00:29:54.430 ⇒ 00:29:54.850 Jasmin Multani: Right.

202 00:29:54.850 ⇒ 00:30:11.020 Uttam Kumaran: I want to be on the edge of, like, using AI and data. I want to be on the edge of, like, strategic analysis. Our stuff is now getting presented to the C-suite and the boards of these, like, couple hundred million dollar companies, and we’re just, like, a little company, you know? Fully remote.

203 00:30:11.540 ⇒ 00:30:26.139 Uttam Kumaran: Like, you know, and that’s, like, what we’re seeing is that a lot of stuff we learned from being in data at these scaling companies is helping, especially coming in as a consultant. Because we’re coming in because there’s a problem. Nobody’s hiring us because it’s, like, everything’s great, like…

204 00:30:26.140 ⇒ 00:30:33.129 Jasmin Multani: keep making it better. There’s usually some… someone’s about to get fired, or the person bringing us in is about to get fired, or something’s about to happen.

205 00:30:33.130 ⇒ 00:30:45.540 Uttam Kumaran: And so, we’re helping people tell these stories, and they’ve never met, I think, folks like us. I could tell you’re kind of in that world, too, where you can do a lot of the hardcore data work, and then still come up for air and, like, translate it.

206 00:30:45.540 ⇒ 00:30:53.470 Uttam Kumaran: And then for us, it’s like, hey, here’s why you need to invest in a data warehouse, ETL, and here’s, like, the types of analysis it can enable.

207 00:30:53.470 ⇒ 00:31:11.049 Uttam Kumaran: we can only get to this far, because your system is totally shit, but if we were to do all these different things on the infra side, you can now ask a question on Monday, and we can get a result by Friday. This so-and-so data people who are about to leave your company, because you never invested.

208 00:31:11.050 ⇒ 00:31:20.140 Uttam Kumaran: are now happy, because they’re able to answer questions for the teams or support, so I’m just, like, being depressed and, like, putting together, like, Excel sheets, right? So those are all the things where

209 00:31:20.850 ⇒ 00:31:27.630 Uttam Kumaran: we, like, talk about those pieces that I feel like are often overlooked, and often a consultancy

210 00:31:27.780 ⇒ 00:31:31.729 Uttam Kumaran: Typically, like, data consultancies, they’re like, you tell us what to do, whatever, like.

211 00:31:31.900 ⇒ 00:31:48.880 Uttam Kumaran: Robert’s, like, a lot… I think, more, like, better than… better at this than me, but we come in and we’re like, you should probably do this. And here’s… now we’ve learned, now we’ve done this for a lot of people, where we’re like, trust us, here’s the roadmap, we will get you there. And a lot of cons… people want to partner.

212 00:31:49.000 ⇒ 00:32:01.190 Uttam Kumaran: They don’t want a consultancy that’s like, oh, we have, like, 100 people somewhere, like, tell us, like, anything we’ll do, we’ll do. It’s like, no, no, no, I don’t do a lot of other things. We do data work, we do AI work, we’re starting to do them both together.

213 00:32:01.350 ⇒ 00:32:01.840 Jasmin Multani: Here.

214 00:32:01.840 ⇒ 00:32:16.120 Uttam Kumaran: the things we can help with. Ideally, the things we find, you should then take to the teams that can affect it. Launch a new product, change discounts, change your emails, like, whatever cadence. But, like, that’s how we come in and support, and then…

215 00:32:16.220 ⇒ 00:32:30.619 Uttam Kumaran: We also sell to the top, so we never sell to someone that’s, like, sitting in the middle somewhere, unless that’s, like, a way in, but usually we… we sort of float our way, because our insights start to get shared, and we just, like, float our way, and then to the top, and then…

216 00:32:30.750 ⇒ 00:32:43.599 Uttam Kumaran: we just are like, how else can we help? You know, and then just, like, things are growing. So we’re… our renewal rate’s really good, we’re starting to expand scope for a lot of people. We’re starting to get shots on goal at, like, some big, big clients.

217 00:32:43.760 ⇒ 00:32:48.060 Uttam Kumaran: But the bigger the client, the longer the sales process is, and the more complex it is.

218 00:32:48.630 ⇒ 00:32:57.589 Uttam Kumaran: And right now, what both of us are selling, probably the only people that could actually truly, like, sell those types of deals, and that’s what the company needs us to go, like.

219 00:32:57.590 ⇒ 00:32:58.120 Jasmin Multani: Yeah.

220 00:32:58.120 ⇒ 00:33:01.639 Uttam Kumaran: Spend way more time thinking about that, the strategy, and, like.

221 00:33:01.790 ⇒ 00:33:09.449 Uttam Kumaran: building, like, the leadership and thinking about the team, you know? Versus, like, I’m still, like, in cursor doing stuff, like, every day, you know? So…

222 00:33:09.450 ⇒ 00:33:23.710 Jasmin Multani: You’re good, you’re good. I also appreciate that, like, because you worked in WeWork as a data person, and you’re working against real estate, basically, and actual finite resources, you have a more realistic expectation as to, like, what does

223 00:33:24.390 ⇒ 00:33:31.600 Jasmin Multani: look like, and where are we spinning our wheels? And I feel like that’s, where…

224 00:33:32.080 ⇒ 00:33:48.450 Jasmin Multani: things get dicey when companies are like, let’s go for infinite growth, and that’s nice to be ambitious, but, I think there’s a point where I’ve had to ask managers to be like, we’re beating our heads on the wrong.

225 00:33:48.760 ⇒ 00:33:53.980 Uttam Kumaran: Yeah, yeah, yeah. But you see, you worked at two very well-funded companies, like TikTok and…

226 00:33:53.980 ⇒ 00:33:55.420 Jasmin Multani: Very lucky, yeah.

227 00:33:55.720 ⇒ 00:33:59.120 Uttam Kumaran: But what you’ll, like, you know, it’s like, we work for companies that…

228 00:33:59.250 ⇒ 00:34:07.929 Uttam Kumaran: there is finite resources, like, they may be in a jam. We also do work with companies that are, like, growing fast, they’re like, we have to take advantage.

229 00:34:07.980 ⇒ 00:34:26.530 Uttam Kumaran: But, yeah, I mean, all of our stuff is not, like… but this is also the thing, like, we’re not doing hardcore data science. Like, at WeWork, they hired, like, a big-ass data science team, they never did anything, they never produced anything. They were, like, completely useless. Like, in one afternoon in, like, SQL, I produced, like, more insights. And so that’s the stuff, like, I can’t recommend.

230 00:34:26.530 ⇒ 00:34:31.420 Jasmin Multani: For the most part, we’re still running, like, 80-20 cohort analysis, like the same thing.

231 00:34:31.429 ⇒ 00:34:50.689 Uttam Kumaran: But what’s not, like… it’s just the fact that we’re going to a place that’s never done it. They’ve, like, never done these things, and we’re coming with our collective experience from all these companies and, like, helping them do it, and nobody on their team is capable of, like, thinking an end-to-end analytics project.

232 00:34:51.170 ⇒ 00:34:52.770 Uttam Kumaran: Coming with a great deck.

233 00:34:52.850 ⇒ 00:35:10.059 Uttam Kumaran: being confident on delivering it, and then being like, here’s what you should do, like, they’ve never… some of these companies are hundreds of millions, have never had that before. And, like, that’s… that’s what I think is, like, the fun about this. I mean, we’re starting to grow. Like, I would love to do, like, stuff in data science, things like, one, I need to… we need to get more money.

234 00:35:10.060 ⇒ 00:35:17.150 Uttam Kumaran: And, like, those timelines on those types of projects, they take a lot of buy-in, right? To be like, give me, like, a few weeks.

235 00:35:17.190 ⇒ 00:35:25.490 Uttam Kumaran: to figure something out, because even me, I’d be like, what on planet Earth could take, like, a few weeks to figure out? You know, so…

236 00:35:26.050 ⇒ 00:35:29.339 Jasmin Multani: What aspects of data science are you trying to bridge into?

237 00:35:29.550 ⇒ 00:35:31.109 Jasmin Multani: For the, for the company.

238 00:35:31.250 ⇒ 00:35:39.940 Uttam Kumaran: I think we’re gonna… I mean, I wanna do way more in, like, forecasting and, like, predictive analytics, especially on the e-comm side,

239 00:35:40.270 ⇒ 00:35:44.240 Uttam Kumaran: On the SaaS side, it’s probably more like,

240 00:35:44.670 ⇒ 00:36:03.030 Uttam Kumaran: churn… churn risk, like, churn scoring, like, lead scoring stuff, like, upsell propensity, that’s… that type of stuff would be interesting. I think if you could also do, like, order velocity and understand, like, you know, high-value customers, so there’s some stuff like that that

241 00:36:03.180 ⇒ 00:36:06.860 Uttam Kumaran: I feel like we could do for some customers, and then start to, like.

242 00:36:07.050 ⇒ 00:36:11.159 Uttam Kumaran: just have… like, it’s really… because every customer of ours is, like.

243 00:36:11.280 ⇒ 00:36:14.749 Uttam Kumaran: if they’re retail, they’re, like, Walmart, they’re whatever. If they’re…

244 00:36:14.920 ⇒ 00:36:17.869 Uttam Kumaran: If they’re omni-channel, there are a couple sources, so…

245 00:36:17.870 ⇒ 00:36:18.380 Jasmin Multani: Yeah.

246 00:36:18.380 ⇒ 00:36:22.719 Uttam Kumaran: I feel like we could start to use those, like, modules of things

247 00:36:23.370 ⇒ 00:36:41.320 Uttam Kumaran: for many different customers, but also we want to start doing AI… I think that’s when we have to start layering on AI things. Like, I want people to get, like, really, really great, like, anomaly detection, and I don’t want you to have to be DoorDash to, like, get that, where you hire, like, 100 people and do that, right? I want to deliver that for companies that are, like.

248 00:36:41.600 ⇒ 00:36:43.250 Uttam Kumaran: No. You know?

249 00:36:43.360 ⇒ 00:36:52.560 Uttam Kumaran: Meaning, I’m not saying, like, they… you guys maybe got it right, but, like, only those companies have, like, the cash to solve those types of problems.

250 00:36:52.730 ⇒ 00:36:57.279 Uttam Kumaran: And it’s like, I want to give that to, like, our average customer, you know?

251 00:36:57.820 ⇒ 00:37:08.179 Jasmin Multani: Truthfully, like, I’ve been on those anomaly detections at both DoorDash and TikTok, I’m managing them, and it really is one person and a script, and the money.

252 00:37:08.180 ⇒ 00:37:09.419 Uttam Kumaran: Yeah, so that’s a…

253 00:37:09.560 ⇒ 00:37:23.150 Jasmin Multani: towards, like, automation pipelines, and, like, we’ve done LLM work, too, we’ve launched some LLMs, and, now the problem about money is about, okay, does our little team

254 00:37:23.290 ⇒ 00:37:32.199 Jasmin Multani: we… the company obviously has GPUs, but how do we advocate for our little team to, get those GPUs?

255 00:37:32.450 ⇒ 00:37:33.000 Uttam Kumaran: Yeah.

256 00:37:33.000 ⇒ 00:37:41.450 Jasmin Multani: Our team has built LLMs that have… that pulls, like, 8,000 videos a day. That’s assessing, you know,

257 00:37:41.560 ⇒ 00:37:49.250 Jasmin Multani: incitement to violence against political actors. And even if it’s at an 80% accuracy rate, right?

258 00:37:49.690 ⇒ 00:38:03.350 Jasmin Multani: our leads are… leaders are like, this is not good enough. Someone else is really something better, and I’m like, are they? Because, I… we just got reorgan into the data science pod, and some of the data scientists

259 00:38:03.690 ⇒ 00:38:21.530 Jasmin Multani: that were already part of the data science pod, they are talking about setting up LLMs, but our team will be like, well, what model have you thought about using? And they’re like, oh, we haven’t really thought about it yet. Which is ironic, because, like, our little pod has already

260 00:38:22.000 ⇒ 00:38:25.480 Jasmin Multani: Launched and tested and iterated with this, and, like.

261 00:38:25.480 ⇒ 00:38:25.880 Uttam Kumaran: Yeah.

262 00:38:25.880 ⇒ 00:38:36.140 Jasmin Multani: I’ve done the prompt engineerings, and, like, we… our analysts have, like, stress-tested the prompt engineering, to understand how do we, improve precision by, like, fixing certain things.

263 00:38:36.140 ⇒ 00:38:36.800 Uttam Kumaran: Yes.

264 00:38:36.800 ⇒ 00:38:39.419 Jasmin Multani: So, my team very much is, like.

265 00:38:39.760 ⇒ 00:38:56.200 Jasmin Multani: we’re all analysts. Technically, our work… we could run the same script over and over again. A lot of the work is, making sure the intel and the information is properly mapped out to the actual bad actors who are doing exploitive work on TikTok.

266 00:38:56.200 ⇒ 00:38:59.820 Jasmin Multani: Okay. So, it’s like, we know…

267 00:39:00.650 ⇒ 00:39:09.710 Jasmin Multani: buying fentanyl off of TikTok is bad, but what are… and, like, we can do a lot of research, get Gemini to analyze all of the words that are being

268 00:39:10.050 ⇒ 00:39:19.799 Jasmin Multani: And all the keywords and the regexes, right? And all the countries and so forth. But we still need to do, boots-on-the-ground work to say, hey.

269 00:39:20.600 ⇒ 00:39:26.290 Jasmin Multani: How are they, being aloof on our automated detect.

270 00:39:26.290 ⇒ 00:39:26.909 Uttam Kumaran: Yeah, yeah, yeah.

271 00:39:26.910 ⇒ 00:39:42.620 Jasmin Multani: automated detections that people in Singapore, who are way smarter, have made. And it’s like, you, you dig deep. I, I was using my DoorDash values, and it’s like, you dig deep, you do the one, one-point, review, and you realize, oh.

272 00:39:43.330 ⇒ 00:39:56.770 Jasmin Multani: some of these drug dealers are posting a video, deleting it within a few hours, and reposting things over and over again. That’s why they can get away with not having their.

273 00:39:56.770 ⇒ 00:39:57.739 Uttam Kumaran: Oh, yeah, yeah, yeah.

274 00:39:57.740 ⇒ 00:40:03.240 Jasmin Multani: band. So there are all these, like, loopholes and layers. Yeah.

275 00:40:03.520 ⇒ 00:40:22.260 Jasmin Multani: that the formal machines have to decide on, because there’s so many… there are so many users, but, across vertical topics, whether it’s, like, trade of drugs or, let’s say, human smuggling, right? Yeah. Using the same bad actor strategy.

276 00:40:22.260 ⇒ 00:40:26.789 Jasmin Multani: But we were able to prove it through this… A few different verticals.

277 00:40:27.140 ⇒ 00:40:27.810 Uttam Kumaran: Yeah, yeah.

278 00:40:27.810 ⇒ 00:40:31.089 Jasmin Multani: I think at DoorDash, let’s, like…

279 00:40:31.290 ⇒ 00:40:37.619 Jasmin Multani: Like, anonymity detection is just my bread and butter, but it really, comes down… like, the resources…

280 00:40:38.220 ⇒ 00:40:48.179 Jasmin Multani: It just takes one person to, build a script, but it takes a lot of people, a few people, to compare what’s happening on the ground.

281 00:40:48.180 ⇒ 00:40:48.680 Uttam Kumaran: Yeah.

282 00:40:48.680 ⇒ 00:40:49.080 Jasmin Multani: Yeah.

283 00:40:49.080 ⇒ 00:40:49.580 Uttam Kumaran: Yeah.

284 00:40:49.580 ⇒ 00:41:07.769 Jasmin Multani: at DoorDash, like, we would have… there was a point where we were doing weekly seminars, where we would review the worst of the worst cases. And this is, like, all hands on deck, senior managers to the associates. We’re all digging into the few cases, we’re all interacting with a doc, and we’re all teaching each other, like, what is.

285 00:41:07.770 ⇒ 00:41:08.110 Uttam Kumaran: Yeah.

286 00:41:08.110 ⇒ 00:41:10.959 Jasmin Multani: score mean? What does this algorithm mean? Like.

287 00:41:11.190 ⇒ 00:41:11.760 Uttam Kumaran: Yeah, yeah, yeah.

288 00:41:11.760 ⇒ 00:41:26.730 Jasmin Multani: The score was this, how did this actually not get violated? How do we talk to the data scientists there? And the data scientists at DoorDash were really good. I’d say they were really good. Over here… Yeah.

289 00:41:27.130 ⇒ 00:41:37.179 Jasmin Multani: from my corner on USDS, I see a lot of dashboarding, a lot of, yeah, we’re monitoring these metrics, and I’m like, but what decisions are you making me so far.

290 00:41:37.180 ⇒ 00:41:40.940 Uttam Kumaran: Yeah, yeah, yeah, no, it’s… but this is the thing, these are just, like… I…

291 00:41:41.490 ⇒ 00:41:50.159 Uttam Kumaran: Like, at WeWork, I had the luxury of, like, it was a company, like, it was a company that was scaling. I joined when it was, like, 5,000 people, and then when I left, it was 15,000 people.

292 00:41:50.170 ⇒ 00:41:53.909 Jasmin Multani: And, like, I came in and we were…

293 00:41:53.910 ⇒ 00:41:56.880 Uttam Kumaran: a six-person BI team.

294 00:41:56.880 ⇒ 00:41:59.789 Jasmin Multani: And then when I left, it was a 40-person…

295 00:41:59.930 ⇒ 00:42:01.669 Uttam Kumaran: Decentralized data team.

296 00:42:01.820 ⇒ 00:42:06.040 Uttam Kumaran: And I joined when I was, like, 21, like, that was my first job.

297 00:42:07.100 ⇒ 00:42:19.720 Uttam Kumaran: And so, you… you’re in, like, a room where it really, really matters, like, nobody’s thinking about the colors on the dashboard. They’re like, dude, I need this by Friday, I have the biggest deal ever, we need to close. So I support account management.

298 00:42:19.720 ⇒ 00:42:30.300 Uttam Kumaran: I worked on keycard data, like, every time you badge into WeWork, we took all that, I repurposed it, we sold that as part of our enterprise offering, like, dashboards, like, of, like.

299 00:42:30.300 ⇒ 00:42:39.739 Uttam Kumaran: your employees for your company, how are they coming in? What spaces are they using? How do we help them optimize and get more space? And so, but again, those are the things where

300 00:42:39.800 ⇒ 00:42:57.680 Uttam Kumaran: at, like, a WeWork and stuff like that, I think it’s one thing, but at a DoorDash or TikTok, you just have a lot of people that are… it’s, like, some… I feel like it’s somewhat performative, you know? And at our company, like, when we go into someplace, these guys are really… have, like, nothing. Which is tough, because, like, sometimes I’m like.

301 00:42:57.790 ⇒ 00:43:05.589 Uttam Kumaran: damn, here, the 10th time I’m, like, setting up DBT, but then that’s also, like, you see people’s eyes light up, they’ve never…

302 00:43:05.590 ⇒ 00:43:06.540 Jasmin Multani: Had it properly.

303 00:43:06.540 ⇒ 00:43:09.859 Uttam Kumaran: data warehouse with naming conventions. They, like.

304 00:43:10.390 ⇒ 00:43:17.729 Uttam Kumaran: They never… they’ve never been able to ask, like, how do we define revenue and, like, and then ask, like, a follow-up question and get an answer.

305 00:43:17.850 ⇒ 00:43:27.329 Uttam Kumaran: like, so that’s the stuff that, like, kind of never gets old. We’re doing it now very often, but it’s just fun. And then for me, it’s figuring out, like, how do we use AI to speed up

306 00:43:27.410 ⇒ 00:43:40.439 Uttam Kumaran: every part of the manufacturing cycle of data, whether it’s setting up ETL, whether it’s setting up data models, whether it’s also, like, assisting with analysis. Like, how can we use AI to actually help speed up

307 00:43:40.440 ⇒ 00:43:51.750 Uttam Kumaran: Like, and I’ll have AI go and, like, ask preliminary questions, gather things, you know, and do a lot of that. And then now, a lot of our clients, they’re… they don’t want a dashboard. They’re like, hey, can we enable, like.

308 00:43:51.970 ⇒ 00:44:09.300 Uttam Kumaran: interacting with data through text, like, through chat, because we have a lot… because I also felt like the dashboard as a medium kind of sucks for a lot of people, like, it hasn’t really changed. I think we started… great data people started moving to notebooks for, like, storytelling, but, like, most stuff… people still get stuff out of dashboards, and

309 00:44:09.620 ⇒ 00:44:27.310 Uttam Kumaran: I don’t know, still think that it’s not great, but now, if you can add enough context, I think your AI can start to actually explain things. How do we take it one step further? For example, we record every meeting, we have notes from everything, we have our project plans. How can all that get layered in this context?

310 00:44:27.310 ⇒ 00:44:39.360 Uttam Kumaran: with the AI to actually start to produce proactive insights, like, go after and run queries on, like, some type of basis to go find things, surface that. So that’s, like, I think, like, a really…

311 00:44:39.360 ⇒ 00:44:48.319 Uttam Kumaran: Holy Grail, where you kind of need a company like ours who’s doing so much context gathering for our… because we have to write documentation to even understand everything.

312 00:44:48.320 ⇒ 00:44:57.009 Uttam Kumaran: And we’re coming in and doing all the data, like, I feel like we truly have a good shot at, like, enabling really great chat with data use cases.

313 00:44:57.010 ⇒ 00:44:57.470 Jasmin Multani: Wow.

314 00:44:57.470 ⇒ 00:45:11.179 Uttam Kumaran: like, AI on top of data to actually power actions, like, in a Klaviyo or, you know, in another system. So, like… but that… see, that’s the stuff where, like, if we’re able to do that and monetize that, like, I won’t… I’m not gonna worry about, like.

315 00:45:11.860 ⇒ 00:45:30.310 Uttam Kumaran: I never wanted to worry about competing with price, with people. Like, I want to go after companies that are like, we need to grow, and we are ambitious, and we’re there to help them grow revenue and profit, right? And data and AI is, like, our way to do that. We don’t go into places that are not growing. Like, we don’t go into places that want to cut costs or fire people.

316 00:45:32.620 ⇒ 00:45:42.599 Jasmin Multani: I mean, it’s an exciting piece of the pie that you guys found, right? It’s clear that you found your stride through your bread and butter, which is, like, data engineering.

317 00:45:42.600 ⇒ 00:45:43.809 Uttam Kumaran: Normal data stuff.

318 00:45:43.810 ⇒ 00:45:55.820 Jasmin Multani: decision-making, but the different decision clients and exposure is gonna stress test your skill set, and even when I was working at Pungle, I’m like, damn.

319 00:45:55.980 ⇒ 00:46:00.829 Jasmin Multani: companies don’t know retention. Retention is so hard.

320 00:46:00.830 ⇒ 00:46:01.600 Uttam Kumaran: Yeah!

321 00:46:02.240 ⇒ 00:46:13.139 Uttam Kumaran: They’re making a lot of money, but you could tell that, like, for them to go from 20 to 100 million, or 100 to 500, you have to kind of, like, understand what your retention is, right?

322 00:46:13.140 ⇒ 00:46:27.029 Jasmin Multani: context is, and, like, that’s the exciting part, too, which is, like, learning about these different industries. My friend from DoorDash, she also left, and she’s running a company with her husband, called Beige. It’s out in LA. Okay.

323 00:46:27.030 ⇒ 00:46:28.109 Uttam Kumaran: What is it really do?

324 00:46:28.430 ⇒ 00:46:34.250 Jasmin Multani: So it’s like a, marketplace for… photographers.

325 00:46:34.410 ⇒ 00:46:35.960 Uttam Kumaran: Okay. And videographers.

326 00:46:36.040 ⇒ 00:46:38.189 Jasmin Multani: And… they’ve been crying.

327 00:46:38.460 ⇒ 00:46:50.960 Jasmin Multani: So I’ve been watching them build this up from the ground up. It’s B-E-I-G-E-A-I. Yeah. They actually asked me, they’re like, Jasmine, you know so much about data, could you help? And I’m like, mmm…

328 00:46:51.380 ⇒ 00:46:53.719 Jasmin Multani: I really value this friendship.

329 00:46:54.510 ⇒ 00:47:04.500 Uttam Kumaran: You say, send me a CSV, I’ll pick them up, I’ll get a couple things. Like, I have a friend that runs a Shopify store for shoes. I actually was wearing his shoes the other day.

330 00:47:04.500 ⇒ 00:47:05.330 Jasmin Multani: Oh, nice!

331 00:47:05.330 ⇒ 00:47:15.240 Uttam Kumaran: But he, I don’t know, he might be at, like, 2 or 3 million, but I was like, I was like, yeah, dude, let me into your Shopify someday, I’ll look up on some stuff to help you with, but…

332 00:47:15.240 ⇒ 00:47:18.390 Jasmin Multani: I mean, sounds like he’s got the funding.

333 00:47:18.390 ⇒ 00:47:26.229 Uttam Kumaran: No, I know, and I met him, you know, he was my account manager at Snowflake when I was buying Snowflake a couple companies ago.

334 00:47:26.240 ⇒ 00:47:29.210 Jasmin Multani: And then I went to go… I was in Denver.

335 00:47:29.210 ⇒ 00:47:31.680 Uttam Kumaran: And he was like, yeah, let’s get coffee, and he was like, dude.

336 00:47:31.790 ⇒ 00:47:50.880 Uttam Kumaran: I don’t even know what Snowflake does. Like, he was like, I don’t care less. And he was… his story was, he was a… he was actually, like, a pro, collegiate athlete in hockey, but then… and then he was about to go to the NHL, but he broke his, tore his ACL. And he… he actually played a little bit in Europe before that, and then tore his ACL.

337 00:47:50.890 ⇒ 00:48:07.800 Uttam Kumaran: But then he came back, and he, like, he’s like, I just gotta get, like, a tech job. It’s in Denver, snowflake is big. He’s like, he was working there, and then, he was like, dude, I’m working on these, like… he was also the first person, this was 2020, he told me about Andrew Huberman, very early. He was like, dude, you gotta listen to Andrew Huberman. This was, like, yeah.

338 00:48:08.370 ⇒ 00:48:15.249 Uttam Kumaran: 2020 or 2021. And then second, he was like, I’m working on these shoes on the side, and he showed me the original design. These are almost like…

339 00:48:15.250 ⇒ 00:48:15.750 Jasmin Multani: Oh, wow.

340 00:48:15.750 ⇒ 00:48:26.540 Uttam Kumaran: You know how Vibroms are, like, sit on the ground? There’s no cushion? He was basically working on these, like, no-sole shoes, that have a wide toe box, and then, like, do a bunch of stuff.

341 00:48:26.540 ⇒ 00:48:27.010 Jasmin Multani: Oh, wow.

342 00:48:27.010 ⇒ 00:48:34.690 Uttam Kumaran: And then, yeah, now they’re selling a lot. He’s a… his company’s really big in, like, the, flat feet, like, the flat feet, like.

343 00:48:34.940 ⇒ 00:48:35.980 Uttam Kumaran: really, like.

344 00:48:35.980 ⇒ 00:48:38.889 Jasmin Multani: Oh my god. Low to the ground, shoot. Stop.

345 00:48:38.890 ⇒ 00:48:47.000 Uttam Kumaran: low to the ground, like, world, because it actually, like, helps your feet get, like, way stronger. It’s called, it’s called POLOS, T-O-L-O-S.

346 00:48:47.620 ⇒ 00:48:51.220 Jasmin Multani: P-O-L-O-F? Okay, I might have been to this, if they’re… if they.

347 00:48:51.220 ⇒ 00:48:54.129 Uttam Kumaran: You should, I’ll get you a discount code.

348 00:48:54.130 ⇒ 00:49:05.460 Jasmin Multani: Let me know, but they’re great. I would love that. I do have flat foot, and, like, my uncle, even though I was a kid, he would make fun of me for it. He’s like, oh, you’re just a freak. I’m like, ugh, I can’t help it.

349 00:49:06.200 ⇒ 00:49:08.449 Jasmin Multani: I know how to fix it, but if this, like…

350 00:49:08.650 ⇒ 00:49:10.780 Jasmin Multani: trains the arch, I would have been.

351 00:49:10.780 ⇒ 00:49:11.550 Uttam Kumaran: Yeah, yeah.

352 00:49:12.060 ⇒ 00:49:15.689 Uttam Kumaran: But, like, a lot of people like the barefoot, like, barefoot shoes.

353 00:49:15.770 ⇒ 00:49:18.010 Jasmin Multani: Like, things that are just… The shoes? The toe shoes?

354 00:49:18.010 ⇒ 00:49:21.450 Uttam Kumaran: Yeah, yeah. So these are just, like, very low to the ground.

355 00:49:21.450 ⇒ 00:49:21.820 Jasmin Multani: Mmm.

356 00:49:21.820 ⇒ 00:49:27.190 Uttam Kumaran: But I don’t know, but that’s a fun one where it’s like, yeah, now that we’ve learned so much about e-comm and…

357 00:49:27.330 ⇒ 00:49:36.580 Uttam Kumaran: I don’t know, it’s just fun to, like, talk to him about it. But he’s, like, shipping, like, shoes out of his, like, garage, you know, for, like, a number of years. I should call him, actually, it’s been a minute.

358 00:49:36.580 ⇒ 00:49:39.510 Jasmin Multani: I know, I appreciate that they’re so wide, too.

359 00:49:39.510 ⇒ 00:49:42.610 Uttam Kumaran: Yeah, so it’s, like, wide, so they’re very wide toe box.

360 00:49:42.610 ⇒ 00:49:43.670 Jasmin Multani: Yeah, yeah.

361 00:49:43.700 ⇒ 00:49:50.440 Uttam Kumaran: Yeah, it’s just, like, great. And you can just throw them in the washing machine. Like, I just throw them in the washing machine, they clean up, like, really, really well. It’s, like, a nice cloth.

362 00:49:50.590 ⇒ 00:49:58.559 Jasmin Multani: Honestly, I probably will buy this, just cause, like, I’m trying to, like, revamp my health and fitness part of my life.

363 00:49:58.560 ⇒ 00:49:59.160 Uttam Kumaran: Okay.

364 00:49:59.160 ⇒ 00:50:09.999 Jasmin Multani: So, I’ve seen videos of people who, like, will go days without wearing, like, those enclosed shoes, and, like, their grippers are, like, this, actually.

365 00:50:10.000 ⇒ 00:50:10.830 Uttam Kumaran: Yes!

366 00:50:10.830 ⇒ 00:50:15.219 Jasmin Multani: Or, like, curled in, so I’m trying to understand, if I can…

367 00:50:15.220 ⇒ 00:50:18.669 Uttam Kumaran: But these will… it will… it will definitely, like…

368 00:50:19.030 ⇒ 00:50:23.080 Uttam Kumaran: You’ll get… your feet will get tired wearing these, but after a few… after a few, like.

369 00:50:23.350 ⇒ 00:50:38.220 Uttam Kumaran: if you wear them more often, you’ll get used to it, because your feet are not used to hitting. Like, I wear hokas also, and Hokas, like, you float. You’re like… it’s like a… it’s like a cushion, so you don’t… but then this, like, you really hit the ground, but it strengthens a lot of the muscles.

370 00:50:38.410 ⇒ 00:50:43.089 Jasmin Multani: Yeah, and like, I’m trying to get back into running and trying to do a half marathon, but I’m gonna.

371 00:50:43.090 ⇒ 00:50:43.470 Uttam Kumaran: Great.

372 00:50:43.470 ⇒ 00:50:48.500 Jasmin Multani: between this time, so maybe… yeah, this’ll be interesting. This is a good plug.

373 00:50:50.250 ⇒ 00:50:51.559 Jasmin Multani: My parents are.

374 00:50:52.060 ⇒ 00:50:53.760 Jasmin Multani: well, business owners, so I get into.

375 00:50:53.760 ⇒ 00:51:02.480 Uttam Kumaran: Oh, really? Okay, cool. Yeah, this is just a good friend of mine, so… Well, so, I guess, what are you thinking about doing? Like, what is, like, what’s next for you?

376 00:51:03.140 ⇒ 00:51:15.929 Jasmin Multani: Okay, what’s next for me is… I’m in a good spot in TikTok where I… my data scientist manager, he was, like, one of the first USDS hires, but I am looking…

377 00:51:16.160 ⇒ 00:51:22.130 Jasmin Multani: to ramp out of TikTok. I feel like there are just limited opportunities, there’s…

378 00:51:22.840 ⇒ 00:51:40.369 Jasmin Multani: just a revolving door, and the perks are nice, but I’m trying to double down on, like, whatever I can learn in data science, and use their resources, but end goal is to leave corporate, and, work with people that I like working with, and that.

379 00:51:40.370 ⇒ 00:51:45.849 Uttam Kumaran: What gives you still, like, energy and data after being in it for, like, A while, Michael.

380 00:51:46.160 ⇒ 00:51:57.410 Jasmin Multani: I’d say the storytelling pieces. I started off in neuroscience, worked in research, kept pivoting, but I do appreciate…

381 00:51:58.270 ⇒ 00:52:05.339 Jasmin Multani: just the real… I mean, it sounds corny, but, like, making data-driven decisions. I know it’s really, really corny, but I…

382 00:52:05.500 ⇒ 00:52:25.230 Jasmin Multani: even with my parents’ liquor store, we use numbers and metrics as, like, the floor, right? And that helps us understand, okay, this is the floor, as long as we are maintaining this volume, in sales, we’re happy. Now we can use this extra space in sales or revenue to experiment

383 00:52:25.570 ⇒ 00:52:44.920 Jasmin Multani: around. Yeah. And so, like, I’m from a big Punjabi family, so my parents are liquor store owners. My dad also sells barbecue grills to the side, so they diversify their portfolio. Some of my… a lot of my uncles also run their own liquor stores, but there’s such a diversity in how they do it, too, right?

384 00:52:45.350 ⇒ 00:52:51.430 Jasmin Multani: in terms of, like, even selecting the location, in Orange County.

385 00:52:51.430 ⇒ 00:52:51.990 Uttam Kumaran: Yeah.

386 00:52:51.990 ⇒ 00:53:10.430 Jasmin Multani: in, like, the demographic that you want to set yourself in. I have an uncle who, you know, he does Instagram Lives, and he’ll be doing shots with… he’ll… he’s just giving shots to his customers, and he’s just squatted there, and…

387 00:53:10.450 ⇒ 00:53:13.729 Jasmin Multani: he’ll do, like, these Kentucky.

388 00:53:13.730 ⇒ 00:53:14.890 Uttam Kumaran: At the liquor store?

389 00:53:15.710 ⇒ 00:53:17.630 Jasmin Multani: Yeah, yeah, yeah, yeah.

390 00:53:18.070 ⇒ 00:53:21.910 Uttam Kumaran: I watched some gas station Instagram Lives, and they’re very funny.

391 00:53:21.910 ⇒ 00:53:25.189 Jasmin Multani: they’re funny. I mean, it’s supposed to be home, right?

392 00:53:25.190 ⇒ 00:53:25.809 Uttam Kumaran: Yeah, yeah, yeah.

393 00:53:26.010 ⇒ 00:53:44.379 Jasmin Multani: it’s an extension of your home, and it’s our bread and butter. My dad actually started off as a data consultant, a data analyst, in biotech, and then after the first couple years, he’s like… and living in America, he’s like, wow, you can’t make much money in a corporate job.

394 00:53:44.380 ⇒ 00:53:44.810 Uttam Kumaran: Yeah.

395 00:53:44.810 ⇒ 00:53:50.159 Jasmin Multani: out in the 90s, so he’s like, I’m gonna use this money to buy a liquor store.

396 00:53:50.160 ⇒ 00:53:51.040 Uttam Kumaran: Wow.

397 00:53:51.040 ⇒ 00:53:57.300 Jasmin Multani: And just, like, the amount of flexibility, I didn’t appreciate it growing up, but now I do.

398 00:53:57.300 ⇒ 00:53:57.740 Uttam Kumaran: Heh, yeah.

399 00:53:57.740 ⇒ 00:54:04.000 Jasmin Multani: humility and, sense of, like, self-respect you can have as a small business owner.

400 00:54:04.360 ⇒ 00:54:15.280 Jasmin Multani: I really appreciate that, and that’s what gets me excited, and that’s what I really thought I was gonna do at DoorDash, but we… because we had to prove

401 00:54:16.300 ⇒ 00:54:20.580 Jasmin Multani: Our value, we had to favor our enterprise, I.

402 00:54:20.580 ⇒ 00:54:21.490 Uttam Kumaran: Yeah, yeah, yeah.

403 00:54:21.490 ⇒ 00:54:28.749 Jasmin Multani: I mean, you get the deal. And it just took a long time for those products to trickle down to,

404 00:54:29.060 ⇒ 00:54:29.570 Jasmin Multani: Actual.

405 00:54:29.570 ⇒ 00:54:29.930 Uttam Kumaran: Yeah.

406 00:54:29.930 ⇒ 00:54:33.660 Jasmin Multani: Like my family that’s also on DoorDash. So I’d.

407 00:54:33.660 ⇒ 00:54:34.010 Uttam Kumaran: Yeah.

408 00:54:34.460 ⇒ 00:54:41.840 Jasmin Multani: that is what keeps me going. I use data as a sanity check, so that people can just, like.

409 00:54:42.390 ⇒ 00:54:57.349 Jasmin Multani: actually explore their own ideas and creativity, and I think that’s why I’m just, like, not feeling as fulfilled at TikTok. I appreciate the detection that we’re making, I appreciate the learning growth, like.

410 00:54:58.040 ⇒ 00:55:02.370 Jasmin Multani: I never asked to be on the LLM project, they were just like, just do this.

411 00:55:02.370 ⇒ 00:55:02.950 Uttam Kumaran: Yeah, yeah.

412 00:55:02.950 ⇒ 00:55:07.419 Jasmin Multani: exposure to so many things, so I really want to milk it as much as I can.

413 00:55:07.420 ⇒ 00:55:08.050 Uttam Kumaran: Yeah, yeah, yeah.

414 00:55:08.050 ⇒ 00:55:14.390 Jasmin Multani: But I also want to give myself a hard stop on when I start plateauing. So that’s…

415 00:55:14.600 ⇒ 00:55:25.730 Jasmin Multani: I appreciate you saying, hey, you want people to be full-time, but if there is a space to do part-time so that I can,

416 00:55:25.730 ⇒ 00:55:26.290 Uttam Kumaran: Sure.

417 00:55:26.290 ⇒ 00:55:33.179 Jasmin Multani: figure out where my pace is in your company, and what I can bring to the table.

418 00:55:33.180 ⇒ 00:55:33.670 Uttam Kumaran: Sure.

419 00:55:33.670 ⇒ 00:55:50.239 Jasmin Multani: And then leaving is going to be very easy. But I’m also holding myself accountable, being like, okay, these are the few projects that I really want under my belt to experiment with, at TikTok. And because I have a data scientist as a manager, like, a true data scientist who…

420 00:55:50.240 ⇒ 00:55:50.880 Uttam Kumaran: Yeah, yeah, yeah.

421 00:55:50.880 ⇒ 00:55:56.049 Jasmin Multani: has a PhD, like, I really want to milk that, because it’s hard to find that.

422 00:55:56.050 ⇒ 00:55:56.919 Uttam Kumaran: Yeah, yeah, it is.

423 00:55:56.920 ⇒ 00:56:00.140 Jasmin Multani: Have really good, like, speaking skills.

424 00:56:00.200 ⇒ 00:56:01.880 Uttam Kumaran: Yeah.

425 00:56:02.020 ⇒ 00:56:03.720 Jasmin Multani: And it’s very rare to find.

426 00:56:03.720 ⇒ 00:56:04.680 Uttam Kumaran: Yeah, yeah.

427 00:56:04.680 ⇒ 00:56:12.850 Jasmin Multani: I’m just trying to milk as much as I can, but the end goal is to leave corporate and just, like, work with direct clients, and that’s.

428 00:56:12.850 ⇒ 00:56:13.170 Uttam Kumaran: Yeah.

429 00:56:13.170 ⇒ 00:56:19.040 Jasmin Multani: Like, I feel like you guys truly, knock on wood, like, Have a golden ticket.

430 00:56:19.040 ⇒ 00:56:19.960 Uttam Kumaran: I appreciate it.

431 00:56:20.530 ⇒ 00:56:25.400 Uttam Kumaran: we… I would say… It’s Golden Ticket in that…

432 00:56:25.530 ⇒ 00:56:31.719 Uttam Kumaran: it’s like, so, people are like, wow, you’ve grown up so much. I’m like, yeah, well, I woke up every day and worked, like.

433 00:56:31.730 ⇒ 00:56:50.029 Uttam Kumaran: 12, 13 hours a day, like, still. So, it’s… we put everything, but I think if you… you know, one thing I totally agree, I think, one, I want you to be able to meet the team, be able to see the clients we’re working with across the board. You know, we have very interesting problems that’s like, how do we scale what we learned to every single one of them, but

434 00:56:50.030 ⇒ 00:56:59.879 Uttam Kumaran: every day we’re going to meetings that are like, oh my god, I’ve never… I didn’t know this about our business type meetings, which is awesome. It’s like the best meetings to be like.

435 00:57:00.010 ⇒ 00:57:11.360 Uttam Kumaran: did you guys know, like, we can predict Amazon PO spikes before they happen? Because, like, you guys… we just did that meeting for someone an hour ago, and they’re like, oh my god, this is, like…

436 00:57:11.360 ⇒ 00:57:26.149 Uttam Kumaran: gonna save us, or we did a huge shipping cost analysis, like, a year ago, where I… I called UPS and renegotiated their UPS contract, and I saved them a half a million dollars. And I just paid for us.

437 00:57:26.150 ⇒ 00:57:26.720 Jasmin Multani: Yeah.

438 00:57:26.720 ⇒ 00:57:46.190 Uttam Kumaran: Like, way… like, paid way more than they were paying us, because I was like, give me all the UPS data, I talked to them, and I’m like, I did the analysis, I’m like, I can think I can get you… cut all these fees, get way better rates. I became friends with a UPS person, and then I got a bad deal, like, almost, like, 70% or 80% less, and, like, some volume spikes that we did.

439 00:57:46.280 ⇒ 00:57:47.440 Uttam Kumaran: And…

440 00:57:47.680 ⇒ 00:57:55.610 Uttam Kumaran: Like, but that’s the time where the normal data company would be like, yeah, here you go, you go do it. I was like, loop me in, who’s UPS? Like, I’ll call her, what’d she know?

441 00:57:56.230 ⇒ 00:58:12.399 Uttam Kumaran: probably know more about data than she does, you know, like, and so that’s the sort of stuff that’s really fun. And we also do a lot with… so we’re doing a lot of e-comm, CPG, and still a lot in B2B software, so still a lot of amplitude, mixed panel stuff there, but

442 00:58:12.400 ⇒ 00:58:17.540 Uttam Kumaran: We’re also working with a couple of just, like, large omni-channel retail and e-com.

443 00:58:17.720 ⇒ 00:58:18.380 Jasmin Multani: You know.

444 00:58:18.380 ⇒ 00:58:23.089 Uttam Kumaran: brands, and then we’re starting to do more in health, like, I… it’s just like…

445 00:58:23.370 ⇒ 00:58:37.310 Uttam Kumaran: Yeah, I don’t know, and every place is just… is just, like, trying to get more work out of us. Like, trying to say, like, how… what more can you take on? Which is… which is, like, really, really promising. Like, it’s taken a long time.

446 00:58:37.490 ⇒ 00:58:43.250 Uttam Kumaran: To get there, and… but now, it’s like, we’re really yearning for people that want to come, like.

447 00:58:43.670 ⇒ 00:58:59.020 Uttam Kumaran: really, like, take on those tasks, like, wow these people. But also, I think we want to build a culture where everybody learns, like, a ton from each other. So we’ve… we buy… we’ve… I bought everybody any AI thing they want. Like, we’re so annoying with just, like.

448 00:58:59.020 ⇒ 00:59:08.280 Uttam Kumaran: trying to show people, like, opportunities for… for leveraging more tools, and, you know, we use every data vendor that exists, and the lovely thing about being consultancy is, like.

449 00:59:08.280 ⇒ 00:59:11.070 Uttam Kumaran: I can hop on the phone with most of the vendors, because we’re, like.

450 00:59:11.070 ⇒ 00:59:12.599 Jasmin Multani: They’re all clients.

451 00:59:12.600 ⇒ 00:59:16.730 Uttam Kumaran: I’m about to make a vendor dis… we go and we make 5 to 10 vendor decisions.

452 00:59:16.730 ⇒ 00:59:19.859 Jasmin Multani: per client. So they pick up every…

453 00:59:19.860 ⇒ 00:59:30.220 Uttam Kumaran: type of data company picks up our phone these days, which is great, because we’re like, we have the keys. Like, and the lovely thing is we only implement the companies we like.

454 00:59:30.220 ⇒ 00:59:44.249 Uttam Kumaran: So, you don’t… we don’t wor… I don’t have to worry, necessarily, unless we walk into a situation about, like, having bad tools, because we’re helping them make those decisions. So, I don’t know, it’s good. I think the biggest challenge for us is gonna think about, like, building a good remote.

455 00:59:44.350 ⇒ 00:59:45.610 Uttam Kumaran: like, culture?

456 00:59:45.610 ⇒ 00:59:49.010 Jasmin Multani: You know, and then building a culture of people that, like.

457 00:59:49.270 ⇒ 00:59:56.869 Uttam Kumaran: want to collaborate and share learnings, like, hey, I’m working on this analysis, this client, like, didn’t you do that, like, a couple months ago? Like.

458 00:59:57.480 ⇒ 01:00:13.460 Uttam Kumaran: that’s the type of stuff we’re trying to build more redundancy in, like, types of functional things we’re doing, so there’s, like, more collaboration there, which is great, you know? And then… and then, like, I want to do more, like, brown bag stuff, like, that’s the stuff that’s what’s really fun about being on a cool team.

459 01:00:13.460 ⇒ 01:00:16.980 Jasmin Multani: Where, like, everybody’s doing cool stuff, they want to share, so… Yeah.

460 01:00:16.980 ⇒ 01:00:20.129 Uttam Kumaran: Yeah, I would love, I would love, if you’re interested, to even just, like.

461 01:00:20.510 ⇒ 01:00:38.919 Uttam Kumaran: come on, maybe, like, one client, take a look at all of our stuff, like, you’ll take a look at all of our internal stuff, and then see if there’s, like, if there’s a fit for, like, also what you want to do. And then my only ask would be, like, we’re shaping, like, what this sort of delivery, lead, or… I don’t know what the name of it is. I also don’t know whether it’s going to be, like, industry versus, like.

462 01:00:39.420 ⇒ 01:00:50.489 Uttam Kumaran: A type of analysis, but, like, if you can give us feedback on, like, what would make sense for you, like, what the responsibilities are, incentives are, like, the goals, like.

463 01:00:50.580 ⇒ 01:01:05.400 Uttam Kumaran: that’s, like, what we need help with. Like, what… what would it need to shape to be able… for you to be like, hell yeah, like, this is gonna be awesome, this is the place, like, I wanna be, and that I can come make a bunch of money in, and I can come, like, just, like.

464 01:01:06.010 ⇒ 01:01:18.949 Uttam Kumaran: dominate on, like, for a lot of these clients, you know? Like, that’s what we’re… we’re thinking. Because for me and Robert, I think I… I try to think about, like, what opportunity… before I started the company, if a company like Brainforge approached me, like.

465 01:01:19.030 ⇒ 01:01:26.910 Uttam Kumaran: what would I ask for? And that’s, like, kind of, like, our first version, and that’s what we’re, like, sort of trying to put in front of more folks like you, so…

466 01:01:26.910 ⇒ 01:01:35.950 Jasmin Multani: Yeah, I guess as a next step, like, for me to answer those questions, are there other people that you recommend I talk to.

467 01:01:35.950 ⇒ 01:01:43.729 Uttam Kumaran: Yeah, so I’ll connect you with a couple people on the team, maybe over email, just say hi, and ask about clients, stuff like that.

468 01:01:44.260 ⇒ 01:02:01.399 Uttam Kumaran: they’re all really, really friendly. So I’ll connect you with some people more on, like, the data modeling side, and then maybe some… some folks on our team that… there’s one person that’s doing a lot of, like, analysis. She’s a little bit junior, but you can talk to her about, sort of, like, what some of the work she’s doing, some of the clients, just get a sense

469 01:02:01.560 ⇒ 01:02:05.800 Uttam Kumaran: You know, so I can… I can set that up, you know, after this over email.

470 01:02:06.160 ⇒ 01:02:15.359 Jasmin Multani: Yeah, I’d love that. I think that helped me understand what does an end-to-end project look like, how does it… Sure. Where do separate,

471 01:02:15.360 ⇒ 01:02:28.400 Jasmin Multani: like, reports lean in, in order to make a decision? Where is there space for, like, experimenting, and where is there, like, an absolutely, like, we need to deliver on this, no matter who the client is?

472 01:02:28.400 ⇒ 01:02:41.169 Jasmin Multani: I think having that blueprint would help me understand, like, okay, how do I lean in with my own skills and my own context, and also, like, give you guys feedback, like, maybe we should divide the work up like this, or…

473 01:02:41.170 ⇒ 01:02:41.530 Uttam Kumaran: Sure.

474 01:02:41.530 ⇒ 01:02:48.290 Jasmin Multani: Maybe there should be more investment in here. Yeah. So, I think that would help.

475 01:02:48.290 ⇒ 01:02:49.660 Uttam Kumaran: But,

476 01:02:50.720 ⇒ 01:03:01.330 Jasmin Multani: Yeah, I know this is gonna be, like, a longer… this… I’m prepared for this to be a long conversation, and Robert also level-set my expectations that, like.

477 01:03:01.550 ⇒ 01:03:06.570 Jasmin Multani: there’s gonna be more information starting January, February, and so forth, yeah.

478 01:03:06.770 ⇒ 01:03:09.979 Uttam Kumaran: Yeah, like, it’s so funny, because…

479 01:03:09.980 ⇒ 01:03:14.049 Jasmin Multani: We’re just growing, and so for us, the more we grow.

480 01:03:14.070 ⇒ 01:03:34.020 Uttam Kumaran: it’s… it would be so painful for me to say no to new business, because we just don’t have these people in the company, you know? And so that’s what we’re trying to figure out. But also, I don’t want to just throw people into stuff. I want to make it so, like, you’re coming in, you’re like, this is the place I want to work, and we’re gonna go, you know, grow, and we’re… like, for us, like, I want to compete with Deloitte.

481 01:03:34.150 ⇒ 01:03:37.220 Uttam Kumaran: Accenture, like, those are the people that, like.

482 01:03:37.240 ⇒ 01:03:43.930 Jasmin Multani: we’re… we want to be on par with them. I don’t want people to hire BCG, they should hire us for work.

483 01:03:43.930 ⇒ 01:03:51.520 Uttam Kumaran: Right? We should… we should charge just as much as they do, we should do way better than they do. Like, that’s who I think. I don’t think about other, like.

484 01:03:51.620 ⇒ 01:04:04.450 Uttam Kumaran: small agencies. I don’t know who those people are. You know, like, our bar and our expectations for our work is so high. So we’ve never sacrificed that. And that’s why, like, we’re able to continue to go. We never, like.

485 01:04:04.550 ⇒ 01:04:11.720 Jasmin Multani: Just, like, said, like, oh, we’re just gonna push random shit out, or, like, stack up all these… stack up all these clients and have a bunch of them churn, like…

486 01:04:11.720 ⇒ 01:04:18.050 Uttam Kumaran: we’ve… we’ve always set the bar so, so, so high, and everybody in the company is very similar. Like, they… they each…

487 01:04:18.170 ⇒ 01:04:22.769 Uttam Kumaran: have really, really high expectations, you know, for themselves, so it’s been good.

488 01:04:23.160 ⇒ 01:04:24.950 Jasmin Multani: Yeah, and I’ve been on the other side of…

489 01:04:25.300 ⇒ 01:04:30.000 Jasmin Multani: even at DoorDash, and I’m like, why did we hire this Deloitte consultant?

490 01:04:30.000 ⇒ 01:04:37.509 Uttam Kumaran: Well, because there’s Deloitte… there’s probably some ex-Deloit person in… at DoorDash that did the homey connection, you know?

491 01:04:37.510 ⇒ 01:04:38.429 Jasmin Multani: Yeah, yeah, yeah.

492 01:04:38.950 ⇒ 01:04:42.369 Jasmin Multani: I’m like, why am I doing more homework

493 01:04:42.680 ⇒ 01:04:46.789 Jasmin Multani: I feel like I was just, like, writing information in spreadsheets, but we weren’t doing…

494 01:04:46.960 ⇒ 01:04:56.759 Uttam Kumaran: product launches at all, so… Yeah. Also been… Yeah, I mean, I’ve hired consultants too, and we hire consultants for our business, and it’s…

495 01:04:57.170 ⇒ 01:05:04.039 Uttam Kumaran: I don’t know. I actually think, like, that’s… that’s the opportunity for us, when you see those types of people, and then you’re like, okay, this is the bar.

496 01:05:04.230 ⇒ 01:05:08.779 Uttam Kumaran: great. Like, I just know if we keep at it, and we’re really good, like.

497 01:05:08.980 ⇒ 01:05:10.590 Uttam Kumaran: We’ll get the business, you know.

498 01:05:10.900 ⇒ 01:05:15.340 Jasmin Multani: Amazing. Maybe I can tell all my friends at Beige. Do a soft plug, be like.

499 01:05:15.660 ⇒ 01:05:19.640 Jasmin Multani: Yeah, I know, I know. Nathan knows a lot more than I do.

500 01:05:19.640 ⇒ 01:05:27.240 Uttam Kumaran: No, it would be… I want to end up doing, like, something for charity, like data work or something, like, where we can dedicate some amount of our…

501 01:05:27.350 ⇒ 01:05:33.299 Uttam Kumaran: resources, like, we were thinking about helping this NGO or something, and that would be fun, like…

502 01:05:33.300 ⇒ 01:05:33.760 Jasmin Multani: Yeah.

503 01:05:33.760 ⇒ 01:05:36.680 Uttam Kumaran: We do help people make more money, it’s not the most, like…

504 01:05:37.030 ⇒ 01:05:42.760 Uttam Kumaran: it’s not like the… it’s like… it’s a mission, but I kind of… it would be fun to take some of our data.

505 01:05:43.450 ⇒ 01:05:50.139 Uttam Kumaran: resources or know-how and, like, go help, like, local stuff and things like that, so something to think about.

506 01:05:50.420 ⇒ 01:05:56.879 Jasmin Multani: Even, like, local counties, local government counties, where they need the tech.

507 01:05:56.880 ⇒ 01:05:59.110 Uttam Kumaran: Like, food banks or something, you know.

508 01:05:59.230 ⇒ 01:06:01.339 Jasmin Multani: And they just don’t have the.

509 01:06:01.340 ⇒ 01:06:04.949 Uttam Kumaran: Yeah, they’re, like, on WordPress, like, not doing anything, yeah.

510 01:06:04.950 ⇒ 01:06:06.010 Jasmin Multani: Yeah, yeah.

511 01:06:06.010 ⇒ 01:06:06.550 Uttam Kumaran: Yeah.

512 01:06:07.080 ⇒ 01:06:08.690 Jasmin Multani: Oh, there’s a lot to build here.

513 01:06:08.690 ⇒ 01:06:14.110 Uttam Kumaran: Yeah. Okay, well, I appreciate the time, thank you. I know we’re, like, way over, so I appreciate you chatting with me.

514 01:06:14.310 ⇒ 01:06:18.150 Jasmin Multani: Of course, of course. I appreciate it, too. Hope you have a great question.

515 01:06:18.150 ⇒ 01:06:19.920 Uttam Kumaran: Yeah, you too. Talk soon. Bye.

516 01:06:20.200 ⇒ 01:06:20.840 Jasmin Multani: Bye.