Meeting Title: BF Interview: Uttam <> Gabriel Date: 2025-10-29 Meeting participants: Gabriel Lam, Uttam Kumaran
WEBVTT
1 00:04:56.100 ⇒ 00:05:02.010 Uttam Kumaran: Hey! How are you? Sorry for the delay, I was just on the phone with Robert, we just had a couple urgent things to, like…
2 00:05:02.010 ⇒ 00:05:03.030 Gabriel Lam: Loris, how are you?
3 00:05:03.030 ⇒ 00:05:03.720 Uttam Kumaran: Figure out stuff.
4 00:05:03.720 ⇒ 00:05:04.890 Gabriel Lam: Austin at the moment?
5 00:05:04.890 ⇒ 00:05:06.239 Uttam Kumaran: I’m in Austin, yes.
6 00:05:06.240 ⇒ 00:05:07.339 Gabriel Lam: How’s that?
7 00:05:07.710 ⇒ 00:05:08.760 Uttam Kumaran: Yeah, it’s good.
8 00:05:09.310 ⇒ 00:05:12.170 Gabriel Lam: Good to hear. It’s really cold in Boston today, so I was like.
9 00:05:12.170 ⇒ 00:05:12.750 Uttam Kumaran: Oh, really?
10 00:05:13.540 ⇒ 00:05:17.530 Uttam Kumaran: It’s getting colder here, but dude, it’s been 100 degrees for 9 months here, so…
11 00:05:17.530 ⇒ 00:05:23.309 Gabriel Lam: Oh my gosh. Does it ever get cooler, or is it sort of, like… Just now. For a couple months, it’ll get cold.
12 00:05:23.500 ⇒ 00:05:23.890 Gabriel Lam: Okay.
13 00:05:23.890 ⇒ 00:05:25.850 Uttam Kumaran: So, yeah, not bad.
14 00:05:26.010 ⇒ 00:05:28.129 Uttam Kumaran: Yeah, how’s our… how’s the week going for you?
15 00:05:28.340 ⇒ 00:05:46.370 Gabriel Lam: It’s good, I’ve got a bunch of, like, errands and, like, interviews on the side, but yeah, thank you for taking the call. I talked with Robert previously, and he was like, hey, if there’s something, that we can do together, like, let me know, and so I reached out, so I appreciate, like, this…
16 00:05:46.370 ⇒ 00:05:51.359 Uttam Kumaran: opportunity, or whatever this collaboration might be. Happy to dive in as soon as possible, but…
17 00:05:51.360 ⇒ 00:06:00.069 Gabriel Lam: I think I’ll just, like, leave it up to you to be like, okay, if there are certain things you guys… you want to hit, or if there’s an agenda you want to follow, otherwise I’m also happy to just…
18 00:06:00.650 ⇒ 00:06:01.839 Gabriel Lam: Jump right in.
19 00:06:01.840 ⇒ 00:06:13.199 Uttam Kumaran: Yeah, please, like, I guess I’m curious if you want to articulate what you know about our business so far, and, like, the different pieces. I’m happy to kind of bill out the details, and then, of course, like, I’m happy to articulate
20 00:06:13.200 ⇒ 00:06:22.279 Uttam Kumaran: everywhere in the company that we have issues. I sort of know… one thing I want to do, is what we do with everybody, is align where our problems are to where your interests are.
21 00:06:22.280 ⇒ 00:06:28.209 Uttam Kumaran: Right? But additionally, of course, not everything is… we don’t have enough
22 00:06:28.370 ⇒ 00:06:47.760 Uttam Kumaran: depth in some areas, so we do have… everybody here wears… tends to wear multiple hats, not 10 hats, but, like, 2 to 3. So, I think hopefully what you’ll, you know, depending on your interests, the great part about waking up Brainforge is you see how to grow a company, a complete bootstrap company, from nothing.
23 00:06:47.760 ⇒ 00:06:56.580 Gabriel Lam: there’s a lot of really great, smart, very kind, nice people here. We’re working with a lot of amazing clients, like, name-brand, great clients.
24 00:06:56.600 ⇒ 00:07:08.209 Uttam Kumaran: And we’re extremely interested in using data and AI to run the business, and that is also how we… the services that we deliver to clients, right? So…
25 00:07:08.350 ⇒ 00:07:09.670 Uttam Kumaran: Those… that’s like…
26 00:07:09.930 ⇒ 00:07:28.830 Uttam Kumaran: sort of, like, in a nutshell, like, what… why I think people like working with us, and, like, why I feel like we have a lot of smart people that come to work every day, like, jazzed. But it doesn’t go without problems, like, there are… there are problems by constraint. We’re limited by people and money. There’s problems just of, like, growing, like.
27 00:07:29.020 ⇒ 00:07:34.100 Uttam Kumaran: Where we were 6 months ago is not where we are now, and so our clients are different, our engagements are different.
28 00:07:34.220 ⇒ 00:07:45.989 Uttam Kumaran: I would say we’ve… we are failing pretty often in a good way. Like, we induce the failure, meaning if something is, like, going off the edge, my job is to push it so that we can learn.
29 00:07:46.190 ⇒ 00:07:51.259 Uttam Kumaran: And then we can iterate, right? Versus, like, a slow-burning death, you know?
30 00:07:51.740 ⇒ 00:07:59.299 Uttam Kumaran: So, there’s a lot of that. So, it’s not a tense environment by any means, but there is a sense of urgency around everything.
31 00:08:00.860 ⇒ 00:08:05.900 Uttam Kumaran: So yeah, that’s just a little… that’s a little bit about the philosophy, kind of, like, where we’re at. Yeah.
32 00:08:07.200 ⇒ 00:08:14.070 Gabriel Lam: Yeah, happy to share a little bit. I think Robert briefed me a little bit, earlier this week.
33 00:08:14.480 ⇒ 00:08:28.349 Gabriel Lam: to my knowledge, there’s the data side and the AI side, which I think you guys have split pretty well, and though there’s a lot of overlap, which I’ve also experienced in terms of, you know, with AI, a lot of it is still making sure that the data streams are…
34 00:08:28.480 ⇒ 00:08:30.930 Gabriel Lam: clean and structured, it’s like…
35 00:08:31.120 ⇒ 00:08:38.479 Gabriel Lam: Garbage in, garbage out, so whatever comes in needs to be, handled and managed really well.
36 00:08:38.659 ⇒ 00:08:39.640 Uttam Kumaran: Okay.
37 00:08:39.640 ⇒ 00:08:44.070 Gabriel Lam: But… He also mentioned that you are…
38 00:08:44.220 ⇒ 00:08:52.369 Gabriel Lam: Taking… Or you head more of the sort of technical delivery, whereas he handles more of the sort of sales and go-to-market aspects.
39 00:08:52.370 ⇒ 00:08:53.110 Uttam Kumaran: Totally.
40 00:08:53.110 ⇒ 00:08:56.469 Gabriel Lam: I see how that’s been split, sort of both in the.
41 00:08:56.890 ⇒ 00:08:59.809 Uttam Kumaran: Yeah, I would say split is… split is probably too harsh of a word.
42 00:08:59.810 ⇒ 00:09:00.310 Gabriel Lam: word, like…
43 00:09:00.310 ⇒ 00:09:03.259 Uttam Kumaran: I would say that’s where we both like to live.
44 00:09:03.260 ⇒ 00:09:03.620 Gabriel Lam: Yeah.
45 00:09:03.620 ⇒ 00:09:12.399 Uttam Kumaran: Like, I love building and architecting systems, I love engineering work. I have learned sales, and we both sell a lot of the business, but
46 00:09:12.590 ⇒ 00:09:15.320 Uttam Kumaran: I would say Robert is the architect on the sales side.
47 00:09:15.320 ⇒ 00:09:15.720 Gabriel Lam: Bye.
48 00:09:15.720 ⇒ 00:09:18.290 Uttam Kumaran: Like, I’m purely, like,
49 00:09:18.460 ⇒ 00:09:26.789 Uttam Kumaran: just an account executive, like, working directly in that org. And then certainly, like, I’m very opinionated about how we deliver work.
50 00:09:26.910 ⇒ 00:09:32.149 Uttam Kumaran: How we deliver great work that keeps clients happy, and we do it at a reasonable margin.
51 00:09:32.250 ⇒ 00:09:36.359 Uttam Kumaran: Like, that’s all I wake up every day thinking about, basically.
52 00:09:36.360 ⇒ 00:09:45.220 Gabriel Lam: Yeah. Awesome. Yeah, I think to defer… to build off on top of that, he… I had…
53 00:09:45.300 ⇒ 00:10:01.409 Gabriel Lam: told them about my interests of moving closer into product management. A lot of the work I did in a large corporate office has more been about, hey, how do we essentially create MVPs and, like, small tools to… for very, very specific pain points, and oftentimes.
54 00:10:01.910 ⇒ 00:10:03.420 Gabriel Lam: In our instance.
55 00:10:04.310 ⇒ 00:10:10.799 Gabriel Lam: even though it is a large company, you don’t have a lot of resources, right? You’re like, hey, what’s the… what’s the cheapest, lowest cost.
56 00:10:11.910 ⇒ 00:10:21.279 Gabriel Lam: solution that we can offer, without, you know, either paying for subscriptions, or buying any credits, or, like, we have automation tools, we have, you know.
57 00:10:21.420 ⇒ 00:10:32.140 Gabriel Lam: In our case, it was Microsoft. But in other cases, it was like, hey, how can we use, you know, Zapier or whatever? Just the lowest bar to deliver some semblance of what they want.
58 00:10:32.380 ⇒ 00:10:37.310 Gabriel Lam: And so, I think that really got me into it, and be like, okay, how do we actually…
59 00:10:38.400 ⇒ 00:10:43.930 Gabriel Lam: Present something and storytell a need into maybe a solution that can be built out into something.
60 00:10:44.450 ⇒ 00:10:46.900 Gabriel Lam: More consolidated, or maybe something a little.
61 00:10:46.900 ⇒ 00:10:53.670 Uttam Kumaran: Yeah, and even before that, tell me a bit about your back… Robert gave me your background, but tell me how you even got into that world.
62 00:10:53.670 ⇒ 00:10:57.419 Gabriel Lam: Yeah, for sure. I came to the U.S.
63 00:10:58.170 ⇒ 00:11:01.539 Gabriel Lam: to study architecture, essentially. I was like, that’s super interesting.
64 00:11:01.540 ⇒ 00:11:01.870 Uttam Kumaran: Exactly.
65 00:11:01.870 ⇒ 00:11:03.280 Gabriel Lam: Around a ton of buildings.
66 00:11:03.400 ⇒ 00:11:07.510 Gabriel Lam: I loved it, but it was totally different to what I thought it was, right?
67 00:11:07.510 ⇒ 00:11:08.130 Uttam Kumaran: When was that?
68 00:11:08.380 ⇒ 00:11:10.420 Gabriel Lam: That was in 2014.
69 00:11:10.520 ⇒ 00:11:11.720 Gabriel Lam: Nice.
70 00:11:11.720 ⇒ 00:11:12.510 Uttam Kumaran: Hell yeah.
71 00:11:12.510 ⇒ 00:11:18.729 Gabriel Lam: And so I was like, this is we… this is wild. It’s so very theoretical, very, sort of.
72 00:11:18.730 ⇒ 00:11:21.170 Uttam Kumaran: And where did you grow up, by the way, with the buildings?
73 00:11:21.170 ⇒ 00:11:22.190 Gabriel Lam: Hong Kong.
74 00:11:22.350 ⇒ 00:11:24.900 Uttam Kumaran: Oh, great, you’ve left tons of buildings, oh my god.
75 00:11:24.900 ⇒ 00:11:25.920 Gabriel Lam: Yeah, yeah, yeah.
76 00:11:25.920 ⇒ 00:11:28.710 Uttam Kumaran: Wonderful place to… to think about architecture.
77 00:11:28.710 ⇒ 00:11:44.989 Gabriel Lam: For sure. So, I had the chance to go through that, and then in grad… in graduate school, I had the chance to really explore out of your typical design area. School is very, very focused onto, like, how do you
78 00:11:45.250 ⇒ 00:11:47.370 Gabriel Lam: Build a building? How do you make a building?
79 00:11:47.770 ⇒ 00:12:06.490 Gabriel Lam: And so I had the chance to go into, like, robotics and do, like, digital fabrication and, like, robotic arms, went into, real estate and development and, like, how do you make, you know, sound financial decisions? But then I think the real… the one that really stuck out was, tooling, and…
80 00:12:06.490 ⇒ 00:12:07.000 Uttam Kumaran: Hmm.
81 00:12:07.000 ⇒ 00:12:13.430 Gabriel Lam: I moved into, like, machine learning and AI and be like, okay, how do we use these emerging technologies to essentially…
82 00:12:14.390 ⇒ 00:12:20.699 Gabriel Lam: you can say disrupt, but I think more so augment, like, a pretty slow, rigid industry, which today it still is.
83 00:12:20.700 ⇒ 00:12:25.210 Uttam Kumaran: Is this for, like, using, like, Revit and for CAD, and, like, what is it… what was the use cases?
84 00:12:25.680 ⇒ 00:12:26.300 Gabriel Lam: There’s…
85 00:12:26.300 ⇒ 00:12:30.659 Uttam Kumaran: And to give you a little bit of background, I worked, I worked at WeWork on the data team.
86 00:12:30.660 ⇒ 00:12:31.040 Gabriel Lam: Okay.
87 00:12:31.040 ⇒ 00:12:37.519 Uttam Kumaran: That was my first job out of college. I worked a lot with their… I worked throughout the business, but heavily with their real estate, outfitting.
88 00:12:37.780 ⇒ 00:12:38.120 Gabriel Lam: planning.
89 00:12:38.960 ⇒ 00:12:39.300 Gabriel Lam: Sure.
90 00:12:39.300 ⇒ 00:12:39.920 Uttam Kumaran: Yeah.
91 00:12:39.920 ⇒ 00:12:49.239 Gabriel Lam: Yeah, I think there’s two sides, as you mentioned. One side is the sort of Revit CAD plugin work, to be like, okay, how do we essentially automate or turn…
92 00:12:49.440 ⇒ 00:13:01.669 Gabriel Lam: you know, words into, like, something that shows up in models. The other side is more, like, how do we make sense of documentation? Like, zoning, building requirements,
93 00:13:02.360 ⇒ 00:13:07.819 Gabriel Lam: A lot of times it was even, like, hey, we’re on site, there’s all these different things. What exactly is necessary? What are we missing?
94 00:13:08.310 ⇒ 00:13:15.220 Gabriel Lam: Things with long lead times, like, essentially there’s a lot of communication and backlog that happens when we build.
95 00:13:15.220 ⇒ 00:13:15.870 Uttam Kumaran: Yeah.
96 00:13:15.870 ⇒ 00:13:18.270 Gabriel Lam: And so… Ironically.
97 00:13:18.270 ⇒ 00:13:19.420 Uttam Kumaran: Arts and stuff like that.
98 00:13:19.420 ⇒ 00:13:28.200 Gabriel Lam: Yeah, ironically, that’s, I think, where the most value-add was, where as opposed to modeling, you could almost, like, throw more people at the problem, and it would be faster.
99 00:13:28.200 ⇒ 00:13:34.480 Uttam Kumaran: It’s a good way, it’s a rare way of thinking about it, by the way, like, it’s also a very… project management’s a very thankless job.
100 00:13:34.480 ⇒ 00:13:34.810 Gabriel Lam: Yeah.
101 00:13:34.810 ⇒ 00:13:39.380 Uttam Kumaran: like, if it goes well, you don’t get any credit. If it goes poorly, you get all the blame, right? And so…
102 00:13:39.380 ⇒ 00:13:39.730 Gabriel Lam: books out.
103 00:13:39.730 ⇒ 00:13:46.039 Uttam Kumaran: My background, I was an engineer for a while, and then I worked as a… in product management for a while, so…
104 00:13:46.520 ⇒ 00:13:52.340 Uttam Kumaran: And that’s how I think about, like, all I think about is sequencing, I think about, risk management.
105 00:13:52.340 ⇒ 00:13:52.710 Gabriel Lam: Right.
106 00:13:52.710 ⇒ 00:13:57.449 Uttam Kumaran: I think about, delivering something when you have nothing to deliver.
107 00:13:57.800 ⇒ 00:14:05.290 Uttam Kumaran: even if you have nothing to deliver, what’s something we can get out the door? I know when to strategically, like, push. Right. I know when to also, like.
108 00:14:05.290 ⇒ 00:14:08.240 Gabriel Lam: let stuff go. Yeah. And, like.
109 00:14:08.240 ⇒ 00:14:10.970 Uttam Kumaran: I would say you’re kind of the heartbeat of a project.
110 00:14:11.320 ⇒ 00:14:11.920 Gabriel Lam: Oh, gosh.
111 00:14:11.920 ⇒ 00:14:18.090 Uttam Kumaran: are the last thing, like, ultimately, it lives or dies. If you… if you… it’s almost like there’s a…
112 00:14:18.120 ⇒ 00:14:35.999 Uttam Kumaran: there’s the, you know, metaphor of, like, you give people, like, the rope to kind of choke themselves. If you don’t… if you don’t do any project management, like, yes, you may get lucky, but odds are, like, your project’s just not gonna… nothing’s gonna get done. If you’re also way too, like, heavy-handed, nobody’s gonna, like, fuck with you, and nobody’s gonna listen to you anyway.
113 00:14:36.000 ⇒ 00:14:36.660 Gabriel Lam: Right.
114 00:14:36.660 ⇒ 00:14:38.080 Uttam Kumaran: So…
115 00:14:38.920 ⇒ 00:14:43.440 Gabriel Lam: Yeah, totally get it. It’s like… You…
116 00:14:43.800 ⇒ 00:14:48.129 Gabriel Lam: You’re sort of the middle person between your own team and the client, and…
117 00:14:48.130 ⇒ 00:14:48.860 Uttam Kumaran: Yeah.
118 00:14:49.200 ⇒ 00:15:04.670 Gabriel Lam: sometimes you’re like, hey, the client really wants this, we gotta respect what they want. Sometimes it’s like, hey, we gotta push in this area. Clients might say something that they think are urgent, but having the sort of overall high-level view of the project, you know, actually, I think, you know.
119 00:15:04.800 ⇒ 00:15:10.859 Gabriel Lam: we need to push this side of the project before we address the thing that they want. Yeah.
120 00:15:11.120 ⇒ 00:15:12.930 Gabriel Lam: Yeah, and slowly…
121 00:15:13.750 ⇒ 00:15:20.730 Gabriel Lam: I… there was a big push for, like, okay, how do we implement new technologies into the work that we do in a very literal sense?
122 00:15:20.830 ⇒ 00:15:23.930 Gabriel Lam: And that’s when I encountered
123 00:15:24.420 ⇒ 00:15:33.979 Gabriel Lam: you know, taking whatever I learned in academia and seeing if I could bring it into… into industry, and that’s where the big, sort of, split, or maybe not a split, but the sort of
124 00:15:34.730 ⇒ 00:15:40.679 Gabriel Lam: moment of learning happened, where I was like, okay, you have to get a lot of buy-in, especially from a large company. It’s like.
125 00:15:41.050 ⇒ 00:15:44.690 Gabriel Lam: You gotta prove that this thing is worth the…
126 00:15:44.930 ⇒ 00:15:52.829 Gabriel Lam: time and resource investment that we think it does. And in some cases, yes, in most cases, no, right? It’s like…
127 00:15:53.200 ⇒ 00:15:58.920 Gabriel Lam: when… when you have a standard… when you have a lot of SOPs that are pretty well-oiled.
128 00:15:59.150 ⇒ 00:16:01.970 Gabriel Lam: Oftentimes, they’re like, well, we don’t really want to…
129 00:16:02.120 ⇒ 00:16:08.879 Gabriel Lam: change the status quo when it more or less works, like, 75-80% of the time. Whereas…
130 00:16:09.100 ⇒ 00:16:19.839 Gabriel Lam: I felt like the last 20%, I was like, hey, there’s actually a lot of opportunity, and it also sets us up later for future business if we’re like, hey, we have all these tools on the side where we could also sell to you.
131 00:16:20.180 ⇒ 00:16:25.280 Gabriel Lam: So that’s when I was like, okay, I love what you guys are doing,
132 00:16:25.620 ⇒ 00:16:33.520 Gabriel Lam: And when Robert and I had been chatting, we were like, hey, maybe there’s something there. I thought I’d reach out, and that was the sort of perfect time to do it, yeah.
133 00:16:33.800 ⇒ 00:16:49.619 Uttam Kumaran: Yeah, so tell me, like, kind of, like, what… I know Robert mentioned some of the visa situation and everything, so tell me, like, in terms of, sort of, our stuff, like, it’s clear that you kind of want to be more involved on the project management side, but where else in the company, to kind of break it down again, we have, like.
134 00:16:49.750 ⇒ 00:16:53.909 Uttam Kumaran: the delivery and, like, the internal side of our business. On the delivery side, of course, we have
135 00:16:54.140 ⇒ 00:17:14.120 Uttam Kumaran: roles all across data engineering, we have AI stuff, we also have, like, project management across all of our projects. Internally as well, we run all of our internal projects like sprints, where we’re the client. And so, there is a lot of project management across operations, finance, marketing, and sales.
136 00:17:14.480 ⇒ 00:17:15.650 Uttam Kumaran: And so…
137 00:17:15.930 ⇒ 00:17:27.050 Uttam Kumaran: I guess, give me a sense of, like, I… from what I gathered, you’re most interested in, like, kind of cross-section on the AI side, and on, like, the project management side. Is that, like, roughly correct?
138 00:17:27.710 ⇒ 00:17:34.260 Gabriel Lam: Yeah, I think that’s pretty accurate. I also feel like it would be good to just have a sort of
139 00:17:34.740 ⇒ 00:17:40.419 Gabriel Lam: an exposure to everything. I don’t really see it as, like, hey, I’m coming in because I want to just do this.
140 00:17:40.420 ⇒ 00:17:41.170 Uttam Kumaran: Totally, totally.
141 00:17:41.170 ⇒ 00:17:42.750 Gabriel Lam: Right, there’s… there’s definitely, like.
142 00:17:42.750 ⇒ 00:17:46.779 Uttam Kumaran: Don’t worry about that, I just… cause our problem is there’s too much to do.
143 00:17:46.780 ⇒ 00:17:47.280 Gabriel Lam: Okay.
144 00:17:47.280 ⇒ 00:18:04.149 Uttam Kumaran: So I can’t… I don’t want to set you up for failure and give you, like… and so I just want to make sure that the scope is well-defined. Like, the moment you see something at the company to do, you can go… there’s no, like… whatever you mentioned about your last company, there’s none of that here.
145 00:18:04.610 ⇒ 00:18:11.600 Uttam Kumaran: We are a very young company, there’s no politics, all we want to do is win. And so, like.
146 00:18:11.890 ⇒ 00:18:16.539 Uttam Kumaran: Yeah, it’s actually more about… it may overwhelm you without…
147 00:18:16.540 ⇒ 00:18:17.450 Gabriel Lam: Okay.
148 00:18:17.450 ⇒ 00:18:19.939 Uttam Kumaran: They look like they could be fixed around here.
149 00:18:19.940 ⇒ 00:18:20.889 Gabriel Lam: Yeah.
150 00:18:20.890 ⇒ 00:18:21.490 Uttam Kumaran: Yeah.
151 00:18:21.490 ⇒ 00:18:29.759 Gabriel Lam: Yeah, I think… I think to… to piggyback out of that, I think the AI practice is really interesting. Robert did mention that the data practice that
152 00:18:29.830 ⇒ 00:18:42.990 Gabriel Lam: he is much more familiar with, seems to be a lot more mature, in terms of, like, the proportion of clients and proportion of business so far. So I think that’s also really interesting just to have an idea of, like, hey.
153 00:18:43.360 ⇒ 00:18:51.330 Gabriel Lam: we have this vision for AI services or AI solutions, but in actuality, what sells, right?
154 00:18:51.330 ⇒ 00:18:51.720 Uttam Kumaran: Yeah.
155 00:18:51.720 ⇒ 00:19:04.469 Gabriel Lam: what actually allows you guys to continue building or continue working on that vision. And so maybe in the future, it becomes more a 50-50 split, or maybe more even towards AI, but for now, it seems like data really…
156 00:19:04.470 ⇒ 00:19:08.450 Uttam Kumaran: Even to the last point, though, there is a lot of data work in our AI world.
157 00:19:08.450 ⇒ 00:19:08.800 Gabriel Lam: Yeah.
158 00:19:08.800 ⇒ 00:19:11.420 Uttam Kumaran: And there’s a lot of AI work that can affect the data world.
159 00:19:11.420 ⇒ 00:19:11.780 Gabriel Lam: Right.
160 00:19:12.450 ⇒ 00:19:24.649 Uttam Kumaran: I would… you know, initially, when we thought about the business, I’m the one that brought a lot of the AI to the table, and we thought about so separately. And I think more we’re finding is that there’s actually a lot of ways that
161 00:19:24.650 ⇒ 00:19:33.649 Uttam Kumaran: what we learn in one area can affect. In addition, all of our AI work we measure. And so, it’s actually more about a cohesive outcome story for the client.
162 00:19:34.330 ⇒ 00:19:52.809 Uttam Kumaran: Data and AI are just like the shovels we use to dig. Right. But what we’re digging for and our ability to get there reliably is what we’re selling to clients. They don’t care, really, how we do it. They’re appreciative that we know what we’re doing, and we have all this experience, all these tools, blah blah blah.
163 00:19:52.940 ⇒ 00:19:56.290 Uttam Kumaran: But they’re more interested in their problem getting solved.
164 00:19:56.290 ⇒ 00:19:56.810 Gabriel Lam: Right.
165 00:19:56.810 ⇒ 00:20:12.990 Uttam Kumaran: And, especially in a world of AI, there’s a ton of jargon, there’s a ton of vendors selling, like, garbage, and there’s a lot of people that have tried stuff that’s, like, halfway worked. So often, we’re walking into that environment where they’re kind of, like, suspect.
166 00:20:12.990 ⇒ 00:20:26.929 Uttam Kumaran: So how do we tell that story on the sales side? And then as soon as we get in, how do we nail the first week, two, four, six weeks? So they’re like, wow, whatever Brainforge can do for us, we gotta send them more money.
167 00:20:26.930 ⇒ 00:20:27.400 Gabriel Lam: Yeah.
168 00:20:27.400 ⇒ 00:20:28.589 Uttam Kumaran: That’s the story, right?
169 00:20:28.590 ⇒ 00:20:29.010 Gabriel Lam: Yeah.
170 00:20:29.010 ⇒ 00:20:43.370 Uttam Kumaran: It’s a true, ultimate, like, consulting, where you… we’re walking in a situation, we don’t really know who’s involved, what’s been done before, what the motives are, like, what the incentives are. All we have is, like, solve this problem for me, and then we come in and we sort of, like.
171 00:20:43.830 ⇒ 00:20:47.480 Uttam Kumaran: go from there, you know? Which is fun, but it’s also, again, like.
172 00:20:47.710 ⇒ 00:20:50.809 Uttam Kumaran: It is truly, like, ultimate, like.
173 00:20:51.070 ⇒ 00:20:54.630 Uttam Kumaran: Okay, there’s a fire, you have to go kind of figure out, like, well, who’s in the house, you know?
174 00:20:54.630 ⇒ 00:21:04.949 Gabriel Lam: Yeah, for sure. Yeah, that seemed to be, like, the biggest, like, a sort of gap that Robert had mentioned, where there’s a sort of value add of packaging all these service offerings to be like.
175 00:21:04.950 ⇒ 00:21:05.800 Uttam Kumaran: Yes.
176 00:21:06.300 ⇒ 00:21:14.560 Gabriel Lam: We can offer these things to you, but… but there’s sort of a gap of, like, what you guys can actually… or what you have done before, what you’re able to do.
177 00:21:14.560 ⇒ 00:21:21.290 Uttam Kumaran: So it seems like you do have some interest, kind of, on the commercial side, too, like, on the packaging, storytelling side.
178 00:21:21.560 ⇒ 00:21:29.630 Gabriel Lam: Yeah, for sure. I think… I mean, I’m coming in as to do a lot of learning as well, I think…
179 00:21:30.400 ⇒ 00:21:32.970 Gabriel Lam: I’ve gotten a very, sort of, niche
180 00:21:33.820 ⇒ 00:21:46.449 Gabriel Lam: exposure or experience into how we build AI tools or build data tools for other people, but because I come in from a sort of practitioner background, I’m like, hey, there’s…
181 00:21:46.450 ⇒ 00:21:55.699 Gabriel Lam: you know, we’re trying to fix this one tiny, tiny solution. That works very, very well for this one client, but not maybe something that can be packaged and sold to multiple.
182 00:21:55.700 ⇒ 00:21:56.100 Uttam Kumaran: Yes.
183 00:21:56.250 ⇒ 00:22:06.799 Gabriel Lam: And I’m sure, you know, with each client, things are different, but there is a general story that comes across for Brain Forge, as opposed to me, it was like, hey, we’ve been working with you guys for, like, 2 years, like.
184 00:22:07.020 ⇒ 00:22:12.389 Gabriel Lam: this is something that you guys need, like, hey, we’re able to do it. Or internally, like, hey, we’ve been noticing this problem for a while.
185 00:22:12.590 ⇒ 00:22:13.940 Gabriel Lam: And so, I think…
186 00:22:14.890 ⇒ 00:22:20.670 Gabriel Lam: I’m coming in with a sort of more open-minded view of just like, hey, these are all the different areas that touch upon
187 00:22:20.680 ⇒ 00:22:35.120 Gabriel Lam: what, what an AI practice would look like, what product management would look like. I’m happy to jump into most of these, things, and so I don’t… I also, from my end, don’t feel like you really have to, like, oh, scope it out too hard either, you know what I mean?
188 00:22:35.350 ⇒ 00:22:50.299 Uttam Kumaran: Yeah, and so there’s, I think even just this conversation, there’s a couple areas where, totally, I think you could affect. So one, and I don’t know if Robert mentioned, we have an internal, like, AI team. I mean, they also work on clients, but we have an internal platform that we’re building just to support our internal operations.
189 00:22:50.400 ⇒ 00:23:02.409 Uttam Kumaran: the lovely part of that is it’s built for us, so the stakes are very low, but it’s… it’s working on, like, a lot of vision that I’ve set, so the expectations are, like.
190 00:23:02.500 ⇒ 00:23:21.090 Uttam Kumaran: really high, meaning we… we have so much low-hanging fruit that we can use AI to help in our business. And so that’s somewhere, I think, purely on… if you want to learn, truly want to learn product management, we have a team of 3 people there that are building stuff for us internally who could definitely use support.
191 00:23:21.340 ⇒ 00:23:24.730 Uttam Kumaran: In the form of… like.
192 00:23:24.880 ⇒ 00:23:44.470 Uttam Kumaran: basically owning, like, what’s gonna get delivered to Sprint, owning the interaction between their work and the client, which can be me, can be someone in sales, can be in marketing. To give you a sense of this platform, the platform is, like, all of our Zoom meetings go into this platform, where you can take… do helpful tasks, like generate summaries, chat over the meeting.
193 00:23:44.470 ⇒ 00:23:56.609 Uttam Kumaran: So, you can start to understand and basically map out the platforms and different functions in the company, and ideally, what we’d be hoping for is that you can start to say.
194 00:23:56.780 ⇒ 00:24:10.920 Uttam Kumaran: okay, I can see where the gaps are, and that these functions in the company aren’t being augmented by AI, and what are ways that we can assist them through the platform? Additionally, there’s a lot of measurement opportunities, so right now, we’re not measuring much, who’s using it.
195 00:24:10.920 ⇒ 00:24:16.939 Uttam Kumaran: what they’re doing in there. Again, we’re not a big company, so… but even just establishing that, like, measurement framework.
196 00:24:16.970 ⇒ 00:24:22.630 Uttam Kumaran: Would be really helpful. And again, I think that’s a very extremely safe But, like.
197 00:24:22.890 ⇒ 00:24:28.820 Uttam Kumaran: really… like, the AI work we’re doing there is more advanced than the stuff our clients ask us for.
198 00:24:28.820 ⇒ 00:24:30.730 Gabriel Lam: So you’ll get exposure to, like.
199 00:24:31.210 ⇒ 00:24:32.400 Uttam Kumaran: basically…
200 00:24:32.930 ⇒ 00:24:37.240 Gabriel Lam: everything you’ve ever heard of in, like, modern AI development.
201 00:24:37.240 ⇒ 00:24:53.549 Uttam Kumaran: The second place that we certainly need help is just on, like, day-to-day project coordination. To give you a sense, like, all of our project management right now, I’m leading all of our projects, and then we have two product… project coordinators on the projects. We previously had, like.
202 00:24:53.980 ⇒ 00:25:00.999 Uttam Kumaran: multiple project managers, we sort of arrived at this model. So, certainly, there’s opportunity for you to actually plug in and help assist
203 00:25:01.160 ⇒ 00:25:15.959 Uttam Kumaran: On coordinating projects. What does that mean? That means helping generate, like, sprint updates, help with decks, help to, like, synthesize, like, new work that’s coming in, think about opportunities for the client, and do that in also a very, like.
204 00:25:16.770 ⇒ 00:25:33.449 Uttam Kumaran: it’s usually typically me or Robert or, like, one of our solution architects that’s in front of the client. So, you could do a lot of that behind the scenes. We do a joint stand-up with everybody in the morning, and then… but we just need a lot of help doing that from someone that has a strategic understanding of, like, an outcomes thinking for clients.
205 00:25:33.530 ⇒ 00:25:41.280 Uttam Kumaran: So those are two, like, really easy areas. And then the last piece is, once you’re able to do both of those, I think you’ll be able to articulate to our
206 00:25:41.790 ⇒ 00:25:52.209 Uttam Kumaran: marketing team into Robert and go to market on how we package the services. Like, once you see what we’re doing internally, once you see what we’re doing externally, you’ll see all of our services, all of our documents.
207 00:25:52.340 ⇒ 00:26:04.839 Uttam Kumaran: totally need someone other than me on the AI side with some commercial sense. How we package this, how we talk about the outcome, how we articulate the customer problem and why they would need the service.
208 00:26:04.860 ⇒ 00:26:13.549 Uttam Kumaran: what are the offers associated with the service, things like that. So I think those kind of three areas, all on the AI side, we could use
209 00:26:13.890 ⇒ 00:26:15.160 Uttam Kumaran: some help with.
210 00:26:16.260 ⇒ 00:26:22.830 Gabriel Lam: That sounds awesome. I think… it seems like this is also not just AI applicable, right? Like, it…
211 00:26:22.830 ⇒ 00:26:23.859 Uttam Kumaran: Totally, totally.
212 00:26:23.860 ⇒ 00:26:26.950 Gabriel Lam: And it’s like, hey, this is just the…
213 00:26:27.130 ⇒ 00:26:39.340 Gabriel Lam: current, like, framework. It’s more of a sort of creating a framework for one area that we’re solving that needs solving most, but it’s not to say, like, hey, this can’t happen for the data side either, which I think is.
214 00:26:39.340 ⇒ 00:26:39.680 Uttam Kumaran: Agreed.
215 00:26:39.680 ⇒ 00:26:40.699 Gabriel Lam: Pretty good idea.
216 00:26:40.700 ⇒ 00:26:41.650 Uttam Kumaran: I agree.
217 00:26:42.090 ⇒ 00:26:44.560 Gabriel Lam: Awesome, I think that sounds… that’s wonderful.
218 00:26:44.560 ⇒ 00:26:45.290 Uttam Kumaran: Yeah, give me a sense of…
219 00:26:45.290 ⇒ 00:26:45.790 Gabriel Lam: about this.
220 00:26:45.790 ⇒ 00:26:51.359 Uttam Kumaran: Yeah, give me a sense of, like, logistics of what’s kind of your availability, and, like, yeah, I can start to, kind of.
221 00:26:51.850 ⇒ 00:26:58.010 Uttam Kumaran: get things, like, we’ll have to sign NDA and something this week, I can get that going, but, like, give me a sense of, like, what your
222 00:26:58.210 ⇒ 00:26:59.790 Uttam Kumaran: What the situation is.
223 00:26:59.790 ⇒ 00:27:02.040 Gabriel Lam: Yeah, I reached out…
224 00:27:02.420 ⇒ 00:27:08.669 Gabriel Lam: seeing this more as a shadowing opportunity, I think for optics and compliance, it makes the most sense, just.
225 00:27:08.670 ⇒ 00:27:09.230 Uttam Kumaran: Okay.
226 00:27:09.230 ⇒ 00:27:13.149 Gabriel Lam: To… as a sort of learning opportunity, as opposed to employment.
227 00:27:13.350 ⇒ 00:27:18.890 Gabriel Lam: I am available for the next… Month, more or less.
228 00:27:19.270 ⇒ 00:27:34.290 Gabriel Lam: half-time… half the full-time, so, like, 20, 25, hours a week. I had mentioned to Robert that it could be full-time, but there’s also other things happening, so I don’t want to, like, set expectations too far. Sure.
229 00:27:34.580 ⇒ 00:27:41.060 Gabriel Lam: And then… come Thanksgiving, my visa process would be fully…
230 00:27:42.680 ⇒ 00:27:46.540 Gabriel Lam: resolved with the US, I’m just essentially waiting for the government to…
231 00:27:46.540 ⇒ 00:27:46.980 Uttam Kumaran: Okay.
232 00:27:46.980 ⇒ 00:27:50.270 Gabriel Lam: respond. This is basically what’s happening.
233 00:27:51.130 ⇒ 00:27:54.980 Gabriel Lam: if… And that ends up in two…
234 00:27:56.320 ⇒ 00:28:02.949 Gabriel Lam: paths. One, basically, where I would start a full-time job that I’ve assigned and
235 00:28:03.180 ⇒ 00:28:07.630 Gabriel Lam: have been sponsored for, and then I see my role here,
236 00:28:08.850 ⇒ 00:28:10.840 Gabriel Lam: what do you call it?
237 00:28:14.190 ⇒ 00:28:16.910 Gabriel Lam: like… Shooting, like, my brain.
238 00:28:17.230 ⇒ 00:28:20.699 Gabriel Lam: Yeah, just… Maybe scaling back, that’s the word.
239 00:28:20.700 ⇒ 00:28:21.030 Uttam Kumaran: Sure.
240 00:28:21.030 ⇒ 00:28:23.260 Gabriel Lam: Sure. Because I think…
241 00:28:23.520 ⇒ 00:28:42.930 Gabriel Lam: there’s a lot of opportunity here that I’m really interested in continuing to learn and contribute with. Sure. But it would be more difficult, you know, balancing two different things, nor do I want you guys to expect, like, hey, I’m gonna be working two jobs. Like, I think it’ll be more like, hey, what are the… what are the key areas that I can still support in with a.
242 00:28:42.930 ⇒ 00:28:43.510 Uttam Kumaran: Given your time.
243 00:28:43.510 ⇒ 00:28:52.939 Gabriel Lam: activity, and sort of, like, the maximum efficiency and effectiveness, as opposed to, like, let’s just, you know, jump into everything. Cool. On the other hand.
244 00:28:53.690 ⇒ 00:29:05.779 Gabriel Lam: if the… if the U.S. government continues to, you know, to stall or whatever, then at… I think in a month’s time, we can then have another discussion of, like, how long do we want to continue this arrangement for?
245 00:29:06.950 ⇒ 00:29:12.590 Gabriel Lam: And what might it look like in that time? So I think I see this as a sort of, like, four-week.
246 00:29:13.420 ⇒ 00:29:19.109 Gabriel Lam: Okay. Exercise, experiment, kind of deal, and then once the four weeks hit, we can then…
247 00:29:19.110 ⇒ 00:29:36.120 Uttam Kumaran: I think you’ll find it very fruitful, you know, like, I don’t know, I just have to jump in a sec, but when I worked at companies, I always wanted to make sure that I got more out of it than the company got, and I promise you that you will get quite a bit out of this. You’ll get exposure to
248 00:29:36.250 ⇒ 00:29:41.820 Uttam Kumaran: Basically, like, everything that’s pretty… except for… building LLMs.
249 00:29:42.180 ⇒ 00:29:45.389 Uttam Kumaran: from scratch, like, we… we do.
250 00:29:45.510 ⇒ 00:29:50.349 Uttam Kumaran: And I think, like, we are… we… we… we’ve, for the most part, under…
251 00:29:50.480 ⇒ 00:30:06.719 Uttam Kumaran: undersold ourselves in how advanced we are on the AI side, so I’m excited for you to come see that. However, we do need a lot of coordination and project management expertise. I’m currently leading all of that with 5% of my time, and it’s, like, a struggle, because I’m, like, doing a hundred other things.
252 00:30:06.720 ⇒ 00:30:07.070 Gabriel Lam: For sure.
253 00:30:07.070 ⇒ 00:30:25.689 Uttam Kumaran: I’m… I’m… I… all that to say, like, I’m very, very excited. Let me send you an email right after this, following up, and then, like, I’ll just send a note and see if we can get stuff signed, and I can get you into stuff even today. Even if we don’t get the chat today, at least you can start poking around at things. Worst case, if you end up in today, then…
254 00:30:25.910 ⇒ 00:30:35.030 Uttam Kumaran: you know, we could chat on the phone, and I can brain dump, and but just for you to start to see, and then I can intro you to everyone, and things like that. So we’ll kind of put a little bit of an action plan together.
255 00:30:35.030 ⇒ 00:30:40.270 Gabriel Lam: That sounds awesome. Okay, well, I know you have probably another call, so I’ll leave you to it, but I hope you…
256 00:30:40.270 ⇒ 00:30:40.740 Uttam Kumaran: Shitter, man.
257 00:30:40.740 ⇒ 00:30:46.099 Gabriel Lam: Yeah. I’ll be waiting. I can also send you a recap of this call, if that’s better.
258 00:30:46.100 ⇒ 00:30:52.050 Uttam Kumaran: Yeah, if you don’t mind sending a quick recap, and then, yeah, that’ll help just to, like, keep things moving forward.
259 00:30:52.050 ⇒ 00:30:53.500 Gabriel Lam: Awesome. Appreciate it.
260 00:30:53.500 ⇒ 00:30:56.060 Uttam Kumaran: Alright, thank you. Have a good one. Talk to you soon. Bye.