Meeting Title: BF Interview: Gabe <> Sheshu Date: 2026-01-07 Meeting participants: Fireflies.ai Notetaker Sheshu, Sheshu Chandrasekar, Gabriel Lam
WEBVTT
1 01:10:58.950 ⇒ 01:11:00.240 Gabriel Lam: Hello.
2 01:11:01.190 ⇒ 01:11:03.290 Sheshu Chandrasekar: Hey Gabe, can you hear me alright?
3 01:11:03.290 ⇒ 01:11:06.610 Gabriel Lam: Sorry, I don’t hear you. Give me one second.
4 01:11:13.320 ⇒ 01:11:16.180 Gabriel Lam: Okay, that’s on me. Hello, do you hear me?
5 01:11:16.860 ⇒ 01:11:18.010 Sheshu Chandrasekar: I can hear you now.
6 01:11:18.560 ⇒ 01:11:24.419 Gabriel Lam: Great. Great to meet you. I apologize for missing the call just now. I totally…
7 01:11:25.140 ⇒ 01:11:28.480 Gabriel Lam: skipped it, that’s not bad, but I hope you’re doing well.
8 01:11:28.780 ⇒ 01:11:29.920 Sheshu Chandrasekar: Yeah, no worries at all.
9 01:11:29.920 ⇒ 01:11:33.329 Gabriel Lam: And, yeah, how’s it… how are you?
10 01:11:33.520 ⇒ 01:11:43.170 Sheshu Chandrasekar: I’m good, and first and foremost, Happy New Year to you as well, and I’m doing well. I’m actually in Houston right now, so tomorrow I’m headed to Austin. So just…
11 01:11:43.170 ⇒ 01:11:44.080 Gabriel Lam: Oh, nice.
12 01:11:44.080 ⇒ 01:11:49.130 Sheshu Chandrasekar: Yeah, yeah, so… Are you, are you based out of, Austin or New York?
13 01:11:49.580 ⇒ 01:11:51.870 Gabriel Lam: No, I am in Boston at the moment.
14 01:11:51.870 ⇒ 01:11:57.859 Sheshu Chandrasekar: Got it. Okay, so you probably… have you ever met Pranav, by any chance? I think he’s at.
15 01:11:57.860 ⇒ 01:12:03.099 Gabriel Lam: I’ve not met Pranav, I’ve only, well, seen him on the screen.
16 01:12:03.490 ⇒ 01:12:04.470 Sheshu Chandrasekar: Okay.
17 01:12:04.470 ⇒ 01:12:07.510 Gabriel Lam: But I have met UTAM in person, yes.
18 01:12:09.730 ⇒ 01:12:10.260 Sheshu Chandrasekar: Got it.
19 01:12:11.040 ⇒ 01:12:16.140 Gabriel Lam: Yeah, so, thanks for making time, I…
20 01:12:17.440 ⇒ 01:12:22.409 Gabriel Lam: I’m not, personally, I’m gonna be a little transparent, I’m not too sure.
21 01:12:23.260 ⇒ 01:12:33.039 Gabriel Lam: what I am expected out of this interview, but I’ll do my best to, like, get you up to speed about Brainforge, and also the head of ops role, which I believe
22 01:12:33.180 ⇒ 01:12:35.579 Gabriel Lam: Is what you applied for, is that correct?
23 01:12:35.830 ⇒ 01:12:37.859 Sheshu Chandrasekar: Yes, that’s correct. Yes.
24 01:12:37.860 ⇒ 01:12:39.220 Gabriel Lam: Okay, yeah.
25 01:12:39.700 ⇒ 01:12:44.880 Sheshu Chandrasekar: Absolutely, so I can… I can kind of give you a little background about me, and…
26 01:12:44.880 ⇒ 01:12:51.730 Gabriel Lam: Yeah, yeah, I would love to get to know you. I’m also happy to share my experience, as well as my experience here, and then we can go from there.
27 01:12:51.730 ⇒ 01:13:04.280 Sheshu Chandrasekar: Yeah, perfect. Yeah, so, prior to, you know, applying for Brainforge and hearing through, hearing Brain… hearing about Brainforge through Pranav, I actually started my career
28 01:13:04.340 ⇒ 01:13:19.739 Sheshu Chandrasekar: At Deloitte, working in the government and public sector practice. So a lot of my projects span from modernization programs that really range from, you know, technology transformation to almost financial planning, and…
29 01:13:19.760 ⇒ 01:13:25.590 Sheshu Chandrasekar: Believe me, I had no business in being in financial planning, because I’m more of the tech side and operations side of things.
30 01:13:25.620 ⇒ 01:13:38.380 Sheshu Chandrasekar: But, you know, a big part of my role at consulting, was making systems usable, so I’ve always been a very systems-building mindset, systems-oriented man. Things like…
31 01:13:38.470 ⇒ 01:13:54.719 Sheshu Chandrasekar: in my projects I’ve worked with, I’ve built onboarding documentation and user guides, building dashboards, for executive leadership, and almost used by 1,300 project managers and program managers, for a large federal agency, and
32 01:13:54.720 ⇒ 01:14:05.850 Sheshu Chandrasekar: Recently, I found out that the dashboards I built was used in a congressional hearing, so that was pretty… that was pretty interesting. Yeah, so for me, Deloitte kind of shaped me into
33 01:14:05.990 ⇒ 01:14:17.030 Sheshu Chandrasekar: becoming a systems thinker slash product and marketing side of things, but I also worked in, I worked with a lot of nonprofits in the Austin area, so I did a lot of pro bono work.
34 01:14:17.140 ⇒ 01:14:31.040 Sheshu Chandrasekar: You know, I did things like building a corporate partnership playbook, and building donor engagement metrics that map to certain user personas for a large Latin American youth education nonprofit.
35 01:14:31.230 ⇒ 01:14:45.009 Sheshu Chandrasekar: That was expanding into the North American market for getting donations. So, my, career has so far been a… pretty much a generalist, but it’s also been…
36 01:14:45.270 ⇒ 01:14:47.370 Sheshu Chandrasekar: very much,
37 01:14:47.610 ⇒ 01:14:52.879 Sheshu Chandrasekar: you know, how do I build systems quickly, and how do we make sure, how do we…
38 01:14:53.150 ⇒ 01:15:14.760 Sheshu Chandrasekar: replicate systems that, you know, is sustainable enough. So, after Deloitte, you know, I’ve really had the idea of going into startups. I’ve always been very intrigued by the world of startups. So, I started getting into more go-to-market strategy, you know, playing around with Clay and other AI tools to, help this one GovTech startup,
39 01:15:14.810 ⇒ 01:15:17.020 Sheshu Chandrasekar: Reach out and build out their pipeline.
40 01:15:17.100 ⇒ 01:15:31.369 Sheshu Chandrasekar: And from there, I learned a lot about what’s good about startups, and, you know, what’s really bad about startups, and what kind of role I really want to play in. So yeah, in this next chapter, hopefully in Brainforge, you know, I can…
41 01:15:31.410 ⇒ 01:15:37.310 Sheshu Chandrasekar: Play that role of operations and building out systems that, you know, will help us scale to a new level.
42 01:15:37.380 ⇒ 01:15:39.779 Sheshu Chandrasekar: And I’m gonna turn on my camera as well, so…
43 01:15:39.780 ⇒ 01:15:49.539 Gabriel Lam: You’re good, you’re good. I also realized, yeah, I’ve… it’s a bad habit of mine, where I’m used to having my camera off for a lot of things, and I’m like, wait, I should probably turn it on, but…
44 01:15:50.220 ⇒ 01:15:50.790 Sheshu Chandrasekar: Yeah, you can see…
45 01:15:50.790 ⇒ 01:15:58.779 Gabriel Lam: Yeah, I’m… I… thanks for the intro. Would love to hear more. I can share a little bit about myself, and then I’ll go,
46 01:15:59.450 ⇒ 01:16:13.259 Gabriel Lam: And ask a bunch of questions, because what you did seems pretty cool. So, I’m Gabe. I started my career as an architect, actually, so very different to the whole tech world. But I also worked for a pretty major
47 01:16:15.090 ⇒ 01:16:30.779 Gabriel Lam: engineering consultancy firm. It’s called Arcadis, based out of New York, and as a part of that, I became exposed from my grad school onwards to AI and how AI is used, specifically in pretty regulated sectors, as I’m sure you’re well.
48 01:16:30.780 ⇒ 01:16:31.230 Sheshu Chandrasekar: Sounds good.
49 01:16:31.230 ⇒ 01:16:32.549 Gabriel Lam: And the government.
50 01:16:32.550 ⇒ 01:16:33.120 Sheshu Chandrasekar: Yeah.
51 01:16:33.550 ⇒ 01:16:45.000 Gabriel Lam: Yeah, so, like, encountering pushback in those ways, and I was like, okay, what sort of roles are out there and are working in the sort of cutting edge and more advanced?
52 01:16:45.170 ⇒ 01:16:50.249 Gabriel Lam: applications for these new tools that we’re using.
53 01:16:50.540 ⇒ 01:16:53.310 Gabriel Lam: Long story short, landed at Ice.
54 01:16:53.730 ⇒ 01:16:58.010 Gabriel Lam: started at Brain Forge a couple months ago, And
55 01:16:58.470 ⇒ 01:17:07.079 Gabriel Lam: what I’ve really been focused on is this sort of internal platform, that… sort of is an R&D.
56 01:17:07.510 ⇒ 01:17:10.840 Gabriel Lam: area for the company.
57 01:17:11.600 ⇒ 01:17:21.159 Gabriel Lam: Brainforge is a consultancy, so as I’m sure you’re well aware of, it’s client-driven, it’s service-driven, and we really go in and are trying to build custom solutions for our clients.
58 01:17:21.200 ⇒ 01:17:36.099 Gabriel Lam: And oftentimes, our clients will want very specific things, right? Either it’s, like, data analysis, or data engineering, or some sort of, you know, pipelines, or… I guess in Pranab’s case also, sometimes it’s, like, we want to build our own chatbot, or something to that extent.
59 01:17:36.640 ⇒ 01:17:37.290 Sheshu Chandrasekar: Alright.
60 01:17:37.480 ⇒ 01:17:46.130 Gabriel Lam: And so, for us, the internal team has really been focused on, like, okay, what does this mean for us? We’re building all these tools for our clients, but…
61 01:17:46.830 ⇒ 01:17:51.800 Gabriel Lam: How are we able to leverage the skills that we have to almost use ourselves?
62 01:17:52.010 ⇒ 01:18:00.249 Gabriel Lam: as a testing ground, right? So, it’s like, we’re, in a way, our own client, which I think has its own exciting opportunities.
63 01:18:00.630 ⇒ 01:18:05.909 Gabriel Lam: So, yeah, the platform we’re working on is really a way for us to…
64 01:18:06.030 ⇒ 01:18:10.070 Gabriel Lam: Supercharge our productivity and really be able to get,
65 01:18:10.290 ⇒ 01:18:26.990 Gabriel Lam: a lot of these processes prototyped, and hopefully that gets extended into new service offerings, and we’re like, hey, you know, clients may ask, you know, have you done something like this before? And we’ll be like, yeah, actually, we can tell you how it’s worked out in our own office.
66 01:18:27.260 ⇒ 01:18:38.599 Gabriel Lam: So yeah, I’m… that’s a little bit about me. I’m really curious when you shared about, like, your dashboards and how that’s been built and how that’s been adopted. I think adoption is a big…
67 01:18:38.750 ⇒ 01:18:41.090 Gabriel Lam: Sort of question for everyone in this…
68 01:18:41.090 ⇒ 01:18:41.940 Sheshu Chandrasekar: Yeah.
69 01:18:41.940 ⇒ 01:18:45.440 Gabriel Lam: current season, so I want to, like, hear about your experience.
70 01:18:48.070 ⇒ 01:18:51.550 Sheshu Chandrasekar: Oh, sorry, you kind of broke up for me there. Was the…
71 01:18:51.550 ⇒ 01:18:52.250 Gabriel Lam: you know.
72 01:18:52.380 ⇒ 01:18:54.360 Gabriel Lam: I guess, yeah, it just… oh, sorry.
73 01:18:54.730 ⇒ 01:19:02.710 Gabriel Lam: Yeah, I just wanted to hear more about, you know, your experience of creating that dashboard and what that adoption process was like for you learning.
74 01:19:02.710 ⇒ 01:19:07.439 Sheshu Chandrasekar: Oh, yeah. No, it was, it was a very tedious, end.
75 01:19:07.740 ⇒ 01:19:20.870 Sheshu Chandrasekar: chaotic and cumbersome process originally. But, yeah, when I first… when I was approached with that problem, the problem was, you know, the federal agency I was working for, they…
76 01:19:21.000 ⇒ 01:19:38.829 Sheshu Chandrasekar: basically, we’re going through a huge IT modernization, program. So, they needed a dashboard that kind of tracked milestones, you know, risk, issues, on a project level, program level, and then also at a, sub-agency level, in a way. So.
77 01:19:39.090 ⇒ 01:19:55.779 Sheshu Chandrasekar: when I was approached with the problem, executive leaders were open to any ideas, but it was more the internal. At Deloitte, you know, you have different siloed work streams. Even though they’re on the same project account, you had to get buy-in from them because, you can’t just
78 01:19:55.780 ⇒ 01:20:20.770 Sheshu Chandrasekar: circumnavigate, in a way, to just implement a dashboard. So, for me, acquiring the data sources and all that stuff was not difficult, because we had already built that infrastructure. So, it was more like figuring out the best way of viewing that data for the executive leaders, right? So, first most problem was getting buy-in from other Deloitte teams. That took a while and a lot of convincing.
79 01:20:20.960 ⇒ 01:20:30.239 Sheshu Chandrasekar: But once we were able to do that, we were able to go from building 5 dashboards into 2 dashboards, which is more looking at a,
80 01:20:30.260 ⇒ 01:20:50.159 Sheshu Chandrasekar: For the executive leaders, they can look at things at a macro level, so they can say, okay, this is our overall portfolio health, this is where we are, this is the budget that, here are the budget requirements that we’re meeting, and here are some that we’re going over, and then the micro is like, okay, on a project level, what are the issues? Like, what milestones are we hitting?
81 01:20:51.300 ⇒ 01:21:06.329 Sheshu Chandrasekar: it was a success, like, we… I was able to build it, executives loved it, but, getting 1,300 users, that had so many questions all the time was such a… that was a cumbersome part, right? It was like, okay.
82 01:21:06.440 ⇒ 01:21:25.529 Sheshu Chandrasekar: And it’s a very logical problem to have, in a way. So, what I had to do is I had to build user documentation, data dictionaries, host office hours every two weeks, so a project manager or program manager can come in and say, hey, I’m having this problem, how do I fix this? But also share updates that we were making to those dashboards.
83 01:21:25.530 ⇒ 01:21:27.239 Sheshu Chandrasekar: And overall to the platform.
84 01:21:27.320 ⇒ 01:21:33.209 Sheshu Chandrasekar: And there’s one key detail I missed out here. This was the ServiceNow implementation, so…
85 01:21:33.370 ⇒ 01:21:52.179 Sheshu Chandrasekar: that was kind of like… a lot of the users that were, using this tool were not used to ServiceNow, so we had to make sure we built some sort of change management policies for them to understand, like, hey, I know we’re moving from this one platform that you had internally built to an overall cohesive platform like ServiceNow, so…
86 01:21:52.340 ⇒ 01:22:09.789 Sheshu Chandrasekar: It was a crazy 4 or 5 months when I was on that project. It felt more like a startup than a traditional consulting, engagement, but, you know, going through that process, and going through that project, and the trials that came along with it was…
87 01:22:09.900 ⇒ 01:22:14.529 Sheshu Chandrasekar: so notable, like, I think it was a career-defining way for me from…
88 01:22:14.820 ⇒ 01:22:23.520 Sheshu Chandrasekar: a technical standpoint, but also from, like, how do I communicate, get a point across, and convince other people to see under my eyes? So, yeah, it was.
89 01:22:23.520 ⇒ 01:22:23.910 Gabriel Lam: Yeah.
90 01:22:23.910 ⇒ 01:22:25.379 Sheshu Chandrasekar: Very notable project for me.
91 01:22:25.950 ⇒ 01:22:29.100 Gabriel Lam: Yeah, I’d love to dive a little deeper,
92 01:22:29.340 ⇒ 01:22:34.660 Gabriel Lam: like, I am not a huge user of ServiceNow, so I’m curious.
93 01:22:34.900 ⇒ 01:22:42.280 Gabriel Lam: you know, when you were approaching this sort of project, were you engaged with… I know you were saying you were engaged with users, and doing interviews and trying to
94 01:22:42.490 ⇒ 01:22:47.529 Gabriel Lam: essentially do upskilling, right, through change management. I think that’s a huge part of.
95 01:22:47.530 ⇒ 01:22:48.260 Sheshu Chandrasekar: Yeah.
96 01:22:48.260 ⇒ 01:22:50.369 Gabriel Lam: I’m curious about, like.
97 01:22:50.610 ⇒ 01:23:03.859 Gabriel Lam: Were you working directly with, like, technical stakeholders? Were there, like, any engineers involved in the automations, or was this more like, we’re gonna do a no-code kind of implementation in a way that really works for specific clients?
98 01:23:04.360 ⇒ 01:23:29.350 Sheshu Chandrasekar: Yeah, in a way, like I said, this entire project was more of a startup, a startup environment, so I had to play… you know, I was building dashboards, but alongside, I was also helping upgrade the platform, so I had to work with a lot of engineers, on the client side, so I was playing a bit of quality assurance as well, as well as writing up user stories to, you know, figure out, hey, like, our user needs this, right? This is the business case that we
99 01:23:29.350 ⇒ 01:23:31.380 Sheshu Chandrasekar: have, and here’s why they
100 01:23:31.380 ⇒ 01:23:53.229 Sheshu Chandrasekar: drastically and urgently need this, right? Because we have a quarterly review that’s coming up, and it’s very important. So, if you’re not familiar with ServiceNow, ServiceNow is pretty much an out-of-box tool, and you can configure a lot of things, but there’s some things that you do need customization for. So, for anything that was more in that customization path, that’s where things would land on me.
101 01:23:53.230 ⇒ 01:24:00.799 Sheshu Chandrasekar: You know, I write user stories, present a business case to the client, project management, product management, and then also engineering team.
102 01:24:00.920 ⇒ 01:24:06.350 Sheshu Chandrasekar: And then, also figure out with compliance if, you know, this checks out, under their policies.
103 01:24:06.480 ⇒ 01:24:25.110 Sheshu Chandrasekar: And then, you know, keep following up with the engineers, doing sprint reviews and stuff like that, and do the testing and say, hey, this is not working, this is working perfectly, I’m good to release, right? So, yeah, I played a lot of roles in that project, so, I hope I answered your question, but…
104 01:24:25.110 ⇒ 01:24:26.420 Gabriel Lam: No, no, no, I think that’s great, yeah, yeah, yeah.
105 01:24:26.420 ⇒ 01:24:37.350 Sheshu Chandrasekar: Yeah, yeah. So, yeah, it was all over the place. One day was not the same. Every day was always different, there was always a different fire. But over time, I think one thing I learned is, like.
106 01:24:37.520 ⇒ 01:24:57.119 Sheshu Chandrasekar: there was patterns I became very aware of, and I was starting to systematize it a little bit better. Every month, it got better and better when it came to quality assurance, like, meetings and stuff like that, or even how we conducted our office hours and supporting my senior manager when it came to that. So, yeah, it was very… it was very,
107 01:24:57.170 ⇒ 01:25:01.039 Sheshu Chandrasekar: transformative project to save us, so, yeah.
108 01:25:01.040 ⇒ 01:25:03.780 Gabriel Lam: Yeah, yeah, yeah. I’m curious, what was it like, I guess.
109 01:25:03.930 ⇒ 01:25:09.809 Gabriel Lam: You know, you’re working with, in this case, external engineers, but, were these engineers…
110 01:25:10.160 ⇒ 01:25:14.919 Gabriel Lam: You know, fully on staff for this transformation project, or was it, like.
111 01:25:15.660 ⇒ 01:25:19.910 Gabriel Lam: Were there moments where you had to sort of ask for capacity, or sort of, you know.
112 01:25:20.960 ⇒ 01:25:23.539 Gabriel Lam: Deal with constraints in terms of capacity?
113 01:25:23.690 ⇒ 01:25:41.989 Sheshu Chandrasekar: Yeah, that was… I would say it was a good mix of both, right? There’d be moments where they’re like, hey, we don’t have any more resources, how many story points is this gonna take us to build? So, I had to kind of, like, work with some of my team members and be like, hey, like, what do you think we can estimate this to be? And what do you…
114 01:25:41.990 ⇒ 01:26:00.510 Sheshu Chandrasekar: what are some trade-offs that I have to sacrifice here, and make those tough calls sometimes, but then there are times where, during the season of, like, non-tax seasons or stuff like that, like, engineers were a little bit more freed, you know, they didn’t have any support tickets to solve and stuff like that, so we were able to use them to the full capacity.
115 01:26:01.090 ⇒ 01:26:01.720 Gabriel Lam: Hmm.
116 01:26:02.070 ⇒ 01:26:02.590 Gabriel Lam: I see.
117 01:26:02.590 ⇒ 01:26:07.590 Sheshu Chandrasekar: But I think, honestly, it was all relationship-based. Like, I remember a lot of times, like.
118 01:26:07.900 ⇒ 01:26:26.830 Sheshu Chandrasekar: there would be a last-minute user story that I had to push, and I had to, like, pull some strings. I had to, like, call in favors and stuff like that, and I would help them out as well, to wherever I can do, right? Because at the end of the day, it’s all relationship-based, and in the world of tech, it’s not really necessarily, like, hey, we’re pushing a feature, and that’s the result. It’s…
119 01:26:26.900 ⇒ 01:26:29.009 Sheshu Chandrasekar: You gotta have good relationships, so…
120 01:26:33.250 ⇒ 01:26:34.929 Sheshu Chandrasekar: Oh, no, I think my Wi-Fi…
121 01:26:36.450 ⇒ 01:26:39.269 Gabriel Lam: No, for sure. I mean, especially…
122 01:26:39.460 ⇒ 01:26:54.479 Gabriel Lam: since I can outside of TechPi, I… I have been experiencing some… some, blackouts, so… it’s probably me. But yeah, I… I was able to get most, or everything that you were saying. Hello.
123 01:27:00.910 ⇒ 01:27:06.570 Sheshu Chandrasekar: Hey, Gabe, I’m so sorry, I think my Wi-Fi is throttling right now. It’s me, I’m pretty sure it’s me. Okay, no worries.
124 01:27:06.570 ⇒ 01:27:09.909 Gabriel Lam: So, no, I literally had this problem earlier today, so…
125 01:27:10.110 ⇒ 01:27:16.059 Gabriel Lam: Yeah, it’s on me. I don’t… I think it’s snowing in Boston, and apparently…
126 01:27:16.060 ⇒ 01:27:16.890 Sheshu Chandrasekar: Whoa.
127 01:27:17.330 ⇒ 01:27:22.589 Gabriel Lam: My Wi-Fi’s been going wonky since it started. It’s been a cold winter, so…
128 01:27:22.860 ⇒ 01:27:24.360 Sheshu Chandrasekar: No, I agree, more than…
129 01:27:24.670 ⇒ 01:27:30.659 Gabriel Lam: Yeah, yeah, yeah. Yeah, no, but I did manage to hear what you were saying. I got the full answer hearing about, you know.
130 01:27:31.030 ⇒ 01:27:32.750 Gabriel Lam: relationship building, and…
131 01:27:33.070 ⇒ 01:27:42.189 Gabriel Lam: I think I mentioned earlier, I don’t know if you heard it, but inside and outside of tech, so much of it is relationship-based, and you know, when you’re dealing with, like, partnerships and, like.
132 01:27:42.700 ⇒ 01:27:51.460 Gabriel Lam: trying to suggest, you know, moving into new markets or whatever it might be, so much of that is… is really the case. I’m also curious about
133 01:27:52.020 ⇒ 01:27:54.740 Gabriel Lam: You know, you mentioned earlier about, like.
134 01:27:55.410 ⇒ 01:28:08.100 Gabriel Lam: the adoption metrics, did you notice any additional OKRs that you felt like you had to really push, or you had to say, like, okay, what does it mean for this to be a successful launch outside of just usage?
135 01:28:08.710 ⇒ 01:28:25.389 Sheshu Chandrasekar: Yeah, so a lot of our OKRs was tied to the platform itself. So, for example, if a project manager was to update a milestone, we would look at it on a monthly-monthly basis to see, like, okay, how well are they managing the milestones module, right? Are they…
136 01:28:25.390 ⇒ 01:28:30.840 Sheshu Chandrasekar: are they clicking on the right fields to update their project milestones? And we would look at that data to say, okay.
137 01:28:30.840 ⇒ 01:28:46.590 Sheshu Chandrasekar: this is where they’re doing really well, like, we see a lot of volume coming in from that end, but then this is where they’re also lacking. And so we would go back to the drawing board and be like, okay, in the next office hours, let’s talk about how do you update a risk tied to a milestone, right? And stuff like that was…
138 01:28:46.720 ⇒ 01:28:58.450 Sheshu Chandrasekar: very transformative for us in that way that we conduct our office hours. And it gave us, like, content, right? So we can make sure they were successful. But along the way, the OCAR is that
139 01:28:58.930 ⇒ 01:29:16.620 Sheshu Chandrasekar: we set was kind of guided by client leadership itself. Like, they knew exactly what they needed, so… and then we would just adopt that and kind of mold it to the platform itself, and figuring out how successful, the platform is for the project managers and the program managers.
140 01:29:16.930 ⇒ 01:29:17.640 Gabriel Lam: Yeah.
141 01:29:17.640 ⇒ 01:29:18.210 Sheshu Chandrasekar: Yeah.
142 01:29:18.920 ⇒ 01:29:21.449 Gabriel Lam: Awesome. I guess on a more…
143 01:29:22.350 ⇒ 01:29:31.480 Gabriel Lam: technical note, you mentioned previously that you were, like, wearing multiple hats, and also, like, essentially managing these stakeholders, like, these engineers on the client side, like.
144 01:29:31.960 ⇒ 01:29:36.979 Gabriel Lam: Or do you feel comfortable… like, how comfortable do you feel in
145 01:29:37.100 ⇒ 01:29:42.310 Gabriel Lam: Technical skills, whether it’s, like, programming all the way to, like, just knowing
146 01:29:43.000 ⇒ 01:29:49.580 Gabriel Lam: tools in general? Like, are you a fan of cloud code, or, like, do you have your own preferences there? Just out of curiosity.
147 01:29:51.260 ⇒ 01:29:58.640 Sheshu Chandrasekar: I’m not a coder, by any means, but I do unders… I used to code a lot in… not a lot, I guess I used to code a lot of Python stuff.
148 01:29:59.120 ⇒ 01:30:07.530 Sheshu Chandrasekar: in college, so I understand the world of coding, and I also have a background in ITS, so I did a lot of Java programming. But…
149 01:30:07.690 ⇒ 01:30:11.840 Sheshu Chandrasekar: I understand technical architecture really well. I think that’s where my strength really lies.
150 01:30:12.240 ⇒ 01:30:25.150 Sheshu Chandrasekar: I even know, like, this past June, I did an AI product management course, understanding how AI works and how to build AI applications, and so I’m very well informed in that realm. I think recently, now I’m…
151 01:30:25.150 ⇒ 01:30:39.069 Sheshu Chandrasekar: getting more experimental. I’m trying to figure out how to do MCP servers, or, figuring out how to create an agent, just, like, watching, like, one-hour videos from Stanford and stuff like that, so I’m always, like, very intrigued by that, and…
152 01:30:39.070 ⇒ 01:30:45.589 Sheshu Chandrasekar: it’s so interesting to me, it’s like, it feels like play, so I love doing that stuff, and one of the things about…
153 01:30:45.810 ⇒ 01:30:51.670 Sheshu Chandrasekar: Me and Pranav, like, we always talk about new things that are happening on the AI front.
154 01:30:51.670 ⇒ 01:30:52.040 Gabriel Lam: Yeah.
155 01:30:52.040 ⇒ 01:31:11.920 Sheshu Chandrasekar: And we just loved, like, just brainstorming stuff. And, I also come back… I also have a deep interest in product design. That’s something I love to do. Like, if I show you my computer right now, I’m on TLDRA, I’m on ChatGPT Gemini, and then Figma’s, like, the biggest, usage of my RAM right now, so…
156 01:31:12.330 ⇒ 01:31:31.009 Sheshu Chandrasekar: Yeah, so I’m very tech-oriented. The only thing I’m not too great at is coding, and that’s something I do want to be a little bit better at, but I think at this point in my career, I think I’m more in the, phase of, like, product management and systems building and… and designing fun. That’s, like, something I love to do.
157 01:31:31.550 ⇒ 01:31:39.030 Gabriel Lam: Yeah, I mean, that’s… that’s super great to hear. I think I resonate with you. I also started in Python. I would also say I’m not a…
158 01:31:39.130 ⇒ 01:31:45.979 Gabriel Lam: like, I’m able to read code and review code, but I’m not your engineer, right? And so I think it’s, like, a whole different level of…
159 01:31:46.100 ⇒ 01:31:47.050 Gabriel Lam: of…
160 01:31:47.550 ⇒ 01:31:55.609 Gabriel Lam: specificity, but I agree with you, and I think the most important part is being able to communicate and understand, right? Because…
161 01:31:56.000 ⇒ 01:32:15.640 Gabriel Lam: you’re never gonna know everything. I think when things scale, you’re gonna be so, you know, some engineers are gonna be so deep in the weeds of, like, systems design. And I’m not gonna know everything, but I’ll be part of it, like, hey, am I able to get their questions to the right people? And am I able to get the right questions from my stakeholders to them, so that we can actually, like.
162 01:32:15.820 ⇒ 01:32:24.199 Gabriel Lam: execute, or they’re able to sort of interpret and understand. But yeah, I’m also a huge fan of Figma. I’m like…
163 01:32:24.670 ⇒ 01:32:25.390 Sheshu Chandrasekar: Yeah.
164 01:32:27.040 ⇒ 01:32:34.450 Gabriel Lam: Yeah, it’s like, ugh, it’s amazing. And I started in Adobe, and so Adobe has been, in some ways, like.
165 01:32:35.490 ⇒ 01:32:38.250 Gabriel Lam: some sort of Stockholm Syndrome, where…
166 01:32:38.410 ⇒ 01:32:40.989 Gabriel Lam: I was just like, ugh, it could be so much better, and then I.
167 01:32:40.990 ⇒ 01:32:41.780 Sheshu Chandrasekar: Yeah, no.
168 01:32:41.780 ⇒ 01:32:43.530 Gabriel Lam: Wow, this is incredible.
169 01:32:43.810 ⇒ 01:32:56.149 Sheshu Chandrasekar: 100%. There’s that… I don’t know if you ever used, I know Apple had their own version of Figma, I forgot what it’s called, but I used it, like… and this is on the precipice of, like, Figma becoming super mainstream, so it was between…
170 01:32:56.310 ⇒ 01:33:06.000 Sheshu Chandrasekar: using Figma and this one, tool that Apple designed, and I was like, I hate this tool. Like, I need to get on Figma. Figma was so much easier to use, it was more intuitive, like…
171 01:33:06.960 ⇒ 01:33:20.919 Sheshu Chandrasekar: And there was more educational resources that were for free, so that’s what was a huge appeal for me to just get into the world of product design. But to also, like, tag on to what you’re saying about engineering, like, yeah, I’m kind of like that person where I understand, like.
172 01:33:21.250 ⇒ 01:33:34.570 Sheshu Chandrasekar: hey, like, what is possible and what is not possible, right? Like, sometimes you gotta talk to the language of the engineers to a certain degree, so, it’s like, kind of like, you have to get your stripes, right? You have to understand, like, what’s… what makes sense, like.
173 01:33:34.620 ⇒ 01:33:41.630 Sheshu Chandrasekar: what’s easy to build, what’s extremely difficult to build, and be mindful of, like, their work, and I think that’s…
174 01:33:41.830 ⇒ 01:33:58.380 Sheshu Chandrasekar: one of the main reasons why I love the technical side of things, like, understand technical architecture and, you know, just being very experimental with new tools and stuff like that, to see, hey, like, okay, we don’t have to use Python to scrape data, like, there’s some loop that’ll do it for us in, like, 30 seconds.
175 01:33:58.380 ⇒ 01:34:03.920 Sheshu Chandrasekar: don’t… no need to worry about it, so… Yeah, that’s kind of, like, how I use…
176 01:34:04.090 ⇒ 01:34:14.459 Sheshu Chandrasekar: Like, tools and just, like, my expertise, in a way, to better suit, like, you know, the team and, you know, make sure we make some forward momentum here.
177 01:34:14.820 ⇒ 01:34:19.740 Gabriel Lam: Yeah. I guess… I have one more question, and then I just want to leave, you know.
178 01:34:19.740 ⇒ 01:34:22.199 Sheshu Chandrasekar: Yeah, no, not wrong at all.
179 01:34:22.480 ⇒ 01:34:29.770 Gabriel Lam: I’m… I’m curious, you know, Having been at…
180 01:34:30.570 ⇒ 01:34:37.409 Gabriel Lam: The… having, like, discussions and, like, having really explored the forefront of all these tools, and then having also been
181 01:34:37.540 ⇒ 01:34:42.970 Gabriel Lam: On the upper side of, like, a large corporate organization, institution, where there’s, you know.
182 01:34:43.430 ⇒ 01:34:50.440 Gabriel Lam: there’s, like, processes and standard best practices for everything. Like, if you were to…
183 01:34:50.800 ⇒ 01:35:09.769 Gabriel Lam: introduce some of these new tools, or introduce some of these new workflows? Have you… like, what has that experience been for you? Like, getting pushback? If you were able to maybe change some things in the way you delivered the dashboard, like, what would you have done, I guess, is maybe… it’s sort of a more aspirational question.
184 01:35:09.770 ⇒ 01:35:26.310 Sheshu Chandrasekar: Yeah, so towards the end of my career at Deloitte, I was working closely with the Office of CTO, and a lot of… a lot of the responsibility I was there was, pushing, like, product design initiatives. So, think, like, cohort-based learning,
185 01:35:26.310 ⇒ 01:35:35.060 Sheshu Chandrasekar: Like, meeting with other product designers within the organization to kind of spread the word about product design, or human-centered design in general.
186 01:35:35.060 ⇒ 01:35:47.969 Sheshu Chandrasekar: So… during that time, like, you know, they were doing… they were always creating these flyers and, messaging on Teams channels and stuff like that, very manually, and…
187 01:35:48.160 ⇒ 01:35:51.150 Sheshu Chandrasekar: you know, I feel like being young is such a…
188 01:35:51.170 ⇒ 01:36:01.920 Sheshu Chandrasekar: double-edged sword, because you want to do something so differently, but then there’s always pushback that, no, we don’t want to break things as it is. So, I think it’s a… it’s a give and take, right?
189 01:36:01.920 ⇒ 01:36:12.859 Sheshu Chandrasekar: So, I remember this one time we were creating, flyers for, this product design course for advanced, users, that had Figma, and…
190 01:36:12.980 ⇒ 01:36:24.540 Sheshu Chandrasekar: you know, I repurposed some of the assets that we already had in Figma, and created, like, 6 or 7 different variations of it. And this is when Figma Make was slowly coming out, so,
191 01:36:25.020 ⇒ 01:36:28.230 Sheshu Chandrasekar: you know, I think my make is great. I don’t understand the hate on it.
192 01:36:28.230 ⇒ 01:36:29.360 Gabriel Lam: I think it’s incredible.
193 01:36:29.360 ⇒ 01:36:43.730 Sheshu Chandrasekar: Yeah, and it doesn’t even have to be, like, front-end designs, but yeah, I digress here. But yeah, I kind of had to bring that to their, to their eyes, and they finally accepted, okay, you know what, maybe we should listen to him, because
194 01:36:43.730 ⇒ 01:36:50.910 Sheshu Chandrasekar: our… his flyers look way better than ours, and what has been used in the past. So, it’s really a double-edged sword, because a lot of times.
195 01:36:51.370 ⇒ 01:36:57.470 Sheshu Chandrasekar: like, people that… older folks, I’ve noticed is that they just don’t want to feel left behind.
196 01:36:57.640 ⇒ 01:37:02.830 Sheshu Chandrasekar: they don’t want to be like, okay, like, this guy knows more than us. It’s more like.
197 01:37:03.180 ⇒ 01:37:10.269 Sheshu Chandrasekar: you gotta let them know and show them the weeds a little bit to understand, hey, this is not gonna replace it, it’s actually gonna elevate your job even more. And…
198 01:37:10.580 ⇒ 01:37:28.109 Sheshu Chandrasekar: So that’s a pushback, but when it comes to standards, like, I think, organizations like Deloitte is so huge, they’ve had a culture that’s been, you know, deeply rooted for, like, many years now, right? So, there were guidelines that was already there, so all I had to do was adapt to it, but the way of doing things was…
199 01:37:28.220 ⇒ 01:37:34.040 Sheshu Chandrasekar: Definitely a little bit more archaic and… and sometimes not open to, you know, change in a way.
200 01:37:34.550 ⇒ 01:37:35.180 Gabriel Lam: Yeah.
201 01:37:35.570 ⇒ 01:37:41.639 Gabriel Lam: Well, okay, I… Matt, thank you for your time. I also want to leave questions for… leave time for any questions you have.
202 01:37:41.650 ⇒ 01:37:46.259 Sheshu Chandrasekar: Yeah. And I’m also happy to run over. I have, like, an extra, like, 5-10 minutes as well, so…
203 01:37:46.260 ⇒ 01:37:51.900 Gabriel Lam: Don’t feel like the 3 minutes is… don’t feel, like, rushed to talk about everything in 3 minutes.
204 01:37:51.900 ⇒ 01:37:57.909 Sheshu Chandrasekar: Yeah, absolutely. So, I’ve actually heard that from Eliza and Rico, that you were the…
205 01:37:57.940 ⇒ 01:38:01.310 Gabriel Lam: the guy for the, internal tool, like… Yeah.
206 01:38:01.370 ⇒ 01:38:05.940 Sheshu Chandrasekar: So, I’m curious, like, like, what problems were you originally
207 01:38:06.150 ⇒ 01:38:14.330 Sheshu Chandrasekar: What problems originally came to you, and how did you go about it, like, saying, like, okay, these are the problems that is high priority right now for us to build?
208 01:38:14.330 ⇒ 01:38:16.749 Gabriel Lam: Yeah, so…
209 01:38:17.280 ⇒ 01:38:23.820 Gabriel Lam: I guess to preface, I had joined during a time where we were really trying to figure out
210 01:38:26.070 ⇒ 01:38:34.280 Gabriel Lam: How to get people to first use the platform, and also to understand, like, what is the point of this whole thing?
211 01:38:34.500 ⇒ 01:38:39.810 Gabriel Lam: And so I think the way we chose to go about it was, like, okay, we had built this
212 01:38:42.030 ⇒ 01:38:46.480 Gabriel Lam: Database of meetings, of assets, of…
213 01:38:46.820 ⇒ 01:38:47.950 Sheshu Chandrasekar: notes.
214 01:38:47.950 ⇒ 01:38:54.380 Gabriel Lam: of, like, and Zoom has transcripts and everything, and so everything is saved into…
215 01:38:55.470 ⇒ 01:39:00.380 Gabriel Lam: let’s call it a platform that we call it, and then we call it a platform.
216 01:39:01.430 ⇒ 01:39:07.150 Gabriel Lam: And what can we do with this information? We’re an AI-first company, and what are all the ways in which
217 01:39:07.540 ⇒ 01:39:16.429 Gabriel Lam: we can actually take that content and use it as context for other things. And so we had noticed an example would be, like.
218 01:39:16.600 ⇒ 01:39:31.080 Gabriel Lam: you know, we’re jumping between a lot of meetings. People are always, you know, context switching, and especially for people leading these services or leading client meetings, oftentimes we sort of just want to know, like, what happened? Like, what are the decisions that happened?
219 01:39:31.410 ⇒ 01:39:39.930 Gabriel Lam: what are the next steps, so that we can actually then delegate and make things and sort of accelerate the velocity. And just really to minimize
220 01:39:40.460 ⇒ 01:39:41.480 Gabriel Lam: the…
221 01:39:43.050 ⇒ 01:39:53.340 Gabriel Lam: sort of lost time in trying to, like, wrap your head around certain things. And so a big part of that is, first of all, like, understanding what it is that
222 01:39:53.550 ⇒ 01:40:04.669 Gabriel Lam: stakeholders needed, which is, like, what is the type of information that needs to be communicated? What are ways that people like to receive information? How do we convert from
223 01:40:05.900 ⇒ 01:40:19.809 Gabriel Lam: you know, once we ingest information to actually, like, actionable steps, whether it’s, like, linear tickets, whether it’s Slack messages, and how do we actually use that to really build and refine? And I think the great thing about AI has been
224 01:40:20.860 ⇒ 01:40:28.449 Gabriel Lam: its ability to almost, like, self-improve, right? Like, the more context you give, the more specificity and, like, steerability you have.
225 01:40:28.660 ⇒ 01:40:33.849 Gabriel Lam: So, that’s sort of, like, how we started this process.
226 01:40:33.960 ⇒ 01:40:41.660 Gabriel Lam: I think… where we are moving towards is saying, okay, we have the input area done, right? Like, we’re…
227 01:40:42.360 ⇒ 01:40:46.570 Gabriel Lam: We’re… at one point, we were thinking about dashboards and that kind of stuff, but then…
228 01:40:47.020 ⇒ 01:40:55.029 Gabriel Lam: the next question is, like, what do we do with dashboards? With dashboards, you know the information. You have an idea of what the sense… you have an idea of the sense of what’s going on.
229 01:40:55.160 ⇒ 01:40:58.260 Gabriel Lam: And then the next part is, like, what do you do with that? And so…
230 01:40:58.600 ⇒ 01:41:02.709 Gabriel Lam: We’re now moving into a stage of, like, how do we get people to…
231 01:41:02.880 ⇒ 01:41:15.019 Gabriel Lam: not only understand what the platform is, but also to then use it to do their own work. And so for, you know, Rico and Eliza, when they’re in ops, a big part of it is, like.
232 01:41:15.820 ⇒ 01:41:24.519 Gabriel Lam: you know, is there… are there best practices that… that we’ve noticed, whether it’s with clients, whether it’s internal? Are there,
233 01:41:25.420 ⇒ 01:41:33.270 Gabriel Lam: like, SOPs that need to be written out? Are there… like, if we’re doing SOWs for… for our… Clients.
234 01:41:33.430 ⇒ 01:41:49.989 Gabriel Lam: are there ways that people are actually able to utilize the platform that we’ve built to then go about and do it? And so, the… we’ve noticed, you know, I’m sure you’ve also encountered, like, knowledge blockers, where your team lead is, like, the guy who knows everything, and you’re like, hey, I don’t know this one thing.
235 01:41:50.330 ⇒ 01:42:02.770 Gabriel Lam: that maybe you addressed in a private meeting with, like, between you and the client lead. Like, I have no visibility into that, therefore I’m blocked on writing, you know, a proposal, for example.
236 01:42:02.880 ⇒ 01:42:10.279 Gabriel Lam: And I think the goal of the platform is really to allow people to do higher level work.
237 01:42:10.450 ⇒ 01:42:13.600 Gabriel Lam: more easily, right? And I think with AI,
238 01:42:14.190 ⇒ 01:42:18.820 Gabriel Lam: The goal is… is not to, like, minimize…
239 01:42:19.930 ⇒ 01:42:33.860 Gabriel Lam: I think… I think the goal is to allow you to increase what you feel comfortable doing, right? It’s not to say, like, you know, like, Robert and Uten, like, they have a bunch going on. I’m, like, taking over their job, like, not at all. It’s more just to say.
240 01:42:34.260 ⇒ 01:42:45.620 Gabriel Lam: are things that they are really good at, and what they would like to focus all their time on is, like, selling, right? I just want to sell, I just want to get more business. And the things that are sort of, like.
241 01:42:46.280 ⇒ 01:42:47.230 Sheshu Chandrasekar: you know.
242 01:42:47.230 ⇒ 01:42:53.440 Gabriel Lam: you have your, like, couple hours of mindless work a day that you’re just like, I just need to get this out, but…
243 01:42:53.540 ⇒ 01:42:58.149 Gabriel Lam: if I had another me, it’d be a lot faster. And so I think the big part of it is, like.
244 01:42:58.430 ⇒ 01:43:06.130 Gabriel Lam: There’s all these, you know, relatively low-hanging fruit, easier tasks that many other people could collaborate on with.
245 01:43:06.330 ⇒ 01:43:07.240 Gabriel Lam: And…
246 01:43:08.060 ⇒ 01:43:16.900 Gabriel Lam: That leads us to really focus on the things that either we really want to do, or focus on the things that actually drive growth, or drive value.
247 01:43:17.370 ⇒ 01:43:23.630 Gabriel Lam: So that’s a sort of high level. I can go more into the weeds, but I’d say
248 01:43:24.270 ⇒ 01:43:26.320 Gabriel Lam: You know, we’re moving towards, like, a…
249 01:43:26.810 ⇒ 01:43:33.129 Gabriel Lam: more technical approach, which is a big part of, like, why I asked more technical questions earlier.
250 01:43:33.130 ⇒ 01:43:35.060 Sheshu Chandrasekar: Yeah. Towards using AI.
251 01:43:35.580 ⇒ 01:43:39.690 Gabriel Lam: And, you know, I… I’ve used…
252 01:43:39.930 ⇒ 01:43:47.390 Gabriel Lam: Gemini, I’ve used GPT, Claude, and we’ve also noticed, like, the benefit of, like, cursor and Claude code.
253 01:43:47.420 ⇒ 01:43:48.380 Sheshu Chandrasekar: Epic.
254 01:43:48.680 ⇒ 01:43:51.369 Gabriel Lam: More, sort of, code-first.
255 01:43:51.560 ⇒ 01:43:53.809 Gabriel Lam: Tools as a way for us to
256 01:43:56.260 ⇒ 01:43:58.880 Gabriel Lam: Leverage the platform and, like, be like, hey.
257 01:43:59.170 ⇒ 01:44:07.839 Gabriel Lam: these are amazing tools that, you know, the engineers are using, and, like, how can we… we’ve seen how well it’s been working. How can we take that and, like.
258 01:44:08.390 ⇒ 01:44:14.160 Gabriel Lam: Upgrade, or, like, make everyone… work better.
259 01:44:14.380 ⇒ 01:44:18.300 Gabriel Lam: And so, you know, we’ve seen great effects, we’ve seen people, like.
260 01:44:18.620 ⇒ 01:44:22.119 Gabriel Lam: Tasks that used to take, like, 2 hours could take, like, 15 minutes.
261 01:44:22.330 ⇒ 01:44:23.809 Gabriel Lam: And it…
262 01:44:24.140 ⇒ 01:44:29.720 Gabriel Lam: for some of us, it went from, like, having to sit down and write a whole document to just, like, hey, I just need to read it.
263 01:44:30.510 ⇒ 01:44:38.740 Gabriel Lam: very quickly see, like, what’s wrong, and that’s a much better use of my time than, like, having to sit through there and, you know, wordsmith. And so things like that.
264 01:44:39.040 ⇒ 01:44:39.500 Sheshu Chandrasekar: Right.
265 01:44:39.500 ⇒ 01:44:46.340 Gabriel Lam: I would say is… has been the main focus, amongst other things. But yeah, I hope… I hope that answers your question.
266 01:44:46.340 ⇒ 01:44:53.300 Sheshu Chandrasekar: Oh, no, it makes a lot of sense. So, a lot of your time is kind of spent, okay, like, how do I…
267 01:44:53.470 ⇒ 01:45:00.069 Sheshu Chandrasekar: Build tools that Kinda takes out that low… like, you know, the low effort.
268 01:45:00.200 ⇒ 01:45:07.510 Sheshu Chandrasekar: like, meet… like, low-effort task out of the way. So, I guess the one thing I heard about is you built the tool where,
269 01:45:07.740 ⇒ 01:45:17.050 Sheshu Chandrasekar: you know, all the meeting notes, anything that’s specific to, like, a certain topic is through a chat-based interface. Is that… is that correct here?
270 01:45:17.780 ⇒ 01:45:21.859 Gabriel Lam: So that… that happened right as I joined. That already exists.
271 01:45:21.860 ⇒ 01:45:23.040 Sheshu Chandrasekar: Oh, okay.
272 01:45:23.040 ⇒ 01:45:36.849 Gabriel Lam: I was part of… one of the first things that I had to do was, like, what are the things that come out of that meeting that need to be really interrogated? And so one example was, like, linear tickets and meeting summaries needed to be really, like.
273 01:45:37.000 ⇒ 01:45:37.770 Gabriel Lam: tight.
274 01:45:38.130 ⇒ 01:45:39.220 Sheshu Chandrasekar: Right. So…
275 01:45:39.590 ⇒ 01:45:42.520 Gabriel Lam: And then that really extends into
276 01:45:42.910 ⇒ 01:45:54.480 Gabriel Lam: you know, when you have, like, documentation with clients, and you’re like, I just need a discovery document that’s constantly updated. What is the type of information that works best coming in, and what is the type of information that works best going out?
277 01:45:54.480 ⇒ 01:46:03.349 Gabriel Lam: So that was one aspect. Another aspect for us was, like, you know, running stand-ups, for example. When you’re running stand-ups, you’re like, I just need to know all the updates from yesterday.
278 01:46:03.360 ⇒ 01:46:07.660 Gabriel Lam: And it could happen in Zoom, Slack, anywhere else.
279 01:46:08.050 ⇒ 01:46:15.919 Gabriel Lam: And so, yeah, I think… My role has been more about Trying to understand.
280 01:46:16.620 ⇒ 01:46:29.599 Gabriel Lam: what the platform does well, what are things that we can do better in, and how do we get there, right? And so, it’s like, there’s… I write PRDs, my day-to-day is, like, PRDs, roadmaps, and also…
281 01:46:30.150 ⇒ 01:46:33.510 Gabriel Lam: trying to… basically, like.
282 01:46:34.610 ⇒ 01:46:44.620 Gabriel Lam: argue for certain features, like, hey, this is probably a good idea for us. And sometimes it’s not an app, right? Sometimes it’s like, hey, if we notice, like, Cloud Code is doing amazing things.
283 01:46:44.660 ⇒ 01:46:57.179 Gabriel Lam: let’s do that instead of trying to build our own version. Or if we notice that, like, you know, Notion is great for certain things, like, why don’t we utilize that? Or if we notice GitHub is great for certain things, why don’t we utilize that? And so it’s not to say.
284 01:46:57.710 ⇒ 01:47:04.020 Gabriel Lam: You know, we’re… it’s not to say doing it ourselves is the best way, it’s more just, like, what’s the landscape out there, and how do we…
285 01:47:04.410 ⇒ 01:47:11.030 Gabriel Lam: mix and match. And sometimes it does take building your own thing, right? And so that’s part of the scope as well, but…
286 01:47:11.910 ⇒ 01:47:16.560 Gabriel Lam: AI is moving so fast, I think we’ve also noticed sometimes it is better to
287 01:47:17.410 ⇒ 01:47:26.229 Gabriel Lam: see what other people have done. They have the funding, they have the resourcing, and then leverage that. It ends up being much cheaper and much faster as well, so…
288 01:47:26.720 ⇒ 01:47:40.740 Sheshu Chandrasekar: No, I totally hear you, because, like, I’ve been using Gumloop for, especially when, in my last contract, with the GovTech, like, I had to scrape a lot of websites, like, startup websites, like, that was super focused on defense tech.
289 01:47:40.760 ⇒ 01:47:54.209 Sheshu Chandrasekar: And I was using Gumloop so much that I didn’t want to pay for it, but I just ended up reaching out to the go-to-market lead on LinkedIn, and he just gave me, like, a bunch of extra credits for free. And I just had to answer some, like.
290 01:47:54.210 ⇒ 01:48:02.989 Sheshu Chandrasekar: like, how satisfied are you with the platform? And, like, what’s the use case that you’re doing? So, you’re so right, there’s certain tools that are out there that,
291 01:48:02.990 ⇒ 01:48:21.620 Sheshu Chandrasekar: sometimes it’s for free, it’s better, and if you have good relationships with them, shoot, like, it will supercharge your work even better, so… I totally hear you. And I know we’re running about time here, so I just want to be mindful, but if not, like, we can end it here, and I can just email you just one last question, if that’s okay.
292 01:48:21.620 ⇒ 01:48:30.400 Gabriel Lam: I can take one more question, I do have, like, a hard stop in 2 minutes, but if you have my email, if you want to email it, I’m also happy to answer there.
293 01:48:30.400 ⇒ 01:48:46.910 Sheshu Chandrasekar: Yeah, absolutely. So, I’m just so curious, so being on the ops side of things now, like, what’s the best way for me to, like, if I have, like, a… let’s say I have, like, a process, right? And then I’m like, okay, you know what, this could be definitely automated. Like, how do I submit things with you? Like, what’s, like, your…
294 01:48:47.400 ⇒ 01:48:50.349 Sheshu Chandrasekar: go to intake, like, saying, okay, I’m gonna look.
295 01:48:50.350 ⇒ 01:48:57.550 Gabriel Lam: Oh. Yeah. I… I think… this is something that Brainforge is trying to really push, which is…
296 01:48:59.320 ⇒ 01:49:05.939 Gabriel Lam: in some ways, my role should not be the gatekeeper, right? Like, my role should be…
297 01:49:06.830 ⇒ 01:49:13.929 Gabriel Lam: And I think what U-Tam is, or the team, is really hoping that the head of ops would…
298 01:49:14.670 ⇒ 01:49:22.190 Gabriel Lam: Would support would be to allow people to, like, Vocalize or offer suggestions.
299 01:49:22.470 ⇒ 01:49:31.369 Gabriel Lam: And then say, what do I need to get it done, right? If it’s like, hey, Gabe, you know, I really need technical depth.
300 01:49:31.510 ⇒ 01:49:46.470 Gabriel Lam: then I can go and say, like, okay, we have engineering capacity in these ways to, like, really handle, you know, the super specifics that, like, you’re not able to do. But if you just wanted to say, like, hey, you know, we have this process, I think it’d be great for the ops team,
301 01:49:47.220 ⇒ 01:49:49.139 Gabriel Lam: Can I just do it? I think what…
302 01:49:49.770 ⇒ 01:49:55.230 Gabriel Lam: The goal the leadership wants is, like, yes, go ahead and do it, and whatever you don’t know.
303 01:49:55.680 ⇒ 01:50:01.839 Gabriel Lam: We want you to be… comfortable and confident reaching out to say, like, I would like this done.
304 01:50:01.950 ⇒ 01:50:15.750 Gabriel Lam: Because I think it’d be really helpful in this way, and how can I get there, and what do I need help with? So it’s not so… the hope is not so much, like, I am the point person for intake, it’s more…
305 01:50:16.610 ⇒ 01:50:18.569 Gabriel Lam: You know, the company as a whole.
306 01:50:19.470 ⇒ 01:50:32.619 Gabriel Lam: ends up being product owners in every way, and, like, we would hope that, like, hey, I… I built this process, I can own it, I think I can improve it in this way. I don’t need Gabe to approve me, I just need…
307 01:50:33.070 ⇒ 01:50:35.820 Gabriel Lam: Someone to say, like, Okay.
308 01:50:36.020 ⇒ 01:50:48.269 Gabriel Lam: is there a better way to do it that exists? We’ll let you know. Do you need help, you know, debugging something, or if, like, you know, the information’s not coming out right, we’ll help you. But,
309 01:50:48.710 ⇒ 01:50:52.619 Gabriel Lam: you don’t need to feel like, oh, I need to get buy-in from Gabe to do it.
310 01:50:52.620 ⇒ 01:50:53.260 Sheshu Chandrasekar: Gotcha.
311 01:50:53.260 ⇒ 01:50:53.850 Gabriel Lam: Yeah.
312 01:50:54.130 ⇒ 01:50:59.999 Sheshu Chandrasekar: So if I were to, like, say, okay, I know this process, I can use AI, and I can go ahead and build it, by all means, I can go ahead and just…
313 01:51:00.000 ⇒ 01:51:17.179 Gabriel Lam: by all means, like, the floor is yours, but if you’re like, hey, you know, Claude’s not talking to, like, fireflies or something, I don’t know, or if, like, ChatGPT’s not talking to my Notion, like, it’s timing out, then we’re like, okay, well, we know where the problem is, we can help you figure it out.
314 01:51:17.310 ⇒ 01:51:30.490 Gabriel Lam: how best to do it. But you know yourself best, you know ops best, right? And so, we don’t think it’s right for us to come in and say, like, oh, you know, you should do ops this way. It’s like, no. You know exactly what you need to do, you know exactly what your deliverables are, you know
315 01:51:31.220 ⇒ 01:51:34.110 Gabriel Lam: What… you know what the outcome needs to be.
316 01:51:34.190 ⇒ 01:51:36.250 Sheshu Chandrasekar: Right. And so, why not give you.
317 01:51:36.430 ⇒ 01:51:39.380 Gabriel Lam: The best tools to just say, well.
318 01:51:39.600 ⇒ 01:51:48.150 Gabriel Lam: I can just build it, and I don’t have to feel like I need to tell anyone to build it. I just need to know that if I have problems building it, that someone will help.
319 01:51:48.540 ⇒ 01:51:49.260 Gabriel Lam: Yeah.
320 01:51:49.810 ⇒ 01:52:02.880 Sheshu Chandrasekar: No, that makes a lot of sense, and I love that, because, like, coming from Deloitte, even the startups a little bit, like, there’s always resource constraints or a way of doing things, so it’s so refreshing to hear, like, I have full autonomy.
321 01:52:02.880 ⇒ 01:52:10.970 Sheshu Chandrasekar: how to go about things, and I can pull in anyone I need. But, Gabe, I know we’re at time, I don’t want to take any more of your time, so thank you so much for…
322 01:52:10.970 ⇒ 01:52:11.350 Gabriel Lam: Good.
323 01:52:11.730 ⇒ 01:52:19.119 Sheshu Chandrasekar: Yeah, it was, this is a very refreshing conversation, I’m super excited, you know, working with you in the future, so.
324 01:52:19.120 ⇒ 01:52:26.360 Gabriel Lam: Yeah, pleasure to meet you, and I don’t know what the next process is, but I’ve also truly enjoyed this conversation, and…
325 01:52:26.570 ⇒ 01:52:28.300 Gabriel Lam: I think it’d be great to have you.
326 01:52:28.520 ⇒ 01:52:31.100 Sheshu Chandrasekar: Absolutely. Thank you so much, Gabe, and take care.
327 01:52:31.100 ⇒ 01:52:31.700 Gabriel Lam: Oh my god.