Meeting Title: Uttam <> Chang Date: 2024-09-12 Meeting participants: Uttam Kumaran, Chang Ho Yoon
WEBVTT
1 00:03:19.100 ⇒ 00:03:20.819 Chang Ho Yoon: Hey, how are you doing?
2 00:03:22.860 ⇒ 00:03:24.180 Chang Ho Yoon: Let’s get the video on.
3 00:03:28.480 ⇒ 00:03:30.250 Uttam Kumaran: Hey! Wow! How are you?
4 00:03:30.250 ⇒ 00:03:31.959 Chang Ho Yoon: You to flash a new deck.
5 00:03:33.180 ⇒ 00:03:34.240 Uttam Kumaran: How’s everything?
6 00:03:34.620 ⇒ 00:03:36.809 Chang Ho Yoon: I’m good. Yeah. Go. Well.
7 00:03:36.820 ⇒ 00:03:43.680 Chang Ho Yoon: quite refreshed after a fairly protracted holiday. Actually. So, yeah, all good. How? How are you doing, man?
8 00:03:43.860 ⇒ 00:03:48.821 Uttam Kumaran: I’m good. I yeah, I feel like, maybe different background. I moved in July,
9 00:03:49.570 ⇒ 00:03:57.959 Uttam Kumaran: and then I was traveling for a bit. But yeah, things are good. Things are getting better slowly. So, yeah.
10 00:03:58.900 ⇒ 00:04:00.649 Chang Ho Yoon: Awesome. Where have you moved to.
11 00:04:00.930 ⇒ 00:04:05.533 Uttam Kumaran: I’m in. I’m in Austin still. But I’m like, sort of like, east of town.
12 00:04:06.202 ⇒ 00:04:09.630 Uttam Kumaran: Yeah, like in a house in like the suburbs. So
13 00:04:09.830 ⇒ 00:04:13.839 Uttam Kumaran: like not an apartment anymore. Finally, so I have a lot of space.
14 00:04:13.910 ⇒ 00:04:19.531 Uttam Kumaran: And I just like stay at home for the most part. So.
15 00:04:20.750 ⇒ 00:04:24.540 Chang Ho Yoon: Romney, who’s at home with you? Do you have a pet or anything? No.
16 00:04:24.540 ⇒ 00:04:31.407 Uttam Kumaran: My girlfriend lives nearby, and there is a there is a dog here right now. He’s like standing. He’s like standing guard.
17 00:04:31.790 ⇒ 00:04:35.039 Uttam Kumaran: Yeah, I’ll see if you can see me standing guard like in the corner there.
18 00:04:37.178 ⇒ 00:04:40.350 Uttam Kumaran: But otherwise just just me.
19 00:04:41.110 ⇒ 00:04:46.740 Chang Ho Yoon: Yes. Well, we’ve we’ve also moved out to Rebecca and I. So we’re just again.
20 00:04:46.820 ⇒ 00:04:50.990 Chang Ho Yoon: but funnily enough, we moved east in Oxford, too, so.
21 00:04:50.990 ⇒ 00:04:51.690 Uttam Kumaran: Okay.
22 00:04:51.690 ⇒ 00:04:57.830 Chang Ho Yoon: And we’ve also moved from a flat to a house weird parallel lives that we’re living. We have a dog.
23 00:04:57.830 ⇒ 00:04:59.809 Uttam Kumaran: What do you think? Okay. Great.
24 00:05:00.140 ⇒ 00:05:05.060 Chang Ho Yoon: But but we we are planning on getting a cat. So maybe there’s the parallel.
25 00:05:05.280 ⇒ 00:05:11.952 Uttam Kumaran: Yeah, I think I’m just thinking about getting an even bigger dog like that’s the that’s where we break fast.
26 00:05:13.223 ⇒ 00:05:15.129 Chang Ho Yoon: Yeah, but
27 00:05:15.330 ⇒ 00:05:18.570 Chang Ho Yoon: it is so pleasant, isn’t having so much room. And then.
28 00:05:18.570 ⇒ 00:05:19.790 Uttam Kumaran: It’s the best.
29 00:05:20.230 ⇒ 00:05:24.360 Uttam Kumaran: I mean, cause I I don’t know. I work a lot, so
30 00:05:24.580 ⇒ 00:05:36.120 Uttam Kumaran: it’s tough to be in the way I compensated before is, I just go work and like a coffee shop, or I like, try to leave, and I still do that because it’s hard to just stay here
31 00:05:36.470 ⇒ 00:05:47.010 Uttam Kumaran: and like, just be inside all the time. But it’s nice to like I have. I have this office, and then I can like, go out and do stuff. Come back to the office.
32 00:05:48.330 ⇒ 00:05:48.860 Chang Ho Yoon: Exactly.
33 00:05:48.860 ⇒ 00:05:55.110 Uttam Kumaran: And you know, like, if I’m at a coffee shop or something, I can’t do meetings, so I can easily sit here, be comfortable and like
34 00:05:55.300 ⇒ 00:06:06.859 Uttam Kumaran: meeting meeting meeting. Then I like go. So it’s really nice. And again, like, I think most of my life is now like centered around trying to make this thing a reality. So it’s
35 00:06:07.220 ⇒ 00:06:12.439 Uttam Kumaran: it’s like, try to try to like, make your environment conducive. For, like your goal a little bit. So.
36 00:06:12.690 ⇒ 00:06:19.219 Chang Ho Yoon: Yeah, while we’re on that note. Let’s let’s talk about what’s happened. Since it sounds like you’ve had some exciting movement.
37 00:06:19.220 ⇒ 00:06:26.639 Uttam Kumaran: Yeah. So a couple I mean, good and bad, I would say across the board, which is usually, I think it’s
38 00:06:27.550 ⇒ 00:06:28.970 Uttam Kumaran: but sometimes
39 00:06:29.060 ⇒ 00:06:30.340 Uttam Kumaran: 2 steps back.
40 00:06:31.190 ⇒ 00:06:36.705 Uttam Kumaran: one step forward, 3 steps forward. You know. It’s like it’s kind of that game, I think.
41 00:06:37.320 ⇒ 00:06:44.910 Uttam Kumaran: probably since last time we talked I had it we were. We were balancing a couple of clients. A couple turned, actually, not because
42 00:06:45.130 ⇒ 00:06:52.689 Uttam Kumaran: it was our fault, actually, like some of their clients, their business was struggling, which is now becomes some more of my criteria, for, like
43 00:06:53.220 ⇒ 00:06:56.599 Uttam Kumaran: the health of like some of our clients, so that’s 1 thing, but
44 00:06:56.920 ⇒ 00:07:05.644 Uttam Kumaran: good and bad. Some clients turned, and they were actually way. More work, some some clients. They were good. But when we re engaged with them
45 00:07:06.350 ⇒ 00:07:17.740 Uttam Kumaran: However, in that same process. We’ve we’ve built out a lot of like partnerships. So built partnerships with like recruiters, partnerships with vendors like some bi tools and things like that.
46 00:07:17.770 ⇒ 00:07:25.219 Uttam Kumaran: who we’ve gotten closer with and sold some of their tools. And so I’ve been able to kind of get some business through them.
47 00:07:25.690 ⇒ 00:07:29.390 Uttam Kumaran: And then, yeah, we started basically
48 00:07:29.500 ⇒ 00:07:44.639 Uttam Kumaran: also working with a recruiting firm that had a lot of enterprise contacts, particularly on the medical side, one of which is like Baylor health. Another is Nvidia and so I’m getting some experience like, I’m I’m actually working that contract.
49 00:07:44.970 ⇒ 00:08:07.439 Uttam Kumaran: Which is good. But it’s tough, because, like I’m doing the work. But it established a relationship with this really large recruiting firm that places like data talent. And particularly, I got really close with one of their recruiters that you know, we’re trying to work more together, placing some of our guys and these sort of situations. So it’s good. And and that’s like keeping.
50 00:08:07.560 ⇒ 00:08:16.610 Uttam Kumaran: That’s like bringing us a good amount of money. And then we have a couple of other clients and another e-commerce client that signed.
51 00:08:16.790 ⇒ 00:08:29.477 Uttam Kumaran: for which we now like pretty tough, templated sort of requirements for Snowflake. And some of these data software in terms of how we implement, like what the 1st month is pretty cookie cutter. Now,
52 00:08:30.200 ⇒ 00:08:40.939 Uttam Kumaran: and then. Yeah, we’ve had some people changes like, I think I’ve I’ve I’ve done a good job at hiring people on the data engineering and data modeling side.
53 00:08:41.740 ⇒ 00:08:51.429 Uttam Kumaran: But then I’ve had some struggles like we had a web designer who was doing really great work. But they had some personal issues, so we couldn’t get a lot of the website stuff done. Now he’s back.
54 00:08:51.530 ⇒ 00:09:05.160 Uttam Kumaran: But then I like, I decided to double up there in case like we, we needed help. So and then we had like, I’m not really great at hiring data analysts, just because I don’t think there’s a ton of extremely talented data analysts. I think a lot of
55 00:09:05.270 ⇒ 00:09:06.700 Uttam Kumaran: people. They
56 00:09:06.710 ⇒ 00:09:10.499 Uttam Kumaran: they’re like, Oh, I’m good at Excel. But I’m actually looking for people who
57 00:09:10.540 ⇒ 00:09:25.340 Uttam Kumaran: become like extensions of the business, basically, and are like, maybe even like banking backgrounds in terms of like the pace that they’re used to working at. So still struggling there a little bit to find, like, great analyst talent, pretty good talent. Otherwise.
58 00:09:25.696 ⇒ 00:09:32.279 Uttam Kumaran: And then operations wise. Yeah, things are getting really interesting. So we started using AI really heavily in the last, like 2 months.
59 00:09:32.801 ⇒ 00:09:42.239 Uttam Kumaran: Everything from, for example, when this meeting, once it finishes, gets transcribed, gets summarized automatically. And then I get slacked
60 00:09:42.500 ⇒ 00:09:44.889 Uttam Kumaran: if I have anything to do, basically
61 00:09:46.606 ⇒ 00:09:54.920 Uttam Kumaran: and then on top of that, we’re starting to evolve AI on like the outbound side. So things get people get personalized messages.
62 00:09:54.960 ⇒ 00:09:58.920 Uttam Kumaran: Another thing we’re doing now is anytime we get a lead.
63 00:09:58.960 ⇒ 00:10:05.780 Uttam Kumaran: I want basically like an a sales hub for that lead, which is like any documents, communications research.
64 00:10:05.800 ⇒ 00:10:13.450 Uttam Kumaran: So we could put so then that way, instead of like using Chat Gbt and having to input that every time you basically can chat
65 00:10:13.530 ⇒ 00:10:18.606 Uttam Kumaran: to like a set of documents, about like a potential client.
66 00:10:19.830 ⇒ 00:10:21.500 Chang Ho Yoon: Yeah, that’s interesting. Suzanne.
67 00:10:21.500 ⇒ 00:10:22.440 Uttam Kumaran: Yeah.
68 00:10:22.440 ⇒ 00:10:25.450 Chang Ho Yoon: Doesn’t that you vectorize the dock? And then.
69 00:10:25.450 ⇒ 00:10:30.809 Uttam Kumaran: Exactly so. Everything from emails Pdfs images.
70 00:10:31.244 ⇒ 00:10:41.929 Uttam Kumaran: so these can be potential contracts. These can be communication. These can be like if I’m texting with like someone I know at the client just like throw all of that in somewhere.
71 00:10:41.930 ⇒ 00:10:42.260 Chang Ho Yoon: Yeah.
72 00:10:42.260 ⇒ 00:10:44.269 Uttam Kumaran: And then you can chat over it.
73 00:10:44.300 ⇒ 00:10:55.345 Uttam Kumaran: And then, additionally, what we’re gonna start to do is pull in research from like the Internet. So you can put in like the client’s website, and that’ll get like, basically scraped and inputted
74 00:10:56.303 ⇒ 00:11:00.959 Uttam Kumaran: and so just basically like I created a list of like
75 00:11:01.060 ⇒ 00:11:05.388 Uttam Kumaran: automations. And I brought on this kid.
76 00:11:05.990 ⇒ 00:11:17.120 Uttam Kumaran: this this kid who just like had used basically, I was looking for someone with who had a particular tool, which is a kind of a tool that came out in the last like 6 months, of which, if you were using it, you’re probably someone who’s like, kind of
77 00:11:17.250 ⇒ 00:11:24.989 Uttam Kumaran: packing around with like these like automation, like web tools. And I found a kid who’s really really good. And then I just basically
78 00:11:25.190 ⇒ 00:11:34.467 Uttam Kumaran: been working with him like, let’s automate like this business as much as possible. And I’ll leave. I can even I can even share with you.
79 00:11:36.230 ⇒ 00:11:39.380 Uttam Kumaran: let me just show you kind of like what this looks like. So
80 00:11:39.630 ⇒ 00:11:41.850 Uttam Kumaran: we have like a
81 00:11:42.900 ⇒ 00:11:47.030 Uttam Kumaran: like an basically like an automations page
82 00:11:47.080 ⇒ 00:11:48.040 Uttam Kumaran: where,
83 00:11:51.185 ⇒ 00:11:57.030 Uttam Kumaran: you can look at like you can look at like all the automations that we’re working on. And so things like
84 00:11:57.150 ⇒ 00:12:17.419 Uttam Kumaran: Zoom Meeting summaries to like send to slack. Additionally, we have all these meetings automatically get put into notion. So we have like notion. And then if you open this, you’ll see all the meetings so like. There’s just things that I cannot scale as like a 1 person leader in this business.
85 00:12:17.692 ⇒ 00:12:31.059 Uttam Kumaran: And I’m not. I’m not really in the mood or like the financial mood, I would say also to hire like kind of like more middle management. But if with a little bit of work we can get some of these tools to go. The distance which is like.
86 00:12:31.060 ⇒ 00:12:47.830 Uttam Kumaran: not only send me the transcription, but actually add it to notion, but then actually email it to the individuals like to do things like that. We have. We have things where, like, for example, if I search for like a data engineer position, we have a slack channel where, if you put that, if you put that job
87 00:12:47.920 ⇒ 00:12:59.939 Uttam Kumaran: a posting in the AI will actually take that, go scrape the listing, find the people at the company to email and then draft like a potential email to them.
88 00:13:00.290 ⇒ 00:13:00.640 Chang Ho Yoon: Whoa!
89 00:13:01.630 ⇒ 00:13:07.129 Uttam Kumaran: so like taking care of some of these things, just like via slack and notion. And like some of these.
90 00:13:07.460 ⇒ 00:13:07.790 Chang Ho Yoon: Yeah.
91 00:13:07.790 ⇒ 00:13:08.940 Uttam Kumaran: Llms
92 00:13:09.416 ⇒ 00:13:11.980 Uttam Kumaran: and then what we’re working on like
93 00:13:12.240 ⇒ 00:13:34.699 Uttam Kumaran: things on like creating opportunity hubs for every sales, opportunity, and like automating content and doing a whole host of things there. So yeah, it’s this kid, Miguel. Who’s working. And so the 1st week I we kind of started doing that. And then I’m basically my hope is that we can start to package some of those and turn those into case studies and then
94 00:13:34.700 ⇒ 00:13:45.937 Uttam Kumaran: again start like the other side of this business, which is like kind of trying to work on more of this AI like Llm side. For all these, we’re using Llms in one way or another.
95 00:13:46.480 ⇒ 00:13:46.960 Chang Ho Yoon: 2 months.
96 00:13:46.960 ⇒ 00:13:49.475 Uttam Kumaran: So yeah, that that I’m really happy started
97 00:13:50.120 ⇒ 00:14:02.149 Uttam Kumaran: we got we’ve made some good movement on the outbound sales side, like we we have like Apollo, which is used for prospecting. Basically, it’s a database of like every professional
98 00:14:02.510 ⇒ 00:14:20.520 Uttam Kumaran: kind of like in the Us and beyond. And you kind of filter to specific leads. And then you can kind of get their email addresses and things like that. So we worked on some processes of actually doing that. Then adding them to Hubspot, doing some sort of lead scoring, and then, of course, like emailing them.
99 00:14:20.700 ⇒ 00:14:25.869 Uttam Kumaran: So the sales side is so slowly coming along, probably the worst out of everything.
100 00:14:26.378 ⇒ 00:14:32.061 Uttam Kumaran: However, we’re still getting business in. That’s the problem with this, though, is that like
101 00:14:32.620 ⇒ 00:14:41.259 Uttam Kumaran: I, it’s just. It’s just hard. It’s just doing everything all at once, and if the website isn’t ready, then I don’t feel comfortable sending people there. So there’s just these little.
102 00:14:41.260 ⇒ 00:14:42.800 Chang Ho Yoon: Yeah, he’s like.
103 00:14:42.800 ⇒ 00:14:55.408 Uttam Kumaran: Multi-step problems, and if I was more Gung ho! I think I would do one thing over another. But I am an engineer, and so I do have some reservations. Also, it’s like some sort of self battle.
104 00:14:56.190 ⇒ 00:15:04.979 Uttam Kumaran: But yeah, I would say, it’s it’s been going. It’s been going better recently where we’re we’re getting like a lot more organized notion. And
105 00:15:05.374 ⇒ 00:15:19.589 Uttam Kumaran: AI are kind of like working hand in hand because we’ve written down so much about the business. That. And the point was hopefully that we can leverage AI to kind of consume a lot of that. And it seems to be like working. You know that way? Yeah.
106 00:15:19.920 ⇒ 00:15:23.079 Uttam Kumaran: So yeah, that’s that’s the most of it. Yeah.
107 00:15:23.080 ⇒ 00:15:25.230 Chang Ho Yoon: So cool. That’s kind of cool that you basically.
108 00:15:25.620 ⇒ 00:15:33.100 Chang Ho Yoon: you’re harnessing the best of what AI has to offer at the moment to essentially scale up faster than you can recruit.
109 00:15:34.260 ⇒ 00:15:42.100 Uttam Kumaran: Yeah, I mean, I think people are going to be the hardest part of this business, not only its largest expense, but
110 00:15:42.170 ⇒ 00:15:44.629 Uttam Kumaran: it’s incredibly hard to get
111 00:15:44.880 ⇒ 00:15:47.224 Uttam Kumaran: really great people. Yeah,
112 00:15:47.890 ⇒ 00:15:55.062 Uttam Kumaran: and I think I’m good at cause I’m look, we are engineering companies, most engineers, that’s my network. But
113 00:15:55.900 ⇒ 00:16:00.499 Uttam Kumaran: I can’t afford to get operations people to do some of these things.
114 00:16:00.520 ⇒ 00:16:05.530 Uttam Kumaran: And frankly, I don’t want to do. I want it to be mostly engineers.
115 00:16:05.823 ⇒ 00:16:27.379 Uttam Kumaran: And actually, I want the engineers like, for example, Miguel, who’s working with us? I base them like, will you? We just call you an AI engineer or automation engineer, and then you work on these things internally, and then as soon we’ll start to get clients for this stuff, and then you can just become client facing right? So there is a revenue component. And then it’s again as we have great people.
116 00:16:27.430 ⇒ 00:16:30.029 Uttam Kumaran: I just want to keep the bar very, very high.
117 00:16:30.290 ⇒ 00:16:30.920 Chang Ho Yoon: Yeah.
118 00:16:31.370 ⇒ 00:16:47.450 Uttam Kumaran: And I made that, and that’s the mistake I’ve made here and there, which is like lowering the bar. And then you kind of you kind of find out like where things break. But at some at some moments I couldn’t do anything but just hire the next person but trying to be a little bit more conscious of like.
119 00:16:47.910 ⇒ 00:16:55.330 Uttam Kumaran: okay, we want to hire people. Everybody needs to have a technical background. Some understanding of like get like having some minimum.
120 00:16:55.330 ⇒ 00:16:55.830 Chang Ho Yoon: Chris.
121 00:16:55.830 ⇒ 00:16:57.083 Uttam Kumaran: Minimum qualities.
122 00:16:57.710 ⇒ 00:17:00.209 Chang Ho Yoon: You’d think they they’d have that
123 00:17:00.510 ⇒ 00:17:01.260 Chang Ho Yoon: as well.
124 00:17:01.260 ⇒ 00:17:10.359 Uttam Kumaran: Analyst side, like, I’m having a real challenging time. I mean, I haven’t done any like hardcore recruiting. It’s been mainly talking to people I know, and being like, send me your
125 00:17:10.490 ⇒ 00:17:12.639 Uttam Kumaran: send me an analyst that you know, but
126 00:17:12.829 ⇒ 00:17:18.179 Uttam Kumaran: I now have really strict requirements on the analyst side of like who to hire just cause. Yeah.
127 00:17:18.180 ⇒ 00:17:18.760 Chang Ho Yoon: Idea of a.
128 00:17:18.760 ⇒ 00:17:19.420 Uttam Kumaran: With them.
129 00:17:19.560 ⇒ 00:17:24.800 Chang Ho Yoon: You’d be amazed. How many people in quant in the finance sector just use excel.
130 00:17:26.010 ⇒ 00:17:27.089 Uttam Kumaran: Really.
131 00:17:27.099 ⇒ 00:17:28.779 Chang Ho Yoon: Yeah, you’d be amazed.
132 00:17:29.350 ⇒ 00:17:31.810 Uttam Kumaran: Wow, yeah, that’s crazy.
133 00:17:31.810 ⇒ 00:17:33.679 Chang Ho Yoon: There are obviously firms.
134 00:17:33.840 ⇒ 00:17:37.650 Chang Ho Yoon: and there are plenty of them. They’re hiring increasingly more and more engineers.
135 00:17:37.800 ⇒ 00:17:46.699 Chang Ho Yoon: They have that size, but not necessarily in the actual investment or the analyst sector. They’ll get some of the they’ll get, you know, a lot of that done by.
136 00:17:47.010 ⇒ 00:17:50.670 Chang Ho Yoon: He will be explicitly hired to do the data engineering for them.
137 00:17:51.350 ⇒ 00:17:58.349 Uttam Kumaran: They don’t mind if they’re like, if they don’t know it, but then they need to have such a compensation in terms of like their agency
138 00:17:58.370 ⇒ 00:18:00.509 Uttam Kumaran: to be like they could figure it out
139 00:18:00.540 ⇒ 00:18:04.160 Uttam Kumaran: right. I mean, I got some people that were both not like
140 00:18:04.330 ⇒ 00:18:06.309 Uttam Kumaran: high agency people meaning like
141 00:18:06.750 ⇒ 00:18:13.254 Uttam Kumaran: it gets not that hard like if you use like, if you use like salesforce Github, it’s like pretty basic.
142 00:18:13.650 ⇒ 00:18:20.660 Uttam Kumaran: So I need someone who’s like that. And right, if you don’t know it, I’m happy. But then I need to see like you’re someone who just figures things out.
143 00:18:20.660 ⇒ 00:18:21.680 Chang Ho Yoon: Yeah, exactly.
144 00:18:21.680 ⇒ 00:18:22.030 Uttam Kumaran: Bought it.
145 00:18:22.030 ⇒ 00:18:22.430 Chang Ho Yoon: Yeah.
146 00:18:22.430 ⇒ 00:18:25.130 Uttam Kumaran: And you don’t know this. Then I’m like.
147 00:18:25.616 ⇒ 00:18:29.989 Chang Ho Yoon: You don’t even need to. Bloody know any coding.
148 00:18:29.990 ⇒ 00:18:37.469 Uttam Kumaran: No, I. And also I’m like, you’re not even doing much development. I just need you to be able to like push like a couple of lines of code.
149 00:18:37.680 ⇒ 00:18:39.399 Uttam Kumaran: I don’t know again, like it’s.
150 00:18:39.700 ⇒ 00:18:55.960 Uttam Kumaran: it’s just so in what I realized is in data engineering. You have a lot of people that come from a software background data modeling. You have people that either were analysts that got more technical or you have data engineers that like were like, oh, I want to work more business on the data analyst side. You have.
151 00:18:56.080 ⇒ 00:19:00.732 Uttam Kumaran: like a whole range of people. Yeah, like, I just think that.
152 00:19:01.500 ⇒ 00:19:04.240 Uttam Kumaran: I think that the worst data engineer.
153 00:19:04.480 ⇒ 00:19:22.150 Uttam Kumaran: right? There’s at least like a floor that’s pretty high, I think, on data analysts, the floor can be very low, right? But the problem is of like it’s, it’s, I would say, there’s really, it’s incredibly hard to get the best data analysts. I would say it’s probably not that hard to get like, really, really good data engineers. Because
154 00:19:22.250 ⇒ 00:19:34.102 Uttam Kumaran: after you don’t really a lot of the really complicated software, you don’t, we’re not really dealing with that, there’s a there’s a pretty low ceiling on the data analyst side that there’s so many people that get that claim to be able to do it.
155 00:19:34.460 ⇒ 00:19:50.397 Uttam Kumaran: but the nice thing is the rate structure is flipped. Data engineers are way. More expensive data analysts can be way cheaper. But I think you have to find the really really great people on the data analyst side. And that’s just like, not my network
156 00:19:51.190 ⇒ 00:19:57.969 Uttam Kumaran: but like, that’s the struggle we’ve had. And the the last problem is that’s the actual thing that gets in in front of the client.
157 00:19:58.060 ⇒ 00:20:01.819 Uttam Kumaran: right? And that’s like the last mile problem for
158 00:20:01.980 ⇒ 00:20:09.909 Uttam Kumaran: this sort of like full stack data is like the data analyst piece is what the client actually like consumes is the dashboards or the
159 00:20:09.980 ⇒ 00:20:12.020 Uttam Kumaran: big point analysis.
160 00:20:12.310 ⇒ 00:20:17.600 Uttam Kumaran: And so that’s something we’re gonna have to figure out like, kind of our data analysts practice a little bit longer term. But
161 00:20:17.700 ⇒ 00:20:20.679 Uttam Kumaran: that’s been challenging like I’ve went through like probably 4 people.
162 00:20:21.460 ⇒ 00:20:22.220 Chang Ho Yoon: Thank you.
163 00:20:22.220 ⇒ 00:20:26.700 Uttam Kumaran: Trying to figure it out, and I have a high I have, I mean, I have a high bar, but also
164 00:20:26.790 ⇒ 00:20:32.480 Uttam Kumaran: I could do the work myself. So if if folks aren’t as good as I am, then I’m going to be like.
165 00:20:33.420 ⇒ 00:20:34.770 Chang Ho Yoon: Or by newly added.
166 00:20:34.770 ⇒ 00:20:37.910 Uttam Kumaran: I’m not even a data analyst like. And I can
167 00:20:38.270 ⇒ 00:20:49.710 Uttam Kumaran: understand, like what this business is about, right? So there’s some. So I don’t know. Maybe it’s I. Unfortunately, like, maybe it’s just hiring ex bankers looking for that. Or like, I need to find some filter. So yeah.
168 00:20:50.470 ⇒ 00:20:55.440 Chang Ho Yoon: Interesting interesting. Hey? Tell me more about this Baylor and health.
169 00:20:55.540 ⇒ 00:20:56.740 Chang Ho Yoon: What was the other one.
170 00:20:57.637 ⇒ 00:21:01.630 Uttam Kumaran: A Kate, a Kate, or wait. It’s a i think it’s Acadia house.
171 00:21:02.000 ⇒ 00:21:04.449 Chang Ho Yoon: Arcadia. I don’t think I’ve ever heard of them.
172 00:21:05.080 ⇒ 00:21:06.580 Uttam Kumaran: A, CADI, a.
173 00:21:06.580 ⇒ 00:21:15.049 Chang Ho Yoon: So so how far? And are you in in discussing? Or is there any role? Is there any sort of yeah, any role for me to help you, man, on, on.
174 00:21:15.050 ⇒ 00:21:20.059 Uttam Kumaran: Yeah. So I think, for I think for these, it looks like it’s just
175 00:21:20.270 ⇒ 00:21:29.679 Uttam Kumaran: I mean, I don’t own the entire contract like I’m coming in kind of as like, just like a mercenary hire, basically where they just needed someone additionally on their team. And I was like.
176 00:21:29.900 ⇒ 00:21:37.410 Uttam Kumaran: Okay, I’ll I’ll I’m happy to do that. But I do think that having this experience gives
177 00:21:37.710 ⇒ 00:21:39.769 Uttam Kumaran: us a lot more credibility than
178 00:21:39.820 ⇒ 00:21:41.619 Uttam Kumaran: you know we had before.
179 00:21:42.132 ⇒ 00:21:44.060 Uttam Kumaran: I will say, though.
180 00:21:44.260 ⇒ 00:21:48.040 Uttam Kumaran: these guys have pretty strict data security requirements.
181 00:21:49.740 ⇒ 00:21:54.359 Uttam Kumaran: like, I’m logging in through like a Vm and things like that. However.
182 00:21:54.520 ⇒ 00:21:58.309 Uttam Kumaran: yeah, workwise, it’s it’s like, for the most part.
183 00:21:58.740 ⇒ 00:22:01.340 Uttam Kumaran: the same. Dbt, like data set up.
184 00:22:01.420 ⇒ 00:22:02.045 Uttam Kumaran: Yeah,
185 00:22:02.840 ⇒ 00:22:04.260 Uttam Kumaran: So I do think that
186 00:22:04.570 ⇒ 00:22:12.820 Uttam Kumaran: there is like, now, I think we have a little bit more like of an angle of trying to like. Say, now that we have some experience with that.
187 00:22:12.840 ⇒ 00:22:18.007 Uttam Kumaran: I still not sure, like what the whole, what the best angle is like, I think.
188 00:22:19.020 ⇒ 00:22:46.249 Uttam Kumaran: I think, coming in. And I think now that we have a good sense of like kind of what these guys are doing. I think there definitely is an angle for us to say, like, Hey, we have people that have done data modeling and healthcare. And we kind of come in in that fashion. I don’t know the AI stuff even them internally like they don’t have any use of any AI tools at all for any of their employees they have like an AI committee that’s like deciding.
189 00:22:46.490 ⇒ 00:22:47.145 Uttam Kumaran: But
190 00:22:47.860 ⇒ 00:22:50.164 Uttam Kumaran: nothing so far. Yeah.
191 00:22:51.260 ⇒ 00:22:54.220 Uttam Kumaran: so I don’t know. Maybe the adoption is slow.
192 00:22:54.300 ⇒ 00:22:56.329 Uttam Kumaran: I do know that they’re
193 00:22:56.590 ⇒ 00:22:59.799 Uttam Kumaran: like I talked to one of the guys at Snowflake who, like
194 00:23:00.133 ⇒ 00:23:06.199 Uttam Kumaran: represents like a lot of the medical community and in terms of Snowflake. And he said that there is
195 00:23:06.650 ⇒ 00:23:15.040 Uttam Kumaran: they’re they’re getting a lot more clients. So I think a lot of them are adopting. They’re moving from on prem to cloud, like via snowflake data.
196 00:23:15.060 ⇒ 00:23:19.249 Uttam Kumaran: But I don’t know any. I’m not sure about the healthcare side of things for AI
197 00:23:19.360 ⇒ 00:23:22.049 Uttam Kumaran: in terms of these big hospital groups.
198 00:23:22.420 ⇒ 00:23:23.100 Chang Ho Yoon: Yeah.
199 00:23:23.990 ⇒ 00:23:29.660 Chang Ho Yoon: yeah. I mean, you’ve seen quite a lot of quite, quite mixed. Take up if you will, and
200 00:23:30.070 ⇒ 00:23:36.369 Chang Ho Yoon: they’re usually very specific areas. I think the the lowest hanging fruit that’s been taken so far is
201 00:23:36.520 ⇒ 00:23:39.460 Chang Ho Yoon: an example out of Pittsburgh called
202 00:23:40.400 ⇒ 00:23:41.460 Chang Ho Yoon: a bridge.
203 00:23:41.710 ⇒ 00:23:42.580 Chang Ho Yoon: which is
204 00:23:42.760 ⇒ 00:23:44.020 Chang Ho Yoon: all about
205 00:23:44.250 ⇒ 00:23:45.580 Chang Ho Yoon: dictation
206 00:23:45.770 ⇒ 00:23:48.720 Chang Ho Yoon: and saving doctors time through dictation.
207 00:23:49.300 ⇒ 00:23:55.430 Chang Ho Yoon: And what this, what, what their gpt essentially does is
208 00:23:56.250 ⇒ 00:24:02.050 Chang Ho Yoon: weed out English and Spanish, and a couple of other languages. And it can. It can basically help navigate
209 00:24:02.520 ⇒ 00:24:20.739 Chang Ho Yoon: conversations that are happening even in pitch and Spanish half the time, whatever. And obviously there’s high variability in its success rate on on accuracy, but a bit like what was happening now with our zoom conversation. It’ll write a transcript for you to the best of its ability, and then.
210 00:24:20.820 ⇒ 00:24:25.820 Chang Ho Yoon: based on that transcript, it will generate jobs just like it does for slack
211 00:24:25.870 ⇒ 00:24:29.070 Chang Ho Yoon: with with, you know the way you’ve automated it.
212 00:24:29.504 ⇒ 00:24:45.719 Chang Ho Yoon: Such that, based on the conversation that you and I might be having. Let’s say I don’t know you, you say. Oh, Doc, you know I’ve got. I’ve had this knee problem after playing football last week. What do you think we should do? It’s swollen, it’s red, it’s sore.
213 00:24:46.010 ⇒ 00:24:57.480 Chang Ho Yoon: Then let’s say, for the sake of argument, I respond. Well, you know I’m sorry to hear that I think what we can do next is give you some pain relief. I’ll order an ultrasound scan of your knees. We can assess.
214 00:24:57.520 ⇒ 00:25:06.800 Chang Ho Yoon: you know, if there’s any meniscal problems or ligamental damage. And what have you with an X-ray of your knee? And then we’ll take it from there.
215 00:25:07.580 ⇒ 00:25:19.509 Chang Ho Yoon: and based on that conversation just now, like that transcript, it will produce a letter with a plan, and then well, they haven’t done yet which I think is probably the next obvious step is
216 00:25:19.730 ⇒ 00:25:21.629 Chang Ho Yoon: start it, it basically
217 00:25:21.860 ⇒ 00:25:25.449 Chang Ho Yoon: interfacing with whatever electronic health record they have in the system.
218 00:25:25.660 ⇒ 00:25:29.470 Chang Ho Yoon: such that in the plans. It can auto populate
219 00:25:30.060 ⇒ 00:25:34.230 Chang Ho Yoon: jobs and the forms required for said jobs.
220 00:25:34.230 ⇒ 00:25:38.700 Uttam Kumaran: Yeah. So it’s the integrations that are the the really, the difficult part here.
221 00:25:38.880 ⇒ 00:25:39.850 Uttam Kumaran: Like.
222 00:25:40.050 ⇒ 00:25:49.459 Uttam Kumaran: right for me, it was actually, I’ve been getting zoom recordings transcribed for the last year. The challenge was like, I have to then go to the transcription.
223 00:25:49.610 ⇒ 00:25:54.959 Uttam Kumaran: then put that in the Chat Gbt. For the tell me the tasks, and then I go create the tasks.
224 00:25:55.090 ⇒ 00:25:58.943 Uttam Kumaran: I’m like that should happen for every single thing.
225 00:26:00.250 ⇒ 00:26:17.589 Uttam Kumaran: you know, because it needs to. It needs to it. You can’t just do 1 5th of an employee. It needs to be like a 3, 4 fifths of an employee, right like, in terms of to go the distance of like, yeah, it would slack me this, then enter it here right and interact with the other products that we use to run the business.
226 00:26:17.590 ⇒ 00:26:18.080 Chang Ho Yoon: Yeah.
227 00:26:18.080 ⇒ 00:26:21.966 Uttam Kumaran: So that’s where I think that there’s like a lot of
228 00:26:23.010 ⇒ 00:26:29.937 Uttam Kumaran: I think that leveraging these like integrations is where there’s like a lot of use case.
229 00:26:30.390 ⇒ 00:26:33.140 Uttam Kumaran: right? And I’m looking at a bridge. It looks like they have something.
230 00:26:34.930 ⇒ 00:26:38.239 Uttam Kumaran: they have like the ability to link with epic. Somehow.
231 00:26:38.240 ⇒ 00:26:40.550 Chang Ho Yoon: They do. Yeah, they’ve linked with epic. Now, yeah.
232 00:26:40.550 ⇒ 00:26:42.430 Uttam Kumaran: Yeah. So those are the things that make
233 00:26:42.460 ⇒ 00:26:52.310 Uttam Kumaran: the pitch like a lot better, I mean, and I don’t know. I I think, like for me. What I’m thinking about is on the data side. I think it’s clear what I can pitch, which is basically
234 00:26:52.410 ⇒ 00:26:55.680 Uttam Kumaran: they are using stuff like in these things like, I think on the
235 00:26:55.700 ⇒ 00:26:57.750 Uttam Kumaran: on the AI side, I think
236 00:26:57.910 ⇒ 00:27:00.760 Uttam Kumaran: what we’ll need to do is basically
237 00:27:02.520 ⇒ 00:27:10.470 Uttam Kumaran: what we’ll need to do is basically it’s kind of like what we what we talked about before and think of like a tool stack where it’s like, Hey, we can come implement
238 00:27:10.480 ⇒ 00:27:14.920 Uttam Kumaran: Xyz tools and take care of like Xyz problems?
239 00:27:16.510 ⇒ 00:27:22.260 Uttam Kumaran: and then just try to find someone who’s willing to like, say, Okay, like, why don’t you? Why don’t we throw you guys at a couple of problems and.
240 00:27:22.260 ⇒ 00:27:22.710 Chang Ho Yoon: Yeah.
241 00:27:23.007 ⇒ 00:27:37.600 Uttam Kumaran: We kind of work towards that. I mean again, I think there’s gonna be tools like a bridge and stuff like that. But some people may want to own that internally, some people may want just a piece of that again. This is mainly transcription. Maybe there’s something that
242 00:27:38.304 ⇒ 00:27:40.560 Uttam Kumaran: that doesn’t touch the
243 00:27:40.670 ⇒ 00:27:41.670 Uttam Kumaran: the
244 00:27:41.790 ⇒ 00:27:44.910 Uttam Kumaran: this at all. And is it in some other part of the business?
245 00:27:46.940 ⇒ 00:27:54.258 Uttam Kumaran: so yeah, I think it’s again. I I the thing I like about using doing the data stuff in this is that they’re kind of hand in hand.
246 00:27:54.976 ⇒ 00:27:58.183 Uttam Kumaran: And so the experience on the it’s not like,
247 00:27:58.720 ⇒ 00:28:12.840 Uttam Kumaran: we do like full. We do like something. So off. And we do like AI stuff. I think they’re really close to each other. So the the data thing might be the way in and then there’s like some layering of more services on top or the other way around. So
248 00:28:12.920 ⇒ 00:28:17.080 Uttam Kumaran: that’s what I’m kind of thinking again. I, as you can tell, I still don’t have like
249 00:28:17.430 ⇒ 00:28:19.010 Uttam Kumaran: what the greatest
250 00:28:19.490 ⇒ 00:28:20.110 Uttam Kumaran: like hot.
251 00:28:20.110 ⇒ 00:28:20.810 Chang Ho Yoon: No, I think.
252 00:28:20.810 ⇒ 00:28:21.440 Uttam Kumaran: You know? Yeah.
253 00:28:21.440 ⇒ 00:28:26.009 Chang Ho Yoon: Yeah, I mean, it’s tricky, because what we don’t have, I guess, is that kind of
254 00:28:26.020 ⇒ 00:28:28.729 Chang Ho Yoon: the the bermute. It’s like the
255 00:28:29.320 ⇒ 00:28:32.719 Chang Ho Yoon: so the black hole of knowledge for us is knowing
256 00:28:32.830 ⇒ 00:28:34.890 Chang Ho Yoon: what many of these different
257 00:28:35.882 ⇒ 00:28:37.809 Chang Ho Yoon: independent medical firms
258 00:28:37.960 ⇒ 00:28:39.110 Chang Ho Yoon: require.
259 00:28:39.120 ⇒ 00:28:52.860 Chang Ho Yoon: whether we’re talking, because obviously, it depends on what we’re talking about. And obviously, Baylor, health care is a different entity from I don’t know. Partners, healthcare Trust or Kaiser, or you know, guys.
260 00:28:52.860 ⇒ 00:28:53.190 Uttam Kumaran: Yeah.
261 00:28:53.190 ⇒ 00:29:12.939 Chang Ho Yoon: Yeah, these these all have different requirements. They have different infrastructure. They have different like legacy software. They have different requirements in terms of what the clinicians want, or what the patients need. And and then, obviously, and they might already have some legacy software from some other vendors, you know who are dealing with certain
262 00:29:13.380 ⇒ 00:29:15.100 Chang Ho Yoon: certain problems.
263 00:29:15.130 ⇒ 00:29:21.119 Chang Ho Yoon: And so the reason why this all sounds so vague is I’m what I’m trying to say, I guess, is that it’s just extremely heterogeneous.
264 00:29:22.270 ⇒ 00:29:23.250 Chang Ho Yoon: And
265 00:29:23.650 ⇒ 00:29:24.650 Chang Ho Yoon: I guess
266 00:29:25.640 ⇒ 00:29:30.359 Chang Ho Yoon: I mean we could just I think the easiest thing might be almost to sort of think of a few
267 00:29:31.480 ⇒ 00:29:36.240 Chang Ho Yoon: almost like hypothetical use cases or hypothetical
268 00:29:37.840 ⇒ 00:29:44.849 Chang Ho Yoon: problems that I I either am aware of, or I can imagine some of these businesses, some of these Med tech companies have.
269 00:29:44.850 ⇒ 00:29:45.600 Uttam Kumaran: Yeah.
270 00:29:45.964 ⇒ 00:29:51.795 Chang Ho Yoon: And then just set them set them out as things that we could help them with.
271 00:29:52.790 ⇒ 00:29:59.830 Chang Ho Yoon: that obviously, I’ll I’ll place them within the boundaries of what I own what I know personally, but also, I guess the potential to hire.
272 00:30:00.317 ⇒ 00:30:05.610 Chang Ho Yoon: Yeah, more to make knowledge as needed on the fly and then
273 00:30:05.750 ⇒ 00:30:08.969 Chang Ho Yoon: we can see if anything sticks.
274 00:30:09.110 ⇒ 00:30:11.209 Chang Ho Yoon: I think that’s where you yeah.
275 00:30:11.210 ⇒ 00:30:13.829 Uttam Kumaran: Yeah, exactly like I think we have, like.
276 00:30:13.930 ⇒ 00:30:17.150 Uttam Kumaran: you know, an industry like personalized.
277 00:30:17.630 ⇒ 00:30:29.639 Uttam Kumaran: you know, offer. Or even if you want to take like, you can take an example of a hospital group or a clinic and say, these guys, probably Xyz problem. And what we’ll do is like we’ll
278 00:30:29.670 ⇒ 00:30:36.519 Uttam Kumaran: W. The way we’ll do it is basically have a prospect list of these sorts of companies will.
279 00:30:36.560 ⇒ 00:30:54.230 Uttam Kumaran: I think, the biggest things to think about, and I can send you kind of like how to think about the filters for this, which is like who the people are like, who’s the persona we’re going after, whether titles, what are come, their responsibilities like? What are keywords and their titles to think about properties of the companies itself so like?
280 00:30:54.310 ⇒ 00:31:09.499 Uttam Kumaran: What if you could put a number on like the revenue size or on the employee size or on the location, or maybe a specific industry inside the hospitals like that’s really helpful. And then, basically, what we’ll do is we’ll we could have, like a 3 email sort of like
281 00:31:09.560 ⇒ 00:31:10.969 Uttam Kumaran: ABC test.
282 00:31:10.980 ⇒ 00:31:18.190 Uttam Kumaran: where we have like 3 different offers right like, do you have? And again, it’s it’s as basic as like, Hi, Cheryl.
283 00:31:18.400 ⇒ 00:31:27.640 Uttam Kumaran: do you? Do you commonly like have problems like with this this or like, is this, yeah, we’ve been noticing that other places have had problems with this. Is this something that you could use help with?
284 00:31:27.700 ⇒ 00:31:34.199 Uttam Kumaran: Let me know. And then that’s it. And then we’ll AV test a couple of those and then just start firing off emails. Basically,
285 00:31:34.470 ⇒ 00:31:34.800 Chang Ho Yoon: Tail.
286 00:31:34.800 ⇒ 00:31:35.250 Uttam Kumaran: With bay.
287 00:31:35.250 ⇒ 00:31:41.399 Chang Ho Yoon: And Arcadia. I mean, you’ve been brought in almost as a mercenary hire you were saying earlier on. But in terms of data access
288 00:31:41.610 ⇒ 00:31:46.070 Chang Ho Yoon: is that something that you’ve had to jump through a lot of different
289 00:31:46.540 ⇒ 00:31:48.050 Chang Ho Yoon: regulatory hoops
290 00:31:48.330 ⇒ 00:31:50.459 Chang Ho Yoon: to be able to access.
291 00:31:51.120 ⇒ 00:31:54.969 Uttam Kumaran: Yeah. I think at a company that size.
292 00:31:55.620 ⇒ 00:32:01.329 Uttam Kumaran: It just seems like it’s I mean, I would say it’s what I expected from like an enterprise.
293 00:32:01.340 ⇒ 00:32:04.949 Uttam Kumaran: It shop? But yeah, I don’t.
294 00:32:05.440 ⇒ 00:32:08.390 Uttam Kumaran: I think their requirements for a vendor
295 00:32:08.550 ⇒ 00:32:09.790 Uttam Kumaran: would be high
296 00:32:09.860 ⇒ 00:32:17.737 Uttam Kumaran: like the vendor I’m sub contract. I’m contracting through basically is like a very large recruiting firm with like a ton of stuff.
297 00:32:19.330 ⇒ 00:32:25.970 Uttam Kumaran: so i i i don’t. I don’t know. I think it would be tough to go after a company this size as like a small outfit.
298 00:32:28.780 ⇒ 00:32:29.815 Uttam Kumaran: yeah,
299 00:32:32.240 ⇒ 00:32:35.789 Uttam Kumaran: but I don’t know again. That’s where, like my lack of industry is like.
300 00:32:36.290 ⇒ 00:32:38.120 Uttam Kumaran: how small does this get
301 00:32:38.510 ⇒ 00:32:47.749 Uttam Kumaran: or like? Is there some small to middle ground where maybe they’re they’re open to like working with vendors like us, and they have budgets for that. Is it more of like.
302 00:32:47.830 ⇒ 00:33:16.230 Uttam Kumaran: yeah, there’s like Baylor and Acadia, and these are like the top enterprise. But then there’s a whole host of people below. So once help, what’s helpful is like taking a example, profile, and like running through the whole example. For example, like, if I was to take like local Hospital YI would go on Linkedin, find the it person, the Vp of operations. You know some of these roles that we’ve talked about and basically say, like, these are the types of people that we would want to hit with this type of messaging.
303 00:33:16.390 ⇒ 00:33:20.489 Uttam Kumaran: So it’s like this type of company, these people within it, this type of messaging
304 00:33:20.610 ⇒ 00:33:25.319 Uttam Kumaran: that’s enough for us to basically create a prospect list. And then of, like.
305 00:33:25.520 ⇒ 00:33:27.729 Uttam Kumaran: you know 200 or 500 people
306 00:33:28.310 ⇒ 00:33:53.340 Uttam Kumaran: share with you to be like? Are these the kind of like people that we want to send this to? And then we just fire off messaging. I think now, also, that I have some good folks on the design side. It’s easy for us to put together like a landing page stuff like that. But I don’t want that to get in the way like, I think our web the website now has. Like all the basics. I think the biggest thing is
307 00:33:53.570 ⇒ 00:33:54.950 Uttam Kumaran: says to try
308 00:33:55.840 ⇒ 00:33:56.800 Uttam Kumaran: right
309 00:33:57.313 ⇒ 00:33:59.779 Uttam Kumaran: just to try to get in front of folks.
310 00:33:59.790 ⇒ 00:34:02.787 Uttam Kumaran: and then we’ll just see if we can book some meetings,
311 00:34:04.870 ⇒ 00:34:05.460 Uttam Kumaran: and then.
312 00:34:05.460 ⇒ 00:34:11.879 Chang Ho Yoon: With what you’re working on for Baylor and Arcadia. Just question, mark that you’ve been. You’ve been brought in as extra. What engineering muscle.
313 00:34:12.770 ⇒ 00:34:16.859 Uttam Kumaran: Yeah, but it’s purely on the data side, and it’s purely on like the Dvt side.
314 00:34:16.900 ⇒ 00:34:18.089 Uttam Kumaran: like, I don’t.
315 00:34:18.100 ⇒ 00:34:22.680 Uttam Kumaran: I’m not. I mean, this is only so. This is so recent. It’s like 2 I’ve been working for like 2 weeks.
316 00:34:24.870 ⇒ 00:34:25.540 Uttam Kumaran: it’s
317 00:34:27.050 ⇒ 00:34:31.850 Uttam Kumaran: yeah. I’m not sure yet. The whole scope. I’m barely. I’m just now learning like the different
318 00:34:32.520 ⇒ 00:34:36.587 Uttam Kumaran: parts of the data model itself. Like, I’m so it’s still really new.
319 00:34:36.909 ⇒ 00:34:40.569 Chang Ho Yoon: And what are they? What are they hoping to achieve with this? What are they hoping to achieve with.
320 00:34:40.570 ⇒ 00:34:48.939 Uttam Kumaran: The biggest stuff they have is they have reporting on, like all sorts of things like patients, clinics, different programs, marketing
321 00:34:48.969 ⇒ 00:34:51.839 Uttam Kumaran: like they have a ton of data
322 00:34:52.280 ⇒ 00:35:21.059 Uttam Kumaran: on a lot of stuff. And basically they have like a, they have like 20 or 30 data people that just, you know, build data models. So like all this, raw data comes in from the application from the marketing side. And then we kind of model it. And they have a huge structure to do that which is like they have like pre dev environments, dev staging production environments. And all that goes to like power dashboards, and they work with a lot of teams to create new metrics and things like that. So there, I would say, pretty like
323 00:35:21.220 ⇒ 00:35:22.473 Uttam Kumaran: they’re pretty
324 00:35:23.710 ⇒ 00:35:30.269 Uttam Kumaran: sophisticated, I would say, compared to the companies that I worked at in terms of data stuff like, I think they’re doing a pretty good job.
325 00:35:30.950 ⇒ 00:35:33.880 Chang Ho Yoon: I mean is that not something that we can offer.
326 00:35:35.750 ⇒ 00:35:43.440 Uttam Kumaran: Yeah, I mean, that’s what that’s exactly what the data side would be. Which is like, hey, you guys don’t have this sort of reporting.
327 00:35:43.800 ⇒ 00:35:44.430 Chang Ho Yoon: Yup!
328 00:35:44.650 ⇒ 00:35:53.320 Uttam Kumaran: We can help basically develop this level of like executive reporting. I would say it’s either it’s either it’s either a pitch for that or a pitch, for
329 00:35:53.520 ⇒ 00:35:55.650 Uttam Kumaran: like operational efficiency.
330 00:35:55.990 ⇒ 00:35:57.710 Uttam Kumaran: like on the AI side.
331 00:35:58.060 ⇒ 00:36:05.070 Uttam Kumaran: So again, we can try that we could have one variant be like towards data, one variant be more on the operational side, or like a mix.
332 00:36:05.250 ⇒ 00:36:10.450 Uttam Kumaran: And then I think the biggest thing we should try to drive for is just test out a campaign.
333 00:36:12.290 ⇒ 00:36:18.700 Uttam Kumaran: What’s the messaging to like a few 100 people? And then we’ll be able to track like, who’s opening the email and things like that?
334 00:36:18.960 ⇒ 00:36:19.569 Uttam Kumaran: Yeah,
335 00:36:20.860 ⇒ 00:36:23.689 Uttam Kumaran: So that’s the biggest thing I think if you could help with like.
336 00:36:24.930 ⇒ 00:36:37.800 Uttam Kumaran: what could? What’s a couple of example companies? Because I also have, like we have an intern this this summer who is helping basically on like creating this prospect list. If you get her like, hey, it’s these types of companies, these types of profiles.
337 00:36:39.480 ⇒ 00:36:45.592 Uttam Kumaran: she’ll be able to go. She’ll put together like lists of hundreds and hundreds of those like look alike, sort of companies.
338 00:36:46.546 ⇒ 00:36:48.489 Uttam Kumaran: That we can review.
339 00:36:50.530 ⇒ 00:36:51.250 Chang Ho Yoon: Got it.
340 00:36:54.010 ⇒ 00:37:00.232 Chang Ho Yoon: it’s basically I was, I think, what could be useful for for you, for us is in fact,
341 00:37:02.150 ⇒ 00:37:27.649 Chang Ho Yoon: quite simply just sort of put it in the last. You said in the last sentence, really, which is that some should prospect companies also prospect companies, also job profiles, and what sorts of problems might they be wanting to address, using skill sets that obviously align with what we can, what we may be able to offer. I kind of feel like until we send this out. As you say, we won’t really get a sense, for where people are looking
342 00:37:27.994 ⇒ 00:37:34.890 Chang Ho Yoon: it’s really difficult to know, because, as you say, there is a certain reticence in the medical sector in general.
343 00:37:35.070 ⇒ 00:37:43.390 Chang Ho Yoon: there’s also particular reticence in the medical sector in general to let a 3rd party vendor in. Who they don’t know personally.
344 00:37:43.660 ⇒ 00:37:44.380 Chang Ho Yoon: Yeah.
345 00:37:44.380 ⇒ 00:37:49.529 Uttam Kumaran: That’s where I think we would like. I mean, we would leverage your your name, I would say.
346 00:37:49.530 ⇒ 00:37:52.640 Chang Ho Yoon: Yeah, I mean that might. That might help to an extent.
347 00:37:52.960 ⇒ 00:37:58.600 Uttam Kumaran: But that’s all we got right, like I do think that, for I do think that there are industries
348 00:37:58.640 ⇒ 00:38:03.609 Uttam Kumaran: where there is an aversion towards 3rd party vendors. I think
349 00:38:03.670 ⇒ 00:38:08.388 Uttam Kumaran: you know, software is very open to it. I think
350 00:38:08.900 ⇒ 00:38:10.630 Uttam Kumaran: manufacturing
351 00:38:10.760 ⇒ 00:38:18.330 Uttam Kumaran: and is really open to it. But there’s also there’s a there’s like a matrix of like open to vendors. But like also techno technologically advanced.
352 00:38:18.380 ⇒ 00:38:33.759 Uttam Kumaran: I think some places like manufacturing and logistics very techno blow advancement, but they’re open. They use a lot of contractors for a ton of stuff like it’s like a whole value chain. That’s basically like mix of contractors. Software and stuff is like high tech.
353 00:38:33.910 ⇒ 00:38:48.816 Uttam Kumaran: Yeah. And they still they. They use external contract. I think healthcare is healthcare, and is like somewhere on the lower side of both right, which is tough. But again, it’s like, how do these firms break in somehow? Right? So
354 00:38:49.900 ⇒ 00:39:00.059 Uttam Kumaran: if we get one personal line, and even if it’s a no for me, I’ll ask like, do you guys? I I asked this. Everybody, I said, do you guys work with external vendors? What’s your vendor procurement process.
355 00:39:00.100 ⇒ 00:39:02.790 Uttam Kumaran: Right? We’ll learn something in the process, I think.
356 00:39:02.930 ⇒ 00:39:03.524 Uttam Kumaran: Yeah.
357 00:39:06.470 ⇒ 00:39:08.586 Chang Ho Yoon: I think that’s cool. Okay,
358 00:39:10.600 ⇒ 00:39:18.499 Chang Ho Yoon: so I’ll I’ll do a bit of I’ll do a bit of plotting a bit of thinking I was. I’m also going to contact some people I can think of as well.
359 00:39:18.530 ⇒ 00:39:23.400 Chang Ho Yoon: I’ll do a bit of scout. I’ll do a bit of scouting, I think. And
360 00:39:23.840 ⇒ 00:39:25.330 Chang Ho Yoon: give you a bit of a
361 00:39:25.360 ⇒ 00:39:28.890 Chang Ho Yoon: well, how about how about we give ourselves a deadline? Actually, so.
362 00:39:28.890 ⇒ 00:39:34.329 Uttam Kumaran: Yeah, let’s give us. Let’s give ourselves a deadline. I’m also gonna start. I’ll start a slack group
363 00:39:34.823 ⇒ 00:39:40.499 Uttam Kumaran: with Abigail on our side, who like is kind of putting together some of these prospect lists
364 00:39:40.600 ⇒ 00:39:43.950 Uttam Kumaran: that way. I’m not in the middle because I
365 00:39:44.120 ⇒ 00:39:53.559 Uttam Kumaran: am all over the place, and so at least there’s like one other person there who we can bounce questions off of. I’ll just start a channel around.
366 00:39:53.560 ⇒ 00:39:57.329 Chang Ho Yoon: I assume I’m going to receive a to do list, anyway, on via slack.
367 00:39:57.330 ⇒ 00:40:13.919 Uttam Kumaran: Yes, I will. So well, once I start the channel, I’ll send this the notes from this conversation immediately, which is great. Look I it’s that is like the dream, like, I don’t need to be in a meeting like jotting down notes unless helps for memory.
368 00:40:13.920 ⇒ 00:40:14.459 Chang Ho Yoon: And I didn’t.
369 00:40:14.460 ⇒ 00:40:14.810 Uttam Kumaran: Like.
370 00:40:14.810 ⇒ 00:40:15.450 Chang Ho Yoon: I think.
371 00:40:15.450 ⇒ 00:40:26.880 Uttam Kumaran: Bounce the meetings, bounce the meetings, you know, and then review it at the end of the day, or, or, for example, like the 2 weeks go by, and I’m like we forgot to follow up. I wonder what ha! Like? What do we agree on? And then I can go look!
372 00:40:27.170 ⇒ 00:40:30.840 Uttam Kumaran: It’s like perfect. I was like, why weren’t we doing this before?
373 00:40:32.190 ⇒ 00:40:35.210 Uttam Kumaran: I guess people now are so open to like record everything.
374 00:40:35.884 ⇒ 00:40:43.129 Uttam Kumaran: Yeah. But I mean, we’re not like we’re not selling opioids, like, you know, so I know I’m not afraid.
375 00:40:44.350 ⇒ 00:40:46.769 Chang Ho Yoon: If only we were called Mckinsey.
376 00:40:46.770 ⇒ 00:40:47.690 Uttam Kumaran: Yeah.
377 00:40:47.690 ⇒ 00:40:48.130 Chang Ho Yoon: Yeah.
378 00:40:48.130 ⇒ 00:40:50.058 Uttam Kumaran: Yeah, yeah, yeah, exactly.
379 00:40:51.030 ⇒ 00:40:53.320 Chang Ho Yoon: Now. Let’s
380 00:40:54.830 ⇒ 00:41:00.480 Chang Ho Yoon: yeah. Let’s say in, let’s touch base next Friday. Then.
381 00:41:01.170 ⇒ 00:41:03.860 Chang Ho Yoon: Okay, that’s the deadline. Yeah.
382 00:41:11.710 ⇒ 00:41:13.759 Chang Ho Yoon: I’m quite happy to sort of talk.
383 00:41:13.930 ⇒ 00:41:17.150 Chang Ho Yoon: It’s maybe slightly earlier than when we did what we planned for today.
384 00:41:17.150 ⇒ 00:41:18.960 Uttam Kumaran: Yes, yes.
385 00:41:19.220 ⇒ 00:41:20.919 Chang Ho Yoon: But only if that works for you, I mean, if you.
386 00:41:20.920 ⇒ 00:41:24.979 Uttam Kumaran: No, that that works usually. Yeah. Let’s just plan on.
387 00:41:25.760 ⇒ 00:41:28.810 Uttam Kumaran: I mean, let me see, next Friday.
388 00:41:28.810 ⇒ 00:41:29.470 Chang Ho Yoon: Yeah.
389 00:41:31.940 ⇒ 00:41:32.760 Uttam Kumaran: next slide.
390 00:41:32.760 ⇒ 00:41:37.230 Chang Ho Yoon: An hour like an hour earlier than what we’ve done today would be, will be ace.
391 00:41:37.230 ⇒ 00:41:39.700 Uttam Kumaran: Yeah. So today was, okay.
392 00:41:40.780 ⇒ 00:41:43.789 Uttam Kumaran: yeah, let’s do that. I’m free. You can even do
393 00:41:44.170 ⇒ 00:41:48.730 Uttam Kumaran: 2 pm, central. So right now, it’s 5 30 central.
394 00:41:48.930 ⇒ 00:41:51.630 Uttam Kumaran: So you can even do 3 h before
395 00:41:51.980 ⇒ 00:41:53.610 Uttam Kumaran: or 4 h before
396 00:41:54.760 ⇒ 00:41:57.689 Uttam Kumaran: I’m basically free. Afternoon. My time.
397 00:41:58.130 ⇒ 00:42:01.179 Chang Ho Yoon: So 3 Pm. Your time. 9 Pm. My time. Great.
398 00:42:01.810 ⇒ 00:42:02.430 Uttam Kumaran: Okay.
399 00:42:02.550 ⇒ 00:42:03.180 Chang Ho Yoon: Yeah.
400 00:42:03.470 ⇒ 00:42:10.029 Chang Ho Yoon: Well, let’s do that, and then by then I’ll I will have a prospect list, and I’ll and then
401 00:42:10.280 ⇒ 00:42:18.359 Chang Ho Yoon: and then we can sort of take you from there, I think, in terms of yeah, I’ve talked about less job profile, sorts of problems which I’ll which I’ve also done some scouting for.
402 00:42:18.520 ⇒ 00:42:24.909 Chang Ho Yoon: And and we can start talking, start creating the emails that we need to send. We want to send out and then
403 00:42:26.290 ⇒ 00:42:32.349 Chang Ho Yoon: chuck them out and see what happens. And if we get absolutely nothing to go to the drawing board. But yeah.
404 00:42:32.350 ⇒ 00:42:44.739 Uttam Kumaran: I’m gonna create a channel in slack, and I’ll introduce you to Abigail from our side. And then just brain dump in there. She’s the she’s actually I mean, I’d love to introduce her to you. She’s a mathematics
405 00:42:44.750 ⇒ 00:42:51.320 Uttam Kumaran: student at Usc. This year. She’s in her senior year. She got introduced by a friend. So we had a couple of interns.
406 00:42:51.681 ⇒ 00:42:59.559 Uttam Kumaran: Working the summer. It’s been great. She’s unfortunately not doing. She’s doing some data work. But it’s actually, I was like, Look, the hardest problem we have is sales.
407 00:42:59.640 ⇒ 00:43:05.771 Uttam Kumaran: But I will give you some data problems in sales which is like figuring out the lists and filters and structure. So
408 00:43:06.330 ⇒ 00:43:11.509 Uttam Kumaran: yeah, I’ll I’ll just add her there and then throw anything at her. She’s she’s really awesome. So.
409 00:43:11.510 ⇒ 00:43:15.340 Chang Ho Yoon: Great, great! I I almost feel like the
410 00:43:16.280 ⇒ 00:43:20.171 Chang Ho Yoon: I I don’t know how also, how how readily would you be able to
411 00:43:22.010 ⇒ 00:43:26.339 Chang Ho Yoon: to sort of think and dream dream about the possibility of
412 00:43:26.950 ⇒ 00:43:28.720 Chang Ho Yoon: making people bespoke.
413 00:43:29.517 ⇒ 00:43:31.340 Chang Ho Yoon: Even smartphone apps.
414 00:43:31.440 ⇒ 00:43:33.200 Chang Ho Yoon: Not necessarily.
415 00:43:33.390 ⇒ 00:43:41.319 Chang Ho Yoon: you know, just web apps. But I mean, obviously a web app slash. Smart. Yeah. One of those, you know, Mobile sent centered design, but.
416 00:43:41.320 ⇒ 00:43:48.939 Uttam Kumaran: I would, I mean I would totally do it. I think the toughest part is before I can hire. We need, like, contract in hand.
417 00:43:49.140 ⇒ 00:43:49.590 Chang Ho Yoon: Yeah, right.
418 00:43:49.590 ⇒ 00:43:51.660 Uttam Kumaran: And that’s the toughest part is like.
419 00:43:51.930 ⇒ 00:43:58.740 Uttam Kumaran: And that’s just the problem. With the maturity of this organization is like, it’s not a we have everybody throw anything it’s like.
420 00:43:58.900 ⇒ 00:44:06.437 Uttam Kumaran: can we? Can we get the contract? And then I can then go find the best people, and we’re somewhere in between.
421 00:44:07.070 ⇒ 00:44:12.219 Uttam Kumaran: you know. So part of our edge is like, I want to get in for data where we’re really sophisticated. And then.
422 00:44:12.450 ⇒ 00:44:20.479 Uttam Kumaran: oh, like we, we meet someone, and they need some Ios stuff. Okay, let me go fine. The other thing I’ve done, though, is I’ve partnered. I’ve now talked to several
423 00:44:21.386 ⇒ 00:44:23.120 Uttam Kumaran: like agencies
424 00:44:23.150 ⇒ 00:44:39.480 Uttam Kumaran: in South America and in Europe that do that do work like across the board. So at minimum, if we have a contract, they are like, you can white label us, and they’ll give us some folks that come on our team. So I think whatever we need we can get done.
425 00:44:39.890 ⇒ 00:44:50.770 Uttam Kumaran: So I would have whatever conversation we need to have. If things look like they’re getting further. I’ll we can re-engage with those folks and look for folks that they can provide us with
426 00:44:50.790 ⇒ 00:44:54.330 Uttam Kumaran: medical backgrounds that do anything from Ios to web.
427 00:44:54.400 ⇒ 00:44:57.480 Uttam Kumaran: So I basically want anything that isn’t data
428 00:44:57.570 ⇒ 00:44:59.270 Uttam Kumaran: or AI, I want
429 00:44:59.310 ⇒ 00:45:02.590 Uttam Kumaran: to find a partner to kind of help us facilitate with
430 00:45:03.890 ⇒ 00:45:12.289 Uttam Kumaran: And I engaged with 1 1 person, Mexico City, one person in Argentina like an agency, and then one another agency in Europe.
431 00:45:12.801 ⇒ 00:45:14.699 Uttam Kumaran: So kind of have, like the main
432 00:45:14.770 ⇒ 00:45:18.630 Uttam Kumaran: faces covered that in case we get something that’s close, I can shop it around
433 00:45:19.076 ⇒ 00:45:20.660 Uttam Kumaran: and we can kind of.
434 00:45:20.850 ⇒ 00:45:31.880 Uttam Kumaran: you know, find the people we need. And that was. And I basically was like, Look I, if in case, I guess software, we need like one back end person. I don’t know. I don’t want to hire like a back end person onto my team.
435 00:45:32.090 ⇒ 00:45:38.200 Uttam Kumaran: And these guys, that’s all they do, and they’re offshore. And I’m like, cool. We can try to make something happen. So yeah.
436 00:45:38.630 ⇒ 00:45:39.300 Chang Ho Yoon: Okay.
437 00:45:41.160 ⇒ 00:45:43.780 Chang Ho Yoon: cool. So I think, well, there’s a bit of a plan.
438 00:45:44.280 ⇒ 00:45:44.840 Uttam Kumaran: Okay.
439 00:45:44.840 ⇒ 00:45:46.450 Chang Ho Yoon: Having a deadline is really helpful.
440 00:45:46.890 ⇒ 00:45:47.440 Uttam Kumaran: Yes, let’s.
441 00:45:47.440 ⇒ 00:45:49.869 Chang Ho Yoon: To. Yeah, let’s aim for them.
442 00:45:50.490 ⇒ 00:45:54.460 Uttam Kumaran: Okay, cool, so you’ll see you’ll see this pop up in your slack. And then
443 00:45:55.650 ⇒ 00:45:57.460 Uttam Kumaran: yeah, let’s go from there.
444 00:45:57.640 ⇒ 00:45:59.670 Chang Ho Yoon: Oh, I look, I look forward to it. Yeah.
445 00:46:00.627 ⇒ 00:46:05.040 Chang Ho Yoon: I don’t force being told by a robot what to do. Yeah.
446 00:46:05.040 ⇒ 00:46:09.160 Uttam Kumaran: Yeah, I mean me, too. Like I it’s again. It’s just like an assistant.
447 00:46:09.900 ⇒ 00:46:10.330 Chang Ho Yoon: I.
448 00:46:10.330 ⇒ 00:46:16.749 Uttam Kumaran: I just need to be. I just need to be put into conversations with. Here’s what you should talk about, or here’s a gist.
449 00:46:16.910 ⇒ 00:46:19.660 Uttam Kumaran: yeah, and like, smile like that’s remember to smile.
450 00:46:20.273 ⇒ 00:46:25.179 Chang Ho Yoon: Excellent mate. Great to see you, Sam, and.
451 00:46:25.180 ⇒ 00:46:29.960 Uttam Kumaran: Yeah, really good to see you. I’m glad everything is going well. Congrats on the on the house and the move.
452 00:46:30.411 ⇒ 00:46:36.539 Chang Ho Yoon: We have. We haven’t bought this house. This is just a temporary measure. Before we had to to double it. Actually.
453 00:46:36.780 ⇒ 00:46:38.480 Uttam Kumaran: Oh, nice. Okay.
454 00:46:38.480 ⇒ 00:46:38.910 Chang Ho Yoon: Yeah, yeah.
455 00:46:38.910 ⇒ 00:46:40.459 Uttam Kumaran: That’s where you’re going to be permanently.
456 00:46:41.654 ⇒ 00:46:45.699 Chang Ho Yoon: We’ll see. I mean, it’s it’s where my wife has
457 00:46:46.220 ⇒ 00:46:52.569 Chang Ho Yoon: received a tenured post. So it’s and it’s no mean feat in the musical world to have that.
458 00:46:52.710 ⇒ 00:46:54.790 Chang Ho Yoon: So I’ll have to go, and then
459 00:46:55.020 ⇒ 00:46:58.500 Chang Ho Yoon: I’ll I’ll be increasingly more like you sort of
460 00:46:59.180 ⇒ 00:47:02.589 Chang Ho Yoon: freelancing it online to to make ends meet. So, yeah.
461 00:47:02.590 ⇒ 00:47:05.792 Uttam Kumaran: Yes, okay. So you’re getting some pressure.
462 00:47:06.250 ⇒ 00:47:09.610 Chang Ho Yoon: Yeah, I think that’s I think that’s important. Actually, if if it’s
463 00:47:09.780 ⇒ 00:47:11.609 Chang Ho Yoon: my livelihood, that depends on it.
464 00:47:11.720 ⇒ 00:47:13.530 Chang Ho Yoon: Survival, then yeah.
465 00:47:13.530 ⇒ 00:47:14.780 Uttam Kumaran: You’re welcome. Yeah, I mean.
466 00:47:14.780 ⇒ 00:47:16.080 Chang Ho Yoon: Forces, you to.
467 00:47:16.510 ⇒ 00:47:16.900 Uttam Kumaran: Yeah.
468 00:47:16.900 ⇒ 00:47:17.360 Chang Ho Yoon: This is.
469 00:47:17.360 ⇒ 00:47:18.810 Uttam Kumaran: To figure it out so
470 00:47:18.930 ⇒ 00:47:20.320 Uttam Kumaran: both awesome.
471 00:47:20.430 ⇒ 00:47:29.159 Uttam Kumaran: Well, I have all the resources on my end, and people to get the bare minimum done. Whether it’s like landing page stuff email stuff. I’ve like assembled
472 00:47:29.750 ⇒ 00:47:37.849 Uttam Kumaran: folks enough to kind of get the whole sales funnel going, except the last part, which is like hopping on with with clients. So that’s where I think
473 00:47:38.000 ⇒ 00:47:43.879 Uttam Kumaran: ideally, we spend most of our time, and we can rely on some folks I have internally for for the rest of it. So.
474 00:47:44.360 ⇒ 00:47:55.679 Chang Ho Yoon: No, it’d be cool. It’d be cool to do something together, even if it’s a small project. That’s kind of what I want ideally, actually is a small thing for us to work on, so we can get a sense for how we both work, how others.
475 00:47:55.680 ⇒ 00:47:56.210 Uttam Kumaran: Yeah.
476 00:47:56.210 ⇒ 00:47:56.740 Chang Ho Yoon: Look.
477 00:47:56.740 ⇒ 00:48:02.770 Uttam Kumaran: Oh, 1, 100% right? And that’s why. Also I will. I want to introduce you more folks on my team, because
478 00:48:02.830 ⇒ 00:48:11.430 Uttam Kumaran: I’ve always said, I’m like I’m the biggest blocker in, like a lot of what goes on at the company. So instead, I’m trying to bring folks together
479 00:48:11.680 ⇒ 00:48:15.509 Uttam Kumaran: to like to like, break through some of that stuff which is like if it goes through me.
480 00:48:15.680 ⇒ 00:48:17.729 Uttam Kumaran: I’m actually, I’m probably like
481 00:48:17.910 ⇒ 00:48:35.050 Uttam Kumaran: at the moment, as productive as I could be. I’m eating very healthy. I’m not drinking a lot exercising like. And I’m still not getting enough done. So I’m I basically have in each area of the company project manager engineering. We have one or 2 people that like
482 00:48:35.320 ⇒ 00:48:39.699 Uttam Kumaran: who, if you just throw things at, they’ll get it done. Those the people that I want
483 00:48:40.280 ⇒ 00:48:42.100 Uttam Kumaran: people to get connected with.
484 00:48:42.140 ⇒ 00:48:46.910 Uttam Kumaran: Yeah, so that some the stuff can run without me. So trying for that.
485 00:48:47.150 ⇒ 00:48:47.790 Uttam Kumaran: yeah.
486 00:48:48.077 ⇒ 00:48:53.539 Chang Ho Yoon: It will happen it will happen. It sounds like you get the the pieces in line. It’s great.
487 00:48:53.540 ⇒ 00:48:55.220 Uttam Kumaran: Starting. Yeah.
488 00:48:55.930 ⇒ 00:48:59.009 Chang Ho Yoon: Have a good rest rest of the day. Rest of the week.
489 00:48:59.010 ⇒ 00:48:59.410 Uttam Kumaran: Thank you.
490 00:48:59.410 ⇒ 00:49:00.030 Chang Ho Yoon: Gosh!
491 00:49:00.330 ⇒ 00:49:01.499 Uttam Kumaran: Yeah, I appreciate it. Okay.
492 00:49:01.500 ⇒ 00:49:02.850 Chang Ho Yoon: Nice one, can you see?
493 00:49:03.230 ⇒ 00:49:03.740 Uttam Kumaran: You too.