Meeting Title: Medical-Industry-Prospects Date: 2024-10-24 Meeting participants: Chang Ho Yoon, Ericson Dalusong, Uttam Kumaran
WEBVTT
1 00:05:52.760 ⇒ 00:05:54.730 Ericson Dalusong: Hey, vitam! How’s it going.
2 00:05:54.730 ⇒ 00:05:56.400 Uttam Kumaran: Hey! How are you?
3 00:05:57.130 ⇒ 00:06:03.789 Ericson Dalusong: Doing good it’s still raining here. But luckily we’re my family, and I are safe.
4 00:06:04.020 ⇒ 00:06:08.439 Uttam Kumaran: Okay? Good. Yeah. I know. I’ve been talking to other people on the team. I hope everything’s okay.
5 00:06:09.160 ⇒ 00:06:11.730 Ericson Dalusong: How are the other team members doing.
6 00:06:11.730 ⇒ 00:06:18.070 Uttam Kumaran: They’re okay, I think. Ryan. You know who works on our content, he said. He’s his apartment is having some rain issues.
7 00:06:19.090 ⇒ 00:06:24.869 Uttam Kumaran: You know. So it just sucks. It’s some something is out of our control, you know. So
8 00:06:24.920 ⇒ 00:06:26.790 Uttam Kumaran: I’m just glad everyone’s okay.
9 00:06:27.730 ⇒ 00:06:28.909 Ericson Dalusong: Yeah, me, too.
10 00:06:32.410 ⇒ 00:06:37.960 Uttam Kumaran: How often I mean I know it storms there a lot. But how often is it like this crazy.
11 00:06:39.350 ⇒ 00:06:40.290 Ericson Dalusong: Yeah.
12 00:06:42.210 ⇒ 00:06:47.559 Ericson Dalusong: this 2024 we usually see, like, you know.
13 00:06:47.830 ⇒ 00:06:49.310 Ericson Dalusong: one or 2
14 00:06:50.150 ⇒ 00:06:55.789 Ericson Dalusong: typhoons, heavy typhoons every every 2 months or every quarter. So
15 00:06:56.590 ⇒ 00:06:58.700 Ericson Dalusong: it’s quite a lot. And
16 00:06:59.510 ⇒ 00:07:02.520 Ericson Dalusong: it’s, you know, for some
17 00:07:02.540 ⇒ 00:07:06.599 Ericson Dalusong: other Filipinos, it’s it’s really difficult to
18 00:07:06.700 ⇒ 00:07:10.109 Ericson Dalusong: to deal with typhoon because of floods and other
19 00:07:10.190 ⇒ 00:07:11.729 Ericson Dalusong: other hassle that
20 00:07:11.750 ⇒ 00:07:13.270 Ericson Dalusong: that it brings.
21 00:07:13.680 ⇒ 00:07:14.690 Uttam Kumaran: Hmm.
22 00:07:21.630 ⇒ 00:07:23.550 Uttam Kumaran: interesting. Yeah, it’s tough.
23 00:07:37.670 ⇒ 00:07:40.910 Ericson Dalusong: How about in Texas? How’s the how’s the weather there.
24 00:07:41.290 ⇒ 00:07:43.527 Uttam Kumaran: Yeah, it’s fine right now. Actually,
25 00:07:44.280 ⇒ 00:07:48.039 Uttam Kumaran: nothing like crazy. It’s just getting a little bit colder. Which is.
26 00:07:48.310 ⇒ 00:07:50.060 Uttam Kumaran: it was actually very nice.
27 00:07:50.560 ⇒ 00:07:52.030 Uttam Kumaran: yeah, I’ve I’ve
28 00:07:53.860 ⇒ 00:07:54.570 Uttam Kumaran: I’ve
29 00:07:56.120 ⇒ 00:08:02.269 Uttam Kumaran: I’ve been enjoying that. It’s been getting a little bit warmer. I mean, like getting a little bit colder. But you know. Otherwise, it’s okay.
30 00:08:02.880 ⇒ 00:08:04.959 Uttam Kumaran: Nothing to report. Really.
31 00:08:06.100 ⇒ 00:08:06.890 Ericson Dalusong: Let’s see.
32 00:08:07.770 ⇒ 00:08:15.327 Uttam Kumaran: Yeah, let me see if changes. I haven’t watched your video yet on loom. But I’m happy that yeah. I’m hoping Miguel and
33 00:08:16.460 ⇒ 00:08:19.100 Uttam Kumaran: they’re gonna add their linkedin. And we can start using that.
34 00:08:19.340 ⇒ 00:08:21.470 Uttam Kumaran: And yeah.
35 00:08:23.390 ⇒ 00:08:25.819 Ericson Dalusong: Yeah, yeah. I’m going to
36 00:08:26.518 ⇒ 00:08:32.729 Ericson Dalusong: set up Nick’s or Nicholas account today. So we can also use this account for
37 00:08:33.049 ⇒ 00:08:34.200 Ericson Dalusong: for outreach.
38 00:08:37.510 ⇒ 00:08:44.919 Uttam Kumaran: Awesome. Yeah, I’m hoping that we can use his account for data, and then Miguel’s for AI, because I know how many connections are we doing
39 00:08:45.140 ⇒ 00:08:46.750 Uttam Kumaran: right now per day
40 00:08:47.220 ⇒ 00:08:49.029 Uttam Kumaran: like 30 right? Or something.
41 00:08:49.620 ⇒ 00:08:52.939 Ericson Dalusong: Per your account. It’s it’s 40 connection request per day.
42 00:08:53.460 ⇒ 00:08:54.060 Uttam Kumaran: Okay.
43 00:08:54.520 ⇒ 00:08:55.110 Ericson Dalusong: Yeah.
44 00:08:56.150 ⇒ 00:08:59.267 Uttam Kumaran: So hopefully, yeah, we can try to hit some scale with him. And then.
45 00:09:01.400 ⇒ 00:09:05.230 Uttam Kumaran: yeah. Also, I saw some updates to twain. I don’t know if that affects us as well.
46 00:09:06.833 ⇒ 00:09:12.470 Ericson Dalusong: No, actually, is it the update about version 2.0.
47 00:09:12.470 ⇒ 00:09:13.300 Uttam Kumaran: Yes.
48 00:09:14.110 ⇒ 00:09:18.501 Ericson Dalusong: Yeah. So we’re using the Api version. So
49 00:09:19.400 ⇒ 00:09:20.450 Ericson Dalusong: I mean.
50 00:09:21.580 ⇒ 00:09:24.820 Ericson Dalusong: we, we can’t use that 2.0 yet.
51 00:09:25.850 ⇒ 00:09:26.860 Ericson Dalusong: But
52 00:09:27.402 ⇒ 00:09:35.059 Ericson Dalusong: eventually, it’s it’s I mean, the the new features in the 2.0 will also be available in the Api version.
53 00:09:35.550 ⇒ 00:09:36.220 Uttam Kumaran: Okay.
54 00:09:37.240 ⇒ 00:09:37.920 Uttam Kumaran: yeah.
55 00:09:39.880 ⇒ 00:09:40.820 Uttam Kumaran: awesome.
56 00:09:44.000 ⇒ 00:09:44.859 Uttam Kumaran: Let me
57 00:09:45.470 ⇒ 00:09:47.540 Uttam Kumaran: let me ping in the channel.
58 00:10:03.960 ⇒ 00:10:08.384 Uttam Kumaran: Otherwise maybe we can, just while we’re waiting for them. Maybe we can just spend
59 00:10:09.250 ⇒ 00:10:11.640 Uttam Kumaran: a little bit of time, I guess. I I
60 00:10:11.920 ⇒ 00:10:20.569 Uttam Kumaran: I wanted to just go through quickly. So you mentioned that the beverage campaign we finished? I guess. What were your ideas about? How to
61 00:10:21.032 ⇒ 00:10:25.269 Uttam Kumaran: change that? Because I know we only were going to like a hundred 30 leads or so, right.
62 00:10:27.196 ⇒ 00:10:29.649 Ericson Dalusong: What do you mean by that? What?
63 00:10:29.650 ⇒ 00:10:39.092 Uttam Kumaran: Like how many, how many? Oh, actually, never mind I I think I was just looking at the lead numbers, and it it was just a filtered
64 00:10:39.740 ⇒ 00:10:45.080 Uttam Kumaran: so that one’s going live. And then I know we have the AI. Indeed! Campaign starting soon, too. Right.
65 00:10:45.860 ⇒ 00:10:49.858 Ericson Dalusong: Yup, Yup, actually I you know
66 00:10:50.480 ⇒ 00:10:55.040 Ericson Dalusong: made it live early this morning. It’s just that we were
67 00:10:55.170 ⇒ 00:11:00.919 Ericson Dalusong: only able to add, you know, a few contacts in that campaign.
68 00:11:03.490 ⇒ 00:11:04.070 Uttam Kumaran: Okay?
69 00:11:07.870 ⇒ 00:11:09.400 Uttam Kumaran: And then I know there’s some.
70 00:11:09.680 ⇒ 00:11:12.930 Uttam Kumaran: There’s some campaigns that finished. But the ones that finished
71 00:11:13.000 ⇒ 00:11:14.349 Uttam Kumaran: are we going to be
72 00:11:14.940 ⇒ 00:11:18.329 Uttam Kumaran: Are we going to be editing those and restarting? Or what do you think we should be doing.
73 00:11:19.270 ⇒ 00:11:21.629 Ericson Dalusong: Yup, that’s a great question.
74 00:11:21.640 ⇒ 00:11:27.304 Ericson Dalusong: So for these campaigns that have already been finished. For example,
75 00:11:30.030 ⇒ 00:11:37.320 Ericson Dalusong: you know, the the flow code look alike, although it’s still says that it’s active. It’s it’s almost finished.
76 00:11:37.430 ⇒ 00:11:39.389 Ericson Dalusong: So I would
77 00:11:40.630 ⇒ 00:11:46.019 Ericson Dalusong: highly suggest that we add leads to that. So I’m gonna be working on
78 00:11:46.110 ⇒ 00:11:48.610 Ericson Dalusong: putting additional leads to it.
79 00:11:48.820 ⇒ 00:11:50.250 Ericson Dalusong: The only
80 00:11:51.633 ⇒ 00:11:58.680 Ericson Dalusong: campaign that has been completed that, you know, we weren’t able, had we weren’t
81 00:11:58.990 ⇒ 00:12:02.299 Ericson Dalusong: have had too much success is the e-commerce, so.
82 00:12:02.300 ⇒ 00:12:03.040 Uttam Kumaran: Yeah.
83 00:12:03.620 ⇒ 00:12:11.590 Ericson Dalusong: Maybe we can put a pause on that campaign, for now and then focus heavily on the steel and manufacturing, because that’s
84 00:12:12.690 ⇒ 00:12:14.610 Ericson Dalusong: where we are. We had some good stuff.
85 00:12:14.610 ⇒ 00:12:15.610 Uttam Kumaran: Please. Yeah.
86 00:12:15.610 ⇒ 00:12:16.200 Ericson Dalusong: Yeah.
87 00:12:16.620 ⇒ 00:12:24.001 Uttam Kumaran: Okay, let’s do that. And then I think this week I can take a look at maybe where we can make some changes, and we have some more AI campaigns
88 00:12:24.380 ⇒ 00:12:26.668 Uttam Kumaran: that we can start as well.
89 00:12:27.580 ⇒ 00:12:31.149 Uttam Kumaran: so great. Yeah, I don’t know if these guys are gonna join today. Maybe we skip.
90 00:12:32.100 ⇒ 00:12:37.811 Uttam Kumaran: For today. But I guess another question I had while I have you. You know I was looking at a lot of these.
91 00:12:38.970 ⇒ 00:12:45.720 Uttam Kumaran: I was looking at these tools. That almost do. God, I don’t know where I have. I don’t know if I even
92 00:12:46.550 ⇒ 00:12:47.779 Uttam Kumaran: add these up
93 00:12:48.469 ⇒ 00:12:51.409 Uttam Kumaran: somewhere. But let me just find them
94 00:12:53.510 ⇒ 00:12:55.100 Uttam Kumaran: one second.
95 00:13:43.330 ⇒ 00:13:45.129 Uttam Kumaran: What was it? What was it?
96 00:14:26.330 ⇒ 00:14:32.969 Uttam Kumaran: Why is this not loading it, you know. Have you ever seen any of those AI like Demo, creating tools.
97 00:14:34.130 ⇒ 00:14:36.109 Ericson Dalusong: Demo creating tools.
98 00:14:36.110 ⇒ 00:14:39.500 Uttam Kumaran: Basically, we can use AI to scale actually like
99 00:14:39.840 ⇒ 00:14:41.450 Uttam Kumaran: demo creation.
100 00:14:44.650 ⇒ 00:14:45.580 Uttam Kumaran: Any of those.
101 00:14:45.980 ⇒ 00:14:46.630 Ericson Dalusong: Is it
102 00:14:47.040 ⇒ 00:14:49.890 Ericson Dalusong: a tool for creating product? Demos.
103 00:14:50.540 ⇒ 00:14:52.427 Uttam Kumaran: Exactly. Yeah. There’s like,
104 00:14:54.210 ⇒ 00:14:57.049 Uttam Kumaran: I want to share you a couple of these.
105 00:15:00.400 ⇒ 00:15:05.350 Uttam Kumaran: there’s like, yeah, drive pipeline a this isn’t 1 of them.
106 00:15:12.400 ⇒ 00:15:17.149 Uttam Kumaran: sorry. I’m just looking through my history because I closed my browsers. Let’s see.
107 00:15:21.710 ⇒ 00:15:23.070 Ericson Dalusong: Yeah, bye, guys.
108 00:15:24.300 ⇒ 00:15:25.790 Uttam Kumaran: Hey? Oh, sorry!
109 00:15:25.790 ⇒ 00:15:26.480 Chang Ho Yoon: Nice.
110 00:15:27.420 ⇒ 00:15:28.440 Uttam Kumaran: All good.
111 00:15:28.460 ⇒ 00:15:29.510 Uttam Kumaran: How are you.
112 00:15:29.910 ⇒ 00:15:31.819 Chang Ho Yoon: Good. Good. Yeah. Just wanted to survive.
113 00:15:32.512 ⇒ 00:15:34.510 Uttam Kumaran: Is this still a good time?
114 00:15:34.770 ⇒ 00:15:41.310 Chang Ho Yoon: Yeah, yeah, absolutely. It’s it’s chat. I’m sorry it’s taking a while for us to get through to Lin Chen. I mean, he’s sort of the
115 00:15:41.540 ⇒ 00:15:44.129 Chang Ho Yoon: primary Us. Contact point for me.
116 00:15:44.900 ⇒ 00:15:50.610 Uttam Kumaran: No problem. I mean, it’s the story of a story of my life, anyways, trying to get in touch with folks. So it’s
117 00:15:50.920 ⇒ 00:15:52.859 Uttam Kumaran: it’s not a surprise. It’s what happens.
118 00:15:54.790 ⇒ 00:15:59.590 Chang Ho Yoon: Anyway, by Tuesday, and I’ll I’ll update as soon as I’ve chatted to him.
119 00:15:59.910 ⇒ 00:16:00.700 Uttam Kumaran: Cool.
120 00:16:00.910 ⇒ 00:16:02.259 Uttam Kumaran: Yeah, I guess I,
121 00:16:02.500 ⇒ 00:16:09.149 Uttam Kumaran: yeah, I just want to spend some time. I think we have. We have. I think we just need a couple of data points, I think, to even start
122 00:16:09.170 ⇒ 00:16:18.980 Uttam Kumaran: like a campaign around the medical sector, and I’ll just introduce you to Erickson on my team. Erickson runs all of our email outbound for us, him and his team. So
123 00:16:19.454 ⇒ 00:16:25.950 Uttam Kumaran: basically, we set up like basically automated campaigns that we take filters like industry
124 00:16:26.366 ⇒ 00:16:34.540 Uttam Kumaran: like filters about companies like revenue type of companies. And then also filters about who we’re tar targeting basically everything we kind of set up.
125 00:16:34.640 ⇒ 00:16:52.449 Uttam Kumaran: And we tried to set up an Apollo. And then, basically, we use AI to actually craft personalized messages. And we’re sort of doing that at scale. So I think one thing that would be nice is just to think about even in this call, like, based on even the calls that you’ve had recently, or the folks that you talk to like
126 00:16:52.550 ⇒ 00:17:03.760 Uttam Kumaran: what is like an initial campaign that we can even set up where we can just begin to put together a list of like, okay, here’s maybe like 500 contacts, we should just go ahead and start emailing and like.
127 00:17:03.820 ⇒ 00:17:08.239 Uttam Kumaran: what do you think is a good hook or a good offer, or even like something to catch their attention.
128 00:17:08.470 ⇒ 00:17:13.619 Uttam Kumaran: and I think Erickson can probably take that back and then provide us with like, what those are.
129 00:17:20.280 ⇒ 00:17:21.930 Chang Ho Yoon: Let me just moved somewhere. Oh.
130 00:17:21.930 ⇒ 00:17:23.069 Uttam Kumaran: Yeah, I’ll go ahead.
131 00:17:23.079 ⇒ 00:17:24.439 Chang Ho Yoon: Bit more privacy.
132 00:17:25.869 ⇒ 00:17:26.599 Chang Ho Yoon: Sure.
133 00:17:27.689 ⇒ 00:17:29.839 Chang Ho Yoon: Yeah, this is probably better.
134 00:17:31.659 ⇒ 00:17:34.409 Chang Ho Yoon: The reason why I was a bit reticent
135 00:17:36.199 ⇒ 00:17:40.969 Chang Ho Yoon: dive straight into a campaign was because of kind of want to get a sense for what?
136 00:17:41.620 ⇒ 00:17:49.039 Chang Ho Yoon: Even a large academic center might be wanting to see by email. I kind of want to get a sense.
137 00:17:49.059 ⇒ 00:17:54.729 Chang Ho Yoon: That’s why I wanted to talk to Lynn really badly last week was because it was, oh, yeah, be before he had to delay it
138 00:17:55.119 ⇒ 00:17:59.739 Chang Ho Yoon: because it’s it’s gonna be critical for trying to shape this email. I
139 00:18:00.349 ⇒ 00:18:06.599 Chang Ho Yoon: I feel like they’re going to be quite different hooks for different players. And that’s the problem.
140 00:18:06.799 ⇒ 00:18:10.449 Chang Ho Yoon: And is that for a large academic center that’s normally quite replete with
141 00:18:11.148 ⇒ 00:18:17.119 Chang Ho Yoon: internal data analysts and and researchers who can also do work that might not be
142 00:18:17.139 ⇒ 00:18:21.609 Chang Ho Yoon: entirely corporate, but will be ancillary to their needs.
143 00:18:21.749 ⇒ 00:18:23.029 Chang Ho Yoon: Will.
144 00:18:23.059 ⇒ 00:18:26.729 Chang Ho Yoon: or often. This is the pro. This is the thing about someone like Harvard or
145 00:18:27.179 ⇒ 00:18:28.069 Chang Ho Yoon: Brigham
146 00:18:28.169 ⇒ 00:18:49.819 Chang Ho Yoon: and women’s is that they will have lots of interesting projects that they want to get up and running, but they won’t necessarily have all the personnel that they require. And so you can imagine the hook for that would be quite different from a much smaller operation where they’re not even thinking about the more exploratory stuff going forward. But in fact, they just need some more operational efficiency.
147 00:18:50.251 ⇒ 00:19:01.909 Chang Ho Yoon: And so they’re looking for quite a different kind of project quite different kind of team, and quite a different set of experiences. A bit more kind of along the lines of what Palantir do if you’re aware of Palantir
148 00:19:02.418 ⇒ 00:19:05.529 Chang Ho Yoon: or about. Have you guys heard of Palantir.
149 00:19:05.530 ⇒ 00:19:10.990 Uttam Kumaran: Yes, yeah, yeah. Palantir, I mean, they’re basically like a large scale service
150 00:19:11.090 ⇒ 00:19:15.610 Uttam Kumaran: software creation like, bespoke software creation. Yeah.
151 00:19:23.090 ⇒ 00:19:24.439 Chang Ho Yoon: Sorry. Can you hear me now?
152 00:19:24.440 ⇒ 00:19:27.670 Uttam Kumaran: I can hear you maybe turn off video. I think maybe it’s
153 00:19:28.500 ⇒ 00:19:31.100 Uttam Kumaran: It’s taking up a lot of data. But we can hear you
154 00:19:40.610 ⇒ 00:19:44.529 Uttam Kumaran: and I can try. We could probably turn off video to Eric’s and see if it helps.
155 00:19:52.380 ⇒ 00:19:53.509 Chang Ho Yoon: Can you hear me now?
156 00:19:53.510 ⇒ 00:19:54.330 Uttam Kumaran: Yes.
157 00:19:54.740 ⇒ 00:19:55.970 Chang Ho Yoon: Okay, perfect.
158 00:19:56.290 ⇒ 00:19:57.920 Chang Ho Yoon: Just sort of trying to
159 00:19:58.380 ⇒ 00:20:01.199 Chang Ho Yoon: edge myself closer to the source of Wi-fi.
160 00:20:01.200 ⇒ 00:20:02.330 Uttam Kumaran: No problem.
161 00:20:02.510 ⇒ 00:20:02.895 Chang Ho Yoon: Yeah.
162 00:20:04.270 ⇒ 00:20:06.560 Chang Ho Yoon: okay, that might be better. Can you hear me?
163 00:20:06.560 ⇒ 00:20:07.200 Uttam Kumaran: Yes.
164 00:20:08.000 ⇒ 00:20:12.069 Chang Ho Yoon: To be a team here as well. Yeah. So just in short.
165 00:20:12.420 ⇒ 00:20:15.219 Chang Ho Yoon: you know different centers with
166 00:20:15.230 ⇒ 00:20:17.280 Chang Ho Yoon: different degrees of
167 00:20:17.340 ⇒ 00:20:27.749 Chang Ho Yoon: like academic, ancillary and academic individuals, people who are doing active research operationally, or just more sort of from a pure academic perspective
168 00:20:27.790 ⇒ 00:20:37.220 Chang Ho Yoon: will have quite different needs. And so that’s why I was very keen to talk to Lin Chen before drafting emails, because I think on the one hand, you’ll have much smaller
169 00:20:37.420 ⇒ 00:20:38.240 Chang Ho Yoon: of
170 00:20:38.410 ⇒ 00:20:40.149 Chang Ho Yoon: hospitals and clinics
171 00:20:40.210 ⇒ 00:20:42.210 Chang Ho Yoon: and as well as
172 00:20:43.493 ⇒ 00:20:46.810 Chang Ho Yoon: much smaller sort of healthcare providers. Unlike.
173 00:20:47.430 ⇒ 00:20:50.050 Chang Ho Yoon: you know, the Brigham and women’s of the world.
174 00:20:50.442 ⇒ 00:21:04.330 Chang Ho Yoon: Who’ll be, you know, who are far more, you know, in the in the Boston sector, at least, they have so many more patients that they have to deal with. And it it’s, you know, incredibly like multifaceted company. I I feel like I feel like when it comes to
175 00:21:04.660 ⇒ 00:21:12.340 Chang Ho Yoon: much smaller clinic. That, you know they might. All they might be looking for is is a little project
176 00:21:12.410 ⇒ 00:21:33.070 Chang Ho Yoon: that’s to do with operational efficiency in a particular domain, whether it be with, you know, their theater operations or with with some of their follow up measures, or whatever it is, and wanting to automate that process there might be, there will be a lot of there’ll probably be an interest in terms of automation of processes, automation of some of the admin.
177 00:21:33.810 ⇒ 00:21:35.330 Chang Ho Yoon: But the
178 00:21:35.968 ⇒ 00:21:41.869 Chang Ho Yoon: with the smaller clinics or smaller hospitals. I feel like that will be the more likely
179 00:21:42.360 ⇒ 00:21:47.539 Chang Ho Yoon: outcome. So along the line. What Palantir offer if you’ve ever heard of Palantir.
180 00:21:47.540 ⇒ 00:21:48.480 Uttam Kumaran: Yes.
181 00:21:48.480 ⇒ 00:21:56.229 Chang Ho Yoon: Yeah. So that’s what parents has been focusing on for Youngs. I mean, obviously, that’s probably the mainstay of their business is in helping to
182 00:21:56.400 ⇒ 00:21:57.240 Chang Ho Yoon: to
183 00:21:58.090 ⇒ 00:22:04.829 Chang Ho Yoon: operationally, like essentially operationally improve or improve the efficiency of some of that admin
184 00:22:05.890 ⇒ 00:22:06.730 Chang Ho Yoon: for example.
185 00:22:06.730 ⇒ 00:22:07.470 Uttam Kumaran: The.
186 00:22:07.470 ⇒ 00:22:09.078 Chang Ho Yoon: Example, with their theatre
187 00:22:09.670 ⇒ 00:22:10.370 Uttam Kumaran: Yes.
188 00:22:10.370 ⇒ 00:22:14.679 Chang Ho Yoon: Like, yeah, like theatre times. Theatre rooms
189 00:22:14.970 ⇒ 00:22:23.740 Chang Ho Yoon: not going spare ensuring that people who are on leave are definitely on the system as being on leave, and so on. So so they don’t waste
190 00:22:24.070 ⇒ 00:22:26.530 Chang Ho Yoon: any any theater slots.
191 00:22:26.950 ⇒ 00:22:55.183 Chang Ho Yoon: So that’s kind of what Palantir would would offer, and that that they do that very successfully to many, many smaller, smaller hospitals, smaller clinics that that are just trying to make that a reality on the flip side. You have. You know, the likes of Harvard and Brigham women’s where they go. They’re trying to push the, you know, the boundaries and frontiers a bit more, and this sort of stuff where they probably lack personnel will be analysts that can help them with some of the internal research projects.
192 00:22:55.870 ⇒ 00:23:08.189 Chang Ho Yoon: partly because they just don’t have enough people to satisfy all the different questions that they have with respect to the, with respect to electronic health record data. And what what kind of scientific insights can be gleaned from those.
193 00:23:08.220 ⇒ 00:23:17.610 Chang Ho Yoon: And so that is far more likely to be a problem in a in something like mass, Gen. Or Brigham women’s than a much smaller
194 00:23:17.900 ⇒ 00:23:19.589 Chang Ho Yoon: healthcare provider.
195 00:23:20.630 ⇒ 00:23:22.429 Uttam Kumaran: Yeah, I guess my only
196 00:23:22.590 ⇒ 00:23:24.989 Uttam Kumaran: my only pushback on
197 00:23:25.050 ⇒ 00:23:47.400 Uttam Kumaran: what you said is, I don’t actually think we need to start any of the campaigns. I think what we can actually do is just begin to set up like exactly what you said, which is you mentioned like, Okay, hospitals associated with the university may have different things, smaller versus bigger. That’s actually all the things that’ll go into basically personalizing those emails. But we can start to just get
198 00:23:47.480 ⇒ 00:23:57.819 Uttam Kumaran: those lists started, and then also, when you go, have the conversation, we can even say, Hey, why don’t? Here’s like 10 people 10. Here’s like 10
199 00:23:57.900 ⇒ 00:24:07.509 Uttam Kumaran: firms that fit each of those categories, and then you can take that as ammo for your conversation. So we don’t. I actually don’t. Wanna yeah, you’re totally right. I don’t want to kick off any.
200 00:24:07.610 ⇒ 00:24:33.999 Uttam Kumaran: any outbound until we’re both solid. But Erickson can actually go through and say, Okay, we do have these distinct categories. We have hospitals that are clinics that are attached to universities. We have maybe revenue bands, or like those are the features that he can actually get started. It’ll take us, you know, a week or so to even just do that. So that’s why I’m just thinking through what we can, you know, even just make an inch of progress on on our side.
201 00:24:37.410 ⇒ 00:24:41.469 Chang Ho Yoon: That sounds good. I I think that they lots of different aspects here. Which I
202 00:24:41.490 ⇒ 00:24:46.199 Chang Ho Yoon: it’s it’s sort of hard to know how it’s how exactly to word the email. But I can think of a few.
203 00:24:46.240 ⇒ 00:24:48.500 Chang Ho Yoon: I can think of a few ways in terms of
204 00:24:48.530 ⇒ 00:25:00.879 Chang Ho Yoon: sub categorizing potential solutions that they might be looking for, whether it be rendering theater space more efficient even booking annual leave. In a way that’s
205 00:25:01.296 ⇒ 00:25:07.480 Chang Ho Yoon: you know, operationally feasible so that they don’t have. They still have adequate levels of staffing.
206 00:25:08.355 ⇒ 00:25:09.070 Chang Ho Yoon: Then.
207 00:25:09.070 ⇒ 00:25:10.290 Uttam Kumaran: And also maybe.
208 00:25:10.520 ⇒ 00:25:11.210 Chang Ho Yoon: Yeah, he.
209 00:25:11.210 ⇒ 00:25:14.730 Uttam Kumaran: Yeah, maybe I will even show you. I’ll even walk you through like in a
210 00:25:14.800 ⇒ 00:25:21.720 Uttam Kumaran: an example of like, how we put together these lists and how they actually get activated in the email, because I think it’ll be super clear and.
211 00:25:21.720 ⇒ 00:25:22.470 Chang Ho Yoon: Yeah.
212 00:25:22.470 ⇒ 00:25:25.340 Uttam Kumaran: And I’m just gonna share. I’m just gonna share clay. And
213 00:25:25.480 ⇒ 00:25:29.359 Uttam Kumaran: I think I just wanna walk there because I think it’ll give you a concrete example. I think
214 00:25:29.590 ⇒ 00:25:31.757 Uttam Kumaran: 2 things for
215 00:25:32.530 ⇒ 00:25:42.470 Uttam Kumaran: today. One is like we can think through what are the, what are the, what are the known segments that you have. You’re like, we know confidently that there’s going to be a difference between
216 00:25:42.590 ⇒ 00:26:01.389 Uttam Kumaran: hospitals or clinics that are associated with universities versus not perfect. That’s that’s a great, at least starting. Here’s how we filter, though. That world. The second thing is, is like, Okay, if you know that there’s roughly like an employee count, or like another sort of trigger that can create some
217 00:26:01.620 ⇒ 00:26:09.190 Uttam Kumaran: segmenting. There, that’s that like you’re confident in. Now we can run with that. Of course we’ll probably learn a couple more things.
218 00:26:09.970 ⇒ 00:26:11.260 Uttam Kumaran: but that would be.
219 00:26:11.260 ⇒ 00:26:11.790 Chang Ho Yoon: Yes.
220 00:26:11.790 ⇒ 00:26:15.155 Uttam Kumaran: You know, that would be really helpful, and then let me just show you this.
221 00:26:15.660 ⇒ 00:26:19.080 Uttam Kumaran: let me just show you what we have right now.
222 00:26:23.900 ⇒ 00:26:29.870 Uttam Kumaran: so to give you a sense of like how we do this. And again, this is evolved a lot. So
223 00:26:30.170 ⇒ 00:26:43.600 Uttam Kumaran: I’m like Super pumped because I think even the even with the small information we have now like, it’s perfect to get started. This is an example we’ve set up for like manufacturing, and I hope you can see us hopefully. It’s not too small on the phone.
224 00:26:44.246 ⇒ 00:26:44.979 Chang Ho Yoon: Can, yeah.
225 00:26:44.980 ⇒ 00:26:49.600 Uttam Kumaran: But basically, like we are able to get like, here are the list of companies
226 00:26:49.730 ⇒ 00:26:53.920 Uttam Kumaran: who they are, the size, anything about any. So sort of
227 00:26:54.010 ⇒ 00:26:57.340 Uttam Kumaran: dimensionality we need whether location size.
228 00:26:57.510 ⇒ 00:27:19.520 Uttam Kumaran: you know. And then we also start to do like lead scoring. Basically. So for part of our other business, I know that like, Hey, we’re looking after. We’re looking at companies in this revenue range. I’m looking at whether they are located in like the Texas or the southern area I’m looking at whether they already have like a head of digital right? So we could set up those advanced rules. But at minimum.
229 00:27:19.520 ⇒ 00:27:31.380 Uttam Kumaran: If you just look at manufacturing companies, there’s going to be a ton. So then we kind of narrow it down to like, how do we get to something that’s around Erickson. What would you say is like a good amount, for, like a campaign in terms of contacts.
230 00:27:33.857 ⇒ 00:27:38.440 Ericson Dalusong: Are you asking for the total addressable market, or the number of contacts that.
231 00:27:38.750 ⇒ 00:27:42.400 Uttam Kumaran: Yeah, like the number of companies that we try to get one table
232 00:27:42.610 ⇒ 00:27:44.090 Uttam Kumaran: to kind of limit to.
233 00:27:45.110 ⇒ 00:27:51.449 Ericson Dalusong: Yeah, ideally 2,000 companies would would give us, like, you know, like.
234 00:27:52.100 ⇒ 00:27:55.150 Ericson Dalusong: between 5 to 6,000 contacts.
235 00:27:55.480 ⇒ 00:27:59.740 Uttam Kumaran: Okay, so yeah, enough, we basically need enough to filter to like
236 00:27:59.780 ⇒ 00:28:04.750 Uttam Kumaran: around 2,000 individual organizations. And then you assume that we find
237 00:28:04.790 ⇒ 00:28:32.240 Uttam Kumaran: 3 to 4 people within each of them to begin to contact. And so that’s what that’s basically what we do. The other thing is, then, if we have these segments, we can say, Hey, we have a we just have a dimension here that’s like, is university associated or not, that actually gets into the email. They, the people associated with the university get a different email which could be everything from a different subject to a different hook to to everything. So it’s all very, very customizable. We’re using.
238 00:28:32.530 ⇒ 00:28:35.337 Uttam Kumaran: you know, Gen. AI for for all this, basically
239 00:28:36.138 ⇒ 00:29:01.990 Uttam Kumaran: and so exactly what you said is like, what we’ll need is like, Okay, what are some good hooks like? What are some good offers where we want a concrete example like, do you guys, one of the things that we do is we’re say, do you have this problem? And we say, like, do you have problems? Understanding who your customers are? You have problems, understanding. Xyz. And you, you kind of like, try to nail like, what could they be thinking of? And that may be different per segment. The second thing is like we think about what an offer is like.
240 00:29:02.379 ⇒ 00:29:04.260 Uttam Kumaran: If we were to take 30 min your time.
241 00:29:04.610 ⇒ 00:29:12.199 Uttam Kumaran: discuss with us with you, or maybe we could give you a 2 week free trial of our service, or do an audit like that’s the things that we think about is.
242 00:29:12.210 ⇒ 00:29:30.900 Uttam Kumaran: how do we gain credibility. How do we like? How do we get them to open the email first? st So, good subject, good. 1st thing, how do we quickly get credibility? Right? I think that’s somewhere where I think we use your resume. And we talk about Brainforge, the firm. And then we say, like, Do you guys have this problem, almost try to induce a yes, of course, I have this problem. Because.
243 00:29:31.070 ⇒ 00:29:35.850 Uttam Kumaran: yeah, I’m I’m this type of a company in this situation. And then they respond right? And that’s the goal.
244 00:29:39.730 ⇒ 00:29:41.819 Chang Ho Yoon: Yeah, sure thing. Sure thing.
245 00:29:42.980 ⇒ 00:29:48.069 Uttam Kumaran: So I mean, I don’t know. What do you do you think like maybe we move forward with the university attached
246 00:29:48.530 ⇒ 00:29:56.850 Uttam Kumaran: versus not as like an initial filter. And then, are there any others that we can just begin to explore to at least get a sense of like, hey? We looked at. There’s about
247 00:29:56.930 ⇒ 00:29:58.840 Uttam Kumaran: x 1,000
248 00:29:58.860 ⇒ 00:30:07.070 Uttam Kumaran: people that like clinics that we found that are attached to university versus not. And then we can start to whittle that down, to try to arrive at like a 2,000 number.
249 00:30:08.520 ⇒ 00:30:12.958 Chang Ho Yoon: I think it’s less to do with University associated or not, and actually more to do with
250 00:30:13.822 ⇒ 00:30:34.390 Chang Ho Yoon: like, whether or not it’s a tertiary or secondary sort of, you know, trauma Center, whether it, you know, they have a lot of theaters in operation or not, and roughly, the size of the personnel. It’s like, how many people how many doctors do they have roughly, and that kind of information may well be easily scrapeable.
251 00:30:35.263 ⇒ 00:30:37.449 Chang Ho Yoon: That might give us a sense. For, like
252 00:30:37.810 ⇒ 00:30:43.719 Chang Ho Yoon: are they? Is this a hospital that is basically just about managing with old technology to make
253 00:30:44.010 ⇒ 00:30:47.850 Chang Ho Yoon: the administrative ends meet, whether it be staffing.
254 00:30:47.940 ⇒ 00:31:01.329 Chang Ho Yoon: you know, leave and vacation ensuring that wards are adequately staffed, and so on, because you can imagine a small enough hospital can probably might be able to just rely on a human Hr. Team to do all that manually
255 00:31:01.370 ⇒ 00:31:10.339 Chang Ho Yoon: versus one where you have significantly more staff, particularly if it’s more like temporary staff people who are what we call locuming like, basically.
256 00:31:10.340 ⇒ 00:31:10.790 Uttam Kumaran: Yes.
257 00:31:10.790 ⇒ 00:31:11.460 Chang Ho Yoon: Alright.
258 00:31:11.940 ⇒ 00:31:15.385 Chang Ho Yoon: you know. Just swing in and out whenever they need to.
259 00:31:16.150 ⇒ 00:31:18.210 Chang Ho Yoon: and they will.
260 00:31:18.220 ⇒ 00:31:36.069 Chang Ho Yoon: That will be fundamentally more difficult to manage, and you can imagine a situation where they will probably be unwilling to believe an untested, unaccredited UN. You know a company without precedent, like like brain forward until
261 00:31:36.450 ⇒ 00:31:49.720 Chang Ho Yoon: we have a good case study to. To simplify. This done, and so for the 1st play it would be more a case of can we help you get to that point where it would still require, obviously a human in the loop to just double check that
262 00:31:49.790 ⇒ 00:31:53.460 Chang Ho Yoon: the the rostering is, is correct, and so on, and so forth.
263 00:31:54.180 ⇒ 00:31:54.730 Uttam Kumaran: Totally.
264 00:31:55.140 ⇒ 00:32:01.740 Chang Ho Yoon: And so I think, yeah, wording. That’s quite tricky. But I I feel like it can be done. I feel like there’s a
265 00:32:02.080 ⇒ 00:32:16.029 Chang Ho Yoon: in terms of the division of how you do that automatically in a table like that. It’s quite, quite difficult. But I would. My 1st suggestion would be, see how quickly or if you, if you’re able to scrape that information about hospitals and.
266 00:32:16.420 ⇒ 00:32:18.719 Uttam Kumaran: Is that on their sites by chance, or like.
267 00:32:18.720 ⇒ 00:32:29.630 Chang Ho Yoon: It might be on Linkedin. I reckon it might be on the Linkedin, so that’ll be where I’d go in terms of employees, and then, and then you’ll get a sense for sort of how many thousands of people work for them, or hundreds of people.
268 00:32:29.630 ⇒ 00:32:34.340 Uttam Kumaran: If you were to. If you were to look up a clinic and try to find this information like, what are some terms
269 00:32:34.640 ⇒ 00:32:42.950 Uttam Kumaran: that you would use like if you were to want to find out, like how many theaters they have, and some of those things. Do you think that’s public, or do you think that’s like
270 00:32:43.090 ⇒ 00:32:44.270 Uttam Kumaran: something we can grab from their.
271 00:32:44.618 ⇒ 00:32:48.800 Chang Ho Yoon: I would look for the particular. I would look for the particular
272 00:32:49.810 ⇒ 00:32:51.070 Chang Ho Yoon: specialisms.
273 00:32:51.340 ⇒ 00:32:55.610 Uttam Kumaran: Oh, you would look at at the constituents in the org to determine
274 00:32:56.500 ⇒ 00:32:58.610 Uttam Kumaran: like you, would, you would look okay. Okay.
275 00:32:59.200 ⇒ 00:33:07.210 Chang Ho Yoon: Yeah, because there you can. If you can look for keywords like orthopedics, or colorectal, or you know
276 00:33:07.537 ⇒ 00:33:16.160 Chang Ho Yoon: or even the words procedures or surgery. Then then you know that they’re gonna have that kind of issue, and we can then unbox a whole set of
277 00:33:16.270 ⇒ 00:33:25.119 Chang Ho Yoon: potential solutions along the lines of operating room efficiency along the lines of aligning up staffing for.
278 00:33:25.130 ⇒ 00:33:27.340 Chang Ho Yoon: and anaesthetic surgeons.
279 00:33:27.500 ⇒ 00:33:29.750 Chang Ho Yoon: you know, operating staff all to be
280 00:33:29.950 ⇒ 00:33:50.059 Chang Ho Yoon: or to be present for for operating rooms to be used efficiently, and so on, so forth. So basically, you can just unbox the the surgical so offerings list. And then for an entire, and then I guess if they are more sort of I don’t know outpatient clinic facing, then you’d be looking for
281 00:33:50.683 ⇒ 00:33:59.886 Chang Ho Yoon: those words like outpatient clinic. You’d be looking for more medical specialties, you know, whether it be rheumatology or gastroenterology, or what have you?
282 00:34:00.400 ⇒ 00:34:03.480 Chang Ho Yoon: If it’s more outpatient clinic facing then you can imagine
283 00:34:03.640 ⇒ 00:34:11.366 Chang Ho Yoon: the areas that you might be able to help. We might be able to help them. Automate will include that Admin, follow up process
284 00:34:12.420 ⇒ 00:34:17.710 Chang Ho Yoon: and that, and then we can sort of unbox it that way, and I kind of feel like that might be the way we go, rather than
285 00:34:18.080 ⇒ 00:34:20.620 Chang Ho Yoon: like necessarily the size of the place.
286 00:34:20.620 ⇒ 00:34:21.400 Uttam Kumaran: Yeah.
287 00:34:22.040 ⇒ 00:34:26.069 Uttam Kumaran: No, I like that. I think that’s Erickson, I mean, I think that’s like a
288 00:34:26.190 ⇒ 00:34:30.610 Uttam Kumaran: I think that’s a good challenge for you and your team. What other information
289 00:34:30.800 ⇒ 00:34:32.232 Uttam Kumaran: do you need?
290 00:34:33.870 ⇒ 00:34:34.590 Chang Ho Yoon: Okay.
291 00:34:35.489 ⇒ 00:34:36.520 Chang Ho Yoon: Yeah.
292 00:34:37.699 ⇒ 00:34:44.014 Ericson Dalusong: Yup, I think I’ve got all the information that I need here.
293 00:34:45.709 ⇒ 00:34:49.549 Ericson Dalusong: but I do think that we can also
294 00:34:52.089 ⇒ 00:34:57.029 Ericson Dalusong: qualify this companies based on their headcount growth. That’s something.
295 00:34:57.030 ⇒ 00:35:01.183 Uttam Kumaran: Yeah, it’s it’s gonna have to be on counts of certain roles.
296 00:35:01.903 ⇒ 00:35:04.629 Uttam Kumaran: But maybe what I’ll do is I’ll give you.
297 00:35:04.650 ⇒ 00:35:08.379 Uttam Kumaran: I’ll give you this transcript of this convo, and maybe you can work
298 00:35:08.620 ⇒ 00:35:12.640 Uttam Kumaran: work with the Claude or Chat Gpt and see like if there’s any.
299 00:35:12.650 ⇒ 00:35:20.430 Uttam Kumaran: If there’s any ways in clay, where basically, what we can do is when you pull a company from Apollo. We want to pull the counts of certain titles.
300 00:35:20.988 ⇒ 00:35:23.529 Uttam Kumaran: And then let’s just try.
301 00:35:23.860 ⇒ 00:35:27.700 Uttam Kumaran: Try to put something in front of bang that that has that
302 00:35:28.160 ⇒ 00:35:32.779 Uttam Kumaran: you know, and we can. And then we can basically spot, check a couple and see whether they line up.
303 00:35:34.600 ⇒ 00:35:37.350 Uttam Kumaran: you know. I think that would be a good good start.
304 00:35:38.730 ⇒ 00:35:39.879 Ericson Dalusong: That sounds good.
305 00:35:39.880 ⇒ 00:35:48.379 Uttam Kumaran: Because there’s somewhere we’re gonna somewhere. We’re gonna land somewhere in the moon. I mean, I think this is a fun challenge. Like, I think some of this stuff definitely is not in Apollo, and not like
306 00:35:48.390 ⇒ 00:35:49.710 Uttam Kumaran: easily.
307 00:35:49.790 ⇒ 00:35:52.690 Uttam Kumaran: We have to deduce this from a couple of different variables.
308 00:35:53.133 ⇒ 00:35:56.826 Uttam Kumaran: But that’s fine. I don’t think anything I don’t. I don’t think it’s
309 00:35:57.690 ⇒ 00:35:59.700 Uttam Kumaran: particularly beyond that.
310 00:36:02.150 ⇒ 00:36:02.840 Ericson Dalusong: Yep.
311 00:36:03.090 ⇒ 00:36:08.259 Uttam Kumaran: It’ll be a it’ll be a work in progress. And then I think, ideally, what I want to do is basically
312 00:36:08.450 ⇒ 00:36:19.099 Uttam Kumaran: give you like, maybe it just like a like a paragraph or 2, saying on like what you could use for your comp for your next conversation as like a Okay, here’s like
313 00:36:19.650 ⇒ 00:36:20.750 Uttam Kumaran: we found.
314 00:36:20.760 ⇒ 00:36:32.709 Uttam Kumaran: we’re basically, here’s how we’re segmenting. And then you can say, Do you think this is in the right area or not? Right? So we’ll I’ll consolidate kind of this conversation. What we end up finding basically come up with a couple of examples.
315 00:36:32.920 ⇒ 00:36:40.320 Uttam Kumaran: Write down kind of what you’re what you mentioned to us about the offer, and give you that in like a little succinct thing, and you could take that to the meeting and say, like.
316 00:36:40.660 ⇒ 00:36:44.089 Uttam Kumaran: you can weave that in and and get feedback on that as well. Okay.
317 00:36:48.240 ⇒ 00:36:49.040 Uttam Kumaran: okay.
318 00:36:49.570 ⇒ 00:36:53.329 Uttam Kumaran: cool anything else. We wanted to chat about
319 00:36:53.470 ⇒ 00:36:57.760 Uttam Kumaran: anything else, Eric said. I mean, you can talk, offline, and and work on it together.
320 00:36:59.442 ⇒ 00:37:01.849 Ericson Dalusong: nothing on my side as of yet. But
321 00:37:02.422 ⇒ 00:37:04.419 Ericson Dalusong: for sure, as I build this
322 00:37:04.820 ⇒ 00:37:05.870 Ericson Dalusong: table.
323 00:37:06.130 ⇒ 00:37:09.859 Ericson Dalusong: I might have some questions and we can.
324 00:37:10.190 ⇒ 00:37:12.870 Ericson Dalusong: We can possibly just use slack for
325 00:37:13.150 ⇒ 00:37:15.449 Ericson Dalusong: for that, if ever.
326 00:37:15.600 ⇒ 00:37:16.280 Ericson Dalusong: Yeah.
327 00:37:17.820 ⇒ 00:37:18.470 Uttam Kumaran: Okay.
328 00:37:18.860 ⇒ 00:37:19.450 Ericson Dalusong: Right
329 00:37:19.740 ⇒ 00:37:20.430 Ericson Dalusong: sounds, great.
330 00:37:20.430 ⇒ 00:37:21.130 Chang Ho Yoon: Nice.
331 00:37:21.130 ⇒ 00:37:39.420 Uttam Kumaran: Okay, thanks. So much for the time. Yeah, I just wanted to. Let’s see, I wanna get somewhere. So let’s I’m gonna keep pushing. And then, yeah, I mean, look, I think, what that conversation as much fodder as I can give you as ammo to be like this was totally wrong assumption. This is totally right. And then, right after that, hopefully, we can take some action and then
332 00:37:39.590 ⇒ 00:37:42.520 Uttam Kumaran: just throw stuff at the wall and see what sticks.
333 00:37:43.480 ⇒ 00:37:49.579 Chang Ho Yoon: Yeah, I mean, I mean, I like the generalist approach like this and sort of how it automates it from the get go. I also believe that
334 00:37:49.660 ⇒ 00:37:51.430 Chang Ho Yoon: maybe engineering it.
335 00:37:52.240 ⇒ 00:37:57.390 Chang Ho Yoon: after a sort of quick micro iteration, is also worthwhile doing. In other words.
336 00:37:57.400 ⇒ 00:38:00.980 Chang Ho Yoon: to actually specifically target a small clinic to do it.
337 00:38:00.980 ⇒ 00:38:01.770 Uttam Kumaran: I’m doing.
338 00:38:02.150 ⇒ 00:38:09.319 Chang Ho Yoon: And see what they’re about, and then that will help guide. How we automate this across the field rather than just going for what we think is is what.
339 00:38:09.320 ⇒ 00:38:16.389 Uttam Kumaran: Exactly. Yeah, no, I’m and I’m totally open to admitting to the yeah, I’m open. I’m totally open to admit to
340 00:38:16.520 ⇒ 00:38:19.967 Uttam Kumaran: those folks, too, that it’s like, Hey,
341 00:38:21.000 ⇒ 00:38:48.910 Uttam Kumaran: this is our like. We we’ve done AI, and across other industries is our 1st one. We’re willing to offer this for free or like we can do a month for free, if you if you like, what we develop. And it’s of course, like we believe that we’re gonna do it right if it if it saves you money, and then we can talk about how to extend. That’s actually what we’re doing across other AI related initiatives now is, we’re planning. We’re basically doing like a free month offer. And we’re kind of formulating is like, partly an audit.
342 00:38:48.910 ⇒ 00:39:18.489 Uttam Kumaran: but partly like, hey, just like, tell us what your problems are. We’re gonna come in and see whether we can prototype a couple of automations because a lot of people don’t know like. They don’t know the tools enough, and they don’t have the applications in their mind yet. But they know they are getting bombarded by either their boss or the industry to be like, use. AI. So instead of saying like, We’re AI, hire us, I’m like, Hey, I’m betting that what we do in a month and
343 00:39:18.520 ⇒ 00:39:26.599 Uttam Kumaran: will blow your socks off, and that you will be like, we need this, just give us an opportunity to show you that for free.
344 00:39:27.050 ⇒ 00:39:41.930 Uttam Kumaran: And then, yeah, yeah, well, let’s, I think that’s a i mean, if someone came to my company, people do come to my company and do that. But then they they they don’t. I don’t feel like a lot a lot of people in engineering do that. And so that’s that’s our bet, you know. I think that’s how we get in
345 00:39:42.262 ⇒ 00:39:51.609 Uttam Kumaran: and again everything else. I think. You know, we have an established company. We have people. I think the biggest thing is you’re right is like getting a 1st case study getting a customer partner. That’s like.
346 00:39:51.610 ⇒ 00:39:52.070 Chang Ho Yoon: Okay.
347 00:39:52.070 ⇒ 00:39:56.090 Uttam Kumaran: You guys are awesome. Let’s work through these like hiccups, and then we can see how it works. You know.
348 00:39:59.140 ⇒ 00:40:00.210 Chang Ho Yoon: Sounds good.
349 00:40:00.510 ⇒ 00:40:01.850 Uttam Kumaran: Okay, perfect.
350 00:40:01.850 ⇒ 00:40:04.310 Chang Ho Yoon: Yeah, I agree. I agree. Thanks. Thanks.
351 00:40:04.310 ⇒ 00:40:04.810 Uttam Kumaran: All right.
352 00:40:05.057 ⇒ 00:40:07.279 Chang Ho Yoon: Catch you guys later on. Thanks for making time.
353 00:40:07.280 ⇒ 00:40:09.409 Uttam Kumaran: Yeah, thank you so much. I’ll talk to you soon.
354 00:40:11.190 ⇒ 00:40:12.020 Chang Ho Yoon: Thanks, then bye.
355 00:40:12.020 ⇒ 00:40:12.580 Uttam Kumaran: Bye.