Meeting Title: Uttam <> Abigail-Weekly Date: 2024-09-24 Meeting participants: Abigail Zhao, Uttam Kumaran
WEBVTT
1 00:02:03.370 ⇒ 00:02:04.040 Uttam Kumaran: Hey!
2 00:02:05.550 ⇒ 00:02:07.179 Abigail Zhao: Hi! How are you?
3 00:02:07.960 ⇒ 00:02:08.639 Abigail Zhao: Cool?
4 00:02:10.880 ⇒ 00:02:11.419 Abigail Zhao: I think.
5 00:02:11.420 ⇒ 00:02:11.960 Uttam Kumaran: Gig.
6 00:02:12.840 ⇒ 00:02:15.699 Abigail Zhao: I think your connection is a little unstable.
7 00:02:15.700 ⇒ 00:02:16.730 Uttam Kumaran: Is it better?
8 00:02:20.000 ⇒ 00:02:20.710 Uttam Kumaran: Hi guys.
9 00:02:21.130 ⇒ 00:02:23.249 Abigail Zhao: Okay, I can hear you at least, which is like.
10 00:02:23.430 ⇒ 00:02:24.950 Abigail Zhao: but yeah, you’re good.
11 00:02:25.330 ⇒ 00:02:28.784 Uttam Kumaran: Hold on! Hold on! Let me see if I can get on the Wi-fi. Hold on.
12 00:02:35.630 ⇒ 00:02:37.189 Uttam Kumaran: Otherwise I’m gonna move.
13 00:02:42.900 ⇒ 00:02:43.660 Uttam Kumaran: and so
14 00:03:07.630 ⇒ 00:03:08.689 Uttam Kumaran: is it better.
15 00:03:11.020 ⇒ 00:03:11.640 Abigail Zhao: Oh.
16 00:03:11.910 ⇒ 00:03:15.200 Abigail Zhao: yeah. So yeah, you were like, frozen for a while.
17 00:03:15.200 ⇒ 00:03:16.569 Uttam Kumaran: Have an hour, have an hour.
18 00:03:18.080 ⇒ 00:03:18.820 Uttam Kumaran: decent.
19 00:03:18.820 ⇒ 00:03:21.099 Abigail Zhao: You’re still frozen.
20 00:03:21.100 ⇒ 00:03:23.190 Uttam Kumaran: Moving. Wait. Hold on
21 00:03:23.880 ⇒ 00:03:25.980 Uttam Kumaran: coming out. Yeah, maybe.
22 00:03:26.270 ⇒ 00:03:27.220 Abigail Zhao: Like.
23 00:03:28.715 ⇒ 00:03:29.370 Abigail Zhao: yeah.
24 00:03:29.370 ⇒ 00:03:33.759 Uttam Kumaran: Alright. We could just do video off like I’m trying. I’m just at a coffee shop. But okay.
25 00:03:34.450 ⇒ 00:03:35.180 Abigail Zhao: Yeah.
26 00:03:37.717 ⇒ 00:03:39.120 Uttam Kumaran: yeah, I guess.
27 00:03:39.130 ⇒ 00:03:40.969 Uttam Kumaran: How’s the week been going.
28 00:03:42.119 ⇒ 00:03:47.519 Abigail Zhao: It’s gone alright. So far. My main thing right now is that
29 00:03:47.850 ⇒ 00:03:53.479 Abigail Zhao: I like have a list of companies for the to like update the spreadsheet, or whatever, but.
30 00:03:53.480 ⇒ 00:03:54.220 Uttam Kumaran: Yeah.
31 00:03:54.220 ⇒ 00:04:05.269 Abigail Zhao: I don’t know. I think right now it’s a little tedious, because I’m like kind of looking at each individual like company to make sure that it like aligns cause like some of these companies.
32 00:04:05.420 ⇒ 00:04:09.819 Abigail Zhao: I either like I don’t know if they’re real or not, or like
33 00:04:10.030 ⇒ 00:04:12.680 Abigail Zhao: I don’t know if I should say like if they’re real or not, but like
34 00:04:13.680 ⇒ 00:04:19.319 Abigail Zhao: some of them, say that like they have like 0 employees or like something like that. And I’m trying to like filter.
35 00:04:19.329 ⇒ 00:04:25.409 Uttam Kumaran: Oh, okay, so like, I mean, that’s so. That’s the thing is like, I wanted to get the filters good enough to basically
36 00:04:25.679 ⇒ 00:04:27.879 Uttam Kumaran: be able to just export all of them.
37 00:04:27.880 ⇒ 00:04:28.350 Abigail Zhao: Yeah.
38 00:04:30.360 ⇒ 00:04:37.100 Uttam Kumaran: like. So did you? Did you initially start by just looking for data? Wait. So you’re this is for the data consulting companies. Right?
39 00:04:37.100 ⇒ 00:04:37.870 Abigail Zhao: Yeah, like.
40 00:04:37.870 ⇒ 00:04:43.460 Uttam Kumaran: So you initially just searched for data consulting companies? Or did you take a couple of examples and like, try to
41 00:04:43.610 ⇒ 00:04:47.990 Uttam Kumaran: cause it’s basically re-engine. It’s like reverse engineering filters, right? Pretty much.
42 00:04:48.490 ⇒ 00:04:53.680 Abigail Zhao: Yeah. At 1st I tried to do it, based on the filters that were coming up for like Brainforge. But
43 00:04:54.183 ⇒ 00:04:57.650 Abigail Zhao: like most of the filters, I think, were
44 00:04:57.670 ⇒ 00:05:03.100 Abigail Zhao: they didn’t. They didn’t involve as much of like the consulting side, more like data engineering.
45 00:05:03.100 ⇒ 00:05:06.200 Uttam Kumaran: Yeah, we’re probably not gonna be a good example, because
46 00:05:07.690 ⇒ 00:05:14.260 Uttam Kumaran: until like, probably 3 months ago, it was pretty like Unclear. What we even done did from the website. So anybody that scraped us
47 00:05:14.560 ⇒ 00:05:25.010 Uttam Kumaran: probably didn’t get a good sense. Also, we’re like a young company. So like looking at like a company like Slalom or analytics. 8 or like Ph, data
48 00:05:25.030 ⇒ 00:05:26.869 Uttam Kumaran: is like a really good
49 00:05:27.230 ⇒ 00:05:28.549 Uttam Kumaran: like alternative.
50 00:05:28.590 ⇒ 00:05:31.760 Uttam Kumaran: Like, if you look up like Ph data, they’re probably like
51 00:05:31.870 ⇒ 00:05:34.809 Uttam Kumaran: what we would be in like a few years.
52 00:05:35.280 ⇒ 00:05:35.900 Abigail Zhao: Hey!
53 00:05:35.900 ⇒ 00:05:39.420 Uttam Kumaran: And they’re whatever they have. They’re probably have like data consulting services.
54 00:05:40.214 ⇒ 00:05:46.079 Uttam Kumaran: So I wonder if you can build like a look alike. Audience, which is kind of like what they call this like? Look alike.
55 00:05:46.540 ⇒ 00:05:48.529 Uttam Kumaran: List from that.
56 00:05:50.560 ⇒ 00:05:52.139 Abigail Zhao: Okay. I’m looking.
57 00:05:52.140 ⇒ 00:05:56.340 Uttam Kumaran: And then, yeah, I mean again, I don’t really care too much about companies with less than
58 00:05:56.500 ⇒ 00:05:57.970 Uttam Kumaran: 10 employees.
59 00:05:58.810 ⇒ 00:05:59.720 Abigail Zhao: okay?
60 00:06:00.550 ⇒ 00:06:03.459 Abigail Zhao: Oh, okay, I can. I mean, I can filter that out pretty easily.
61 00:06:04.230 ⇒ 00:06:04.900 Abigail Zhao: And
62 00:06:09.285 ⇒ 00:06:11.340 Abigail Zhao: i’m looking at.
63 00:06:11.370 ⇒ 00:06:16.130 Abigail Zhao: oh, I lost the tab. I’m looking at ph, data right now.
64 00:06:16.270 ⇒ 00:06:17.880 Uttam Kumaran: Do you want to share, by the way, so I can just see.
65 00:06:17.880 ⇒ 00:06:18.740 Abigail Zhao: Yeah, yeah.
66 00:06:20.450 ⇒ 00:06:21.120 Abigail Zhao: second.
67 00:06:26.040 ⇒ 00:06:26.890 Abigail Zhao: think it’s
68 00:06:28.155 ⇒ 00:06:30.480 Abigail Zhao: yeah. Yeah, okay, see? This.
69 00:06:31.340 ⇒ 00:06:32.240 Uttam Kumaran: Yeah.
70 00:06:32.240 ⇒ 00:06:34.348 Abigail Zhao: Okay, yeah, so this is ph, data,
71 00:06:34.650 ⇒ 00:06:35.400 Uttam Kumaran: Yeah.
72 00:06:35.400 ⇒ 00:06:38.369 Abigail Zhao: I think this also is just very much like.
73 00:06:38.370 ⇒ 00:06:39.280 Uttam Kumaran: Generic.
74 00:06:39.690 ⇒ 00:06:44.680 Abigail Zhao: Yeah, very much more like data. And like, I feel like, if I were to put these in, I would probably get.
75 00:06:44.680 ⇒ 00:06:45.560 Uttam Kumaran: Yeah.
76 00:06:45.560 ⇒ 00:06:47.259 Abigail Zhao: We’re in like data, consulting.
77 00:06:48.570 ⇒ 00:06:51.037 Uttam Kumaran: So can you also look at like
78 00:06:52.310 ⇒ 00:06:54.340 Uttam Kumaran: analytics? 8
79 00:06:54.790 ⇒ 00:06:59.750 Uttam Kumaran: cause on cause on Linkedin. It says it consulting services. Right? So I’m like, what.
80 00:07:00.120 ⇒ 00:07:01.530 Abigail Zhao: Analytics. 8.
81 00:07:01.920 ⇒ 00:07:03.619 Uttam Kumaran: Yeah, literally, just the number 8.
82 00:07:04.170 ⇒ 00:07:04.720 Abigail Zhao: Okay.
83 00:07:07.030 ⇒ 00:07:09.760 Uttam Kumaran: I don’t ask how I know. Like all these companies.
84 00:07:11.090 ⇒ 00:07:11.910 Uttam Kumaran: okay.
85 00:07:12.240 ⇒ 00:07:20.340 Uttam Kumaran: information technology services. So that’s the probably the one like, maybe we should go for that. See? Under under 180 employees.
86 00:07:20.490 ⇒ 00:07:23.619 Uttam Kumaran: It says, information technology services.
87 00:07:24.330 ⇒ 00:07:27.400 Uttam Kumaran: Maybe we should try that. And then
88 00:07:27.580 ⇒ 00:07:32.929 Uttam Kumaran: also put in like something on the data side. And then it looks like they also have technology implementation
89 00:07:33.380 ⇒ 00:07:34.740 Uttam Kumaran: as like a key.
90 00:07:36.160 ⇒ 00:07:38.310 Uttam Kumaran: So maybe we try a couple of those.
91 00:07:39.160 ⇒ 00:07:43.729 Uttam Kumaran: And then we can also put in like employees above, like a certain amount in us.
92 00:07:43.730 ⇒ 00:07:46.789 Abigail Zhao: Yeah, right now, I think I do have this industry.
93 00:07:48.040 ⇒ 00:07:51.430 Uttam Kumaran: So how many, how many hits do you get when you put in just that
94 00:07:52.200 ⇒ 00:07:53.359 Uttam Kumaran: like a ton?
95 00:07:53.840 ⇒ 00:07:56.669 Abigail Zhao: Yeah, like, probably over. Let me let me
96 00:07:58.270 ⇒ 00:08:00.339 Abigail Zhao: let me just try.
97 00:08:02.450 ⇒ 00:08:03.230 Abigail Zhao: And
98 00:08:07.910 ⇒ 00:08:09.589 Abigail Zhao: there there you go.
99 00:08:10.130 ⇒ 00:08:11.169 Abigail Zhao: Oh, there we go!
100 00:08:14.560 ⇒ 00:08:18.180 Abigail Zhao: yeah, I think this one will give you like a lot. Wait also.
101 00:08:19.270 ⇒ 00:08:19.900 Uttam Kumaran: Yeah.
102 00:08:21.840 ⇒ 00:08:25.069 Uttam Kumaran: so and then maybe try with employees over 10.
103 00:08:28.310 ⇒ 00:08:30.739 Uttam Kumaran: You could probably just do custom range.
104 00:08:30.760 ⇒ 00:08:32.409 Abigail Zhao: Oh, yeah. Return.
105 00:08:34.400 ⇒ 00:08:36.049 Abigail Zhao: 10. Max.
106 00:08:37.900 ⇒ 00:08:39.280 Uttam Kumaran: Yeah, it’s funny.
107 00:08:39.972 ⇒ 00:08:43.200 Uttam Kumaran: And then you wanna do us based. That’s probably why.
108 00:08:43.200 ⇒ 00:08:43.789 Abigail Zhao: Yeah.
109 00:08:46.020 ⇒ 00:08:46.650 Abigail Zhao: what?
110 00:08:46.650 ⇒ 00:08:49.039 Uttam Kumaran: I think it’s an account location. Yeah.
111 00:08:50.200 ⇒ 00:08:52.640 Abigail Zhao: Wait. I don’t know if this is all in Chinese.
112 00:08:52.770 ⇒ 00:08:53.510 Abigail Zhao: Okay.
113 00:08:54.380 ⇒ 00:08:56.350 Uttam Kumaran: Oh, wait! I don’t know. Okay, maybe that’s
114 00:08:56.410 ⇒ 00:08:58.019 Uttam Kumaran: it’s your laptop.
115 00:08:58.478 ⇒ 00:08:59.749 Abigail Zhao: I don’t know. Okay.
116 00:09:00.170 ⇒ 00:09:01.050 Uttam Kumaran: Wait, what.
117 00:09:01.050 ⇒ 00:09:01.909 Abigail Zhao: I don’t know.
118 00:09:01.910 ⇒ 00:09:04.349 Uttam Kumaran: Maybe it’s alphabetically ranked. Okay? Well.
119 00:09:04.360 ⇒ 00:09:07.050 Uttam Kumaran: see if you can put in like something about data
120 00:09:07.790 ⇒ 00:09:11.380 Uttam Kumaran: like what the fuck. I don’t know how else to filter this down.
121 00:09:13.230 ⇒ 00:09:23.590 Abigail Zhao: There’s like what I’ve been doing mainly is like the keywords. So like, just like putting a bunch of like data related keywords and, like consulting related keywords.
122 00:09:23.590 ⇒ 00:09:27.239 Uttam Kumaran: So what did so on in terms of keywords like we put in,
123 00:09:27.870 ⇒ 00:09:32.669 Uttam Kumaran: what did that company had had like data manage? This is like data manage like, what was that
124 00:09:33.640 ⇒ 00:09:36.230 Uttam Kumaran: technology implementation? Yeah, try that.
125 00:09:41.220 ⇒ 00:09:42.190 Abigail Zhao: Oh, okay.
126 00:09:42.190 ⇒ 00:09:43.130 Uttam Kumaran: 33.
127 00:09:43.600 ⇒ 00:09:45.910 Uttam Kumaran: Okay, so looks like that
128 00:09:46.290 ⇒ 00:09:47.250 Uttam Kumaran: helped.
129 00:09:51.460 ⇒ 00:09:58.399 Uttam Kumaran: So I still want it. I still want to put in like included keywords is like we. We just need everything to try to point us towards like
130 00:09:58.650 ⇒ 00:10:02.849 Uttam Kumaran: consulting. But I don’t know if you click on analytics. 8 within Apollo.
131 00:10:03.240 ⇒ 00:10:06.909 Uttam Kumaran: Is there anywhere where it shows you like similar companies in here? Or
132 00:10:07.010 ⇒ 00:10:08.590 Uttam Kumaran: is there anything like that.
133 00:10:08.860 ⇒ 00:10:09.970 Abigail Zhao: M.
134 00:10:10.900 ⇒ 00:10:11.890 Uttam Kumaran: Maybe not.
135 00:10:12.160 ⇒ 00:10:16.482 Abigail Zhao: I don’t think I don’t think it shows like related companies or anything. It just shows, like the people
136 00:10:16.810 ⇒ 00:10:17.830 Abigail Zhao: that you can.
137 00:10:17.830 ⇒ 00:10:25.429 Uttam Kumaran: Can you search on Google like Apollo related companies, or Apollo similar companies, or something like that? See if there’s anything like that
138 00:10:28.590 ⇒ 00:10:34.260 Uttam Kumaran: you may have. Oh, you may have to do like Apolloio, similar companies, or something like that.
139 00:10:34.790 ⇒ 00:10:37.296 Uttam Kumaran: Oh, it’s gonna give you alternatives to Apollo.
140 00:10:38.340 ⇒ 00:10:43.230 Uttam Kumaran: maybe just try Googling like how to find. Oh, I don’t know to Google something.
141 00:10:46.060 ⇒ 00:10:47.060 Abigail Zhao: And.
142 00:10:47.550 ⇒ 00:10:50.180 Uttam Kumaran: It’s almost like you’re trying to find. Like.
143 00:10:51.690 ⇒ 00:10:53.669 Uttam Kumaran: yeah, see that top reddit one.
144 00:10:53.820 ⇒ 00:10:55.570 Abigail Zhao: This one. Yeah.
145 00:10:55.680 ⇒ 00:10:56.740 Abigail Zhao: you find.
146 00:10:57.720 ⇒ 00:11:00.080 Abigail Zhao: use AI to plug in the company.
147 00:11:02.010 ⇒ 00:11:04.090 Uttam Kumaran: Okay, never mind.
148 00:11:07.050 ⇒ 00:11:13.649 Uttam Kumaran: I wonder if you just search in Apollo. Can you search for data, consulting companies? And does anything come up.
149 00:11:14.310 ⇒ 00:11:15.490 Abigail Zhao: Oh, like
150 00:11:22.702 ⇒ 00:11:24.630 Abigail Zhao: like as a.
151 00:11:25.020 ⇒ 00:11:27.214 Uttam Kumaran: Yeah, I mean, I don’t know.
152 00:11:28.460 ⇒ 00:11:29.590 Abigail Zhao: We’re like and.
153 00:11:30.390 ⇒ 00:11:35.549 Uttam Kumaran: Maybe in you could go, probably go back. Yeah. Maybe in industry, you can type in
154 00:11:36.370 ⇒ 00:11:38.070 Uttam Kumaran: consulting companies.
155 00:11:39.070 ⇒ 00:11:42.110 Abigail Zhao: Oh, this this part is like, really, like
156 00:11:42.840 ⇒ 00:11:43.360 Abigail Zhao: specific.
157 00:11:43.360 ⇒ 00:11:46.080 Uttam Kumaran: That’s gonna okay. Okay? So maybe it’s not.
158 00:11:46.110 ⇒ 00:11:50.350 Uttam Kumaran: Maybe it’s not there. Maybe it’s always on the we just have to search using the keywords.
159 00:11:50.480 ⇒ 00:11:52.339 Uttam Kumaran: because everything’s gonna be it
160 00:11:52.390 ⇒ 00:11:53.619 Uttam Kumaran: in our world.
161 00:11:54.530 ⇒ 00:11:58.129 Uttam Kumaran: So maybe you want to search for like consulting.
162 00:11:58.200 ⇒ 00:12:02.980 Uttam Kumaran: or whatever the other one was which was like data implementation services or something like that.
163 00:12:03.100 ⇒ 00:12:04.580 Uttam Kumaran: right? Something like that
164 00:12:05.010 ⇒ 00:12:08.309 Uttam Kumaran: or technical implementation technology implementation.
165 00:12:10.350 ⇒ 00:12:15.199 Uttam Kumaran: So so these are all consulting companies on the right. Okay, so we’re in like, we’re in like the right
166 00:12:16.100 ⇒ 00:12:17.280 Uttam Kumaran: world.
167 00:12:17.300 ⇒ 00:12:21.570 Uttam Kumaran: Maybe if you can also find, how do we? We just have to filter this to like data
168 00:12:21.640 ⇒ 00:12:23.030 Uttam Kumaran: data related?
169 00:12:23.290 ⇒ 00:12:24.380 Uttam Kumaran: So.
170 00:12:28.020 ⇒ 00:12:35.280 Uttam Kumaran: okay, there’s still like more than a million. I wonder if you can just do? Can you filter out anyone less than 10 employees.
171 00:12:35.280 ⇒ 00:12:35.960 Abigail Zhao: Yeah.
172 00:12:36.410 ⇒ 00:12:38.269 Uttam Kumaran: And then we could also look at.
173 00:12:46.200 ⇒ 00:12:46.800 Abigail Zhao: Let’s.
174 00:12:46.800 ⇒ 00:12:48.289 Uttam Kumaran: This is getting better.
175 00:12:49.060 ⇒ 00:12:51.530 Uttam Kumaran: And then, okay, this is getting better.
176 00:12:52.705 ⇒ 00:12:55.240 Uttam Kumaran: Let’s also do
177 00:12:57.510 ⇒ 00:12:59.450 Uttam Kumaran: technologies.
178 00:13:00.520 ⇒ 00:13:01.070 Abigail Zhao: Can you.
179 00:13:01.070 ⇒ 00:13:04.179 Uttam Kumaran: You look at like, what can you just look at tape and data.
180 00:13:06.660 ⇒ 00:13:07.990 Abigail Zhao: I think it’s like specific, like.
181 00:13:07.990 ⇒ 00:13:08.980 Uttam Kumaran: Or
182 00:13:09.470 ⇒ 00:13:12.920 Uttam Kumaran: data analytics. Just type in data analytics is at the top there.
183 00:13:13.650 ⇒ 00:13:14.600 Uttam Kumaran: Yeah.
184 00:13:19.020 ⇒ 00:13:21.730 Uttam Kumaran: see if that. Okay, it’s the better.
185 00:13:23.470 ⇒ 00:13:24.330 Uttam Kumaran: Right?
186 00:13:24.450 ⇒ 00:13:25.370 Abigail Zhao: Yeah, yeah.
187 00:13:29.130 ⇒ 00:13:34.029 Uttam Kumaran: okay, this is pretty good. Let’s filter out. Oh, actually, okay, that’s fine.
188 00:13:36.440 ⇒ 00:13:39.990 Uttam Kumaran: what else can we try to filter? Can you type in data warehouse.
189 00:13:40.950 ⇒ 00:13:41.790 Abigail Zhao: The keeper.
190 00:13:42.610 ⇒ 00:13:43.810 Uttam Kumaran: More technologies.
191 00:13:43.890 ⇒ 00:13:44.799 Abigail Zhao: Okay, got it.
192 00:13:48.370 ⇒ 00:13:49.259 Abigail Zhao: It does not come.
193 00:13:49.260 ⇒ 00:13:50.206 Uttam Kumaran: Coming up.
194 00:13:51.150 ⇒ 00:13:53.510 Uttam Kumaran: can you type in like business intelligence.
195 00:13:56.190 ⇒ 00:13:57.050 Abigail Zhao: There’s like
196 00:13:57.790 ⇒ 00:13:58.960 Abigail Zhao: specific.
197 00:13:59.930 ⇒ 00:14:03.129 Uttam Kumaran: Is there? Business? Intelligence is like a broad category or no.
198 00:14:03.130 ⇒ 00:14:04.080 Abigail Zhao: I don’t think so. It’s.
199 00:14:04.080 ⇒ 00:14:05.949 Uttam Kumaran: Right now. Okay, like.
200 00:14:07.107 ⇒ 00:14:09.080 Uttam Kumaran: okay, maybe try.
201 00:14:09.390 ⇒ 00:14:11.999 Uttam Kumaran: Well, I think maybe we could try to hit
202 00:14:12.410 ⇒ 00:14:14.819 Uttam Kumaran: trying to think what’s the best way to do this
203 00:14:15.740 ⇒ 00:14:23.240 Uttam Kumaran: cause that gives us 5,000. Can you keep? Can you just like, see if we can scroll through a couple of them and see how we can filter out some of the ones we don’t want.
204 00:14:23.280 ⇒ 00:14:24.890 Uttam Kumaran: These all look fine.
205 00:14:26.150 ⇒ 00:14:28.059 Uttam Kumaran: Yeah, this is looks like
206 00:14:29.630 ⇒ 00:14:31.399 Uttam Kumaran: this is like a consulting company.
207 00:14:38.930 ⇒ 00:14:39.410 Abigail Zhao: Hello!
208 00:14:41.700 ⇒ 00:14:42.790 Uttam Kumaran: What happened? Okay.
209 00:14:42.790 ⇒ 00:14:43.840 Abigail Zhao: Oh, no! No!
210 00:14:44.190 ⇒ 00:14:45.630 Uttam Kumaran: Oh, shit you lost everything, dude.
211 00:14:45.630 ⇒ 00:14:46.280 Abigail Zhao: It’s cool.
212 00:14:46.280 ⇒ 00:14:49.809 Uttam Kumaran: Go forward. Okay? Oh, wait. Keep going forward until you
213 00:14:53.440 ⇒ 00:14:54.310 Uttam Kumaran: click on.
214 00:14:55.140 ⇒ 00:14:56.000 Uttam Kumaran: Okay.
215 00:14:56.250 ⇒ 00:14:57.540 Abigail Zhao: Is that right? Okay.
216 00:14:57.820 ⇒ 00:15:00.370 Uttam Kumaran: Save! Wait! Save the search, save the search!
217 00:15:00.370 ⇒ 00:15:00.780 Abigail Zhao: Yeah.
218 00:15:01.750 ⇒ 00:15:02.670 Abigail Zhao: okay.
219 00:15:03.612 ⇒ 00:15:04.880 Uttam Kumaran: I don’t know.
220 00:15:06.520 ⇒ 00:15:07.250 Abigail Zhao: Boom.
221 00:15:08.590 ⇒ 00:15:09.692 Uttam Kumaran: I don’t know.
222 00:15:12.550 ⇒ 00:15:15.918 Uttam Kumaran: You’re the you’re the one in Apollo.
223 00:15:16.400 ⇒ 00:15:17.740 Abigail Zhao: I’ll just name it like
224 00:15:19.170 ⇒ 00:15:20.019 Abigail Zhao: man. I don’t know.
225 00:15:20.490 ⇒ 00:15:21.740 Abigail Zhao: and.
226 00:15:22.750 ⇒ 00:15:27.339 Uttam Kumaran: Could just name it like test search with for the day or something, or like cause. I want to keep updating this one
227 00:15:27.510 ⇒ 00:15:31.020 Uttam Kumaran: so you could just name it like consulting like data consulting search
228 00:15:31.190 ⇒ 00:15:33.990 Uttam Kumaran: that way, we can just come, continue to iterate on this.
229 00:15:37.950 ⇒ 00:15:41.020 Uttam Kumaran: so keep going to the right on like this list.
230 00:15:41.260 ⇒ 00:15:43.269 Uttam Kumaran: Like, let’s see a couple of others.
231 00:15:46.310 ⇒ 00:15:48.250 Uttam Kumaran: Okay, this is all good
232 00:15:52.790 ⇒ 00:15:54.990 Uttam Kumaran: big data mobile.
233 00:15:55.560 ⇒ 00:15:59.097 Uttam Kumaran: Let’s see, the problem is, there’s gonna be a lot of people that
234 00:16:00.450 ⇒ 00:16:09.929 Uttam Kumaran: I honestly probably want to prefer to just 5 people that primarily do data. So wondering if we can. Can you also filter? Yeah, filter this to us.
235 00:16:10.150 ⇒ 00:16:13.370 Uttam Kumaran: And then, okay? And then let’s also
236 00:16:13.860 ⇒ 00:16:16.489 Uttam Kumaran: see if we can filter this to
237 00:16:17.030 ⇒ 00:16:19.819 Uttam Kumaran: exclude, like mobile from the
238 00:16:20.110 ⇒ 00:16:21.820 Uttam Kumaran: from, like, the keywords.
239 00:16:22.050 ⇒ 00:16:23.047 Abigail Zhao: Oh, yeah. Okay.
240 00:16:26.390 ⇒ 00:16:28.890 Uttam Kumaran: So can you do? Yeah, exclude mobile.
241 00:16:31.480 ⇒ 00:16:34.650 Uttam Kumaran: And can you also exclude front end and back end?
242 00:16:36.440 ⇒ 00:16:37.929 Uttam Kumaran: Just one word each.
243 00:16:43.470 ⇒ 00:16:45.240 Uttam Kumaran: Okay, we’re getting better.
244 00:16:46.130 ⇒ 00:16:49.043 Uttam Kumaran: San Ramon. That’s where I’m from. Okay.
245 00:16:50.530 ⇒ 00:16:52.810 Uttam Kumaran: devops, Fintech.
246 00:16:56.350 ⇒ 00:16:59.039 Uttam Kumaran: What other stuff can we like? Try to?
247 00:17:07.160 ⇒ 00:17:11.770 Uttam Kumaran: Maybe keep searching like, keep going some more to see if there’s anything else.
248 00:17:17.000 ⇒ 00:17:18.699 Uttam Kumaran: Big data.
249 00:17:18.800 ⇒ 00:17:20.910 Uttam Kumaran: it solutions
250 00:17:21.890 ⇒ 00:17:23.359 Uttam Kumaran: big data.
251 00:17:23.890 ⇒ 00:17:25.580 Uttam Kumaran: I mean, I kind of like.
252 00:17:25.790 ⇒ 00:17:27.729 Uttam Kumaran: I basically kind of like
253 00:17:28.170 ⇒ 00:17:35.980 Uttam Kumaran: this, I want to make sure that data is in the keyword. So how can we make sure it is like click on advanced under, include.
254 00:17:43.770 ⇒ 00:17:44.260 Abigail Zhao: How.
255 00:17:44.260 ⇒ 00:17:48.850 Uttam Kumaran: Can we? Oh, so this is include keywords. Okay?
256 00:17:49.210 ⇒ 00:17:51.659 Uttam Kumaran: And what does include all? What is that thing.
257 00:17:52.160 ⇒ 00:17:52.950 Abigail Zhao: This is in.
258 00:17:52.950 ⇒ 00:17:54.150 Uttam Kumaran: Underneath SEO.
259 00:17:54.150 ⇒ 00:17:59.609 Abigail Zhao: Like they have. If you put a bunch like it’s all of them in the company like.
260 00:17:59.610 ⇒ 00:18:03.340 Uttam Kumaran: Oh, so then, so then, type in type in here, just type in data.
261 00:18:04.380 ⇒ 00:18:05.270 Uttam Kumaran: So it’s like.
262 00:18:05.340 ⇒ 00:18:09.979 Abigail Zhao: Like group of them, like every single one of these words, would have to be like included.
263 00:18:09.980 ⇒ 00:18:10.685 Uttam Kumaran: Okay.
264 00:18:11.440 ⇒ 00:18:14.089 Uttam Kumaran: okay. So in this case, data has to be there.
265 00:18:14.460 ⇒ 00:18:18.780 Uttam Kumaran: So let’s just so let’s so I like this list. See analytics data, is there?
266 00:18:19.360 ⇒ 00:18:20.970 Uttam Kumaran: That’s not. That’s not bad.
267 00:18:21.710 ⇒ 00:18:22.450 Uttam Kumaran: right?
268 00:18:23.360 ⇒ 00:18:24.140 Abigail Zhao: yeah.
269 00:18:25.913 ⇒ 00:18:28.080 Uttam Kumaran: Can you also include all?
270 00:18:28.860 ⇒ 00:18:32.579 Uttam Kumaran: Oh, so if you do so, click on what’s under advanced under include all?
271 00:18:35.050 ⇒ 00:18:36.270 Uttam Kumaran: Oh, okay, okay.
272 00:18:36.270 ⇒ 00:18:36.950 Abigail Zhao: I see it.
273 00:18:42.020 ⇒ 00:18:44.210 Uttam Kumaran: okay, I mean, I like this.
274 00:18:44.970 ⇒ 00:18:46.969 Uttam Kumaran: I kind of like this list. Right?
275 00:18:47.540 ⇒ 00:18:48.160 Uttam Kumaran: Hmm.
276 00:18:51.360 ⇒ 00:18:53.470 Uttam Kumaran: these all look like data companies.
277 00:18:53.610 ⇒ 00:18:58.190 Uttam Kumaran: So should we put consult. So, okay, so the other keywords.
278 00:19:00.870 ⇒ 00:19:05.410 Uttam Kumaran: Okay, so I wonder if, should we? Should we remove these other keywords or no.
279 00:19:08.900 ⇒ 00:19:10.949 Abigail Zhao: I mean, we can see what it does.
280 00:19:14.030 ⇒ 00:19:15.390 Abigail Zhao: Okay? Okay.
281 00:19:17.090 ⇒ 00:19:24.710 Uttam Kumaran: I mean, at least these are. But these are it. Companies. So scroll down on the list to the right. Let’s see if there’s any of them that like shouldn’t show up here.
282 00:19:25.653 ⇒ 00:19:31.309 Uttam Kumaran: Like data robot click, click on that like. Is that a is that a consulting opening.
283 00:19:31.600 ⇒ 00:19:36.230 Abigail Zhao: Like, since we got rid of. A few of these probably will not be consulting companies.
284 00:19:40.920 ⇒ 00:19:42.819 Uttam Kumaran: This looks like a consulting company actually.
285 00:19:44.120 ⇒ 00:19:45.720 Abigail Zhao: Oh, yeah.
286 00:19:46.500 ⇒ 00:19:49.409 Uttam Kumaran: Under keywords like what other keywords are there?
287 00:19:52.880 ⇒ 00:19:53.530 Uttam Kumaran: Oh.
288 00:19:54.020 ⇒ 00:19:56.290 Abigail Zhao: Oh, it came back anyway.
289 00:19:56.290 ⇒ 00:19:57.789 Uttam Kumaran: Okay, well, that’s fine. Yeah.
290 00:19:59.730 ⇒ 00:20:04.670 Uttam Kumaran: okay, so keep wait. Keep scrolling. Let let’s go to like couple couple more pages in, and we’ll click on a random one.
291 00:20:06.800 ⇒ 00:20:09.950 Uttam Kumaran: So for example, yeah, okay, so yeah, just keep going.
292 00:20:10.130 ⇒ 00:20:11.359 Uttam Kumaran: And then.
293 00:20:15.000 ⇒ 00:20:17.139 Uttam Kumaran: okay, I mean, these all look like pretty
294 00:20:19.410 ⇒ 00:20:20.990 Uttam Kumaran: heavy. It right.
295 00:20:24.070 ⇒ 00:20:24.690 Abigail Zhao: Yeah.
296 00:20:25.050 ⇒ 00:20:26.960 Abigail Zhao: I, mean, yeah, I think they.
297 00:20:26.960 ⇒ 00:20:29.900 Uttam Kumaran: I just wanna make sure we don’t have like software companies in here.
298 00:20:29.980 ⇒ 00:20:33.700 Uttam Kumaran: So, for example, Mother Duck is a software company.
299 00:20:35.270 ⇒ 00:20:37.679 Abigail Zhao: Oh, okay, they don’t have any keywords.
300 00:20:37.890 ⇒ 00:20:39.520 Uttam Kumaran: So they’ll have data.
301 00:20:39.530 ⇒ 00:20:42.619 Uttam Kumaran: But they won’t have. Okay. So I don’t know.
302 00:20:43.470 ⇒ 00:20:45.800 Uttam Kumaran: I feel like we should keep in
303 00:20:46.160 ⇒ 00:20:47.420 Uttam Kumaran: consulting.
304 00:20:55.010 ⇒ 00:20:55.710 Abigail Zhao: Okay. That’ll.
305 00:20:55.710 ⇒ 00:20:58.259 Uttam Kumaran: Okay, I’m happy with this list honestly.
306 00:20:59.890 ⇒ 00:21:02.940 Uttam Kumaran: So maybe just save this as the search, and then
307 00:21:03.738 ⇒ 00:21:07.020 Uttam Kumaran: see if you can add all of these to a new
308 00:21:08.810 ⇒ 00:21:10.490 Uttam Kumaran: tab on the Google sheet.
309 00:21:12.780 ⇒ 00:21:13.910 Uttam Kumaran: that’d be dope.
310 00:21:16.611 ⇒ 00:21:18.780 Abigail Zhao: Do you want me to like
311 00:21:19.450 ⇒ 00:21:22.180 Abigail Zhao: export these, or.
312 00:21:23.900 ⇒ 00:21:27.370 Uttam Kumaran: yeah, that’d be great if we could just export like, how do you do that on here?
313 00:21:27.820 ⇒ 00:21:29.480 Abigail Zhao: That’s a good question. I.
314 00:21:29.480 ⇒ 00:21:33.310 Uttam Kumaran: If you click on the the dots like the selects.
315 00:21:34.330 ⇒ 00:21:38.409 Uttam Kumaran: Yeah, like above company. There’s like a little selector button
316 00:21:39.460 ⇒ 00:21:42.480 Uttam Kumaran: on the right, like, where the scrolling thing is.
317 00:21:43.250 ⇒ 00:21:43.610 Abigail Zhao: Oh.
318 00:21:44.310 ⇒ 00:21:45.100 Abigail Zhao: wait!
319 00:21:45.390 ⇒ 00:21:47.860 Uttam Kumaran: All the way to the left, under hide filters.
320 00:21:47.860 ⇒ 00:21:48.510 Abigail Zhao: Oh, left!
321 00:21:48.510 ⇒ 00:21:49.200 Uttam Kumaran: Yeah.
322 00:21:50.870 ⇒ 00:21:53.009 Uttam Kumaran: so can you select all these?
323 00:21:53.980 ⇒ 00:21:56.830 Uttam Kumaran: And then there should be some way to export from here.
324 00:21:57.040 ⇒ 00:21:58.939 Uttam Kumaran: So click on the 3 dots.
325 00:21:59.240 ⇒ 00:22:01.029 Uttam Kumaran: See if there’s a way to export.
326 00:22:04.600 ⇒ 00:22:07.910 Uttam Kumaran: oh, you probably have to add these guys to a list, or
327 00:22:09.810 ⇒ 00:22:11.440 Uttam Kumaran: like click on plus.
328 00:22:13.210 ⇒ 00:22:14.440 Abigail Zhao: Oh, okay.
329 00:22:16.720 ⇒ 00:22:19.179 Abigail Zhao: Save. Account. Sorry.
330 00:22:19.650 ⇒ 00:22:23.129 Uttam Kumaran: Also update the owner. Yeah, just save these accounts somewhere.
331 00:22:26.190 ⇒ 00:22:27.999 Uttam Kumaran: And like, can you add them to a list?
332 00:22:30.550 ⇒ 00:22:32.700 Uttam Kumaran: Or we can’t create a new list.
333 00:22:34.370 ⇒ 00:22:36.340 Abigail Zhao: Enter or create, list.
334 00:22:38.030 ⇒ 00:22:39.360 Uttam Kumaran: Yeah, just type in like
335 00:22:39.510 ⇒ 00:22:41.369 Uttam Kumaran: data consulting companies
336 00:22:45.320 ⇒ 00:22:46.620 Uttam Kumaran: and then hit, save.
337 00:22:50.810 ⇒ 00:22:52.129 Uttam Kumaran: Okay, cool. That’s it.
338 00:22:53.050 ⇒ 00:22:55.279 Uttam Kumaran: So now, how do we go to save list. Yep.
339 00:22:57.304 ⇒ 00:23:00.510 Uttam Kumaran: You just yeah. Companies list cool.
340 00:23:01.800 ⇒ 00:23:02.520 Uttam Kumaran: Oh, so.
341 00:23:02.520 ⇒ 00:23:03.060 Abigail Zhao: Way. I just.
342 00:23:03.060 ⇒ 00:23:03.862 Uttam Kumaran: How do we.
343 00:23:04.680 ⇒ 00:23:05.290 Abigail Zhao: Okay.
344 00:23:07.140 ⇒ 00:23:08.999 Uttam Kumaran: How do we like export from here.
345 00:23:15.760 ⇒ 00:23:16.470 Abigail Zhao: Oh!
346 00:23:27.250 ⇒ 00:23:28.000 Abigail Zhao: What!
347 00:23:28.690 ⇒ 00:23:33.460 Uttam Kumaran: Yeah, export. Well, we’re not exporting contacts. We want to export the accounts. Right? So I don’t know.
348 00:23:35.980 ⇒ 00:23:38.569 Abigail Zhao: Find the list and click, export.
349 00:23:39.990 ⇒ 00:23:42.480 Uttam Kumaran: Go to the list, tab, find the list. Click export. Okay?
350 00:23:43.330 ⇒ 00:23:44.870 Abigail Zhao: I mean this is.
351 00:23:44.870 ⇒ 00:23:48.299 Uttam Kumaran: Yeah, click into it. And then it’s probably one of those buttons above company.
352 00:23:48.830 ⇒ 00:23:50.559 Uttam Kumaran: It’s gotta be like one of those
353 00:23:52.500 ⇒ 00:23:55.139 Uttam Kumaran: like, make sure this. Yeah, select all of them.
354 00:23:58.400 ⇒ 00:24:00.789 Uttam Kumaran: I feel like, it’s okay. There it is.
355 00:24:01.810 ⇒ 00:24:03.070 Uttam Kumaran: Wait this.
356 00:24:04.150 ⇒ 00:24:05.280 Uttam Kumaran: Yeah, this is it.
357 00:24:05.880 ⇒ 00:24:06.899 Abigail Zhao: Okay, got it. Got it.
358 00:24:07.880 ⇒ 00:24:09.020 Abigail Zhao: bye.
359 00:24:10.030 ⇒ 00:24:12.572 Uttam Kumaran: Just check the settings or go on her.
360 00:24:13.300 ⇒ 00:24:14.335 Abigail Zhao: My bad, sorry.
361 00:24:15.500 ⇒ 00:24:20.810 Uttam Kumaran: You’re good. Okay, cool. Alright. I’ll let you. I’ll let you paste that in. You want to talk about the medical stuff real quick.
362 00:24:21.750 ⇒ 00:24:22.400 Abigail Zhao: Yeah.
363 00:24:23.472 ⇒ 00:24:24.740 Uttam Kumaran: Yeah. Go ahead.
364 00:24:24.910 ⇒ 00:24:27.210 Abigail Zhao: Oh, okay, I feel like it might
365 00:24:27.480 ⇒ 00:24:29.990 Abigail Zhao: be beneficial
366 00:24:30.110 ⇒ 00:24:34.160 Abigail Zhao: like for the time we meet with Chang to like if.
367 00:24:34.160 ⇒ 00:24:34.860 Uttam Kumaran: Yeah.
368 00:24:35.410 ⇒ 00:24:39.100 Abigail Zhao: Like how Apollo works, because I think, based on
369 00:24:39.150 ⇒ 00:24:41.020 Abigail Zhao: the like feedback, like
370 00:24:41.140 ⇒ 00:24:44.029 Abigail Zhao: feedback he gave me for how to
371 00:24:44.780 ⇒ 00:24:46.289 Abigail Zhao: let me just find it
372 00:24:47.510 ⇒ 00:24:49.100 Abigail Zhao: how to go about
373 00:24:53.090 ⇒ 00:24:59.446 Abigail Zhao: these things like it. It doesn’t really work out very well, for, like the filters that Apollo has
374 00:25:00.730 ⇒ 00:25:01.320 Abigail Zhao: there’s no.
375 00:25:01.320 ⇒ 00:25:06.690 Uttam Kumaran: So is there? So one. So one thing I’ll suggest is, is there any chance you can record like a quick loom
376 00:25:06.710 ⇒ 00:25:11.640 Uttam Kumaran: just showing him like an overview of Apollo, and like what you can filter people by
377 00:25:11.850 ⇒ 00:25:17.119 Uttam Kumaran: cause. I feel like he’s not, gonna I would say, send him the documentation. But
378 00:25:17.130 ⇒ 00:25:20.383 Uttam Kumaran: Apollo is good. It’s like really fucking, confusing.
379 00:25:20.970 ⇒ 00:25:22.629 Uttam Kumaran: I’ll just invite you to loom.
380 00:25:22.860 ⇒ 00:25:28.890 Uttam Kumaran: and if you can record like you’ve used loom before, or like any of those like really chill, like recording softwares.
381 00:25:30.350 ⇒ 00:25:33.779 Abigail Zhao: I’ve not used loom before, but I’ve used like other like.
382 00:25:34.180 ⇒ 00:25:36.380 Abigail Zhao: I’m sure it’ll it’ll be fine.
383 00:25:36.380 ⇒ 00:25:52.179 Uttam Kumaran: It’s like it’s like a little bit of a souped up version of like screen record. The nice thing is like you just install a chrome extension. You install like the app you hit. Record it like gives you like 5 seconds. You can literally talk. You have your face, and then you could just just walk through like, hey?
384 00:25:52.280 ⇒ 00:25:53.740 Uttam Kumaran: Actually, I would say.
385 00:25:53.860 ⇒ 00:26:09.840 Uttam Kumaran: if you just say, Hey, like I saw your reply. Here’s what I actually can filter by, and you explain, like, why his current thing was won’t work. And actually what you need, I think it’ll take like 2 min to record a loom. It’ll just be way easier than waiting for that call. And then also, whatever ways are than trying to figure it out over
386 00:26:10.040 ⇒ 00:26:32.669 Uttam Kumaran: slack. So I’ll invite you to loom. And then, if you just want to record that real quick, as soon as you record the loom, it’ll give you like a link to share. So it’s like so easy. I’m trying to get everyone to use loom more more because it’s like there’s some stuff where, you know. Like, if you’re if you’re texting or like, you’re waiting for slack, just like can’t explain it properly, and you’d like need to show you need to talk. And so
387 00:26:32.750 ⇒ 00:26:40.480 Uttam Kumaran: it’s easier. But then, also, we don’t have to wait for the meeting to basically have it be like an Apollo session. I’d rather that meeting be like about substance. So
388 00:26:41.036 ⇒ 00:26:42.500 Uttam Kumaran: maybe we do that.
389 00:26:43.350 ⇒ 00:26:46.740 Abigail Zhao: I I’m also just a little confused on, like
390 00:26:47.260 ⇒ 00:26:48.320 Abigail Zhao: what
391 00:26:48.960 ⇒ 00:26:56.980 Abigail Zhao: the goal of this is. Cause, he said, that this is just like an initial scoping exercise, right? So like
392 00:26:57.520 ⇒ 00:26:58.480 Abigail Zhao: what
393 00:27:00.080 ⇒ 00:27:01.090 Abigail Zhao: the goal.
394 00:27:01.550 ⇒ 00:27:08.820 Uttam Kumaran: Yeah, good question. So Chang, Chang is basically our resource into the medical industry.
395 00:27:09.417 ⇒ 00:27:15.079 Uttam Kumaran: He’s like a really good friend of mine and is basically helping us try to sell our services into the medical industry.
396 00:27:16.560 ⇒ 00:27:21.729 Uttam Kumaran: when you talk about an industry and who we sell to, there’s gonna be a whole host of different properties.
397 00:27:21.810 ⇒ 00:27:48.909 Uttam Kumaran: Right? There’s everything from like the roles we’re selling to what their pain points are, the types of companies so basically similar to how for manufacturing we created like a we have like a manufacturing list, like in notion. If you go and look at the manufacturing industry, we have, like basic all the properties which is like, what are the titles? What are their pain points? What are the geographies? We basically create a list of the filters. We’re trying to do a very similar thing here. And then we’re gonna start to do outbound emails towards those
398 00:27:49.240 ⇒ 00:27:50.750 Uttam Kumaran: medical contacts.
399 00:27:52.400 ⇒ 00:27:57.710 Uttam Kumaran: So kind of our edge here is that Chang is like in the medical industry, like.
400 00:27:58.310 ⇒ 00:28:05.750 Uttam Kumaran: you know, pretty much entirely so he has some unique insight into who to target and what the messaging could be for that industry.
401 00:28:05.900 ⇒ 00:28:12.359 Uttam Kumaran: And that’s basically what we’re trying to dissect and turn into an initial list of like potential leads that we could target.
402 00:28:15.110 ⇒ 00:28:16.045 Abigail Zhao: Alright.
403 00:28:18.150 ⇒ 00:28:21.429 Abigail Zhao: yeah, okay. I guess my main question was just like, why.
404 00:28:22.000 ⇒ 00:28:26.740 Abigail Zhao: why, he was asking me to plug in like the major healthcare clinics, for like
405 00:28:29.170 ⇒ 00:28:30.000 Abigail Zhao: this.
406 00:28:30.351 ⇒ 00:28:35.630 Uttam Kumaran: We’re. Oh, yeah. So I mean, cause those are companies that we would go after.
407 00:28:36.850 ⇒ 00:28:45.319 Abigail Zhao: Oh, I but then I thought, he said, right after he said, our main targets will probably be the small peripheral, possibly even rural players without such close ties with universities.
408 00:28:46.060 ⇒ 00:28:49.619 Uttam Kumaran: Oh, okay. So he says, well, let me read plug in.
409 00:28:49.850 ⇒ 00:28:54.390 Abigail Zhao: Little like, confused, like what our, what our goal actually was.
410 00:28:57.380 ⇒ 00:29:12.380 Uttam Kumaran: Well, I think he’s probably talking about 2 things. But honestly, this is probably a good question just to ask him directly, so I would just plug him. But my thought is, he’s probably like, let’s do an initial search where we just search for everybody, and then we’ll also do a search where we look at
411 00:29:12.520 ⇒ 00:29:13.580 Uttam Kumaran: smaller
412 00:29:14.380 ⇒ 00:29:20.140 Uttam Kumaran: targets. So I would. I mean I I would say. It’s probably pretty easy just to produce both lists and send it to him.
413 00:29:20.280 ⇒ 00:29:27.989 Uttam Kumaran: I mean, you could just send them like, just explain, like, kind of how the filters work and what we can do. But yeah, I mean reading that it seems like you.
414 00:29:29.400 ⇒ 00:29:30.060 Uttam Kumaran: Yeah, sure.
415 00:29:30.980 ⇒ 00:29:32.290 Uttam Kumaran: lest lest
416 00:29:32.590 ⇒ 00:29:35.379 Uttam Kumaran: as like the initial exercise, and then
417 00:29:35.780 ⇒ 00:29:39.819 Uttam Kumaran: where we’ll probably end up is like the second list. So
418 00:29:40.130 ⇒ 00:29:47.769 Uttam Kumaran: again. It’s probably just a little bit of a of an evolution. But I would just like, even in that loom. Just explain that you’re having that confusion. You’ll answer it so.
419 00:29:48.060 ⇒ 00:29:49.369 Uttam Kumaran: But it’s a good question.
420 00:29:49.420 ⇒ 00:30:06.810 Uttam Kumaran: basically, like when we when we enter these new industries, I don’t particularly have an edge in the medical world. So we would be getting into a pretty like from a blank canvas perspective, having someone like Chang who who can gather the information about who we should be going after. What are the pain points and like how we should be targeting
421 00:30:06.820 ⇒ 00:30:08.490 Uttam Kumaran: is like is our edge.
422 00:30:08.560 ⇒ 00:30:13.590 Uttam Kumaran: And so basically, we’re trying to put together the initial list of things that I could provide to our outbound team.
423 00:30:13.640 ⇒ 00:30:21.440 Uttam Kumaran: which is like, here’s how here’s the people we want to go after. Here’s the messaging and like start to send off emails, you know. So that’s basically what we’re getting now.
424 00:30:23.710 ⇒ 00:30:24.690 Abigail Zhao: Sounds good.
425 00:30:27.030 ⇒ 00:30:29.679 Abigail Zhao: really quickly. Where do?
426 00:30:29.710 ⇒ 00:30:32.219 Abigail Zhao: Where does this get exported to.
427 00:30:34.003 ⇒ 00:30:36.550 Uttam Kumaran: It’s probably exporting it to your email.
428 00:30:36.650 ⇒ 00:30:37.480 Uttam Kumaran: I think.
429 00:30:37.480 ⇒ 00:30:39.889 Abigail Zhao: Great. Well, then, cause isn’t this year.
430 00:30:40.999 ⇒ 00:30:44.890 Uttam Kumaran: Maybe my email, I don’t know. I thought it should be in the platform, but.
431 00:30:44.950 ⇒ 00:30:45.520 Abigail Zhao: Yeah, I, thought.
432 00:30:45.520 ⇒ 00:30:46.350 Uttam Kumaran: Let me see.
433 00:30:49.320 ⇒ 00:30:51.559 Uttam Kumaran: Oh, yeah, I got an email. Okay, cool.
434 00:30:52.380 ⇒ 00:31:01.079 Uttam Kumaran: Okay, cool. So I’ll download it. And then maybe I’ll just send it to you. And then, if you could just add to that Google Sheet, I don’t know what the fields are and stuff, but you could just add a new sheet in here
435 00:31:01.450 ⇒ 00:31:04.359 Uttam Kumaran: and do it in there so that we can keep this one separate.
436 00:31:08.000 ⇒ 00:31:08.950 Uttam Kumaran: Oh.
437 00:31:11.330 ⇒ 00:31:17.530 Uttam Kumaran: okay, anything else? Yeah. I think we’re just starting to start out. We’re starting to send out campaigns
438 00:31:17.570 ⇒ 00:31:22.189 Uttam Kumaran: this week hopefully by the end of this week. But we also posted a bunch of stuff on Linkedin today.
439 00:31:22.380 ⇒ 00:31:27.319 Uttam Kumaran: And yeah, I don’t know. Did you have a chance to talk to Miguel at all?
440 00:31:27.770 ⇒ 00:31:30.940 Abigail Zhao: Yeah, he sent me a lot of like resources for
441 00:31:33.750 ⇒ 00:31:34.965 Abigail Zhao: They’re all called
442 00:31:37.100 ⇒ 00:31:40.149 Abigail Zhao: Sorry. He sent me a lot of
443 00:31:40.590 ⇒ 00:31:42.340 Abigail Zhao: documentation type of stuff.
444 00:31:42.340 ⇒ 00:31:43.550 Uttam Kumaran: For relevance.
445 00:31:45.460 ⇒ 00:31:51.029 Abigail Zhao: Yeah, wait I haven’t gotten a good chance to take a look at it yet. He gave me access to like the.
446 00:31:51.030 ⇒ 00:31:51.660 Uttam Kumaran: Okay. Okay.
447 00:31:51.660 ⇒ 00:31:54.010 Abigail Zhao: You build the job post scraper and then
448 00:31:54.766 ⇒ 00:31:57.250 Abigail Zhao: like AI agent development stuff.
449 00:31:57.760 ⇒ 00:32:00.050 Abigail Zhao: And then I don’t think anything
450 00:32:00.120 ⇒ 00:32:01.160 Abigail Zhao: actually.
451 00:32:03.350 ⇒ 00:32:03.900 Abigail Zhao: Oh.
452 00:32:03.900 ⇒ 00:32:16.760 Uttam Kumaran: I was just worried that he might hit you with like a bunch of stuff, but nothing concrete. So I’m gonna like, did he give you, like a concrete like task to try to do, or just basically like, go read on this stuff.
453 00:32:19.860 ⇒ 00:32:24.799 Abigail Zhao: yeah, he didn’t. I don’t think he really gave me like any specific task to do.
454 00:32:26.750 ⇒ 00:32:30.189 Uttam Kumaran: Okay, let me I’m gonna ping him and just be like.
455 00:32:30.450 ⇒ 00:32:33.449 Abigail Zhao: Besides, how we can create like a notion, Doc, for, like
456 00:32:34.130 ⇒ 00:32:35.589 Abigail Zhao: a lot of the different.
457 00:32:36.490 ⇒ 00:32:43.400 Uttam Kumaran: We have a. We have a list of all the stuff that we need done. But I want him to kind of pick a 1 that you guys can actually take on because
458 00:32:43.440 ⇒ 00:33:00.080 Uttam Kumaran: some of the stuff is kind of complicated. And then we can kind of get to the meteor stuff. So let me just ping him and say, and I’ll tell him to just pick one. That’s easy to do. But yeah, just poke around in relevance. It’s gonna be like, even for me. All that stuff was brand new as of like 3 months ago. So don’t be
459 00:33:00.090 ⇒ 00:33:02.496 Uttam Kumaran: like worried if it’s too crazy. But
460 00:33:02.880 ⇒ 00:33:06.560 Uttam Kumaran: yeah, I would just play around. There’s a lot of like examples and stuff in there.
461 00:33:11.820 ⇒ 00:33:13.480 Uttam Kumaran: Okay, cool.
462 00:33:16.050 ⇒ 00:33:29.099 Uttam Kumaran: Otherwise, I think it may just be once we kind of fish finish up some of the Apollo stuff. I think it may just be the relevant stuff. But really, I’m hoping that you guys can kind of start to work with some of the AI tools at least by next week.
463 00:33:29.370 ⇒ 00:33:35.689 Uttam Kumaran: but it’s gonna be a high learning curve. But I I kind of want you guys to start to rely on Miguel, because I’m getting like, really, really busy. So
464 00:33:36.970 ⇒ 00:33:39.359 Uttam Kumaran: don’t want you guys to just get completely swamped.
465 00:33:44.110 ⇒ 00:33:46.379 Uttam Kumaran: Okay, cool, that’s all. I had.
466 00:33:47.970 ⇒ 00:33:50.090 Abigail Zhao: Yeah, I think I’m good on my end, too. So.
467 00:33:50.750 ⇒ 00:33:58.070 Uttam Kumaran: Okay, cool. Then I think we’ll probably have a team meeting on Friday. But maybe talk before. Actually, we’ll talk on Thursday. So.
468 00:33:59.050 ⇒ 00:33:59.700 Abigail Zhao: Alright.
469 00:34:00.110 ⇒ 00:34:00.980 Abigail Zhao: Okay.
470 00:34:01.110 ⇒ 00:34:02.000 Uttam Kumaran: Have a good one.
471 00:34:02.310 ⇒ 00:34:04.180 Abigail Zhao: Yeah. Bye, thank you. Bye.