Meeting Title: Default | Brainforge Weekly Sync Date: 2025-12-04 Meeting participants: Scratchpad Notetaker, Mustafa Raja, Uttam Kumaran, Amber Lin, Caitlyn Vaughn
WEBVTT
1 00:01:13.130 ⇒ 00:01:14.120 Uttam Kumaran: Hey.
2 00:01:14.290 ⇒ 00:01:19.540 Uttam Kumaran: Is the, is the stuff ready? I didn’t see any messages, like, are we ready to present?
3 00:01:20.460 ⇒ 00:01:22.050 Mustafa Raja: So…
4 00:01:22.380 ⇒ 00:01:29.929 Mustafa Raja: Yeah, so, so for the master sheet, for the fields, it’s still work in progress, but…
5 00:01:29.930 ⇒ 00:01:31.590 Uttam Kumaran: now, like, I just don’t underst… like.
6 00:01:31.590 ⇒ 00:01:32.240 Mustafa Raja: Hmm.
7 00:01:32.240 ⇒ 00:01:35.940 Uttam Kumaran: I don’t get what’s taking so long. It’s just an Excel sheet.
8 00:01:37.390 ⇒ 00:01:52.269 Mustafa Raja: Yeah, so, motivation I talked about is, having, each of the fields, as the column itself, and then check boxes, for, the vendors.
9 00:01:52.270 ⇒ 00:01:55.269 Uttam Kumaran: This is, like, way… this is, like, way overcomplicated.
10 00:01:56.260 ⇒ 00:02:03.960 Mustafa Raja: Yeah, but yeah… I mean, I could meet him, meet with him again, and…
11 00:02:04.160 ⇒ 00:02:07.639 Mustafa Raja: Trying to figure… But I just, like, this is what I’m… I’m just…
12 00:02:07.640 ⇒ 00:02:13.460 Uttam Kumaran: I’m just frustrated, like, nope, this should not take 5 days to put together a spreadsheet.
13 00:02:13.570 ⇒ 00:02:18.009 Uttam Kumaran: Like, where it’s just a list of the fields, dude.
14 00:02:18.190 ⇒ 00:02:21.679 Uttam Kumaran: Like, all we need is the name of the field.
15 00:02:24.150 ⇒ 00:02:27.739 Uttam Kumaran: and then, like, the API name, you know?
16 00:02:28.990 ⇒ 00:02:35.969 Mustafa Raja: The only thing that I’m confused about is, how should we then format it, if not this one?
17 00:02:38.540 ⇒ 00:02:44.510 Uttam Kumaran: But there’s, like, you should just put the vendor, the endpoint, and the field.
18 00:02:46.830 ⇒ 00:02:50.359 Uttam Kumaran: And then, just… Copy all of them in, right?
19 00:02:52.180 ⇒ 00:02:53.630 Mustafa Raja: Sorry, could you say that again?
20 00:02:54.000 ⇒ 00:02:57.059 Uttam Kumaran: Like, the vendor, the name of the field.
21 00:02:57.960 ⇒ 00:03:00.510 Mustafa Raja: The name of the endpoint, and then the name of the field.
22 00:03:00.860 ⇒ 00:03:02.950 Uttam Kumaran: And then just, like, put it in one table.
23 00:03:06.210 ⇒ 00:03:09.519 Mustafa Raja: So that… that would just mean that we are bringing all of the tables here.
24 00:03:09.820 ⇒ 00:03:11.109 Uttam Kumaran: Yeah. We already have.
25 00:03:11.600 ⇒ 00:03:12.180 Uttam Kumaran: Yeah.
26 00:03:13.920 ⇒ 00:03:14.670 Mustafa Raja: Okay.
27 00:03:17.220 ⇒ 00:03:23.060 Uttam Kumaran: But, like, I don’t know, I’m just like, what do… I don’t… I don’t… I don’t… I don’t get what’s going on, like, why…
28 00:03:23.180 ⇒ 00:03:26.090 Uttam Kumaran: Can we just do that now? Because this is, like, super late.
29 00:03:27.570 ⇒ 00:03:34.340 Mustafa Raja: Yeah… so, just to be sure.
30 00:03:35.380 ⇒ 00:03:36.049 Uttam Kumaran: Can I get…
31 00:03:36.050 ⇒ 00:03:36.500 Mustafa Raja: Everyone…
32 00:03:36.500 ⇒ 00:03:38.889 Uttam Kumaran: I think I’ll just… I’ll just show, exactly, like.
33 00:03:38.890 ⇒ 00:03:39.880 Mustafa Raja: Yeah.
34 00:03:41.680 ⇒ 00:03:44.219 Uttam Kumaran: And then what else do we owe her?
35 00:03:45.250 ⇒ 00:03:50.779 Mustafa Raja: Apart from that, this window comparison?
36 00:03:50.780 ⇒ 00:03:51.560 Uttam Kumaran: Donna?
37 00:03:52.150 ⇒ 00:03:55.560 Mustafa Raja: Yeah, this one, it’s… it’s just this sheet.
38 00:04:03.150 ⇒ 00:04:07.190 Uttam Kumaran: Okay, but this is still not, like… Ugh.
39 00:04:07.800 ⇒ 00:04:09.860 Uttam Kumaran: She wanted a Notion document.
40 00:04:13.330 ⇒ 00:04:17.879 Mustafa Raja: Hmm… Avish and I thought that this might be a little better.
41 00:04:19.430 ⇒ 00:04:30.649 Uttam Kumaran: I know, but guys, like, you didn’t… in the… the meeting we had, I mean, we can talk about it, but I think we just need a clear, super clear Notion document, like, this is okay, but…
42 00:04:31.540 ⇒ 00:04:32.280 Uttam Kumaran: Hello.
43 00:04:32.620 ⇒ 00:04:33.810 Caitlyn Vaughn: Hi!
44 00:04:34.160 ⇒ 00:04:35.159 Uttam Kumaran: How’s it going?
45 00:04:35.890 ⇒ 00:04:36.890 Caitlyn Vaughn: Chaos.
46 00:04:37.970 ⇒ 00:04:38.969 Uttam Kumaran: What’s the latest?
47 00:04:41.100 ⇒ 00:04:50.229 Caitlyn Vaughn: everything’s good. We’re, like, finally on the other side of product and engineering, and now we’re pivoting to launch plan, so…
48 00:04:50.410 ⇒ 00:04:53.699 Caitlyn Vaughn: It’s good, just, like, finishing the last few SKUs.
49 00:04:53.930 ⇒ 00:04:56.770 Uttam Kumaran: for Phoenix, and then also planning.
50 00:04:57.080 ⇒ 00:04:58.090 Caitlyn Vaughn: the launch.
51 00:04:58.430 ⇒ 00:05:01.370 Uttam Kumaran: Is there a… do you guys have, like, a demo instance yet?
52 00:05:02.460 ⇒ 00:05:11.309 Caitlyn Vaughn: Not yet. December 31st, we will. So, if you guys wanna, like, get in there and test around, actually, we’d be super down for that.
53 00:05:12.080 ⇒ 00:05:18.209 Uttam Kumaran: Okay, sick, yeah, I would love to. And I wanted to see, like, whether we could get Amplitude and stuff set up.
54 00:05:20.180 ⇒ 00:05:21.969 Caitlyn Vaughn: Like, in your account?
55 00:05:23.080 ⇒ 00:05:26.260 Uttam Kumaran: No, for, like, just product analytics, like, in general.
56 00:05:26.260 ⇒ 00:05:34.019 Caitlyn Vaughn: Oh, oh, oh, I’m thinking of Amplify. Yes, Amplitude. Yes, we’ll definitely need to do that. We’ll have, like.
57 00:05:34.330 ⇒ 00:05:38.900 Caitlyn Vaughn: I can give you kind of a rough breakdown as well.
58 00:05:47.540 ⇒ 00:05:50.580 Caitlyn Vaughn: Do you have, like, subfolders in your subfolders?
59 00:05:51.630 ⇒ 00:05:52.669 Caitlyn Vaughn: in motion.
60 00:05:52.730 ⇒ 00:05:53.970 Uttam Kumaran: Oh, yeah.
61 00:05:54.120 ⇒ 00:05:54.959 Caitlyn Vaughn: It’s like…
62 00:05:55.090 ⇒ 00:05:56.680 Uttam Kumaran: Little rabbit hole.
63 00:05:58.670 ⇒ 00:06:05.900 Caitlyn Vaughn: Here are the, like, rough timelines. So… December 31st is code complete?
64 00:06:05.900 ⇒ 00:06:08.539 Uttam Kumaran: And then we have two cycles of QA.
65 00:06:08.540 ⇒ 00:06:19.639 Caitlyn Vaughn: And then January 31st is when we start, like, migrating a handful of our current customers over to Phoenix. Like, let’s say a dozen of them. They test,
66 00:06:20.010 ⇒ 00:06:25.729 Caitlyn Vaughn: And then we’re not gonna sell the old platform anymore, starting January 31st.
67 00:06:25.860 ⇒ 00:06:33.360 Caitlyn Vaughn: And then we’ll go through, like, a few cycles, and, like, hopefully we’ll actually do the full go-to-market launch on, like, March 1st.
68 00:06:33.780 ⇒ 00:06:40.609 Uttam Kumaran: So, also, if you guys are down to do some, like, co-marketing stuff, yeah. …be, like, a really good time to get involved.
69 00:06:41.270 ⇒ 00:06:52.280 Uttam Kumaran: Okay, great. Yeah, so one thing I want to make sure is for, like, after… I guess after December 31st, is that you guys, like, have amplitude set up well, and that you’re starting to track things.
70 00:06:54.150 ⇒ 00:06:56.120 Caitlyn Vaughn: Like, after or before?
71 00:06:56.540 ⇒ 00:07:00.570 Uttam Kumaran: I guess whenever the… whenever the new platform exists.
72 00:07:01.240 ⇒ 00:07:05.520 Uttam Kumaran: And if you guys are already starting to do QA, we should already start to test, like, new events.
73 00:07:06.360 ⇒ 00:07:13.969 Caitlyn Vaughn: Yeah, yeah, we hope to finish December 31st. Honestly, there’s, like, a few SKUs that are a few weeks behind, like.
74 00:07:14.080 ⇒ 00:07:17.799 Caitlyn Vaughn: workflows, like, that’s just gonna take a little bit longer, so…
75 00:07:18.260 ⇒ 00:07:20.520 Caitlyn Vaughn: Yeah, I’ll keep you guys posted.
76 00:07:20.640 ⇒ 00:07:21.429 Uttam Kumaran: Okay, okay.
77 00:07:22.720 ⇒ 00:07:26.470 Caitlyn Vaughn: But what’s up with you? Show me the tea! What did you have up there?
78 00:07:26.860 ⇒ 00:07:45.699 Uttam Kumaran: Well, we wanted to… we wanted to put together the duplicate fields, but I just think we’re overthinking it a bit, so I may just finish it up and send it over, probably later today, but, basically, I guess it’s up to you, I guess, like, who… who’s gonna be using this sheet, but I…
79 00:07:45.960 ⇒ 00:07:50.880 Uttam Kumaran: We were thinking, like, if you want to just see… Kind of, like, filter by…
80 00:07:51.140 ⇒ 00:07:58.479 Uttam Kumaran: the type of field here, or if you literally just wanted, like, a full list of everything. But, like, what’s… what’s helpful?
81 00:07:59.360 ⇒ 00:08:02.390 Caitlyn Vaughn: Yeah, good question.
82 00:08:04.020 ⇒ 00:08:09.369 Uttam Kumaran: like, is this gonna be used by engineers in terms of, like, the API fields, or is this more of, like.
83 00:08:10.270 ⇒ 00:08:11.670 Uttam Kumaran: Yeah, I don’t know.
84 00:08:13.430 ⇒ 00:08:24.199 Caitlyn Vaughn: the people that have been asking for this so far have been on sales, because they’re like, we have a deal that is gonna close, but only if we have X field is this, like, in scope.
85 00:08:24.200 ⇒ 00:08:25.020 Uttam Kumaran: Okay.
86 00:08:25.020 ⇒ 00:08:31.839 Caitlyn Vaughn: And I’ll be like, yes, or like, maybe, because there’s so many fields, it’s not like I remember every single one of them.
87 00:08:31.840 ⇒ 00:08:37.309 Uttam Kumaran: Do you think, like, this is helpful like this, then, for that use case? Because they basically can…
88 00:08:38.700 ⇒ 00:08:46.929 Uttam Kumaran: basically look through. What this is, basically, is that columns are going to be all available fields, and you’ll see which ones have them.
89 00:08:47.900 ⇒ 00:08:48.960 Caitlyn Vaughn: That’s fine.
90 00:08:48.960 ⇒ 00:08:49.600 Uttam Kumaran: Okay.
91 00:08:50.910 ⇒ 00:08:57.630 Uttam Kumaran: for, like, an engineering view, if the engineers need it, then I was thinking about one where it’s literally just, like, the endpoint.
92 00:08:57.840 ⇒ 00:08:59.220 Uttam Kumaran: And the field name.
93 00:08:59.490 ⇒ 00:09:00.180 Caitlyn Vaughn: Hmm.
94 00:09:01.220 ⇒ 00:09:04.929 Uttam Kumaran: Otherwise, like, we can make sure that these titles are really clear on, like.
95 00:09:05.120 ⇒ 00:09:14.429 Uttam Kumaran: This is related to employees, this is related to company revenue, people, and so it’s easy for a salesperson to come in here and basically see, like, what
96 00:09:15.280 ⇒ 00:09:16.830 Uttam Kumaran: What’s gonna be available?
97 00:09:18.020 ⇒ 00:09:20.700 Caitlyn Vaughn: The only other thing I’m thinking here is…
98 00:09:21.970 ⇒ 00:09:31.279 Caitlyn Vaughn: This might be, like, slightly prohibitive to engineers, because if there is one field that multiple providers.
99 00:09:32.140 ⇒ 00:09:36.320 Uttam Kumaran: Yeah. And satisfy, like, it’s probably not called the exact same thing.
100 00:09:36.740 ⇒ 00:09:42.030 Uttam Kumaran: Yeah, exactly. So we, like… basically merged it.
101 00:09:42.060 ⇒ 00:09:49.319 Caitlyn Vaughn: Yeah, that’s fine for sales. Do we have, like, a separate list of just, like, every field?
102 00:09:49.320 ⇒ 00:09:52.780 Uttam Kumaran: Yeah, like, this is an example of what we have for every single vendor.
103 00:09:52.780 ⇒ 00:09:53.930 Caitlyn Vaughn: Oh, awesome. Yeah.
104 00:09:53.930 ⇒ 00:09:57.490 Uttam Kumaran: This is, like, per swarm, per people data.
105 00:09:57.490 ⇒ 00:09:57.880 Caitlyn Vaughn: Hmm.
106 00:09:57.880 ⇒ 00:10:00.869 Uttam Kumaran: Color, we have… we have basically everything here.
107 00:10:00.870 ⇒ 00:10:01.380 Caitlyn Vaughn: Okay.
108 00:10:01.380 ⇒ 00:10:12.170 Uttam Kumaran: And so I was just gonna merge all of these into just, like, one table, but if anyone wants… basically, if anyone wants to come see, like, what’s available from these APIs, all of this documentation is here.
109 00:10:12.170 ⇒ 00:10:15.659 Caitlyn Vaughn: Yeah, that’s fine. It’s, like, perfectly… that’s great.
110 00:10:16.290 ⇒ 00:10:17.290 Uttam Kumaran: Okay, okay.
111 00:10:17.290 ⇒ 00:10:21.829 Caitlyn Vaughn: Yeah, those two sheets. Having, like, the individual, just, vendor laid out.
112 00:10:21.960 ⇒ 00:10:24.940 Caitlyn Vaughn: With each, like, API name, and then…
113 00:10:25.070 ⇒ 00:10:33.160 Caitlyn Vaughn: with what is available for each one. We are gonna cut Owler, probably. Maybe we’ll just leave it in there for now, but…
114 00:10:33.520 ⇒ 00:10:37.380 Caitlyn Vaughn: I think I, like, just didn’t respond to them, because they’re so expensive.
115 00:10:37.380 ⇒ 00:10:39.329 Uttam Kumaran: Okay. They weren’t that impressive.
116 00:10:39.500 ⇒ 00:10:40.130 Uttam Kumaran: Okay.
117 00:10:40.780 ⇒ 00:10:44.459 Caitlyn Vaughn: But yeah, can we get the rest of these finished? Or basically, like…
118 00:10:45.090 ⇒ 00:10:48.159 Uttam Kumaran: And then, dude, like, I guess, is it helpful to have it in Notion?
119 00:10:48.970 ⇒ 00:10:49.840 Caitlyn Vaughn: No.
120 00:10:49.850 ⇒ 00:10:50.520 Uttam Kumaran: Okay.
121 00:10:51.350 ⇒ 00:10:53.200 Caitlyn Vaughn: Yeah.
122 00:10:53.310 ⇒ 00:11:07.040 Caitlyn Vaughn: So, this… I mean, I’m just, like, so underwater, but I really need to figure out… I need to, like, calculate the cost of… probably, like, a blended cost of credits. I’ll probably… I’ll do this on my own, because I’m not sure, like.
123 00:11:07.440 ⇒ 00:11:13.849 Caitlyn Vaughn: the opinions that I want to have on it, if I want to do, like, blended, if I want to do, like, cost per vendor…
124 00:11:14.360 ⇒ 00:11:29.959 Caitlyn Vaughn: unsure, but I have, like, the rough pricing breakdown, and I’m gonna have to, like… I’m writing the PRD for billing credits right now, I need to figure out, like, how much we’re gonna charge people, and, like, what a credit can be used for. So, I’m assuming it’s just…
125 00:11:30.870 ⇒ 00:11:38.639 Caitlyn Vaughn: like, our actual, like, enrichment providers and, like, maybe AI, but… I don’t know. Anyways, I’m, like, rambling.
126 00:11:38.640 ⇒ 00:11:39.200 Uttam Kumaran: Okay.
127 00:11:39.760 ⇒ 00:11:41.489 Caitlyn Vaughn: The faster this can be done.
128 00:11:41.700 ⇒ 00:12:00.969 Caitlyn Vaughn: the better position I will be in to be like, yes, we are gonna have this vendor, here are all the fields available. And I know for PDL, like, I negotiated a pretty small contract with them initially, but they do have a lot of, like, pretty valuable and unique signals, and if we’re not gonna go with Owler, then I’ll probably invest more funding into PDL.
129 00:12:01.650 ⇒ 00:12:02.670 Uttam Kumaran: Okay, okay.
130 00:12:02.810 ⇒ 00:12:06.449 Uttam Kumaran: Okay, yeah, we’ll have this done, like, probably in the next few hours.
131 00:12:06.910 ⇒ 00:12:11.109 Caitlyn Vaughn: Okay, cool. There are a few other vendors, right?
132 00:12:13.050 ⇒ 00:12:15.460 Uttam Kumaran: Mustafa, are we testing any others?
133 00:12:17.170 ⇒ 00:12:22.590 Mustafa Raja: not right now, but we did, mention…
134 00:12:22.910 ⇒ 00:12:24.989 Mustafa Raja: One in the last meeting, I believe.
135 00:12:25.900 ⇒ 00:12:28.939 Caitlyn Vaughn: Okay, I think we have, like, 6 more to add.
136 00:12:29.490 ⇒ 00:12:33.120 Caitlyn Vaughn: That has been in there. Let me just look really quick.
137 00:12:33.570 ⇒ 00:12:36.830 Caitlyn Vaughn: Like, even looking at this list, I can tell you…
138 00:12:36.960 ⇒ 00:12:41.079 Caitlyn Vaughn: We need Clearbit, we need Apollo, we need…
139 00:12:41.470 ⇒ 00:12:42.160 Uttam Kumaran: Yeah.
140 00:12:42.420 ⇒ 00:12:50.820 Caitlyn Vaughn: Let’s see… I feel like I just haven’t looked at this list in a long time.
141 00:12:51.110 ⇒ 00:12:59.120 Caitlyn Vaughn: Product growth, vendor procurement… okay. Demand base, we already tested demand base. Do we have Snitcher?
142 00:13:02.010 ⇒ 00:13:03.690 Caitlyn Vaughn: Ocean I.O.
143 00:13:04.160 ⇒ 00:13:05.470 Caitlyn Vaughn: Harmonic.
144 00:13:05.470 ⇒ 00:13:06.310 Uttam Kumaran: Yeah, harmonic.
145 00:13:06.830 ⇒ 00:13:17.760 Caitlyn Vaughn: We did Captain Data… After CMI, I think we’re cutting… Wiza?
146 00:13:18.420 ⇒ 00:13:20.130 Caitlyn Vaughn: Yeah, let’s do Wizza.
147 00:13:21.120 ⇒ 00:13:21.740 Uttam Kumaran: Okay.
148 00:13:27.710 ⇒ 00:13:28.820 Caitlyn Vaughn: Yeah.
149 00:13:29.340 ⇒ 00:13:39.689 Caitlyn Vaughn: I think Snitcher, Snitcher’s gonna be company-level… company-level website reveal, like, IP?
150 00:13:40.220 ⇒ 00:13:45.519 Caitlyn Vaughn: So, potentially, we could cut them too, but if they have enrichment, then I’d like to test them for enrichment.
151 00:13:47.190 ⇒ 00:13:47.930 Uttam Kumaran: Okay.
152 00:13:48.860 ⇒ 00:13:54.389 Caitlyn Vaughn: And same with Ocean.io, it’s gonna be, like, relational. I don’t know if we can test that in here, necessarily.
153 00:13:54.650 ⇒ 00:13:55.370 Uttam Kumaran: Yeah.
154 00:13:57.390 ⇒ 00:14:02.499 Uttam Kumaran: But that’s what I’m… I think both for… for anything that’s, like, unique, we’ll just have to come up with a new, like, testing…
155 00:14:04.140 ⇒ 00:14:07.539 Caitlyn Vaughn: Okay. Yeah, I don’t know if Ocean may… I mean, it’s just like…
156 00:14:08.840 ⇒ 00:14:24.449 Caitlyn Vaughn: it’s a pretty specific vendor, we don’t really have any others like it, and I don’t know that it’s, like, a main use case for default, so… I feel like we can just, like, add it in and see if people use it. It’s, like, lower risk than… no one’s gonna, like, waterfall with it, for example.
157 00:14:26.520 ⇒ 00:14:27.729 Uttam Kumaran: Okay. What do you think?
158 00:14:33.160 ⇒ 00:14:36.680 Uttam Kumaran: Yeah… Ocean Iowa’s an interesting one.
159 00:14:37.520 ⇒ 00:14:38.430 Caitlyn Vaughn: Also.
160 00:14:38.930 ⇒ 00:14:42.980 Uttam Kumaran: Well, isn’t it… this is, like, isn’t it, like, you need two people to, like, find something?
161 00:14:43.100 ⇒ 00:14:47.539 Uttam Kumaran: So that’s why it’s like… I’m trying to think about how we would test, like, I guess we would…
162 00:14:49.070 ⇒ 00:14:56.060 Uttam Kumaran: because a lot of our testing is, like, testing on accuracy, so I feel like I’m not sure… I guess we could just look at response time and, like.
163 00:14:56.380 ⇒ 00:14:58.230 Uttam Kumaran: How many results we get, but…
164 00:14:58.230 ⇒ 00:15:14.819 Caitlyn Vaughn: Yeah, actually, that would be a good one. Response time would be… good. Although, honestly, like, what it actually is, it’s like Crossbeam. It’s like, find me more companies or people like this profile, and it will just, like, spit out a bunch of similar profiles, which…
165 00:15:14.820 ⇒ 00:15:20.490 Caitlyn Vaughn: Feels like not that deep. Seems pretty simple, if we’re gonna be completely honest. Like, if they’re fucking this up…
166 00:15:20.790 ⇒ 00:15:21.790 Uttam Kumaran: Yeah, yeah.
167 00:15:22.090 ⇒ 00:15:29.480 Caitlyn Vaughn: And it’s also, like, I don’t know, it seems kind of straightforward.
168 00:15:33.560 ⇒ 00:15:34.380 Uttam Kumaran: Okay.
169 00:15:34.380 ⇒ 00:15:36.770 Caitlyn Vaughn: And people are not gonna use it for routing, either.
170 00:15:36.960 ⇒ 00:15:37.560 Uttam Kumaran: Okay.
171 00:15:39.090 ⇒ 00:15:41.620 Uttam Kumaran: I mean, I don’t know, yeah, you wouldn’t really, like…
172 00:15:42.010 ⇒ 00:15:44.089 Uttam Kumaran: What would you even do in routing with?
173 00:15:44.090 ⇒ 00:15:44.700 Caitlyn Vaughn: Yeah.
174 00:15:45.950 ⇒ 00:15:49.740 Caitlyn Vaughn: Seems like a non-low-latency use case. Maybe,
175 00:15:51.000 ⇒ 00:15:57.860 Caitlyn Vaughn: Hit it with, like, 100, see if it returns everything properly, like, if it doesn’t fail, that’s probably a good one.
176 00:15:57.860 ⇒ 00:15:58.490 Uttam Kumaran: Okay.
177 00:16:00.810 ⇒ 00:16:02.820 Uttam Kumaran: The outing’s okay, yeah.
178 00:16:03.090 ⇒ 00:16:03.870 Caitlyn Vaughn: Yeah.
179 00:16:04.840 ⇒ 00:16:07.910 Caitlyn Vaughn: Harmonic’s gonna be more, like, startup stuff.
180 00:16:08.650 ⇒ 00:16:12.300 Caitlyn Vaughn: And then, yeah, snitcher also might be not relevant.
181 00:16:12.300 ⇒ 00:16:15.610 Uttam Kumaran: Snitcher, but Snitcher is like RB2B, right? Or…
182 00:16:15.950 ⇒ 00:16:19.860 Caitlyn Vaughn: But I think it’s for company…
183 00:16:20.170 ⇒ 00:16:26.350 Caitlyn Vaughn: It’s… okay, also, Snitcher’s a little bit different than, like, we’re using Vector.
184 00:16:26.550 ⇒ 00:16:28.899 Caitlyn Vaughn: Instead of our B2B, but it’s the same thing.
185 00:16:29.880 ⇒ 00:16:41.689 Caitlyn Vaughn: but Vector’s, like, a much stronger provider, than RB2B, but Snitcher, they technically do company and person, but they can’t reveal the person, so they do it by
186 00:16:42.580 ⇒ 00:16:47.340 Caitlyn Vaughn: Identifying or, like, revealing the company, and then showing the most likely people that it is.
187 00:16:48.100 ⇒ 00:16:50.760 Caitlyn Vaughn: So it’s like… Yeah.
188 00:16:50.970 ⇒ 00:16:51.990 Uttam Kumaran: Interesting.
189 00:16:51.990 ⇒ 00:16:52.800 Caitlyn Vaughn: Yeah.
190 00:16:53.180 ⇒ 00:16:55.110 Caitlyn Vaughn: Because they’re, they’re based in Europe.
191 00:16:55.510 ⇒ 00:16:56.770 Uttam Kumaran: Oh, okay.
192 00:16:56.770 ⇒ 00:16:57.450 Caitlyn Vaughn: Yeah.
193 00:16:57.730 ⇒ 00:16:59.200 Uttam Kumaran: You guys like Vector?
194 00:16:59.630 ⇒ 00:17:00.950 Caitlyn Vaughn: We love Vector, yeah.
195 00:17:01.270 ⇒ 00:17:03.120 Uttam Kumaran: Do you guys use it on default site?
196 00:17:03.120 ⇒ 00:17:03.740 Caitlyn Vaughn: Yeah.
197 00:17:04.170 ⇒ 00:17:07.219 Uttam Kumaran: Oh, okay. We were thinking about Deal Front…
198 00:17:08.180 ⇒ 00:17:11.510 Uttam Kumaran: But then someone was like, I don’t know, it’s, like, not great anymore, so…
199 00:17:11.510 ⇒ 00:17:12.500 Caitlyn Vaughn: Deal Friends?
200 00:17:12.500 ⇒ 00:17:13.230 Uttam Kumaran: Yeah.
201 00:17:16.609 ⇒ 00:17:22.639 Caitlyn Vaughn: All of… all of the, like, person-level reveal, it’s just, like, a tough game to play.
202 00:17:22.639 ⇒ 00:17:23.379 Uttam Kumaran: Okay.
203 00:17:24.149 ⇒ 00:17:29.029 Caitlyn Vaughn: Our B2B is… I mean, if you’re gonna, like.
204 00:17:29.309 ⇒ 00:17:39.499 Caitlyn Vaughn: If you were going to use RB2B, I would say just use 5x5 and pay, like, a tenth of the price for… it’s the same data. Like, RB2B just, like.
205 00:17:40.189 ⇒ 00:17:42.519 Caitlyn Vaughn: White labels 5x5 data.
206 00:17:42.520 ⇒ 00:17:43.310 Uttam Kumaran: Oh, really?
207 00:17:43.310 ⇒ 00:17:46.129 Caitlyn Vaughn: It’s just wholesale data that’s, like, uncleaned, so it’s
208 00:17:46.510 ⇒ 00:17:51.370 Caitlyn Vaughn: data, but they’ve managed to make it really expensive and just brand it really well.
209 00:17:51.370 ⇒ 00:17:52.159 Uttam Kumaran: Yeah, yeah.
210 00:17:53.380 ⇒ 00:17:56.690 Caitlyn Vaughn: It’s like Kirkland vodka vibe.
211 00:17:57.350 ⇒ 00:18:01.320 Uttam Kumaran: Everyone starts with RB2B, though, because they do a lot on LinkedIn.
212 00:18:01.320 ⇒ 00:18:05.309 Caitlyn Vaughn: Yeah, yeah, they’ve done so well on the marketing front.
213 00:18:05.310 ⇒ 00:18:07.620 Uttam Kumaran: Yeah, they only have, like, 3 employees or something.
214 00:18:07.620 ⇒ 00:18:10.619 Caitlyn Vaughn: Yeah, it’s just fuckin’ Adam, right?
215 00:18:10.680 ⇒ 00:18:11.699 Uttam Kumaran: Yeah, yeah, yeah.
216 00:18:12.090 ⇒ 00:18:13.799 Caitlyn Vaughn: He lives in Austin, do you know him?
217 00:18:13.800 ⇒ 00:18:15.670 Uttam Kumaran: Oh, no! I didn’t know that.
218 00:18:15.670 ⇒ 00:18:21.599 Caitlyn Vaughn: Yeah, he’s, like, buddies with fucking Nick, who owns 5x5. That’s the only reason why I know him.
219 00:18:21.600 ⇒ 00:18:22.900 Uttam Kumaran: Oh, okay.
220 00:18:26.530 ⇒ 00:18:35.690 Uttam Kumaran: Okay, cool, so we’ll finish this up, and then I think I’ll get an estimation from Mustafa on how fast we can knock these out.
221 00:18:36.300 ⇒ 00:18:39.760 Uttam Kumaran: I think we’re ready to also do an Omni walkthrough.
222 00:18:39.760 ⇒ 00:18:40.910 Caitlyn Vaughn: Oh, cool!
223 00:18:41.190 ⇒ 00:18:42.040 Caitlyn Vaughn: Can we hooked up?
224 00:18:42.310 ⇒ 00:18:43.070 Uttam Kumaran: Huh?
225 00:18:43.070 ⇒ 00:18:44.059 Caitlyn Vaughn: We’re hooked up.
226 00:18:44.060 ⇒ 00:18:49.950 Uttam Kumaran: Yeah, yeah, yeah, so we’re hooked up, and I know Mustafa made the, the edit that you wanted as well.
227 00:18:49.950 ⇒ 00:18:50.510 Caitlyn Vaughn: Oh, good.
228 00:18:50.510 ⇒ 00:18:51.550 Uttam Kumaran: integrations.
229 00:18:51.550 ⇒ 00:18:52.050 Caitlyn Vaughn: Hmm.
230 00:18:52.050 ⇒ 00:18:59.019 Uttam Kumaran: Piece, so I think… yeah, I don’t know if everybody’s, like, slammed, but I wanted to try to do… do it before…
231 00:18:59.570 ⇒ 00:19:00.360 Uttam Kumaran: Bye.
232 00:19:00.820 ⇒ 00:19:02.410 Uttam Kumaran: Christmas, basically.
233 00:19:02.410 ⇒ 00:19:08.059 Caitlyn Vaughn: Yeah, yeah, me too. Honestly, what I would love to do with you is, like.
234 00:19:08.170 ⇒ 00:19:16.089 Caitlyn Vaughn: have just you and I one-on-oneing, like, doing, like, a free version of it, just because I know that I will pay much more attention, and like…
235 00:19:16.090 ⇒ 00:19:16.630 Uttam Kumaran: Okay.
236 00:19:16.630 ⇒ 00:19:21.340 Caitlyn Vaughn: put more effort into it in my brain. But we should also do, like, a company one.
237 00:19:21.520 ⇒ 00:19:23.730 Uttam Kumaran: Maybe, like, Tuesday?
238 00:19:23.890 ⇒ 00:19:25.260 Uttam Kumaran: Next week?
239 00:19:25.520 ⇒ 00:19:30.500 Caitlyn Vaughn: Yep. Let’s do… I have all hands. Can we do Wednesday morning?
240 00:19:30.800 ⇒ 00:19:32.480 Caitlyn Vaughn: Or I could do Monday.
241 00:19:33.420 ⇒ 00:19:34.870 Uttam Kumaran: Yeah, what time Monday?
242 00:19:35.190 ⇒ 00:19:38.090 Caitlyn Vaughn: Anytime after 1PM.
243 00:19:39.100 ⇒ 00:19:40.819 Uttam Kumaran: Okay, yeah, let’s aim for Monday.
244 00:19:41.050 ⇒ 00:19:41.700 Caitlyn Vaughn: Okay.
245 00:19:44.150 ⇒ 00:19:45.470 Caitlyn Vaughn: Put something on.
246 00:19:52.430 ⇒ 00:19:53.500 Uttam Kumaran: Okay…
247 00:19:57.960 ⇒ 00:19:58.930 Uttam Kumaran: Okay.
248 00:19:59.160 ⇒ 00:20:04.119 Uttam Kumaran: You can tell me, like, what part of it we want to share… scale the other folks.
249 00:20:04.280 ⇒ 00:20:05.370 Caitlyn Vaughn: Yeah.
250 00:20:05.490 ⇒ 00:20:11.539 Caitlyn Vaughn: So, was Thomas able to, like, get everything hooked up and live?
251 00:20:12.070 ⇒ 00:20:16.560 Uttam Kumaran: Yeah, I guess, Mustafa, do we have, like,
252 00:20:16.970 ⇒ 00:20:22.050 Uttam Kumaran: Are you guys… you’re speaking directly with Thomas on, like, updates for the product data?
253 00:20:25.500 ⇒ 00:20:36.539 Mustafa Raja: Yeah, so, we had a meeting, with Thomas regarding that, but, since then, we haven’t heard, for an update. So, yeah, I’m still waiting on…
254 00:20:36.890 ⇒ 00:20:38.650 Mustafa Raja: Wanting to update us.
255 00:20:39.700 ⇒ 00:20:40.080 Caitlyn Vaughn: You’re away.
256 00:20:40.080 ⇒ 00:20:42.029 Mustafa Raja: If the pieces are ready for us to take over.
257 00:20:42.420 ⇒ 00:20:42.990 Caitlyn Vaughn: Okay.
258 00:20:42.990 ⇒ 00:20:43.730 Mustafa Raja: Yeah.
259 00:20:44.320 ⇒ 00:20:54.319 Caitlyn Vaughn: Let me… Ping him… I’ve been, like, a thorn in the side of everybody for this.
260 00:20:55.090 ⇒ 00:20:57.839 Mustafa Raja: Also, also for the,
261 00:20:57.840 ⇒ 00:21:03.779 Uttam Kumaran: let me know, we can keep… I mean, I can keep trying to ping every day, I just don’t know his timing that well, but…
262 00:21:05.830 ⇒ 00:21:07.029 Uttam Kumaran: Yeah, we could use.
263 00:21:07.030 ⇒ 00:21:07.710 Caitlyn Vaughn: Okay.
264 00:21:07.840 ⇒ 00:21:17.509 Caitlyn Vaughn: He said yesterday, I pinged him, and he said, just got off a call with Mustafa, and the setup seems pretty simple, waiting on approval from Vic.
265 00:21:18.170 ⇒ 00:21:20.770 Caitlyn Vaughn: Okay, cool, I’ll blast him today.
266 00:21:20.770 ⇒ 00:21:25.839 Uttam Kumaran: And then, what about the, stuff for Catalyst, Mustafa?
267 00:21:26.930 ⇒ 00:21:31.300 Mustafa Raja: Yeah, that depends on, On Thomas’s update.
268 00:21:32.320 ⇒ 00:21:35.040 Uttam Kumaran: Okay, he’s on the hook for the catalyst, though. On our end, we’re…
269 00:21:35.040 ⇒ 00:21:35.860 Mustafa Raja: Yeah.
270 00:21:35.860 ⇒ 00:21:38.670 Uttam Kumaran: Yeah, we’re good, we’re… we’re ready on that, so…
271 00:21:38.870 ⇒ 00:21:39.750 Caitlyn Vaughn: Okay.
272 00:21:39.750 ⇒ 00:21:53.169 Mustafa Raja: Yeah, yeah, also, also one more thing, so, you mentioned, the, default has about 20 integrations, right? So, in the data, I was, I was only able to find these ones that I have laid out in the, in the.
273 00:21:53.530 ⇒ 00:22:01.600 Mustafa Raja: Dashboard, so, maybe, maybe we need to add, more data, to include these integrations that we are missing.
274 00:22:02.430 ⇒ 00:22:05.410 Caitlyn Vaughn: Yeah, what’s up with that? So we have, like…
275 00:22:06.330 ⇒ 00:22:11.429 Caitlyn Vaughn: we have AI block… like, I basically want to see who is using every block.
276 00:22:12.010 ⇒ 00:22:26.289 Caitlyn Vaughn: like, how many unique people are using each of these, because I’m curious from, like, a product lens, we’re about to port over to, Pipedream, and so if we’re gonna rebuild all of these integrations, I wanna make sure it’s, like, worth our time, you know?
277 00:22:26.290 ⇒ 00:22:27.710 Uttam Kumaran: Yeah, yeah, yeah.
278 00:22:28.890 ⇒ 00:22:30.940 Caitlyn Vaughn: Like, I don’t know who the fuck’s using loops.
279 00:22:31.350 ⇒ 00:22:33.450 Caitlyn Vaughn: It’s such a random integration.
280 00:22:35.000 ⇒ 00:22:47.400 Uttam Kumaran: Okay, then let’s try to get… I’ll try to get you a concise thing. We’ll also, like, by tomorrow, on, like, integration usage. That’s… I mean, I think we’ll… we’ll find that all in the existing product data.
281 00:22:47.650 ⇒ 00:22:51.749 Uttam Kumaran: But that also would be helpful for, like.
282 00:22:51.880 ⇒ 00:22:58.179 Uttam Kumaran: when we get the new system to have Amplitude hooked up to literally the buttons, because then it’ll be way easier to see that, versus…
283 00:22:58.440 ⇒ 00:22:59.010 Caitlyn Vaughn: Yeah.
284 00:22:59.180 ⇒ 00:22:59.850 Uttam Kumaran: Okay.
285 00:23:00.080 ⇒ 00:23:05.290 Caitlyn Vaughn: Okay, so Mustafa, I looked at Omni, and obviously I’m like.
286 00:23:05.540 ⇒ 00:23:17.729 Caitlyn Vaughn: have not put the effort into, like, actually learn how to build things out, but I looked at the integrations that we can add in on that one that you had already built, and there wasn’t even the other integrations available, so I don’t think it’s in the data.
287 00:23:23.650 ⇒ 00:23:28.469 Mustafa Raja: Like the harmonic and other stuff that you just showed.
288 00:23:30.190 ⇒ 00:23:31.339 Caitlyn Vaughn: Wait, what’d you say?
289 00:23:32.310 ⇒ 00:23:40.150 Mustafa Raja: by integrations, do we mean the, if it… if the users are Slack-enabled or HubSpot enabled?
290 00:23:40.480 ⇒ 00:23:41.230 Caitlyn Vaughn: Yes.
291 00:23:42.020 ⇒ 00:23:49.420 Mustafa Raja: Okay, yeah, for these ones, yeah, I could only find, these, fields in this.
292 00:23:49.740 ⇒ 00:23:51.720 Caitlyn Vaughn: Yeah, same.
293 00:23:52.410 ⇒ 00:23:57.479 Mustafa Raja: Hmm, we will… we would need more data if we have… if we have more integrations.
294 00:23:57.730 ⇒ 00:24:01.149 Caitlyn Vaughn: Okay, so we’re just, like, missing the actual data, is what you’re saying.
295 00:24:03.530 ⇒ 00:24:04.280 Mustafa Raja: Yes.
296 00:24:05.450 ⇒ 00:24:07.269 Caitlyn Vaughn: Okay, how do we fix this?
297 00:24:09.500 ⇒ 00:24:12.199 Caitlyn Vaughn: Is it something that we’re… we need to, like, add on our side?
298 00:24:12.200 ⇒ 00:24:14.280 Mustafa Raja: I could get… get on with…
299 00:24:15.580 ⇒ 00:24:17.029 Mustafa Raja: I think I could get on here.
300 00:24:17.030 ⇒ 00:24:21.860 Uttam Kumaran: Can you, Mustafa, can you just send, like,
301 00:24:22.980 ⇒ 00:24:34.519 Uttam Kumaran: either a CSV of the usage data, and, like, maybe tag Vic and Thomas, and say, like, hey, is this accurate? Like, this is the integration usage data we’re seeing from the product.
302 00:24:34.700 ⇒ 00:24:35.759 Uttam Kumaran: Like, is this accurate?
303 00:24:35.760 ⇒ 00:24:36.600 Mustafa Raja: Yep.
304 00:24:36.600 ⇒ 00:24:37.339 Caitlyn Vaughn: What’s the total list?
305 00:24:37.340 ⇒ 00:24:38.789 Mustafa Raja: I’ll, I’ll do that.
306 00:24:39.730 ⇒ 00:24:40.630 Caitlyn Vaughn: Cool.
307 00:24:41.620 ⇒ 00:24:44.150 Uttam Kumaran: Yeah, because if it’s not there, then, yeah, okay.
308 00:24:44.150 ⇒ 00:24:44.720 Caitlyn Vaughn: Yeah.
309 00:24:45.060 ⇒ 00:24:46.470 Caitlyn Vaughn: We’re missing something.
310 00:24:46.760 ⇒ 00:24:50.690 Caitlyn Vaughn: Yeah. Like, how do we send partial workflow data? Like, how did the.
311 00:24:50.690 ⇒ 00:24:52.189 Uttam Kumaran: Yeah, so I’m like, okay.
312 00:24:54.280 ⇒ 00:24:55.440 Uttam Kumaran: Okay, cool.
313 00:24:55.730 ⇒ 00:24:56.490 Caitlyn Vaughn: Yay!
314 00:24:58.550 ⇒ 00:25:04.370 Caitlyn Vaughn: Well, we have to have everything set up before we do the Omni thing, but we’ll keep it on Monday,
315 00:25:04.750 ⇒ 00:25:08.110 Caitlyn Vaughn: I would love to just… be…
316 00:25:08.250 ⇒ 00:25:11.080 Caitlyn Vaughn: up to speed on Omni in general, obviously.
317 00:25:11.080 ⇒ 00:25:11.540 Uttam Kumaran: F.
318 00:25:11.540 ⇒ 00:25:14.980 Caitlyn Vaughn: Not on you guys, you guys have been great, it’s on, on us, but…
319 00:25:14.980 ⇒ 00:25:19.130 Uttam Kumaran: I want to show you how to create, at least start to create dashboards and things like that.
320 00:25:19.370 ⇒ 00:25:20.120 Caitlyn Vaughn: Hmm.
321 00:25:21.060 ⇒ 00:25:21.510 Uttam Kumaran: Yeah.
322 00:25:21.510 ⇒ 00:25:24.630 Caitlyn Vaughn: Oh yeah, we don’t really need the, like, live product data for that, do we?
323 00:25:25.040 ⇒ 00:25:38.019 Uttam Kumaran: No, I mean, we… the data… I… we don’t need… no, we have the data we have right now. Basically, once we get the live data, we’ll swap it in so that it’ll start to be up-to-date, but I want to show you… basically, we’ll walk through almost recreating, like, our existing dashboard.
324 00:25:38.140 ⇒ 00:25:40.049 Uttam Kumaran: And then…
325 00:25:40.390 ⇒ 00:25:48.450 Uttam Kumaran: Kind of just, like, show you how to walk through some of the nuances, and then part of it will be you just have to carve out time to just, like, spend here.
326 00:25:48.450 ⇒ 00:25:49.390 Caitlyn Vaughn: Thank you.
327 00:25:49.390 ⇒ 00:26:01.099 Uttam Kumaran: as you’ll learn, like, the quirks of, like, how to create different things, but ideally, at least I want to show you, like, in a… in a quick thing, like, how to do basic bar charts, line charts.
328 00:26:01.230 ⇒ 00:26:08.439 Uttam Kumaran: the organization, how we did the models. And so, you could be able to start to produce things.
329 00:26:08.710 ⇒ 00:26:09.040 Caitlyn Vaughn: Yeah.
330 00:26:09.040 ⇒ 00:26:13.429 Uttam Kumaran: And then, yeah, it’ll just take a little bit of time to spend in the platform, so…
331 00:26:14.640 ⇒ 00:26:20.650 Caitlyn Vaughn: Cool, yeah, I used to use Tableau when I was at Hershey’s, and it was so awesome, so I assume it’s, like.
332 00:26:20.650 ⇒ 00:26:24.730 Uttam Kumaran: Yeah, this is, like, there’s a better version of Tableau. Like, it doesn’t glitch out and stuff.
333 00:26:24.730 ⇒ 00:26:26.320 Caitlyn Vaughn: Yeah, that was rough.
334 00:26:26.320 ⇒ 00:26:26.990 Uttam Kumaran: Yeah.
335 00:26:27.110 ⇒ 00:26:31.910 Uttam Kumaran: Is the integration stuff at the bottom of this? Where is it again?
336 00:26:32.220 ⇒ 00:26:33.359 Caitlyn Vaughn: It’s right here.
337 00:26:37.070 ⇒ 00:26:38.719 Uttam Kumaran: Okay, yeah, yeah, yeah.
338 00:26:39.180 ⇒ 00:26:39.900 Caitlyn Vaughn: Yeah.
339 00:26:40.090 ⇒ 00:26:44.540 Caitlyn Vaughn: So, I think I looked in, and these are all the integrations that I could see as well.
340 00:26:46.230 ⇒ 00:26:52.039 Caitlyn Vaughn: On the bright side, now we have 60 people enabled on audio, which we had, like, 2 before, so…
341 00:26:52.040 ⇒ 00:26:52.680 Uttam Kumaran: Yeah.
342 00:26:52.680 ⇒ 00:26:59.540 Caitlyn Vaughn: That is good. Things like this, like… Member creating forms?
343 00:26:59.540 ⇒ 00:27:00.160 Uttam Kumaran: Yeah.
344 00:27:00.350 ⇒ 00:27:04.369 Caitlyn Vaughn: Teams, booking meetings, workflows with successful triggers?
345 00:27:04.370 ⇒ 00:27:15.260 Uttam Kumaran: The reason why I don’t like these is they’re round numbers, and they don’t have a time, so, like, you kind of want to see these over time, like, what percentage of members are creating forms each month.
346 00:27:17.190 ⇒ 00:27:18.370 Caitlyn Vaughn: Yeah…
347 00:27:18.560 ⇒ 00:27:28.520 Caitlyn Vaughn: I guess that first one is good. I just, like, I feel like I look at this and I, like, don’t understand what it is. Same with, like, test unlabeled submissions versus valid submissions. Like, what does that mean?
348 00:27:30.550 ⇒ 00:27:32.570 Uttam Kumaran: Yeah, we can clean this up a little bit more.
349 00:27:32.930 ⇒ 00:27:41.230 Caitlyn Vaughn: And then, even looking at this specific screen, here’s the total members, 4,100, and then here’s the member count.
350 00:27:41.250 ⇒ 00:27:57.889 Caitlyn Vaughn: And those two pieces of data are, like, completely conflicting. And I know it’s because we do have genuinely, like, 5,200 members, but there’s probably only 4,100 that have, like, enabled some integration, right? So this is probably more reflective of our actual true member count.
351 00:27:59.500 ⇒ 00:28:02.580 Uttam Kumaran: Okay, yeah, we can clean this up, too. So we’ll clean… I’ll clean this up.
352 00:28:03.150 ⇒ 00:28:11.600 Uttam Kumaran: And then by Monday, and then we can go through. But this is also where I… our initial goal in getting this whole dashboard set up was, like, I want to show you how to make edits.
353 00:28:11.600 ⇒ 00:28:20.969 Caitlyn Vaughn: Yeah, we can… why don’t we just edit it together on Monday? I’m not, like, I’m not that worried about it, it’s not like this is, like, a burning issue or anything. Like, there’s a lot of really good data in here, but there’s.
354 00:28:20.970 ⇒ 00:28:29.919 Uttam Kumaran: I also want to walk you, because we started filtering out, like, default and test users, internal stuff, and so I want to show you, like, how some of those filters work.
355 00:28:30.080 ⇒ 00:28:48.670 Uttam Kumaran: And also, like, we’ve… at the moment, we’ve included all fields. So one thing we’ll go through on Monday is, like, what stuff are you never gonna look at that I can, like, hide? Because bringing in all fields from all tables, and for example, if you’re like, oh, I’m never gonna look at, like, the meta field from this table, I’m like, okay, well, like…
356 00:28:49.230 ⇒ 00:28:50.420 Uttam Kumaran: Clean it up.
357 00:28:50.570 ⇒ 00:28:51.120 Uttam Kumaran: Yeah.
358 00:28:51.310 ⇒ 00:28:57.189 Caitlyn Vaughn: Okay, cool. Yeah. I mean, it’s, like, mostly good. There’s just things that would probably be more valuable.
359 00:28:57.190 ⇒ 00:28:57.830 Uttam Kumaran: Okay.
360 00:28:59.110 ⇒ 00:29:01.109 Caitlyn Vaughn: But fuck yeah, okay, cool, this is…
361 00:29:01.260 ⇒ 00:29:08.339 Caitlyn Vaughn: Coming along! It’s so nice to just, like, have this done every week, and, like, be able to focus on not this.
362 00:29:08.340 ⇒ 00:29:12.430 Uttam Kumaran: Yeah. You guys, do you know this company, Hydra? Have you heard of them?
363 00:29:13.090 ⇒ 00:29:15.739 Caitlyn Vaughn: Yes, that sounds familiar.
364 00:29:16.300 ⇒ 00:29:19.440 Uttam Kumaran: I am… These guys.
365 00:29:19.550 ⇒ 00:29:22.089 Uttam Kumaran: So this, throw us into here, let’s see.
366 00:29:24.700 ⇒ 00:29:32.120 Uttam Kumaran: Yeah, we started, doing some work with them recently. I don’t… I guess I… Yeah.
367 00:29:32.120 ⇒ 00:29:33.300 Caitlyn Vaughn: Cool website!
368 00:29:37.150 ⇒ 00:29:41.700 Uttam Kumaran: They’re based at SF, but it’s all… they’re all, it’s, like, really consumer-heavy.
369 00:29:41.700 ⇒ 00:29:46.489 Caitlyn Vaughn: Mmm… Wow, the Descript.
370 00:29:51.580 ⇒ 00:29:55.130 Caitlyn Vaughn: Cool company. You just got them as a client?
371 00:29:55.600 ⇒ 00:30:04.729 Uttam Kumaran: Yeah, we’ve been working with them for a month or so, and then for them, we’re starting to… we’re basically pitching them on, can we start to do more product analytics work?
372 00:30:04.900 ⇒ 00:30:08.649 Caitlyn Vaughn: Mmm, that’s great. Everyone in product analytics.
373 00:30:08.650 ⇒ 00:30:25.230 Uttam Kumaran: Yes. And usually, like, once we come in and set up the basics, that’s, like, where we like to go, because it takes a while to understand every company’s, like, unique objects. Like, the default has, like, forms and these things, and so once we understand the product, then it’s like, okay, cool, we should start looking at
374 00:30:25.310 ⇒ 00:30:42.089 Uttam Kumaran: okay, what combinations of things people are using? How do we move people from pricing tiers? Typically, what they say is, like, what is, like, the golden event? Like, what are the events that people need to do to move them to show that they’re, like… those are the fun things and data, but there just takes so much setup work, and, like.
375 00:30:43.120 ⇒ 00:30:50.970 Uttam Kumaran: to understand the business, understand the product, and then be like, okay, let’s start to, like, take a look at amplitude data, and so we’re starting to do more of that, you know.
376 00:30:50.970 ⇒ 00:30:55.510 Caitlyn Vaughn: Yeah, that’s so great. What was I gonna ask you?
377 00:30:57.300 ⇒ 00:30:59.189 Caitlyn Vaughn: How many clients do you have right now?
378 00:30:59.890 ⇒ 00:31:04.960 Uttam Kumaran: We have, I actually don’t know, maybe 14 or 15?
379 00:31:04.960 ⇒ 00:31:06.020 Caitlyn Vaughn: What?
380 00:31:06.020 ⇒ 00:31:10.119 Uttam Kumaran: We had an insane month and a half, dude.
381 00:31:10.300 ⇒ 00:31:11.140 Caitlyn Vaughn: Really?
382 00:31:12.120 ⇒ 00:31:20.460 Uttam Kumaran: Yeah, I mean, I haven’t… I just haven’t slept in, like, probably 3 or 4 weeks. But yeah, I mean, we…
383 00:31:21.210 ⇒ 00:31:26.349 Uttam Kumaran: we signed with Hedra, we’re doing work with Element.
384 00:31:26.530 ⇒ 00:31:27.730 Caitlyn Vaughn: No way!
385 00:31:27.730 ⇒ 00:31:32.189 Uttam Kumaran: Yeah, so we’re basically setting up their entire data infrastructure.
386 00:31:32.400 ⇒ 00:31:34.710 Uttam Kumaran: For element, which is great.
387 00:31:34.710 ⇒ 00:31:36.389 Caitlyn Vaughn: That’s so exciting!
388 00:31:36.390 ⇒ 00:31:39.740 Uttam Kumaran: And then we’re… Do you know Honey Stinger?
389 00:31:40.020 ⇒ 00:31:41.210 Caitlyn Vaughn: Yeah, I do!
390 00:31:41.210 ⇒ 00:31:43.270 Uttam Kumaran: We work for Honey Stinger as well.
391 00:31:43.270 ⇒ 00:31:44.180 Caitlyn Vaughn: What?
392 00:31:44.410 ⇒ 00:31:47.340 Caitlyn Vaughn: You guys are closing some great clients.
393 00:31:47.340 ⇒ 00:32:00.670 Uttam Kumaran: So there’s some great brands, all e-com, so it’s… it’s like… but I think we have a lot in SaaS and e-com, and then, we’re talking… we’re sort of at the finish line with Magic Spoons, too.
394 00:32:01.970 ⇒ 00:32:03.599 Caitlyn Vaughn: I don’t know if I know mattress spoons.
395 00:32:03.600 ⇒ 00:32:05.329 Uttam Kumaran: Protein cereal company.
396 00:32:05.330 ⇒ 00:32:07.899 Caitlyn Vaughn: Oh my gosh, actually I do. That’s so.
397 00:32:07.900 ⇒ 00:32:11.579 Uttam Kumaran: I bought it once, but then it was so expensive, I was like, I’m never gonna buy this again.
398 00:32:11.580 ⇒ 00:32:12.870 Caitlyn Vaughn: Like, fuck.
399 00:32:12.870 ⇒ 00:32:14.090 Uttam Kumaran: Yeah, I’d love to.
400 00:32:14.090 ⇒ 00:32:15.080 Caitlyn Vaughn: Really?
401 00:32:15.080 ⇒ 00:32:15.510 Uttam Kumaran: Yeah.
402 00:32:15.510 ⇒ 00:32:17.100 Caitlyn Vaughn: Magic Spoon.
403 00:32:17.500 ⇒ 00:32:21.830 Caitlyn Vaughn: Childhood Classics Grown Up Ingredients. Oh, wait, this is so cute!
404 00:32:21.830 ⇒ 00:32:28.810 Uttam Kumaran: Yeah, yeah. So, we have a couple really cool… CPG, e-commerce clients.
405 00:32:29.220 ⇒ 00:32:31.439 Caitlyn Vaughn: And then on the SaaS side.
406 00:32:31.550 ⇒ 00:32:41.439 Uttam Kumaran: Of course, y’all, Hedra, and then we’re talking to, a few others as well.
407 00:32:41.630 ⇒ 00:32:53.790 Uttam Kumaran: I don’t even… like, I feel like I’m just, like, always, like, underestimating how many books we’re working with, but it’s, like, it’s tough, because we’re… we have to… we have to now grow, like, to… to keep up, so…
408 00:32:53.790 ⇒ 00:32:54.140 Caitlyn Vaughn: Yeah.
409 00:32:54.140 ⇒ 00:32:56.489 Uttam Kumaran: We’re thinking about how do we build
410 00:32:56.710 ⇒ 00:33:00.209 Uttam Kumaran: Like, kind of the next… like, folks to actually come drive
411 00:33:00.480 ⇒ 00:33:11.899 Uttam Kumaran: clients. You know, like, I think about, like, for y’all, I think it’s… we’re now at a point where it’s actually really… there’s a couple work streams, right? There’s, like, some supporting the Catalyst work.
412 00:33:11.900 ⇒ 00:33:12.290 Caitlyn Vaughn: Yeah.
413 00:33:12.290 ⇒ 00:33:20.330 Uttam Kumaran: like, data ingestion, there is, like, the dashboarding, like, operational reporting, and then I also wanted, like, you guys totally…
414 00:33:20.530 ⇒ 00:33:27.070 Uttam Kumaran: once you start launching your products, as you hire more product people, they’re gonna be like, I need usage data.
415 00:33:27.070 ⇒ 00:33:28.100 Caitlyn Vaughn: Yeah.
416 00:33:28.130 ⇒ 00:33:37.460 Uttam Kumaran: Right? And so, the amplitude work is also that, but on our side, it’s like, okay, I want to get someone who just comes in and just thinks about, like, y’all every day, you know?
417 00:33:37.460 ⇒ 00:33:38.000 Caitlyn Vaughn: Yeah!
418 00:33:38.000 ⇒ 00:33:39.499 Uttam Kumaran: I’m thinking about doing this.
419 00:33:39.500 ⇒ 00:33:44.229 Caitlyn Vaughn: That would be so nice. Although I feel like you guys are so on top of it, like, you’re… you guys respect.
420 00:33:44.230 ⇒ 00:33:49.239 Uttam Kumaran: So we definitely, we definitely, like, try, but it’s coming at, like, the…
421 00:33:49.350 ⇒ 00:33:51.239 Uttam Kumaran: It’s gonna be, like, my health expenses.
422 00:33:51.240 ⇒ 00:33:55.170 Caitlyn Vaughn: Yeah. Or, like, minus one HP, minus one HP.
423 00:33:55.170 ⇒ 00:34:04.450 Uttam Kumaran: Like, I love… I mean, I love it, but it’s also helpful to have someone that thinks about it, and then I’m still, like, I can guide the overall strategy.
424 00:34:04.450 ⇒ 00:34:04.800 Caitlyn Vaughn: Yeah.
425 00:34:04.800 ⇒ 00:34:05.840 Uttam Kumaran: And then…
426 00:34:05.860 ⇒ 00:34:15.950 Caitlyn Vaughn: You know, but that’s what’s fun, like, hearing your feedback on the work we did on pricing was really great. That’s the sort of stuff that I think we want to get utilized more.
427 00:34:16.380 ⇒ 00:34:23.609 Uttam Kumaran: in, like, true analytics and, like, analysis of, like, hey, there’s, like, this, like, mystery thing over here, like, go figure it. That’s the stuff I
428 00:34:24.400 ⇒ 00:34:29.209 Uttam Kumaran: Yeah, yeah, yeah. We’ll implement the warehouse and whatever, like, that’s table stakes for us, but…
429 00:34:29.219 ⇒ 00:34:29.759 Caitlyn Vaughn: Yeah.
430 00:34:29.760 ⇒ 00:34:31.560 Uttam Kumaran: But yeah.
431 00:34:31.900 ⇒ 00:34:34.320 Caitlyn Vaughn: It’s gonna get so much more fun once we launch.
432 00:34:34.320 ⇒ 00:34:35.210 Uttam Kumaran: I know.
433 00:34:35.710 ⇒ 00:34:40.039 Caitlyn Vaughn: Because I’m, like, I’ve been talking about it with Nico so much, I’m like.
434 00:34:40.170 ⇒ 00:34:45.589 Caitlyn Vaughn: shitting myself, I’m so stressed about it, because I know that this is what I’ve been, like, leading up to for the last.
435 00:34:45.590 ⇒ 00:34:45.980 Uttam Kumaran: Yeah.
436 00:34:45.989 ⇒ 00:34:58.039 Caitlyn Vaughn: two years, year and a half, yeah, it’ll be two years almost. But our, like, big milestones, Nico and I had, like, this meeting yesterday where we talked about, like, what are our priorities with launching.
437 00:34:58.039 ⇒ 00:34:59.419 Uttam Kumaran: Yeah. And…
438 00:34:59.419 ⇒ 00:35:00.599 Caitlyn Vaughn: It’s like…
439 00:35:00.659 ⇒ 00:35:22.659 Caitlyn Vaughn: new revenue expansion, new revenue growth, and whatever else. But, like, the main thing being, when we launch self-serve, we hope that we have, like, fucking 5% conversion or something, right? Like, we hope that some amount of people are converting, both into, like, paid self-serve, and then also getting poached into, like, enterprise contracts.
440 00:35:22.729 ⇒ 00:35:26.159 Caitlyn Vaughn: Also, we’re starting our enterprise contracts at $50K.
441 00:35:26.159 ⇒ 00:35:34.350 Uttam Kumaran: We’re, like, going into the big leagues. Right. We’re launching… We use, you know, we use defaults, we, we did a… I did a webinar.
442 00:35:34.850 ⇒ 00:35:37.719 Uttam Kumaran: American Chamber of Commerce of Kyrgyzstan this morning.
443 00:35:37.720 ⇒ 00:35:38.680 Caitlyn Vaughn: What?
444 00:35:38.680 ⇒ 00:35:43.419 Uttam Kumaran: for 25 or 26 people, and we use default for all of our forms.
445 00:35:43.420 ⇒ 00:35:44.380 Caitlyn Vaughn: Really?
446 00:35:44.380 ⇒ 00:35:55.600 Uttam Kumaran: perfect, because all last week and this week through Slack, we were getting swarm submissions. It’s great. The feedback loop from the product is really, really satisfying, and Ryan on our team loves default, so we’re like…
447 00:35:56.250 ⇒ 00:35:59.569 Uttam Kumaran: We’re tossing it everywhere, and he’s like, yeah, he loves it, so…
448 00:35:59.570 ⇒ 00:36:02.289 Caitlyn Vaughn: Hell yeah. We paid him out so much, so he better love it.
449 00:36:02.290 ⇒ 00:36:06.729 Uttam Kumaran: How’s a new partnerships person?
450 00:36:06.730 ⇒ 00:36:12.529 Caitlyn Vaughn: She’s so good. We’re actually kicking off tomorrow our first implementation pilot.
451 00:36:12.890 ⇒ 00:36:13.390 Uttam Kumaran: Great.
452 00:36:13.390 ⇒ 00:36:22.530 Caitlyn Vaughn: So we’re going, like, clay model. Like, we’re gonna pass off all of our deals to partners, essentially, but we just have one agency. It’s called Lean Scale. I don’t know if you’ve heard of them.
453 00:36:22.840 ⇒ 00:36:24.059 Uttam Kumaran: Lean scale?
454 00:36:24.060 ⇒ 00:36:35.009 Caitlyn Vaughn: Yeah, if you haven’t, you definitely should. Their COO, his name’s Joe, he’s fucking awesome. Literally such a cool guy, and you guys should definitely do some work together, but…
455 00:36:35.010 ⇒ 00:36:35.680 Uttam Kumaran: Yeah.
456 00:36:35.680 ⇒ 00:36:43.059 Caitlyn Vaughn: They have managed to scale up to, like, they probably have, like, 70 employees now. They’re doing, like, I think $15 million in revenue.
457 00:36:43.060 ⇒ 00:36:43.810 Uttam Kumaran: Wow.
458 00:36:43.810 ⇒ 00:36:56.130 Caitlyn Vaughn: So they’re doing really well, and they just have, like, a whole team of, like, technical implementation people, and they’re like, we’ll just scale up or down with whatever you have, and we’re like, okay, great.
459 00:36:56.130 ⇒ 00:36:56.840 Uttam Kumaran: Great.
460 00:36:57.250 ⇒ 00:37:10.450 Caitlyn Vaughn: Yeah, so it’s going really well. Hopefully we can bring her on full-time. It’s actually, like, she’s doing 10 hours a week right now, and it seems to be, like, plenty at the moment, so maybe eventually we’ll… we’ll hire, but we just got a chief of staff.
461 00:37:10.740 ⇒ 00:37:17.530 Caitlyn Vaughn: We just hired a new forward deployed engineer. We actually have, like, a bunch of new women that just started, so… Great. Stoked on it.
462 00:37:17.530 ⇒ 00:37:18.210 Uttam Kumaran: Nice.
463 00:37:18.210 ⇒ 00:37:19.630 Caitlyn Vaughn: Yeah, it’s been good.
464 00:37:19.730 ⇒ 00:37:22.309 Uttam Kumaran: Also, Rush is opening next weekend.
465 00:37:22.490 ⇒ 00:37:23.559 Uttam Kumaran: Oh, really?
466 00:37:23.560 ⇒ 00:37:24.240 Caitlyn Vaughn: Yeah!
467 00:37:24.240 ⇒ 00:37:25.100 Uttam Kumaran: Probably…
468 00:37:25.100 ⇒ 00:37:35.519 Caitlyn Vaughn: Yeah, we have a dinner on Monday, an investor dinner, and we gotta raise an extra $6 million, and then once that’s closed, we just bought 2 more pieces of land, and it’s opening.
469 00:37:35.520 ⇒ 00:37:36.819 Uttam Kumaran: Wow, what a journey.
470 00:37:36.820 ⇒ 00:37:41.199 Caitlyn Vaughn: Yeah, next Sunday… next Saturday. So, if you’re around, you should come!
471 00:37:41.200 ⇒ 00:37:44.940 Uttam Kumaran: Yeah, tell me, send me a portable, I’m here. I’m here all month.
472 00:37:44.940 ⇒ 00:37:45.960 Caitlyn Vaughn: Yay!
473 00:37:46.470 ⇒ 00:37:48.480 Caitlyn Vaughn: So exciting. So much going on!
474 00:37:48.730 ⇒ 00:37:49.500 Caitlyn Vaughn: That’s right.
475 00:37:49.960 ⇒ 00:37:52.340 Uttam Kumaran: Are you, are you in Austin today?
476 00:37:52.340 ⇒ 00:37:53.100 Caitlyn Vaughn: Yeah.
477 00:37:53.270 ⇒ 00:37:59.400 Uttam Kumaran: I’m… I’m judging this, AI… competition at UT today.
478 00:37:59.400 ⇒ 00:38:00.270 Caitlyn Vaughn: Today?
479 00:38:00.270 ⇒ 00:38:02.420 Uttam Kumaran: It’s like a… yeah, like, later tonight.
480 00:38:02.420 ⇒ 00:38:03.380 Caitlyn Vaughn: What time?
481 00:38:03.540 ⇒ 00:38:06.200 Uttam Kumaran: At, like, 6?
482 00:38:06.410 ⇒ 00:38:07.319 Caitlyn Vaughn: At 6.
483 00:38:07.320 ⇒ 00:38:07.880 Uttam Kumaran: Yeah.
484 00:38:07.880 ⇒ 00:38:11.050 Caitlyn Vaughn: Okay, I might have a jumping lesson, but if it gets canceled, I’ll actually.
485 00:38:11.050 ⇒ 00:38:24.170 Uttam Kumaran: Okay, I’ll send it to you. They… Michael put me in touch with these guys, and I was like, I’m trying to hire AI people, like, if there’s any kids here that are actually decent, and they were like, actually, you want to be a judge? We’re running, like, a hackathon thing. I’m like, yeah, I’ll go.
486 00:38:24.170 ⇒ 00:38:26.090 Caitlyn Vaughn: What? That’s so fun!
487 00:38:26.090 ⇒ 00:38:27.600 Uttam Kumaran: So, that should be nice.
488 00:38:27.600 ⇒ 00:38:32.559 Caitlyn Vaughn: That’ll be really fun, amazing. Better than the Soho House AI events, those are shit.
489 00:38:32.560 ⇒ 00:38:37.230 Uttam Kumaran: Yeah, I’m not trying to go… I’m not trying to go to any of those.
490 00:38:37.440 ⇒ 00:38:42.689 Caitlyn Vaughn: So funny. I keep seeing people post… who was it? Do you know Tan? Fam?
491 00:38:45.000 ⇒ 00:38:46.280 Caitlyn Vaughn: H-A-N.
492 00:38:46.820 ⇒ 00:38:48.739 Uttam Kumaran: I feel like maybe it’s familiar.
493 00:38:48.740 ⇒ 00:38:54.030 Caitlyn Vaughn: He’s, like, really well-connected, but also has, like, the personality of, like, a piece of wood.
494 00:38:56.990 ⇒ 00:38:58.320 Uttam Kumaran: Bo’s last name?
495 00:38:59.330 ⇒ 00:39:00.729 Uttam Kumaran: Yeah, P-H-A-N?
496 00:39:00.730 ⇒ 00:39:04.830 Caitlyn Vaughn: Yeah, I think it’s P-H-A-M?
497 00:39:07.970 ⇒ 00:39:17.730 Caitlyn Vaughn: T-H-A-N… Human Router, P-H-A-M, yeah. Human Router is his Instagram that he just posted.
498 00:39:19.570 ⇒ 00:39:35.110 Caitlyn Vaughn: As I reflect on the last year, one of my favorite moments was teaching an AI workshop to friends in Austin. It all started with a conversation. If what I knew about AI… if I knew about AI, it’d be unstoppable. I’m like, you don’t know anything about AI. You’re literally so irrelevant.
499 00:39:35.390 ⇒ 00:39:36.479 Uttam Kumaran: What the fuck?
500 00:39:36.480 ⇒ 00:39:47.369 Caitlyn Vaughn: like, who is taking information from you and doing something with it? Sorry, I’m, like, really talking shit now. But anyway, TLDR, really glad that you’re gonna be a judge, because you know, you know things and stuff.
501 00:39:47.370 ⇒ 00:39:52.160 Uttam Kumaran: I’m just… I want to hire people locally, dude, I’m not able to do that, like, there’s just not, like…
502 00:39:52.210 ⇒ 00:39:56.160 Caitlyn Vaughn: Yeah, finding great people here, and it’s such… Really?
503 00:39:56.560 ⇒ 00:39:57.360 Caitlyn Vaughn: Damn!
504 00:39:57.360 ⇒ 00:40:15.860 Uttam Kumaran: Because I can’t… we’re not here to, like, teach. Like, I need people to run, you know? Yeah. And then I’m, like, I can now… I’ll show you how to, like… even if you’re running, like, a 6-minute mile, I’ll show you how to run a 4. But if you come, you’re like, I’ve never run a mile, like, it’s really hard for me, you know, at least now, we can’t afford that, so…
505 00:40:16.470 ⇒ 00:40:23.640 Uttam Kumaran: Getting some really, really good people, and then now what we’re trying to do is get, like, people that want to come and, like, run projects and run service lines.
506 00:40:23.640 ⇒ 00:40:28.239 Caitlyn Vaughn: Yeah. For example, product analytics. We’re doing product analytics for, like, 4 clients.
507 00:40:28.270 ⇒ 00:40:31.530 Uttam Kumaran: Someone needs to just become, like, the owner of, like.
508 00:40:31.590 ⇒ 00:40:39.060 Caitlyn Vaughn: our product analytics service. Clients shouldn’t care that they’re getting product analytics work from us, dashboarding.
509 00:40:39.060 ⇒ 00:40:43.840 Uttam Kumaran: data, but, like, There is a need to have, like, a functional owner.
510 00:40:44.560 ⇒ 00:40:45.360 Uttam Kumaran: Of, like, these.
511 00:40:45.360 ⇒ 00:40:45.730 Caitlyn Vaughn: Yeah.
512 00:40:45.730 ⇒ 00:40:49.450 Uttam Kumaran: this is that we’re doing. So that’s kind of how we’re thinking about it.
513 00:40:49.600 ⇒ 00:40:55.490 Caitlyn Vaughn: Yeah, it seems like in Austin, there’s, like, anyone who is really good is, like, already running their own stuff, you know?
514 00:40:55.490 ⇒ 00:41:01.170 Uttam Kumaran: Yeah, or they… yeah, exactly. I… I think, yeah, that’s most… that’s most of the folks.
515 00:41:02.150 ⇒ 00:41:10.530 Uttam Kumaran: Yeah. I mean, like, yeah, I just… it’s tough, because, like, the consultants and senior engineers here, they’re, like, working for big enterprise companies.
516 00:41:10.780 ⇒ 00:41:12.710 Uttam Kumaran: They’re not really, like, doing much.
517 00:41:13.670 ⇒ 00:41:14.370 Caitlyn Vaughn: Yeah.
518 00:41:14.560 ⇒ 00:41:19.920 Uttam Kumaran: Let’s be chillin’, and it’s gonna be a shock. It’s not that we’re crazy, but…
519 00:41:20.160 ⇒ 00:41:23.009 Uttam Kumaran: Like, we have a sense of urgency.
520 00:41:23.830 ⇒ 00:41:30.130 Uttam Kumaran: And so, it’ll be… people do call me, and they’re like, I would love to work, but I’m like, dude, you’ve been at IBM for, like, 6 years, like…
521 00:41:30.400 ⇒ 00:41:34.830 Uttam Kumaran: I don’t know whether you can work hard, again, like, in your life.
522 00:41:34.830 ⇒ 00:41:38.269 Caitlyn Vaughn: No, and it’s like they’re making 400K.
523 00:41:38.270 ⇒ 00:41:41.790 Uttam Kumaran: Yeah. I mean, they’re making less, dude, but they just don’t.
524 00:41:41.790 ⇒ 00:41:42.380 Caitlyn Vaughn: What?
525 00:41:42.380 ⇒ 00:41:43.530 Uttam Kumaran: They don’t want to work.
526 00:41:43.750 ⇒ 00:41:45.079 Caitlyn Vaughn: They’re making less?
527 00:41:45.290 ⇒ 00:41:45.910 Uttam Kumaran: Yeah.
528 00:41:47.270 ⇒ 00:41:49.689 Uttam Kumaran: And they don’t… just don’t wanna… they just don’t wanna, like…
529 00:41:50.370 ⇒ 00:42:05.130 Uttam Kumaran: they’re interested in AI and stuff, but then, like, they… people just aren’t… don’t want to, like, come in and, like, I’m like, okay, you’re gonna work on, like, 2-3 clients. Yeah. Everybody on our team right now is, like, really genuinely interested in, like, learning and trying everything new.
530 00:42:05.910 ⇒ 00:42:21.289 Uttam Kumaran: And we don’t… Yeah, and so we’re trying to basically find, like, ex-consultants at, like, Deloitte, or EY, or, like, BCG, who are in the middle of their career, that are like, damn, I’m not gonna make partner, it’ll take me, like, mad long.
531 00:42:21.290 ⇒ 00:42:21.760 Caitlyn Vaughn: Yeah.
532 00:42:21.760 ⇒ 00:42:25.580 Uttam Kumaran: But they’re also, like… I kind of want more ownership.
533 00:42:25.710 ⇒ 00:42:31.930 Uttam Kumaran: And we want those people to come in and not only, like, do what we do, like the technical stuff, but also help sell.
534 00:42:32.880 ⇒ 00:42:39.169 Uttam Kumaran: And that is… those are, like, those are tough people, you know, to find. Because otherwise, then I have to get salespeople, and…
535 00:42:39.540 ⇒ 00:42:43.380 Uttam Kumaran: technical owners, and then there’s, like, this handoff, and I think the client experience.
536 00:42:44.030 ⇒ 00:42:46.629 Uttam Kumaran: not good. Like, it’s… it goes well now because…
537 00:42:46.940 ⇒ 00:42:49.590 Uttam Kumaran: like, I’m with you, and a bunch of people are with you from the whole…
538 00:42:49.940 ⇒ 00:42:53.779 Caitlyn Vaughn: all the whole way, right? Yeah. We layer on more stuff as we…
539 00:42:53.780 ⇒ 00:42:56.179 Uttam Kumaran: As we can help you out more, you know? And that’s…
540 00:42:56.180 ⇒ 00:42:57.330 Caitlyn Vaughn: Yeah. Yeah.
541 00:42:57.900 ⇒ 00:43:02.380 Caitlyn Vaughn: Yeah. If you can find someone that’s technical and social, it’s like…
542 00:43:02.630 ⇒ 00:43:07.620 Caitlyn Vaughn: So niche, and so great. But also, those people are in, like, very high demand.
543 00:43:07.620 ⇒ 00:43:09.720 Uttam Kumaran: Yes, yes.
544 00:43:10.270 ⇒ 00:43:11.399 Caitlyn Vaughn: lot of options.
545 00:43:11.400 ⇒ 00:43:13.560 Uttam Kumaran: Yeah, yeah, I know.
546 00:43:14.010 ⇒ 00:43:31.030 Caitlyn Vaughn: Okay, cool. Well, you guys are killing it. Thank you so much for everything. Mustafa, let me know how I can help get those last couple across the line, because we kind of need to close this project out as soon as possible. And then let’s get kicking with the Omni stuff starting next week.
547 00:43:31.030 ⇒ 00:43:32.280 Uttam Kumaran: Okay, perfect.
548 00:43:32.600 ⇒ 00:43:33.900 Caitlyn Vaughn: Well, these are great, thanks!
549 00:43:34.120 ⇒ 00:43:35.240 Uttam Kumaran: Thank you. Bye.