Meeting Title: Default | Brainforge Weekly Sync Date: 2025-11-20 Meeting participants: Scratchpad Notetaker, Uttam Kumaran, Caitlyn Vaughn, Mustafa Raja, Amber Lin
WEBVTT
1 00:04:47.540 ⇒ 00:04:49.140 Caitlyn Vaughn: Hello?
2 00:04:53.550 ⇒ 00:04:54.530 Uttam Kumaran: Hello!
3 00:04:55.030 ⇒ 00:04:56.369 Caitlyn Vaughn: How’s it going?
4 00:04:56.370 ⇒ 00:04:57.350 Uttam Kumaran: Good.
5 00:04:57.640 ⇒ 00:04:58.840 Uttam Kumaran: How’s everything?
6 00:05:00.450 ⇒ 00:05:05.590 Caitlyn Vaughn: It’s good, it’s a lot of context switching, it’s a lot of, deep work.
7 00:05:06.310 ⇒ 00:05:07.410 Uttam Kumaran: Yeah, hey, good!
8 00:05:08.210 ⇒ 00:05:15.050 Caitlyn Vaughn: So it’s, like, kind of going in and out. It’s like coding and then, like, talking to people, you know, vibe.
9 00:05:15.820 ⇒ 00:05:16.620 Uttam Kumaran: Yes.
10 00:05:16.970 ⇒ 00:05:22.450 Uttam Kumaran: Yeah, we’re just, like… Scrambling to end the year well, but…
11 00:05:22.750 ⇒ 00:05:30.460 Uttam Kumaran: It’s also good over here, where we added another team member, kind of bringing on… thinking about bringing on a few more people, yeah, so…
12 00:05:30.460 ⇒ 00:05:31.790 Caitlyn Vaughn: What did you add in?
13 00:05:32.200 ⇒ 00:05:35.549 Uttam Kumaran: We brought on another data engineer, and then…
14 00:05:35.950 ⇒ 00:05:39.079 Uttam Kumaran: We’re interviewing for a few more analysts as well.
15 00:05:40.670 ⇒ 00:05:44.300 Caitlyn Vaughn: That’s fucking amazing! Wow, I’m so excited for you!
16 00:05:44.530 ⇒ 00:05:47.820 Uttam Kumaran: Thanks, yeah, we’re up to, like, 15 people, I think.
17 00:05:47.820 ⇒ 00:05:49.050 Caitlyn Vaughn: Whoa!
18 00:05:49.590 ⇒ 00:05:50.400 Uttam Kumaran: Yeah.
19 00:05:50.590 ⇒ 00:05:57.340 Uttam Kumaran: Amber and… Amber and Mustafa are just a couple of the… couple of the Brainforged friends?
20 00:05:57.340 ⇒ 00:05:59.799 Caitlyn Vaughn: So you’re, like, building a small army over there.
21 00:06:00.610 ⇒ 00:06:04.949 Uttam Kumaran: It’s good, I mean, we’re… one is, like, we’re ramping everybody up to…
22 00:06:05.450 ⇒ 00:06:11.990 Uttam Kumaran: full-time. Most of the people are… most of the people in engineering are full-time, except for 3, and they’re getting ramped up.
23 00:06:12.280 ⇒ 00:06:14.660 Uttam Kumaran: We’re expanding a lot of our…
24 00:06:14.910 ⇒ 00:06:20.790 Uttam Kumaran: Existing client engagements, which is giving folks more scope, and then people are just getting better, so…
25 00:06:21.020 ⇒ 00:06:28.449 Uttam Kumaran: as people get better and more senior, I’m able to just move them to, like, tougher projects, and then sort of fill in
26 00:06:28.940 ⇒ 00:06:31.849 Uttam Kumaran: Fill… fill in where they are on smaller stuff.
27 00:06:32.070 ⇒ 00:06:36.879 Uttam Kumaran: I think the biggest piece for us is we’re… we have to go… I need to go higher, like.
28 00:06:37.030 ⇒ 00:06:43.519 Uttam Kumaran: a Caitlin, a Ryan. I think we were… we gotta build, like, a lieutenant class.
29 00:06:43.700 ⇒ 00:06:44.340 Caitlyn Vaughn: Yeah.
30 00:06:44.340 ⇒ 00:06:47.689 Uttam Kumaran: And so that’s our next, like, challenge, because…
31 00:06:48.600 ⇒ 00:06:54.399 Uttam Kumaran: I don’t… none of… I don’t… we… I don’t know if that… I don’t necessarily know if that… we have that internally.
32 00:06:55.040 ⇒ 00:06:55.590 Caitlyn Vaughn: Yeah.
33 00:06:55.590 ⇒ 00:07:00.379 Uttam Kumaran: And it costs a lot of money to get a Caitlin and a Ryan, and a…
34 00:07:00.380 ⇒ 00:07:03.240 Caitlyn Vaughn: A lot of money, Tom, you can never afford…
35 00:07:04.370 ⇒ 00:07:08.540 Uttam Kumaran: No, I’m not gonna… I don’t have an offer for you, don’t worry, I would never lowball you.
36 00:07:08.620 ⇒ 00:07:11.329 Caitlyn Vaughn: You’re like, oh, I would never hire you, but…
37 00:07:11.330 ⇒ 00:07:16.670 Uttam Kumaran: No, I would never lowball you. Believe me, if we can make money together, that would be amazing.
38 00:07:16.670 ⇒ 00:07:19.609 Caitlyn Vaughn: I know, that’d be so sick. And another, like, 12.
39 00:07:19.610 ⇒ 00:07:22.120 Uttam Kumaran: In another life, yeah, in the next life.
40 00:07:22.120 ⇒ 00:07:23.560 Caitlyn Vaughn: our next company.
41 00:07:23.560 ⇒ 00:07:25.560 Uttam Kumaran: In the next… in the next business, yes.
42 00:07:26.020 ⇒ 00:07:31.749 Caitlyn Vaughn: Honestly, would you ever consider going back into, like, building a product, or are you, like, set on sort.
43 00:07:31.750 ⇒ 00:07:33.110 Uttam Kumaran: Totally, totally.
44 00:07:33.110 ⇒ 00:07:33.429 Caitlyn Vaughn: That’s true.
45 00:07:33.430 ⇒ 00:07:37.499 Uttam Kumaran: Yeah. I don’t… I… I care about…
46 00:07:37.690 ⇒ 00:07:44.210 Uttam Kumaran: building cool businesses and making a lot of money, and there’s a lot of different business models. I just… this is just, like.
47 00:07:44.400 ⇒ 00:07:48.299 Uttam Kumaran: I feel like services is easy to get into, hard to scale.
48 00:07:48.770 ⇒ 00:07:50.610 Uttam Kumaran: Product is, like, the other way.
49 00:07:50.610 ⇒ 00:07:50.960 Caitlyn Vaughn: Yeah.
50 00:07:50.960 ⇒ 00:07:53.759 Uttam Kumaran: So you kind of, like, pick your boys in,
51 00:07:54.770 ⇒ 00:08:03.140 Uttam Kumaran: I mean, I think, like, it would be a shame if I did… got this far in this business and never, like, did another thing, because it just meets so many friends, and, like.
52 00:08:03.370 ⇒ 00:08:09.449 Uttam Kumaran: learned to do so much stuff that, like, yeah, I would… I would totally do another thing. In fact, like, that’s what I’m sort of hoping, is that…
53 00:08:09.550 ⇒ 00:08:14.869 Uttam Kumaran: I mean, our… we’re trying to get an outcome out of this business. I don’t know how long it’ll… it’ll take.
54 00:08:16.530 ⇒ 00:08:25.760 Uttam Kumaran: But I’m kind of hopeful that I can continue to work with a lot of the people that are at Brain Forge if we do a next thing. And, like, we just met a lot of people on the way, but…
55 00:08:25.840 ⇒ 00:08:39.330 Uttam Kumaran: Yeah, whatever is next has to be 10 times bigger, 10 times crazier than this, so… yeah, I would love to. I’m getting… I’m actually, like… this has been so hard, but I also learned so much that I’m like…
56 00:08:39.520 ⇒ 00:08:42.640 Uttam Kumaran: I can move so much faster on the next one.
57 00:08:42.640 ⇒ 00:08:46.270 Caitlyn Vaughn: Yeah, totally. I love that, though. Thanks.
58 00:08:46.270 ⇒ 00:08:46.770 Uttam Kumaran: Yeah.
59 00:08:46.770 ⇒ 00:08:51.260 Caitlyn Vaughn: I need, like, I need 3 more years at… probably at default.
60 00:08:51.260 ⇒ 00:08:52.480 Uttam Kumaran: Totally, totally.
61 00:08:52.480 ⇒ 00:08:54.230 Caitlyn Vaughn: And then we should talk about it.
62 00:08:54.230 ⇒ 00:08:59.540 Uttam Kumaran: We should! 100%, I mean, yeah. That’d be awesome.
63 00:08:59.540 ⇒ 00:09:03.839 Caitlyn Vaughn: Have some ideas. I’ll pitch you on girl gas soon, but not today.
64 00:09:03.840 ⇒ 00:09:07.250 Uttam Kumaran: Is that, like, galaxy gas? Is that, like.
65 00:09:07.250 ⇒ 00:09:11.090 Caitlyn Vaughn: No, it’s like a gas station for… it’s, like, upscale gas station.
66 00:09:11.090 ⇒ 00:09:11.729 Uttam Kumaran: Oh, no, no, like.
67 00:09:11.730 ⇒ 00:09:14.940 Caitlyn Vaughn: I want to disrupt the gas station business.
68 00:09:15.590 ⇒ 00:09:19.880 Uttam Kumaran: Interesting. Girls do like gas stations, you know?
69 00:09:19.880 ⇒ 00:09:21.420 Caitlyn Vaughn: Eight gas stations.
70 00:09:21.420 ⇒ 00:09:24.979 Uttam Kumaran: No, but my girlfriend loves just shopping in the gas stations, like…
71 00:09:24.980 ⇒ 00:09:27.150 Caitlyn Vaughn: getting, like, getting, like…
72 00:09:27.340 ⇒ 00:09:28.390 Uttam Kumaran: Yeah, yeah.
73 00:09:28.770 ⇒ 00:09:43.969 Caitlyn Vaughn: Literally, it’s like, I literally got gas yesterday on my way out to, like, ride, which is kind of out of town, and I stopped in, and I fucking hate getting gas, and I especially try not to get gas, like, at nighttime.
74 00:09:44.200 ⇒ 00:09:47.970 Caitlyn Vaughn: Because there’s, like, tons of creepos there, and homeless, and it’s just, like, very.
75 00:09:47.970 ⇒ 00:09:51.179 Uttam Kumaran: I know, it’s what I tell her, too. So I bought her pepper spray.
76 00:09:51.180 ⇒ 00:09:58.479 Caitlyn Vaughn: Yeah. Anyway, this whole premise, I’ll talk to you about it later, but let’s get into brain-forged stuff.
77 00:09:59.360 ⇒ 00:10:00.450 Uttam Kumaran: Okay, cool.
78 00:10:00.520 ⇒ 00:10:01.399 Caitlyn Vaughn: I think they’re…
79 00:10:02.010 ⇒ 00:10:08.109 Caitlyn Vaughn: like, dedicated 0% of my brain to Brain Forge this week, so loop me in, catch me up.
80 00:10:08.110 ⇒ 00:10:11.930 Uttam Kumaran: Yeah, Mustafa, maybe let’s start with the vendor evals.
81 00:10:12.410 ⇒ 00:10:13.300 Caitlyn Vaughn: Thank God.
82 00:10:15.910 ⇒ 00:10:20.370 Mustafa Raja: Yeah, do you want me to share your screen and go through it?
83 00:10:20.370 ⇒ 00:10:24.140 Uttam Kumaran: Yeah, if you can send the notion, and then… You can share.
84 00:10:25.370 ⇒ 00:10:26.760 Mustafa Raja: Yeah, let me do that.
85 00:10:29.640 ⇒ 00:10:36.469 Caitlyn Vaughn: Also, Mustafa, thank you so much for, like, the quick dashboarding things. On Monday, you, like, saved my ass so hard.
86 00:10:38.410 ⇒ 00:10:39.370 Mustafa Raja: Thank you.
87 00:10:40.430 ⇒ 00:10:42.509 Uttam Kumaran: How’d it go? What was the… what was the meeting?
88 00:10:42.980 ⇒ 00:10:53.880 Caitlyn Vaughn: It was Product All Hands, and so I was just, like, scrambling to get shit together. I try to do, like, an analytics portion in the start of all of my Product All Hands,
89 00:10:54.380 ⇒ 00:11:02.609 Caitlyn Vaughn: But it’s just, like, I just need to learn how to use Omni and, like, be able to quickly set things up. But I still, I was, like, staring at it, I was just like, I have no idea what’s going on, honestly.
90 00:11:03.670 ⇒ 00:11:12.640 Uttam Kumaran: Yeah, we… we have a few people that are training, are getting certified, and then… yeah, this week, because I wanted to make sure we get the pricing stuff done, and we do this, and then…
91 00:11:12.790 ⇒ 00:11:19.460 Uttam Kumaran: It’s also Thanksgiving, so I was like, we’ll do the training. We migrated everyone over, at least, so…
92 00:11:19.460 ⇒ 00:11:19.860 Caitlyn Vaughn: Oh, God.
93 00:11:19.860 ⇒ 00:11:21.809 Uttam Kumaran: We should do the training when we’re back.
94 00:11:21.810 ⇒ 00:11:22.590 Caitlyn Vaughn: Oh, God.
95 00:11:23.450 ⇒ 00:11:25.180 Uttam Kumaran: So yeah, we, we kind of…
96 00:11:25.270 ⇒ 00:11:28.840 Caitlyn Vaughn: Ran through the swarm and Captain Data, so both of these…
97 00:11:29.440 ⇒ 00:11:30.839 Uttam Kumaran: are in the Zoom chat.
98 00:11:31.470 ⇒ 00:11:37.520 Caitlyn Vaughn: Okay, I’m looking at the captain data right now. Lustafa, do you want to walk me through it?
99 00:11:38.970 ⇒ 00:11:41.290 Mustafa Raja: Yeah, give me a moment.
100 00:11:41.630 ⇒ 00:11:47.830 Mustafa Raja: So let’s start with this one, that is, the one we did first.
101 00:11:48.680 ⇒ 00:11:51.289 Mustafa Raja: So… I’m sharing my screen. Updated.
102 00:11:54.100 ⇒ 00:11:56.870 Mustafa Raja: Okay, we want to do captain data.
103 00:11:57.290 ⇒ 00:11:59.760 Caitlyn Vaughn: Oh, no, the swarm is fine, too.
104 00:12:02.760 ⇒ 00:12:05.380 Uttam Kumaran: Yeah, do the swarm first, Mustafa.
105 00:12:27.300 ⇒ 00:12:29.179 Uttam Kumaran: Okay, maybe he glitched out?
106 00:12:30.200 ⇒ 00:12:31.760 Caitlyn Vaughn: Bye, Mustafa!
107 00:12:31.850 ⇒ 00:12:37.439 Uttam Kumaran: Okay, I’ll, let me… Let me share… okay, he’s back, nevermind.
108 00:12:37.710 ⇒ 00:12:39.010 Mustafa Raja: Sorry…
109 00:12:39.720 ⇒ 00:12:40.250 Uttam Kumaran: good.
110 00:12:40.280 ⇒ 00:12:41.050 Caitlyn Vaughn: We missed you.
111 00:12:41.530 ⇒ 00:12:55.500 Mustafa Raja: Yeah, let me share again. Okay, so, the latency takes a big hit, it’s about, you know, 5 to 7 seconds, and we would be using this endpoint for…
112 00:12:55.620 ⇒ 00:13:02.060 Mustafa Raja: company, and… Search people endpoint for the people.
113 00:13:02.920 ⇒ 00:13:04.130 Caitlyn Vaughn: Is that 18 seconds?
114 00:13:04.140 ⇒ 00:13:04.860 Mustafa Raja: Mmm…
115 00:13:07.170 ⇒ 00:13:08.250 Caitlyn Vaughn: For the people?
116 00:13:08.250 ⇒ 00:13:09.270 Mustafa Raja: Yes.
117 00:13:09.500 ⇒ 00:13:10.780 Caitlyn Vaughn: What?
118 00:13:12.060 ⇒ 00:13:13.850 Caitlyn Vaughn: That’s so bad.
119 00:13:13.850 ⇒ 00:13:16.050 Mustafa Raja: So… Yeah.
120 00:13:16.640 ⇒ 00:13:17.630 Mustafa Raja: Not good.
121 00:13:18.360 ⇒ 00:13:20.380 Caitlyn Vaughn: Yeah, oh my gosh. Also…
122 00:13:20.380 ⇒ 00:13:22.499 Uttam Kumaran: That’s herself… that’s herself reported.
123 00:13:23.000 ⇒ 00:13:23.770 Caitlyn Vaughn: Jeez…
124 00:13:23.770 ⇒ 00:13:25.529 Mustafa Raja: Yeah, and they’re reporting this.
125 00:13:25.920 ⇒ 00:13:31.269 Caitlyn Vaughn: 98% is so bad, also, like, on the uptime.
126 00:13:32.710 ⇒ 00:13:38.169 Caitlyn Vaughn: I think our SLA is, like, 99.9999.
127 00:13:38.410 ⇒ 00:13:39.400 Uttam Kumaran: Yeah.
128 00:13:39.590 ⇒ 00:13:41.130 Caitlyn Vaughn: So this is pretty bad.
129 00:13:42.890 ⇒ 00:13:43.700 Mustafa Raja: Yes.
130 00:13:45.840 ⇒ 00:13:57.019 Mustafa Raja: Also, so the way they are structured is, right now, what we need to do is, to use this enriched Company endpoint, we need to have
131 00:13:57.060 ⇒ 00:14:14.859 Mustafa Raja: the company’s LinkedIn. So prior to this, what we were doing was, we would use domain to find the company and enrich it. They require LinkedIn, our set did not have that, so, I… I took… took the one from Aula.
132 00:14:16.520 ⇒ 00:14:26.140 Mustafa Raja: But yeah, that’s pretty much it. And then, this is just availability…
133 00:14:26.280 ⇒ 00:14:29.290 Mustafa Raja: Let me reload this so the images are loaded.
134 00:14:35.590 ⇒ 00:14:38.620 Mustafa Raja: Sorry, my network is just taking a hit right now.
135 00:14:39.480 ⇒ 00:14:40.329 Uttam Kumaran: It’s all good.
136 00:14:43.910 ⇒ 00:14:47.049 Uttam Kumaran: Well, did we find any, like, interesting fields in Captain Data, or…
137 00:14:47.050 ⇒ 00:14:48.230 Mustafa Raja: Yeah.
138 00:14:48.510 ⇒ 00:14:55.380 Mustafa Raja: Yeah, so, yeah, so the accuracy, really takes a big hit. The coverage is good, but the…
139 00:14:55.480 ⇒ 00:15:05.500 Mustafa Raja: But the accuracy, isn’t really good. So, we see, yeah, over here, this, this would be…
140 00:15:05.630 ⇒ 00:15:16.170 Mustafa Raja: So, within, within, 5% tolerance, we’ll have about 10% accuracy.
141 00:15:18.480 ⇒ 00:15:32.449 Mustafa Raja: Which is… which really isn’t… which really isn’t good. So this is actually for the, employee count. So, if… if… the difference would be plus-minus 5%, and only 10% of the companies
142 00:15:32.450 ⇒ 00:15:38.550 Mustafa Raja: would have, correct, numbers accordingly. Does that make sense?
143 00:15:39.640 ⇒ 00:15:43.439 Caitlyn Vaughn: Okay, wait, can you repeat that? I think I just did not grok that at all.
144 00:15:45.030 ⇒ 00:15:47.100 Mustafa Raja: Yeah, so,
145 00:15:47.720 ⇒ 00:16:00.120 Mustafa Raja: So if, so if we, if we tolerate, 10% to 5% difference from, from the employee count that they are giving us.
146 00:16:00.440 ⇒ 00:16:03.490 Mustafa Raja: 10% companies come under that, right?
147 00:16:04.360 ⇒ 00:16:04.960 Uttam Kumaran: Damn.
148 00:16:05.180 ⇒ 00:16:06.120 Caitlyn Vaughn: That’s really bad.
149 00:16:06.120 ⇒ 00:16:06.890 Mustafa Raja: Yeah.
150 00:16:06.990 ⇒ 00:16:15.009 Mustafa Raja: Yeah, and with 10% plus minus 10% tolerance, it goes closer to 20%.
151 00:16:15.070 ⇒ 00:16:29.530 Mustafa Raja: These are the numbers for that. So, if we tolerate 25%, then only 45% companies would come closer to the number. So, yeah, not good at all.
152 00:16:29.530 ⇒ 00:16:32.789 Caitlyn Vaughn: Okay, so wait, for the exact match, if you go up.
153 00:16:32.920 ⇒ 00:16:35.339 Caitlyn Vaughn: So, exact match is just, like.
154 00:16:36.990 ⇒ 00:16:37.350 Mustafa Raja: There’s a bunch of.
155 00:16:37.350 ⇒ 00:16:40.239 Caitlyn Vaughn: Is just, like, all of them matching?
156 00:16:40.620 ⇒ 00:16:48.279 Uttam Kumaran: Well, like, we have… yeah, we have a… so we have a control data set, basically, that we work from across all of the different vendors.
157 00:16:48.850 ⇒ 00:16:51.249 Uttam Kumaran: And then, yeah, exact matches, like, if the…
158 00:16:51.740 ⇒ 00:16:58.399 Uttam Kumaran: if the number exactly matches. So, for the most part, we’re looking for within 10%, like, and the other vendors did…
159 00:16:58.820 ⇒ 00:17:01.259 Uttam Kumaran: get that really well, at least half of them.
160 00:17:01.430 ⇒ 00:17:04.850 Uttam Kumaran: for the most part, I think 50% were within, like, 10%, right, or so.
161 00:17:05.819 ⇒ 00:17:11.479 Mustafa Raja: Yes. Aule was really good. Aule was really good, with all of its hackers.
162 00:17:11.480 ⇒ 00:17:19.430 Uttam Kumaran: Like, for example, if you’re… if you’re within… if you’re out of 10% on 300,000 employees, you’re… you’re low on 30,000 employees.
163 00:17:19.430 ⇒ 00:17:20.450 Caitlyn Vaughn: Huh.
164 00:17:20.589 ⇒ 00:17:22.009 Uttam Kumaran: You know.
165 00:17:22.010 ⇒ 00:17:22.739 Caitlyn Vaughn: What do you mean, love?
166 00:17:22.740 ⇒ 00:17:29.090 Uttam Kumaran: Like, for example, let’s take, Infosys, right? Infosys has 300,000 employees.
167 00:17:29.090 ⇒ 00:17:29.540 Caitlyn Vaughn: Huh.
168 00:17:29.540 ⇒ 00:17:37.579 Uttam Kumaran: Let’s say you are within 10%, that means you’re within either $330,000 or 270,000.
169 00:17:37.750 ⇒ 00:17:40.350 Caitlyn Vaughn: So, like, the accuracy…
170 00:17:40.350 ⇒ 00:17:49.229 Uttam Kumaran: here is not great, in particular because most of the companies, most of their enrichments are actually outside of 25%.
171 00:17:49.400 ⇒ 00:17:50.000 Caitlyn Vaughn: Oh, wow.
172 00:17:50.000 ⇒ 00:18:05.380 Uttam Kumaran: So, they’re, like, very off from our controlled data set, and then the question is, like, okay, how do you know the control dataset is right? Well, that’s what we’ve been using throughout all the vendor analyses, and Owler and some of the other ones actually were… were basically matching, like, really.
173 00:18:05.380 ⇒ 00:18:09.599 Caitlyn Vaughn: Okay. And then what is the actual field that you’re testing for this?
174 00:18:11.140 ⇒ 00:18:12.020 Caitlyn Vaughn: Is it employee?
175 00:18:12.020 ⇒ 00:18:18.280 Mustafa Raja: And then, yeah, yeah, this one, this one really is just employee count.
176 00:18:18.550 ⇒ 00:18:19.530 Caitlyn Vaughn: Oh, wow.
177 00:18:20.030 ⇒ 00:18:22.489 Caitlyn Vaughn: Yeah, that’s so bad. Okay.
178 00:18:23.190 ⇒ 00:18:30.179 Mustafa Raja: Yeah, and the other ones would be industry alignment, and then,
179 00:18:30.870 ⇒ 00:18:35.640 Mustafa Raja: The address, which… which is good.
180 00:18:36.470 ⇒ 00:18:37.489 Mustafa Raja: Those were good.
181 00:18:37.930 ⇒ 00:18:45.610 Mustafa Raja: Yeah, addresses and then industry alignment is good. I have the… yeah, so this is all for employees.
182 00:18:46.480 ⇒ 00:18:50.750 Mustafa Raja: Yeah, so this is the alignment for… so this is the alignment,
183 00:18:51.050 ⇒ 00:18:57.160 Mustafa Raja: And the employee count here is, with 25% tolerance.
184 00:18:57.310 ⇒ 00:19:15.180 Mustafa Raja: But for, HQ location and this, this is just if they are, exactly matching or not. And these are, these are pretty good. The coverage isn’t very good, it’s about 85%, I believe.
185 00:19:15.480 ⇒ 00:19:15.910 Caitlyn Vaughn: Huh.
186 00:19:15.910 ⇒ 00:19:17.920 Mustafa Raja: Yeah, that… that is pretty much it.
187 00:19:18.530 ⇒ 00:19:24.199 Caitlyn Vaughn: Okay, so, like, out of the 85% that it did cover, then the accuracy comes from there.
188 00:19:24.950 ⇒ 00:19:26.900 Mustafa Raja: Yeah, the accuracy comes from the…
189 00:19:27.180 ⇒ 00:19:27.850 Caitlyn Vaughn: Okay.
190 00:19:29.470 ⇒ 00:19:37.430 Mustafa Raja: And then… for people…
191 00:19:39.140 ⇒ 00:19:42.809 Mustafa Raja: Yeah, for people, let’s open up their dataset.
192 00:19:46.620 ⇒ 00:20:03.690 Mustafa Raja: So, so the coverage is very good, for people, almost, 90… 98, 98%. We got 98% people from, from the…
193 00:20:03.820 ⇒ 00:20:19.850 Mustafa Raja: set that we have. We really don’t have very much to compare, so we are just, looking at, the quality of the data that we… that they are sending us back.
194 00:20:20.480 ⇒ 00:20:32.609 Mustafa Raja: So, the fields that they have, education majors about headlines, and the, and the company, if they, if the company aligns, with
195 00:20:32.820 ⇒ 00:20:35.229 Mustafa Raja: With this company that is mentioned.
196 00:20:35.960 ⇒ 00:20:49.789 Mustafa Raja: Or not, and that is pretty much it. So, so the company one, the company one, the, the people one, sorry, the people one really, really is good, but… but the company one really takes a hit.
197 00:20:50.690 ⇒ 00:20:52.400 Mustafa Raja: That’s what I would say. Yeah.
198 00:20:54.330 ⇒ 00:20:55.540 Caitlyn Vaughn: Final recommendation?
199 00:20:55.540 ⇒ 00:20:55.900 Mustafa Raja: I’m…
200 00:20:55.900 ⇒ 00:20:56.600 Caitlyn Vaughn: moved?
201 00:20:57.510 ⇒ 00:20:59.369 Mustafa Raja: For people.
202 00:20:59.370 ⇒ 00:20:59.920 Caitlyn Vaughn: Oh, so…
203 00:20:59.920 ⇒ 00:21:02.600 Mustafa Raja: Yeah, yeah, true recommendation.
204 00:21:02.600 ⇒ 00:21:05.779 Uttam Kumaran: The final recognition is not approved.
205 00:21:06.530 ⇒ 00:21:06.980 Caitlyn Vaughn: Yeah.
206 00:21:06.980 ⇒ 00:21:11.799 Mustafa Raja: Yeah, for company endpoint, it’s not approved.
207 00:21:12.330 ⇒ 00:21:18.170 Mustafa Raja: And then we can, we can use it for, what’s it called? The people.
208 00:21:18.520 ⇒ 00:21:21.860 Caitlyn Vaughn: Okay, but then the People API was, like, 18 seconds.
209 00:21:23.340 ⇒ 00:21:28.310 Mustafa Raja: Oh, yeah, the latency, I didn’t… didn’t mention here, so…
210 00:21:28.900 ⇒ 00:21:29.680 Caitlyn Vaughn: Okay.
211 00:21:29.680 ⇒ 00:21:32.460 Mustafa Raja: Oh, the rate limits aren’t good, actually, too, so…
212 00:21:33.080 ⇒ 00:21:34.140 Caitlyn Vaughn: So what is that?
213 00:21:35.270 ⇒ 00:21:35.670 Uttam Kumaran: the rate.
214 00:21:35.670 ⇒ 00:21:41.589 Mustafa Raja: Heat limits, let’s take a look at…
215 00:21:43.550 ⇒ 00:21:56.900 Mustafa Raja: Yeah. So I believe we, we were using the low plant here, I’m not sure, and that is, 2 requests per second, or 120 requests per minute.
216 00:21:57.210 ⇒ 00:21:58.770 Caitlyn Vaughn: That’s great.
217 00:22:00.060 ⇒ 00:22:00.730 Mustafa Raja: Yeah.
218 00:22:01.590 ⇒ 00:22:04.640 Mustafa Raja: Yeah, that is pretty much it for captain data.
219 00:22:05.050 ⇒ 00:22:06.159 Mustafa Raja: Let’s go to the spot.
220 00:22:06.160 ⇒ 00:22:11.799 Caitlyn Vaughn: Do we have, like, a side-by-side of all of these, like, data points for each vendor?
221 00:22:11.800 ⇒ 00:22:12.590 Mustafa Raja: Yes.
222 00:22:13.380 ⇒ 00:22:16.750 Mustafa Raja: Let me go to default…
223 00:22:27.640 ⇒ 00:22:32.070 Uttam Kumaran: Like, a side-by-side of, like, what we pulled, or side-by-side of, like, the results?
224 00:22:33.220 ⇒ 00:22:35.450 Caitlyn Vaughn: Of the results of each vendor.
225 00:22:37.180 ⇒ 00:22:37.610 Uttam Kumaran: Oh.
226 00:22:38.030 ⇒ 00:22:39.290 Uttam Kumaran: We do have a, like, a…
227 00:22:39.290 ⇒ 00:22:44.190 Mustafa Raja: Yeah, I don’t think… yeah, yeah. Yeah, for now, we don’t have, I guess we can create a table for that.
228 00:22:44.660 ⇒ 00:22:45.739 Uttam Kumaran: Yeah, we can do that.
229 00:22:46.880 ⇒ 00:22:49.819 Uttam Kumaran: Right now, these are all sitting as isolated docs.
230 00:22:49.820 ⇒ 00:22:51.680 Caitlyn Vaughn: Yeah, cause it’s like…
231 00:22:51.960 ⇒ 00:23:07.880 Caitlyn Vaughn: I feel like everyone… I feel like when I look at each one of these, I’m like, wow, this vendor kind of sucks, but I think if they were all side-by-side, maybe it would help, like, put it into context a little bit better, and it would make it more obvious if one didn’t suck.
232 00:23:08.470 ⇒ 00:23:17.249 Uttam Kumaran: Yeah, I think basically what we wanted to find in the beginning here is that, like, out of 100, yes, I don’t think everything is gonna be 100% right. It’s sort of like…
233 00:23:17.400 ⇒ 00:23:21.259 Uttam Kumaran: Making sure that the ones that are always wrong, you kind of don’t use.
234 00:23:21.260 ⇒ 00:23:21.580 Caitlyn Vaughn: Right.
235 00:23:21.580 ⇒ 00:23:26.950 Uttam Kumaran: So, there are some clear, really good ones that we found. So, yeah, Mustafa, maybe we can make
236 00:23:27.100 ⇒ 00:23:30.180 Uttam Kumaran: We can just create, like, a document that is…
237 00:23:30.300 ⇒ 00:23:37.560 Uttam Kumaran: With… that has these sort of sub-documents, and maybe it’s just a table with, like, the high-level results across each vendor.
238 00:23:39.960 ⇒ 00:23:45.480 Uttam Kumaran: And then we can, in that table, you can just link to these notions, so that we can start to build that.
239 00:23:46.040 ⇒ 00:23:57.969 Caitlyn Vaughn: Keep it, like, as simple as possible, like, what are the kind of dots that we can compare across all of the vendors that help us understand, like, who’s coming ahead versus, like, who has fallen behind?
240 00:23:59.480 ⇒ 00:24:00.140 Mustafa Raja: Okay.
241 00:24:01.960 ⇒ 00:24:09.150 Mustafa Raja: Okay, so, for the swamp, it is pretty much the same story, except it’s just a little worse.
242 00:24:09.360 ⇒ 00:24:09.880 Caitlyn Vaughn: Oh, no.
243 00:24:09.880 ⇒ 00:24:15.820 Mustafa Raja: So… So the coverage is pretty good on this one, but,
244 00:24:16.220 ⇒ 00:24:30.990 Mustafa Raja: The accuracy takes a hit, on locations and on, employee count a little bit more, so it’s closer to 40%. The captain data was 45, and then, captain data had good
245 00:24:31.060 ⇒ 00:24:45.859 Mustafa Raja: location data, but this… this is just closer to 60%. Industry seems good, and… and has a similar story for funding rounds. The coverage isn’t good for funding rounds, also. It’s about 60%.
246 00:24:46.050 ⇒ 00:24:50.050 Mustafa Raja: And the amounts, do take a hit also.
247 00:24:50.550 ⇒ 00:24:51.310 Caitlyn Vaughn: Yeah, that’s bad.
248 00:24:51.310 ⇒ 00:24:51.940 Mustafa Raja: Yep.
249 00:24:52.110 ⇒ 00:25:01.010 Mustafa Raja: Yeah, but… The latency is good. They don’t have any, public documents for, their uptimes.
250 00:25:01.980 ⇒ 00:25:04.250 Caitlyn Vaughn: Okay, also, on the latency piece…
251 00:25:04.250 ⇒ 00:25:24.880 Caitlyn Vaughn: Just for your information. So, the interesting part about default is something that we have to consider when looking at all these enrichment vendors, is that where most of these will sit is in between somebody submitting a form, and then us enriching them, qualifying, routing them, and then getting them set up on the scheduler to book a call.
252 00:25:24.880 ⇒ 00:25:35.359 Caitlyn Vaughn: So you can imagine the, like, amount of seconds that they have to sit there and wait in between the form submit and, like, getting the calendar is where these will live. So, if we could get, like.
253 00:25:35.750 ⇒ 00:25:42.849 Caitlyn Vaughn: Ideally, under 200 milliseconds. We’ve kind of set that as our maximum for, like.
254 00:25:43.240 ⇒ 00:25:47.250 Caitlyn Vaughn: Essentially, so even 600 to 800 milliseconds is a lot.
255 00:25:49.140 ⇒ 00:25:49.750 Mustafa Raja: Okay.
256 00:25:50.420 ⇒ 00:25:52.559 Mustafa Raja: Yeah, yeah, that makes sense.
257 00:25:52.810 ⇒ 00:25:53.350 Caitlyn Vaughn: Hmm.
258 00:25:54.810 ⇒ 00:26:01.490 Mustafa Raja: Okay, so… so yeah, this is, this is just the gist of the data that we are getting from this.
259 00:26:01.820 ⇒ 00:26:12.459 Mustafa Raja: Let’s move to… The people won… the people are… Are… good… with this?
260 00:26:13.550 ⇒ 00:26:15.170 Mustafa Raja: Let’s actually pull that up.
261 00:26:15.330 ⇒ 00:26:26.030 Mustafa Raja: Yeah, so, So 90… 96%, coverage,
262 00:26:26.540 ⇒ 00:26:41.330 Mustafa Raja: And… yeah, it was pretty good. They… they had fields about, current function, work… they had work emails, education, and much more. Social media, they had social media.
263 00:26:41.450 ⇒ 00:26:43.940 Mustafa Raja: X and Facebook and all.
264 00:26:44.380 ⇒ 00:26:47.870 Mustafa Raja: So… overall, this was… this was pretty good.
265 00:26:48.980 ⇒ 00:26:50.270 Caitlyn Vaughn: Okay, amazing.
266 00:26:51.500 ⇒ 00:26:52.210 Mustafa Raja: Yeah.
267 00:26:54.420 ⇒ 00:27:01.449 Mustafa Raja: And, all of the data that I am pulling actually lives, in default clay.
268 00:27:01.840 ⇒ 00:27:11.439 Mustafa Raja: I’m just using that, I’m just using the Clay’s HTTP API, to pull all of this data.
269 00:27:11.610 ⇒ 00:27:15.880 Mustafa Raja: So, you have visibility over it also.
270 00:27:16.570 ⇒ 00:27:17.230 Caitlyn Vaughn: Amazing.
271 00:27:17.230 ⇒ 00:27:18.750 Mustafa Raja: I’ll share the link after.
272 00:27:19.300 ⇒ 00:27:19.970 Caitlyn Vaughn: Okay.
273 00:27:20.830 ⇒ 00:27:21.949 Caitlyn Vaughn: I’m gonna post these.
274 00:27:21.950 ⇒ 00:27:29.370 Mustafa Raja: This is pretty much it. But overall, Owl has been pretty good, but they don’t do people, I believe.
275 00:27:31.040 ⇒ 00:27:34.449 Caitlyn Vaughn: They do company data, and they were pretty good at it.
276 00:27:34.780 ⇒ 00:27:36.160 Caitlyn Vaughn: Wait, who? The swarm.
277 00:27:36.350 ⇒ 00:27:37.050 Uttam Kumaran: Howler.
278 00:27:37.050 ⇒ 00:27:37.510 Caitlyn Vaughn: Oh.
279 00:27:37.510 ⇒ 00:27:39.000 Mustafa Raja: The owler, owler.
280 00:27:39.000 ⇒ 00:27:41.529 Caitlyn Vaughn: Oh, yeah, Owler, we kind of…
281 00:27:41.870 ⇒ 00:27:44.669 Caitlyn Vaughn: I think we kind of kicked them. They’re so expensive.
282 00:27:46.040 ⇒ 00:27:46.460 Mustafa Raja: Oh.
283 00:27:46.460 ⇒ 00:27:56.559 Caitlyn Vaughn: Unless they’re really good, and then we can go back to them, but they were, like, they were wanting to charge us, like, $60K for, like, a fraction of data, versus, like…
284 00:27:57.020 ⇒ 00:28:00.730 Caitlyn Vaughn: PDL is charging us $7K annual.
285 00:28:00.990 ⇒ 00:28:01.959 Uttam Kumaran: Yeah, yeah, yeah.
286 00:28:01.960 ⇒ 00:28:05.199 Caitlyn Vaughn: So that’s what we should put into the table as well, Mustafa, like.
287 00:28:05.200 ⇒ 00:28:05.650 Mustafa Raja: Yeah.
288 00:28:05.650 ⇒ 00:28:06.570 Uttam Kumaran: quotes we got.
289 00:28:07.910 ⇒ 00:28:10.370 Mustafa Raja: Yeah, yeah, I’ll definitely work on that.
290 00:28:10.740 ⇒ 00:28:12.520 Caitlyn Vaughn: Yeah, I can send those over to you.
291 00:28:15.240 ⇒ 00:28:24.110 Caitlyn Vaughn: What’s I gotta say? Oh, next, so this is really interesting. All of these vendors are, like, surprising me a lot, actually. It’s… it’s funny, because…
292 00:28:24.380 ⇒ 00:28:33.339 Caitlyn Vaughn: vendors like Captain Data are, like, supposed to be up and coming and, like, better than past data providers, and that’s how they’re kind of positioning themselves.
293 00:28:33.550 ⇒ 00:28:43.910 Caitlyn Vaughn: And this is kind of telling me that they’re not really that much better. So, right now, Sid, our other, product lead, is…
294 00:28:44.430 ⇒ 00:28:47.350 Caitlyn Vaughn: really hot on crust data.
295 00:28:49.100 ⇒ 00:28:49.960 Caitlyn Vaughn: Would it be possible?
296 00:28:49.960 ⇒ 00:28:51.900 Uttam Kumaran: That’s, like, the worst name, oh my god.
297 00:28:51.900 ⇒ 00:28:58.029 Caitlyn Vaughn: Yeah, would it be possible for us to test crust data next, and do some comparisons? Because…
298 00:28:58.030 ⇒ 00:28:58.680 Mustafa Raja: Yes.
299 00:28:58.680 ⇒ 00:29:00.799 Caitlyn Vaughn: I would love to, like, either…
300 00:29:01.670 ⇒ 00:29:07.240 Caitlyn Vaughn: be done with this conversation on crust data, or humor it longer,
301 00:29:07.430 ⇒ 00:29:13.269 Caitlyn Vaughn: He’s driving me nuts, so… I need this… I need the information on if this is actually a good vendor.
302 00:29:15.420 ⇒ 00:29:16.030 Mustafa Raja: Okay.
303 00:29:16.530 ⇒ 00:29:18.010 Caitlyn Vaughn: Great.
304 00:29:18.810 ⇒ 00:29:22.289 Caitlyn Vaughn: Okay, perfect, this all looks really good,
305 00:29:23.160 ⇒ 00:29:25.190 Caitlyn Vaughn: Cross data, that’s the one, yeah.
306 00:29:28.850 ⇒ 00:29:30.780 Uttam Kumaran: They’re, like, a brand new company.
307 00:29:38.310 ⇒ 00:29:42.899 Uttam Kumaran: But it says real time, so I wonder if it’s… maybe they’re trying to compete on latency.
308 00:29:43.670 ⇒ 00:29:50.220 Caitlyn Vaughn: Yeah… I think their whole thing is they go, like, scrape data live, but I don’t know. So does, like…
309 00:29:50.220 ⇒ 00:29:51.360 Uttam Kumaran: Oh…
310 00:29:51.950 ⇒ 00:29:54.029 Caitlyn Vaughn: Captain Data does that, apparently, too.
311 00:29:56.830 ⇒ 00:30:00.889 Uttam Kumaran: Interesting, okay. And then, I know,
312 00:30:01.920 ⇒ 00:30:10.090 Uttam Kumaran: What’s it called? Amber has some follow-ups on pricing, wonder if I need to… Relay anything to her.
313 00:30:10.820 ⇒ 00:30:16.300 Caitlyn Vaughn: I actually think we’re sorted on the pricing side. Okay.
314 00:30:17.870 ⇒ 00:30:23.449 Caitlyn Vaughn: I can share with you, or if you don’t care, that’s fine too. Let’s see…
315 00:30:23.660 ⇒ 00:30:25.399 Caitlyn Vaughn: Where did I put pricing?
316 00:30:26.720 ⇒ 00:30:29.100 Caitlyn Vaughn: Product Girls!
317 00:30:31.470 ⇒ 00:30:33.870 Caitlyn Vaughn: Let me see… okay, here we go.
318 00:30:37.180 ⇒ 00:30:45.310 Caitlyn Vaughn: I mean, TLDR, we just want to keep it as simple as possible, so… Like, oh, here she is.
319 00:30:45.580 ⇒ 00:30:46.580 Caitlyn Vaughn: So perfect.
320 00:30:46.580 ⇒ 00:30:47.430 Uttam Kumaran: a star.
321 00:30:47.730 ⇒ 00:30:49.600 Caitlyn Vaughn: The star of the show.
322 00:30:49.710 ⇒ 00:30:51.390 Caitlyn Vaughn: The bell of the ball.
323 00:30:53.660 ⇒ 00:30:54.760 Caitlyn Vaughn: Hey, Amber.
324 00:30:54.760 ⇒ 00:30:58.380 Amber Lin: Hi there. I totally didn’t see the meeting.
325 00:30:58.380 ⇒ 00:31:03.399 Caitlyn Vaughn: No, you actually came so perfectly on time, like, I literally just shared my screen, and I showed up.
326 00:31:04.590 ⇒ 00:31:06.430 Amber Lin: Cool, glad to hear.
327 00:31:06.430 ⇒ 00:31:22.460 Caitlyn Vaughn: Okay, so this is what I think we’re gonna end up going with for pricing. So we’re gonna have two test tiers. Actually, I’m gonna start here. We’ve been calling everything a SKU in default, because we have so many different product lines, but we’re, like.
328 00:31:22.550 ⇒ 00:31:37.460 Caitlyn Vaughn: delineating from this, because we’re rolling out a tables platform that’s gonna be pretty similar to, like, Clay.com. So we’ll basically have two, like, quote-unquote parts of the platform. One is tables, one is workflows. Both of these you can basically
329 00:31:37.460 ⇒ 00:31:44.959 Caitlyn Vaughn: run processes in, and then here are the SKUs that you can purchase on top of the platform, and then we’re making sure to, like.
330 00:31:45.290 ⇒ 00:31:51.300 Caitlyn Vaughn: not call features SKUs, because it’s giving them a little too much cred, I think.
331 00:31:51.840 ⇒ 00:31:54.840 Caitlyn Vaughn: So, with that in mind,
332 00:31:55.130 ⇒ 00:31:59.180 Caitlyn Vaughn: the goal of PLG for us is to, like, Git?
333 00:31:59.560 ⇒ 00:32:17.080 Caitlyn Vaughn: people in the door so that we can poach them for, like, sales-led sales cycles, essentially, and, like, expand them into larger enterprise deals. We don’t really… we’re not, like, that worried about making money on self-serve at the moment, it’s just, like, an outlet for smaller accounts to
334 00:32:17.080 ⇒ 00:32:29.639 Caitlyn Vaughn: you know, help themselves, and then, like, a top of funnel for our larger accounts. So anyway, I’m gonna A-B test, I think, one with tables and one with workflows. I don’t know if we need to do templates or not.
335 00:32:29.660 ⇒ 00:32:32.420 Caitlyn Vaughn: We’ll have people sign up.
336 00:32:32.590 ⇒ 00:32:47.319 Caitlyn Vaughn: see how conversion goes. We’ll give them, like, 100 credits of our, like, basic providers, enrichment providers. And then we’ll just have one self-serve tier to begin. It’ll be 500 bucks a month, we’ll have 5 seats on it.
337 00:32:47.320 ⇒ 00:32:53.189 Caitlyn Vaughn: Unlimited workflows and tables, and then some bucket of credits, and then you can purchase credits on top of it.
338 00:32:53.190 ⇒ 00:32:57.619 Caitlyn Vaughn: Will allow people to, like, talk to sales
339 00:32:57.860 ⇒ 00:33:01.519 Caitlyn Vaughn: For the self-serve, for routing and scheduling, but…
340 00:33:01.730 ⇒ 00:33:03.629 Caitlyn Vaughn: I think we’ve actually been, like.
341 00:33:04.820 ⇒ 00:33:08.020 Caitlyn Vaughn: Bundling the product in a way where we’re…
342 00:33:08.790 ⇒ 00:33:16.809 Caitlyn Vaughn: we’re saying that the value of our product is routing and scheduling, and I… I don’t really think it is. I think it’s, like, a great part of our platform, but it’s not, like, the point.
343 00:33:17.190 ⇒ 00:33:33.200 Caitlyn Vaughn: So I’m just gonna keep them on here, and then I’m gonna, like, do some feature gating on this tier with things like sync back to CRM, match update CRM, webhook, premium enrichment, and then everything else is still gonna be on the sales lead, and we’re gonna start with the 20K minimum.
344 00:33:34.610 ⇒ 00:33:35.350 Uttam Kumaran: Nice.
345 00:33:35.910 ⇒ 00:33:36.740 Uttam Kumaran: Great.
346 00:33:37.870 ⇒ 00:33:38.470 Caitlyn Vaughn: Yeah.
347 00:33:38.850 ⇒ 00:33:39.990 Caitlyn Vaughn: So…
348 00:33:40.420 ⇒ 00:33:50.549 Caitlyn Vaughn: a lot of this pricing came from, like, Amber’s insights that she did on this, which is great. I feel like this is a good place for us to start, and we’ll probably explore.
349 00:33:50.550 ⇒ 00:33:50.930 Uttam Kumaran: Yes.
350 00:33:50.930 ⇒ 00:34:00.769 Caitlyn Vaughn: with, like, more self-service tiers before we go into, like, the horizontal product, offering, but yeah, I feel pretty good about it.
351 00:34:01.520 ⇒ 00:34:15.729 Uttam Kumaran: Great, yeah, I mean, I think probably… I think the most important thing is once you implement it, then we should start to… we can start to put together reporting, like, on a monthly basis. The other thing I told Amber is to start to move her analysis
352 00:34:15.810 ⇒ 00:34:29.660 Uttam Kumaran: into live dashboards in Omni, so that way, they don’t just live as, like, in Notion docs, like, we can… you can start to see a lot of what she found just, like, as rolling dashboards, so that’s… that’s the next ask that I had.
353 00:34:29.889 ⇒ 00:34:34.989 Caitlyn Vaughn: Okay, awesome. Yeah, that would be really helpful. And question, have you guys,
354 00:34:35.199 ⇒ 00:34:39.689 Caitlyn Vaughn: Have you guys worked on billing at all?
355 00:34:41.090 ⇒ 00:34:45.980 Uttam Kumaran: In terms of billing systems, or billing data?
356 00:34:46.400 ⇒ 00:35:05.170 Caitlyn Vaughn: I’m, like, right now, I’m looking at self-serve billing and just, like, building out a new billing across the platform, and I’m, I’m looking into, like, which entitlement sync system would work best for Stripe. I’m thinking WorkOS, I don’t know if you guys have, like, done anything like that or not, or if it’s, like, irrelevant to you.
357 00:35:05.790 ⇒ 00:35:18.650 Uttam Kumaran: I don’t think we’ve done anything. I have heard about WorkOS recently, actually. The only… from everybody that I work for, they just say, do not build your own building system, like, build it on Stripe.
358 00:35:18.870 ⇒ 00:35:19.370 Caitlyn Vaughn: Yeah.
359 00:35:19.370 ⇒ 00:35:22.389 Uttam Kumaran: But… I can ask.
360 00:35:23.990 ⇒ 00:35:25.479 Caitlyn Vaughn: You can ask who.
361 00:35:25.480 ⇒ 00:35:27.680 Uttam Kumaran: Just some, like, I can just ask around.
362 00:35:28.060 ⇒ 00:35:36.040 Uttam Kumaran: Sorry, that was really vague. No, I’ll just… I can ask some friends in product world what they think.
363 00:35:36.040 ⇒ 00:35:37.640 Caitlyn Vaughn: Anyway, yeah, yeah.
364 00:35:37.640 ⇒ 00:35:44.549 Uttam Kumaran: So basically, this is the… this is around, like, how do you bill for product, like, for product feature usage, things like that, right?
365 00:35:44.550 ⇒ 00:35:52.619 Caitlyn Vaughn: Yeah, like, entitlement is basically how you tie your pricing system to, like, the actual product offerings on each tier.
366 00:35:52.620 ⇒ 00:35:53.720 Uttam Kumaran: I see, I see.
367 00:35:53.720 ⇒ 00:36:05.849 Caitlyn Vaughn: So, I don’t know, it seems like WorkOS looks the most promising. There’s, like, not really that many solutions out there for entitlement syncing. It’s, like, typically something that is done by engineers in the backend. It’s, like, a new thing.
368 00:36:05.980 ⇒ 00:36:13.790 Caitlyn Vaughn: for… for it to be offered as a service, but… I don’t know. I guess I was just wondering if you have any insights, but… if not, it’s fine.
369 00:36:14.230 ⇒ 00:36:23.099 Uttam Kumaran: I mean, I’ve been part of teams that have built this from scratch, which, like, was always a nightmare, so I’m… I would love to just ping some of my friends that are…
370 00:36:23.430 ⇒ 00:36:26.050 Uttam Kumaran: At our other companies, and I’ll just see what they say.
371 00:36:26.050 ⇒ 00:36:27.470 Caitlyn Vaughn: Yeah, phone a friend.
372 00:36:27.650 ⇒ 00:36:28.320 Uttam Kumaran: Yes.
373 00:36:28.660 ⇒ 00:36:30.350 Uttam Kumaran: Okay. Nice.
374 00:36:31.710 ⇒ 00:36:46.320 Caitlyn Vaughn: Okay, cool. Yeah, I think, the most helpful thing right now, then, would be to just, like, finish off these, vendor assessments, and then… did I send you the message that I… I was chatting with Victor? He, like, approved S3?
375 00:36:47.010 ⇒ 00:36:48.440 Uttam Kumaran: Oh, let’s go, okay.
376 00:36:48.690 ⇒ 00:36:49.710 Caitlyn Vaughn: This is, like, it’s.
377 00:36:49.710 ⇒ 00:36:55.059 Uttam Kumaran: Sorry to… sorry to be annoying to him, you can apologize, I didn’t… I’m not meaning to be annoying, but…
378 00:36:55.350 ⇒ 00:36:55.740 Caitlyn Vaughn: No.
379 00:36:55.740 ⇒ 00:36:58.749 Uttam Kumaran: I was, like, just wanted to… we sort of, like…
380 00:36:58.990 ⇒ 00:37:02.519 Uttam Kumaran: we have everything ready to go to test for Deanna, so…
381 00:37:02.520 ⇒ 00:37:05.780 Caitlyn Vaughn: No, it’s… it’s super annoying. Super valid.
382 00:37:06.030 ⇒ 00:37:10.500 Caitlyn Vaughn: He’s like, we just got our SOC 2 window open.
383 00:37:10.870 ⇒ 00:37:11.600 Uttam Kumaran: Okay.
384 00:37:11.600 ⇒ 00:37:19.729 Caitlyn Vaughn: So we’re like… in the frickin’… he’s just, like, in the weeds. But, he did approve S3. He said…
385 00:37:20.060 ⇒ 00:37:37.530 Caitlyn Vaughn: I was like, can you please approve or deny S3? Analytics execution currently blocked by you. And he said, I think there’s no alternative to doing these things in S3, so I approve, but execution is next, and a blocker. Either I’ll personally do it or work directly with Thomas. I’m working urgently.
386 00:37:37.620 ⇒ 00:37:40.380 Caitlyn Vaughn: On the whatever stuff, anyway.
387 00:37:40.380 ⇒ 00:37:43.630 Uttam Kumaran: Oh, okay, so… Yeah. Should we provide him with…
388 00:37:44.150 ⇒ 00:37:45.779 Uttam Kumaran: what we… what do we need, Don?
389 00:37:46.760 ⇒ 00:37:58.180 Caitlyn Vaughn: Yes, Thomas also just pinged me yesterday and said he just finished up with Candela, so now he has capacity to, like, work on this, and he would bother Victor, but it’s probably a good time for us to just, like…
390 00:37:58.470 ⇒ 00:38:00.910 Caitlyn Vaughn: quarter to get this across the line.
391 00:38:01.240 ⇒ 00:38:08.909 Uttam Kumaran: Cool, so maybe, Mustafa, wondering if we could… maybe let’s just put a Notion doc together in terms of what we need to do to conduct the first test?
392 00:38:09.200 ⇒ 00:38:13.390 Uttam Kumaran: I don’t mind if they’re managing the S3, because we can just land stuff there.
393 00:38:14.050 ⇒ 00:38:20.989 Uttam Kumaran: let’s facilitate what we need for the first test, and in that same document, Mustafa, let’s write what we would need for ongoing sync.
394 00:38:22.710 ⇒ 00:38:24.060 Mustafa Raja: Oh yeah, I’ll do that.
395 00:38:24.470 ⇒ 00:38:26.849 Uttam Kumaran: So, that way they can… we can… and then we can send that…
396 00:38:27.020 ⇒ 00:38:31.569 Uttam Kumaran: Yeah, ideally, we can send that today or tomorrow and tag Victor and Thomas.
397 00:38:32.770 ⇒ 00:38:33.320 Mustafa Raja: Okay.
398 00:38:33.810 ⇒ 00:38:41.809 Caitlyn Vaughn: Perfect. And then Crest Data Next, and whoever else is on the list. I think we have a handful more vendors, right?
399 00:38:42.140 ⇒ 00:38:42.990 Uttam Kumaran: Yes.
400 00:38:44.200 ⇒ 00:38:47.189 Caitlyn Vaughn: Okay, great! Anything else?
401 00:38:49.780 ⇒ 00:38:50.820 Uttam Kumaran: I think that’s it.
402 00:38:51.220 ⇒ 00:38:59.350 Uttam Kumaran: And then, yeah, I’m gonna… I’m gonna plan… I think what we’re… now that we’re sort of, we have some bandwidth and pricing, I’m gonna work with Amber, and we’re sort of planning out a…
403 00:38:59.800 ⇒ 00:39:02.379 Uttam Kumaran: Omni training for week after Thanksgiving.
404 00:39:02.520 ⇒ 00:39:07.089 Caitlyn Vaughn: Yes, okay, perfect, that would be great. We obviously, like, need set up before we do.
405 00:39:07.090 ⇒ 00:39:07.450 Uttam Kumaran: Yes.
406 00:39:07.450 ⇒ 00:39:08.810 Caitlyn Vaughn: But, fingers.
407 00:39:08.810 ⇒ 00:39:14.200 Uttam Kumaran: No, so it’s all… so it’s all kind of set up, like, the databases are all connected and things like that.
408 00:39:14.940 ⇒ 00:39:18.560 Uttam Kumaran: In your instance, and everybody’s invited, so I think…
409 00:39:18.850 ⇒ 00:39:25.789 Uttam Kumaran: we’ll just kind of make sure, and then I want to walk… I want to do, like, a create a first dashboard, create a metric, like.
410 00:39:25.790 ⇒ 00:39:26.960 Caitlyn Vaughn: Walk through.
411 00:39:26.960 ⇒ 00:39:29.480 Uttam Kumaran: Some of the most common things.
412 00:39:29.490 ⇒ 00:39:30.510 Caitlyn Vaughn: Pretty cool.
413 00:39:30.510 ⇒ 00:39:31.190 Uttam Kumaran: So…
414 00:39:31.420 ⇒ 00:39:38.689 Caitlyn Vaughn: Yay! Okay, that’s… that sounds great. Yeah, let’s plan for after Thanksgiving, and I’ll, like, send out a big team invite for it.
415 00:39:39.350 ⇒ 00:39:40.510 Uttam Kumaran: Okay, perfect.
416 00:39:40.830 ⇒ 00:39:42.639 Caitlyn Vaughn: Yay! Okay, thanks guys!
417 00:39:43.100 ⇒ 00:39:43.960 Uttam Kumaran: Thank you.
418 00:39:44.370 ⇒ 00:39:45.090 Caitlyn Vaughn: You see?
419 00:39:45.090 ⇒ 00:39:45.780 Mustafa Raja: Thank you.
420 00:39:45.780 ⇒ 00:39:46.540 Amber Lin: Thanks.
421 00:39:46.770 ⇒ 00:39:47.450 Mustafa Raja: Bye.