Meeting Title: Default | Brainforge Weekly Sync Date: 2025-10-02 Meeting participants: Scratchpad Notetaker, Caitlyn Vaughn, Uttam Kumaran, Justin Breshears
WEBVTT
1 00:00:58.420 ⇒ 00:00:59.770 Uttam Kumaran: Hello?
2 00:01:00.230 ⇒ 00:01:02.340 Caitlyn Vaughn: Hi, how’s it going?
3 00:01:02.340 ⇒ 00:01:03.550 Uttam Kumaran: Good morning.
4 00:01:03.550 ⇒ 00:01:10.259 Caitlyn Vaughn: Okay, I’m literally so annoyed, my fucking AirPods are just not. Just not.
5 00:01:12.370 ⇒ 00:01:15.479 Caitlyn Vaughn: Microphone speakers. AirPods.
6 00:01:17.450 ⇒ 00:01:18.910 Caitlyn Vaughn: Okay, now talk.
7 00:01:19.220 ⇒ 00:01:20.090 Uttam Kumaran: Talk.
8 00:01:22.070 ⇒ 00:01:24.450 Uttam Kumaran: Great.
9 00:01:24.620 ⇒ 00:01:26.919 Caitlyn Vaughn: laughs. What a crisis on my side.
10 00:01:27.470 ⇒ 00:01:28.440 Caitlyn Vaughn: How’s everything?
11 00:01:29.590 ⇒ 00:01:33.569 Caitlyn Vaughn: We’re gonna do some yoga, just stretch my body, you know?
12 00:01:34.170 ⇒ 00:01:39.570 Uttam Kumaran: Yeah, I can’t believe it’s Thursday, this is, feels like a short week.
13 00:01:39.570 ⇒ 00:01:42.410 Caitlyn Vaughn: I thought it was Friday. It just ruined my whole day.
14 00:01:44.810 ⇒ 00:01:56.699 Uttam Kumaran: I got in, like, I was… I was out of town, I was in Maryland this weekend, last weekend, and I got in, like, I was visiting a friend… oh, we went to a horse race! You’d be interested, we went to Shawan Downs!
15 00:01:56.700 ⇒ 00:01:57.880 Caitlyn Vaughn: Wallets?
16 00:01:57.880 ⇒ 00:02:05.969 Uttam Kumaran: I don’t know if you’ve been there, I don’t… that’s… I’m just trying to name-drop of the only horse place, the only… but you know, I used to do dressage when I was growing up.
17 00:02:06.530 ⇒ 00:02:07.700 Caitlyn Vaughn: What?
18 00:02:07.700 ⇒ 00:02:11.030 Uttam Kumaran: Yeah, see, it’s just… it’s just more you know.
19 00:02:11.039 ⇒ 00:02:12.679 Caitlyn Vaughn: I do dressage.
20 00:02:12.680 ⇒ 00:02:15.440 Uttam Kumaran: Briefly, briefly. Yeah, I know you do, I know you have.
21 00:02:15.440 ⇒ 00:02:16.190 Caitlyn Vaughn: Oh, boy, this is me.
22 00:02:16.190 ⇒ 00:02:16.829 Uttam Kumaran: I think.
23 00:02:17.270 ⇒ 00:02:21.099 Uttam Kumaran: Wait, that’s so crazy! My horse just finally came home.
24 00:02:21.530 ⇒ 00:02:22.530 Uttam Kumaran: Sounds great.
25 00:02:22.990 ⇒ 00:02:27.879 Uttam Kumaran: Wow, yeah, we just… We survived. We watched… we watched… I’ll have to send you some pictures, we watched, like.
26 00:02:28.050 ⇒ 00:02:33.790 Uttam Kumaran: 4 or 5 races. It was actually kind of dangerous, like, some people really got tossed off.
27 00:02:34.050 ⇒ 00:02:34.540 Caitlyn Vaughn: Yeah.
28 00:02:34.540 ⇒ 00:02:41.139 Uttam Kumaran: And I felt sad for some of the horses, like, it just felt like it would hurt.
29 00:02:41.540 ⇒ 00:02:41.880 Uttam Kumaran: But.
30 00:02:41.880 ⇒ 00:02:42.360 Caitlyn Vaughn: Yeah.
31 00:02:42.360 ⇒ 00:02:42.910 Uttam Kumaran: Israel.
32 00:02:42.910 ⇒ 00:02:45.689 Caitlyn Vaughn: Did you do some bedding while you were out there?
33 00:02:45.690 ⇒ 00:02:51.610 Uttam Kumaran: They had some people doing cash bets, but no, we mainly just drank and hung out.
34 00:02:51.610 ⇒ 00:02:56.809 Caitlyn Vaughn: That’s, like, the best part, is betting. So then you have some skin in the game. You care so much more, you know?
35 00:02:56.810 ⇒ 00:03:02.560 Uttam Kumaran: Yeah, but it was nice, it was a great day, and it was just nice to… everybody was, like, dressed up, super nice.
36 00:03:03.290 ⇒ 00:03:06.980 Uttam Kumaran: It was… it was nice. I’ve never been to, like, a derby-type thing.
37 00:03:07.230 ⇒ 00:03:08.210 Caitlyn Vaughn: Oh, really?
38 00:03:08.210 ⇒ 00:03:09.020 Uttam Kumaran: Yeah.
39 00:03:09.430 ⇒ 00:03:16.050 Caitlyn Vaughn: Yeah, it’s fun. In San Diego, we had the Del Mar Racetrack, which is, like, the big, you know, the big one down there.
40 00:03:16.270 ⇒ 00:03:23.039 Caitlyn Vaughn: So it was always fun to go as a kid. But yeah, the older you get, the more you know, and then the less fun it is, you know?
41 00:03:23.040 ⇒ 00:03:25.660 Uttam Kumaran: What do you mean? Oh, like a… because… well, yeah, because…
42 00:03:25.820 ⇒ 00:03:30.280 Uttam Kumaran: my friend was like, yeah, usually, like, one horse has to get put down every race, and I’m like.
43 00:03:30.280 ⇒ 00:03:30.830 Caitlyn Vaughn: Oops.
44 00:03:32.210 ⇒ 00:03:35.720 Caitlyn Vaughn: Yeah, it’s not every race, but it’s definitely not uncommon.
45 00:03:36.160 ⇒ 00:03:38.029 Uttam Kumaran: It’s just so sad, but…
46 00:03:38.190 ⇒ 00:03:38.910 Caitlyn Vaughn: Yeah.
47 00:03:38.910 ⇒ 00:03:39.650 Uttam Kumaran: You know.
48 00:03:40.170 ⇒ 00:03:44.779 Caitlyn Vaughn: Yeah, we, my family rescues off the track their overbreds.
49 00:03:44.780 ⇒ 00:03:46.150 Uttam Kumaran: Oh, great.
50 00:03:46.330 ⇒ 00:03:56.060 Caitlyn Vaughn: Yeah, so after they, are done racing, like, if they’re too slow, or like… generally, if they’re too slow, then they just, like, try to sell them, and so they’ll go to the meat farm.
51 00:03:56.200 ⇒ 00:04:02.010 Caitlyn Vaughn: And so we, we’ll pick them up for, like, a grand, and rehabilitate them, and when we get them, they’re, like.
52 00:04:02.440 ⇒ 00:04:09.329 Caitlyn Vaughn: That’s gonna make you more sad, probably, but, like, their necks are covered in, like, lumps for when they, like, inject them with steroids and stuff.
53 00:04:09.330 ⇒ 00:04:10.250 Uttam Kumaran: Oh my god…
54 00:04:10.250 ⇒ 00:04:13.140 Caitlyn Vaughn: And they, like, hate people. Not all of them, but a lot of the ones.
55 00:04:13.140 ⇒ 00:04:15.000 Uttam Kumaran: Yeah, I know they’re very, very skittish.
56 00:04:15.270 ⇒ 00:04:17.899 Caitlyn Vaughn: Yeah, yeah, people are not that nice to them.
57 00:04:19.660 ⇒ 00:04:21.019 Uttam Kumaran: Not explicitly.
58 00:04:21.029 ⇒ 00:04:22.399 Caitlyn Vaughn: Some of them are nice.
59 00:04:22.400 ⇒ 00:04:28.399 Uttam Kumaran: I’m glad your family’s very nice to them, at least. Towards the end of their life, at least, they get some reprieve, so…
60 00:04:28.400 ⇒ 00:04:34.770 Caitlyn Vaughn: Yeah, honestly, like, the horses that we bring home are, like, so mean, and so is my sister, so they’re, like, good.
61 00:04:34.770 ⇒ 00:04:39.790 Uttam Kumaran: Oh. Just for each other, you know? That’s funny.
62 00:04:39.790 ⇒ 00:04:41.520 Caitlyn Vaughn: She’s the bitch whisperer.
63 00:04:45.670 ⇒ 00:04:47.010 Caitlyn Vaughn: Oh, so good.
64 00:04:47.450 ⇒ 00:04:57.470 Justin Breshears: Anyways, back to work. It’s like a whole different view of horses than, like, I grew up with, because I grew up in the middle of nowhere in Texas, so horses were…
65 00:04:57.470 ⇒ 00:04:58.090 Caitlyn Vaughn: Really?
66 00:04:58.090 ⇒ 00:04:59.590 Justin Breshears: Or workers, they were…
67 00:05:00.360 ⇒ 00:05:06.080 Justin Breshears: On ranches, and had to shoo them off of our practice fields to play football, and things like that, so…
68 00:05:06.080 ⇒ 00:05:06.800 Caitlyn Vaughn: What?
69 00:05:06.800 ⇒ 00:05:09.699 Justin Breshears: That’s an entirely different world.
70 00:05:09.700 ⇒ 00:05:14.899 Caitlyn Vaughn: Wow, that’s honestly way more fun. That’s, like, such a pleasant world for horses.
71 00:05:14.900 ⇒ 00:05:23.919 Justin Breshears: Yeah, it was, yeah, my family has a ranch and a farm, so cows, horses, all of that, like, but in that context, not the…
72 00:05:23.920 ⇒ 00:05:24.630 Caitlyn Vaughn: Yeah.
73 00:05:24.630 ⇒ 00:05:34.140 Justin Breshears: Like, dressed up, bedding, and drinking, you know, mint juleps, or whatever they drink at the derbies. We grew up in that one.
74 00:05:34.810 ⇒ 00:05:38.059 Caitlyn Vaughn: That’s amazing. Justin, do you ride, too? Did you grow up riding?
75 00:05:38.060 ⇒ 00:05:41.439 Justin Breshears: I did, I have not in a long time.
76 00:05:41.440 ⇒ 00:05:41.990 Caitlyn Vaughn: Nice.
77 00:05:41.990 ⇒ 00:05:50.389 Justin Breshears: Yeah, so I have done a lot of that when I was younger, but I can’t even remember the last time, because I went to college and then moved to Austin, and…
78 00:05:50.500 ⇒ 00:05:54.320 Caitlyn Vaughn: And no horses riding through Austin, so… Yeah, it’s so hard.
79 00:05:54.320 ⇒ 00:05:56.520 Uttam Kumaran: Horses on 6th Street, yeah, that’s the only one.
80 00:05:56.520 ⇒ 00:06:02.609 Caitlyn Vaughn: Oh, yeah! Well, they board up at, this place called White Fences in Manor, so it’s not.
81 00:06:02.610 ⇒ 00:06:03.240 Uttam Kumaran: Oh, I like that.
82 00:06:03.240 ⇒ 00:06:03.990 Caitlyn Vaughn: soon.
83 00:06:04.330 ⇒ 00:06:04.870 Uttam Kumaran: Okay.
84 00:06:05.270 ⇒ 00:06:07.020 Justin Breshears: the good to Northeast.
85 00:06:09.150 ⇒ 00:06:17.420 Caitlyn Vaughn: Well, you guys are crushing it. Let’s talk about… Data… .
86 00:06:17.420 ⇒ 00:06:21.710 Uttam Kumaran: Yeah, I guess we had two things on our side. We wanted to talk either about the data…
87 00:06:21.820 ⇒ 00:06:24.000 Uttam Kumaran: Vendor stuff.
88 00:06:24.310 ⇒ 00:06:29.719 Uttam Kumaran: And then I wanted to talk about the dashboard. You can start wherever your mind is.
89 00:06:30.010 ⇒ 00:06:36.129 Caitlyn Vaughn: Okay, let’s talk about… dashboard, like, all of the customer analytics stuff, I had you guys… Sure.
90 00:06:36.570 ⇒ 00:06:44.709 Caitlyn Vaughn: To… so I ended up GPT creating some charts out of it, which was great. Cool. Very helpful.
91 00:06:45.350 ⇒ 00:06:49.100 Caitlyn Vaughn: The only thing that is missing is the contracts.
92 00:06:49.300 ⇒ 00:06:57.720 Caitlyn Vaughn: So I think… What’s his name? I want to call him Lufasa.
93 00:06:57.890 ⇒ 00:06:59.160 Uttam Kumaran: Mustafa.
94 00:06:59.160 ⇒ 00:07:08.459 Caitlyn Vaughn: Yeah, Mustafa. Mustafa. He… he said that the CSV that I had sent to him with the Hyperline data
95 00:07:08.690 ⇒ 00:07:18.260 Caitlyn Vaughn: the contract value was associated with the company name, but it didn’t have the default ID, which is how he was associating the contracts
96 00:07:18.530 ⇒ 00:07:19.610 Caitlyn Vaughn: I think.
97 00:07:19.610 ⇒ 00:07:28.219 Uttam Kumaran: with the customer data. So, you similarly, like, if you look in… in the Hyperline CSV, you have Salesforce Customer ID, Stripe Customer ID,
98 00:07:28.340 ⇒ 00:07:33.050 Uttam Kumaran: I thought you guys would have, like, a linear, a default team ID.
99 00:07:33.970 ⇒ 00:07:39.710 Uttam Kumaran: Matched, because otherwise, we’re just trying to… we’re basically matching on… the email address.
100 00:07:41.140 ⇒ 00:07:49.850 Uttam Kumaran: And it’s, like, sometimes not the same thing. Like, some people… for example, 5Touch Solutions Inc, their email domain is eventmobi.com.
101 00:07:50.120 ⇒ 00:07:58.479 Uttam Kumaran: So, like, it was just hard for us to… to do that matching. So maybe we could talk about that first, like.
102 00:07:59.410 ⇒ 00:08:04.260 Uttam Kumaran: One, we could go and just, like, manually Do the matching.
103 00:08:04.960 ⇒ 00:08:06.810 Uttam Kumaran: It’s like a hu- it’s not like a huge list.
104 00:08:06.890 ⇒ 00:08:08.500 Caitlyn Vaughn: It’s, like, 200. Yeah.
105 00:08:08.500 ⇒ 00:08:13.859 Uttam Kumaran: Yeah, so… but then also, like, in Hyperline, just wanted to confirm, like, if there was any
106 00:08:14.710 ⇒ 00:08:19.069 Uttam Kumaran: Because there’s some way you guys… there’s no way the systems are synced right now.
107 00:08:19.250 ⇒ 00:08:21.210 Uttam Kumaran: It’s, like, all… okay, okay.
108 00:08:21.820 ⇒ 00:08:24.899 Caitlyn Vaughn: Yeah, everything is, like, pretty fractional.
109 00:08:24.900 ⇒ 00:08:28.029 Uttam Kumaran: So, like, when… when someone up… when someone adds seats.
110 00:08:28.500 ⇒ 00:08:30.250 Caitlyn Vaughn: And default…
111 00:08:30.250 ⇒ 00:08:36.140 Uttam Kumaran: what is a sales process? Like, someone just catches at some point and ups their contract? Okay.
112 00:08:36.140 ⇒ 00:08:36.980 Caitlyn Vaughn: Yeah.
113 00:08:36.980 ⇒ 00:08:37.370 Uttam Kumaran: Okay.
114 00:08:37.370 ⇒ 00:08:43.049 Caitlyn Vaughn: Basically, there’s, like, a per-seat cost with default, right?
115 00:08:43.250 ⇒ 00:08:51.450 Caitlyn Vaughn: So, if somebody wants to add a seat, then technically they’re supposed to ping sales, and then sales works out a new contract with them and adds seats.
116 00:08:51.450 ⇒ 00:08:54.799 Uttam Kumaran: But we actually have no way of…
117 00:08:54.860 ⇒ 00:09:05.309 Caitlyn Vaughn: knowing if someone has, like, added seats, and there’s no restrictions on it. So people have been adding seats for the last, like, 2 years, and we’re, like, playing a game of catch-up, but…
118 00:09:06.000 ⇒ 00:09:10.230 Caitlyn Vaughn: We are, like, manually charging people, so there’s, like, no…
119 00:09:10.970 ⇒ 00:09:15.900 Uttam Kumaran: And Hyperline you’re using for the billing? You’re using Stripe and Hyperline?
120 00:09:16.290 ⇒ 00:09:17.290 Caitlyn Vaughn: Not Stripe.
121 00:09:17.620 ⇒ 00:09:19.069 Uttam Kumaran: It’s all through Hyperline.
122 00:09:19.070 ⇒ 00:09:19.720 Caitlyn Vaughn: Huh.
123 00:09:20.030 ⇒ 00:09:23.479 Uttam Kumaran: Okay. Okay, so that’s fine. I mean, one is…
124 00:09:24.400 ⇒ 00:09:31.510 Uttam Kumaran: you should… whoever is… if you could somehow get that into the roadmap for the next product, you totally should. So whoever…
125 00:09:31.510 ⇒ 00:09:32.839 Caitlyn Vaughn: What specifically?
126 00:09:32.970 ⇒ 00:09:38.469 Uttam Kumaran: Basically, the Teams table needs to have a Hyperline
127 00:09:39.230 ⇒ 00:09:42.730 Uttam Kumaran: equivalent ID on it. So your default teams
128 00:09:43.120 ⇒ 00:09:51.640 Uttam Kumaran: y’all should 100% have some way to link that to the Hyperline equivalent. This is a very common pattern, like.
129 00:09:51.850 ⇒ 00:09:53.759 Uttam Kumaran: So, again, you guys have…
130 00:09:54.340 ⇒ 00:10:01.370 Uttam Kumaran: typical teams or organizations, there’s a one-to-one mapping between that and the Hyperline version of that.
131 00:10:03.180 ⇒ 00:10:04.719 Caitlyn Vaughn: Yeah, that makes sense.
132 00:10:04.720 ⇒ 00:10:11.200 Uttam Kumaran: And then that way they can increment the seats. Second is… I’m happy to…
133 00:10:11.650 ⇒ 00:10:20.690 Uttam Kumaran: I think what we could do initially is we’ll just go through, and I’ll… we’ll do our best to do the mapping, and then I can tell you the ones where maybe we need, like.
134 00:10:21.400 ⇒ 00:10:24.009 Uttam Kumaran: Hey, like, this one, we don’t know who this is.
135 00:10:24.180 ⇒ 00:10:27.670 Uttam Kumaran: So I can map… we can map all the IDs manually.
136 00:10:27.860 ⇒ 00:10:34.840 Uttam Kumaran: After that point, it’ll be pretty easy for me to tell you who’s, like, under billing, basically. That’s…
137 00:10:35.110 ⇒ 00:10:40.620 Uttam Kumaran: like, one good outcome, but then, of course, we should… we could talk about the ARR stuff, so…
138 00:10:42.250 ⇒ 00:10:43.920 Caitlyn Vaughn: Okay, so…
139 00:10:45.970 ⇒ 00:10:55.959 Caitlyn Vaughn: Yes, I’m looking at Hyperline right now, I’ll just share my screen. I’m pretty sure this is what is exported. Okay, so you see where it says subscriptions?
140 00:10:56.480 ⇒ 00:10:59.050 Uttam Kumaran: Yeah, like, let’s use an example like Search Party.
141 00:10:59.960 ⇒ 00:11:00.489 Uttam Kumaran: If that’s true.
142 00:11:00.490 ⇒ 00:11:01.070 Caitlyn Vaughn: Sure.
143 00:11:01.400 ⇒ 00:11:01.990 Uttam Kumaran: Yeah.
144 00:11:02.160 ⇒ 00:11:05.989 Caitlyn Vaughn: So, the 7.08 is…
145 00:11:06.790 ⇒ 00:11:09.710 Uttam Kumaran: Okay, so ARR, and then they’re doing 590 times…
146 00:11:09.710 ⇒ 00:11:10.450 Caitlyn Vaughn: value.
147 00:11:10.630 ⇒ 00:11:11.880 Uttam Kumaran: Okay, okay.
148 00:11:11.880 ⇒ 00:11:20.040 Caitlyn Vaughn: Yeah, so what I’m looking for is, like, the annual cost that we’re… the annual price we’re charging them, right? Or they’re paying us, so…
149 00:11:20.390 ⇒ 00:11:23.510 Caitlyn Vaughn: this, like, six and a half. They’re paying us…
150 00:11:23.690 ⇒ 00:11:30.719 Caitlyn Vaughn: $548 on, like, a monthly basis, right? Okay. But the total contract is six and a half.
151 00:11:31.390 ⇒ 00:11:35.880 Uttam Kumaran: And then, is there… can you go to the… where you did the export from?
152 00:11:36.260 ⇒ 00:11:44.610 Uttam Kumaran: And is… was there any options for additional fields at all? Oh, this is just it. Yeah. Okay, cool. Alright, very cool.
153 00:11:45.020 ⇒ 00:11:45.670 Caitlyn Vaughn: Cool.
154 00:11:45.670 ⇒ 00:11:55.589 Uttam Kumaran: Okay, this is fine for now. I… we’re basically just gonna… I’m just gonna do… you all… all we have is ARR, so I’m just gonna divide it into 12 to get the MRR.
155 00:11:55.590 ⇒ 00:11:57.020 Caitlyn Vaughn: No, just keep.
156 00:11:57.020 ⇒ 00:11:57.340 Uttam Kumaran: Why?
157 00:11:57.340 ⇒ 00:11:58.670 Caitlyn Vaughn: ARR.
158 00:11:58.670 ⇒ 00:12:02.130 Uttam Kumaran: Well, you’re gonna need to measure MRR from these guys.
159 00:12:03.110 ⇒ 00:12:04.360 Caitlyn Vaughn: No!
160 00:12:04.520 ⇒ 00:12:05.630 Uttam Kumaran: Why?
161 00:12:05.730 ⇒ 00:12:07.389 Uttam Kumaran: Like, for each customer?
162 00:12:07.580 ⇒ 00:12:09.710 Caitlyn Vaughn: We only do annual contracts.
163 00:12:11.620 ⇒ 00:12:14.529 Uttam Kumaran: But it’s still relevant to know how much you’re bringing
164 00:12:14.990 ⇒ 00:12:17.960 Uttam Kumaran: On a monthly… on a monthly basis, right?
165 00:12:18.410 ⇒ 00:12:20.940 Caitlyn Vaughn: But we’re not charging these people monthly.
166 00:12:20.940 ⇒ 00:12:26.200 Uttam Kumaran: No, no, no, but, like, but also, all of your other product KPIs are monthly.
167 00:12:27.610 ⇒ 00:12:28.510 Caitlyn Vaughn: Like, what.
168 00:12:29.160 ⇒ 00:12:32.630 Uttam Kumaran: Like, if we’re looking at meetings booked and things like that, like.
169 00:12:33.010 ⇒ 00:12:36.579 Uttam Kumaran: Obviously, you want to look at, like, how much revenue is coming in, and then what is the type of…
170 00:12:37.530 ⇒ 00:12:46.370 Uttam Kumaran: output every… like, again, the reason I’m pushing for it is, like, you want to have monthly… you want to have the same time dimensionality as much as possible.
171 00:12:46.370 ⇒ 00:13:00.769 Uttam Kumaran: across all of your sources. So one source is Hyperline cost data, one source is default data. The default data, of course, we have down to, like, the hour, basically. But in order to look at, like, how much money came in this month, and how much
172 00:13:00.910 ⇒ 00:13:04.939 Uttam Kumaran: Product usage was there in the same month, and see that in one chart.
173 00:13:05.770 ⇒ 00:13:15.289 Uttam Kumaran: like, I’ll just take this… we’ll just take this ARR and divide it by 12 to create an MRR. Of course, like, it’s not billed MRR, like, the billing schedule is still annually.
174 00:13:15.420 ⇒ 00:13:20.099 Uttam Kumaran: But it helps you see, like, how much money is actually, like.
175 00:13:20.920 ⇒ 00:13:25.810 Uttam Kumaran: coming in, and then there are other KPIs that are built on MRR and ARR.
176 00:13:26.430 ⇒ 00:13:30.719 Caitlyn Vaughn: Okay, well, can we do both? Like, are you able to just include in and then.
177 00:13:30.720 ⇒ 00:13:33.280 Uttam Kumaran: No, this is… this is above. This is above, this is a both thing.
178 00:13:33.280 ⇒ 00:13:34.030 Caitlyn Vaughn: Okay.
179 00:13:34.030 ⇒ 00:13:34.580 Uttam Kumaran: Yeah, yeah.
180 00:13:35.610 ⇒ 00:13:42.010 Caitlyn Vaughn: I’m less… less concerned. Okay, that sounds good, and then… I’ve obviously, like…
181 00:13:42.320 ⇒ 00:13:51.769 Caitlyn Vaughn: I sent you guys a link of, like, what I actually had built out. I also built out something for, like, the data enrichment testing thing.
182 00:13:51.770 ⇒ 00:13:52.450 Uttam Kumaran: Cool.
183 00:13:52.580 ⇒ 00:13:57.199 Caitlyn Vaughn: But… the point being in here…
184 00:13:58.990 ⇒ 00:14:02.589 Caitlyn Vaughn: Oh, I forgot to link the customer segmentation, that’s, like, the whole point.
185 00:14:08.300 ⇒ 00:14:10.870 Caitlyn Vaughn: patient report… okay.
186 00:14:11.210 ⇒ 00:14:12.770 Caitlyn Vaughn: So this is what I like.
187 00:14:13.370 ⇒ 00:14:18.030 Caitlyn Vaughn: pulled out of the shared data so far. Basically, like.
188 00:14:18.560 ⇒ 00:14:23.560 Caitlyn Vaughn: Current average funding for a default customer is $60 million.
189 00:14:24.060 ⇒ 00:14:26.650 Uttam Kumaran: Like, average series is A…
190 00:14:26.730 ⇒ 00:14:33.109 Caitlyn Vaughn: customer segmentation, mostly into enterprise, actually. And then…
191 00:14:33.270 ⇒ 00:14:47.580 Caitlyn Vaughn: second is mid-market, but if you look at the actual, like, customer breakdown… can I make this bigger? I don’t think so. I actually broke it down by, like, what series they are in, and what they sell into, and basically everybody.
192 00:14:47.580 ⇒ 00:14:47.910 Uttam Kumaran: Great.
193 00:14:47.910 ⇒ 00:14:50.010 Caitlyn Vaughn: like, trying to sell enterprise. So…
194 00:14:50.340 ⇒ 00:14:59.239 Caitlyn Vaughn: The point being, like, when we are thinking about what quote-unquote good data looks like, I think we need to make sure that we’re covering our bases, which is, like.
195 00:14:59.240 ⇒ 00:15:11.559 Caitlyn Vaughn: for sure, enterprise, also mid-market, and we do have SMB and startup, so… Okay. We have a pretty good spread. I’m, like, very not concerned about having, like, B2C data or, like, public or government data,
196 00:15:11.740 ⇒ 00:15:16.289 Caitlyn Vaughn: We actually have, like, quite a few companies across the board selling into, like, each scale.
197 00:15:16.900 ⇒ 00:15:17.570 Uttam Kumaran: Okay.
198 00:15:18.010 ⇒ 00:15:18.820 Uttam Kumaran: Okay.
199 00:15:19.610 ⇒ 00:15:23.430 Uttam Kumaran: So then we had, like, on our side, basically.
200 00:15:23.530 ⇒ 00:15:26.279 Uttam Kumaran: We were gonna look at, okay,
201 00:15:28.570 ⇒ 00:15:34.870 Uttam Kumaran: We’re gonna look at the domain-to-company enrichment, we’re gonna look at, the…
202 00:15:35.480 ⇒ 00:15:47.589 Uttam Kumaran: like, okay, do they have all the funding data? Do they have employee trends? Things like that. I guess, yeah, I saw that you kind of combined a little bit from our side. Was there anything that we were, like, missing?
203 00:15:49.090 ⇒ 00:15:52.730 Caitlyn Vaughn: There’s… I think this is, like, slight overkill, so…
204 00:15:52.730 ⇒ 00:15:53.420 Uttam Kumaran: Okay.
205 00:15:53.980 ⇒ 00:15:57.940 Caitlyn Vaughn: What I don’t think we need, we don’t need pricing.
206 00:15:58.310 ⇒ 00:15:58.980 Uttam Kumaran: Okay.
207 00:15:59.290 ⇒ 00:16:01.190 Caitlyn Vaughn: I’m gonna strike this.
208 00:16:01.860 ⇒ 00:16:04.929 Uttam Kumaran: Okay, that’s what I thought. Under responsiveness…
209 00:16:05.570 ⇒ 00:16:08.170 Uttam Kumaran: Because you already have the list of people we’re evaluating.
210 00:16:08.580 ⇒ 00:16:09.430 Caitlyn Vaughn: Yes.
211 00:16:09.430 ⇒ 00:16:10.310 Uttam Kumaran: Okay, okay.
212 00:16:14.100 ⇒ 00:16:22.849 Caitlyn Vaughn: Yes, so let me, like… actually, I haven’t really read through this. For sure, sure. Scoring criteria, weighted, data accuracy and coverage, does the vendor provide really…
213 00:16:22.980 ⇒ 00:16:25.190 Caitlyn Vaughn: Data, how broad is our coverage?
214 00:16:26.650 ⇒ 00:16:30.790 Caitlyn Vaughn: Okay, so yeah, I’m gonna add in your… can I type in your Yakim?
215 00:16:31.090 ⇒ 00:16:31.640 Uttam Kumaran: Checked.
216 00:16:32.040 ⇒ 00:16:37.489 Caitlyn Vaughn: Yeah, I can. Look at customer segmentation.
217 00:16:39.240 ⇒ 00:16:46.559 Caitlyn Vaughn: port for… Best data coverage practices.
218 00:16:48.410 ⇒ 00:16:58.389 Caitlyn Vaughn: TLTR… Enterprise… Data is LDK.
219 00:16:59.220 ⇒ 00:17:04.300 Caitlyn Vaughn: Okay. Esmer… oh, you have that second, sorry. Does the vendor of deep accuracy coverage?
220 00:17:04.300 ⇒ 00:17:04.609 Uttam Kumaran: That’s fine.
221 00:17:04.619 ⇒ 00:17:06.629 Caitlyn Vaughn: segments… yeah.
222 00:17:09.359 ⇒ 00:17:10.369 Caitlyn Vaughn: Okay.
223 00:17:11.659 ⇒ 00:17:13.089 Caitlyn Vaughn: Uniqueness…
224 00:17:15.730 ⇒ 00:17:16.140 Uttam Kumaran: This was, like.
225 00:17:16.140 ⇒ 00:17:16.650 Caitlyn Vaughn: Nope.
226 00:17:16.960 ⇒ 00:17:33.499 Uttam Kumaran: Yeah, this… yeah, this is the… the, like, okay, who has the… who either on the roadmap or on their existing thing, like, actually has, like, really deeper stuff. The nice thing is, given your segmentation, we’re not gonna… we’re gonna… people have more stuff into government or consumer, like, we don’t care, mainly looking for
227 00:17:34.230 ⇒ 00:17:36.600 Uttam Kumaran: deeper, like, stuff like what PDL has.
228 00:17:38.180 ⇒ 00:17:46.069 Caitlyn Vaughn: Exactly, so… like I said, I did this too, which is probably good, because am I in your instance?
229 00:17:48.080 ⇒ 00:17:49.010 Caitlyn Vaughn: Whoa.
230 00:17:49.920 ⇒ 00:17:52.600 Uttam Kumaran: Oh yeah, so I see your kill list, basically, or…
231 00:17:52.600 ⇒ 00:18:06.549 Caitlyn Vaughn: Yes. Okay, so I’ll go through. This is, like, I tried to do the same thing. So, I think we should test with, like, 400-ish samples, if possible. I don’t know if you guys need me to provide, like, a list of companies, or…
232 00:18:06.630 ⇒ 00:18:18.479 Caitlyn Vaughn: If you guys could create a list, but essentially, now that we know, we are… we’re trying to sell to larger companies as well. That’s the other… other piece of this. We’re trying to go upmarket, so I would optimize for those, like.
233 00:18:18.560 ⇒ 00:18:26.440 Caitlyn Vaughn: Series B plus companies, and, like, what they’re trying to sell into. So if we could pull companies, like…
234 00:18:27.760 ⇒ 00:18:34.680 Caitlyn Vaughn: Series B, C, D, plus, and use that as our sample set, that would probably be the best.
235 00:18:35.640 ⇒ 00:18:41.959 Caitlyn Vaughn: What matters? Yeah. Robustness. So, something that was missed is latency.
236 00:18:42.150 ⇒ 00:18:52.969 Caitlyn Vaughn: That is, like, a very important piece of our product, obviously, that, like, submitting a form, and then it needs to enrich, qualify, and route, all in, like, a couple hundred milliseconds.
237 00:18:53.350 ⇒ 00:18:58.090 Caitlyn Vaughn: So, we’re only interested in using APIs under, like, 200 milliseconds.
238 00:18:58.560 ⇒ 00:19:00.710 Uttam Kumaran: Great, okay, great. That’s a great filter.
239 00:19:01.030 ⇒ 00:19:08.620 Caitlyn Vaughn: Obviously rate limits and up times. As we move into PLG, we’re gonna kinda open the floodgates, right?
240 00:19:09.450 ⇒ 00:19:20.829 Caitlyn Vaughn: And people should be able to, like, upload lists and enrich them, kind of like Clay. So, if they enrich a list of a million companies, like, it’s okay if we have to, like, run in parallel and or, like.
241 00:19:21.290 ⇒ 00:19:24.680 Caitlyn Vaughn: what do you call it? Like… Do it in chunks.
242 00:19:25.900 ⇒ 00:19:33.549 Uttam Kumaran: So, of the… a quick question, of the 400, let’s say I was gonna do… Enterprise, mid-market.
243 00:19:34.050 ⇒ 00:19:37.269 Uttam Kumaran: Well, I guess it’s gonna be Series C, D+,
244 00:19:37.750 ⇒ 00:19:41.280 Uttam Kumaran: like, do you care if I do, like,
245 00:19:42.700 ⇒ 00:19:51.009 Uttam Kumaran: like, basically series CD, and then private, mid-market, and then enterprise, so, like, Maybe, like, 100, 100, 200.
246 00:19:51.890 ⇒ 00:19:55.490 Caitlyn Vaughn: Yeah, actually, as I’m thinking about this,
247 00:19:55.610 ⇒ 00:20:05.929 Caitlyn Vaughn: I guess what we’re really trying to test is, like, if we have data on who our customers need to see, right? Which would be enterprise mid-market SMB startups.
248 00:20:06.290 ⇒ 00:20:07.050 Uttam Kumaran: Yes.
249 00:20:07.320 ⇒ 00:20:08.580 Caitlyn Vaughn: So maybe, like…
250 00:20:08.940 ⇒ 00:20:13.639 Uttam Kumaran: Well, the thing is, like, you have… you guys have a lot of stuff before… you guys have a lot of customers that are pretty Series C.
251 00:20:13.850 ⇒ 00:20:16.909 Uttam Kumaran: So I don’t want to… I was gonna use just your…
252 00:20:17.520 ⇒ 00:20:22.570 Uttam Kumaran: I’m just gonna use your customers as it, too, but I also want to throw in ones that, like.
253 00:20:23.190 ⇒ 00:20:29.080 Uttam Kumaran: I know would be harder to enrich, so I want to build… like, want to build a good 400 sample, basically.
254 00:20:30.870 ⇒ 00:20:38.680 Caitlyn Vaughn: Yeah, you could definitely use our customers. I think the only thing that we’re missing is the enterprise. I don’t think we have very many enterprise companies.
255 00:20:40.340 ⇒ 00:20:48.239 Uttam Kumaran: Yeah, we’ll run with some… we’ll run with, like, some from the Fortune 1000, basically, and then pick a couple different industries.
256 00:20:48.740 ⇒ 00:20:50.360 Caitlyn Vaughn: Okay, perfect.
257 00:20:51.000 ⇒ 00:20:57.299 Caitlyn Vaughn: Yeah, so maybe, like, 200 enterprise, and then you can split the rest.
258 00:20:57.690 ⇒ 00:20:58.340 Uttam Kumaran: Sure.
259 00:20:59.610 ⇒ 00:21:06.610 Caitlyn Vaughn: Okay… return rate, how often does it return value, accuracy? Yeah, it’s probably another one, like…
260 00:21:07.280 ⇒ 00:21:23.030 Caitlyn Vaughn: if it’s getting a value in general, that’s great, and then, like, what is the actual accuracy of those values? Yeah. Because a lot of these workflows, like, hinge on some kind of return, like, even if it’s… even if the return is not correct, it’s not great, but at least the workflow will keep running, right?
261 00:21:24.070 ⇒ 00:21:24.890 Uttam Kumaran: Yeah.
262 00:21:25.780 ⇒ 00:21:29.600 Caitlyn Vaughn: Area, person signals, freshness, cost.
263 00:21:29.920 ⇒ 00:21:34.260 Caitlyn Vaughn: And then here’s the list, so these are the actual providers that I need.
264 00:21:34.420 ⇒ 00:21:36.290 Caitlyn Vaughn: You guys test with.
265 00:21:36.630 ⇒ 00:21:37.020 Uttam Kumaran: Okay.
266 00:21:37.020 ⇒ 00:21:49.549 Caitlyn Vaughn: But, actually, I don’t even know… yeah, I’ll probably have you assess this. This is the list of, like, vendors that I had assessed and, like, had meetings with. These are their price ranges. I have notes on each of them, so if you open it up.
267 00:21:49.720 ⇒ 00:21:55.560 Caitlyn Vaughn: This is, like… My rough meeting notes that are chaotic, but get the gist.
268 00:21:56.970 ⇒ 00:21:59.830 Caitlyn Vaughn: Okay. And then, yeah, that’s kind of everything.
269 00:22:00.630 ⇒ 00:22:02.820 Uttam Kumaran: So you just want us to go through for these…
270 00:22:03.060 ⇒ 00:22:07.550 Uttam Kumaran: like, 10 or so, or should we go through everything on the vendor list that’s.
271 00:22:07.550 ⇒ 00:22:08.860 Caitlyn Vaughn: just these.
272 00:22:08.860 ⇒ 00:22:09.440 Uttam Kumaran: Okay.
273 00:22:09.770 ⇒ 00:22:10.440 Uttam Kumaran: Okay.
274 00:22:14.180 ⇒ 00:22:15.110 Caitlyn Vaughn: Cool.
275 00:22:16.430 ⇒ 00:22:24.380 Uttam Kumaran: Okay, great. I feel like it’s actually… yeah, I think I just wanna… I think probably next steps, Justin, is, like, we’ll…
276 00:22:24.550 ⇒ 00:22:35.299 Uttam Kumaran: we have to build out this, like, 400 set, and then I just want to run it by… by you, Caitlin. I think we’ll… we’ll pick some people we… we’re… we’re familiar with.
277 00:22:35.450 ⇒ 00:22:39.349 Uttam Kumaran: So that we can spot check. We’ll also pick some ones that
278 00:22:39.510 ⇒ 00:22:48.039 Uttam Kumaran: I know, like, they’ll have a hard… only, like, it’ll have a harder time getting that data, so we can test. Like, for example, if you look up, like, a company like Salesforce.
279 00:22:48.300 ⇒ 00:22:55.980 Uttam Kumaran: all that data is online, but there’s other large enterprise companies with not so much, so I think that’s helpful.
280 00:22:56.400 ⇒ 00:23:06.550 Uttam Kumaran: And then… I’ll… we’ll modify the criteria a bit. I just won’t over… we’re not gonna overthink, like, the scoring and stuff too much. If anything, I may even reduce it to, like, 1, 2, or 3.
281 00:23:06.710 ⇒ 00:23:08.969 Uttam Kumaran: Just so you have that.
282 00:23:08.970 ⇒ 00:23:09.560 Caitlyn Vaughn: Yep.
283 00:23:10.510 ⇒ 00:23:12.560 Uttam Kumaran: And then we’ll save all the,
284 00:23:12.820 ⇒ 00:23:17.350 Uttam Kumaran: Like, the resulting output data somewhere, too, for those 400 tests.
285 00:23:18.120 ⇒ 00:23:26.440 Uttam Kumaran: guys have that, and it’s actually helpful to know the 200 milliseconds and return something versus nothing.
286 00:23:26.700 ⇒ 00:23:28.730 Uttam Kumaran: So yeah, I don’t think this should take as much time.
287 00:23:29.290 ⇒ 00:23:32.170 Caitlyn Vaughn: Okay, cool. The other thing to note is…
288 00:23:32.300 ⇒ 00:23:35.060 Caitlyn Vaughn: We have a contract with Owler.
289 00:23:35.250 ⇒ 00:23:39.759 Caitlyn Vaughn: we have, like, a POC with them until the 15th of October.
290 00:23:39.870 ⇒ 00:23:50.860 Caitlyn Vaughn: And then we’re supposed to start, like, a full annual contract for a lot of money. Okay. So, this needs to be returned before then. This is, like, kind of what’s catalyzing it.
291 00:23:51.090 ⇒ 00:23:51.430 Uttam Kumaran: Okay.
292 00:23:51.430 ⇒ 00:23:56.090 Caitlyn Vaughn: Probably, I would say, do Owler first, so at least we have the data for that, and then do everything else.
293 00:23:56.640 ⇒ 00:24:04.619 Uttam Kumaran: So let’s… let’s, so 15th is two Wednesdays from now, so yeah, Justin, I think let’s aim for something…
294 00:24:05.200 ⇒ 00:24:06.400 Uttam Kumaran: next week.
295 00:24:07.390 ⇒ 00:24:08.589 Caitlyn Vaughn: Maybe next Friday.
296 00:24:09.100 ⇒ 00:24:10.120 Uttam Kumaran: Yeah…
297 00:24:10.120 ⇒ 00:24:11.160 Caitlyn Vaughn: Or next Thursday.
298 00:24:11.160 ⇒ 00:24:12.740 Uttam Kumaran: Next Thursday…
299 00:24:13.190 ⇒ 00:24:18.620 Uttam Kumaran: Ideally, we have the 400 set by Tuesday, and then we can run it on hourly by Thursday.
300 00:24:20.170 ⇒ 00:24:28.239 Uttam Kumaran: And then, yeah, and if there’s any… like, are there any other ones? Like, Harmonic would be another one that I feel pretty confident we’ll go with, just because of their startup.
301 00:24:28.650 ⇒ 00:24:30.359 Uttam Kumaran: Data is really, really good.
302 00:24:32.090 ⇒ 00:24:36.509 Uttam Kumaran: You guys didn’t, think about, like, Crunchbase for…
303 00:24:37.160 ⇒ 00:24:39.560 Uttam Kumaran: the startup data, either. Just trying to think of.
304 00:24:39.560 ⇒ 00:24:40.020 Caitlyn Vaughn: Cool.
305 00:24:40.020 ⇒ 00:24:40.860 Uttam Kumaran: would be…
306 00:24:41.790 ⇒ 00:24:45.630 Caitlyn Vaughn: Yeah, I haven’t considered Crunch Face.
307 00:24:45.850 ⇒ 00:24:46.480 Uttam Kumaran: Okay.
308 00:24:46.480 ⇒ 00:24:52.450 Caitlyn Vaughn: I mean, Harmonix says that they’re, like, better than Crunchbase. We can definitely, like, test it and see.
309 00:24:52.530 ⇒ 00:24:54.440 Uttam Kumaran: Okay. But I, I don’t think I…
310 00:24:54.440 ⇒ 00:24:57.539 Caitlyn Vaughn: I think I reached out to Crunchbase and didn’t hear back, so…
311 00:24:57.680 ⇒ 00:25:01.169 Caitlyn Vaughn: Also, I’m kind of going off of, like, who I’ve already met with.
312 00:25:01.170 ⇒ 00:25:01.650 Uttam Kumaran: Okay, good.
313 00:25:01.650 ⇒ 00:25:04.930 Caitlyn Vaughn: I think you’re meeting with people, but… I would rather not.
314 00:25:05.750 ⇒ 00:25:10.830 Uttam Kumaran: No, I think Harmonic is, like, I follow them on Twitter, like, they’re doing really, like, their stuff is really good.
315 00:25:11.110 ⇒ 00:25:13.790 Uttam Kumaran: Yeah, and we also have them in platform.
316 00:25:14.310 ⇒ 00:25:15.719 Uttam Kumaran: Okay, okay, great, alright.
317 00:25:17.160 ⇒ 00:25:18.190 Caitlyn Vaughn: Cool. Okay.
318 00:25:18.270 ⇒ 00:25:22.109 Uttam Kumaran: And then I think once you… once we get you these reports.
319 00:25:22.330 ⇒ 00:25:34.329 Uttam Kumaran: like, I feel like if you need us to go talk to whoever the contact is directly, like, if we have open questions, or we see questions about latency and stuff, you may be able to ask them to give you a more…
320 00:25:34.590 ⇒ 00:25:39.519 Uttam Kumaran: Like, priority queue or something like that, but we’ll just get you all this data on these.
321 00:25:40.080 ⇒ 00:25:53.570 Caitlyn Vaughn: Okay, cool. Depending on how Owler does, if we do want to move forward with them, maybe I’ll pull you in, Utong, on the call, because we have a new rep who probably sucks less than our last one. But to negotiate the actual contract.
322 00:25:53.930 ⇒ 00:25:54.690 Uttam Kumaran: Okay, okay.
323 00:25:54.690 ⇒ 00:25:56.210 Caitlyn Vaughn: It’s so expensive.
324 00:25:56.870 ⇒ 00:25:58.679 Uttam Kumaran: Yeah, that would be great, it’s a great…
325 00:25:59.150 ⇒ 00:26:03.460 Uttam Kumaran: I will push them down and get you some money.
326 00:26:04.350 ⇒ 00:26:11.910 Uttam Kumaran: Trying to think if there’s anything else, let’s see… Okay.
327 00:26:12.660 ⇒ 00:26:25.579 Uttam Kumaran: Yeah, and then, you know, additionally, look, if we test the hour alert data, and we test it on the R400, and we find discrepancies, and you still want to go with them, it’s a great way to be like, yo, your stuff is not that good, so give us a discount.
328 00:26:25.580 ⇒ 00:26:26.710 Caitlyn Vaughn: Yeah,
329 00:26:28.410 ⇒ 00:26:35.029 Uttam Kumaran: I mean, still people are using Apollo, like, people are using Apollo and stuff, but I don’t know, I think the data’s kind of bad.
330 00:26:35.030 ⇒ 00:26:40.760 Caitlyn Vaughn: It’s terrible, yeah. I think, I mean, we’re all on the same page, but also if we have some, like.
331 00:26:40.870 ⇒ 00:26:46.230 Caitlyn Vaughn: hard numbers on how bad Apollo is, it’s probably easier for me to, like, rip it out of the platform.
332 00:26:46.390 ⇒ 00:26:47.390 Uttam Kumaran: Okay, okay.
333 00:26:48.050 ⇒ 00:26:51.290 Caitlyn Vaughn: But yeah, I have, different motives for each vendor.
334 00:26:51.480 ⇒ 00:27:05.259 Uttam Kumaran: Yeah, yeah. Okay, so we’ll tell. Yeah, I think this 400 data set, I’ll try to make it diverse, and then again, like, what you’re probably gonna see is Harmonic exists now, in 6 months, there’ll be another one, so hopefully you could just plug them into the same, like, kind of test criteria.
335 00:27:05.370 ⇒ 00:27:07.789 Caitlyn Vaughn: Or what you should do is you should just…
336 00:27:07.930 ⇒ 00:27:13.079 Uttam Kumaran: give them the company set and say, go run this, and we need a report on XYZ.
337 00:27:13.390 ⇒ 00:27:15.200 Caitlyn Vaughn: Mmm, that’s a good idea.
338 00:27:15.200 ⇒ 00:27:17.499 Uttam Kumaran: And they can do it for you if they want your business.
339 00:27:18.580 ⇒ 00:27:20.010 Caitlyn Vaughn: That’s a really good idea.
340 00:27:20.820 ⇒ 00:27:23.799 Uttam Kumaran: They lie about the latency and stuff, but I don’t know.
341 00:27:23.960 ⇒ 00:27:27.220 Caitlyn Vaughn: At least, you can check the accuracy first.
342 00:27:27.290 ⇒ 00:27:28.800 Uttam Kumaran: There’s a latency…
343 00:27:29.190 ⇒ 00:27:35.309 Uttam Kumaran: That week, you guys, you could do really, really quickly, but having them be like, here’s our test data set of 400,
344 00:27:35.650 ⇒ 00:27:38.639 Uttam Kumaran: You need to prove that you can hit all these, basically, or hit some percent.
345 00:27:38.640 ⇒ 00:27:40.330 Caitlyn Vaughn: Right.
346 00:27:40.600 ⇒ 00:27:41.639 Caitlyn Vaughn: Good idea.
347 00:27:43.100 ⇒ 00:27:44.200 Caitlyn Vaughn: Okay, cool.
348 00:27:44.570 ⇒ 00:27:54.099 Uttam Kumaran: Okay, and then for the dashboard, so we’ll add that. Anything else, like, on the dash, we should… I mean, it’s getting… I basically said, throw everything into one dashboard. We can start to section it out, but…
349 00:27:55.100 ⇒ 00:27:55.820 Caitlyn Vaughn: Hmm.
350 00:27:55.820 ⇒ 00:27:59.520 Uttam Kumaran: I was just gonna basically create a bottom section that’s, like,
351 00:27:59.900 ⇒ 00:28:03.450 Uttam Kumaran: About default customers, and then the top is about usage.
352 00:28:03.590 ⇒ 00:28:08.210 Uttam Kumaran: But… Yeah, I mean, it’s been really great to, like, to look at and…
353 00:28:08.420 ⇒ 00:28:12.450 Uttam Kumaran: I’ve also been, like, trying to look at, like, okay, what are some trends we’re seeing, so…
354 00:28:12.660 ⇒ 00:28:19.850 Caitlyn Vaughn: Oh yeah, that was the other thing I was gonna say. Well, one, are you able to put it in a separate dashboard, or no?
355 00:28:20.180 ⇒ 00:28:21.110 Uttam Kumaran: Yeah, we can.
356 00:28:21.480 ⇒ 00:28:25.439 Caitlyn Vaughn: Okay, I would probably just start anyone for customer segmentation.
357 00:28:25.440 ⇒ 00:28:26.429 Uttam Kumaran: Okay, okay.
358 00:28:27.110 ⇒ 00:28:39.540 Caitlyn Vaughn: And then the other thing is, I obviously… so I showed you the report of the customer segmentation that I had built out, like, who’s, you know, Series A, Series B, who are they selling to? If we… if you guys could help me, like.
359 00:28:39.680 ⇒ 00:28:44.630 Caitlyn Vaughn: I feel like this is maybe where a strength could be?
360 00:28:45.130 ⇒ 00:28:48.550 Caitlyn Vaughn: Is, like, figuring out what some, like.
361 00:28:48.660 ⇒ 00:28:54.520 Caitlyn Vaughn: cross patterns are, or, like, trends with multiple data points, because I’m able to obviously, like.
362 00:28:54.650 ⇒ 00:28:59.989 Caitlyn Vaughn: pull a single data point, but I’m not able to, like, create relational data for, like, several.
363 00:29:01.020 ⇒ 00:29:06.700 Uttam Kumaran: And this is relational data between, like, SMB behavior on the platform versus mid-market.
364 00:29:07.060 ⇒ 00:29:09.220 Uttam Kumaran: And this is, like, behavior of, like.
365 00:29:09.860 ⇒ 00:29:14.009 Uttam Kumaran: The meetings booked, the usage of workflows, things like that.
366 00:29:14.670 ⇒ 00:29:17.660 Uttam Kumaran: Yeah, I mean, I… I don’t know. That’s why I’m like… Okay.
367 00:29:17.660 ⇒ 00:29:30.039 Caitlyn Vaughn: Asking you if you can find, like, patterns inside of here where you’re like, oh, that’s, like, a really interesting data point, or, like, those are two… two things that might be related, and you’re able to, like, pair them together, that could be really cool.
368 00:29:30.240 ⇒ 00:29:31.800 Uttam Kumaran: Okay. 13. Cool.
369 00:29:32.480 ⇒ 00:29:37.249 Uttam Kumaran: Yeah, all of our joins are already there, so we can poke it. I already have a couple of
370 00:29:37.410 ⇒ 00:29:44.710 Uttam Kumaran: I mean, like, I already have a couple of thoughts on, like, where you’ll probably see. I mean, basically, one thing I want to do is, now that we have the money.
371 00:29:44.810 ⇒ 00:29:49.789 Caitlyn Vaughn: And is the… are the go… are the contracts, like, standard at all, or was it just, like…
372 00:29:49.840 ⇒ 00:29:52.419 Uttam Kumaran: Like, should we see that the enterprise clients
373 00:29:53.000 ⇒ 00:29:57.900 Uttam Kumaran: Are paying more per seat? Or, like, how… what were the… or is it just sort of, like, how much you can get?
374 00:29:58.700 ⇒ 00:29:59.980 Caitlyn Vaughn: Kind of vibes.
375 00:30:00.140 ⇒ 00:30:07.279 Uttam Kumaran: Okay, because instead, I was going to try to look at, like, who was most valuable customers, and potentially, like, who should be paying more.
376 00:30:07.520 ⇒ 00:30:09.979 Uttam Kumaran: Kind of look at where, like, the behavior…
377 00:30:10.260 ⇒ 00:30:12.680 Uttam Kumaran: How the behavior is associated with
378 00:30:12.960 ⇒ 00:30:15.590 Uttam Kumaran: The amount of money people are spending, but if it was sort of, like.
379 00:30:16.070 ⇒ 00:30:19.169 Uttam Kumaran: If the contract wasn’t negotiated so much on that, then…
380 00:30:19.440 ⇒ 00:30:22.969 Uttam Kumaran: more what I’m gonna look at is behavior by each segment.
381 00:30:23.670 ⇒ 00:30:35.170 Uttam Kumaran: And then, ideally, what you should see is that the enterprise customers, or your ideal customer, is using the platform very heavily, they’re using multiple products in the platform, they’ve onboard a lot of people, and therefore, like.
382 00:30:35.440 ⇒ 00:30:37.989 Uttam Kumaran: They should be driving more money, you know?
383 00:30:37.990 ⇒ 00:30:45.790 Caitlyn Vaughn: Yeah, like, usage and size. That’s also another good point. I mean, we do have, like, what…
384 00:30:45.900 ⇒ 00:30:50.960 Caitlyn Vaughn: we do have, like, prices, right? Like, it should be per seat pricing.
385 00:30:51.140 ⇒ 00:30:55.850 Caitlyn Vaughn: And there’s, like, some kind of, like, base platforms, see price.
386 00:30:56.720 ⇒ 00:31:00.370 Uttam Kumaran: Is there a platform… is there a platform price, or is it all per seat?
387 00:31:00.770 ⇒ 00:31:03.629 Caitlyn Vaughn: There’s a platform price and per seat.
388 00:31:04.550 ⇒ 00:31:07.909 Uttam Kumaran: Like, 200 bucks plus, like, something incremental, or something like that.
389 00:31:07.910 ⇒ 00:31:17.659 Caitlyn Vaughn: It’s, like, 8K annual, plus, like, it’s, like, 25 for scheduling or something, but I’ll get you, like, the exact numbers on it.
390 00:31:17.660 ⇒ 00:31:20.939 Uttam Kumaran: Yeah, if you can get me that, because then I’ll break… I’ll break that out.
391 00:31:21.110 ⇒ 00:31:24.969 Uttam Kumaran: And then we could truly see the per-seat cost, and then you could basically go.
392 00:31:25.360 ⇒ 00:31:28.999 Uttam Kumaran: You’ll see, like, who’s just… you should go probably fix billing on.
393 00:31:29.390 ⇒ 00:31:39.929 Caitlyn Vaughn: Yeah, I think we have that going, but what would be interesting would be to see, like, who is on average getting, like, the highest discounts? Like, what is our discount per segment, like…
394 00:31:40.260 ⇒ 00:31:45.570 Caitlyn Vaughn: who… how much should each company be paying? How much are they actually paying? Like, what’s the delta?
395 00:31:45.780 ⇒ 00:31:48.859 Uttam Kumaran: Yeah, exactly, so if you can give me the standard rates…
396 00:31:49.020 ⇒ 00:31:49.490 Caitlyn Vaughn: Huh.
397 00:31:49.490 ⇒ 00:31:52.219 Uttam Kumaran: then I will basically… can tell you, like, what the…
398 00:31:52.690 ⇒ 00:31:54.489 Uttam Kumaran: I’ll take out the fixed costs.
399 00:31:54.670 ⇒ 00:31:57.220 Uttam Kumaran: And then I can tell you, like, okay, who… who’s…
400 00:31:58.480 ⇒ 00:32:04.559 Uttam Kumaran: Yeah, I guess I’ll have to look at the data, because some people, if you… I don’t know how the discounting works. Like, are you… are the discounts on, like.
401 00:32:04.800 ⇒ 00:32:10.190 Uttam Kumaran: Are you also doing discounts on the fixed cost, or is it always on, like, the seats.
402 00:32:11.190 ⇒ 00:32:13.300 Uttam Kumaran: Or is it, like, willy-nilly?
403 00:32:14.350 ⇒ 00:32:17.110 Caitlyn Vaughn: I think it’s, like…
404 00:32:18.650 ⇒ 00:32:23.969 Uttam Kumaran: The question is, would you… do you ever discount the default platform and then the additional 2,500 features?
405 00:32:25.810 ⇒ 00:32:27.560 Uttam Kumaran: Because that’s not in the Hyperline.
406 00:32:28.030 ⇒ 00:32:28.880 Caitlyn Vaughn: Yeah.
407 00:32:28.880 ⇒ 00:32:29.750 Uttam Kumaran: right now.
408 00:32:30.100 ⇒ 00:32:33.889 Caitlyn Vaughn: Alright, well, I’m asking sales right now. I’ll let you know. Okay.
409 00:32:33.890 ⇒ 00:32:37.160 Uttam Kumaran: Okay. Yeah, if you can get me the standard, then at least, because…
410 00:32:37.520 ⇒ 00:32:46.759 Uttam Kumaran: I’ll show… like, we could do whatever you get me, and then for the ones that turn out negative, you’ll… we’ll realize that we… something… there’s some discount.
411 00:32:47.070 ⇒ 00:32:53.269 Caitlyn Vaughn: Okay, I have it, I’m gonna send it to you. I’ll send it to the Brainforge group.
412 00:32:54.620 ⇒ 00:32:56.140 Caitlyn Vaughn: Brain Forge…
413 00:33:00.820 ⇒ 00:33:05.620 Caitlyn Vaughn: Okay, I just sent it in. Growth plan 6K, scale is 20K.
414 00:33:06.150 ⇒ 00:33:07.959 Caitlyn Vaughn: C is 45.
415 00:33:07.960 ⇒ 00:33:09.830 Uttam Kumaran: Okay. Per month per user.
416 00:33:10.840 ⇒ 00:33:13.419 Caitlyn Vaughn: What is the seat, though? Pretty sure…
417 00:33:14.460 ⇒ 00:33:16.120 Caitlyn Vaughn: Let me do some more digging.
418 00:33:16.770 ⇒ 00:33:23.319 Uttam Kumaran: Okay, just… yeah, okay. Well, basically, ideally, what I was gonna do was just… I was gonna look at…
419 00:33:24.150 ⇒ 00:33:26.149 Uttam Kumaran: Who is growth, who is scale?
420 00:33:26.360 ⇒ 00:33:27.410 Uttam Kumaran: take out…
421 00:33:27.720 ⇒ 00:33:34.550 Uttam Kumaran: the… that out of the ARR to basically get you the… and then I can back into the number of seats they should be using.
422 00:33:35.140 ⇒ 00:33:39.069 Uttam Kumaran: and the price per sheet at 45, then what I’ll do is…
423 00:33:39.190 ⇒ 00:33:41.729 Uttam Kumaran: I will then look at the actual seats they have.
424 00:33:41.990 ⇒ 00:33:46.850 Uttam Kumaran: And then look at, like, okay, what are they actually paying per seat? And you should start to see the ones where
425 00:33:47.020 ⇒ 00:33:51.700 Uttam Kumaran: That’ll indicate either we gave them a per-seat discount, or we’re just…
426 00:33:51.950 ⇒ 00:33:54.189 Uttam Kumaran: Not billing them for these additional seats.
427 00:33:54.410 ⇒ 00:34:04.309 Uttam Kumaran: Right, so then we can start to dig into, like, how our discounts are given. But maybe that’s a good question, like, you can ask if discounts were given on the plan, or on the seats, or on both.
428 00:34:05.560 ⇒ 00:34:12.379 Uttam Kumaran: Not a rush, though, because the data will show, where it doesn’t match.
429 00:34:19.489 ⇒ 00:34:22.619 Caitlyn Vaughn: Okay, I wonder if I can pull from Paperline.
430 00:34:24.349 ⇒ 00:34:25.579 Caitlyn Vaughn: descriptions.
431 00:34:26.719 ⇒ 00:34:35.689 Uttam Kumaran: Yeah, the subscriptions would be good. And ideally, if there are discounts there, that would… that would be best. And these are fairly whole numbers, so…
432 00:34:36.349 ⇒ 00:34:40.249 Uttam Kumaran: It should be hard for me to back in as long as we even have the discount amount, but…
433 00:34:42.159 ⇒ 00:34:47.669 Caitlyn Vaughn: But, like, why the fuck is there 200? I don’t know. 183? I don’t even look like we.
434 00:34:47.670 ⇒ 00:34:49.409 Uttam Kumaran: Click on… click on one of them.
435 00:34:50.020 ⇒ 00:34:53.539 Uttam Kumaran: Is Kit, can you even click… is there anything to click on? No, I don’t want it. Alright.
436 00:34:55.670 ⇒ 00:34:57.960 Uttam Kumaran: Yeah… okay, oh, here.
437 00:34:58.550 ⇒ 00:35:01.429 Uttam Kumaran: So, see, for this one, this is a 100% discount, well…
438 00:35:02.600 ⇒ 00:35:06.719 Uttam Kumaran: Right, this is 5 seats at 100… oh, V2 routing, 15 seats.
439 00:35:06.910 ⇒ 00:35:09.629 Uttam Kumaran: 5 platform seats, Workflow Studio.
440 00:35:12.930 ⇒ 00:35:20.300 Caitlyn Vaughn: So… Studio is our, like, Premier tier.
441 00:35:20.580 ⇒ 00:35:21.380 Caitlyn Vaughn: Terrific.
442 00:35:23.110 ⇒ 00:35:25.149 Uttam Kumaran: But then, what is scale?
443 00:35:26.420 ⇒ 00:35:27.949 Uttam Kumaran: That scales above that.
444 00:35:30.520 ⇒ 00:35:31.380 Caitlyn Vaughn: Or is that?
445 00:35:33.940 ⇒ 00:35:38.940 Caitlyn Vaughn: Oh yeah, we have, like, growth and scale, but we used to call it platform and studio.
446 00:35:39.110 ⇒ 00:35:42.139 Caitlyn Vaughn: So, I think there’s… Oh, okay, okay. There’s, like, some…
447 00:35:42.340 ⇒ 00:35:47.040 Uttam Kumaran: So it looks like with the platform or growth, you get 5 free seats?
448 00:35:47.190 ⇒ 00:35:49.979 Caitlyn Vaughn: Yeah, it comes with 5 seats.
449 00:35:50.650 ⇒ 00:35:53.009 Uttam Kumaran: Okay, I’m just doing it last time.
450 00:35:56.150 ⇒ 00:36:01.300 Uttam Kumaran: And then it looks like for this case, they got 15 seats at 6K a year.
451 00:36:02.000 ⇒ 00:36:05.840 Uttam Kumaran: So, what is that?
452 00:36:05.840 ⇒ 00:36:07.870 Caitlyn Vaughn: It should be 45 bucks a seat.
453 00:36:07.870 ⇒ 00:36:09.960 Uttam Kumaran: Michael, this is 33 bucks a seat.
454 00:36:10.150 ⇒ 00:36:10.680 Uttam Kumaran: Per month.
455 00:36:10.680 ⇒ 00:36:11.260 Caitlyn Vaughn: Okay.
456 00:36:11.380 ⇒ 00:36:13.960 Caitlyn Vaughn: So there’s, like, $11 per seat discount.
457 00:36:13.960 ⇒ 00:36:15.100 Uttam Kumaran: potentially a discount.
458 00:36:16.750 ⇒ 00:36:20.980 Uttam Kumaran: So basically, that was… that’s the math I would… that’s the math I’ll do, so you’ll see…
459 00:36:21.210 ⇒ 00:36:23.860 Uttam Kumaran: So we’ll see, hey, these guys are getting…
460 00:36:24.180 ⇒ 00:36:33.839 Uttam Kumaran: Well, I guess the one thing I don’t have in the Hyperline data is I don’t… I don’t know how much… how many seats they’ve been allotted. Like, on… like, I don’t know this 15,
461 00:36:34.090 ⇒ 00:36:36.549 Uttam Kumaran: I will, like, for example, if they have 20 here, then…
462 00:36:37.310 ⇒ 00:36:43.539 Uttam Kumaran: It’s gonna look like a really bad perk seat, so… is there any way to get me this, like, subscription?
463 00:36:44.530 ⇒ 00:36:45.550 Uttam Kumaran: Data?
464 00:36:45.950 ⇒ 00:36:47.469 Caitlyn Vaughn: I wonder if we can just…
465 00:36:51.810 ⇒ 00:36:53.540 Caitlyn Vaughn: Oh, you already have it.
466 00:36:54.120 ⇒ 00:36:57.089 Uttam Kumaran: Oh, okay, then I’ll just come in and try to export the subscription data.
467 00:36:57.390 ⇒ 00:36:59.369 Caitlyn Vaughn: Yeah, yeah, you’re already in here.
468 00:36:59.370 ⇒ 00:37:00.020 Uttam Kumaran: Okay.
469 00:37:00.500 ⇒ 00:37:06.149 Uttam Kumaran: Yeah, because I just want to see what… and then if I can get the subscription details, then it’s, like, way easier for me to just tell you.
470 00:37:07.660 ⇒ 00:37:11.330 Caitlyn Vaughn: Yeah, it’s hard, there’s not, like, an easy way to export it, I think.
471 00:37:13.320 ⇒ 00:37:14.480 Uttam Kumaran: No, that’s fine, that’s…
472 00:37:14.480 ⇒ 00:37:15.650 Caitlyn Vaughn: Maybe you can figure it out.
473 00:37:15.650 ⇒ 00:37:16.170 Uttam Kumaran: Okay.
474 00:37:16.970 ⇒ 00:37:18.879 Caitlyn Vaughn: How do you like it versus Stripe?
475 00:37:21.890 ⇒ 00:37:23.849 Uttam Kumaran: Or, like, why was the decision on Hyperline?
476 00:37:25.570 ⇒ 00:37:27.790 Caitlyn Vaughn: Why are we not using Stripe? I think…
477 00:37:28.630 ⇒ 00:37:34.940 Caitlyn Vaughn: I forget why. We were using… we… this is our, like, third or fourth CPQ product in, like, a year.
478 00:37:35.580 ⇒ 00:37:36.590 Uttam Kumaran: Oh, okay.
479 00:37:36.780 ⇒ 00:37:41.040 Caitlyn Vaughn: That’s why it’s such a disaster, because we were on Databricks, And then we left.
480 00:37:41.430 ⇒ 00:37:42.980 Caitlyn Vaughn: We went to…
481 00:37:44.310 ⇒ 00:37:52.199 Caitlyn Vaughn: something else, and then that other one closed, and then we went to sequence, and then sequenced Alert Data, so then we went to Hyperline.
482 00:37:52.740 ⇒ 00:37:54.270 Uttam Kumaran: That’s right.
483 00:37:55.280 ⇒ 00:37:57.060 Uttam Kumaran: Dude, you should see those guys.
484 00:37:57.670 ⇒ 00:38:03.929 Caitlyn Vaughn: Yeah, it’s been kind of a shitshow, to be honest. But it’s, like, every time we move over, all the data gets kind of fucked up, and we have to, like.
485 00:38:03.930 ⇒ 00:38:04.260 Uttam Kumaran: Okay.
486 00:38:04.260 ⇒ 00:38:14.500 Caitlyn Vaughn: Yeah. And so, after 4 moves, it’s like, we have no source of truth. Bryn’s like, what is going on? How many, like, really, did we hit 2 million in ARR? I don’t know, maybe.
487 00:38:14.500 ⇒ 00:38:20.130 Uttam Kumaran: That’s the thing, that’s what I’ll hope… I want to show you guys what it is, and then ideally, at least in this exercise.
488 00:38:20.310 ⇒ 00:38:24.760 Uttam Kumaran: You could go tell sales that, hey, we need to go adjust, we’re paying for all these seats.
489 00:38:24.760 ⇒ 00:38:26.929 Caitlyn Vaughn: Of course, we’re not charging for all these seeds.
490 00:38:27.140 ⇒ 00:38:28.420 Caitlyn Vaughn: Is there something…
491 00:38:28.420 ⇒ 00:38:32.790 Uttam Kumaran: At least you can do that now through this system on, like, a monthly basis or something, you know?
492 00:38:32.790 ⇒ 00:38:35.479 Caitlyn Vaughn: Yeah, goddamn.
493 00:38:36.200 ⇒ 00:38:38.919 Uttam Kumaran: You guys are probably sitting on some good ARR.
494 00:38:39.200 ⇒ 00:38:42.879 Caitlyn Vaughn: Yeah, I think we have, like, 300K in unselected ARR.
495 00:38:44.470 ⇒ 00:38:45.620 Uttam Kumaran: Yeah.
496 00:38:47.080 ⇒ 00:38:51.299 Uttam Kumaran: Okay, cool, so we’ll get you this by next week, and then…
497 00:38:51.940 ⇒ 00:38:54.550 Uttam Kumaran: We’ll try to hit… we’ll hit the hour-lower deadline, for sure.
498 00:38:54.820 ⇒ 00:38:56.890 Caitlyn Vaughn: Okay, cool. Thank you guys so much.
499 00:38:56.890 ⇒ 00:38:58.230 Uttam Kumaran: Okay, thanks, Caitlin.
500 00:38:58.450 ⇒ 00:38:59.210 Caitlyn Vaughn: Bye.
501 00:38:59.510 ⇒ 00:39:00.060 Uttam Kumaran: Right.