Meeting Title: Brainforge x Default Weekly Sync Date: 2026-04-02 Meeting participants: Scratchpad Notetaker, Greg Stoutenburg, Demilade Agboola, Nandika Jhunjhunwala, Caitlyn Vaughn, Lev Katreczko, Uttam Kumaran
WEBVTT
1 00:01:26.430 ⇒ 00:01:26.930 Nandika Jhunjhunwala: Oh…
2 00:01:26.930 ⇒ 00:01:28.630 Greg Stoutenburg: Good to see you again.
3 00:01:28.630 ⇒ 00:01:29.640 Nandika Jhunjhunwala: Yeah, you too.
4 00:01:32.500 ⇒ 00:01:36.450 Greg Stoutenburg: Who’s the, car person at default, who seems to have decorated the wall behind you?
5 00:01:36.450 ⇒ 00:01:44.369 Nandika Jhunjhunwala: Yeah, I think it’s Nico. I think Nico and Victor had, like, an F1 racing gig, or, like, they started a company around it.
6 00:01:44.550 ⇒ 00:01:44.960 Greg Stoutenburg: Yeah.
7 00:01:44.960 ⇒ 00:01:51.569 Nandika Jhunjhunwala: Pretty default, so we have a meeting room with, kind of.
8 00:01:51.570 ⇒ 00:01:52.080 Greg Stoutenburg: Nice.
9 00:01:52.080 ⇒ 00:01:55.469 Nandika Jhunjhunwala: Of course, that’s the extent of my knowledge here.
10 00:01:55.470 ⇒ 00:02:00.960 Greg Stoutenburg: Yeah, that’d be pretty sweet. You run a company, so you just get to go, like, well, I’m in charge of decoration, I’m gonna put up stuff I like.
11 00:02:00.960 ⇒ 00:02:02.600 Nandika Jhunjhunwala: Yeah, yeah.
12 00:02:03.880 ⇒ 00:02:05.940 Greg Stoutenburg: Salesforce logos? No. Porsche.
13 00:02:05.940 ⇒ 00:02:08.360 Nandika Jhunjhunwala: Yeah.
14 00:02:10.009 ⇒ 00:02:11.099 Caitlyn Vaughn: Hi, guys!
15 00:02:11.310 ⇒ 00:02:12.389 Greg Stoutenburg: Hey, how’s it going?
16 00:02:13.210 ⇒ 00:02:15.499 Caitlyn Vaughn: It is good, how are you?
17 00:02:15.500 ⇒ 00:02:16.800 Greg Stoutenburg: Yeah, I’m good.
18 00:02:16.940 ⇒ 00:02:19.739 Greg Stoutenburg: I’m good. Lots of default action this week.
19 00:02:19.970 ⇒ 00:02:20.790 Caitlyn Vaughn: Is there?
20 00:02:20.790 ⇒ 00:02:22.110 Greg Stoutenburg: Reviewing dashes, you know?
21 00:02:22.110 ⇒ 00:02:22.850 Caitlyn Vaughn: Oh, yeah.
22 00:02:22.850 ⇒ 00:02:23.440 Greg Stoutenburg: forwards.
23 00:02:23.440 ⇒ 00:02:24.150 Caitlyn Vaughn: Yo!
24 00:02:24.150 ⇒ 00:02:45.539 Greg Stoutenburg: Making tickets? Good! It was good, yeah. Demi sort of gave a walkthrough of the CS Dash, and we had, I mean, we just had a nice conversation about, like, what’s there that’s gonna be useful for CSMs talking to customers and getting those renewals, and, like, some additional things that would be helpful to add so they have that additional context to
25 00:02:45.710 ⇒ 00:02:51.139 Greg Stoutenburg: yeah, you know, show the value that Defaults had for their company, so… Yeah, it was good.
26 00:02:51.440 ⇒ 00:02:53.069 Caitlyn Vaughn: Good, I’m glad you’re here.
27 00:02:53.070 ⇒ 00:02:53.840 Greg Stoutenburg: Yeah.
28 00:02:55.280 ⇒ 00:02:57.960 Greg Stoutenburg: I think that…
29 00:02:58.190 ⇒ 00:03:14.659 Greg Stoutenburg: I think Utam’s gonna be here, yeah. But let’s just… let’s get started. Alright, I’ll just jump right in, and I wanted to… I’ll try to get through the deck sort of quickly, like, you know, like I said a couple of weeks ago, as we try to use this to be more of a working session.
30 00:03:14.880 ⇒ 00:03:23.439 Greg Stoutenburg: And so, I’ll get through the deck, and then we’ll… chat. So, share button… Desktop… here we go.
31 00:03:25.510 ⇒ 00:03:32.909 Greg Stoutenburg: Alright, cool. Summary stuff. The RR sync with Ryan and Laura… that was, what, Demi? Tuesday? I don’t know, this week’s such a blur.
32 00:03:33.000 ⇒ 00:03:38.649 Greg Stoutenburg: Ryan added some context to the gaps, and we have a clearer picture of what we need to
33 00:03:38.650 ⇒ 00:04:03.289 Greg Stoutenburg: do there, like I said, the customer reporting enablement dashboard, that went well. For both of those dashboards, and this is something we’re going to do broadly for the reporting work that we’re doing for you all, is provide more clarity in the dashboards themselves on what the metrics are that are relevant, how we’re defining those metrics, so that you have that context, and it’s just easier to understand what it is you’re digesting when you come in. So we’re going to try to continue to make it more user-friendly.
34 00:04:03.290 ⇒ 00:04:13.230 Greg Stoutenburg: as we do things like train up Lobby to be more accurate, the goal being that when you interact with your data in Omni, it’s just really clear what you’re looking at, and you get insights that you can trust and use.
35 00:04:14.010 ⇒ 00:04:21.930 Greg Stoutenburg: Hyperline data is now in Mother Duck, ready to use. For product analytics, Nanika, I know we talked about this very briefly last week, just touched on this.
36 00:04:21.930 ⇒ 00:04:32.800 Greg Stoutenburg: I took the initial step of creating a user activation dashboard and just moved in your charts that are relevant to that, and then scoped out the things that still need to be added to it that we can create, and we’ll just start
37 00:04:32.800 ⇒ 00:04:45.260 Greg Stoutenburg: we’ll start doing that work and updating each other as we go. But I think we’re in really good shape, actually, to have everything that we want tracked, tracked in post-hog by the time Phoenix goes up. So, I’m… I’m feeling really good about our progress there.
38 00:04:46.000 ⇒ 00:04:55.720 Greg Stoutenburg: On the data side, close out the remaining customer reporting and enablement metrics, you know, based on that conversation today, and then moving on to the… these other dashboards.
39 00:04:56.820 ⇒ 00:04:59.490 Greg Stoutenburg: Demi, do you want to speak to these?
40 00:04:59.910 ⇒ 00:05:01.890 Greg Stoutenburg: Or is this more or less self-explanatory?
41 00:05:02.490 ⇒ 00:05:10.210 Demilade Agboola: I mean, I can speak to it. I think it kind of is, but, like, yeah, so we had our sync with, Deanna and Laura this week.
42 00:05:10.380 ⇒ 00:05:14.939 Demilade Agboola: And basically… actually, no, that should be Lauren.
43 00:05:15.070 ⇒ 00:05:23.830 Demilade Agboola: But yes, but it’s the charts for Diana, and the concept is we’ve been able to put out the… get a fresh review, understand what’s missing.
44 00:05:24.140 ⇒ 00:05:34.659 Demilade Agboola: Understand what else she needs, and, like, the modifications we need to make, so that it’s useful in the first capacity for, like, internal use, as well as external use.
45 00:05:34.930 ⇒ 00:05:41.399 Demilade Agboola: In terms of Hyperline, we’ve been able to get the connection set up this week,
46 00:05:41.550 ⇒ 00:05:52.600 Demilade Agboola: So we ingested the raw Hyperline data on Monday. We’ve already started utilizing it in some of our models, and we’ll utilize it for some more, because I know…
47 00:05:52.900 ⇒ 00:05:57.450 Demilade Agboola: based off our call with Ryan, he said, in some cases, it’s better to use
48 00:05:57.580 ⇒ 00:06:01.859 Demilade Agboola: the Hyperline data instead of, like, what is directly in Salesforce.
49 00:06:02.090 ⇒ 00:06:05.099 Demilade Agboola: So, we will make those adjustments there as well.
50 00:06:05.480 ⇒ 00:06:14.659 Demilade Agboola: And… which leads us nicely to the ARL-Sync, and so basically, yeah, we had those numbers reviewed with Ryan and Laura, and we’ve just basically been able to build trust
51 00:06:14.910 ⇒ 00:06:19.809 Demilade Agboola: And hopefully, we can just drive adoption and usability in the dashboard.
52 00:06:20.910 ⇒ 00:06:22.499 Greg Stoutenburg: Cool, yeah, thanks, Emmy.
53 00:06:22.870 ⇒ 00:06:35.249 Greg Stoutenburg: There’s the dashboard as it looks now. Laura signed off, and so anything left to do to the dashboard now is just sort of, you know, brushing up some things, improvements, things like that. And so, you know, we’re happy to notch that.
54 00:06:35.550 ⇒ 00:06:43.840 Greg Stoutenburg: Like I said before, the headline on product analytics is dashboard development is in sync… is on time to be in sync with Phoenix launch.
55 00:06:43.860 ⇒ 00:07:06.060 Greg Stoutenburg: I’ve got those, got the dashboard for user activation up and ready to go, and then next week we’ll follow on very quickly with user retention as well, because a lot of that, you know, thanks to Nautica’s work, a lot of those charts are built already, and so what we’re doing is more tweaking, organizing, finding the gaps, those things that we don’t currently have dashboards, sorry, charts for, and add those to the dashboards, and just keep moving forward.
56 00:07:07.410 ⇒ 00:07:12.700 Greg Stoutenburg: In progress, polishing up the customer reporting and enablement dashboard, as noted.
57 00:07:13.210 ⇒ 00:07:21.450 Greg Stoutenburg: And then, yeah, you know, sign off on the AR dashboard. We’ve got that from…
58 00:07:21.840 ⇒ 00:07:28.160 Greg Stoutenburg: Sorry, we got that from earlier this week. My kids are off school, so I think there was some kind of fight with video games in the other room.
59 00:07:28.710 ⇒ 00:07:35.809 Greg Stoutenburg: That I’m monitoring from here. I have the door closed, but that doesn’t mean that I can’t hear the shouting. And now we’ve got a little bit of sulking.
60 00:07:36.650 ⇒ 00:07:44.180 Greg Stoutenburg: Okay. All right. And then, yeah, reconciling that Salesforce and Omni, data.
61 00:07:44.720 ⇒ 00:07:45.690 Greg Stoutenburg: Okay.
62 00:07:46.210 ⇒ 00:08:01.840 Greg Stoutenburg: So, that’s what we did this week, and wanted to open to discussion now. And, so these are things that were on my mind that I thought we might chat about. If there’s anything that you have, of course, you know, raised that as well. But, you know, broad question, how’s Blobby going for you?
63 00:08:02.170 ⇒ 00:08:08.690 Greg Stoutenburg: Do you feel like it’s speeding you up? The goal, again, is, like, self-service analytics. Do you feel like that’s happening so far?
64 00:08:09.210 ⇒ 00:08:16.540 Caitlyn Vaughn: Did you guys check… sorry, I haven’t checked our channel. Did you check that data that I had sent over for accuracy, or no?
65 00:08:16.910 ⇒ 00:08:23.130 Greg Stoutenburg: I know that we had started work on it, was this the one about…
66 00:08:23.230 ⇒ 00:08:26.089 Greg Stoutenburg: the… the ARR calculation?
67 00:08:26.090 ⇒ 00:08:32.539 Caitlyn Vaughn: No, this is the one that I sent yesterday around the… Enrichment? Enrichment usage, yeah.
68 00:08:32.549 ⇒ 00:08:33.199 Greg Stoutenburg: Oh, right.
69 00:08:34.000 ⇒ 00:08:37.610 Demilade Agboola: So yes, in terms of enrichment usage,
70 00:08:37.890 ⇒ 00:08:43.649 Demilade Agboola: Basically, we are trying to, like, double-check everything.
71 00:08:43.880 ⇒ 00:08:54.770 Demilade Agboola: And so we’ve been able to, like, just get the list of all enrichments, the utilization of it. The numbers we’re getting are very similar, we’re trying to find out, like, where the delta comes from.
72 00:08:55.130 ⇒ 00:08:58.390 Demilade Agboola: Potentially, we might also need to define, like.
73 00:08:58.800 ⇒ 00:09:04.820 Demilade Agboola: What enrich… like, is, like, is every single enrichment valid.
74 00:09:05.330 ⇒ 00:09:06.160 Caitlyn Vaughn: Like, if…
75 00:09:06.160 ⇒ 00:09:17.080 Demilade Agboola: Like, there are certain things we might need to filter out, but yeah, we’ll get that back to you today, tomorrow, and just let you know, like, what the numbers are, like, when we do our, like, queries itself.
76 00:09:17.820 ⇒ 00:09:22.809 Demilade Agboola: And what those disparities, like, why a blobby did what it did, and what those disparities are.
77 00:09:23.150 ⇒ 00:09:39.430 Caitlyn Vaughn: Yeah, I’m feeling like Blobby is, like, I’m starting to get why it’s valuable, and I’m starting to use it more, as I assume our whole team will start using it more, like, once we launch. I think I don’t have enough data back yet, in general, on, like, the queries that I’ve given.
78 00:09:39.430 ⇒ 00:09:44.779 Caitlyn Vaughn: for if the data is, like, accurate or not. Like, the first one I sent was the ARR stuff.
79 00:09:44.890 ⇒ 00:09:50.300 Caitlyn Vaughn: And then you guys clean that up on your end. I think it’s, like, in the beginning, just a process of
80 00:09:50.620 ⇒ 00:10:04.110 Caitlyn Vaughn: you know, us querying, and then getting wrong results, and then adjusting the results to figure out, like, where the data’s off. So I am getting value out of it, but not without you guys right now.
81 00:10:04.400 ⇒ 00:10:12.410 Greg Stoutenburg: Yeah, yeah, yeah. No, and that’s great, and that’s to be expected as well. So, for any live source that’s connected to Omni,
82 00:10:12.730 ⇒ 00:10:29.490 Greg Stoutenburg: If you’ve run a query using Blobby, or however, and you get data back that looks like it’s wrong, that’s gonna be something that is on the Omni side of things, and so we’ll resolve that by adjusting the topics that Blobby is using, by training Blobby specifically to look at
83 00:10:29.490 ⇒ 00:10:53.029 Greg Stoutenburg: to provide certain types of responses instead of others. The AI itself, they use a mix of Claude models, and then, you know, if the data coming in is clean, then it’s… then it’s, you know, it’s on the Brainforge team, based on feedback that you provide, you know, to make sure that it’s actually delivering the responses that are the relevant ones and not something else. So, something that we haven’t seen is, like, hallucination.
84 00:10:53.030 ⇒ 00:10:54.970 Greg Stoutenburg: What we have seen is
85 00:10:54.970 ⇒ 00:11:14.730 Greg Stoutenburg: calculations that are wrong, given the goal that Bobby’s been given. So, that’s where it’s, like, kind of on us to, like, you know, train this… train this little baby analyst to, you know, focus on the right things, and… and give good responses, yeah. So, yeah, please keep that coming. And, you know, just since you… since you asked about, like.
86 00:11:15.080 ⇒ 00:11:18.069 Greg Stoutenburg: you know, did we work on this? I think maybe…
87 00:11:18.080 ⇒ 00:11:36.700 Greg Stoutenburg: you know, consider, like, I don’t know, like, an unofficial SLA or something like that. Anytime you ask a question about Blobby, I’ll make sure that we, you know, we tell you, you know, hey, we’re working on it, and make sure to provide that update really explicitly. I remember getting this and ticketing it right away, like, you know, hey, let’s look into this. So we just need to make sure that we’re getting back to you on that.
88 00:11:37.010 ⇒ 00:11:39.659 Caitlyn Vaughn: Yeah, I’m not super concerned about, like.
89 00:11:39.660 ⇒ 00:11:40.180 Greg Stoutenburg: Yeah.
90 00:11:40.180 ⇒ 00:11:56.640 Caitlyn Vaughn: 24 hours versus 72 is fine. What could be interesting, I don’t know, Nandica, if this is, like, this would be interesting to you, but as you guys are adjusting Blobby and the, like, figuring out, you know, where it needs to be tweaked.
91 00:11:56.640 ⇒ 00:12:01.000 Caitlyn Vaughn: potentially having Nandica in some of those conversations, and, like.
92 00:12:01.000 ⇒ 00:12:01.320 Greg Stoutenburg: Yeah.
93 00:12:01.320 ⇒ 00:12:15.009 Caitlyn Vaughn: to understand, like, how we make tweaks would be a great, like, she’d be a great internal person for us. Great. Eventually, when we, like, don’t have you anymore, and then we’re just, like, sitting around, having no idea what to do.
94 00:12:15.160 ⇒ 00:12:16.070 Greg Stoutenburg: Yeah. Come on.
95 00:12:16.070 ⇒ 00:12:18.809 Caitlyn Vaughn: person internally that, like, knows what’s going on.
96 00:12:18.810 ⇒ 00:12:23.610 Greg Stoutenburg: Yeah, yeah, yeah. No, that makes a lot of sense. And, yeah, we can…
97 00:12:24.320 ⇒ 00:12:29.990 Greg Stoutenburg: we’ll get you in those conversations, and we’ll, nonika, you can be, as well, you know, the voice of…
98 00:12:29.990 ⇒ 00:12:48.970 Greg Stoutenburg: default on what kinds of questions, what kinds of answers are going to be most valuable. When we think about… I like to think about stakeholder questions that are going to be sort of, you know, core questions that someone at default would want to answer as, like, acceptance criteria for how effective Blobby is. So, you know, Nandica can inform us on the
99 00:12:49.590 ⇒ 00:12:57.289 Greg Stoutenburg: well, what sorts of things you’d want to ask regularly to make sure that it’s delivering those answers. So, yeah, that’s great, we’ll do that.
100 00:12:57.430 ⇒ 00:12:58.340 Caitlyn Vaughn: Okay, great.
101 00:12:58.540 ⇒ 00:13:09.680 Demilade Agboola: Also, just to chip in on the blobby, QAing path, I will suggest that if you have any, like, blobby questions that you ask.
102 00:13:11.100 ⇒ 00:13:18.380 Demilade Agboola: you’re sending over the response, like, the answer and the data. It’ll also be helpful if you send the blobby chat.
103 00:13:18.380 ⇒ 00:13:18.980 Caitlyn Vaughn: the coordinates.
104 00:13:18.980 ⇒ 00:13:19.370 Demilade Agboola: Okay.
105 00:13:19.370 ⇒ 00:13:19.720 Caitlyn Vaughn: Yeah.
106 00:13:19.720 ⇒ 00:13:24.429 Demilade Agboola: Yeah, so… so we can also look through, like, how Blobby thought through.
107 00:13:24.430 ⇒ 00:13:25.220 Caitlyn Vaughn: Hmm…
108 00:13:25.220 ⇒ 00:13:26.190 Demilade Agboola: the solution.
109 00:13:26.320 ⇒ 00:13:31.849 Demilade Agboola: And potentially add either more context that would be appropriate, or we can just,
110 00:13:32.600 ⇒ 00:13:36.580 Demilade Agboola: Yeah, just basically add more context as appropriate to the solution, and use that to…
111 00:13:36.710 ⇒ 00:13:39.550 Demilade Agboola: Get blobby much closer and more accurate, like…
112 00:13:42.010 ⇒ 00:13:45.799 Caitlyn Vaughn: Okay, do I just send you the whole URL? I mean, how do I send this whole chat to you?
113 00:13:45.800 ⇒ 00:13:51.160 Demilade Agboola: Oh, yeah, just the URL is fine, so once you send the URL, it’ll link us to the conversation on the blobby.
114 00:13:51.160 ⇒ 00:13:54.780 Caitlyn Vaughn: Oh, awesome. Okay, I just sent it in the Slack channel.
115 00:13:54.880 ⇒ 00:13:55.380 Greg Stoutenburg: Cool.
116 00:13:55.380 ⇒ 00:13:56.280 Demilade Agboola: Thank you.
117 00:13:56.960 ⇒ 00:14:00.710 Greg Stoutenburg: Let me just check real quick, because this just came up.
118 00:14:01.280 ⇒ 00:14:07.089 Greg Stoutenburg: I was gonna show you anyway your adoption stats in Omni. You can see…
119 00:14:07.460 ⇒ 00:14:12.390 Greg Stoutenburg: Who’s using, how many queries they’ve run, token usage, things like that.
120 00:14:13.320 ⇒ 00:14:14.350 Greg Stoutenburg: Come on.
121 00:14:15.450 ⇒ 00:14:22.329 Greg Stoutenburg: It’s cloudy and rainy out, so I think Omni’s feeling sleepy when accessed from York, Pennsylvania.
122 00:14:22.930 ⇒ 00:14:25.399 Caitlyn Vaughn: So sad if nobody pops up.
123 00:14:25.820 ⇒ 00:14:27.610 Greg Stoutenburg: Oh, no, there’s a lot, I mean, yeah.
124 00:14:27.610 ⇒ 00:14:28.240 Caitlyn Vaughn: Yeah.
125 00:14:28.610 ⇒ 00:14:31.690 Greg Stoutenburg: Hey, Caitlin, there’s you!
126 00:14:31.690 ⇒ 00:14:32.730 Caitlyn Vaughn: Yo, let’s go!
127 00:14:32.730 ⇒ 00:14:33.570 Nandika Jhunjhunwala: Yeah, check it out.
128 00:14:33.570 ⇒ 00:14:36.610 Greg Stoutenburg: running that leaderboard!
129 00:14:38.210 ⇒ 00:14:39.160 Greg Stoutenburg: Lev.
130 00:14:39.470 ⇒ 00:14:40.800 Greg Stoutenburg: What’s going on, man?
131 00:14:40.800 ⇒ 00:14:42.130 Caitlyn Vaughn: Love getting there.
132 00:14:43.510 ⇒ 00:14:46.159 Lev Katreczko: Sorry, guys. Gotta get on Blobby.
133 00:14:47.450 ⇒ 00:14:47.800 Greg Stoutenburg: I’m missing.
134 00:14:47.800 ⇒ 00:14:50.540 Caitlyn Vaughn: Are you Claude Code told yet, Lev?
135 00:14:51.100 ⇒ 00:14:54.980 Lev Katreczko: I’m actually double-tasking here with two clods open.
136 00:14:54.980 ⇒ 00:14:55.520 Caitlyn Vaughn: You are?
137 00:14:55.520 ⇒ 00:14:56.580 Lev Katreczko: So…
138 00:14:56.580 ⇒ 00:14:58.439 Caitlyn Vaughn: Cloud Code Work or Cloud Code?
139 00:14:58.740 ⇒ 00:14:59.740 Lev Katreczko: All good.
140 00:14:59.740 ⇒ 00:15:06.250 Caitlyn Vaughn: Okay, well, you can use Omni’s MPC to pull data, so… get on it.
141 00:15:07.090 ⇒ 00:15:08.210 Lev Katreczko: Will do. You can just.
142 00:15:08.210 ⇒ 00:15:12.970 Greg Stoutenburg: Just run an automation to have a query run every 10 minutes, and just blow this leaderboard out of the water.
143 00:15:13.920 ⇒ 00:15:16.769 Greg Stoutenburg: You’ll be, you’ll be all top 10 of the users.
144 00:15:16.770 ⇒ 00:15:18.569 Lev Katreczko: I’m about to max the leaderboard.
145 00:15:18.570 ⇒ 00:15:19.030 Caitlyn Vaughn: Boy.
146 00:15:20.150 ⇒ 00:15:20.830 Caitlyn Vaughn: So funny.
147 00:15:20.830 ⇒ 00:15:24.019 Greg Stoutenburg: Yeah, token maxing. Okay.
148 00:15:24.030 ⇒ 00:15:42.859 Greg Stoutenburg: So, I mean, we get this… this is pretty cool, like, summary of most frequently asked questions, ACV seats, user count, Caitlin, I’m impressed, you know, despite being number four. And now, obviously, this is, like, you can’t compete with them because they’re here building stuff. But despite being number 4, you’ve got, you know, I think this was your question just the other day. You’re at number 1, so that’s good.
149 00:15:43.120 ⇒ 00:15:50.760 Caitlyn Vaughn: I’m kind of laughing, looking at this, like, leaderboard up above your mouse here, because as I’m looking at number of prompts to sessions.
150 00:15:50.960 ⇒ 00:15:56.270 Caitlyn Vaughn: the, like, number of prompts I have is so much higher.
151 00:15:56.270 ⇒ 00:15:57.250 Greg Stoutenburg: Yeah. You know?
152 00:15:57.540 ⇒ 00:16:01.389 Caitlyn Vaughn: Right. Like, everyone else is basically, like, one-shotting, and I’m, like, 50-shotting.
153 00:16:01.390 ⇒ 00:16:02.640 Greg Stoutenburg: You’re like, what is this?
154 00:16:02.640 ⇒ 00:16:03.830 Caitlyn Vaughn: Yeah.
155 00:16:03.830 ⇒ 00:16:06.810 Greg Stoutenburg: Tell me how to use you, Blubby.
156 00:16:07.090 ⇒ 00:16:13.009 Greg Stoutenburg: No, that’s great, yeah, yeah, yeah, I should be proud of my quality score here then, right? It’s 1 to 1.
157 00:16:13.010 ⇒ 00:16:14.249 Caitlyn Vaughn: Yeah, literally, wow.
158 00:16:14.250 ⇒ 00:16:22.450 Greg Stoutenburg: Yeah, okay, so we’ve… what was I saying I wanted to do? Oh yeah, to see if we can actually find the session.
159 00:16:23.020 ⇒ 00:16:23.990 Caitlyn Vaughn: Oh, nice.
160 00:16:25.470 ⇒ 00:16:27.080 Caitlyn Vaughn: Can you filter, or no?
161 00:16:30.070 ⇒ 00:16:40.510 Greg Stoutenburg: I feel like we should be able to, I mean, because this is just another dashboard in Omni. I haven’t tried to explore that. I think that would be interesting to drop a filter on here that’s like, you know, even just user.
162 00:16:40.880 ⇒ 00:16:44.890 Caitlyn Vaughn: Right, because then you could just filter it to me, and then find the conversation.
163 00:16:44.890 ⇒ 00:16:49.089 Greg Stoutenburg: Oh, here we go. Yep. Here we go. So here’s… this says session URL.
164 00:16:50.270 ⇒ 00:16:53.250 Greg Stoutenburg: Check it. Cool. There’s the whole conversation.
165 00:16:53.560 ⇒ 00:16:54.130 Caitlyn Vaughn: Great.
166 00:16:54.470 ⇒ 00:16:55.850 Greg Stoutenburg: There’s the enrichment question?
167 00:16:56.610 ⇒ 00:17:05.459 Greg Stoutenburg: And… Blobby’s responses, what Blobby’s looking for, and then, yeah, you had a follow-up.
168 00:17:05.990 ⇒ 00:17:09.160 Greg Stoutenburg: Cool. Alright, so the whole session has its own page.
169 00:17:09.569 ⇒ 00:17:12.149 Greg Stoutenburg: Well, that’s good for us to look into, Demi.
170 00:17:12.930 ⇒ 00:17:17.079 Greg Stoutenburg: I actually just… Link this to you right now.
171 00:17:17.510 ⇒ 00:17:20.809 Greg Stoutenburg: No, I won’t. I have too many tabs open, it’ll ruin everything.
172 00:17:20.810 ⇒ 00:17:22.629 Caitlyn Vaughn: I linked it in, in Slack already.
173 00:17:22.630 ⇒ 00:17:23.779 Greg Stoutenburg: Oh, cool, thank you.
174 00:17:24.530 ⇒ 00:17:26.670 Greg Stoutenburg: Cool. Okay.
175 00:17:28.050 ⇒ 00:17:37.639 Greg Stoutenburg: So, we’re able to see usage here, we’re able to see how many tokens are being used, make sure you stay in contract limits. I’m sure there will be a chart if we wait a moment.
176 00:17:38.610 ⇒ 00:17:48.579 Greg Stoutenburg: Yeah, great. Alright, projected monthly tokens. I haven’t seen the details of your contract, I think they normally default to $50 million a month, so it looks like you’ll be comfortably within that.
177 00:17:49.420 ⇒ 00:17:51.279 Caitlyn Vaughn: Okay, that’s fine.
178 00:17:51.880 ⇒ 00:17:53.509 Greg Stoutenburg: We can look at your overview.
179 00:17:54.430 ⇒ 00:17:56.680 Greg Stoutenburg: 5,000 queries in the last 7 days.
180 00:17:57.710 ⇒ 00:18:02.170 Greg Stoutenburg: 9 users active, and we know that that’s including now more on the default team, which is great.
181 00:18:02.960 ⇒ 00:18:13.679 Greg Stoutenburg: So yeah, cool. So it looks like adoption is going okay. Who else do you think would be a natural fit to be in Omni, using Blobby, interacting with the data here?
182 00:18:14.290 ⇒ 00:18:14.640 Caitlyn Vaughn: Exactly.
183 00:18:14.640 ⇒ 00:18:15.900 Greg Stoutenburg: As noted, yeah.
184 00:18:15.900 ⇒ 00:18:22.560 Caitlyn Vaughn: Yeah, it should be Lev and Nanda, definitely Ryan and Laura, Nico.
185 00:18:22.560 ⇒ 00:18:23.230 Greg Stoutenburg: Okay.
186 00:18:23.230 ⇒ 00:18:24.780 Caitlyn Vaughn: Who else, you guys?
187 00:18:28.070 ⇒ 00:18:29.520 Nandika Jhunjhunwala: Deanna, maybe?
188 00:18:29.520 ⇒ 00:18:31.530 Caitlyn Vaughn: Oh yeah, Deanna. Yeah, the CS team.
189 00:18:31.530 ⇒ 00:18:32.980 Greg Stoutenburg: Deanna. Okay.
190 00:18:32.980 ⇒ 00:18:33.670 Caitlyn Vaughn: Sid…
191 00:18:34.840 ⇒ 00:18:36.180 Nandika Jhunjhunwala: Dude, yeah.
192 00:18:36.530 ⇒ 00:18:37.960 Caitlyn Vaughn: On the product side.
193 00:18:37.960 ⇒ 00:18:38.670 Greg Stoutenburg: Okay.
194 00:18:38.960 ⇒ 00:18:40.079 Caitlyn Vaughn: Yeah, so did an I.
195 00:18:40.080 ⇒ 00:18:41.750 Greg Stoutenburg: Let me make sure they’ve been invited.
196 00:18:44.880 ⇒ 00:18:47.439 Caitlyn Vaughn: They have been, some of them have expired.
197 00:18:47.440 ⇒ 00:18:50.630 Greg Stoutenburg: Yeah, Deanna, Laura Lauren…
198 00:18:51.260 ⇒ 00:18:55.560 Greg Stoutenburg: Nico, you mentioned… alright, so… Yeah.
199 00:18:55.670 ⇒ 00:18:59.790 Greg Stoutenburg: Jana… okay, Ryan… alright, Ryan’s an outlier. I’ll make sure Ryan gets invited.
200 00:18:59.980 ⇒ 00:19:01.509 Caitlyn Vaughn: Is he not in?
201 00:19:01.880 ⇒ 00:19:04.329 Demilade Agboola: He’s invited, I’m not sure he’s accepted it.
202 00:19:04.650 ⇒ 00:19:05.089 Caitlyn Vaughn: miss it.
203 00:19:05.090 ⇒ 00:19:08.389 Greg Stoutenburg: I don’t… I don’t see Orion on this list.
204 00:19:08.720 ⇒ 00:19:09.809 Caitlyn Vaughn: Can you look at expired inventory?
205 00:19:09.810 ⇒ 00:19:11.280 Greg Stoutenburg: Oh, that could be.
206 00:19:11.680 ⇒ 00:19:12.740 Greg Stoutenburg: Oh.
207 00:19:12.910 ⇒ 00:19:17.820 Greg Stoutenburg: Oh, well, okay, he was out, I feel bad. We’ll renew. Come on back.
208 00:19:19.750 ⇒ 00:19:22.590 Greg Stoutenburg: Are we concerned about Philip not being in here? Phil?
209 00:19:24.490 ⇒ 00:19:26.819 Greg Stoutenburg: Whoa, that expired a long time ago. He’s not interested.
210 00:19:26.820 ⇒ 00:19:27.870 Caitlyn Vaughn: That’s right.
211 00:19:27.870 ⇒ 00:19:28.560 Greg Stoutenburg: Okay.
212 00:19:29.130 ⇒ 00:19:30.070 Greg Stoutenburg: Okay.
213 00:19:30.330 ⇒ 00:19:31.250 Greg Stoutenburg: Okay.
214 00:19:31.640 ⇒ 00:19:34.939 Greg Stoutenburg: Cool, so we’ve got probably the right list, and just need to make sure that these folks get in.
215 00:19:35.760 ⇒ 00:19:36.230 Caitlyn Vaughn: Yeah.
216 00:19:36.230 ⇒ 00:19:37.349 Greg Stoutenburg: Festival with it.
217 00:19:37.350 ⇒ 00:19:38.390 Caitlyn Vaughn: Yes, it’s fine.
218 00:19:38.560 ⇒ 00:19:39.170 Greg Stoutenburg: Yeah, great.
219 00:19:39.170 ⇒ 00:19:42.200 Caitlyn Vaughn: I can invite people as they… are curious.
220 00:19:42.460 ⇒ 00:19:43.660 Greg Stoutenburg: Okay, yeah, great.
221 00:19:43.970 ⇒ 00:20:02.380 Greg Stoutenburg: Okay, cool. And then, just, like, a couple of other things. So, as I was looking at the user activation dashboard that is now in posthog, I was thinking, you know, one of the things we want to do, and you know, Caitlin, we had the conversation about onboarding a few weeks ago, and Annika, we’ve talked about what some of the success metrics are.
222 00:20:02.600 ⇒ 00:20:25.310 Greg Stoutenburg: something we should think about, and we don’t have to, like, decide this right now, but as we’re building those charts and preparing for the rollout, we’ll want to have in mind what that final step in our funnels is, so we understand how we’re performing, like, with the initial user activation experience. So, I think it would make sense to just, you know, think about, chat a little bit about what we think an activated user of Phoenix would be.
223 00:20:25.560 ⇒ 00:20:44.250 Greg Stoutenburg: So, some things we’ve mentioned in the past would be, like, user enriches a record for the first time, user creates a query, user connects to CRM, login will be a step, and now, it could be that we think a user who’s activated does… does just one of those things.
224 00:20:44.530 ⇒ 00:20:50.069 Greg Stoutenburg: It could be that they do more than one thing. It could be that they do one of those things a certain number of times.
225 00:20:50.210 ⇒ 00:20:54.669 Greg Stoutenburg: So all of these are, are options.
226 00:20:55.400 ⇒ 00:20:59.050 Greg Stoutenburg: I would think that someone who…
227 00:20:59.450 ⇒ 00:21:03.199 Greg Stoutenburg: Enriches a record, has gotten some value.
228 00:21:03.330 ⇒ 00:21:13.099 Greg Stoutenburg: from the product. What we’re really looking for then, right, is, like, what’s sometimes called, like, the aha moment. When someone comes into the product, they do some stuff, and they go, oh, you know, this is neat. Like, I’m actually using the product.
229 00:21:13.370 ⇒ 00:21:16.809 Greg Stoutenburg: All of these make sense to me.
230 00:21:17.990 ⇒ 00:21:20.519 Caitlyn Vaughn: Yeah, so…
231 00:21:21.420 ⇒ 00:21:31.500 Caitlyn Vaughn: I finished onboarding for the most part, and then I’m working on permissions right now, so, like, RBAC and, like, fine-grade access.
232 00:21:31.500 ⇒ 00:21:32.130 Greg Stoutenburg: Yeah.
233 00:21:33.310 ⇒ 00:21:40.790 Caitlyn Vaughn: And I will say, not everybody… actually, most people will not have permission to actually run enrichment.
234 00:21:40.790 ⇒ 00:21:41.400 Greg Stoutenburg: Okay.
235 00:21:41.400 ⇒ 00:21:43.970 Caitlyn Vaughn: only admins will. I think, actually, what…
236 00:21:44.130 ⇒ 00:21:47.360 Caitlyn Vaughn: Maybe a good thesis for what the…
237 00:21:49.080 ⇒ 00:21:55.199 Caitlyn Vaughn: what an activated user would look like would be somebody running, running our agent. Like.
238 00:21:55.440 ⇒ 00:22:00.480 Caitlyn Vaughn: kind of similar to Omni, almost, like… Is somebody,
239 00:22:01.250 ⇒ 00:22:06.290 Caitlyn Vaughn: making a query inside of our AI chat, and then are they…
240 00:22:06.660 ⇒ 00:22:12.570 Caitlyn Vaughn: probably, like, okay, A, that. B, are they coming back, like, week over week?
241 00:22:12.570 ⇒ 00:22:14.000 Greg Stoutenburg: Yeah.
242 00:22:15.130 ⇒ 00:22:15.740 Greg Stoutenburg: Yep.
243 00:22:15.930 ⇒ 00:22:22.359 Caitlyn Vaughn: And then C, potentially, like, are they moving from the chat into a SKU?
244 00:22:28.760 ⇒ 00:22:35.440 Greg Stoutenburg: Yeah, okay. Okay, yeah. Okay, so thinking of… thinking of, you know, a user who’s just getting up and running.
245 00:22:35.790 ⇒ 00:22:55.260 Greg Stoutenburg: we probably don’t want to require week-over-week engagement, because that’s more like they’ve just become a ready user. But thinking more of, like, that, you know, that early sign that we’ve got one who’s going to become a successful user, I think, yeah, something like using the agent. Do you think they just need to use the agent, or do they need to, like.
246 00:22:55.260 ⇒ 00:22:59.879 Greg Stoutenburg: do anything from it. I mean, I sort of like this idea here, that they use the chat and then
247 00:22:59.880 ⇒ 00:23:14.360 Greg Stoutenburg: they perform some action, because, like, I could see, for example, to take the Omni analogy you’ve made, right? I could see someone coming into Omni, asking Blobby a question, and then just, like, never coming back. You know, like, okay, okay, cool, it has a chat, everything’s got a chat. But, like.
248 00:23:14.780 ⇒ 00:23:23.329 Greg Stoutenburg: then, like, act on it, that seems like it’d be a good signal. And we’re just conversing right now, right? Like, it might turn out that any one of these is just…
249 00:23:23.460 ⇒ 00:23:27.609 Greg Stoutenburg: Not the signal we think it is, and it’ll take some work, but it still helps to have a hypothesis.
250 00:23:27.610 ⇒ 00:23:32.169 Caitlyn Vaughn: Yeah, exactly. We’ll definitely need some, like, actual data before we can.
251 00:23:32.170 ⇒ 00:23:42.149 Greg Stoutenburg: Yeah, yeah, yeah, and this’ll… right, I mean, like, once… once Phoenix is running and everything’s streaming into PostHog, then we, you know, then we do something else for two weeks, and then we… we see what we find.
252 00:23:42.420 ⇒ 00:23:46.300 Caitlyn Vaughn: Yeah, totally. I can show, too.
253 00:23:46.440 ⇒ 00:23:55.440 Caitlyn Vaughn: Or, like, agent onboarding. Let’s see… share… Okay, so…
254 00:23:55.830 ⇒ 00:24:04.770 Caitlyn Vaughn: It’s, like, still a little bit chaotic, but… the goal is… so this is our, like, agentic onboarding, and I will say, like, we’re gonna launch without agent.
255 00:24:05.330 ⇒ 00:24:08.819 Caitlyn Vaughn: actually, but we should have it pretty quickly. So…
256 00:24:08.920 ⇒ 00:24:17.259 Caitlyn Vaughn: It’s the login, and then it’s gonna ask people to connect in their CRM, right? Because you’re not able to do anything until you connect in the CRM.
257 00:24:17.730 ⇒ 00:24:22.289 Greg Stoutenburg: Real quick, did you decide if someone declines that? Are you gonna allow them to decline it?
258 00:24:22.610 ⇒ 00:24:27.050 Caitlyn Vaughn: If they… so this is the initial,
259 00:24:28.080 ⇒ 00:24:35.589 Caitlyn Vaughn: Where are we? The initial version is creating a workspace, connect your CRM. If you cannot…
260 00:24:36.000 ⇒ 00:24:38.139 Caitlyn Vaughn: Then, you have to email your admin.
261 00:24:38.300 ⇒ 00:24:39.150 Greg Stoutenburg: Okay.
262 00:24:39.150 ⇒ 00:24:42.289 Caitlyn Vaughn: And then the flow to, like, can’t connect.
263 00:24:42.990 ⇒ 00:24:44.679 Caitlyn Vaughn: It will send an email, but yeah.
264 00:24:44.680 ⇒ 00:24:45.010 Greg Stoutenburg: Okay.
265 00:24:45.010 ⇒ 00:24:46.290 Caitlyn Vaughn: into the platform.
266 00:24:46.290 ⇒ 00:24:49.399 Greg Stoutenburg: Okay, no dummy data or anything, they’re just… they’re locked out.
267 00:24:49.400 ⇒ 00:24:51.769 Caitlyn Vaughn: Exactly. I like it. Bold.
268 00:24:51.770 ⇒ 00:24:54.229 Greg Stoutenburg: I like it. Cool, I know that was one of the options we talked about before.
269 00:24:54.230 ⇒ 00:25:03.899 Caitlyn Vaughn: It was, yeah. So this is the version that we’ll launch with, which is introducing to the revenue graph, you’re mapping your fields.
270 00:25:04.160 ⇒ 00:25:07.840 Caitlyn Vaughn: And then, we’re pushing people directly into tables.
271 00:25:08.050 ⇒ 00:25:08.380 Greg Stoutenburg: Cool.
272 00:25:08.380 ⇒ 00:25:14.979 Caitlyn Vaughn: So, we’re showing them how to use the revenue graph, which is a similar concept to, like, once we have the agent.
273 00:25:14.980 ⇒ 00:25:15.630 Greg Stoutenburg: Yup.
274 00:25:15.790 ⇒ 00:25:21.809 Caitlyn Vaughn: people connect in their CRM, then the chat is starting, right?
275 00:25:22.220 ⇒ 00:25:26.649 Caitlyn Vaughn: With that data, and then the goal is to, once again, push them into…
276 00:25:26.870 ⇒ 00:25:36.839 Caitlyn Vaughn: I don’t know if we finished this yet, but basically push them into a SKU, like, okay, great, now you have some amount of data, and now we can either, like, generate charts, or, like, let’s view this
277 00:25:37.110 ⇒ 00:25:41.060 Caitlyn Vaughn: You know, in a table, or let’s set up a workflow so that in the future, like.
278 00:25:41.060 ⇒ 00:25:41.420 Greg Stoutenburg: Okay.
279 00:25:41.420 ⇒ 00:25:43.270 Caitlyn Vaughn: Notifies, stuff like that.
280 00:25:45.990 ⇒ 00:25:55.030 Greg Stoutenburg: Okay, cool. That’s great. That’s great. Nandika, is that set up? Is that in staging yet?
281 00:25:56.300 ⇒ 00:25:57.700 Nandika Jhunjhunwala: the onboarding?
282 00:25:57.700 ⇒ 00:25:58.570 Greg Stoutenburg: Yeah.
283 00:25:59.240 ⇒ 00:26:00.700 Nandika Jhunjhunwala: I don’t think so.
284 00:26:00.700 ⇒ 00:26:01.890 Greg Stoutenburg: Okay. Okay.
285 00:26:01.890 ⇒ 00:26:02.540 Nandika Jhunjhunwala: Yeah.
286 00:26:02.540 ⇒ 00:26:04.080 Greg Stoutenburg: Okay, great.
287 00:26:04.640 ⇒ 00:26:11.960 Greg Stoutenburg: Oh, okay, it’s not… okay, great. Yeah, I was in… I was in staging yesterday, but, you know, it wasn’t, like… I didn’t see, like, a button for new user flow or something like that, so…
288 00:26:12.530 ⇒ 00:26:19.019 Greg Stoutenburg: Alright, cool, yeah, that’s, that’s great. We can include that in the mapping and make sure it’s in the tracking plan.
289 00:26:19.230 ⇒ 00:26:30.019 Greg Stoutenburg: I think… yeah, I mean, something like it is, but we’ll say that our initial hypothesis is that they take some action from chat, and then we can refine what that action is as we get closer.
290 00:26:30.340 ⇒ 00:26:35.769 Caitlyn Vaughn: Cool. Yeah, I’m actually, like, less worried about what the actual action is second, as long as they’re, like.
291 00:26:36.160 ⇒ 00:26:41.369 Caitlyn Vaughn: doing one of, you know, 10… using one of 10 SKUs that we have within chat.
292 00:26:41.370 ⇒ 00:26:41.990 Greg Stoutenburg: Yep.
293 00:26:42.190 ⇒ 00:26:58.220 Greg Stoutenburg: Yeah, agreed, and you know, and it could just… it could just vary. I mean, given that there’s a lot that someone can do from the chat, we can just watch and find out what paths users take. Posthog, I can’t remember the word they use for it, but they have, like, a journeys chart, so we can…
294 00:26:58.380 ⇒ 00:27:04.420 Greg Stoutenburg: like I said, when I said, you know, we just stand it up and then wait 2 weeks and see what happens, we can set up a journeys chart.
295 00:27:04.880 ⇒ 00:27:07.090 Greg Stoutenburg: Watch what happens when users first
296 00:27:07.290 ⇒ 00:27:21.580 Greg Stoutenburg: like, they get a response in chat, and then what happens? And then just watch that branch and see what it looks like users do afterward, and then correlate those things with, like, week two retention. Because if someone is still coming back two weeks in, like.
297 00:27:21.580 ⇒ 00:27:34.840 Greg Stoutenburg: It’s a great sign. Probably in good shape, right, yeah, it’s a good sign, that’s a great early signal. And then we can go, alright, so our hypothesis is that the user, you know, did this particular thing, or, you know, they did any action at all, we don’t care what it is, but they did.
298 00:27:34.840 ⇒ 00:27:35.200 Caitlyn Vaughn: it at least.
299 00:27:35.200 ⇒ 00:27:36.429 Greg Stoutenburg: At least 3 times.
300 00:27:36.860 ⇒ 00:27:42.499 Greg Stoutenburg: Over the course of a week, or something like that. So, we’ll figure it out, but this gives us a starting point and direction.
301 00:27:43.780 ⇒ 00:27:44.360 Greg Stoutenburg: Cool.
302 00:27:44.930 ⇒ 00:27:50.180 Greg Stoutenburg: Alright, that’s all I had in mind. Any thoughts?
303 00:27:50.890 ⇒ 00:27:52.880 Greg Stoutenburg: Concerns, follow-ups?
304 00:27:52.880 ⇒ 00:27:59.830 Caitlyn Vaughn: No, that was helpful. I’m glad we got through the, like, dashboard, meetings with the stakeholders this week, and…
305 00:27:59.950 ⇒ 00:28:03.459 Caitlyn Vaughn: What are we starting on next, dashboard-wise?
306 00:28:04.630 ⇒ 00:28:08.209 Greg Stoutenburg: I think this was mentioned before. Yeah, go ahead, Demi.
307 00:28:08.210 ⇒ 00:28:14.830 Demilade Agboola: So we’re going to be looking at the, GRR and NRR?
308 00:28:15.000 ⇒ 00:28:20.240 Demilade Agboola: Views, so, like, we’re just gonna look at the gross recurring revenue, the net recurring revenue.
309 00:28:20.390 ⇒ 00:28:24.960 Demilade Agboola: also do it by CSMs, so Laura has access to those views.
310 00:28:25.100 ⇒ 00:28:29.190 Demilade Agboola: And also, we’ll start working on, like, livestream around, like.
311 00:28:29.310 ⇒ 00:28:36.599 Demilade Agboola: Just, like, BDR stuff, and so at least Lev can start using Blobby as well for what he needs on a day-to-day.
312 00:28:37.420 ⇒ 00:28:42.129 Caitlyn Vaughn: Okay, amazing. Lev, if you’re not using Blobby next week, you’re in big trouble.
313 00:28:42.970 ⇒ 00:28:44.949 Lev Katreczko: I’m ready to get on Blobby.
314 00:28:45.420 ⇒ 00:29:01.070 Greg Stoutenburg: You know, yeah, we’re gonna… we’re gonna tell everyone, at Eden… I mean, sorry, at Omni. Lev, I’ll be curious to see, when you connect to the MCP, I want to see how that shows up in the… in the usage data.
315 00:29:01.070 ⇒ 00:29:08.399 Greg Stoutenburg: Like, if it still does the exact same thing as it does for a logged-in session through the browser, I’m… I’m curious, I’m not sure.
316 00:29:08.400 ⇒ 00:29:11.969 Greg Stoutenburg: So, when you’ve… when you run a query from MCP, let me know.
317 00:29:12.450 ⇒ 00:29:13.530 Lev Katreczko: Will do.
318 00:29:13.530 ⇒ 00:29:14.180 Greg Stoutenburg: Beautiful.
319 00:29:14.760 ⇒ 00:29:18.099 Greg Stoutenburg: All right. I’m feeling like that’s a wrap. It’s a wrap.
320 00:29:18.100 ⇒ 00:29:18.820 Caitlyn Vaughn: Stop!
321 00:29:18.820 ⇒ 00:29:22.760 Greg Stoutenburg: All right. All right. Go team! Have a good rest of your week.
322 00:29:22.760 ⇒ 00:29:24.630 Caitlyn Vaughn: Thank you, Greg. Thanks, Demi.
323 00:29:24.630 ⇒ 00:29:25.310 Lev Katreczko: Bye, guys. See you guys.
324 00:29:25.310 ⇒ 00:29:26.690 Greg Stoutenburg: Bye. See ya.