Meeting Title: Omni Dashboard QA and Updates Date: 2026-03-06 Meeting participants: Demilade Agboola, Caitlyn Vaughn, Scratchpad Notetaker, Laura Krivec, Uttam Kumaran
WEBVTT
1 00:00:13.460 ⇒ 00:00:14.990 Caitlyn Vaughn: Hello!
2 00:00:15.750 ⇒ 00:00:16.660 Laura Krivec: Bye.
3 00:00:17.630 ⇒ 00:00:18.810 Caitlyn Vaughn: How’s it going?
4 00:00:19.310 ⇒ 00:00:20.930 Laura Krivec: Good. How are you?
5 00:00:20.930 ⇒ 00:00:22.539 Caitlyn Vaughn: Your hair looks so good.
6 00:00:22.790 ⇒ 00:00:23.670 Laura Krivec: My hair?
7 00:00:23.670 ⇒ 00:00:25.240 Caitlyn Vaughn: I do straighten it?
8 00:00:25.420 ⇒ 00:00:29.910 Laura Krivec: No, I just washed it. It was a hair wash day today.
9 00:00:31.180 ⇒ 00:00:32.040 Caitlyn Vaughn: What’s that?
10 00:00:32.439 ⇒ 00:00:36.009 Laura Krivec: You as well. I mean, I did… I guess I… I blow-dried it a bit.
11 00:00:36.010 ⇒ 00:00:54.340 Caitlyn Vaughn: You did. Yeah, I can tell. I, just… I went to a workout class this morning, and, my friend is staying with me, and she was like, do you want to go to yoga this morning? And I was like, sure! And then it was actually a really hard workout class, and she tricked me into it, but anyways, I feel good.
12 00:00:54.340 ⇒ 00:00:55.940 Laura Krivec: Now you’re glad you did it, I’m sure.
13 00:00:55.940 ⇒ 00:01:00.660 Caitlyn Vaughn: Yeah, yeah, yeah, but it was in the middle, and I was like… Eww.
14 00:01:04.060 ⇒ 00:01:05.740 Caitlyn Vaughn: Hi, Demi, how are you?
15 00:01:06.150 ⇒ 00:01:08.100 Demilade Agboola: I’m pretty good, how’s everyone doing?
16 00:01:08.550 ⇒ 00:01:09.470 Laura Krivec: Good, thank you.
17 00:01:09.470 ⇒ 00:01:10.500 Caitlyn Vaughn: Yes.
18 00:01:10.920 ⇒ 00:01:12.689 Laura Krivec: Good to hear, it’s good to hear.
19 00:01:13.380 ⇒ 00:01:18.280 Demilade Agboola: I know Otam said he was gonna be on this call… I’m not sure…
20 00:01:20.500 ⇒ 00:01:23.309 Demilade Agboola: Let me message him and see what he has to say.
21 00:01:24.560 ⇒ 00:01:25.180 Caitlyn Vaughn: Cool.
22 00:01:25.570 ⇒ 00:01:27.619 Demilade Agboola: Okay, he says he’ll be on in a minute, so…
23 00:01:27.970 ⇒ 00:01:28.650 Caitlyn Vaughn: Okay, amazing.
24 00:01:28.650 ⇒ 00:01:32.189 Demilade Agboola: give him… Couple seconds, and he should be here.
25 00:01:32.650 ⇒ 00:01:38.290 Caitlyn Vaughn: Cool. I also have a hard stop at 10.30, so I’m free game until then.
26 00:01:38.490 ⇒ 00:01:40.259 Demilade Agboola: Oh, okay. Sounds good.
27 00:02:29.720 ⇒ 00:02:34.009 Caitlyn Vaughn: Laura, you know what we need to do next? I say we as in…
28 00:02:34.270 ⇒ 00:02:41.539 Caitlyn Vaughn: really me, but I need your help, is I need to figure out what we should be charging for, like, AI tokens.
29 00:02:43.170 ⇒ 00:02:48.429 Laura Krivec: Like, if they wanna…
30 00:02:49.280 ⇒ 00:02:57.730 Laura Krivec: Sorry, where are you? I’m just responding to an email. Like, if they use… to default… Agent.
31 00:02:57.730 ⇒ 00:03:03.639 Caitlyn Vaughn: Yeah, or if they used an AI block, or if they used just a regular research agent.
32 00:03:03.890 ⇒ 00:03:06.270 Laura Krivec: And that wouldn’t be including the price?
33 00:03:06.770 ⇒ 00:03:07.469 Caitlyn Vaughn: I don’t know.
34 00:03:08.280 ⇒ 00:03:22.290 Caitlyn Vaughn: I mean, that’s, like, how companies are making money. Like, we’re charging on credits right now for, like, enrichment, but I would love to get to a place where we’re not making money on enrichment, and we’re making money on tokens.
35 00:03:22.920 ⇒ 00:03:23.720 Laura Krivec: Hmm.
36 00:03:24.620 ⇒ 00:03:27.490 Laura Krivec: Well, that will come whenever the agents launch, right?
37 00:03:28.210 ⇒ 00:03:29.640 Caitlyn Vaughn: Yes.
38 00:03:29.640 ⇒ 00:03:32.109 Laura Krivec: Oh, in the summer, but yeah, okay, good point.
39 00:03:32.730 ⇒ 00:03:36.919 Caitlyn Vaughn: Yeah. Tid was asking how he price… prices out, like, renewals.
40 00:03:36.920 ⇒ 00:03:40.320 Laura Krivec: For people that he knows are gonna use, like, the AI.
41 00:03:41.150 ⇒ 00:03:42.620 Caitlyn Vaughn: Whatever we do.
42 00:03:43.000 ⇒ 00:03:45.429 Caitlyn Vaughn: And I don’t know. I don’t know what’s telling me.
43 00:03:47.780 ⇒ 00:03:48.670 Laura Krivec: Okay.
44 00:03:50.420 ⇒ 00:03:52.160 Laura Krivec: Yeah, let’s chat.
45 00:03:52.930 ⇒ 00:03:53.580 Caitlyn Vaughn: Okay.
46 00:03:54.150 ⇒ 00:03:58.400 Caitlyn Vaughn: Demi, do we want to maybe kick off, and Tom can filter in when he gets here?
47 00:03:59.090 ⇒ 00:04:02.529 Demilade Agboola: Alright, sounds good, I’ll do… let’s start that right now.
48 00:04:05.760 ⇒ 00:04:10.510 Demilade Agboola: So, a couple of things… To note…
49 00:04:10.660 ⇒ 00:04:13.970 Demilade Agboola: Give me one second, I’m about to start sharing my screen.
50 00:04:17.040 ⇒ 00:04:21.749 Demilade Agboola: To do this…
51 00:04:29.720 ⇒ 00:04:36.880 Demilade Agboola: Okay, so a couple of things to note is that not all the queries that get the numbers that you’re using
52 00:04:37.630 ⇒ 00:04:42.399 Demilade Agboola: player. Like, I can’t necessarily do the SQL queries for all of them.
53 00:04:42.720 ⇒ 00:04:48.960 Demilade Agboola: But I have been able to get, like, to an approximate range, and it is quite, similar.
54 00:04:49.870 ⇒ 00:04:53.919 Demilade Agboola: And so any, any, like, disparities can easily be fixed.
55 00:04:59.290 ⇒ 00:05:01.019 Demilade Agboola: Alright, let me share my screen.
56 00:05:05.210 ⇒ 00:05:07.119 Demilade Agboola: I’m looking for the Zoom thingy to share.
57 00:05:09.750 ⇒ 00:05:16.059 Demilade Agboola: Okay, so… We have a couple… Dashboards, I know.
58 00:05:16.980 ⇒ 00:05:25.840 Demilade Agboola: You shared the ER dash, and so this is, like, the… Our, like, this is all.
59 00:05:26.710 ⇒ 00:05:28.750 Demilade Agboola: What we’ve been able to create out of it.
60 00:05:28.980 ⇒ 00:05:36.750 Demilade Agboola: Couple of things to note is that some things just don’t seem to match, based off the logic.
61 00:05:37.550 ⇒ 00:05:44.439 Demilade Agboola: and… Let me give an example. And I… sometimes I had to go to Hyperline to confirm…
62 00:05:45.140 ⇒ 00:05:50.829 Demilade Agboola: what was going on, so… And give an example…
63 00:05:59.600 ⇒ 00:06:02.090 Demilade Agboola: Second, welcome to Open Hypline.
64 00:06:05.640 ⇒ 00:06:06.520 Demilade Agboola: Oh…
65 00:06:43.720 ⇒ 00:06:46.470 Demilade Agboola: Sorry, this… this couple…
66 00:07:04.990 ⇒ 00:07:06.260 Uttam Kumaran: Hey guys, sorry.
67 00:07:07.140 ⇒ 00:07:08.170 Demilade Agboola: Bye.
68 00:07:09.850 ⇒ 00:07:13.060 Demilade Agboola: Alright, so we’re showing the dashboard that we’ve done so far.
69 00:07:13.640 ⇒ 00:07:16.710 Demilade Agboola: So yeah, these are the numbers.
70 00:07:17.750 ⇒ 00:07:18.400 Demilade Agboola: have.
71 00:07:18.580 ⇒ 00:07:19.780 Uttam Kumaran: Sure.
72 00:07:22.370 ⇒ 00:07:25.630 Demilade Agboola: A couple… thing. So, yes.
73 00:07:26.280 ⇒ 00:07:31.700 Demilade Agboola: First things, let me show the equals dash side by side, or… It’s on…
74 00:07:31.700 ⇒ 00:07:37.069 Uttam Kumaran: So just to set the stage, again, like, we’re mainly trying to just recreate what’s an equals
75 00:07:37.390 ⇒ 00:07:39.719 Uttam Kumaran: within what’s in Omni.
76 00:07:40.360 ⇒ 00:07:46.219 Uttam Kumaran: I did a review of the dashboard yesterday. It seems like we have most of the core metrics.
77 00:07:46.440 ⇒ 00:07:52.879 Uttam Kumaran: But yeah, I guess I would love to hear, Laura, like, if there are discrepancies now.
78 00:07:53.040 ⇒ 00:08:03.509 Laura Krivec: Yeah, I think the ARR, if you look at there, you are at, like, 3.5 in April. First of all, why are we showing April? It’s now March.
79 00:08:05.360 ⇒ 00:08:07.169 Laura Krivec: So, that’s…
80 00:08:07.610 ⇒ 00:08:11.300 Demilade Agboola: Yeah, it’s just the label in… it’s actually March.
81 00:08:11.300 ⇒ 00:08:15.400 Laura Krivec: Oh, we’re saying 3.5 in March, and…
82 00:08:15.400 ⇒ 00:08:22.369 Uttam Kumaran: So yeah, I just… maybe I just want to set up two things, for this call. Like, one, I just want to talk about the structure first.
83 00:08:22.370 ⇒ 00:08:23.029 Laura Krivec: So that we can.
84 00:08:23.030 ⇒ 00:08:33.649 Uttam Kumaran: and nail that, like, we have the right graphs, we have the right, like, cuts of data, and then I want to get into QA. It can sometimes be overwhelming to sort of share
85 00:08:33.900 ⇒ 00:08:50.350 Uttam Kumaran: to see everything, especially because I know you’re deep in the weeds, to be like, okay, this is off. But first thing I want to do is just make sure, like, this has all of the metrics that you’re looking at, and then… and then we can go into basically QAing from… from the top.
86 00:08:50.790 ⇒ 00:08:56.860 Laura Krivec: Yeah. Metrics, I think… one second, let me just check something.
87 00:09:02.800 ⇒ 00:09:08.060 Laura Krivec: Okay. I think on the top, makes sense, ARR…
88 00:09:08.580 ⇒ 00:09:18.170 Laura Krivec: net ARR change, so that means total ARR in February versus March, yeah?
89 00:09:18.170 ⇒ 00:09:18.780 Uttam Kumaran: Yeah.
90 00:09:19.030 ⇒ 00:09:32.949 Laura Krivec: Okay. Customers, new customers… I… one thing that I would add on the top is churned customers. So, like, just having, you have net new, sorry, new customers, and then also churned customers.
91 00:09:33.540 ⇒ 00:09:34.070 Demilade Agboola: Okay.
92 00:09:34.330 ⇒ 00:09:37.370 Laura Krivec: If we add that, that would be helpful.
93 00:09:39.140 ⇒ 00:09:40.060 Laura Krivec: Mmm…
94 00:09:43.430 ⇒ 00:09:48.870 Laura Krivec: And I think other than that, that makes sense. What I would also…
95 00:09:53.780 ⇒ 00:10:00.289 Laura Krivec: Actually, one thing that can we add, where do we have total ARR in the table?
96 00:10:01.940 ⇒ 00:10:04.869 Uttam Kumaran: We can add totals at the bottom that sort of add up each piece.
97 00:10:05.070 ⇒ 00:10:07.230 Laura Krivec: Yeah, in the ARR breakdown, what I.
98 00:10:07.230 ⇒ 00:10:08.380 Demilade Agboola: Oh, yeah, yes, yes, sir.
99 00:10:08.380 ⇒ 00:10:15.930 Laura Krivec: So add total in the bottom, and then add a line that says month-on-month ARR growth, and show the percentage, please.
100 00:10:16.280 ⇒ 00:10:21.600 Laura Krivec: Okay, okay. And same with the customer count. Yep.
101 00:10:22.140 ⇒ 00:10:29.650 Laura Krivec: And then A, total ARI by month, that makes sense. ARI by component… So
102 00:10:30.940 ⇒ 00:10:36.410 Laura Krivec: I… I think that’s fine. Equals shows it a bit differently. Like, I…
103 00:10:36.540 ⇒ 00:10:46.850 Laura Krivec: I’m wondering if we should just show the ARR by component chart the way equals does, because the way you’re showing it now, the churn, it’s so small in comparison that it’s kind of hard.
104 00:10:46.850 ⇒ 00:10:47.290 Uttam Kumaran: Yeah.
105 00:10:47.290 ⇒ 00:10:53.009 Laura Krivec: So if you remove the end-of-month ARR and just show everything else, I think that’s fine.
106 00:10:53.180 ⇒ 00:10:53.590 Uttam Kumaran: Okay.
107 00:10:53.590 ⇒ 00:10:55.639 Demilade Agboola: Oh, okay, but another thing.
108 00:10:55.640 ⇒ 00:10:56.930 Uttam Kumaran: It’s adjust the changes.
109 00:10:57.540 ⇒ 00:11:03.220 Demilade Agboola: Yeah, exactly. Another thing to note is if you, just click on, like.
110 00:11:03.630 ⇒ 00:11:04.720 Laura Krivec: Oh, I see.
111 00:11:04.720 ⇒ 00:11:05.420 Demilade Agboola: You can actually see the.
112 00:11:06.100 ⇒ 00:11:09.640 Uttam Kumaran: Yeah, but still the… still, I want to remove the… yeah.
113 00:11:09.640 ⇒ 00:11:10.280 Demilade Agboola: Yeah.
114 00:11:11.670 ⇒ 00:11:16.810 Laura Krivec: Okay, but that’s, that’s helpful. Okay, yeah, so I think overall…
115 00:11:18.370 ⇒ 00:11:26.020 Laura Krivec: That’s fine, and wait, well, one thing that’s not here that maybe you’ll do in a separate,
116 00:11:26.490 ⇒ 00:11:33.829 Laura Krivec: dashboard, but, if you go down, is there ACV somewhere? Oh, there is, okay, okay, yeah.
117 00:11:35.570 ⇒ 00:11:37.310 Laura Krivec: Mmm…
118 00:11:37.310 ⇒ 00:11:40.439 Demilade Agboola: So, cost… yeah, new customers, yeah.
119 00:11:41.070 ⇒ 00:11:47.300 Uttam Kumaran: Well, the new, all customers, new customers. What do you think about the, like, we’re truncating to, like, one…
120 00:11:47.630 ⇒ 00:11:49.919 Uttam Kumaran: One significant digit, for the most part.
121 00:11:50.030 ⇒ 00:11:53.580 Uttam Kumaran: Like, do you have any concerns on that?
122 00:11:53.750 ⇒ 00:11:54.599 Laura Krivec: No, even for…
123 00:11:54.860 ⇒ 00:11:55.640 Uttam Kumaran: Okay.
124 00:11:55.640 ⇒ 00:11:56.310 Demilade Agboola: Okay.
125 00:11:56.310 ⇒ 00:11:57.030 Laura Krivec: Yep.
126 00:11:57.530 ⇒ 00:12:05.229 Demilade Agboola: Also, I noticed you had top customer changes over the last 30 days. Do you still want to keep the last 30 days? Do you want to reduce the time frame? Do you want to increase the time frame?
127 00:12:05.660 ⇒ 00:12:09.799 Laura Krivec: Mmm… Last three days, fine, no.
128 00:12:09.800 ⇒ 00:12:10.460 Demilade Agboola: Okay.
129 00:12:12.840 ⇒ 00:12:14.529 Demilade Agboola: Oh, okay,
130 00:12:16.080 ⇒ 00:12:20.339 Demilade Agboola: And also, in terms of, like, customer changes, is there any other thing, customer change, you would want to keep?
131 00:12:20.670 ⇒ 00:12:22.470 Demilade Agboola: Track of, or note of.
132 00:12:23.860 ⇒ 00:12:26.630 Demilade Agboola: For instance, you might want to see churn over, like.
133 00:12:26.630 ⇒ 00:12:31.210 Laura Krivec: Yes, actually, that would be great. If we add…
134 00:12:31.550 ⇒ 00:12:41.709 Laura Krivec: So, I can see, if you go up, where can I see churn by Man? Okay, it’s churned customers, I see that, yes. You know what we should add?
135 00:12:43.170 ⇒ 00:12:54.620 Laura Krivec: churn reason by month. So, in Salesforce, there is a field Where…
136 00:12:54.800 ⇒ 00:13:12.210 Laura Krivec: customer success marks churn, and they mark regrettable, non-regrettable, and then they mark reason for churn. I would like to see top, like, top 3 or top 5 reasons by churn. There’s a few ways to do this. We could do it on a bar chart and show it by month.
137 00:13:12.210 ⇒ 00:13:22.660 Laura Krivec: Or we can just do it, like, you know, in the last 6 months, a few top few reasons by churn, but I want to see churn reasons, basically.
138 00:13:22.660 ⇒ 00:13:28.159 Uttam Kumaran: I would suggest two charts, Demi, like, one that is, like, a bar chart by month, by the reason.
139 00:13:28.550 ⇒ 00:13:41.279 Uttam Kumaran: And then I want… in that, I want to see customer count and dollars. And then similarly, we want a table that’s churn… basically last 6 months, churn reason, customer count, dollar.
140 00:13:42.180 ⇒ 00:13:46.080 Demilade Agboola: I said… The last churn customer account
141 00:13:46.360 ⇒ 00:13:49.469 Demilade Agboola: By reason, we want that to be a…
142 00:13:49.700 ⇒ 00:13:52.919 Demilade Agboola: bar chart, or do we want that to be, like, a table?
143 00:13:52.920 ⇒ 00:13:53.610 Uttam Kumaran: Both.
144 00:13:55.080 ⇒ 00:13:55.960 Uttam Kumaran: Both.
145 00:13:56.090 ⇒ 00:14:00.050 Uttam Kumaran: Like, you have… you can have the reason per month, Yeah.
146 00:14:00.240 ⇒ 00:14:00.930 Demilade Agboola: Go.
147 00:14:01.980 ⇒ 00:14:03.330 Demilade Agboola: Okay, that’s fair.
148 00:14:03.950 ⇒ 00:14:04.780 Laura Krivec: in…
149 00:14:04.780 ⇒ 00:14:06.890 Demilade Agboola: And then we want it to be, what, 60 days?
150 00:14:07.050 ⇒ 00:14:07.990 Uttam Kumaran: Yeah. Or…
151 00:14:08.350 ⇒ 00:14:10.369 Demilade Agboola: No, we wanna do the last 6 months.
152 00:14:10.880 ⇒ 00:14:12.240 Demilade Agboola: Plastics, okay.
153 00:14:12.240 ⇒ 00:14:14.960 Laura Krivec: Yeah, that’s the last response.
154 00:14:15.780 ⇒ 00:14:22.740 Laura Krivec: But ideally, I mean, if we can, like, change the time frame, like, with a drop-down, that could be helpful also, I don’t…
155 00:14:23.010 ⇒ 00:14:28.860 Demilade Agboola: Yeah, yeah, we’ll def… we’ll definitely add that there, so, these are…
156 00:14:28.990 ⇒ 00:14:36.630 Demilade Agboola: Yeah, so we’ve been able to build this out, and so we’ll add, like, filters. So this right now is static. It’s just refreshing as the data refreshes every day.
157 00:14:36.720 ⇒ 00:14:40.319 Laura Krivec: But the idea is we’ll add filters based off this once we’re.
158 00:14:40.680 ⇒ 00:14:44.960 Demilade Agboola: Once we finalize the format, we can then just add filters to ensure that
159 00:14:45.200 ⇒ 00:14:54.319 Demilade Agboola: we can start to break it down by customer, if you want to look and see the ARR by customer, for instance, or by, time frame as well.
160 00:14:55.840 ⇒ 00:14:56.510 Demilade Agboola: And also, I know.
161 00:14:56.510 ⇒ 00:14:56.940 Laura Krivec: the initial…
162 00:14:56.940 ⇒ 00:15:01.029 Demilade Agboola: Like, mid-markets as well, so, like, enterprise and mid-market, so that’s… that would be the.
163 00:15:01.030 ⇒ 00:15:18.379 Laura Krivec: Yes, so that will become more relevant in the future. We don’t have that many, like, enterprise customers yet, but, like, in the future, hopefully, so yeah. Okay, that’s good. But yeah, other than that, I think we’re… in terms of the structure and what we cover in this one, it’s pretty much set.
164 00:15:19.980 ⇒ 00:15:20.800 Demilade Agboola: Sounds good.
165 00:15:21.140 ⇒ 00:15:26.639 Caitlyn Vaughn: And is the goal for this, Laura, to show to, like, sales team kind of thing? Or is it…
166 00:15:27.500 ⇒ 00:15:28.310 Laura Krivec: Like…
167 00:15:28.450 ⇒ 00:15:29.970 Caitlyn Vaughn: Executive.
168 00:15:29.970 ⇒ 00:15:37.309 Laura Krivec: I think it’s more executive, so, like, one, it’s for all hands, two, it’s for the board meetings, and then, just, like.
169 00:15:38.940 ⇒ 00:15:56.140 Laura Krivec: Yeah, I mean, eventually, when we launch… when we launch Phoenix, I want to do weekly metrics meetings for, all revenue teams, and basically, probably I’ll be pulling data from here, maybe, or the model, but yeah.
170 00:15:56.380 ⇒ 00:16:05.909 Caitlyn Vaughn: Okay, the other thing that I was thinking about yesterday when I was looking at this, when we have ARR by component, did you see the,
171 00:16:06.050 ⇒ 00:16:09.879 Caitlyn Vaughn: browser-based investor update, Laura?
172 00:16:11.210 ⇒ 00:16:12.999 Laura Krivec: No, which one? What do you mean?
173 00:16:13.000 ⇒ 00:16:15.570 Caitlyn Vaughn: You know Browser-based, you know that company?
174 00:16:15.570 ⇒ 00:16:17.050 Laura Krivec: Yeah, I haven’t seen it, no.
175 00:16:17.050 ⇒ 00:16:33.359 Caitlyn Vaughn: Okay, yeah, they put out an investor update, only because Nico shared it with me. I’m not an investor. But they had a really cool chart, which was the breakdown of all of their revenue, like, ARR by component, but it would be, like, enterprise, mid-market, PLG, so on and so forth.
176 00:16:33.360 ⇒ 00:16:37.549 Laura Krivec: And that, for sure, we should have. It’s just we cannot have that yet, right?
177 00:16:37.550 ⇒ 00:16:38.600 Caitlyn Vaughn: Right, right.
178 00:16:38.600 ⇒ 00:16:40.690 Laura Krivec: Yeah, but that I… equally.
179 00:16:41.060 ⇒ 00:16:43.640 Caitlyn Vaughn: And I feel like we could probably set it up
180 00:16:43.840 ⇒ 00:16:50.150 Caitlyn Vaughn: Now, by just adding a field in Salesforce that says what customer they are.
181 00:16:50.150 ⇒ 00:16:57.970 Laura Krivec: Actually, let me check with Ryan, because we should already have mid-market and enterprise, or Demi, do you know if we have that?
182 00:16:58.790 ⇒ 00:17:03.850 Demilade Agboola: In terms of classifications, I haven’t seen it yet in Salesforce, but I haven’t.
183 00:17:03.850 ⇒ 00:17:04.319 Laura Krivec: That’s true.
184 00:17:04.329 ⇒ 00:17:06.409 Demilade Agboola: Just at every single table.
185 00:17:06.880 ⇒ 00:17:09.290 Laura Krivec: I’ll check when we’re adding this.
186 00:17:09.710 ⇒ 00:17:11.070 Demilade Agboola: But yes, we…
187 00:17:11.859 ⇒ 00:17:16.479 Demilade Agboola: If you can let me know where it’s been added, I can know what tables to go look for and ingest as well.
188 00:17:16.940 ⇒ 00:17:17.859 Laura Krivec: I’ll do that.
189 00:17:18.480 ⇒ 00:17:19.599 Demilade Agboola: Alright,
190 00:17:20.300 ⇒ 00:17:26.050 Demilade Agboola: Also, is there… if there’s any other classification you want to add, like, that’s currently in Salesforce, do let me know as well, so we.
191 00:17:26.050 ⇒ 00:17:28.720 Laura Krivec: Yeah, I think it’s fine for now, but…
192 00:17:28.810 ⇒ 00:17:34.010 Demilade Agboola: Alright, sounds good. So yes, this is, in terms of structure.
193 00:17:34.310 ⇒ 00:17:36.149 Demilade Agboola: Utam, did you have anything you wanted to add?
194 00:17:36.150 ⇒ 00:17:45.029 Uttam Kumaran: Yeah, so I think we’re gonna go through, and we’ll do a deeper QA on the ARR numbers. I also know there’s… some things are listed as zero, so we’ll go basically match
195 00:17:45.180 ⇒ 00:17:48.229 Uttam Kumaran: Line by line, to what’s in equals.
196 00:17:48.480 ⇒ 00:18:02.800 Uttam Kumaran: But the bigger piece here is, like, I think we’ve driven towards creating this dashboard, but Laura, you also have the ability to just create dashboards as well pretty easily. So we’ll add, like, dashboard-level filters, but you could easily just duplicate this and, like, edit it.
197 00:18:03.030 ⇒ 00:18:14.739 Uttam Kumaran: And so most of what we’re doing is making sure that, like, when you see ARR anywhere, anyone else uses it, it’s, like, the confirmed number, and so that’s the one thing I just want to make sure with you, that it’s all lined up.
198 00:18:14.860 ⇒ 00:18:18.229 Uttam Kumaran: So that, like, as other teams start to use these figures.
199 00:18:18.300 ⇒ 00:18:20.260 Laura Krivec: We’re, like, we’re comfortable.
200 00:18:20.260 ⇒ 00:18:24.769 Uttam Kumaran: I think the other piece that I mentioned to the team yesterday, you know, as you know, like, these are all…
201 00:18:24.880 ⇒ 00:18:33.550 Uttam Kumaran: like, stuff from Salesforce is all, like, operational, versus, like, stuff from QBO is all gonna be, like, closed book, you know, like, accounting certified, so that’s the biggest…
202 00:18:33.650 ⇒ 00:18:36.959 Uttam Kumaran: kind of just thing I want to align on is that
203 00:18:37.160 ⇒ 00:18:45.859 Uttam Kumaran: Like, a lot of the company is gonna report on, like, revenue or, like, bookings, but of course, there’s also, like, what you guys do at the close a month.
204 00:18:45.860 ⇒ 00:18:47.229 Laura Krivec: To close books.
205 00:18:47.230 ⇒ 00:18:53.730 Uttam Kumaran: So I want to make that crystal clear, and I think even internally, we want to make that crystal clear, that there is a difference. We know that.
206 00:18:53.730 ⇒ 00:18:54.759 Laura Krivec: I think… Okay.
207 00:18:54.760 ⇒ 00:19:18.640 Laura Krivec: Yeah, like, and honestly, we haven’t, like, I mean, Equals does have some stuff done on QuickBooks, but I never even checked that. We always, like, we go off of Salesforce numbers in terms of, you know, for, like, for investors and all that. Now, in terms of, like, our books, obviously that’s, done by our accounting firm that then goes into QuickBooks, so that’s a separate thing, but yeah.
208 00:19:18.640 ⇒ 00:19:20.110 Laura Krivec: I understand that.
209 00:19:20.350 ⇒ 00:19:20.880 Uttam Kumaran: Cool.
210 00:19:21.680 ⇒ 00:19:29.079 Demilade Agboola: Okay, also, speaking of QuickBooks, we’ve also been able to build out some of the, numbers in terms of, like, burn rate and COGS.
211 00:19:29.080 ⇒ 00:19:29.430 Laura Krivec: Yeah.
212 00:19:29.530 ⇒ 00:19:30.989 Demilade Agboola: Would you like to see that as well?
213 00:19:30.990 ⇒ 00:19:31.870 Laura Krivec: Yes.
214 00:19:31.870 ⇒ 00:19:32.540 Demilade Agboola: Okay.
215 00:19:32.950 ⇒ 00:19:40.709 Demilade Agboola: Alright, so we’ve been able to… Yeah… Give me one second…
216 00:19:48.270 ⇒ 00:19:49.700 Demilade Agboola: Yeah, I believe it’s this.
217 00:19:51.600 ⇒ 00:19:53.630 Demilade Agboola: Oh yeah, so this is the burn rate.
218 00:19:58.250 ⇒ 00:19:59.449 Demilade Agboola: Okay, yeah.
219 00:20:00.890 ⇒ 00:20:09.879 Laura Krivec: this… A burn rate… it can’t be burn rate, because it’s showing we’re burning 7 million, that’s not right.
220 00:20:10.440 ⇒ 00:20:16.720 Demilade Agboola: I’ll… So for this, we’re looking at, like, the, so in terms of QuickBooks.
221 00:20:17.110 ⇒ 00:20:22.549 Demilade Agboola: The bills and payment… the bills payment tags were not necessarily, like, explicitly used.
222 00:20:22.740 ⇒ 00:20:25.599 Demilade Agboola: For some reason, like, in the API.
223 00:20:26.020 ⇒ 00:20:28.770 Demilade Agboola: So it was hard to be able to reconcile that. I had to use, like.
224 00:20:29.100 ⇒ 00:20:32.579 Demilade Agboola: account type, so where the account was going, like, the
225 00:20:33.520 ⇒ 00:20:37.469 Demilade Agboola: How do I put it? So, you could see the account that the money was going out of.
226 00:20:37.610 ⇒ 00:20:52.910 Demilade Agboola: the tag on the type of the accounts was both being used. So we have, like, typed in COGS, we have type been, like, expenses, and so being able to use that to say, hey, this is… this counts as this, this counts as, brand rates, this counts as COGS.
227 00:20:53.130 ⇒ 00:20:56.770 Demilade Agboola: Do you have an explicit, like.
228 00:20:56.980 ⇒ 00:21:02.199 Demilade Agboola: classification of expenses in QuickBooks that we can then recategorize into this chart.
229 00:21:02.990 ⇒ 00:21:23.060 Laura Krivec: I’m not sure… I don’t, like, we don’t use QuickBooks. Our accounting firm uses QuickBooks. I mean, we have… I look at QuickBooks all the time for, like, expenses or whatnot, but I don’t actually do anything in QuickBooks other than, you know, like, downloading statements, or actually, I can download statements from our accounting firm portal.
230 00:21:23.060 ⇒ 00:21:28.779 Laura Krivec: So… I don’t know how to answer to this. I think…
231 00:21:28.870 ⇒ 00:21:44.460 Laura Krivec: the… I’m not sure where you’re pulling the data from in QuickBooks, but the most accurate way of doing it would be to pull it from the P&L, which is, in… under reports in QuickBooks, and that’s basically.
232 00:21:44.460 ⇒ 00:21:45.540 Uttam Kumaran: Pull the items out.
233 00:21:45.540 ⇒ 00:21:50.639 Laura Krivec: Exactly, and just chart them, right? Because, I mean, I can do this quickly in…
234 00:21:50.850 ⇒ 00:22:03.439 Laura Krivec: in, like, a Google Sheet, but it would be nice to have it here. So just use that data. That data is already, reviewed by accounting. The only thing is that it would be, up until
235 00:22:03.540 ⇒ 00:22:10.899 Laura Krivec: January, as of today, and then February in, like, a few weeks, right? Because they take a few weeks to close the books.
236 00:22:13.820 ⇒ 00:22:20.850 Uttam Kumaran: Is there any other view, Laura, in this? Like, beyond just, like, a view of the P&L? Like, and we can provide that both
237 00:22:21.130 ⇒ 00:22:28.260 Uttam Kumaran: just, like, basically all the line items, as well as, like, some of these charts over time. Is there anything else that’s, like, valuable?
238 00:22:28.660 ⇒ 00:22:31.009 Laura Krivec: I would show… so we have…
239 00:22:31.010 ⇒ 00:22:35.930 Uttam Kumaran: Assuming, like, you want to see percentage of revenues per categories, and then…
240 00:22:37.130 ⇒ 00:22:40.109 Uttam Kumaran: Just growth and… growth or decreases in categories, like, on the front.
241 00:22:40.110 ⇒ 00:22:48.680 Laura Krivec: Yeah, I think we should add, like, with some of this book revenue, can we add, like, a line chart… line on top that shows percentage? Okay.
242 00:22:48.680 ⇒ 00:22:49.140 Demilade Agboola: No.
243 00:22:49.140 ⇒ 00:22:59.879 Laura Krivec: That, for burn rates, please check it, because it’s… it’s wrong, it should be below a million, and we’re, like, at 700, not over. Cogs…
244 00:23:00.170 ⇒ 00:23:03.389 Laura Krivec: Yeah, Cox, that’s fine. And then a runway?
245 00:23:04.140 ⇒ 00:23:05.020 Demilade Agboola: Okay, so…
246 00:23:05.020 ⇒ 00:23:05.690 Uttam Kumaran: Okay, cool.
247 00:23:06.220 ⇒ 00:23:08.499 Laura Krivec: Line chart for book driving your runway.
248 00:23:10.050 ⇒ 00:23:12.459 Laura Krivec: I’m trying to think what else…
249 00:23:13.900 ⇒ 00:23:22.149 Uttam Kumaran: Like, are you guys, are you guys keeping benchmarks? Like, I mean, again, it’s gonna be just, like, either revenue per employee, or, like, either, like.
250 00:23:22.260 ⇒ 00:23:26.830 Uttam Kumaran: any of the common, like, SaaS benchmarks on…
251 00:23:27.260 ⇒ 00:23:39.049 Uttam Kumaran: yeah, I guess it’s, like, burn multiple, or rule of 40, or any of that, like… We could do, let’s do burn multiple, actually, that would be useful, because we do share that with the board. Okay.
252 00:23:40.220 ⇒ 00:23:46.959 Laura Krivec: burn multiple… rule of 40, we haven’t really calculated, we haven’t been asked to yet. We could add it, I’m sure.
253 00:23:46.960 ⇒ 00:23:59.810 Uttam Kumaran: Yeah, I think the biggest thing is, like, whatever’s… whatever’s helpful, whatever just, like, gets you to produce that… the investor update faster, and then, yeah, like, if there’s any other things that’s like, okay, maybe we’ve never calculated it, we can take a crack at, like, some of the common…
254 00:23:59.810 ⇒ 00:24:05.570 Laura Krivec: Sure, yeah, sure, do R40, that could be helpful.
255 00:24:07.020 ⇒ 00:24:16.199 Laura Krivec: And I think that’s it. Like, ideally, eventually we could add things like CAC and all this, but we need to clean our books. We cannot…
256 00:24:16.200 ⇒ 00:24:16.780 Uttam Kumaran: Yeah.
257 00:24:16.780 ⇒ 00:24:25.329 Laura Krivec: You cannot do it very simply from QuickBooks, so that’s work on our side. But I think if you just add,
258 00:24:25.940 ⇒ 00:24:27.549 Laura Krivec: AR40, yeah.
259 00:24:27.970 ⇒ 00:24:28.460 Uttam Kumaran: Okay.
260 00:24:28.460 ⇒ 00:24:28.930 Demilade Agboola: Okay.
261 00:24:28.930 ⇒ 00:24:35.289 Uttam Kumaran: Yeah, we’ll show margins, and then show… just show expenses. I guess you could tell us how far deep to go if you want a breakdown of…
262 00:24:35.440 ⇒ 00:24:40.330 Uttam Kumaran: Expense types, but yeah, we’ll just add a couple of the most common.
263 00:24:40.330 ⇒ 00:24:41.110 Laura Krivec: yam.
264 00:24:41.110 ⇒ 00:24:51.250 Uttam Kumaran: you know, SaaS finance metrics, so that could be helpful. And then, yeah, over time, I think we’ll be able to add, like, retention, cohorting, CAC, LTV, like, ratios.
265 00:24:52.040 ⇒ 00:25:04.810 Laura Krivec: So, for cohorting, did you manage to do the, customer success? So, what was it, NRR and, GRR?
266 00:25:05.640 ⇒ 00:25:06.940 Demilade Agboola: So, not yet.
267 00:25:06.940 ⇒ 00:25:07.320 Laura Krivec: Okay.
268 00:25:07.320 ⇒ 00:25:22.450 Demilade Agboola: initially, the ARR was a bit off, and so that was kind of why I was trying to get the ARR down, and so until we get the ARR to the point where things are looking good, doing, like, NRR will just be wasted effort. So once we’re, like, in the ballpark now.
269 00:25:22.830 ⇒ 00:25:30.020 Demilade Agboola: Yes, there’s some slight disparities, but then we can easily do NRR, and then the logic will trickle down as we verify…
270 00:25:30.020 ⇒ 00:25:30.520 Laura Krivec: webinar.
271 00:25:30.520 ⇒ 00:25:33.350 Demilade Agboola: get ARR to… to the spots we want it to be.
272 00:25:35.120 ⇒ 00:25:39.330 Laura Krivec: Okay, yeah, we need to add the cohorted NRR.
273 00:25:39.680 ⇒ 00:25:44.649 Demilade Agboola: Yeah, so we’ll do that, and I would also add, I remember you mentioned, like, benchmarks being
274 00:25:45.700 ⇒ 00:25:49.589 Demilade Agboola: you mentioned the benchmarks we want to get to, ideally, so I can always put that…
275 00:25:49.730 ⇒ 00:25:51.590 Demilade Agboola: As well, in the notes, like a text.
276 00:25:51.590 ⇒ 00:25:52.360 Laura Krivec: Excellent.
277 00:25:52.360 ⇒ 00:25:54.630 Demilade Agboola: So it’s easy for whoever’s using the,
278 00:25:55.440 ⇒ 00:26:04.129 Demilade Agboola: dashboard to be aware of either, like, what months are we doing well at, or we’re doing well in, and what months where, like, we can also try and ramp up.
279 00:26:06.000 ⇒ 00:26:06.940 Laura Krivec: Okay.
280 00:26:09.890 ⇒ 00:26:18.919 Demilade Agboola: Okay, so, like, these are the numbers we have so far. The… we’ll take the feedback, improve the structure, and work on some of the numbers as well to ensure, like.
281 00:26:19.090 ⇒ 00:26:21.250 Demilade Agboola: The numbers match what they should match.
282 00:26:21.890 ⇒ 00:26:31.140 Uttam Kumaran: Yeah, I think maybe, Laura, one thing that I can… if we have some figures here that we want further QA on, I think we might just have to do, like, a working session to just drill into…
283 00:26:31.410 ⇒ 00:26:36.460 Uttam Kumaran: Like, for example, if we’re a few hundred thousand off, it’s just gonna require us to sort of.
284 00:26:36.850 ⇒ 00:26:38.800 Laura Krivec: Yeah. Start comparing apples to apples.
285 00:26:38.900 ⇒ 00:26:43.270 Uttam Kumaran: So, in that sense, what I… what we can do is, like, typically we’ll take a month.
286 00:26:43.400 ⇒ 00:26:55.700 Uttam Kumaran: we’ll look at all the customers, which I think is… and then we’ll just basically try to compare, like, ARR contribution from either each category or the customers, and then we’ll find, like, these are off, okay, like.
287 00:26:55.990 ⇒ 00:27:03.959 Uttam Kumaran: what from Hyperline, or what from Sale… like, what’s… where’s the error coming from? So that’ll get… that’ll… that’ll get us to the… to the remaining issues.
288 00:27:04.140 ⇒ 00:27:06.430 Uttam Kumaran: So that’s how we’ll kind of go through.
289 00:27:07.120 ⇒ 00:27:08.260 Laura Krivec: Okay.
290 00:27:08.500 ⇒ 00:27:12.779 Demilade Agboola: Quick question. I was wondering who runs, like, who operates, like, equals?
291 00:27:13.750 ⇒ 00:27:15.700 Demilade Agboola: Because there are some…
292 00:27:16.270 ⇒ 00:27:22.909 Demilade Agboola: views in equals that produce some of this data that I would like to have access to, and I can’t seem to see.
293 00:27:23.620 ⇒ 00:27:24.310 Demilade Agboola: Good.
294 00:27:24.310 ⇒ 00:27:37.389 Laura Krivec: I can give you access, but who runs Equals is equals, basically. We have, you know, an FDE that we constantly talk to, but I can give you access to the view that you want if you just let me know.
295 00:27:38.130 ⇒ 00:27:43.549 Demilade Agboola: Okay, alright. I will let you know, because usually the way things are built in equals right now is, like.
296 00:27:43.650 ⇒ 00:27:46.920 Demilade Agboola: They keep going, like, referring to, like, higher level stuff.
297 00:27:47.060 ⇒ 00:27:49.689 Demilade Agboola: And so, I have been able to see some things.
298 00:27:50.100 ⇒ 00:27:52.310 Demilade Agboola: I’ve hit the wall in terms of access or potential.
299 00:27:52.310 ⇒ 00:27:55.750 Uttam Kumaran: Can you show an example of what that is?
300 00:27:56.060 ⇒ 00:28:01.720 Demilade Agboola: Alright, so, N equals, if you… Come in here…
301 00:28:01.880 ⇒ 00:28:03.980 Demilade Agboola: To the main page, you can see views.
302 00:28:04.240 ⇒ 00:28:10.580 Demilade Agboola: And so I can kind of see, like, oh, this is error built by customer, I can see the logic behind it, which is cool.
303 00:28:11.460 ⇒ 00:28:16.400 Demilade Agboola: But then they’re referring to another…
304 00:28:16.820 ⇒ 00:28:20.250 Demilade Agboola: view. In this case, it is the…
305 00:28:20.960 ⇒ 00:28:24.430 Demilade Agboola: Arrow build impute, which, again, fine.
306 00:28:24.780 ⇒ 00:28:32.830 Demilade Agboola: But error build imputes is referring to another call… another, view called Opportunities Clean.
307 00:28:33.200 ⇒ 00:28:41.389 Demilade Agboola: And I don’t have… so, from Opportunities Clean, and I don’t have access to Opportunities Clean to see what’s going on in there, and how they’re cleaning the opportunities.
308 00:28:41.500 ⇒ 00:28:59.700 Demilade Agboola: To get out the data that we’re using. So I would like to look at that, get an idea of the logic that is going on behind the scenes, but potentially, if I see what’s going on in Opportunities Clean, they could also be referring to another view, and so I might need more than one view, I might budget more than one time, is kind of, like, the heads-up I’m giving you around that.
309 00:29:00.500 ⇒ 00:29:04.719 Laura Krivec: Oh, I don’t have access to opportunities inputs view, I don’t see it here.
310 00:29:05.060 ⇒ 00:29:11.219 Demilade Agboola: Oh, okay. So, yeah, that’s kind of why I asked, because I saw that it was done by someone called Sam.
311 00:29:11.390 ⇒ 00:29:14.410 Laura Krivec: Yeah, he’s, he works at Equals.
312 00:29:14.820 ⇒ 00:29:15.740 Demilade Agboola: Okay.
313 00:29:16.660 ⇒ 00:29:20.469 Demilade Agboola: Can you… would it be possible for you to put me through to him, so I could just work on that piece?
314 00:29:21.050 ⇒ 00:29:21.380 Laura Krivec: Yeah.
315 00:29:21.380 ⇒ 00:29:23.710 Demilade Agboola: Same here. Alright, I’ll appreciate that.
316 00:29:25.220 ⇒ 00:29:29.240 Caitlyn Vaughn: Alright, I’ve gotta jump to my next call, but I’ll see you guys later. Thanks for this.
317 00:29:29.240 ⇒ 00:29:29.830 Uttam Kumaran: Thank you.
318 00:29:29.830 ⇒ 00:29:30.270 Laura Krivec: Caitlin.
319 00:29:31.600 ⇒ 00:29:34.289 Uttam Kumaran: So, I think probably next steps, I think, yeah, if we can…
320 00:29:34.760 ⇒ 00:29:43.729 Uttam Kumaran: Laura, if we can get down to the root in equals, then we can do that QA, actually, and then basically can come to you with, like, these are off, and, like, here’s the reasoning.
321 00:29:43.880 ⇒ 00:29:50.209 Uttam Kumaran: But yeah, maybe a connection with Sam, but even if, like, that takes a while, like, I think we still can drive.
322 00:29:50.220 ⇒ 00:29:53.579 Laura Krivec: And so our first goal here is just to try to get.
323 00:29:53.580 ⇒ 00:29:55.100 Uttam Kumaran: that ARR dash usable.
324 00:29:55.100 ⇒ 00:29:55.530 Laura Krivec: Yep.
325 00:29:55.530 ⇒ 00:30:02.029 Uttam Kumaran: And then, I think hearing how you want the QuickBooks stuff modeled, like, we can quickly move to that as well.
326 00:30:02.030 ⇒ 00:30:03.120 Laura Krivec: Yeah.
327 00:30:03.120 ⇒ 00:30:08.110 Uttam Kumaran: I think you let us know, like, how much… I think as you get more comfortable.
328 00:30:08.300 ⇒ 00:30:12.789 Laura Krivec: editing the dashboard and stuff, you just let us know, like, how you kind of want to operate with us, I think.
329 00:30:13.030 ⇒ 00:30:28.300 Uttam Kumaran: Of course, our goal is, like, we will go end-to-end if you’re like, hey, I need this chart, but I think it’s also, if you’re in a… if you’re in a bind or you want to quickly see something, we’d love to show you, like, how to quickly make some of these. It’s not like… it’s not like… it’s… it’s probably a little bit easier than equals, actually.
330 00:30:28.300 ⇒ 00:30:31.720 Laura Krivec: Can you send me the access to Omni? I don’t even know if I have it, but I.
331 00:30:31.720 ⇒ 00:30:32.050 Uttam Kumaran: Yeah.
332 00:30:32.550 ⇒ 00:30:32.920 Demilade Agboola: Okay.
333 00:30:32.920 ⇒ 00:30:42.819 Laura Krivec: Okay, that, and then a question for you. So, you know how in our doc we had a list of all these dashboards? So, what is the plan? Like, that we do it, or you’re gonna continue to do it, some of this?
334 00:30:42.820 ⇒ 00:30:43.969 Uttam Kumaran: We’re gonna continue to do it.
335 00:30:43.970 ⇒ 00:30:45.480 Laura Krivec: Okay, great, yeah. Okay.
336 00:30:45.480 ⇒ 00:30:59.279 Uttam Kumaran: Yeah, so we’re gonna do it, we are working with Nandica closely at default, but again, we’re working on dashboards not only for finance, for sales, and, like, kind of further, so she’s helping in some places, but we’re gonna drive towards the first versions.
337 00:30:59.280 ⇒ 00:31:16.599 Uttam Kumaran: Of as many of them as possible. And then tweaks and things, like, we’ll keep taking on as much as we can take on, but I think it’ll be easy for you to be like, okay, I want this column. Most of our job is making sure, like, actually all the columns you pick and join are accurate, but we’re gonna drive towards those dashboards.
338 00:31:17.200 ⇒ 00:31:18.730 Laura Krivec: Okay, great.
339 00:31:19.080 ⇒ 00:31:24.679 Demilade Agboola: I’m sharing the dash, the ARR dash with you, so…
340 00:31:25.610 ⇒ 00:31:29.359 Demilade Agboola: you’ll have access to it, and as I’m updating it, it will be…
341 00:31:30.500 ⇒ 00:31:35.929 Demilade Agboola: up-to-date. But once we get to a spot that we feel everything is good, I can then make it a bit, like.
342 00:31:36.070 ⇒ 00:31:39.270 Demilade Agboola: I can give access to more people on the default team, so they can.
343 00:31:39.270 ⇒ 00:31:39.670 Laura Krivec: Christmas.
344 00:31:39.670 ⇒ 00:31:47.590 Demilade Agboola: to use it as well. So for now, it will be just, like, you QAing it, letting me know, like, hey, these numbers seem a bit off today, or this…
345 00:31:47.850 ⇒ 00:31:53.350 Demilade Agboola: We should definitely look at this, and I will be able to add these charts to it, and I’ll do that to the chart.
346 00:31:54.170 ⇒ 00:31:55.190 Laura Krivec: Okay, great.
347 00:31:55.400 ⇒ 00:31:56.380 Demilade Agboola: Sounds good.
348 00:31:56.550 ⇒ 00:32:06.830 Uttam Kumaran: Okay, perfect, so I think we’ll get back to you on Monday, probably end of day with some changes, and then probably Monday with a few more, and then maybe we can see if we need to start QAing on a call, or if we can just go back and forth.
349 00:32:07.490 ⇒ 00:32:09.070 Laura Krivec: Yep, that sounds good.
350 00:32:09.070 ⇒ 00:32:09.680 Uttam Kumaran: Okay.
351 00:32:09.790 ⇒ 00:32:10.380 Uttam Kumaran: Awesome.
352 00:32:10.380 ⇒ 00:32:10.720 Laura Krivec: Right.
353 00:32:11.140 ⇒ 00:32:11.970 Uttam Kumaran: Thank you so much.
354 00:32:11.970 ⇒ 00:32:14.070 Laura Krivec: Cool, thank you, have a good day, bye!