Meeting Title: CSO Client Presentation Dry Run Date: 2026-02-18 Meeting participants: Greg Stoutenburg, Pranav Narahari, Demilade Agboola, Robert Tseng
WEBVTT
1 00:01:33.390 ⇒ 00:01:34.450 Greg Stoutenburg: Hey, perhaps.
2 00:01:34.450 ⇒ 00:01:35.730 Pranav Narahari: Hey, Greg, how’s it going?
3 00:01:37.690 ⇒ 00:01:38.859 Greg Stoutenburg: Pretty good, how are you?
4 00:01:39.220 ⇒ 00:01:42.690 Pranav Narahari: Pretty good, pretty good. Give me one sec, just setting up my camera.
5 00:02:01.750 ⇒ 00:02:11.870 Pranav Narahari: Is this, Eden Omni training have to do with, I know that you responded to, like, one of my threads earlier this week about the Claude Code training. Is this kind of in a similar vein, or totally different?
6 00:02:14.920 ⇒ 00:02:32.230 Pranav Narahari: earlier this week feels like 100 years ago. The co-coding was, it was, like, the vibe coding environment that we were, like, using to help, like, Lilo ship features, and then we would basically review them, and then push them into, like, production, basically.
7 00:02:32.230 ⇒ 00:02:32.850 Greg Stoutenburg: and then…
8 00:02:33.550 ⇒ 00:02:34.770 Pranav Narahari: But, if that doesn’t.
9 00:02:34.770 ⇒ 00:02:37.539 Greg Stoutenburg: Yeah, I don’t know. Yeah, not ringing a bell. Yeah.
10 00:02:37.990 ⇒ 00:02:39.800 Pranav Narahari: Yeah.
11 00:02:40.500 ⇒ 00:02:43.070 Greg Stoutenburg: Yeah, sorry. One moment.
12 00:02:45.280 ⇒ 00:02:46.199 Pranav Narahari: How’s it going, dummy?
13 00:02:49.320 ⇒ 00:02:49.750 Greg Stoutenburg: Shit.
14 00:02:49.750 ⇒ 00:02:51.630 Demilade Agboola: Pretty good. How’s everyone doing?
15 00:02:52.500 ⇒ 00:02:54.180 Greg Stoutenburg: Hey, doing alright.
16 00:02:54.460 ⇒ 00:02:55.709 Pranav Narahari: Yup, doing alright.
17 00:03:00.350 ⇒ 00:03:02.640 Greg Stoutenburg: I need, 40 seconds.
18 00:03:03.060 ⇒ 00:03:03.800 Pranav Narahari: Cool, cool.
19 00:03:41.370 ⇒ 00:03:49.109 Greg Stoutenburg: Okay, yeah, alright, thanks for your patience, sorry, I’m just very,
20 00:03:49.960 ⇒ 00:03:55.269 Greg Stoutenburg: the… the Tableau to Omni migration is a lot of cognitive bandwidth.
21 00:03:55.830 ⇒ 00:03:58.109 Pranav Narahari: Yeah. This week.
22 00:03:58.280 ⇒ 00:04:00.930 Greg Stoutenburg: So let’s see, Robert said yes, Oren said yes.
23 00:04:01.460 ⇒ 00:04:07.389 Greg Stoutenburg: And then it’s us, hugh Tom said yes, but he is on a flight, I saw.
24 00:04:07.510 ⇒ 00:04:08.340 Greg Stoutenburg: Hmm.
25 00:04:08.790 ⇒ 00:04:15.759 Greg Stoutenburg: Well, maybe we should just get started, and for now, if you could introduce us to the challenge that you’re facing that,
26 00:04:16.070 ⇒ 00:04:16.850 Greg Stoutenburg: Yep.
27 00:04:17.829 ⇒ 00:04:19.129 Greg Stoutenburg: Yeah, maybe let’s go there.
28 00:04:19.640 ⇒ 00:04:21.679 Greg Stoutenburg: Perfect.
29 00:04:21.890 ⇒ 00:04:23.500 Pranav Narahari: Yeah, so…
30 00:04:24.190 ⇒ 00:04:31.390 Pranav Narahari: Last week, I was kind of discussing how we were having a little bit of change in priority with the…
31 00:04:31.710 ⇒ 00:04:32.870 Pranav Narahari: with Lilo.
32 00:04:33.100 ⇒ 00:04:34.419 Pranav Narahari: They were…
33 00:04:34.600 ⇒ 00:04:51.430 Pranav Narahari: basically, for the last month and a half, we’ve realized that the Gantt chart hasn’t been a great indicator of what we’re actually working on week to week, because they have so many ad hoc things that come up, and then they… they’re like, oh, this is number one priority next week, this is number one priority, and so…
34 00:04:51.650 ⇒ 00:05:04.740 Pranav Narahari: the thing is, it wasn’t even us that had to, like, elevate that to them. It was actually internally, like, there’s two of, like, two points of contact, basically, at Lilo. They kind of discussed amongst themselves, they were like, hey guys, like.
35 00:05:04.740 ⇒ 00:05:17.399 Pranav Narahari: we are being all over the place for y’all. We understand that. I mean, we… I’ve brought it up a couple times as well, but I think they came back to us and were like, okay, let’s align on, like, what our priorities are internally first, and we’ll bring that up to y’all.
36 00:05:17.790 ⇒ 00:05:29.590 Pranav Narahari: So, that finally happened this week. They gave us a document with, like, 6 priorities, and then very sparse details for the things that they wanted shipped.
37 00:05:29.950 ⇒ 00:05:34.519 Pranav Narahari: And so, what I did was…
38 00:05:34.830 ⇒ 00:05:44.159 Pranav Narahari: yesterday and the day before, talk with our Lilo team, like, internal, like, Brainforge team that’s working on the Lilo project, estimated those things.
39 00:05:44.970 ⇒ 00:05:51.609 Pranav Narahari: And then, yesterday, I had, like, Utom and Clarence do, like, a pass over what I wrote, and…
40 00:05:51.920 ⇒ 00:06:03.669 Pranav Narahari: basically, they were like, the information that you’re working… that you’re working with to create that estimate is just not enough information, you know? Like, you need to, like, really be able to define, like, what you’re going to ship.
41 00:06:05.170 ⇒ 00:06:13.999 Pranav Narahari: And so, before you even give an estimate for, like, how long it’s gonna take. And that makes total sense. So, yesterday, I kind of just, like, worked pretty late to figure out…
42 00:06:14.210 ⇒ 00:06:20.970 Pranav Narahari: based on what they wrote, using all the context that I have for the client, writing down the exact
43 00:06:21.270 ⇒ 00:06:27.099 Pranav Narahari: like… KPIs, as well as the feature-by-feature
44 00:06:27.500 ⇒ 00:06:33.730 Pranav Narahari: Almost like a breakdown of, like, the main thing that they want shipped into, like, individual features.
45 00:06:33.930 ⇒ 00:06:40.820 Pranav Narahari: And then I sent that back to them. So, without any estimates, because I wanted them to double-check.
46 00:06:40.990 ⇒ 00:06:48.209 Pranav Narahari: that I’ve got everything right, that I, like, we’re synced on, like, what their priorities are. And then I also gave them an additional column.
47 00:06:48.310 ⇒ 00:06:55.240 Pranav Narahari: that I would like for them to, like, fill in to give even more detail and additional comments based on what I wrote.
48 00:06:55.590 ⇒ 00:06:58.480 Pranav Narahari: Now, that was yesterday night.
49 00:06:59.330 ⇒ 00:07:11.620 Pranav Narahari: to… I kind of wanted to push that as, like, okay, this is number one priority, so we can actually start working. They haven’t done that yet today. I gave them, like, I bumped the message earlier today, haven’t gone back to me.
50 00:07:11.900 ⇒ 00:07:16.870 Pranav Narahari: Or they did get back to me, saying that I’ll get back to you later.
51 00:07:17.380 ⇒ 00:07:20.869 Pranav Narahari: Yeah, so that’s just kind of where I’m at. I’ve kind of told our, like.
52 00:07:21.370 ⇒ 00:07:24.730 Pranav Narahari: our team at Brainforge is like, okay, let’s stop…
53 00:07:24.900 ⇒ 00:07:30.990 Pranav Narahari: shipping for right now, like, any more features, because I don’t want to work on stuff that isn’t priority.
54 00:07:32.970 ⇒ 00:07:38.150 Pranav Narahari: And then, yeah, I could go on and on, I kind of want to pause, because there’s a thing, but yeah.
55 00:07:38.660 ⇒ 00:07:44.589 Greg Stoutenburg: Yeah, yeah, I think what stood out to me in what you just described is that the client says.
56 00:07:44.740 ⇒ 00:07:53.729 Greg Stoutenburg: Pranav, this is top priority, and then you work into the evening on what they said is top priority, and then you give it to them, and they say, we’ll look at this later.
57 00:07:56.030 ⇒ 00:07:56.589 Pranav Narahari: I didn’t.
58 00:07:56.590 ⇒ 00:07:58.040 Greg Stoutenburg: Sound like top priority.
59 00:07:58.040 ⇒ 00:07:59.570 Pranav Narahari: That’s so true, yeah.
60 00:07:59.570 ⇒ 00:08:07.259 Greg Stoutenburg: Does not sound like top priority. So, yeah, yeah, I mean… As far as, like.
61 00:08:07.980 ⇒ 00:08:13.969 Greg Stoutenburg: what to do? I mean, it sounds like they’re confused about… it sounds like they’re confused about what they want. Now…
62 00:08:13.970 ⇒ 00:08:14.420 Pranav Narahari: Yep.
63 00:08:14.420 ⇒ 00:08:27.759 Greg Stoutenburg: I think maybe you hinted at this, so maybe you can fill it in. Have you… have you tried yet, or has this been a piece of it, like, where you’ve said, hey, here’s what I think should be the priority? I’ve taken a look at it, here’s what I think should be the priority.
64 00:08:28.170 ⇒ 00:08:39.220 Greg Stoutenburg: Like, are they maybe… and the reason I’m suggesting that is, like, could they be confused internally and don’t really know what to ask for, so that’s why they look like they’re just scrambling a little bit and could use some direction?
65 00:08:39.580 ⇒ 00:08:42.330 Greg Stoutenburg: Or… Yeah, you know, yeah.
66 00:08:42.330 ⇒ 00:08:47.649 Pranav Narahari: I think you’re… I think you’re on point there. I’m glad that you said that, because…
67 00:08:47.850 ⇒ 00:08:54.310 Pranav Narahari: earlier today, Utam said something kind of similar on, like, a specific feedback that they gave, and I think…
68 00:08:54.520 ⇒ 00:08:59.180 Pranav Narahari: You’re absolutely right. Like, I was thinking about, like, They are not…
69 00:08:59.500 ⇒ 00:09:10.179 Pranav Narahari: doing a great job of defining the product that they want. And I think it’s because they’re trying to get too much in the weeds of, like, the technical. They’re trying to, like, I think they…
70 00:09:11.230 ⇒ 00:09:15.619 Pranav Narahari: from, like, they’re getting influence from just, like, what they’re seeing, like, on Twitter, or just, like.
71 00:09:15.930 ⇒ 00:09:17.690 Pranav Narahari: Anywhere else where, like.
72 00:09:18.010 ⇒ 00:09:26.140 Pranav Narahari: People talk about, like, these super dope, like, technical features, but from a non-technical person, you could get that confused for, like, the…
73 00:09:26.500 ⇒ 00:09:29.650 Pranav Narahari: the outcome you’re actually looking for. So an example of that is…
74 00:09:30.370 ⇒ 00:09:39.450 Pranav Narahari: One of the priorities that they set for us was we want to create a knowledge base feature in our chat… for our chat interface.
75 00:09:39.880 ⇒ 00:09:46.240 Pranav Narahari: Now… That makes sense, like, that’s… like, a lot of people would like knowledge bases, but…
76 00:09:46.360 ⇒ 00:09:50.979 Pranav Narahari: They’ve never described to us exactly why they need knowledge bases.
77 00:09:51.290 ⇒ 00:09:55.780 Pranav Narahari: They’re just telling us we want knowledge bases, can you guys develop it? Of course we can, but it’s like…
78 00:09:55.780 ⇒ 00:09:56.440 Greg Stoutenburg: Yeah.
79 00:09:56.700 ⇒ 00:10:01.180 Greg Stoutenburg: how do I know this is solving y’all’s problem? Are we just gonna, like, two weeks from now, just be like…
80 00:10:01.660 ⇒ 00:10:08.999 Pranav Narahari: okay, we build the knowledge bases, and then find out, like, okay, this is what y’all needed at all. That’s kind of what it needs to be, like…
81 00:10:09.340 ⇒ 00:10:13.409 Pranav Narahari: Okay, you guys are spending, like, a lot of time, and there seems to be a little bit of, like…
82 00:10:13.790 ⇒ 00:10:19.639 Pranav Narahari: back and forth about what exactly you guys want. Like, it’s not… it doesn’t seem like you guys are…
83 00:10:20.870 ⇒ 00:10:26.929 Pranav Narahari: convinced on exactly what the priority is, and so let me help you there. Like, I have a lot of context on…
84 00:10:27.110 ⇒ 00:10:37.560 Pranav Narahari: what problems we can solve using AI, so if you tell me more of your problems, I can, like, help you define, like, certain priorities to help you, like, fix those problems.
85 00:10:37.560 ⇒ 00:10:40.489 Greg Stoutenburg: Yeah. We just need to have that conversation.
86 00:10:41.000 ⇒ 00:10:46.830 Greg Stoutenburg: Yeah, that… that… yeah, that sounds likely to me. I mean, you know, for the old, you know, the old…
87 00:10:46.980 ⇒ 00:10:47.860 Greg Stoutenburg: Whatever.
88 00:10:48.030 ⇒ 00:10:56.539 Greg Stoutenburg: example of, like, you know, you think you need a, you know, you think you need a drill, but what you really want is a hole. Like, that kind of thing, you know.
89 00:10:56.680 ⇒ 00:10:57.460 Greg Stoutenburg: Yeah.
90 00:10:58.030 ⇒ 00:10:59.169 Greg Stoutenburg: What do you think, Demi?
91 00:11:00.660 ⇒ 00:11:04.439 Demilade Agboola: So I’m thinking that, like, it does appear…
92 00:11:04.770 ⇒ 00:11:15.560 Demilade Agboola: I’m not exactly sure what they do, but it does appear like you do need to have, like, a very clear business outcome. Like, it might be very helpful to have, like, a very clear, this is what we need.
93 00:11:15.660 ⇒ 00:11:17.430 Demilade Agboola: So, like, we don’t need…
94 00:11:18.270 ⇒ 00:11:30.959 Demilade Agboola: ultimately, whatever AI thing you’re building is for something, right? Like, there has to be that outcome that we need to attach it to. And so, once we start to have, like, a list of outcomes that we need.
95 00:11:31.470 ⇒ 00:11:43.829 Demilade Agboola: And we start to rank the priorities of those business, like, outcomes, so this will probably make us the most money. This would probably, like, just be a waste of… be a fun project, but we don’t, like, you know, turn around, like, a profit or any huge revenue.
96 00:11:44.110 ⇒ 00:11:47.260 Demilade Agboola: Once we start to get, like, an idea of what that looks like.
97 00:11:47.760 ⇒ 00:11:54.909 Demilade Agboola: then it starts to go, okay, so how… what can we do to drive this? So, for instance, if I need to drive,
98 00:11:55.300 ⇒ 00:11:56.000 Demilade Agboola: like…
99 00:11:56.450 ⇒ 00:12:02.750 Demilade Agboola: let’s just use this simple example of a chatbot. The reason why people, like, lean into chatbots heavily is because
100 00:12:02.950 ⇒ 00:12:06.139 Demilade Agboola: Customer service needs to be paid round the clock.
101 00:12:06.890 ⇒ 00:12:16.339 Demilade Agboola: If you can reduce the volume of people that you need to pay around the clock, that drives… you can increase your profit margins. So there’s a very clear use case.
102 00:12:16.490 ⇒ 00:12:22.999 Demilade Agboola: And that drives what you’re doing. And so, like, just being able to break whatever we’re doing down to those basics.
103 00:12:23.210 ⇒ 00:12:28.530 Demilade Agboola: Ultimately, it can be, okay, so are we trying to keep, are we trying to drive engagement?
104 00:12:28.960 ⇒ 00:12:48.519 Demilade Agboola: And so, like, if we’re trying to drive engagement, because we need more people on the platform, because we want to make more money the longer they’re on the platform, this would not be driving that. This would just be a nice tool to have, but it doesn’t drive any business outcome. I think sometimes just breaking it down to, like, those, like, core fundamentals would always be helpful in being able to, like.
105 00:12:48.730 ⇒ 00:12:52.599 Demilade Agboola: Ensure that you’re not just doing stuff just because it’s cool, I guess.
106 00:12:52.600 ⇒ 00:12:53.200 Pranav Narahari: Yeah.
107 00:12:53.880 ⇒ 00:12:56.590 Pranav Narahari: I, I think… That’s really good feedback.
108 00:12:57.670 ⇒ 00:13:02.549 Pranav Narahari: Yeah, I’m realizing, too, it’s just, like, a little bit of, like, a mindset shift of, like…
109 00:13:03.290 ⇒ 00:13:12.959 Pranav Narahari: the client wanting something, and me just saying yes, versus me saying, like, no, you don’t need that, you need something else. Because I think, Demi, like, what you’re describing is just, like.
110 00:13:13.170 ⇒ 00:13:24.960 Pranav Narahari: they’re almost trying to tell us, like, what the solution is, and then we’re just kind of developing it for them. Whereas, like, we should kind of, like, work together, collaborate on, like, what the solution is, and then…
111 00:13:25.400 ⇒ 00:13:35.269 Pranav Narahari: we’ll probably have to do much less work, a lot less patches, a lot less pivots, and we’ll also build a better solution for them, so… Yeah, I think that’s the next step.
112 00:13:35.500 ⇒ 00:13:39.430 Pranav Narahari: I just need to get ahold of them, and then once I can get ahold of them, then…
113 00:13:39.590 ⇒ 00:13:41.340 Pranav Narahari: We can have this conversation.
114 00:13:42.960 ⇒ 00:13:48.470 Demilade Agboola: Yeah, I agree, like, it’s one of those things where, like, everyone… it was one of the things I had to learn.
115 00:13:48.640 ⇒ 00:13:51.960 Demilade Agboola: My, like, with data on my first job.
116 00:13:52.070 ⇒ 00:13:57.620 Demilade Agboola: Let me just very back… random backstory. So I used to work in fintech at one point in time.
117 00:13:57.720 ⇒ 00:14:01.679 Demilade Agboola: And what we used to do was we used to have, like, POSs, like, like.
118 00:14:01.950 ⇒ 00:14:04.200 Demilade Agboola: Like, those, tap-to-pay.
119 00:14:04.480 ⇒ 00:14:04.830 Pranav Narahari: Yeah.
120 00:14:04.830 ⇒ 00:14:06.329 Demilade Agboola: Different parts of the country.
121 00:14:06.720 ⇒ 00:14:09.670 Demilade Agboola: However, we were being paid by…
122 00:14:10.380 ⇒ 00:14:23.380 Demilade Agboola: the number of transactions, we were able to garner, right? So even if… if someone did a huge transaction, we didn’t… we didn’t… it wasn’t a cut of the transactions, like, of the amount, it was the volume, like, the…
123 00:14:23.450 ⇒ 00:14:32.859 Demilade Agboola: number, basically, how many transactions you did. So it made… it was better for you to have a thousand transactions of small volume than for you to have one really big transaction, right?
124 00:14:33.020 ⇒ 00:14:42.019 Demilade Agboola: And I… initially, when I joined, I didn’t realize that, so I was building dashboards showing volume, but volume wasn’t, like, the end goal. It wasn’t useful.
125 00:14:42.190 ⇒ 00:14:43.200 Demilade Agboola: Right? Like…
126 00:14:43.310 ⇒ 00:14:53.780 Demilade Agboola: you couldn’t look at the volume done. It was a vanity metric, basically. It was one of those things where you look at the volume done, oh, a lot of money went through, but when we… when the finance team sat down.
127 00:14:54.180 ⇒ 00:15:00.680 Demilade Agboola: and were reconciling, and had to be like, oh, we made this amount of money. It wasn’t, like, a direct correlation.
128 00:15:01.010 ⇒ 00:15:05.640 Demilade Agboola: And so eventually, once I started to realize that I had to get to the point where I was doing
129 00:15:05.800 ⇒ 00:15:14.250 Demilade Agboola: charts where we started looking at the volume of transactions. We started looking at what merchants were doing lower volumes.
130 00:15:14.250 ⇒ 00:15:26.520 Demilade Agboola: And we can start, like, saying, hey, you have… this is your daily target of number of transactions you have to do in a day. If not, we would have to repossess this and, you know, give to people who we believe can do higher volumes than you are currently doing.
131 00:15:26.660 ⇒ 00:15:35.829 Demilade Agboola: But, like, that was one of my, like, eye-opening lessons, because I was just doing, like, oh, let me just show the number, like, oh, we’re doing $100,000, just doing our numbers.
132 00:15:36.010 ⇒ 00:15:42.389 Demilade Agboola: per day, sounds really nice, sounds really fancy, but if those 100,000 orders were, like, literally
133 00:15:42.590 ⇒ 00:15:46.710 Demilade Agboola: just a hundred orders of $1,000, that’s not that much money.
134 00:15:47.860 ⇒ 00:16:00.539 Demilade Agboola: It was better if we had, you know, a hundred… a hundred one dollar orders, for instance, $100,001 orders instead. So, like, that’s just kind of context. It’s always important, like, so I always use that to always ensure that I’m always going back
135 00:16:01.000 ⇒ 00:16:06.550 Demilade Agboola: To what is the important, like, thing we need out of all of this?
136 00:16:08.230 ⇒ 00:16:08.870 Pranav Narahari: Yeah.
137 00:16:09.370 ⇒ 00:16:16.380 Pranav Narahari: I can see how that relates to me, too. It’s like, I need to make sure I have a clear understanding of what they’re trying to get out of this product.
138 00:16:16.780 ⇒ 00:16:17.680 Demilade Agboola: Yeah.
139 00:16:18.080 ⇒ 00:16:26.360 Pranav Narahari: And I don’t think I’m really there yet. And especially with the new priorities, like, what they’re trying to accomplish with these new priorities, like, I’m definitely, like…
140 00:16:27.670 ⇒ 00:16:30.380 Pranav Narahari: I’m not exactly sure.
141 00:16:30.650 ⇒ 00:16:32.510 Pranav Narahari: Like, I have a lot of questions there, so…
142 00:16:32.670 ⇒ 00:16:35.250 Pranav Narahari: Yeah, no, that’s really good feedback. I appreciate it, guys.
143 00:16:35.900 ⇒ 00:16:36.980 Demilade Agboola: Yeah, definitely.
144 00:16:37.510 ⇒ 00:16:38.490 Demilade Agboola: Definitely.
145 00:16:41.050 ⇒ 00:16:47.060 Greg Stoutenburg: Yeah, same thought. Robert has to remind me all the time on Eden. Like… like, there’s a million stakeholders.
146 00:16:47.150 ⇒ 00:16:48.919 Demilade Agboola: They all have different things they want.
147 00:16:48.980 ⇒ 00:16:53.739 Greg Stoutenburg: And they’ll just send you messages, they’ll put time on your calendar,
148 00:16:54.130 ⇒ 00:17:13.210 Greg Stoutenburg: they just want to make money, and… and you gotta tell them how, so I feel reminded of that. And I don’t know about you guys, like, I don’t know, your background super well, but, like, this is my first time in a consulting role, rather than in-house full-time, and there’s something about the idea that your title is, you know, is consulting
149 00:17:13.349 ⇒ 00:17:18.510 Greg Stoutenburg: that… it feels like I should be listening to them to tell me what to do.
150 00:17:18.839 ⇒ 00:17:24.209 Greg Stoutenburg: Whereas if I’m internal full-time, and I know, like, my KPIs, you know, self-service sign-ups, like.
151 00:17:24.760 ⇒ 00:17:33.560 Greg Stoutenburg: Alright, here I come. I’m gonna drive self-service signups, rather than it’s like, what do you want to do? Okay, I’ll help you do it.
152 00:17:33.740 ⇒ 00:17:37.170 Greg Stoutenburg: But maybe, you know, as I think about this, like.
153 00:17:37.350 ⇒ 00:17:40.570 Greg Stoutenburg: And I’ve been thinking about this a lot, recently.
154 00:17:41.010 ⇒ 00:17:42.480 Greg Stoutenburg: Probably shouldn’t think about it that way.
155 00:17:44.240 ⇒ 00:17:44.860 Demilade Agboola: Yeah.
156 00:17:45.050 ⇒ 00:17:49.160 Demilade Agboola: I mean, this isn’t necessarily my first rodeo,
157 00:17:49.420 ⇒ 00:17:52.979 Demilade Agboola: in consulting, because I have done this before.
158 00:17:53.810 ⇒ 00:17:56.839 Demilade Agboola: And it’s one of the things I had to learn in my first rodeo, was just, like.
159 00:17:58.410 ⇒ 00:18:10.089 Demilade Agboola: you know, the customer isn’t always right, like, sometimes the customer has no clue what they want. Usually what you can help them get to is, like, business outcomes, because they tend to.
160 00:18:10.090 ⇒ 00:18:10.470 Greg Stoutenburg: So…
161 00:18:10.470 ⇒ 00:18:19.249 Demilade Agboola: business outcomes they need. We need to drive profits through increasing our user sign-up. Great! That’s a business outcome.
162 00:18:19.500 ⇒ 00:18:28.919 Demilade Agboola: How do you track that? How, what, you know, have you noticed that? And then you can start going back and back and back, and then now you have what you need to do.
163 00:18:28.990 ⇒ 00:18:43.029 Demilade Agboola: But if you just let the customer come and say, hey, we need to track this, we need to track this, I need a dashboard train me this, you can do that dashboard, and then they get it, and then they realize that whatever the dashboard is.
164 00:18:43.760 ⇒ 00:18:50.129 Demilade Agboola: isn’t really driving anything, and so now that dashboard is a waste of time. So it’s just, like, one of those things where you kind of…
165 00:18:50.720 ⇒ 00:18:58.180 Demilade Agboola: yes, you need to hear what they… what, like, what they’re seeing they want, but, you know, sometimes you just have to ask them, okay, so who needs this? Why do they need this?
166 00:19:00.320 ⇒ 00:19:06.689 Demilade Agboola: And sometimes, if they tell you that, you can then say, potentially, I don’t think this is… this is not… this is not what you need, you know?
167 00:19:06.690 ⇒ 00:19:07.310 Greg Stoutenburg: No.
168 00:19:07.310 ⇒ 00:19:07.960 Demilade Agboola: Yeah.
169 00:19:12.630 ⇒ 00:19:17.540 Pranav Narahari: Yeah, I mean, right now I’m just, like, thinking too, like, because our…
170 00:19:18.090 ⇒ 00:19:32.020 Pranav Narahari: main contact with Lilo is just the founders, and they’re not… they’re using the tool, but they also have other people within their agency that are using the tool as well, so it’s like, I could also…
171 00:19:32.130 ⇒ 00:19:43.429 Pranav Narahari: benefit from having conversations with them, because, you know, there’s dozens of them that are using the tool. I wonder, you know, Bobby and Zach were the point of contacts with Lilo, like.
172 00:19:43.800 ⇒ 00:19:48.840 Pranav Narahari: They’re going to have a really good understanding of How they operate, but not…
173 00:19:49.170 ⇒ 00:19:56.750 Pranav Narahari: They’re not gonna know everything of just, like, how their internal, employees are using the platform, and what they would benefit from as well, so…
174 00:19:57.050 ⇒ 00:20:00.909 Pranav Narahari: Yeah, even having conversations with them might be beneficial.
175 00:20:01.700 ⇒ 00:20:02.940 Pranav Narahari: Have to set that up.
176 00:20:09.530 ⇒ 00:20:19.420 Pranav Narahari: There’s one other thing with, Lilo, that’s kind of unrelated to this problem, but still kind of like CSO, is,
177 00:20:19.720 ⇒ 00:20:24.810 Pranav Narahari: How are… Deal with them works is just, like, it’s a flat fee per month.
178 00:20:25.090 ⇒ 00:20:28.910 Pranav Narahari: And… basically…
179 00:20:30.130 ⇒ 00:20:45.280 Pranav Narahari: for the month of January, we realized we used a lot of hours to, like, get them to where they wanted to get to, and they were… they were super happy with just, like, our speed, but it’s just not sustainable for us. Like, we shouldn’t be putting in that many hours.
180 00:20:45.410 ⇒ 00:20:49.150 Pranav Narahari: And so… Just dialing that back.
181 00:20:49.660 ⇒ 00:20:53.500 Pranav Narahari: I think it’s gonna be super important for me to be like, okay, like…
182 00:20:54.650 ⇒ 00:20:59.340 Pranav Narahari: And it’s kind of difficult, because, like, we’ve done so much for them, like, now, like, telling, like…
183 00:20:59.940 ⇒ 00:21:05.950 Pranav Narahari: I think… I think we can still, like, ship a lot, but… It’s,
184 00:21:06.570 ⇒ 00:21:12.200 Pranav Narahari: how to just, like, set the expectations for, like, okay, you’re paying this much, this is how much you’re gonna get.
185 00:21:13.380 ⇒ 00:21:15.909 Pranav Narahari: I just gotta do that. But I don’t know if there’s really…
186 00:21:17.880 ⇒ 00:21:20.059 Pranav Narahari: I don’t know what advice could be…
187 00:21:20.740 ⇒ 00:21:22.730 Pranav Narahari: given there. I think it’s just, like.
188 00:21:24.370 ⇒ 00:21:26.160 Pranav Narahari: You just gotta set the expectation.
189 00:21:27.530 ⇒ 00:21:28.290 Greg Stoutenburg: Yeah.
190 00:21:28.400 ⇒ 00:21:35.729 Greg Stoutenburg: Yeah, I think ultimately, as far as, like, direction, maybe you’re gonna have to tell them what it is, and I’m basically just passing along the same kind of advice people give me, so…
191 00:21:35.970 ⇒ 00:21:36.630 Pranav Narahari: Yeah.
192 00:21:37.380 ⇒ 00:21:38.130 Greg Stoutenburg: Yes.
193 00:21:40.700 ⇒ 00:21:45.700 Greg Stoutenburg: Hey, Pranav, I know that, you’d worked on the dock.
194 00:21:45.900 ⇒ 00:21:50.250 Greg Stoutenburg: Do you have the link handy to the CSO meeting?
195 00:21:50.940 ⇒ 00:21:53.209 Greg Stoutenburg: The running, agenda that we’ve had?
196 00:21:54.990 ⇒ 00:21:56.420 Pranav Narahari: Yeah, is it…
197 00:21:56.420 ⇒ 00:21:58.080 Greg Stoutenburg: I want to drop a doc in there.
198 00:21:59.200 ⇒ 00:22:01.929 Greg Stoutenburg: Yeah, it’s… yeah, it’s not on the calendar invite today.
199 00:22:02.300 ⇒ 00:22:03.570 Pranav Narahari: Oh, really? Okay.
200 00:22:04.140 ⇒ 00:22:07.780 Demilade Agboola: It’s the other, you know, there are two… we have two meetings, so I think it’s the… probably the other meeting.
201 00:22:09.720 ⇒ 00:22:11.079 Greg Stoutenburg: Oh, it’s the Monday one?
202 00:22:11.550 ⇒ 00:22:12.880 Demilade Agboola: Probably. Hey, Robert.
203 00:22:13.550 ⇒ 00:22:21.299 Robert Tseng: Sorry I’m late. This discovery call just, like, kind of went in a million different directions, this guy.
204 00:22:21.470 ⇒ 00:22:24.720 Robert Tseng: is… I mean, just had a lot to say.
205 00:22:26.030 ⇒ 00:22:27.900 Greg Stoutenburg: So, you must have discovered a lot.
206 00:22:28.370 ⇒ 00:22:38.979 Robert Tseng: Yeah, he definitely gave me more than I was asked. He was, yeah, telling me about his sister and everything, and I was like, alright, I think… I think we can… we can wrap it up.
207 00:22:38.980 ⇒ 00:22:44.849 Greg Stoutenburg: Yeah, I mean, does that mean you’ve closed the deal? I mean, like, once you start hearing about family members, that’s probably good.
208 00:22:45.130 ⇒ 00:23:04.389 Robert Tseng: Yeah, well, I mean, I don’t… I don’t know if it was for… for first call, but, yeah, no, he gave me a couple… couple good leads. They’re actually… I mean, I don’t have to spend too much time on it, but Contextual AI is a partner that we’re… that we’re… that we’ve been exploring. They’re basically… this is like a finance-related compliance platform, so…
209 00:23:04.440 ⇒ 00:23:15.609 Robert Tseng: I don’t know, I got connected to them through a friend at PwC, and they basically are in a pinch, because, like, in order for firms to
210 00:23:15.790 ⇒ 00:23:31.980 Robert Tseng: work with them, they need to get their data cleaned up. And so, it, you know, it’s usually, like, a 3-6 month period before they can even really use their product, and so I’m trying to explore, like, hey, can Brainforge come in and basically do the discovery and cleaning for them?
211 00:23:32.360 ⇒ 00:23:46.929 Robert Tseng: we could do it in 3 months or less, and then, like, that kind of helps on their… on their sales motion, and I mean, their average contract size is, like, 500 grand, so, like, I think it’d be… it’d be, it’d be interesting if we could actually do it for them.
212 00:23:47.320 ⇒ 00:23:48.060 Robert Tseng: Yeah.
213 00:23:50.210 ⇒ 00:23:50.830 Demilade Agboola: Oh, wow.
214 00:23:51.090 ⇒ 00:23:53.259 Demilade Agboola: I wouldn’t mind a 500 grand contract.
215 00:23:53.550 ⇒ 00:24:08.359 Robert Tseng: Yeah, yeah, so, and he’s… he’s a pretty well-connected, hedge fund guy, was at Citadel for, like, 8 years, so he kind of just kind of picked up a bunch of the algorithms that they built there, and then he’s been building stuff himself, so…
216 00:24:08.400 ⇒ 00:24:14.760 Robert Tseng: Interesting, like, really nerdy products, but, like, they have no idea how to do sales at this point, so…
217 00:24:18.370 ⇒ 00:24:19.590 Demilade Agboola: That’s fair, that’s fair.
218 00:24:20.190 ⇒ 00:24:20.810 Robert Tseng: Yeah.
219 00:24:21.710 ⇒ 00:24:36.219 Robert Tseng: Cool. Yeah, I know we wanted to… I know not to cut you guys off when you were doing… at least I’m interested in the Omni thing that I was supposed to do with Greg, but I guess, I don’t know, wherever you guys left off, I can just kinda…
220 00:24:36.640 ⇒ 00:24:37.740 Robert Tseng: Go with… go with us.
221 00:24:37.740 ⇒ 00:24:38.480 Greg Stoutenburg: Yeah.
222 00:24:38.610 ⇒ 00:24:50.889 Greg Stoutenburg: Yeah, I think we can jump into Omni. So, I don’t have anything resembling, like, a full-on prepared training to do today. Okay. What I would like to do is just go over the things I want to hit.
223 00:24:50.900 ⇒ 00:25:04.210 Greg Stoutenburg: And, get… get comments on that, and thank you very much. And my son has just delivered the potent nasal spray. I’m 4 days into a cold and breathing through my mouth, so I’m gonna go mute and dark for one moment.
224 00:25:04.470 ⇒ 00:25:09.049 Robert Tseng: Yeah, no, those things work like magic. It’ll come back, and then your sinus will be cleared.
225 00:25:09.590 ⇒ 00:25:11.620 Robert Tseng: Yeah.
226 00:25:11.750 ⇒ 00:25:14.890 Robert Tseng: Are these the things that… And Pranav, I just saw your message.
227 00:25:14.890 ⇒ 00:25:16.760 Pranav Narahari: You’re good, you’re good. Yeah.
228 00:25:17.360 ⇒ 00:25:23.330 Pranav Narahari: Yeah, I’m thinking about using those, Greg. I am also, like, sniffling, like, all day, all night, so…
229 00:25:23.660 ⇒ 00:25:29.580 Greg Stoutenburg: Yeah. In about… in my experience, in about three and a half minutes, I’m gonna feel like a new man.
230 00:25:29.660 ⇒ 00:25:30.800 Greg Stoutenburg: Yeah.
231 00:25:30.850 ⇒ 00:25:48.569 Greg Stoutenburg: Okay, yeah, it’s been… it’s been a rough one today. Okay, so for… so this is the way that I’m thinking about it. I dropped in a link, I had cursor look at just everything that I had from Omni so far, and ask for, like, a training presentation that’s about 45 minutes long.
232 00:25:48.570 ⇒ 00:25:53.100 Greg Stoutenburg: That is geared toward the Eden folks as users.
233 00:25:53.220 ⇒ 00:26:15.909 Greg Stoutenburg: And, so there’s… in some ways, Cursor kind of didn’t listen, but I also just haven’t refined it. There’s a lot that you can do in Omni as a developer, you know, connecting additional data sources, creating topics, things like that. My thought had been that I would just skip all of those things, because, just like for Tableau, Brainforge will be managing the setup of the tool.
234 00:26:15.910 ⇒ 00:26:24.069 Greg Stoutenburg: And what we really want to enable is, is two things. So, viewer usership as, like, in the same way that they were using Tableau.
235 00:26:24.110 ⇒ 00:26:32.760 Greg Stoutenburg: I’m an executive, I want to log in and look at a particular dashboard, that dashboard is there, I’m happy. I’ll ask questions, you know, about the dashboard, and that’s it.
236 00:26:33.140 ⇒ 00:26:47.720 Greg Stoutenburg: And then, second, users who want to interrogate the data a little bit more by using AI to create new charts or to ask questions about charts or dashboards. So, I want the… my thought is that the training will be geared toward that.
237 00:26:47.740 ⇒ 00:27:04.019 Greg Stoutenburg: And understanding how to do those things. And then, secondarily, but related, as far as the Omni account goes, since there isn’t a signed deal, I would go through things like, here’s what the package is that you’re looking at for your tier. You know, they’ve got…
238 00:27:05.500 ⇒ 00:27:11.809 Greg Stoutenburg: 5, 6, 7, 8, 9, 10, 11, 12 users total. When I look at the list that you gave me the other day, Robert.
239 00:27:12.300 ⇒ 00:27:17.300 Greg Stoutenburg: Most of them would be viewers, so…
240 00:27:20.130 ⇒ 00:27:37.249 Greg Stoutenburg: Omni distinguishes developers who have access to, like, all of the tools, and that’s one kind of seat. Actually, standard is the one where you can use some AI, and they get 10 on the tier that they’re looking at. They get 10 users who can use the AI tools, and then anybody after that is viewer, and that’s the third tier seat.
241 00:27:38.570 ⇒ 00:27:46.009 Greg Stoutenburg: So I’d go into that a little bit as well, just, like, to say, you know, here’s how many tokens you get, and, you know, here…
242 00:27:46.200 ⇒ 00:27:50.139 Greg Stoutenburg: see if they have any questions about that, or how I can work with Max to…
243 00:27:50.890 ⇒ 00:27:54.050 Greg Stoutenburg: Get their, get their sale moving along.
244 00:27:54.810 ⇒ 00:27:56.170 Greg Stoutenburg: Any questions so far?
245 00:27:57.420 ⇒ 00:27:58.750 Greg Stoutenburg: Okay, let’s keep going.
246 00:27:59.020 ⇒ 00:28:03.889 Greg Stoutenburg: So my thought is for… for training, like, the order of operation would be,
247 00:28:04.280 ⇒ 00:28:06.400 Greg Stoutenburg: Like, here are the different user types.
248 00:28:06.740 ⇒ 00:28:13.040 Greg Stoutenburg: here’s what pricing looks like for your package, which I think is just gonna be what they’ve already had put in front of them. Robert, by you.
249 00:28:13.310 ⇒ 00:28:21.260 Greg Stoutenburg: Yeah. Showing them what the AI features are and how they access them, showing them how to interrogate data and also how to create charts using AI.
250 00:28:22.070 ⇒ 00:28:28.450 Greg Stoutenburg: And then how to save them, if they want to actually save them, versus if it’s just, you know, a one-off inquiry, inquiry.
251 00:28:29.240 ⇒ 00:28:31.999 Greg Stoutenburg: Show them their familiar data dashboards.
252 00:28:32.170 ⇒ 00:28:38.349 Greg Stoutenburg: And, I’ll explain what topics are, probably as I go into any of the AI stuff.
253 00:28:39.890 ⇒ 00:28:48.250 Greg Stoutenburg: I’ll tell them that they have a mobile view, and which dashboards are set up for mobile view, but they don’t need to know how to enable that. That’s more… one of the management pieces that we’ll do.
254 00:28:48.430 ⇒ 00:28:52.890 Greg Stoutenburg: And then, tell them about how we can use snapshots and create a workflow.
255 00:28:53.670 ⇒ 00:28:56.220 Greg Stoutenburg: So, my thought would be of doing something like this.
256 00:28:57.820 ⇒ 00:28:59.110 Greg Stoutenburg: It’s coming here.
257 00:29:00.070 ⇒ 00:29:00.750 Greg Stoutenburg: Back.
258 00:29:01.020 ⇒ 00:29:05.800 Greg Stoutenburg: I’m starting to breathe my nose again. Alright, so the user list is this.
259 00:29:06.530 ⇒ 00:29:14.600 Greg Stoutenburg: That Robert gave me the other day. Yep. At Eden, we’ve got CEO, VP, COO.
260 00:29:15.020 ⇒ 00:29:18.800 Robert Tseng: Mitesh is the CMO, but he just… yeah, yeah.
261 00:29:18.990 ⇒ 00:29:20.969 Greg Stoutenburg: Oh, is he?
262 00:29:23.920 ⇒ 00:29:27.710 Greg Stoutenburg: I feel like I just saw his name and it’s gone. Oh, this says Eden Marketing. Okay.
263 00:29:27.710 ⇒ 00:29:31.930 Robert Tseng: Yeah, they, they, they just… they tried… he tried to share it with his team, yeah.
264 00:29:32.150 ⇒ 00:29:34.240 Greg Stoutenburg: Okay, got it.
265 00:29:35.440 ⇒ 00:29:38.040 Greg Stoutenburg: Yeah, I asked Kersha to add titles, and that’s why.
266 00:29:38.300 ⇒ 00:29:46.090 Greg Stoutenburg: Those are there. Okay, so when you said, do ELT last, That’s… is that Adam, Brad?
267 00:29:46.540 ⇒ 00:30:01.109 Robert Tseng: Yeah, the C-suite, we can add Mitesh in, or… actually, I would say, yeah, Danny Mitesh should probably be one of the early ones, because Danny is the… is the… is the… is the C-suite sponsor, like, he wants this more than the others, and then Mitesh is…
268 00:30:01.320 ⇒ 00:30:10.070 Robert Tseng: I mean, I think he’s… he’s… it’s just good to keep him in the loop, since he’s… he’s kind of the most engaged, exec on… for our… for our work role.
269 00:30:10.800 ⇒ 00:30:17.969 Greg Stoutenburg: Okay, great, yeah. I kind of thought this would be sort of a two-parter, so do one… do one training for everyone who’s not an ELT.
270 00:30:18.300 ⇒ 00:30:22.939 Robert Tseng: And let’s actually remove Stewart. He’s actually, I think he got let go, so… yeah.
271 00:30:22.940 ⇒ 00:30:23.620 Greg Stoutenburg: Okay.
272 00:30:23.740 ⇒ 00:30:24.370 Robert Tseng: Yeah.
273 00:30:24.370 ⇒ 00:30:25.080 Greg Stoutenburg: Alright.
274 00:30:26.180 ⇒ 00:30:29.120 Greg Stoutenburg: Okay, so for a first training.
275 00:30:29.360 ⇒ 00:30:30.709 Greg Stoutenburg: Who do you think that should be?
276 00:30:32.010 ⇒ 00:30:41.040 Robert Tseng: First training, I mean, Ryan, brian, Matt, Schwartz,
277 00:30:44.510 ⇒ 00:31:01.099 Robert Tseng: I think you should add Brad, but, like, I don’t… I’m also, like, how much are we gonna show… is it… if it’s mostly marketing data, maybe we just do the marketing stuff. Like, maybe Brad doesn’t need to be in the first one. Yeah, Ryan brought… yeah, Ryan, ryan, Matt, and
278 00:31:01.870 ⇒ 00:31:04.149 Robert Tseng: Judd, probably? Yeah.
279 00:31:04.150 ⇒ 00:31:07.439 Greg Stoutenburg: Okay. And then maybe everybody else is the second one?
280 00:31:07.610 ⇒ 00:31:08.850 Robert Tseng: Yeah, yeah.
281 00:31:11.640 ⇒ 00:31:14.180 Greg Stoutenburg: Okay, you said Brian, I don’t see Brian on this list.
282 00:31:14.810 ⇒ 00:31:17.670 Robert Tseng: Yeah, that might have been a misspeak, I don’t think, but I am.
283 00:31:17.670 ⇒ 00:31:19.330 Greg Stoutenburg: Oh, okay. Okay, sure.
284 00:31:21.420 ⇒ 00:31:27.040 Greg Stoutenburg: Alright, Ryan, Matt, Chudd. And Matt is… You said he’s also marketing?
285 00:31:27.890 ⇒ 00:31:28.540 Robert Tseng: Yeah.
286 00:31:30.110 ⇒ 00:31:38.909 Greg Stoutenburg: Okay, great. So that can be, like, partly a test run for me. Alright, I’ll set that up, and since we’re on the topic, I mean, can I just start sending invites for…
287 00:31:38.910 ⇒ 00:31:47.569 Robert Tseng: department makes sense, so if we do marketing first, and… or… and then if we want to do ops as for the second group, then it would be Brad, Sarah.
288 00:31:47.790 ⇒ 00:31:59.739 Robert Tseng: They may ask a couple other people to be brought in. I could… I could… but I’m not, like, gonna proactively name them, because they’re not really the leads. But I would just drop it in that FarmOps channel if you’re in it, and…
289 00:31:59.740 ⇒ 00:32:04.019 Greg Stoutenburg: Okay. You know, at least Brad and Sarah, because they’re the two leads there.
290 00:32:04.610 ⇒ 00:32:06.090 Greg Stoutenburg: Okay. Good. Yeah.
291 00:32:06.200 ⇒ 00:32:07.710 Robert Tseng: And then…
292 00:32:07.820 ⇒ 00:32:09.969 Greg Stoutenburg: Third can be ELT.
293 00:32:10.540 ⇒ 00:32:11.090 Robert Tseng: Yeah.
294 00:32:11.860 ⇒ 00:32:12.430 Greg Stoutenburg: Okay.
295 00:32:13.840 ⇒ 00:32:19.110 Greg Stoutenburg: All right, good, that’s helpful, thanks. Alright, so the thought is that we’d come here.
296 00:32:19.990 ⇒ 00:32:28.039 Greg Stoutenburg: by default, there wouldn’t be anything in the home area, and nothing in the… in the for you paid. So, Omni looks pretty blank.
297 00:32:28.360 ⇒ 00:32:41.040 Greg Stoutenburg: the way that I’d want to structure this is, as a user, when you log in, you don’t see anything, so you would navigate to shared with me, and… okay, well, there would be something there.
298 00:32:41.040 ⇒ 00:32:41.659 Robert Tseng: Can I add something?
299 00:32:41.660 ⇒ 00:32:42.250 Greg Stoutenburg: Good, yeah.
300 00:32:42.970 ⇒ 00:32:53.619 Robert Tseng: Yeah, for the ELT one, it’ll probably have to be mobile. They really cared about this being mobile first, so, like, I think if… yeah, just FYI on that one.
301 00:32:53.620 ⇒ 00:32:55.280 Greg Stoutenburg: Mobile first. Okay, got it.
302 00:32:55.300 ⇒ 00:33:01.150 Robert Tseng: Like, they’re only on their… looking at it on mobile, usually. They’re not really going to stop looking at it. Yeah.
303 00:33:01.150 ⇒ 00:33:05.280 Greg Stoutenburg: Okay, great, yeah. So, so my plan had been…
304 00:33:05.480 ⇒ 00:33:21.970 Greg Stoutenburg: I was gonna get to it today, I’m not gonna get to it today now, would be to look at Tableau viewer stats and see who on ELT is looking at what, and then optimize those for mobile, because in Omni, for any dashboard, you can create a separate view that’s a mobile view.
305 00:33:22.920 ⇒ 00:33:37.919 Robert Tseng: Yeah, that’s what we’ve been doing on Tableau, because they’re… yeah, Tableau doesn’t do good mobile optimization, so we’ve pretty much had to duplicate mobile views for each of them. I would say everything in the exec folder, or whatever I called it, it needs to have a mobile view.
306 00:33:38.160 ⇒ 00:33:40.080 Robert Tseng: The others don’t, necessarily.
307 00:33:45.200 ⇒ 00:33:46.999 Greg Stoutenburg: Cool, that helps me plan.
308 00:33:47.910 ⇒ 00:33:50.720 Greg Stoutenburg: Alright, so they come to the hub, And…
309 00:33:50.780 ⇒ 00:34:04.200 Greg Stoutenburg: you know, depending on which group it is, let’s just say it’s, let’s say it’s the marketing folks. They’ll come here, and then we’ll… we’ll look at the, the marketing dashboards there, and we’ll… we’ll proceed that way. That content is not in there yet, so…
310 00:34:04.240 ⇒ 00:34:11.010 Greg Stoutenburg: we’ll go to what we do see, and just kind of kick it off from there. So, let’s go to, the exec view.
311 00:34:12.190 ⇒ 00:34:30.820 Greg Stoutenburg: And from here… actually backing up… from here, this view should resemble pretty closely what they see in Tableau when they look at all published dashboards. So anything that is something that they’re using that we’ve put together for them will be here. And then I’ll show them that if they star it, it’ll show up on their home view page.
312 00:34:31.719 ⇒ 00:34:42.980 Greg Stoutenburg: Now, you come in here, and the goal here would be that this ticks the box of the curated dashboards that you’re used to engaging with in Tableau. You log in, the thing is there, ready for you to use.
313 00:34:43.350 ⇒ 00:34:48.360 Greg Stoutenburg: Now, if you’re in The dashboard, and you want to…
314 00:34:48.690 ⇒ 00:34:52.489 Greg Stoutenburg: learn something more, you can click Explore here, or you can click the tab here.
315 00:34:52.980 ⇒ 00:34:55.070 Greg Stoutenburg: And just go to Explore.
316 00:34:56.000 ⇒ 00:35:01.409 Greg Stoutenburg: And now, you can interact with Blobby by clicking on, the stars here.
317 00:35:02.350 ⇒ 00:35:08.680 Greg Stoutenburg: So then we can ask a question, you know, if this is the chart that we write next to. We can say,
318 00:35:08.870 ⇒ 00:35:17.119 Greg Stoutenburg: you know, I’ll have some predefined questions that we might spit out at this point, just to kind of show it off. So if we want to know something like,
319 00:35:21.170 ⇒ 00:35:23.599 Greg Stoutenburg: I don’t know, some… some…
320 00:35:24.180 ⇒ 00:35:25.689 Greg Stoutenburg: Let’s just ask for the sum.
321 00:35:26.100 ⇒ 00:35:31.999 Greg Stoutenburg: What is the sum of new customer revenue in the past?
322 00:35:33.380 ⇒ 00:35:34.460 Greg Stoutenburg: 20 days.
323 00:35:34.610 ⇒ 00:35:38.360 Greg Stoutenburg: Something that’s gonna be a little bit more granular than what’s on the table.
324 00:35:39.050 ⇒ 00:35:43.090 Greg Stoutenburg: And just show them that Blobby is able to look at this, and come up with an answer.
325 00:35:44.680 ⇒ 00:35:45.220 Robert Tseng: Yeah.
326 00:35:45.220 ⇒ 00:35:58.190 Greg Stoutenburg: da-da-da, lobby’s being fast, says this chart needs help, but, like, don’t worry, it’s gonna come up with something. So, if I want to look at just the last 20 days, I get this number. Now, is that number in touch with reality? I wanna go back and see.
327 00:36:00.600 ⇒ 00:36:07.090 Greg Stoutenburg: Yeah, okay, cool. It actually created a new chart for me. And basically, I changed the filter, but using natural language. I didn’t have to use any of these
328 00:36:07.690 ⇒ 00:36:08.580 Greg Stoutenburg: buttons.
329 00:36:09.460 ⇒ 00:36:11.450 Greg Stoutenburg: So that’s cool.
330 00:36:11.770 ⇒ 00:36:15.410 Greg Stoutenburg: We can ask some more analytical questions that I’ll prepare in advance as well.
331 00:36:16.370 ⇒ 00:36:22.679 Greg Stoutenburg: I think that’s nice for showing them the power of this, alongside what they’re already able to see just by logging in.
332 00:36:23.260 ⇒ 00:36:28.139 Greg Stoutenburg: Now, the way that we’re able to do that…
333 00:36:28.410 ⇒ 00:36:31.039 Greg Stoutenburg: I think that’s here? No, I don’t need model changes.
334 00:36:37.280 ⇒ 00:36:40.790 Greg Stoutenburg: Live demo, so, you know, required lag.
335 00:36:42.170 ⇒ 00:36:42.810 Robert Tseng: Yeah.
336 00:36:46.240 ⇒ 00:36:49.569 Greg Stoutenburg: Yeah, alright, this is… Just showing it.
337 00:36:50.010 ⇒ 00:36:53.120 Greg Stoutenburg: All these tables… okay, good.
338 00:36:53.630 ⇒ 00:36:56.490 Greg Stoutenburg: Here, I’ll show them that the way that…
339 00:36:56.640 ⇒ 00:37:07.189 Greg Stoutenburg: Blobby is able to do this is because in Omni, there’s this concept of a topic, and a topic is a predefined set of tables. So, when you type something here into Blobby to ask a question.
340 00:37:07.650 ⇒ 00:37:20.909 Greg Stoutenburg: The first thing that’s going to happen is that it’s going to look through all of these different topics where those are particular datasets that are designed to address some subject matter.
341 00:37:21.190 ⇒ 00:37:34.299 Greg Stoutenburg: these topics were chosen because these are the dashboard categories that were used in Tableau. So we already know that this is the kind of information that they’re interested in, this is how they have their data structured, so,
342 00:37:34.300 ⇒ 00:37:46.909 Greg Stoutenburg: we’ve… we’ve duplicated that here. It’s not just, like, pointing an LLM at all of your data. It’s actually structured in a particular way, so that when you ask a question, it’s gonna find the right topic to answer you from, and then deliver an answer from there.
343 00:37:47.940 ⇒ 00:37:58.119 Greg Stoutenburg: Which, hopefully, at that point, there’s a little bit of, like, you know, because that is actually pretty sweet. We’ll see how that goes. So, yep, and then…
344 00:37:58.390 ⇒ 00:38:03.629 Greg Stoutenburg: And then from here, what I think I would show them is, alright, so Brainforge will continue to manage
345 00:38:03.750 ⇒ 00:38:16.169 Greg Stoutenburg: your Omni instance, but if there is something that you come up with as a new insight that you think that you would want to refer back to, there’s a way to take this, for example, and save it somewhere. Now, let’s see if I remember how to do that.
346 00:38:18.570 ⇒ 00:38:20.459 Greg Stoutenburg: No, that’s a filter.
347 00:38:21.450 ⇒ 00:38:36.620 Greg Stoutenburg: It’s gonna… yeah, okay, cool. It’s gonna be this. So, I can take this chart that I just made using AI and add it to a workbook. And maybe it’s just my own, you know, private one, so, you know, revenue,
348 00:38:37.270 ⇒ 00:38:38.230 Greg Stoutenburg: scratch pad.
349 00:38:39.830 ⇒ 00:38:55.060 Greg Stoutenburg: And now I’ll save this in my documents, because this is something that I care about. So, I’m going to want to look back at that, so I’ll put this here, and I can publish it, or I can leave it in draft state for now, but I can now work with the data further if I want to, to create something custom of my own.
350 00:38:56.920 ⇒ 00:39:02.320 Greg Stoutenburg: And I’ll pause here, as far as…
351 00:39:02.550 ⇒ 00:39:08.639 Greg Stoutenburg: data work that they would do on their own. I think this is probably about as far as I would want to take it.
352 00:39:08.640 ⇒ 00:39:09.000 Robert Tseng: Yeah.
353 00:39:09.000 ⇒ 00:39:23.519 Greg Stoutenburg: We want them to be able to self-serve, but, like, that’s what Blobby and the dashboards are for. Yeah. Aside from, like, their own, you know, like, workspace like this, I don’t… I don’t think I want to say much more about, like, go ahead, here’s how you change filters, here’s how you connect new data sources, like, that’s just for us.
354 00:39:23.910 ⇒ 00:39:24.480 Robert Tseng: Yeah.
355 00:39:24.750 ⇒ 00:39:25.290 Greg Stoutenburg: Yeah.
356 00:39:26.470 ⇒ 00:39:27.960 Greg Stoutenburg: Yep. Okay.
357 00:39:28.700 ⇒ 00:39:36.549 Greg Stoutenburg: If, by the time… We’re having the training.
358 00:39:37.040 ⇒ 00:39:47.379 Greg Stoutenburg: I’m… I’ve already set up a workflow that takes a snapshot and uses a webhook to push it to Slack. I’ll show them where that lives. Otherwise, I think I’ll just say that we can do it.
359 00:39:47.600 ⇒ 00:39:49.120 Greg Stoutenburg: And leave it at that.
360 00:39:52.110 ⇒ 00:39:53.190 Robert Tseng: Yeah, that makes sense.
361 00:39:53.580 ⇒ 00:39:59.890 Greg Stoutenburg: Yep Alright, so it was a very dry, dry run. Any thoughts?
362 00:40:01.500 ⇒ 00:40:11.939 Robert Tseng: Yeah, no, I think that that’s the extent. I mean, obviously showing that we still have the same reporting capability as we did before, and then walking them through, like, how to answer their own question.
363 00:40:12.200 ⇒ 00:40:20.520 Robert Tseng: I mean, I’ve asked for this before, but I don’t think we ever logged all the questions. Like, we get a bunch of random ad… it’s less now. It’s probably…
364 00:40:20.830 ⇒ 00:40:22.820 Robert Tseng: At some point, we were getting…
365 00:40:24.060 ⇒ 00:40:34.489 Robert Tseng: 5-plus questions a week. Now it’s probably less than that. But yeah, I mean, those questions should be all answerable within Omni. That’s what I would ideally like to see.
366 00:40:34.730 ⇒ 00:40:39.590 Robert Tseng: I don’t think everybody would be building out their own dashboards, but, like, the leads themselves, like…
367 00:40:39.740 ⇒ 00:40:58.560 Robert Tseng: right now, they… yeah, they just export data from BASC, and they run it in Monday, but that’s kind of the extent of what they do. They just, like, take raw CSV-type files, run it in Monday, and get bar charts or line charts. So, like, that’s a framing of, like, that’s the extent of, like, the analytics
368 00:40:58.700 ⇒ 00:41:02.419 Robert Tseng: Usage of people there, so…
369 00:41:02.800 ⇒ 00:41:20.890 Robert Tseng: I mean, I’m worried that it’s gonna be, like, here’s a shiny tool, they don’t really know what to do with it. I mean, we gotta find out who the power users are. Like, yeah, so I think that’s really gonna be key for us. Like, who’s really gonna spend their time, like, digging into the data? Like, how do we train them to dig into the data?
370 00:41:21.010 ⇒ 00:41:23.309 Robert Tseng: So I, I think.
371 00:41:23.650 ⇒ 00:41:28.519 Greg Stoutenburg: I mean, that’s… this won’t be for the first training, but I’m just thinking for subsequent things, like…
372 00:41:29.370 ⇒ 00:41:33.160 Robert Tseng: Ideally, we end up having our analysts
373 00:41:33.310 ⇒ 00:41:44.809 Robert Tseng: whoever is, like, using Omni to kind of basically drill into things and assemble analysis using Omni. Like, that would be ideal if people can actually
374 00:41:46.230 ⇒ 00:41:54.210 Robert Tseng: spot something, and then go deeper on their own, like, I think that would be… I… that would be a good,
375 00:41:56.120 ⇒ 00:42:06.230 Robert Tseng: win. Like, I don’t know how we’re gonna define the success criteria of this yet. I think they just… they just hate Tableau and eager to get off, but, like, they don’t really know what this is supposed to, like…
376 00:42:06.340 ⇒ 00:42:14.019 Robert Tseng: like, how much this really raises the bar for them, like, I think we have to be intentional about how we’re gonna coach people through this.
377 00:42:14.420 ⇒ 00:42:14.980 Greg Stoutenburg: Yeah.
378 00:42:15.280 ⇒ 00:42:25.639 Greg Stoutenburg: Yeah. Yeah. Yeah, and yeah, and I think that the… I think that the plan of an initial training is the right way to start, but yeah, there will need to continue to be more.
379 00:42:25.750 ⇒ 00:42:32.530 Greg Stoutenburg: more check-ins, more sessions to encourage adoption, and make sure that they’re seeing what they want to see, and know that Omni can deliver it.
380 00:42:33.320 ⇒ 00:42:42.410 Robert Tseng: Yeah, and then ideally, in the future, when we’re answering questions for them, because we’ll have ad hoc questions, we’ll be able to answer them through Omni. Like, I think our team should try to default to doing that.
381 00:42:42.580 ⇒ 00:43:00.199 Robert Tseng: Rather than, like, Demulade sending a CSV and saying, here’s your answer, or me writing this long, like, Slack message. Like, ideally, it’s like, that’s… the report is there, Omni has the context, the message is all in Omni. Like, if we can get that… that in there, then I think that would be great. So this will hopefully stop us from having to…
382 00:43:00.760 ⇒ 00:43:04.670 Robert Tseng: to do the random CSV, Excel kind of sharing that we do.
383 00:43:05.760 ⇒ 00:43:06.310 Greg Stoutenburg: Yeah.
384 00:43:07.280 ⇒ 00:43:07.910 Robert Tseng: Yeah.
385 00:43:08.970 ⇒ 00:43:09.480 Greg Stoutenburg: Cool.
386 00:43:10.140 ⇒ 00:43:10.760 Robert Tseng: Cool.
387 00:43:11.810 ⇒ 00:43:13.800 Greg Stoutenburg: Any other, comments from Erdogan?
388 00:43:14.030 ⇒ 00:43:17.870 Demilade Agboola: I was gonna ask if… Is Jonah gonna be on the call?
389 00:43:19.570 ⇒ 00:43:20.210 Greg Stoutenburg: Let’s see…
390 00:43:20.210 ⇒ 00:43:27.699 Robert Tseng: Not on the first one, but if you want, I mean, maybe we should bring him in if he’s… if you feel like he’s gonna be… I feel like he’s just gonna be, like.
391 00:43:27.900 ⇒ 00:43:35.000 Robert Tseng: He… I mean, he’s a finance guy, he doesn’t really care about any of these features, he just wants to be able to get data, you’ll always want it in CSVs.
392 00:43:35.180 ⇒ 00:43:46.180 Demilade Agboola: Because I know, like, sometimes he’s asked me some… or, like, his team, people on his team have asked questions around, hey, how many unique customers do we have in the entire year of last year? Which, again, is something you can always just ask.
393 00:43:46.370 ⇒ 00:43:54.130 Demilade Agboola: Omni, or, I need to get the… like, yearly… Report.
394 00:43:54.360 ⇒ 00:44:05.820 Demilade Agboola: Of every single state by gross revenue, number of refunds, like, the amount of refunds in terms of, like, dollar amount of refunds, dollar amount of, like, discounts.
395 00:44:06.180 ⇒ 00:44:09.339 Demilade Agboola: And then, the net revenue, like, things like that.
396 00:44:09.610 ⇒ 00:44:12.250 Demilade Agboola: I’ve had to do that for, I think, like.
397 00:44:12.400 ⇒ 00:44:20.529 Demilade Agboola: 3 quarter of the year, and then end of year, they had… they asked me to do that as well, for the entirety of the year. I mean, those kind of, like, requests…
398 00:44:20.690 ⇒ 00:44:25.599 Demilade Agboola: are stuff that, like, ideally you would want to use Omni to kind of answer.
399 00:44:26.010 ⇒ 00:44:30.010 Demilade Agboola: And I know in terms of, like, Ops…
400 00:44:30.710 ⇒ 00:44:35.900 Demilade Agboola: I know Katie has reached out to me a couple times to ask me for, say,
401 00:44:36.420 ⇒ 00:44:41.520 Demilade Agboola: Based off of, like, people who are, like, using, you know, SAMA.
402 00:44:41.780 ⇒ 00:44:45.890 Demilade Agboola: How many… how many, shipments are left?
403 00:44:47.160 ⇒ 00:44:48.970 Demilade Agboola: And when is your next shipping date?
404 00:44:49.390 ⇒ 00:44:50.989 Demilade Agboola: You know, things like that, so…
405 00:44:51.410 ⇒ 00:45:00.320 Demilade Agboola: Those are, like, kind of, like, the sample questions that come to mind, like, quickly, based off of, you know, the kind of things I’ve been helping people answer across different domains.
406 00:45:01.050 ⇒ 00:45:11.130 Greg Stoutenburg: Yeah, sweet, that’s all full, yeah. Mustafa took old transcripts to try to find questions that have been asked, and feed those in for topics,
407 00:45:11.130 ⇒ 00:45:11.800 Demilade Agboola: Okay.
408 00:45:11.930 ⇒ 00:45:19.980 Greg Stoutenburg: My… my goal and hope is that we’ll have all the dashboards stood up by the end of the day tomorrow, and then Friday can be focused on QA.
409 00:45:19.980 ⇒ 00:45:33.399 Greg Stoutenburg: And, praying for the next steps. So, if you could, you know, so… so that’s an ask. It’s turning into an ask. So, if on Friday you can go in and ask Bobby those questions, and, you know, imagine that you’re Katie or that you’re Jonah, that would be really helpful.
410 00:45:34.400 ⇒ 00:45:35.540 Demilade Agboola: Alright, sounds good.
411 00:45:38.430 ⇒ 00:45:39.600 Greg Stoutenburg: Cool.
412 00:45:39.600 ⇒ 00:45:45.529 Pranav Narahari: For, slide 13, and I don’t know if this is, like, a conversation for a later time, but
413 00:45:45.940 ⇒ 00:45:48.320 Pranav Narahari: How are you planning on doing, like, that validation?
414 00:45:52.030 ⇒ 00:45:55.830 Greg Stoutenburg: My plan for slide 13 would be to delete that slide.
415 00:45:56.150 ⇒ 00:46:14.720 Greg Stoutenburg: Okay. This is… yeah, yeah, yeah, I asked… I asked Chris to, like, plan a training, and, like, some of the things that… some of the things that I saw right away is, like, way… this is, like, way too developer-focused. Like, the developers for… the people in the developer seats will be Brain Forge folks, and, you know, if the day comes to hand over
416 00:46:14.720 ⇒ 00:46:30.749 Greg Stoutenburg: that kind of thing to Eden, then, you know, that stakeholder gets in. But otherwise, I want to focus this on people in the viewer and on the standard role, so they can use AI to answer their questions, or to… like, the only creating would be to, like, create their own workbook, like I saw, but I don’t…
417 00:46:30.870 ⇒ 00:46:37.179 Greg Stoutenburg: I don’t think we actually want them going in and, you know, messing with things like that. And to the degree that, like.
418 00:46:37.430 ⇒ 00:46:46.660 Greg Stoutenburg: to the degree that they need to pay attention to particular filters, they already know that from working with Tableau or other data sources, like… so I… yeah. I think 13 can just go.
419 00:46:47.530 ⇒ 00:47:02.169 Pranav Narahari: Yeah. I mean, in case, like, you feel like you need to prepare for just, like, the question of, like, you know, AI does hallucinate, AI does, like, come up with just, like, random insights that are not, based on any, like, source of truth, like.
420 00:47:02.260 ⇒ 00:47:10.100 Pranav Narahari: we can talk about that, but if you feel like the conversation’s not gonna go there, then that’s fine. I just bring that up because we’re literally having that same conversation with Leela right now, so…
421 00:47:10.900 ⇒ 00:47:30.250 Greg Stoutenburg: Yeah, no, that’s a good call. And so, the top… the concept of topics is designed in part to prevent that kind of hallucination, because, you know, like, we’ve identified the right data source, and it’s supposed to reference that data source, but they can also always ask us if there’s concern about any individual report that they’re seeing.
422 00:47:31.170 ⇒ 00:47:40.229 Greg Stoutenburg: And then hopefully they just, like, for the most part, hopefully that won’t happen, and they won’t take us up on that, because part of the goal here is that that doesn’t happen. That they don’t give us those ad hoc requests.
423 00:47:43.900 ⇒ 00:47:48.259 Greg Stoutenburg: Okay, as far as, as far as scheduling these things,
424 00:47:48.690 ⇒ 00:47:52.010 Greg Stoutenburg: Robert, will you recommend I just sort of propose times on the calendar?
425 00:47:52.370 ⇒ 00:47:55.790 Greg Stoutenburg: for these people, or should I, like, reach out to them and say, hey, what works for you?
426 00:47:56.200 ⇒ 00:48:03.609 Robert Tseng: Do you have the data triadin access? I think it’s a 1Pass? If you do, you can go in and just find people’s, like… you can just book it yourself.
427 00:48:04.210 ⇒ 00:48:04.960 Greg Stoutenburg: Oh, okay.
428 00:48:06.550 ⇒ 00:48:10.079 Robert Tseng: Yeah, like, from Google Calendar, it’ll show their… it’ll show their availability.
429 00:48:10.330 ⇒ 00:48:11.920 Greg Stoutenburg: It’ll be data at Triaden.
430 00:48:12.250 ⇒ 00:48:12.980 Robert Tseng: Yeah.
431 00:48:14.730 ⇒ 00:48:15.370 Greg Stoutenburg: Okay.
432 00:48:17.680 ⇒ 00:48:22.789 Robert Tseng: And then, obviously, you can add your own email to use it, but just to get a better viz on, like, where…
433 00:48:22.790 ⇒ 00:48:24.240 Greg Stoutenburg: Yeah. What their availability is.
434 00:48:24.680 ⇒ 00:48:26.530 Greg Stoutenburg: Yeah, good. Yep, cool.
435 00:48:27.300 ⇒ 00:48:31.120 Greg Stoutenburg: Alright, yeah, so far so good.
436 00:48:31.420 ⇒ 00:48:46.009 Greg Stoutenburg: topics for everything are built, and then the next stage is getting, is getting the dashboards all set up. So, Mustafa’s working on that, and we’re allocating tickets to divide that up amongst some other data engineers to get everything in there.
437 00:48:46.500 ⇒ 00:48:47.830 Greg Stoutenburg: As quickly as possible.
438 00:48:49.290 ⇒ 00:48:49.810 Robert Tseng: Yep.
439 00:48:50.250 ⇒ 00:48:51.180 Robert Tseng: Sounds good.
440 00:48:52.150 ⇒ 00:48:55.489 Greg Stoutenburg: Yep, cool, that’s it for me.
441 00:49:03.310 ⇒ 00:49:04.020 Robert Tseng: Okay.
442 00:49:05.000 ⇒ 00:49:06.100 Robert Tseng: Anything else?
443 00:49:08.990 ⇒ 00:49:10.020 Pranav Narahari: Think I’m all set.
444 00:49:11.590 ⇒ 00:49:21.499 Robert Tseng: Cool, then let’s… let’s end it. I’m all meeting it out. I know who Tom likes to run… run it all the way, but I’m… I’m… I’m more for… we… if we’re good, we just end early.
445 00:49:22.020 ⇒ 00:49:24.430 Greg Stoutenburg: I think today set a brain forge record for me.
446 00:49:24.580 ⇒ 00:49:26.879 Greg Stoutenburg: I’m gonna… I’m gonna count, and I want y’all to listen.
447 00:49:27.690 ⇒ 00:49:28.170 Robert Tseng: Yeah.
448 00:49:28.170 ⇒ 00:49:28.880 Greg Stoutenburg: one.
449 00:49:29.490 ⇒ 00:49:30.280 Greg Stoutenburg: 2.
450 00:49:31.140 ⇒ 00:49:32.000 Greg Stoutenburg: Three.
451 00:49:33.250 ⇒ 00:49:35.260 Greg Stoutenburg: 4, 5, 6, 7.
452 00:49:36.000 ⇒ 00:49:36.750 Greg Stoutenburg: 8.
453 00:49:36.960 ⇒ 00:49:37.990 Greg Stoutenburg: 8 meetings.
454 00:49:38.450 ⇒ 00:49:39.470 Robert Tseng: Yeah. Not bad.
455 00:49:40.010 ⇒ 00:49:41.160 Greg Stoutenburg: I bet for an IC.
456 00:49:44.830 ⇒ 00:49:46.670 Greg Stoutenburg: Boom. Alright.
457 00:49:46.870 ⇒ 00:49:49.599 Greg Stoutenburg: See you guys. Have a good one.
458 00:49:49.600 ⇒ 00:49:50.190 Pranav Narahari: He does.