Meeting Title: Brainforge x FoamPro Demo and Pricing Date: 2025-10-09 Meeting participants: Robert Tseng, Uttam Kumaran, Samuel Roberts
WEBVTT
1 00:01:04.599 ⇒ 00:01:05.899 Uttam Kumaran: Nice sweater, dude.
2 00:01:08.000 ⇒ 00:01:09.009 Robert Tseng: Hey. Thanks.
3 00:01:09.010 ⇒ 00:01:10.249 Uttam Kumaran: It looks cozy.
4 00:01:10.910 ⇒ 00:01:14.239 Robert Tseng: Yeah, it’s, it’s getting cold here, so…
5 00:01:14.240 ⇒ 00:01:15.970 Uttam Kumaran: Nice.
6 00:01:15.970 ⇒ 00:01:16.530 Robert Tseng: Yeah.
7 00:01:19.440 ⇒ 00:01:21.910 Uttam Kumaran: You should have seen me, I rocked it this morning.
8 00:01:24.120 ⇒ 00:01:25.259 Robert Tseng: What happened?
9 00:01:26.080 ⇒ 00:01:30.790 Uttam Kumaran: I just dominated project management, like, I just should…
10 00:01:31.450 ⇒ 00:01:37.269 Uttam Kumaran: It’s just like, I… and then I went straight into the hype meeting, and then straight into the default meeting.
11 00:01:37.370 ⇒ 00:01:38.759 Uttam Kumaran: Crushed hype.
12 00:01:38.920 ⇒ 00:01:40.190 Uttam Kumaran: Crush default.
13 00:01:40.190 ⇒ 00:01:40.880 Robert Tseng: camp.
14 00:01:42.050 ⇒ 00:01:48.090 Uttam Kumaran: We’re… we… we… we walked through the demo, Sam’s… Sam’s… we got rat ready, so we’re just rolling today.
15 00:01:50.670 ⇒ 00:01:51.999 Uttam Kumaran: Yeah, we’re good.
16 00:02:07.240 ⇒ 00:02:10.940 Robert Tseng: Sorry, I’m, like, responding to the insomnia folks. I have a call with them right after this.
17 00:02:10.949 ⇒ 00:02:11.999 Uttam Kumaran: Okay, okay.
18 00:02:12.310 ⇒ 00:02:12.970 Robert Tseng: Yeah.
19 00:03:24.200 ⇒ 00:03:27.060 Samuel Roberts: Alrighty, got everything set up and ready to go.
20 00:03:28.250 ⇒ 00:03:28.980 Uttam Kumaran: Nice.
21 00:03:31.070 ⇒ 00:03:33.290 Robert Tseng: Alright, let me text… let me text them real quick.
22 00:03:35.200 ⇒ 00:03:37.640 Uttam Kumaran: I think we can do what he wants for…
23 00:03:38.000 ⇒ 00:03:41.980 Uttam Kumaran: for our… we could do it for 5K, and then it may be another…
24 00:03:42.230 ⇒ 00:03:45.459 Uttam Kumaran: like, 1 to 2K on his side, like… Yeah.
25 00:03:45.460 ⇒ 00:03:46.350 Robert Tseng: We’re just talking about.
26 00:03:46.630 ⇒ 00:03:47.589 Uttam Kumaran: I think it’s…
27 00:03:47.870 ⇒ 00:03:55.260 Uttam Kumaran: I don’t think… I’m gonna be very honest, I’m like, I don’t think you can do… I don’t think you’ll ever get this pretty… cheaper than what we’re offering, basically, so…
28 00:03:55.470 ⇒ 00:03:56.050 Robert Tseng: Yeah.
29 00:03:57.850 ⇒ 00:03:59.260 Uttam Kumaran: I think he’ll be in a good spot.
30 00:04:02.210 ⇒ 00:04:03.680 Robert Tseng: Alright, I just texted him, we’re on.
31 00:04:19.190 ⇒ 00:04:24.009 Samuel Roberts: Just some… Redemo track? Okay, good. Database is set.
32 00:04:35.120 ⇒ 00:04:36.539 Samuel Roberts: Glad we noted that down.
33 00:04:36.680 ⇒ 00:04:43.169 Samuel Roberts: to set the database properly, because I just created a new one and totally forgot about it until I checked the notion, so good note.
34 00:04:43.700 ⇒ 00:04:50.790 Uttam Kumaran: Yeah, this is a good demo. I’ve been really impressed by Omni. I’m glad we don’t have to build this ourselves.
35 00:04:50.790 ⇒ 00:04:58.890 Samuel Roberts: No, this is really… it’s… it’s… it’s good. I wasn’t sure how, you know, AI chat agent on top of the data could be a few different things, I feel like, and this is…
36 00:04:59.080 ⇒ 00:05:01.190 Samuel Roberts: as natural as I want it to be.
37 00:05:30.310 ⇒ 00:05:33.889 Robert Tseng: Okay, he read my message, I think he’ll come on. We’ll give him a couple minutes, but…
38 00:05:33.890 ⇒ 00:05:34.250 Samuel Roberts: Sure.
39 00:05:34.250 ⇒ 00:05:40.210 Robert Tseng: Anyway, like, with the… with insomnia, obviously, a lot is going on, but yeah, like, I…
40 00:05:40.390 ⇒ 00:05:48.130 Robert Tseng: I pretty much, like, finished out an analysis that Shred kind of left last week, and then…
41 00:05:50.100 ⇒ 00:05:57.509 Robert Tseng: I guess the team liked it, and then the data guy… the tech guys there were like, oh, can you send us, like, everything, and we’ll, like.
42 00:05:58.040 ⇒ 00:06:04.630 Robert Tseng: we’ll host it and integrate it into the data warehouse, and I’m like, no, like, add me. Like, I’ll do it myself.
43 00:06:04.860 ⇒ 00:06:10.159 Robert Tseng: So… You want this to be used more than once? You have to give me access, so…
44 00:06:10.160 ⇒ 00:06:10.570 Uttam Kumaran: Nice.
45 00:06:10.570 ⇒ 00:06:11.680 Robert Tseng: I definitely…
46 00:06:12.160 ⇒ 00:06:18.960 Robert Tseng: being more, yeah, I’m not just giving them whatever they want, like, they gotta give us more access.
47 00:06:19.340 ⇒ 00:06:27.030 Uttam Kumaran: Yeah, and hopefully by the end of the weekend, I’ll review all the stuff, and I’ll start to build out… I’ll help you build out a little bit larger analysis backlog.
48 00:06:27.180 ⇒ 00:06:30.429 Uttam Kumaran: And the team can naturally start to just build you
49 00:06:30.830 ⇒ 00:06:36.179 Uttam Kumaran: pretty easy data sets to explore. And then I’m gonna… we’re gonna do an hour on just the…
50 00:06:36.680 ⇒ 00:06:37.620 Robert Tseng: the…
51 00:06:37.780 ⇒ 00:06:40.319 Uttam Kumaran: Scorecard, and we’ll blast through.
52 00:06:40.860 ⇒ 00:06:42.710 Uttam Kumaran: Whatever the heck is going on there.
53 00:06:43.950 ⇒ 00:06:50.719 Samuel Roberts: Yeah, if you want to loop me into that, I saw Casey and Awash were getting on together, and I asked if they needed my help, but I don’t know exactly what Awish…
54 00:06:51.230 ⇒ 00:06:54.229 Samuel Roberts: Has planned for that and stuff, but if there’s some more…
55 00:06:54.490 ⇒ 00:06:57.950 Samuel Roberts: We can do on top of that, because even just, like, filling those things seems like…
56 00:06:58.650 ⇒ 00:07:03.859 Samuel Roberts: I don’t know, so I don’t fully have my teeth into it yet to know exactly what’s going on, but…
57 00:07:04.760 ⇒ 00:07:05.440 Uttam Kumaran: Yeah.
58 00:07:05.560 ⇒ 00:07:08.099 Samuel Roberts: I think there’s probably more we can do, and I’m not sure.
59 00:07:09.290 ⇒ 00:07:12.110 Samuel Roberts: Why? It’s not, but we’ll get to it.
60 00:08:07.570 ⇒ 00:08:12.410 Uttam Kumaran: You know, I want to start to do some more stuff, Sam, with, notebook LM.
61 00:08:13.000 ⇒ 00:08:14.150 Samuel Roberts: Mmm.
62 00:08:14.370 ⇒ 00:08:22.570 Uttam Kumaran: It’d be cool, you know, one thing I was thinking about was, like, can we push all the meetings for Brainforge into a notebook LM every week and give everyone, like, a podcast?
63 00:08:25.360 ⇒ 00:08:26.400 Uttam Kumaran: Brainforge.
64 00:08:26.400 ⇒ 00:08:35.169 Samuel Roberts: Yeah, seriously, I haven’t touched that since, like, it got real, you know, its first spike in, like, the podcast thing. So I don’t know what it’s up to now, but I definitely…
65 00:08:35.570 ⇒ 00:08:40.690 Samuel Roberts: Actually, I don’t know how much you can dump into it. What I… my experience with it was, when I was working on the travel startup.
66 00:08:41.070 ⇒ 00:08:55.609 Samuel Roberts: he sent me, like, a big dump of, like, the travel industry trends and everything, and I was like, I’m not reading a several hundred page thing. And I dumped it right into there and listened to, like, a decent podcast, the highlights of it, you know, it wasn’t…
67 00:08:55.860 ⇒ 00:08:59.810 Samuel Roberts: It’s a little uncanny still, but, you know, I knew what I was listening to, so it was fine.
68 00:09:00.110 ⇒ 00:09:04.559 Uttam Kumaran: Yeah, I, I think at some point, like,
69 00:09:04.730 ⇒ 00:09:13.879 Uttam Kumaran: like, first, Amber and Alex worked on, like, a PMO proposal, and I was like, I’m… I was, like, driving, and I’m like, I can’t… I want to beat this, but I was like, I’ll just shove it in, and…
70 00:09:14.570 ⇒ 00:09:22.090 Uttam Kumaran: I mean, like, it wasn’t the most interesting thing, but I feel like we could probably use… we could probably use Notebook LM pretty… in pretty interesting ways.
71 00:09:22.090 ⇒ 00:09:23.100 Samuel Roberts: Yeah.
72 00:09:23.670 ⇒ 00:09:24.929 Samuel Roberts: I don’t know…
73 00:09:25.460 ⇒ 00:09:30.460 Samuel Roberts: Yeah, I’ll have to look at it a little bit more, because like I said, it’s been a minute since I’ve been there, but…
74 00:09:33.310 ⇒ 00:09:38.329 Samuel Roberts: Yeah, even if we just, like, get a dump of everything and put it in there, or try to get… I don’t know if they have…
75 00:09:38.940 ⇒ 00:09:44.209 Samuel Roberts: Anything open to, like, generate the podcast from an API or something, but…
76 00:09:44.660 ⇒ 00:09:45.570 Uttam Kumaran: Yeah.
77 00:09:45.570 ⇒ 00:09:49.639 Samuel Roberts: We can take a look. Even if it is just, like, get a big dump of the week’s meetings or something.
78 00:09:50.500 ⇒ 00:09:51.350 Uttam Kumaran: Yeah.
79 00:09:52.780 ⇒ 00:09:53.790 Samuel Roberts: That’d be kinda cool.
80 00:09:57.670 ⇒ 00:10:01.020 Samuel Roberts: Yeah, the driving thing was big with that travel thing, because I was like.
81 00:10:01.300 ⇒ 00:10:05.829 Samuel Roberts: I don’t have time for this, I need to go somewhere and do something else, and it was perfect. I love…
82 00:10:06.800 ⇒ 00:10:10.500 Samuel Roberts: I love that. So I’m a big, like, audiobook guy anyway in the car, so…
83 00:10:11.360 ⇒ 00:10:15.779 Uttam Kumaran: Yeah, I just listen to so many… if everything could be podcast form, it’d be good, yeah.
84 00:10:17.060 ⇒ 00:10:21.329 Uttam Kumaran: You know what I did before that? Oh, so I just had to go to this hype meeting earlier?
85 00:10:21.440 ⇒ 00:10:24.549 Uttam Kumaran: And I didn’t have time to watch the meeting.
86 00:10:24.940 ⇒ 00:10:29.670 Uttam Kumaran: like, earlier, and so I started watching the meeting, but I had, like, 3 minutes left before it, and I was like.
87 00:10:30.030 ⇒ 00:10:35.620 Uttam Kumaran: I took the transcript, shoved it in chat GPT, I said, I’m about to go to this meeting, and I have very little context.
88 00:10:36.290 ⇒ 00:10:38.799 Uttam Kumaran: Can you just tell me what I need to know? And it, like…
89 00:10:39.910 ⇒ 00:10:54.920 Uttam Kumaran: Audrey, right off the bat, asked me a question about Healthy, and I ripped it, because I just… the transcript they want… it said, like, this is what Healthy Can’t do, like, because they gave us an overview, and… and she was like, yeah, did you… do you remember from that meeting, like, if Healthy can do this? And I was like, yes, it can, totally.
90 00:10:56.060 ⇒ 00:11:02.199 Uttam Kumaran: It was crazy. I literally was like, like, I’m a beast, like, I’m using AI to the max, like…
91 00:11:02.200 ⇒ 00:11:03.250 Samuel Roberts: Yeah, that’s… that’s good.
92 00:11:05.700 ⇒ 00:11:12.880 Uttam Kumaran: It was… it was… it was worth it. And then I had… and then I quickly, like, scanned through Healthy API and figured everything out, but it was good.
93 00:11:12.880 ⇒ 00:11:13.540 Samuel Roberts: I guess.
94 00:11:14.750 ⇒ 00:11:15.849 Samuel Roberts: That’s… yeah.
95 00:11:16.100 ⇒ 00:11:17.610 Samuel Roberts: A good use case there.
96 00:11:18.550 ⇒ 00:11:21.059 Uttam Kumaran: Like, meeting prep, I think, is a big thing, you know, that we.
97 00:11:21.060 ⇒ 00:11:21.490 Samuel Roberts: Yeah.
98 00:11:21.490 ⇒ 00:11:22.390 Uttam Kumaran: up with.
99 00:11:22.710 ⇒ 00:11:28.610 Samuel Roberts: Definitely, definitely, yeah.
100 00:11:30.460 ⇒ 00:11:32.350 Samuel Roberts: Did you get the, nope.
101 00:11:33.110 ⇒ 00:11:42.339 Robert Tseng: Sorry, no, I was just gonna think, hey, you know what, maybe with the rest of this call, if he’s not gonna jump on, can we just run through our demo, and then we’ll just send him a clip of this meeting, this call?
102 00:11:43.510 ⇒ 00:11:44.010 Samuel Roberts: Good.
103 00:11:44.170 ⇒ 00:11:50.209 Uttam Kumaran: Maybe we’ll just do that. Just that way we don’t have to hop on another call. Honestly, I love, you watch this, and that’s it. We’re not gonna get on another call with you.
104 00:11:50.570 ⇒ 00:11:51.120 Robert Tseng: Okay.
105 00:11:51.120 ⇒ 00:11:58.780 Uttam Kumaran: So let me, let me… let me also record a loom, Sam, and then we can… I will also have backup.
106 00:11:58.950 ⇒ 00:12:00.460 Samuel Roberts: Yeah, that’s fine.
107 00:12:00.740 ⇒ 00:12:04.790 Uttam Kumaran: And then, yeah, let me… let’s do that.
108 00:12:27.880 ⇒ 00:12:30.320 Robert Tseng: Okay, I’m gonna mute, you guys, I’ll let you guys run up.
109 00:12:31.350 ⇒ 00:12:32.140 Uttam Kumaran: Okay.
110 00:12:32.270 ⇒ 00:12:33.880 Uttam Kumaran: Let me turn this off.
111 00:12:42.920 ⇒ 00:12:46.850 Samuel Roberts: Actually, while we’re doing that, let me make sure my audio’s not gonna go crazy on me.
112 00:12:47.120 ⇒ 00:12:51.579 Samuel Roberts: I think it does some percentage of the time, but it’s always when our booms get recorded.
113 00:12:52.600 ⇒ 00:12:53.520 Samuel Roberts: We’re good.
114 00:13:04.820 ⇒ 00:13:14.810 Uttam Kumaran: Okay, cool. So, today we wanted to demo, you know, basically how I think PhonePro can actually accomplish some of the objectives.
115 00:13:15.050 ⇒ 00:13:17.110 Uttam Kumaran: Which I’ll just list out.
116 00:13:17.120 ⇒ 00:13:29.500 Uttam Kumaran: you know, to kick this off, which is, one, taking Sage data and getting it into a simple warehouse, and implementing some light BI AI layer on top to be able to query that, both
117 00:13:29.500 ⇒ 00:13:38.560 Uttam Kumaran: you know, using typical SQL and, like, a BI layer, but also, using natural language. So Sam today is going to demo
118 00:13:38.560 ⇒ 00:13:55.359 Uttam Kumaran: the natural language piece, which I think is the kind of most innovative, but also, like, the most unique part of all this, and then after, I can talk through, like, the architecture of the warehouse, BI, talk about, like, what pricing could look like.
119 00:13:55.390 ⇒ 00:14:03.920 Uttam Kumaran: I know that there was a 5K budget, you know, for this project, so I can kind of talk through how we can work within that, potentially.
120 00:14:04.270 ⇒ 00:14:11.190 Uttam Kumaran: And yeah, you can… you guys can make a decision. So, Sam, if you want to go ahead and share, and we can walk through the questions that we’ve…
121 00:14:11.440 ⇒ 00:14:13.539 Uttam Kumaran: Prepared,
122 00:14:13.620 ⇒ 00:14:28.570 Uttam Kumaran: You know, for this demo. And so, to get a… set the stage here, this is a view of Omni. Omni is, sort of, like, one of our preferred, business intelligence tools that we recommend for a couple of reasons. One, pretty price effective compared to other enterprise tools, like
123 00:14:28.570 ⇒ 00:14:41.329 Uttam Kumaran: Looker, Tableau. Second, it comes out of the box with a lot of amazing AI functionality that we’ll be demoing today. This is something that, without a tool like this, you would have to build, and it is very, very expensive to…
124 00:14:41.440 ⇒ 00:14:56.739 Uttam Kumaran: pay us or other folks to build stuff like this, and it’s sort of a great way to make a decision on the BI tool that you can use longer term across your business. So Sam, yeah, you can go ahead and, you know, run through our demo about
125 00:14:56.930 ⇒ 00:15:00.450 Uttam Kumaran: Yeah, you can go ahead and kind of take it from here.
126 00:15:01.020 ⇒ 00:15:12.920 Samuel Roberts: Yeah, so, this is just the AI assistant that’s built into Omni. You can see I’ve got some e-commerce demo, data here. I’m just gonna start asking some questions about the data.
127 00:15:13.260 ⇒ 00:15:27.389 Samuel Roberts: I’m not super familiar with how the data is even structured, so I can just kind of ask, and it’ll help. So it’ll say, give me a snapshot of our orders for this or…
128 00:15:28.360 ⇒ 00:15:40.390 Samuel Roberts: And so, what this is doing is just, figuring out what it needs to query…
129 00:15:40.580 ⇒ 00:15:42.989 Samuel Roberts: Getting all that data, and then it’ll present it.
130 00:15:43.420 ⇒ 00:15:48.870 Samuel Roberts: Right now, how it thinks it’s best, but you can kind of specify different graphs and things you want to see.
131 00:15:51.310 ⇒ 00:16:02.940 Uttam Kumaran: So under the hood, it’s taking your question, it’s translating it to, SQL, issuing the SQL query on a database, grabbing the results.
132 00:16:03.080 ⇒ 00:16:18.819 Uttam Kumaran: And then it actually pulls the results back into AI context, and then runs a bunch of further questions on it to actually give us, like, a natural language summary. So there are a lot of steps being orchestrated here.
133 00:16:18.980 ⇒ 00:16:24.330 Uttam Kumaran: And… Yeah, you can kind of go ahead, Sam, and just outline what we’re seeing on the screen.
134 00:16:24.330 ⇒ 00:16:39.309 Samuel Roberts: Yeah, so, you know, it seemed to do exactly what I asked for. It gave us a snapshot of orders, for the quarter. So you obviously can see all the data here. Again, this is just some kind of demo data, so things might not necessarily be, like, realistic-looking, but,
135 00:16:39.430 ⇒ 00:16:49.980 Samuel Roberts: you know, that’s all there, you can dig into that as much as you’d like, but what I really like about this so far, as I’ve been using it, is the summary, where I can, at a glance, know what’s going on here, so you can see the total orders, revenue.
136 00:16:50.100 ⇒ 00:17:06.460 Samuel Roberts: average order value, unique customers, active across all categories, which I think is a weird thing I found in this test data, but it doesn’t matter right now. But the other nice thing I like is these suggestions down here to keep diving in without having to get into the data.
137 00:17:06.589 ⇒ 00:17:07.349 Samuel Roberts: Excuse me.
138 00:17:07.359 ⇒ 00:17:11.769 Uttam Kumaran: Go ahead and click the top products, by chance, because that was just another demo question anyways.
139 00:17:11.770 ⇒ 00:17:13.529 Samuel Roberts: Totally, yeah, exactly.
140 00:17:14.490 ⇒ 00:17:32.849 Uttam Kumaran: So one thing that, you know, our sort of idea here is there’s a lot of customers and users within our customers that commonly aren’t trained on how to use dashboards, to explore, and usually the dashboard is a limiting factor, and so being able to actually answer these questions in natural language opens up
141 00:17:32.880 ⇒ 00:17:46.459 Uttam Kumaran: You know, the number of questions, this sort of iterative process, almost like you had your own analyst, that could work through a problem for you. And so what you’re seeing here is we… the top 5 products,
142 00:17:46.540 ⇒ 00:17:50.489 Uttam Kumaran: You know, and it gives you helpful information. You’re not just looking for
143 00:17:50.630 ⇒ 00:18:06.290 Uttam Kumaran: you don’t have to specify, like, I just want to see revenue, I want to see… it’s gonna… it’s gonna use natural language to sort of hypothesize what are helpful KPIs to see. And then, of course, this is, like, in a chat GPT-style interface, so all the past context is saved, and you can keep iterating.
144 00:18:06.290 ⇒ 00:18:22.500 Uttam Kumaran: With this, this data set is just a couple of tables around orders, customers, sales. Should mimic, probably some of your data. Of course, maybe from ERP, you may be doing quotes and inventory, and have your SKU information.
145 00:18:22.500 ⇒ 00:18:23.880 Samuel Roberts: inventory stuff here, too.
146 00:18:23.880 ⇒ 00:18:24.760 Uttam Kumaran: Yeah.
147 00:18:25.480 ⇒ 00:18:33.490 Uttam Kumaran: And then maybe we want to, we can… maybe we can just move to the… the next demo, which is this, the, the, yeah, with the chat to dashboard.
148 00:18:33.490 ⇒ 00:18:48.089 Samuel Roberts: Yeah, so here I’ve just thrown together a couple little things I was playing with, for the dashboard, but, really the benefit here is that this is a dashboard you can publish and share, but if I want to create a new element here, this is basically gonna open up this workbook.
149 00:18:48.130 ⇒ 00:18:54.480 Samuel Roberts: that, I can select what specific topic I want to dig into, and this is based on the data.
150 00:18:54.540 ⇒ 00:18:59.050 Samuel Roberts: Underneath. So, let’s look at,
151 00:18:59.410 ⇒ 00:19:02.959 Samuel Roberts: The order and tran- order transactions. And then here.
152 00:19:03.120 ⇒ 00:19:10.269 Samuel Roberts: what the big thing here is that this AI query helper is very similar to what we just chatted about, but now it’s going to be specifically generating
153 00:19:10.460 ⇒ 00:19:23.440 Samuel Roberts: charts and graphs that I want. So, let’s look at, monthly… Sales… Trends for the last…
154 00:19:23.830 ⇒ 00:19:29.909 Samuel Roberts: Oops. Two years… as a line chart.
155 00:19:31.130 ⇒ 00:19:35.899 Samuel Roberts: So, it’s gonna do very similar to what it did before,
156 00:19:36.420 ⇒ 00:19:38.890 Samuel Roberts: But now it knows it’s specifically looking at orders.
157 00:19:39.070 ⇒ 00:19:45.320 Samuel Roberts: And so here we go. This is a chart that just got generated. It’s showing the last two complete years.
158 00:19:45.400 ⇒ 00:19:54.639 Samuel Roberts: You can customize the chart all you want, but it did a pretty good job with it here. And then, you’re able to just jump back to the dashboard.
159 00:19:54.670 ⇒ 00:20:05.869 Samuel Roberts: And there it is. And so you could build… you could see how you could build a dashboard, without really digging into the data yourself too much, and just chatting with the AI and making new charts or… or graphs that you want there.
160 00:20:06.110 ⇒ 00:20:22.600 Uttam Kumaran: Yeah, so commonly, if you’re used to Tableau or a Looker Flow, it takes a long time to figure out, like, x-axis, Y-axis, the chart. So, you can get started pretty fast, and then any of these things, you can not only pull up a query, you can edit it, but also ask data, ask questions about it.
161 00:20:22.600 ⇒ 00:20:26.580 Uttam Kumaran: And Omni has a bunch of helpful features to add context.
162 00:20:26.580 ⇒ 00:20:33.859 Uttam Kumaran: to your data, to your columns, and so, the answers you get back from the LLM can be pretty rich.
163 00:20:34.140 ⇒ 00:20:46.469 Samuel Roberts: There’s even an AI chat here for the whole dashboard, which is off, and I believe is only available once it’s published, because they’re still editing it right now, but it’s the same basic chat function, but over disinformation, I bet, so…
164 00:20:46.920 ⇒ 00:20:50.760 Uttam Kumaran: Cool. And then, yeah, Sam, we could probably show the last… Demo.
165 00:20:51.010 ⇒ 00:20:53.970 Samuel Roberts: Yes, let me flip over.
166 00:20:55.150 ⇒ 00:20:58.950 Samuel Roberts: To… Here, okay.
167 00:21:00.650 ⇒ 00:21:17.230 Samuel Roberts: Alright, so here we are, in a Slack channel that we use for, like, testing different bots, and I have a bot here that we put together that’s just chatting with Omni. Very similar to the AI thing, you can imagine
168 00:21:17.310 ⇒ 00:21:25.140 Samuel Roberts: you could expand this in a few different ways, but, let’s see, let’s just ask for, tell me about…
169 00:21:25.260 ⇒ 00:21:29.580 Samuel Roberts: the highest… Selling SKUs.
170 00:21:30.180 ⇒ 00:21:38.340 Samuel Roberts: And so, it’s running locally on my machine, so it might take a minute, but it’s doing the same kind of thing that the AI chat was doing, where it generates the query.
171 00:21:38.560 ⇒ 00:21:56.179 Samuel Roberts: executes the query and then returns the data. Right now, it just returns it as a CSV file, but all the data is there, and you can imagine downloading that, bringing it into something else, eventually potentially even just having graphs right in Slack for a quick, kind of, jumping in and seeing the trends of something.
172 00:21:56.730 ⇒ 00:22:00.779 Samuel Roberts: But yeah, this is… this is something that we kind of threw together using their
173 00:22:01.040 ⇒ 00:22:12.629 Samuel Roberts: API, so they have all that accessible. I believe they’re working on a Slack bot as well, that was probably going to be a little more polished than this, so that’s exciting in the near future, too.
174 00:22:13.160 ⇒ 00:22:16.940 Uttam Kumaran: Cool, and then maybe I can just take the last, kind of, couple minutes to just talk about
175 00:22:17.090 ⇒ 00:22:23.729 Uttam Kumaran: like, pricing across all this. So, one thing that I wanted to share,
176 00:22:24.170 ⇒ 00:22:34.050 Uttam Kumaran: And to just, like, kind of talk a little bit about, pricing… Is this,
177 00:22:34.140 ⇒ 00:22:42.560 Uttam Kumaran: sort of, like, our sort of expectation for how we share and procure products for clients. So, you know, you kind of mentioned your 5K budget.
178 00:22:42.560 ⇒ 00:23:03.240 Uttam Kumaran: that is, of course, not, like, a ton of money, and so we want to kind of prescribe you guys, like, a little bit of a solution that can work short-term, but also sets you guys up for success. So, there’s going to be two components to this. One, of course, is time for us to set up these tools, set up these systems. There’s also the price you’re going to pay for the tooling themselves.
179 00:23:03.240 ⇒ 00:23:13.939 Uttam Kumaran: We do a lot of work with a lot of various vendors, and we don’t take any kickbacks from any of them, so we try to really give you, like, what we feel is the best solution.
180 00:23:13.940 ⇒ 00:23:22.090 Uttam Kumaran: And so, let me walk through, sort of, the entire stack for you guys. So, in terms of moving SAGE into a data warehouse.
181 00:23:22.100 ⇒ 00:23:28.329 Uttam Kumaran: You’re gonna need some level of an ETL tool or a simple, you know, Python.
182 00:23:28.330 ⇒ 00:23:44.260 Uttam Kumaran: API process to do that. It’s until I kind of see how, you know, complicated the data you need to get out is, it’s hard to understand whether that’s something we can build one-off, or you may want to invest an ETL tool like Fivetran or Polytomic.
183 00:23:44.260 ⇒ 00:23:48.410 Uttam Kumaran: Those can be anywhere from, you know, a few hundred dollars a month.
184 00:23:48.490 ⇒ 00:24:03.920 Uttam Kumaran: And kind of scale from there. But again, the reason for investing in a tool there is you don’t want to write custom Python code to pull from Sage. Those guys will have connectors already built to do that. Next is, like, where you’re storing it. So,
185 00:24:04.410 ⇒ 00:24:23.580 Uttam Kumaran: in your price range, like, Mother Duck, which is the, sort of, owner of DuckDB, is probably the best option. They have a really generous, free, and, like, $25 a month tier. Just for the couple of data sets that you guys need, I feel like you should be totally fine there. The other options here are, like, Snowflake, BigQuery.
186 00:24:23.600 ⇒ 00:24:37.229 Uttam Kumaran: Those are gonna be much more expensive, and kind of overkill for just a simple analytics use case here. And then in terms of BI, so what you have in front of you is sort of our, like, pricing sheet for BI tools.
187 00:24:37.260 ⇒ 00:24:47.270 Uttam Kumaran: As we show, the demo is… we showed is an Omni. Roughly, you know, given our relationship with them, we can probably get you somewhere where it’s, like, one to two grand a month.
188 00:24:47.410 ⇒ 00:24:48.830 Uttam Kumaran: To sort of…
189 00:24:48.940 ⇒ 00:25:06.559 Uttam Kumaran: get the tool set up. With that, you get, like, a bunch of seats out of the box, which should cover things. And then you also get all that… this AI functionality. The alternative here is to go with, like, a cheaper tool like Power BI, or maybe Tableau, but
190 00:25:06.790 ⇒ 00:25:13.900 Uttam Kumaran: you will have to use, like, an agency like us or other engineering resources to build out the AI piece, which…
191 00:25:13.900 ⇒ 00:25:30.339 Uttam Kumaran: will be expensive and will not be as high quality as what Omni has built here. So, our recommendation is to go with Omni. You can also consider a tool like RIL. It’s a bit cheaper. They do have some AI features there as well. Happy to, you know, sort of
192 00:25:30.410 ⇒ 00:25:39.730 Uttam Kumaran: demo that as well, but both of those are probably the tools that are gonna be in, you know, your price range with the AI functionality built in.
193 00:25:39.850 ⇒ 00:25:54.270 Uttam Kumaran: And then for our time, that’s gonna be sort of on top of this. So, for the 5K, that’ll probably give us, you know, anywhere from, like, 20 to 30 hours, and we can probably get this sort of basic setup in a month.
194 00:25:54.400 ⇒ 00:26:06.249 Uttam Kumaran: But, like, that… it wouldn’t really get you much more time after that month, so that’s something that, if you need ongoing time from us, we would either do hourly or, sort of, kind of have a commitment. So that’s the general…
195 00:26:06.370 ⇒ 00:26:09.790 Uttam Kumaran: Like, pitch on how you could probably accomplish this.
196 00:26:09.840 ⇒ 00:26:26.799 Uttam Kumaran: in something close to your price range, so again, it would be the 5K for our services, and then maybe an additional 2K a month just to host all of the, you know, solutions and software you need to execute this. But overall, it’s very cost-effective. I don’t think
197 00:26:26.930 ⇒ 00:26:34.510 Uttam Kumaran: I’m not sure if there’s gonna be another, agency or consultancy that can kind of do it in our range, and also give you guys
198 00:26:34.640 ⇒ 00:26:36.590 Uttam Kumaran: software,
199 00:26:36.910 ⇒ 00:26:52.240 Uttam Kumaran: that can execute this with that range. That is a very, very tight budget. So hope you guys consider us, and yeah, I feel like, just let us know over email if you guys have any questions about… about Omni, or about the demo, or, pricing.
200 00:26:54.620 ⇒ 00:26:55.980 Uttam Kumaran: Cool. Okay.
201 00:26:56.670 ⇒ 00:26:57.320 Robert Tseng: Cool.
202 00:26:58.480 ⇒ 00:27:01.840 Uttam Kumaran: I’ll chop the loom up, and then give that… I’ll send it to you.
203 00:27:02.090 ⇒ 00:27:16.549 Robert Tseng: Perfect. Yeah, we’ll just, send him over an email. I sent him a text as well, like, hey, you know, we’re not gonna reschedule. This is it, so… Yeah, so I guess we’ll just… we’ll shoot it their way, and I’ll follow up with him.
204 00:27:16.610 ⇒ 00:27:17.320 Uttam Kumaran: Okay, okay.
205 00:27:17.320 ⇒ 00:27:19.490 Robert Tseng: Thanks, guys. Sorry, sorry I didn’t show up.
206 00:27:19.620 ⇒ 00:27:24.419 Uttam Kumaran: No, no, you’re good. I guess while I have you, what else do you need? Maybe we can just do a brief, like.
207 00:27:24.610 ⇒ 00:27:25.180 Robert Tseng: Yeah.
208 00:27:25.560 ⇒ 00:27:30.270 Uttam Kumaran: thing about today, so, I’m gonna kind of follow up on, like.
209 00:27:30.570 ⇒ 00:27:35.280 Uttam Kumaran: insomnia stuff, like, towards the end of the day. Yeah, okay. Generally, hip…
210 00:27:35.410 ⇒ 00:27:49.990 Uttam Kumaran: default, are in a good spot. Ellie is also in a pretty good spot. Then Readme, I’m gonna also kind of get into the weeds with, and just help Henry push there. So I’m in the channel, and so I’ll just, like.
211 00:27:50.120 ⇒ 00:27:55.579 Uttam Kumaran: start making noise. Basically, I want to get… read me and Ellie on some cadence with me.
212 00:27:55.850 ⇒ 00:27:59.110 Uttam Kumaran: and Zoran are me and Henry, and then…
213 00:27:59.110 ⇒ 00:27:59.740 Robert Tseng: Okay.
214 00:28:00.020 ⇒ 00:28:03.480 Uttam Kumaran: I feel like… Those look pretty chill. Yeah.
215 00:28:03.480 ⇒ 00:28:04.190 Robert Tseng: their show.
216 00:28:04.870 ⇒ 00:28:07.010 Uttam Kumaran: So, we’ll rock it.
217 00:28:07.640 ⇒ 00:28:12.630 Uttam Kumaran: Default is going really well. They’re probably gonna purchase Omni through us, which is great.
218 00:28:12.630 ⇒ 00:28:13.150 Robert Tseng: Great.
219 00:28:13.160 ⇒ 00:28:15.360 Uttam Kumaran: They just onboarded, like.
220 00:28:15.370 ⇒ 00:28:15.930 Robert Tseng: Okay, buddy.
221 00:28:15.930 ⇒ 00:28:19.019 Uttam Kumaran: 12 of their people from their company onto our Omni dashboard.
222 00:28:19.020 ⇒ 00:28:19.969 Samuel Roberts: Oh, I saw that.
223 00:28:19.970 ⇒ 00:28:30.159 Uttam Kumaran: Kalen shared it. She was like, everybody now, like, wants to see all this stuff, and we’re gonna build out their use cases for dashboarding for their customer service department or account management.
224 00:28:30.320 ⇒ 00:28:33.020 Uttam Kumaran: So, like, that client is still going great.
225 00:28:33.160 ⇒ 00:28:38.499 Uttam Kumaran: The Remo thing is, like, that’s probably where it’s up to you to…
226 00:28:39.460 ⇒ 00:28:48.539 Uttam Kumaran: to decide tomorrow, like, what the gist is, but overall, I think you should have enough information to go into that call, and then Surf will be there with you to dance, so…
227 00:28:48.540 ⇒ 00:28:57.300 Robert Tseng: Okay. Yeah, so with Eden, I’m gonna… there’s, like, 3 budgets, so we’ve kind of just been doing one budget. Basically, what I’ve been pushing for in the past week has been…
228 00:28:57.380 ⇒ 00:29:14.039 Robert Tseng: Basically, Zoran’s time should just be billed on the marketing budget, so that’ll be something separate. I think that’ll just be on top, so we’ll just basically staff all Zoran, and that’ll give us our… give our team back the… kind of… I mean, it’ll just increase our budget, but just kind of from there.
229 00:29:14.040 ⇒ 00:29:22.379 Robert Tseng: their perspective, Loron’s time, if he’s gonna stay on longer term, he should just be… that should be… that should not be taken away from… from our current budget with them.
230 00:29:22.780 ⇒ 00:29:31.580 Robert Tseng: Remo’s already a separate budget. Whether or not that project continues, obviously things are kind of shaky right now, so we’ll see kind of how that goes.
231 00:29:31.680 ⇒ 00:29:33.859 Robert Tseng: But they also have needs for, like.
232 00:29:34.040 ⇒ 00:29:37.700 Robert Tseng: they have… they have AI needs as well, so…
233 00:29:37.970 ⇒ 00:29:51.210 Robert Tseng: you know, Adam keeps saying, like, I want to be able to chat with the… with the data that you guys have set up in your warehouse. So, kind of a similar ask to this, like, I’m not really sure, but, like, so Amber’s been kind of logging a few different AI requests that have been.
234 00:29:51.210 ⇒ 00:29:54.489 Uttam Kumaran: We’ll just move them on to Omni, dude, and I can get it paid for.
235 00:29:56.030 ⇒ 00:29:56.630 Robert Tseng: Oh, God.
236 00:29:57.350 ⇒ 00:30:06.199 Robert Tseng: I guess they’re just gonna be like, oh, another tool or whatever, but I’m not really sure how we want to stack that with Tableau. I mean, I’m fine with it. I mean…
237 00:30:07.690 ⇒ 00:30:09.900 Robert Tseng: Yeah, so we’ll just have to.
238 00:30:09.900 ⇒ 00:30:21.850 Uttam Kumaran: I’ll try to come up… I’ll get you the demo, I’ll get you a really good demo first, so you can get the buy-in on, like, oh shit, this is, like, really sick, and then you could lay it on to them about, like, what it would cost.
239 00:30:21.850 ⇒ 00:30:30.050 Robert Tseng: We could… we could move off of Tableau by February. Like, that’s kind of when the renewal is up, so I don’t know how much of the lift will be to, like, move everything over, but…
240 00:30:30.280 ⇒ 00:30:39.210 Robert Tseng: yeah, I guess, like, we’re open to that. So I think that… I think there’s, like, a real shot at a proposal with them as well, just because they’re asking for it,
241 00:30:39.500 ⇒ 00:30:42.319 Robert Tseng: And yeah, so I just want to put that on your guys’ radar.
242 00:30:42.320 ⇒ 00:30:46.299 Uttam Kumaran: The other thing is, it’s gonna be way easier for our team to develop on me.
243 00:30:46.630 ⇒ 00:30:48.329 Uttam Kumaran: And we’re gonna avoid all those issues.
244 00:30:48.330 ⇒ 00:30:48.960 Robert Tseng: asleep.
245 00:30:48.960 ⇒ 00:30:58.969 Uttam Kumaran: So, okay. So, like, if that’s… yeah, so basically, I think over this next… over this month, I’ll work with, like, probably a wish to get you a simple Omni
246 00:30:59.150 ⇒ 00:31:05.830 Uttam Kumaran: a chat with data demo that… it’ll just be in our instance, so you can sort of, like, if you’re on a call with them, you can say, hey, we’re, like.
247 00:31:06.230 ⇒ 00:31:09.499 Uttam Kumaran: We’re testing things out with this tool. Could be…
248 00:31:09.810 ⇒ 00:31:10.460 Robert Tseng: Yeah.
249 00:31:10.630 ⇒ 00:31:11.330 Uttam Kumaran: Yeah, or whatever.
250 00:31:11.330 ⇒ 00:31:17.089 Robert Tseng: I already pay around, like, 30, 35K a year for Tableau, so, like, it’s… it’s not a…
251 00:31:17.090 ⇒ 00:31:17.540 Uttam Kumaran: It’s showing.
252 00:31:17.540 ⇒ 00:31:19.070 Robert Tseng: It’d be a budget problem, yeah.
253 00:31:19.070 ⇒ 00:31:19.610 Uttam Kumaran: No.
254 00:31:21.070 ⇒ 00:31:31.969 Uttam Kumaran: This’ll be cheaper, and Omni will pay for some of our services, so it’s like a complete no-brainer on the cost side, if I’ll just… so I’ll just get you all the details.
255 00:31:31.980 ⇒ 00:31:32.890 Robert Tseng: Okay.
256 00:31:33.400 ⇒ 00:31:34.090 Robert Tseng: Cool.
257 00:31:35.480 ⇒ 00:31:39.050 Robert Tseng: Alright. That’s… those are the main things. Thanks, guys.
258 00:31:39.310 ⇒ 00:31:41.240 Uttam Kumaran: Okay. Thank you guys. Thanks, Sam. Appreciate it.