Meeting Title: AI - Data Services Overview Date: 2026-01-13 Meeting participants: Samuel Roberts, Luke Scorziell, Awaish Kumar, Luke’s Notetaker
WEBVTT
1 00:00:38.380 ⇒ 00:00:40.180 Luke Scorziell: Hey, Sam, how’s it going?
2 00:00:40.180 ⇒ 00:00:43.030 Samuel Roberts: Hey, pretty good. How about yourself?
3 00:00:43.830 ⇒ 00:00:45.030 Luke Scorziell: Doing good.
4 00:00:49.710 ⇒ 00:00:52.329 Luke Scorziell: I’m jumping in the deep end here.
5 00:00:55.130 ⇒ 00:00:57.989 Luke Scorziell: So… What have you been working on today?
6 00:00:58.480 ⇒ 00:01:11.909 Samuel Roberts: We’ve been working on, the MCP servers for Lilo. They have a… they had a platform that could talk to all their ad accounts and everything, and we were trying to rebuild that for them, and I hit a bunch of…
7 00:01:12.140 ⇒ 00:01:17.459 Samuel Roberts: errors deploying it today, and it was just fixing a few of the errors, but it’s working, so hopefully that’s a good sign.
8 00:01:17.790 ⇒ 00:01:21.420 Luke Scorziell: MCP being, what is MCP?
9 00:01:21.420 ⇒ 00:01:30.460 Samuel Roberts: the Model Context Protocol. It’s basically how the LLMs, the AI models, know what tools they have access to.
10 00:01:31.330 ⇒ 00:01:31.970 Luke Scorziell: Oh, okay.
11 00:01:31.970 ⇒ 00:01:44.700 Samuel Roberts: So they have a… it’s a, you know, chat interface that they have, and they can toggle on whether or not it has access to Shopify data, or Meta Ads data, or Google Ads data for all their different brands that they work with.
12 00:01:45.540 ⇒ 00:01:51.390 Luke Scorziell: Got it. And so it’s just a… it’s just a layer in between, basically, the AI and the API that…
13 00:01:51.390 ⇒ 00:01:52.990 Samuel Roberts: search the data.
14 00:01:52.990 ⇒ 00:01:54.879 Luke Scorziell: Is that where, like, contextual would sit?
15 00:01:55.930 ⇒ 00:02:02.509 Samuel Roberts: A little bit… It’s… it’s… yeah, it’s a little different, because this is, like…
16 00:02:04.910 ⇒ 00:02:09.649 Samuel Roberts: This is, like, hitting the APIs of Shopify and, like, fetching, you know, all the information from there.
17 00:02:10.009 ⇒ 00:02:16.569 Samuel Roberts: Contextual seems more like the document side of things. This is more like the accessing the API that’s already there.
18 00:02:17.070 ⇒ 00:02:18.990 Luke Scorziell: Yeah. Anyways, how’s it going?
19 00:02:19.360 ⇒ 00:02:20.160 Samuel Roberts: Hello.
20 00:02:20.160 ⇒ 00:02:22.870 Awaish Kumar: Oh, all good, how about you?
21 00:02:23.520 ⇒ 00:02:29.780 Luke Scorziell: Good. I had an early morning on my end. I guess, I don’t know, maybe you guys are up in the middle of the night, or I guess…
22 00:02:29.780 ⇒ 00:02:31.109 Awaish Kumar: the late nights.
23 00:02:31.220 ⇒ 00:02:33.080 Awaish Kumar: It’s late night here.
24 00:02:33.080 ⇒ 00:02:34.700 Samuel Roberts: Yeah, it’s crazy.
25 00:02:35.950 ⇒ 00:02:37.170 Luke Scorziell: Yeah, I don’t know.
26 00:02:37.870 ⇒ 00:02:41.220 Awaish Kumar: Yeah, I’m in Pakistan, so it’s 10 PM here.
27 00:02:41.830 ⇒ 00:02:42.260 Samuel Roberts: Oh, really?
28 00:02:42.800 ⇒ 00:02:46.190 Luke Scorziell: And are you just… Are you, like, kind of midway through the day right now?
29 00:02:48.280 ⇒ 00:02:49.410 Awaish Kumar: It’s kind of a…
30 00:02:49.710 ⇒ 00:02:55.209 Awaish Kumar: like, 10 PM, so it’s kind of night, but I’m… I just normally work with EST hours.
31 00:02:56.480 ⇒ 00:02:58.059 Luke Scorziell: Yeah, so you’ve still got a queue.
32 00:02:58.290 ⇒ 00:03:00.150 Luke Scorziell: Yeah.
33 00:03:00.440 ⇒ 00:03:01.490 Awaish Kumar: Cool.
34 00:03:01.490 ⇒ 00:03:07.769 Luke Scorziell: Well, yeah, thank you guys, Juan, so much for making the time, to meet with me, and
35 00:03:08.300 ⇒ 00:03:13.380 Luke Scorziell: Definitely let me know, too, like, you know, we plan on… quit it for…
36 00:03:14.440 ⇒ 00:03:16.580 Luke Scorziell: Yeah, 45 minutes, so if anything…
37 00:03:16.690 ⇒ 00:03:20.060 Luke Scorziell: If you gotta hop off or something comes up, just let me know.
38 00:03:20.620 ⇒ 00:03:29.949 Luke Scorziell: But, yeah, I think the goal that, you know, I’m hoping to get is, one, like, personally, to know the services that we’re offering better.
39 00:03:31.450 ⇒ 00:03:34.379 Luke Scorziell: And know, like, what’s active right now.
40 00:03:34.620 ⇒ 00:03:39.909 Luke Scorziell: Maybe which services are, like… I’m just looking at, like, the offers list that we have on Notion, like.
41 00:03:40.130 ⇒ 00:03:45.350 Luke Scorziell: Which offers are most, popular, which… and then also getting an idea of, like.
42 00:03:45.490 ⇒ 00:03:50.380 Luke Scorziell: What is high effort and low effort, and then also what’s kind of, like, high leverage, low leverage.
43 00:03:50.500 ⇒ 00:03:53.979 Luke Scorziell: And what that is, is just from, like, a business sense, like.
44 00:03:54.130 ⇒ 00:03:57.520 Luke Scorziell: Are there services that we’re offering that,
45 00:03:59.400 ⇒ 00:04:03.290 Luke Scorziell: like, are very good for Brainforge as a company.
46 00:04:03.290 ⇒ 00:04:04.000 Samuel Roberts: Hmm.
47 00:04:04.910 ⇒ 00:04:10.290 Luke Scorziell: And then maybe, like, what services are, like, more on the entry side, and then which services are more, like.
48 00:04:10.490 ⇒ 00:04:16.940 Luke Scorziell: you know, a little more complicated, we’ve already been working with someone for a while. So…
49 00:04:17.450 ⇒ 00:04:22.719 Luke Scorziell: Yeah, I guess, and Awash, you are more on the data side, correct?
50 00:04:23.820 ⇒ 00:04:24.690 Awaish Kumar: Yep.
51 00:04:24.690 ⇒ 00:04:25.590 Luke Scorziell: Okay.
52 00:04:25.700 ⇒ 00:04:32.179 Luke Scorziell: So, yeah, I mean, I don’t know, would it maybe just be helpful, I could, like, share my screen, maybe, and we could just…
53 00:04:32.390 ⇒ 00:04:33.579 Luke Scorziell: like, walk through.
54 00:04:33.920 ⇒ 00:04:34.840 Luke Scorziell: Okay.
55 00:04:36.190 ⇒ 00:04:36.970 Luke Scorziell: Cool.
56 00:04:37.340 ⇒ 00:04:40.149 Luke Scorziell: Either of you guys have any thoughts on…
57 00:04:42.120 ⇒ 00:04:43.699 Samuel Roberts: Just felt pretty good.
58 00:04:44.250 ⇒ 00:04:49.720 Samuel Roberts: Yeah, I mean, I just pulled up the Notion myself, just to look. I hadn’t looked a ton yet.
59 00:04:50.980 ⇒ 00:04:54.110 Samuel Roberts: One thing I think is that there’s… A lot of, like.
60 00:04:54.510 ⇒ 00:04:57.140 Samuel Roberts: There’s some AI stuff here, but…
61 00:04:57.250 ⇒ 00:05:02.399 Samuel Roberts: I think the data side of it is maybe a little more fleshed out at this point.
62 00:05:02.940 ⇒ 00:05:04.949 Samuel Roberts: Is that… is that fair, awaish?
63 00:05:07.320 ⇒ 00:05:15.130 Awaish Kumar: Yeah, so… That’s true, and,
64 00:05:16.240 ⇒ 00:05:24.710 Awaish Kumar: Like, these offers, like, not everything is, like, kind of… Once you say refined?
65 00:05:26.100 ⇒ 00:05:34.620 Awaish Kumar: In terms of, like, we have an offpring, but we… Maybe not, like…
66 00:05:35.040 ⇒ 00:05:40.480 Awaish Kumar: Like, find it yet, or, like, try to…
67 00:05:40.620 ⇒ 00:05:47.210 Awaish Kumar: focus it more, focus on it more. So, these are kind of… Still,
68 00:05:49.300 ⇒ 00:06:00.659 Awaish Kumar: Yeah, some of it are live, and some of it are just, like, you can say, in development, or, like, still, we are reviewing what, what, like, how to offer, how to refine these.
69 00:06:01.570 ⇒ 00:06:05.090 Luke Scorziell: Yeah, yeah. Well, cause… and I’m assuming, you know.
70 00:06:05.390 ⇒ 00:06:09.860 Luke Scorziell: Since we’re a services company, we don’t, you know, it’s not necessarily like we just have
71 00:06:10.410 ⇒ 00:06:28.419 Luke Scorziell: here’s the rinse and repeat thing that we do over and over again, as much as, like, what are the problems that you’re dealing with. I do think that as we’re moving more towards the enterprise side of things, it may help… and then just as a… from a point of, like, being able to scale the company more, having some services that are
72 00:06:28.910 ⇒ 00:06:35.839 Luke Scorziell: Like, high profit for us, and then also… Aren’t, like, super complicated.
73 00:06:40.150 ⇒ 00:06:48.579 Luke Scorziell: will be… will be helpful. So… so yeah, so you don’t need to, like, don’t stress over, like, this is gonna be, like, the final…
74 00:06:48.860 ⇒ 00:06:49.180 Samuel Roberts: Right.
75 00:06:49.180 ⇒ 00:06:55.690 Luke Scorziell: Final, like, set of services that we go to market with or anything like that, but yeah.
76 00:06:55.690 ⇒ 00:06:56.810 Awaish Kumar: shit, I think?
77 00:06:57.080 ⇒ 00:06:59.869 Luke Scorziell: Yeah, yeah, I think I let you share.
78 00:07:01.140 ⇒ 00:07:12.070 Awaish Kumar: Like this, basically… But Jure sharing is kind of, The document which I… I shared, right?
79 00:07:12.380 ⇒ 00:07:13.090 Luke Scorziell: Yes.
80 00:07:14.410 ⇒ 00:07:20.259 Awaish Kumar: Like, that is one of the documents where this is a kind of list of offers, which
81 00:07:20.370 ⇒ 00:07:21.870 Awaish Kumar: We don’t know, like…
82 00:07:22.320 ⇒ 00:07:29.779 Awaish Kumar: Some of it is, like, kind of well-refined, and we already offer it to people, and some of it is, like, not…
83 00:07:29.970 ⇒ 00:07:32.210 Awaish Kumar: Not yet, like, but the focus…
84 00:07:32.360 ⇒ 00:07:36.489 Awaish Kumar: for, like, in the last meeting we had with Robert, where we were focusing, okay.
85 00:07:36.610 ⇒ 00:07:39.259 Awaish Kumar: What industries we are targeting?
86 00:07:39.660 ⇒ 00:07:40.680 Awaish Kumar: So…
87 00:07:41.180 ⇒ 00:07:42.190 Luke Scorziell: Yeah.
88 00:07:43.600 ⇒ 00:07:49.930 Awaish Kumar: On… on that level, like, this is the thing that we are basically focused… these are… these were things which…
89 00:07:50.820 ⇒ 00:07:53.020 Awaish Kumar: Kind of, we were focusing on.
90 00:07:53.150 ⇒ 00:07:55.180 Samuel Roberts: like, CPG companies?
91 00:07:55.850 ⇒ 00:07:56.760 Awaish Kumar: Right?
92 00:07:57.060 ⇒ 00:08:01.540 Awaish Kumar: So, companies,
93 00:08:01.780 ⇒ 00:08:06.960 Awaish Kumar: with the, like, like, one of our client drink element is one of this kind of CPG…
94 00:08:06.960 ⇒ 00:08:07.540 Samuel Roberts: Hmm.
95 00:08:07.540 ⇒ 00:08:09.859 Awaish Kumar: Company, consumable packaged goods.
96 00:08:10.720 ⇒ 00:08:17.990 Awaish Kumar: They are, like, in revenue, like, maybe 100 million or something, so…
97 00:08:18.700 ⇒ 00:08:28.409 Awaish Kumar: like, companies of that type, and we are targeting them to, like, build the data warehouse. So, one of the offers is, like, setting up
98 00:08:28.780 ⇒ 00:08:30.790 Awaish Kumar: Data warehouse, right?
99 00:08:31.170 ⇒ 00:08:31.520 Luke Scorziell: Yeah, I’m.
100 00:08:31.520 ⇒ 00:08:33.410 Awaish Kumar: In the list of offers.
101 00:08:33.919 ⇒ 00:08:36.619 Luke Scorziell: Because do some companies not even have that set up yet?
102 00:08:38.070 ⇒ 00:08:42.100 Awaish Kumar: Yeah, like, Nick Gallimon didn’t have that set up already.
103 00:08:42.400 ⇒ 00:08:46.530 Luke Scorziell: And how did… what was the… so, Element didn’t have a data warehouse set up
104 00:08:46.880 ⇒ 00:08:48.460 Luke Scorziell: Did they come to us?
105 00:08:49.490 ⇒ 00:08:52.549 Luke Scorziell: Or were… how wide did they come to us, I guess?
106 00:08:53.190 ⇒ 00:08:55.300 Luke Scorziell: What was the kind of, like… Yeah.
107 00:08:55.300 ⇒ 00:08:59.309 Awaish Kumar: That’s what I’m trying to say, is that in the list of offers, we might have some offer, like.
108 00:08:59.610 ⇒ 00:09:02.450 Awaish Kumar: Setting up data warehouse and data stack.
109 00:09:02.580 ⇒ 00:09:03.380 Awaish Kumar: Like…
110 00:09:04.130 ⇒ 00:09:15.150 Awaish Kumar: thing. That is the offer we are telling them, that we are going to build a data warehouse where we are going to ingest all your data coming from across the sources, head to one place.
111 00:09:15.350 ⇒ 00:09:15.700 Luke Scorziell: Yeah.
112 00:09:15.700 ⇒ 00:09:22.019 Awaish Kumar: like… Help you… help with some, like, basic reporting.
113 00:09:22.710 ⇒ 00:09:24.530 Awaish Kumar: Looks like the financial system.
114 00:09:25.400 ⇒ 00:09:27.200 Luke Scorziell: Okay, and so, so they…
115 00:09:27.200 ⇒ 00:09:41.139 Awaish Kumar: Once they are in, we offer something, they came, we built a data warehouse, they are happy. Now we can, like, keep on adding and stuff, like, okay, we can help now with dashboarding, we can help with analysis, we can help with…
116 00:09:41.260 ⇒ 00:09:45.699 Awaish Kumar: product analytics, like, we could keep on adding this scope.
117 00:09:46.800 ⇒ 00:09:50.649 Luke Scorziell: Okay. So the data warehouse is really, like, the key part.
118 00:09:51.130 ⇒ 00:09:57.940 Luke Scorziell: Of it all. If they have that, then we can do other things, but if they don’t have it, then… and is that the same as dbt?
119 00:09:59.380 ⇒ 00:10:03.139 Awaish Kumar: Actually, DBD is kind of just a transformation layer on top of…
120 00:10:03.380 ⇒ 00:10:10.960 Awaish Kumar: warehouse. So some people do have data in a warehouse, but they don’t have… they don’t know what to do with it.
121 00:10:11.530 ⇒ 00:10:13.030 Samuel Roberts: Hmm. Huh.
122 00:10:13.030 ⇒ 00:10:19.449 Awaish Kumar: So, we have, like, some companies have data, what, for example.
123 00:10:20.450 ⇒ 00:10:31.470 Awaish Kumar: Eden, like, something like… someone like Eden. So they had the data, they had some reporting layer, but it was not well organized, and they didn’t know, like, how to…
124 00:10:31.580 ⇒ 00:10:34.849 Awaish Kumar: Get answers for this data properly.
125 00:10:35.030 ⇒ 00:10:38.059 Awaish Kumar: Or the people they had, like, the analysts they had, like.
126 00:10:38.390 ⇒ 00:10:48.770 Awaish Kumar: maybe they were not able to cope up with the pace. So they come to us, okay, let’s help us with building a proper reporting on top of the data we have.
127 00:10:50.220 ⇒ 00:10:51.339 Awaish Kumar: Data warehouse.
128 00:10:51.470 ⇒ 00:10:57.530 Awaish Kumar: So they’d already had some data stack ready. We came on… we come on the…
129 00:10:57.610 ⇒ 00:11:13.130 Awaish Kumar: on the project, and we can help them with refining their data setup, or optimizing the data setup, but, like, the major part is that they have the data, right? Now our task is to optimize it and help them
130 00:11:13.460 ⇒ 00:11:16.750 Awaish Kumar: I’ll create reporting on top of it.
131 00:11:18.110 ⇒ 00:11:19.080 Luke Scorziell: Got it, okay.
132 00:11:19.500 ⇒ 00:11:21.909 Luke Scorziell: So, at the core, that’s kind of the…
133 00:11:22.280 ⇒ 00:11:26.070 Luke Scorziell: That’s the core thing that Brainforge does, right? Pretty much.
134 00:11:26.350 ⇒ 00:11:32.919 Luke Scorziell: Who’s… has set up the data infrastructure so that every… everything’s going into one source of truth.
135 00:11:33.630 ⇒ 00:11:42.950 Awaish Kumar: So, yeah, like, but that’s what we want to separate, right? In the services, like, main thing is, like, if you talk about data, it’s all about
136 00:11:43.290 ⇒ 00:11:46.360 Awaish Kumar: Data warehousing and dashboarding.
137 00:11:46.670 ⇒ 00:11:47.530 Awaish Kumar: Alright.
138 00:11:48.780 ⇒ 00:11:55.210 Awaish Kumar: Right, but then we… we can… we split each part, like, okay, we can do… Boom.
139 00:11:55.630 ⇒ 00:12:04.739 Awaish Kumar: For example, like, there was one client, like, Hydra, where we wanted to pitch
140 00:12:04.980 ⇒ 00:12:07.240 Awaish Kumar: I think we have somewhere here.
141 00:12:08.490 ⇒ 00:12:15.790 Awaish Kumar: So, yeah, I think this… Data Foundation… Something is…
142 00:12:16.230 ⇒ 00:12:21.680 Awaish Kumar: Is what we are… Okay, so, like, this doesn’t seem to be refined.
143 00:12:22.580 ⇒ 00:12:24.750 Awaish Kumar: So…
144 00:12:25.530 ⇒ 00:12:26.000 Luke Scorziell: Yeah.
145 00:12:26.680 ⇒ 00:12:29.150 Awaish Kumar: Yeah, let me just go back, let’s…
146 00:12:33.180 ⇒ 00:12:37.260 Awaish Kumar: So, like, so this is what I remember from, like, what we are targeting.
147 00:12:37.580 ⇒ 00:12:41.740 Awaish Kumar: The industries we are targeting in terms of data,
148 00:12:42.090 ⇒ 00:12:45.910 Awaish Kumar: times. So, number one, KCPG companies.
149 00:12:46.120 ⇒ 00:12:47.789 Luke Scorziell: Number 2 is…
150 00:12:47.790 ⇒ 00:12:55.679 Awaish Kumar: Companies, healthcare companies, like, we had a client, like, called, a hype access.
151 00:12:56.140 ⇒ 00:12:58.350 Awaish Kumar: This is one of the worst data.
152 00:12:58.490 ⇒ 00:13:15.410 Awaish Kumar: healthcare-related client. So we were helping them also with the… they were some… trying to establish their first clinic, and we were helping them with all the data flows, and… and ultimately building the data warehouse, and then,
153 00:13:16.740 ⇒ 00:13:21.229 Awaish Kumar: for example, building the reporting on top of it.
154 00:13:22.320 ⇒ 00:13:26.360 Awaish Kumar: The on-top level, these are kind of…
155 00:13:26.740 ⇒ 00:13:34.929 Awaish Kumar: kind of companies we are targeting. There’s one that was, like, I don’t see it here, but it was, like, more, regarding,
156 00:13:35.610 ⇒ 00:13:40.820 Awaish Kumar: SaaS, like, companies, so targeting, those.
157 00:13:41.390 ⇒ 00:13:43.200 Luke Scorziell: Oh, yeah, yeah.
158 00:13:43.940 ⇒ 00:13:49.669 Luke Scorziell: And then, just from a zooming out then, At what point does…
159 00:13:50.000 ⇒ 00:13:54.880 Luke Scorziell: At what point does AI then… enter into…
160 00:13:55.070 ⇒ 00:13:59.739 Luke Scorziell: Like, is that something that, you know, you await the data team finishes up?
161 00:13:59.840 ⇒ 00:14:05.149 Luke Scorziell: structuring, and then AI kind of enters, is there any overlap right now? Are they two kind of completely different?
162 00:14:05.850 ⇒ 00:14:08.620 Luke Scorziell: Like, yeah, what is… tell me more about that mix.
163 00:14:08.620 ⇒ 00:14:11.460 Samuel Roberts: Yeah, I mean, they’ve… oh, go ahead.
164 00:14:12.250 ⇒ 00:14:17.400 Awaish Kumar: Yeah, sorry. So, like, okay, you can see it here, like, what we basically did.
165 00:14:18.730 ⇒ 00:14:27.609 Awaish Kumar: like, this is kind of… if you look at the ICPs and industry, point number 2, so you can see there is a…
166 00:14:28.120 ⇒ 00:14:34.170 Awaish Kumar: some… But, What do you say? Sticky notes, and we…
167 00:14:34.170 ⇒ 00:14:34.630 Luke Scorziell: Yeah, sure.
168 00:14:35.440 ⇒ 00:14:38.410 Awaish Kumar: This is what we discussed. Okay, yeah, here it is.
169 00:14:39.940 ⇒ 00:14:50.990 Awaish Kumar: In the November-focused ICP. So, in the December, we didn’t add it, basically. I don’t know if we are… we are following the same, like, what we are focusing on, like, in terms of
170 00:14:51.220 ⇒ 00:14:52.000 Awaish Kumar: Boom.
171 00:14:52.600 ⇒ 00:15:00.009 Awaish Kumar: ICP. So, if you, if you see my screen, this is what we discussed, Utam. So, sorry, Robert,
172 00:15:00.470 ⇒ 00:15:11.560 Awaish Kumar: So, one thing is, we are, like, SaaS founders wanting to stand up their data stack before the holidays. So it was, like, kind of helping them build a data warehouse.
173 00:15:11.670 ⇒ 00:15:15.149 Awaish Kumar: In a month, kind of thing, like, in 4 or 5 weeks.
174 00:15:15.290 ⇒ 00:15:25.250 Awaish Kumar: help them set up the… all the data stack, and… and we start ingesting the data. So we, like, we have… as an example, we have companies like Hydra.
175 00:15:25.870 ⇒ 00:15:26.750 Awaish Kumar: Right?
176 00:15:27.160 ⇒ 00:15:40.980 Awaish Kumar: Then we… this is what we suggested, like, we could do this, we could do this, so these are, like, all the… how… I don’t know how to better… now it’s up to you to help us with how to better calculate it for an offer.
177 00:15:41.150 ⇒ 00:15:43.330 Awaish Kumar: But, like, data…
178 00:15:43.460 ⇒ 00:15:49.439 Awaish Kumar: think, like, these are different things we can do with this. Like, we can offer all these things
179 00:15:51.450 ⇒ 00:15:52.719 Awaish Kumar: To a startup.
180 00:15:52.920 ⇒ 00:15:54.630 Awaish Kumar: So, like, okay.
181 00:15:55.290 ⇒ 00:16:07.670 Awaish Kumar: for someone like Hydra, we can say, okay, we will… we are going to adjust your data. As a package, we have, like, this four-week sprint plan, and we are going to,
182 00:16:07.910 ⇒ 00:16:20.920 Awaish Kumar: work with this velocity, and this is our pricing. And what you are get… you will get as a deliverable is, you are going to get a snowflake ready to use, you are going to get a polytopic ready to use.
183 00:16:20.960 ⇒ 00:16:33.240 Awaish Kumar: all the connection in just, like, all the connectors set up, and you have data in your warehouse, and we can run dbt on top of it. We can set up dbt for CICD, and…
184 00:16:33.420 ⇒ 00:16:38.270 Awaish Kumar: and basic modeling. So all that is done in a four-week plan.
185 00:16:39.310 ⇒ 00:16:41.060 Awaish Kumar: So this is what I’m looking for.
186 00:16:41.420 ⇒ 00:16:54.949 Awaish Kumar: And the other one was, like, this is, okay. So this is omnichannel CPG brands, something like Drake Element, where we have 10 million plus, like, companies with 10 million plus revenue, we are targeting them.
187 00:16:55.710 ⇒ 00:17:01.980 Awaish Kumar: And then this, the third one was, like, the MarTech leader, so this is basically…
188 00:17:02.300 ⇒ 00:17:09.470 Awaish Kumar: Kind of a… we are offering companies to, Like, we are going to…
189 00:17:09.730 ⇒ 00:17:26.880 Awaish Kumar: get data from whatever source you want, we can put it to Snowflake, run transformation on top of it, and get it back to your customer I.O. Like, if you want to run targeted campaigns, right, to your customer base, okay, you want to target… I want to target my…
190 00:17:26.910 ⇒ 00:17:29.740 Awaish Kumar: Customers with a higher…
191 00:17:29.870 ⇒ 00:17:36.370 Awaish Kumar: LTV, right, right? And then I, I want, like, I don’t know, but…
192 00:17:36.530 ⇒ 00:17:41.920 Awaish Kumar: who those, like, customers are. I’m a CIO, like, customer I.O. person.
193 00:17:42.040 ⇒ 00:17:47.060 Awaish Kumar: I don’t know, I can get the emails, but I don’t know how to get this data, so we can help you.
194 00:17:47.220 ⇒ 00:17:55.150 Awaish Kumar: with creating those segments of your customer and loading that data back to your customer I.O. from Snowflake, and then…
195 00:17:55.310 ⇒ 00:18:03.380 Awaish Kumar: In CIO, you can just use, okay, for this segment of customers, send this email. For this segment of customers, send this email, kind of thing.
196 00:18:03.380 ⇒ 00:18:05.239 Luke Scorziell: So, this is profile.
197 00:18:06.250 ⇒ 00:18:10.790 Luke Scorziell: But none of that can be done without the initial data warehouse, right?
198 00:18:11.550 ⇒ 00:18:21.740 Awaish Kumar: Yeah, but this offer is not there for someone who didn’t have the data warehouse. It’s for someone who already has the data warehouse, struggling with
199 00:18:21.950 ⇒ 00:18:24.050 Awaish Kumar: Targeting campaigning.
200 00:18:26.660 ⇒ 00:18:31.299 Luke Scorziell: Okay, so there’s different levels of clients, yeah.
201 00:18:31.570 ⇒ 00:18:44.119 Awaish Kumar: This is all the data work. If you talk about data, it’s just building a data warehouse and reporting. But it’s… when we say just, like, there’s a lot of things. And different people are in different stages.
202 00:18:44.840 ⇒ 00:18:50.310 Luke Scorziell: Yeah, I was… I was walking through some of the stuff with Zoran this morning, because I was like, just show me the…
203 00:18:50.670 ⇒ 00:18:57.759 Luke Scorziell: Show me the, like, screen share, because, you know, it’s one thing to hear about all this stuff, and then another to see it.
204 00:18:57.920 ⇒ 00:19:07.340 Awaish Kumar: So there are thousands of, like, now tools, like, this is one of the offers where I’m mentioning Snowflake, Snowflake, Snowflake. There could be an offer, like, okay, we are going to get you set up.
205 00:19:07.740 ⇒ 00:19:12.239 Awaish Kumar: With mother death, polytopics, DBT, and
206 00:19:12.430 ⇒ 00:19:24.089 Awaish Kumar: And, like, in 4 weeks, this is our offer. Maybe one with Snowflake is much more costly, because maybe it includes more effort, or some… something with the mother duck is…
207 00:19:24.560 ⇒ 00:19:32.040 Awaish Kumar: is a little bit, like, less costlier, because we want to target the clients. We are… who are not looking for…
208 00:19:33.430 ⇒ 00:19:42.529 Awaish Kumar: for systems like Snowflake, because they can’t pay for it, like, if they are looking for tools which are kind of budget tools, then our offer is also, like, kind of matching
209 00:19:43.750 ⇒ 00:19:45.000 Awaish Kumar: matching that.
210 00:19:46.770 ⇒ 00:19:50.049 Luke Scorziell: So Snowflake is quite expensive, is what you’re saying, to use?
211 00:19:50.430 ⇒ 00:19:54.739 Awaish Kumar: Yeah, it’s a usage… it’s a usage-based, right? Not, like… Oh, yeah.
212 00:19:55.820 ⇒ 00:20:01.780 Awaish Kumar: But, like, mother can be cheaper, and… Snowflake.
213 00:20:03.310 ⇒ 00:20:08.170 Luke Scorziell: Got it. Okay.
214 00:20:08.620 ⇒ 00:20:14.309 Luke Scorziell: I want to, like, ask you all a bunch of questions, and then I know that Sam probably has a whole different,
215 00:20:14.810 ⇒ 00:20:18.820 Luke Scorziell: Set of, but… So…
216 00:20:18.820 ⇒ 00:20:24.459 Awaish Kumar: So… so, like, for… for your context, you can look at this, you know, what we focused in terms of
217 00:20:24.930 ⇒ 00:20:26.980 Awaish Kumar: ICPs in November.
218 00:20:27.100 ⇒ 00:20:31.099 Awaish Kumar: Then we have this list of offers which I already sent.
219 00:20:31.310 ⇒ 00:20:39.719 Awaish Kumar: And I… like, this is just… we named them maybe in a meeting with Kutam or something, but we didn’t really work on what’s supposed to be in there.
220 00:20:39.720 ⇒ 00:20:46.760 Luke Scorziell: So, I guess, like, maybe just, you know, simply for this meeting, and then we can all… I can schedule more time with you, too. What…
221 00:20:47.150 ⇒ 00:20:56.539 Luke Scorziell: like… An order of complexity, or to accomplish these different tasks, what…
222 00:20:57.280 ⇒ 00:21:07.679 Luke Scorziell: like, what are we looking at? Like, a 4-week sprint is… is that pretty easy relative to other offers that we have? What are the ones that, you know, if you… if you would say, like.
223 00:21:08.390 ⇒ 00:21:11.200 Luke Scorziell: If I asked you, like, who’s paying the most for this.
224 00:21:11.440 ⇒ 00:21:14.310 Luke Scorziell: And it’s easiest for us to do.
225 00:21:14.570 ⇒ 00:21:19.020 Luke Scorziell: Like, does… do what comes to mind there?
226 00:21:19.680 ⇒ 00:21:24.609 Luke Scorziell: Because I think in the context, too, is that we’re kind of looking to switch up to…
227 00:21:25.130 ⇒ 00:21:30.179 Luke Scorziell: more enterprise-level clients, and my assumption, I guess, there would be that they already have
228 00:21:30.470 ⇒ 00:21:37.739 Luke Scorziell: the warehousing complete? Maybe they just need better reporting or optimization of the warehouse?
229 00:21:42.610 ⇒ 00:21:43.130 Awaish Kumar: Sorry.
230 00:21:44.470 ⇒ 00:21:48.220 Luke Scorziell: So, I guess, like, on your end, if we’re thinking about more of an inter…
231 00:21:48.690 ⇒ 00:21:53.030 Luke Scorziell: enterprise… I mean, have you worked with, kind of, more people on the enterprise level?
232 00:21:54.000 ⇒ 00:21:54.550 Luke Scorziell: Official.
233 00:21:54.550 ⇒ 00:22:00.140 Awaish Kumar: So, Drink Element is one of our enterprise clients I’m working with right now.
234 00:22:00.840 ⇒ 00:22:04.839 Luke Scorziell: And they didn’t have a warehouse set up at all, right? Is what you said?
235 00:22:05.210 ⇒ 00:22:05.750 Awaish Kumar: Yeah.
236 00:22:06.860 ⇒ 00:22:09.150 Awaish Kumar: So they didn’t have this data warehouse, so…
237 00:22:09.420 ⇒ 00:22:13.620 Luke Scorziell: And what are they paying? Because they’re one of our bigger clients, right?
238 00:22:14.900 ⇒ 00:22:17.540 Awaish Kumar: Well, I don’t know right now.
239 00:22:17.710 ⇒ 00:22:21.630 Awaish Kumar: I got bang… It should be.
240 00:22:21.770 ⇒ 00:22:22.780 Awaish Kumar: Somewhere.
241 00:22:25.260 ⇒ 00:22:28.159 Luke Scorziell: Yeah, maybe, I guess, I don’t know if this could be homework, but…
242 00:22:28.390 ⇒ 00:22:33.250 Luke Scorziell: If you could figure out what services Are kind of, like.
243 00:22:33.650 ⇒ 00:22:40.920 Luke Scorziell: the easiest on our end to do, and then maybe rank them in order of complexity, I think that would be, like, that would be quite helpful.
244 00:22:42.250 ⇒ 00:22:43.270 Luke Scorziell: For me.
245 00:22:45.200 ⇒ 00:22:57.440 Awaish Kumar: Hmm… yeah, so… I don’t know, like… Like, how… Do you want us to… go with, like, we…
246 00:22:58.140 ⇒ 00:23:01.409 Awaish Kumar: Like, do you need just the names? Do you need, like.
247 00:23:01.410 ⇒ 00:23:07.750 Luke Scorziell: Or, I mean, even if you want to go to that… even in this meeting, if you just go to that, other database of, like, the,
248 00:23:09.160 ⇒ 00:23:10.860 Luke Scorziell: Services that we’re offering?
249 00:23:13.010 ⇒ 00:23:15.129 Awaish Kumar: Rick element here, basically.
250 00:23:16.580 ⇒ 00:23:23.450 Awaish Kumar: Let me just… Yeah, so this is… 15,000 per month.
251 00:23:24.170 ⇒ 00:23:26.769 Luke Scorziell: But Eden… and Eden is also a data client?
252 00:23:27.490 ⇒ 00:23:28.100 Awaish Kumar: Yeah.
253 00:23:29.680 ⇒ 00:23:34.879 Luke Scorziell: Yeah, so maybe just going back to the services…
254 00:23:36.440 ⇒ 00:23:43.900 Awaish Kumar: Tack. Yeah, so I… I was looking for something which I…
255 00:23:48.750 ⇒ 00:23:49.850 Luke Scorziell: Yeah, we have a lot of…
256 00:23:51.760 ⇒ 00:23:53.540 Awaish Kumar: Yeah, I mean, that’s tough.
257 00:23:57.140 ⇒ 00:24:02.709 Awaish Kumar: So, yeah, you see, like… Okay, so these are basically the playbooks.
258 00:24:05.020 ⇒ 00:24:05.790 Awaish Kumar: Okay.
259 00:24:08.560 ⇒ 00:24:11.100 Awaish Kumar: Okay, so yeah, that was, like,
260 00:24:12.320 ⇒ 00:24:14.989 Awaish Kumar: This… this is the services page.
261 00:24:15.420 ⇒ 00:24:19.940 Awaish Kumar: We already have some there, listed there, and
262 00:24:21.230 ⇒ 00:24:25.730 Awaish Kumar: Oh, and, like, I, I understand can, like, add a few more.
263 00:24:26.100 ⇒ 00:24:29.069 Awaish Kumar: And the list of offers, and then we can refine them.
264 00:24:29.820 ⇒ 00:24:34.450 Luke Scorziell: Yeah, maybe if you go to just Active Services, just so I can get an idea right now.
265 00:24:35.620 ⇒ 00:24:39.820 Awaish Kumar: Actually, I’m… I’m about to see it, like…
266 00:24:40.400 ⇒ 00:24:45.859 Luke Scorziell: If you click the view, so go down, or up, up, and then click Active Services View.
267 00:24:46.500 ⇒ 00:24:47.519 Awaish Kumar: Oh, this one.
268 00:24:48.850 ⇒ 00:24:51.980 Luke Scorziell: Yeah, and then, yeah, that should give you the…
269 00:24:52.790 ⇒ 00:24:56.820 Luke Scorziell: So, which, which of these are… our data?
270 00:24:57.510 ⇒ 00:25:02.429 Awaish Kumar: So, for the data, you see, like, there’s nothing much here. This one is data.
271 00:25:02.700 ⇒ 00:25:05.519 Awaish Kumar: This is, like, in development yet.
272 00:25:06.770 ⇒ 00:25:09.600 Awaish Kumar: And this one is data, but this is also in development.
273 00:25:09.930 ⇒ 00:25:15.390 Awaish Kumar: And then we have this, these boxes… Seems to be AI.
274 00:25:16.650 ⇒ 00:25:20.240 Awaish Kumar: I think this is also something data-related.
275 00:25:20.710 ⇒ 00:25:30.130 Awaish Kumar: This is data-related. CDP is… kind of data, but it’s more like product, like, we categorize our data work
276 00:25:30.620 ⇒ 00:25:33.129 Awaish Kumar: Into, also into the product analytics.
277 00:25:35.320 ⇒ 00:25:39.490 Awaish Kumar: So this CDP is… comes under data, but…
278 00:25:39.820 ⇒ 00:25:43.300 Awaish Kumar: It’s a specialized version of it, you can say.
279 00:25:43.500 ⇒ 00:25:46.320 Awaish Kumar: I… so you’re gonna, like…
280 00:25:46.840 ⇒ 00:25:56.810 Awaish Kumar: For CDP, you can meet with Robert and his team, and his team includes Zorad and Henry, who are our product analytics experts.
281 00:25:56.930 ⇒ 00:25:58.200 Awaish Kumar: And Greg.
282 00:25:58.890 ⇒ 00:25:59.470 Awaish Kumar: Basically.
283 00:26:02.280 ⇒ 00:26:06.830 Awaish Kumar: Chief Automation, yeah, this is something I think… or AI.
284 00:26:07.430 ⇒ 00:26:11.860 Awaish Kumar: cycle intelligence sprint. It could be under…
285 00:26:12.030 ⇒ 00:26:18.980 Awaish Kumar: It is under data, you can say, but similarly, we might have to ask
286 00:26:19.150 ⇒ 00:26:24.280 Awaish Kumar: Robert’s team for that, and same for product analytics. It’s also data, but…
287 00:26:24.840 ⇒ 00:26:39.019 Awaish Kumar: data, like, data strategy, or data analysis, and then data engineering. So it will come under, under data, and then product analytics. So we… for product analytics, we are going to go to maybe somewhere, like.
288 00:26:39.460 ⇒ 00:26:42.200 Awaish Kumar: Robert or his team, like Greg.
289 00:26:42.330 ⇒ 00:26:48.610 Awaish Kumar: our Henry and Zoran. Zoran is more of a… A marketing guy?
290 00:26:48.890 ⇒ 00:26:51.940 Awaish Kumar: MarTech engineering, say? So…
291 00:26:52.790 ⇒ 00:27:02.120 Awaish Kumar: Yeah. For a market engineer, and then, like, for Martech, we don’t have any offer here. We can build a few offers with Zora.
292 00:27:03.780 ⇒ 00:27:07.710 Awaish Kumar: Okay? So, if you… if you want me to be…
293 00:27:07.950 ⇒ 00:27:18.849 Awaish Kumar: like, to, like, get all that list, so I can meet with them, so I can include what we are working on, or what we can offer, and I can also…
294 00:27:19.570 ⇒ 00:27:22.950 Awaish Kumar: Read, read
295 00:27:23.310 ⇒ 00:27:30.770 Awaish Kumar: Zolan, and we come up with some more offers, and then we can go over… over there.
296 00:27:31.190 ⇒ 00:27:33.900 Luke Scorziell: No, that’d be great. I think… I think… I think if…
297 00:27:34.820 ⇒ 00:27:38.170 Luke Scorziell: like, a simple… I mean, obviously, I know this is complicated, but as…
298 00:27:38.450 ⇒ 00:27:42.910 Luke Scorziell: If you’re just thinking of, like, what are the three stages of
299 00:27:43.280 ⇒ 00:27:48.730 Luke Scorziell: Like, offers that we could present of, like, low, medium, in-depth.
300 00:27:49.110 ⇒ 00:27:56.429 Luke Scorziell: maybe entry, mid-level, like, area expertise, like, that would be maybe a helpful framework. And even if it means grouping, like.
301 00:27:56.680 ⇒ 00:27:59.870 Awaish Kumar: I don’t know if we want to run it like this, because…
302 00:28:00.790 ⇒ 00:28:06.599 Awaish Kumar: like, the robot ran it differently, so this one is, like, I…
303 00:28:06.810 ⇒ 00:28:18.040 Awaish Kumar: as a… as a data engineer, go back, come up with some offer, and write down something, and I can tell you, okay, this is for entry-level clients, right? That’s… that’s what you mean, right?
304 00:28:18.360 ⇒ 00:28:18.930 Luke Scorziell: Yeah.
305 00:28:19.460 ⇒ 00:28:26.669 Awaish Kumar: clients on a… on, like, we don’t have a data setup. So, we have this data stack setup service, which is already live.
306 00:28:28.190 ⇒ 00:28:29.320 Awaish Kumar: Yeah, okay.
307 00:28:29.740 ⇒ 00:28:35.129 Luke Scorziell: Well, yeah, why don’t we… I want to hear Sam, too, just on the AI side.
308 00:28:35.320 ⇒ 00:28:36.040 Awaish Kumar: But…
309 00:28:36.040 ⇒ 00:28:36.790 Samuel Roberts: Yeah.
310 00:28:36.790 ⇒ 00:28:38.170 Luke Scorziell: Thank you, Awish.
311 00:28:38.170 ⇒ 00:28:40.129 Awaish Kumar: Good. Just a minute, I just…
312 00:28:40.270 ⇒ 00:28:41.739 Luke Scorziell: Follow up with one more question.
313 00:28:42.750 ⇒ 00:28:49.069 Awaish Kumar: just a minute, I can just tell you that the process for current offers, and then I can, like.
314 00:28:49.340 ⇒ 00:28:57.159 Awaish Kumar: any letters, like, Sam to speak. So, this is what we did last time. So, this was kind of Figma exercise. We’ve worked with Robert.
315 00:28:57.370 ⇒ 00:29:05.959 Awaish Kumar: like, what are the problems for these kind of companies? What are the solutions? And basically, we came up with decision on two offers.
316 00:29:06.110 ⇒ 00:29:09.460 Awaish Kumar: Right? Which is all in this.
317 00:29:10.630 ⇒ 00:29:11.160 Luke Scorziell: Stigma.
318 00:29:11.520 ⇒ 00:29:12.270 Awaish Kumar: Yeah.
319 00:29:12.470 ⇒ 00:29:23.240 Awaish Kumar: And then, like, Robert mentioned that he’s going to go back and standardize that, those offers, but, like, maybe he didn’t got time or whatever. Maybe you can discuss that with Toy. Yeah, that’s all.
320 00:29:23.240 ⇒ 00:29:24.600 Luke Scorziell: Okay.
321 00:29:24.810 ⇒ 00:29:25.750 Luke Scorziell: Thank you.
322 00:29:27.410 ⇒ 00:29:30.900 Luke Scorziell: Yeah, it’s… I mean, like… Taking it all in.
323 00:29:32.510 ⇒ 00:29:36.569 Luke Scorziell: Yeah, Sam, maybe if you just want to give an overview, then, of,
324 00:29:38.740 ⇒ 00:29:42.960 Samuel Roberts: Yeah, so, I mean, I think the data stuff’s a little more…
325 00:29:43.290 ⇒ 00:29:49.389 Samuel Roberts: you know, what we’ve done in the past. So when I joined over the summer, we were working on a couple
326 00:29:49.840 ⇒ 00:29:51.010 Samuel Roberts: clients…
327 00:29:51.430 ⇒ 00:30:00.519 Samuel Roberts: The one we’ve been doing the longest was ABC, which has also become kind of a data thing, so we’ve kind of spread that, but the AI side of it was this…
328 00:30:00.750 ⇒ 00:30:05.849 Samuel Roberts: chatbot for their customer service representatives to
329 00:30:06.110 ⇒ 00:30:11.620 Samuel Roberts: Access the data that they have, and all of the, the different…
330 00:30:11.620 ⇒ 00:30:13.310 Luke Scorziell: ABC, right?
331 00:30:13.310 ⇒ 00:30:29.929 Samuel Roberts: Yeah, this is for ABC, so they have technicians and service people that go out, and they’re all assigned different places, and so this is helping them find that, find all the documentation and things, so it’s… I have a little bit of a harder time thinking of, like, services, necessarily, that we’ve done at this point, because they’re kind of…
332 00:30:30.260 ⇒ 00:30:33.029 Samuel Roberts: a little more bespoke projects.
333 00:30:33.300 ⇒ 00:30:33.940 Luke Scorziell: Yeah.
334 00:30:34.310 ⇒ 00:30:41.729 Samuel Roberts: So, I’m trying to think, like, what is, you know, a way to group that beyond the little bit that we had in that list already?
335 00:30:41.730 ⇒ 00:30:46.439 Luke Scorziell: Well, maybe… why don’t you tell me more about… so is that… you’ve worked on,
336 00:30:47.450 ⇒ 00:30:51.329 Luke Scorziell: Is that the only one that you’ve worked on, is ABC, or what else?
337 00:30:51.330 ⇒ 00:30:57.090 Samuel Roberts: No, so we’ve had ABC, we worked on a project with a, a,
338 00:30:57.390 ⇒ 00:31:08.320 Samuel Roberts: I don’t know what they were called, like, a design firm that did, they made pitch decks for… for companies raising money. We were helping them with their, ideation process.
339 00:31:08.560 ⇒ 00:31:17.990 Samuel Roberts: they had… they had been using Claude a bunch, back and forth, and we’d helped them kind of automate that a little bit. And then, more recently has been Lilo, which was… we’re helping them rebuild their…
340 00:31:18.260 ⇒ 00:31:24.089 Samuel Roberts: Stitch platform, which is their way of accessing all of the data that they have on Shopify and…
341 00:31:24.230 ⇒ 00:31:27.480 Samuel Roberts: Meta and Google, and then they have a whole
342 00:31:27.950 ⇒ 00:31:31.220 Samuel Roberts: Roadmap of different features that they’re looking to add as well, so…
343 00:31:31.670 ⇒ 00:31:34.870 Luke Scorziell: Which is a chat. It’s like a chat with our data type thing.
344 00:31:34.870 ⇒ 00:31:36.060 Samuel Roberts: Yeah, yeah.
345 00:31:36.710 ⇒ 00:31:37.970 Luke Scorziell: Okay.
346 00:31:37.970 ⇒ 00:31:41.519 Samuel Roberts: And there’s an element there that’s also… this is what I’m saying, it’s very…
347 00:31:41.660 ⇒ 00:31:44.509 Samuel Roberts: It’s been very specific per client. It’s been less of a, like.
348 00:31:45.010 ⇒ 00:31:52.429 Samuel Roberts: oh, we go in and set up a thing that’s always the same. So I’m not sure where to go with that. I mean, there’s definitely ideas we could… we could brainstorm a little bit more, but…
349 00:31:52.950 ⇒ 00:31:59.220 Luke Scorziell: Maybe, I don’t know, could you even… would you mind sharing even your screen of just, like, maybe something you’ve been working on?
350 00:31:59.490 ⇒ 00:32:00.090 Samuel Roberts: Yeah, sure.
351 00:32:00.090 ⇒ 00:32:01.809 Luke Scorziell: So I could kind of get a flow.
352 00:32:01.810 ⇒ 00:32:02.690 Samuel Roberts: Yeah, so…
353 00:32:03.050 ⇒ 00:32:05.709 Luke Scorziell: Yeah, the way to think about some of this stuff is, like.
354 00:32:07.450 ⇒ 00:32:20.690 Luke Scorziell: whether it’s… you can group by the offer that you’re giving, or, like, the type of client, you know, that we’re going after, too. So it doesn’t always need to be, like, a specific set of services, as much as, like, are you in this boat?
355 00:32:20.920 ⇒ 00:32:22.229 Luke Scorziell: And if you’re in this boat…
356 00:32:22.230 ⇒ 00:32:23.100 Samuel Roberts: Right, right, okay.
357 00:32:23.100 ⇒ 00:32:24.670 Luke Scorziell: The things that we might do.
358 00:32:25.400 ⇒ 00:32:26.440 Luke Scorziell: So, like, for the.
359 00:32:26.440 ⇒ 00:32:27.470 Samuel Roberts: Yeah.
360 00:32:27.470 ⇒ 00:32:29.760 Luke Scorziell: Do you have a warehouse? No.
361 00:32:29.760 ⇒ 00:32:31.969 Samuel Roberts: Okay, well then, obviously, you’re gonna be here.
362 00:32:31.980 ⇒ 00:32:34.980 Luke Scorziell: Do you, you know, for… so I don’t know, on the AI side.
363 00:32:35.260 ⇒ 00:32:37.540 Luke Scorziell: How we would think about some of that.
364 00:32:37.720 ⇒ 00:32:39.330 Samuel Roberts: Yeah.
365 00:32:40.910 ⇒ 00:32:42.190 Luke Scorziell: But, anyways, feel free to…
366 00:32:42.190 ⇒ 00:32:46.350 Samuel Roberts: Yeah, sorry, I’m sorry, I was thinking. So, this is… this is what we’ve been working on, this is kind of their…
367 00:32:46.480 ⇒ 00:32:49.289 Samuel Roberts: They had a platform…
368 00:32:49.770 ⇒ 00:33:04.749 Samuel Roberts: that another dev shop had built, and wasn’t really doing what they wanted, and they were getting frustrated with them, so… I’m not exactly sure how the connection was made, but that they, you know, we approached them, and they approached us, kind of thing. But, this is basically… they are,
369 00:33:06.430 ⇒ 00:33:25.699 Samuel Roberts: excuse me, these guys here. So they do a lot of e-commerce ad stuff, and so they have a ton of brands that they work with. And so, for each of these, the idea… this is still, like, a test environment here, but they’re gonna have different brands that they can click into. And right now, the functionality is just, like, chat over these different, sources.
370 00:33:25.770 ⇒ 00:33:31.989 Samuel Roberts: So, it’s a little bit broken right now, that’s kind of what I was in the middle of, so it might be hard to demo a little bit, but as you can see, like.
371 00:33:32.070 ⇒ 00:33:37.750 Samuel Roberts: You know, it can pull… This is just using the data directly from
372 00:33:37.960 ⇒ 00:33:49.790 Samuel Roberts: the sources. There’s another side of this that’s gonna be actually, like, doing a little bit of the stuff that Awave talked about, where we’re gonna warehouse some of the Shopify data and things like that to do more forecasting and things,
373 00:33:50.480 ⇒ 00:33:54.059 Samuel Roberts: For predictions and whatnot, but,
374 00:33:54.620 ⇒ 00:33:58.230 Samuel Roberts: You know, this is just setting up these connections to…
375 00:33:58.620 ⇒ 00:34:05.680 Samuel Roberts: Google, Klaviyo for email, Meta ads, and Shopify for e-commerce stuff, so…
376 00:34:06.140 ⇒ 00:34:10.559 Samuel Roberts: I don’t know where to go with that from here. Like, these guys are, you know.
377 00:34:10.960 ⇒ 00:34:17.610 Samuel Roberts: in terms of the type of client, like, they’re… I don’t know how big they are in terms of the total team size, but the two founders.
378 00:34:17.840 ⇒ 00:34:20.759 Samuel Roberts: Talk about at least, like, maybe, like, 15 other people.
379 00:34:20.760 ⇒ 00:34:25.459 Luke Scorziell: And, so that’s on the marketing side, too, and…
380 00:34:25.460 ⇒ 00:34:27.379 Samuel Roberts: Yeah. So, functionally…
381 00:34:27.580 ⇒ 00:34:31.380 Luke Scorziell: Does that replace, like, amplitude, almost? I don’t know.
382 00:34:31.690 ⇒ 00:34:33.930 Samuel Roberts: No, I think this is more…
383 00:34:34.909 ⇒ 00:34:37.550 Samuel Roberts: You know, they want to be able to,
384 00:34:38.469 ⇒ 00:34:42.759 Samuel Roberts: you know, compare, pull this up together, talk about… this is not a good one to go with.
385 00:34:42.760 ⇒ 00:34:43.270 Awaish Kumar: Yeah.
386 00:34:43.270 ⇒ 00:34:47.290 Samuel Roberts: It’s like chatting with the data, right? Chatting with the data, yeah.
387 00:34:47.290 ⇒ 00:34:50.810 Luke Scorziell: Versus, like, a lot of the other stuff is visualizing the data.
388 00:34:51.690 ⇒ 00:34:52.350 Luke Scorziell: So…
389 00:34:52.350 ⇒ 00:35:01.030 Samuel Roberts: Yeah, yeah, visualizing, like, trends and things. This is a little bit more digging in, to the data that’s… and right now, it’s coming straight from those sources. We’re not warehousing anything yet, so…
390 00:35:01.210 ⇒ 00:35:02.460 Samuel Roberts: It’s…
391 00:35:02.460 ⇒ 00:35:03.140 Luke Scorziell: Virtually.
392 00:35:03.140 ⇒ 00:35:04.129 Samuel Roberts: whatever is in.
393 00:35:04.380 ⇒ 00:35:07.460 Luke Scorziell: In the future, could we build this off of the warehouse?
394 00:35:07.460 ⇒ 00:35:22.590 Samuel Roberts: That’s… that’s kind of the plan here, is eventually… not necessarily for the chat here, that’s gonna be, you know, you want to get the fresh data that way, but for… they have a whole forecasting app that they want to build for modeling out ad spend and cost per clicks and stuff.
395 00:35:22.850 ⇒ 00:35:27.910 Samuel Roberts: And that we will be doing a little bit more of the ingestion side. So it’s a little bit of a hybrid thing there.
396 00:35:28.530 ⇒ 00:35:29.260 Awaish Kumar: Yeah.
397 00:35:29.510 ⇒ 00:35:38.909 Awaish Kumar: But I think offer has to stand alone, right? So even if it requires data expertise, they might get our help, but offer should be, like, something…
398 00:35:39.300 ⇒ 00:35:47.919 Awaish Kumar: Using, like, Kind of, what do you say, chat with your… spend data, right? So…
399 00:35:47.920 ⇒ 00:35:53.850 Samuel Roberts: Yeah. Yeah, I mean, that’s kind of what this is here, yeah. I mean, the other one, yeah, it’s a lot of chat, that’s kind of the…
400 00:35:54.020 ⇒ 00:36:00.059 Samuel Roberts: a very easy, idea for, like, how to use the LLMs, how to use the automation stuff.
401 00:36:00.220 ⇒ 00:36:02.539 Samuel Roberts: A chat interface.
402 00:36:02.900 ⇒ 00:36:05.489 Samuel Roberts: But there’s other things we’re doing,
403 00:36:05.840 ⇒ 00:36:15.270 Samuel Roberts: Well, the other one, we’re not really working on anymore, Interlude. That was a little bit of a chat thing that was in Slack, but it was… it was an iterative thing that was, you know, you’d feed it the,
404 00:36:15.300 ⇒ 00:36:28.320 Samuel Roberts: questionnaires from the brands and some of their other, interviews and things, and then it had a series of prompts that it kind of went through in this kind of iterative way to generate a bunch of copy for a proposed,
405 00:36:29.200 ⇒ 00:36:34.250 Samuel Roberts: slide deck for raising money, so… It’s a little… you know.
406 00:36:34.940 ⇒ 00:36:43.979 Samuel Roberts: The AI stuff right now is just everything’s changing quickly, so there’s a lot of different ways to go with it, but in terms of, like, who’s good to work with, or…
407 00:36:44.370 ⇒ 00:36:48.159 Samuel Roberts: You know, what things we can package more easily, like…
408 00:36:48.590 ⇒ 00:36:55.029 Samuel Roberts: you know, chat over X is a pretty standard thing, I feel like, at this point, but.
409 00:36:55.030 ⇒ 00:36:56.650 Luke Scorziell: Yeah, yeah. Huh.
410 00:36:57.480 ⇒ 00:37:01.179 Samuel Roberts: It’s not groundbreaking, necessarily, either, but that does need to be, you know.
411 00:37:08.660 ⇒ 00:37:09.420 Awaish Kumar: Okay.
412 00:37:10.680 ⇒ 00:37:11.549 Luke Scorziell: Let me think.
413 00:37:11.940 ⇒ 00:37:15.979 Awaish Kumar: How… Do you want to, like, take it forward, no?
414 00:37:16.830 ⇒ 00:37:18.629 Luke Scorziell: Yeah, I think,
415 00:37:22.080 ⇒ 00:37:26.349 Luke Scorziell: So, well, just to clarify, so Sam, it seems like the AI stuff is pretty bespoke.
416 00:37:27.070 ⇒ 00:37:29.330 Samuel Roberts: It has been to this point,
417 00:37:29.800 ⇒ 00:37:34.609 Samuel Roberts: I don’t think it necessarily has to stay that way, and it might, you know, I mean.
418 00:37:35.680 ⇒ 00:37:42.039 Samuel Roberts: it could. It could be something that, you know, either we approach someone who has an AI thing, or looking for AI, and then…
419 00:37:42.240 ⇒ 00:37:46.190 Samuel Roberts: data is also a thing, or, like, ABC, that’s kind of how it went, I think. We’re…
420 00:37:46.190 ⇒ 00:37:50.709 Awaish Kumar: I’ve been working with them on the AI side, and now we’re doing some discovery data stuff with them.
421 00:37:52.310 ⇒ 00:37:58.290 Samuel Roberts: It could be totally separate, like the interlude stuff that we did, which was just… they had some automation ideas and wanted to…
422 00:37:58.790 ⇒ 00:38:04.200 Samuel Roberts: build that out. It’s been much more, like, product-y, it’s been a little more, like.
423 00:38:04.530 ⇒ 00:38:07.069 Samuel Roberts: building a… a tool. It’s… it’s…
424 00:38:07.330 ⇒ 00:38:10.490 Samuel Roberts: Again, it doesn’t have to be that way. In fact, I would like to see…
425 00:38:10.740 ⇒ 00:38:16.509 Samuel Roberts: maybe a few things that we could package a little bit better and go out and sell.
426 00:38:16.820 ⇒ 00:38:19.839 Samuel Roberts: But up to this point, it hasn’t been a ton of that.
427 00:38:20.190 ⇒ 00:38:21.390 Samuel Roberts: At least.
428 00:38:21.530 ⇒ 00:38:22.720 Luke Scorziell: Yeah. This way.
429 00:38:24.250 ⇒ 00:38:26.999 Samuel Roberts: I’m even trying to think, like, what are good, like, what are.
430 00:38:27.000 ⇒ 00:38:27.510 Awaish Kumar: There’s a big deal.
431 00:38:27.510 ⇒ 00:38:29.099 Samuel Roberts: Client for this side, but…
432 00:38:29.680 ⇒ 00:38:35.010 Awaish Kumar: I think in the short term, on the data side, at least I can suggest, if we can go to that Figma.
433 00:38:35.210 ⇒ 00:38:41.000 Awaish Kumar: Where we have decision to… Offers. That was discussed, like, quite…
434 00:38:41.210 ⇒ 00:38:44.359 Awaish Kumar: With the big team, like, we were all in there.
435 00:38:45.560 ⇒ 00:38:54.090 Awaish Kumar: Me, Henry, Zoran, Robert, everybody was there. I was brainstorming on a few things, came up with two offers.
436 00:38:54.210 ⇒ 00:38:56.479 Awaish Kumar: Which, we can easily package them.
437 00:38:56.610 ⇒ 00:39:02.829 Awaish Kumar: For… for others, like, that’s up to you how you want to now I’ll drive it.
438 00:39:03.980 ⇒ 00:39:10.520 Luke Scorziell: Okay, well, yeah, maybe then… I can take a look through the Figma.
439 00:39:11.600 ⇒ 00:39:18.580 Luke Scorziell: Maybe just let you guys… was that… that was pretty recent, that you guys did that exercise?
440 00:39:18.800 ⇒ 00:39:22.240 Awaish Kumar: Yeah, I think it has been at least 4 weeks.
441 00:39:22.330 ⇒ 00:39:25.170 Luke Scorziell: Oh, so it’s literally not even been a… it’s barely been a month.
442 00:39:26.150 ⇒ 00:39:31.049 Awaish Kumar: We had a decision on offers, but, like, I think none of us had
443 00:39:31.160 ⇒ 00:39:36.129 Awaish Kumar: Enough, like, availability to go and formalize it, basically.
444 00:39:37.790 ⇒ 00:39:42.630 Awaish Kumar: Write down, like, what… What a standard offer looks like.
445 00:39:43.220 ⇒ 00:39:43.710 Awaish Kumar: We just…
446 00:39:43.710 ⇒ 00:39:53.009 Luke Scorziell: Yeah, yeah. I mean, I’ll look through this, and then I’ll probably chat with Robert and Tom more, and I don’t know, maybe you guys could recommend more people to talk with, too. I think.
447 00:39:54.350 ⇒ 00:40:04.090 Luke Scorziell: Yeah, I’m still learning the business a lot, so I think that I don’t want to make any, like, premature, like, this is what we need to do. My kind of gut sense is maybe having, like.
448 00:40:04.750 ⇒ 00:40:14.029 Luke Scorziell: Particularly as I’m thinking about, like, LinkedIn content, like, just having, like, one or two pain points that were really hitting home, and, like, an offer that we’re speaking about pretty consistently.
449 00:40:14.240 ⇒ 00:40:16.470 Luke Scorziell: We could have more, too.
450 00:40:17.090 ⇒ 00:40:22.239 Awaish Kumar: There is one thing Hena used to do, like, I don’t know if that’s, like, case studies.
451 00:40:22.460 ⇒ 00:40:25.899 Awaish Kumar: I don’t know if you are looking for that, or if you’re looking for offers.
452 00:40:27.080 ⇒ 00:40:30.929 Luke Scorziell: Yeah, we have, we have, like, the case studies, too, so,
453 00:40:31.620 ⇒ 00:40:42.410 Luke Scorziell: Yeah, I think, like, in my mind, as I’m thinking, it’s like, how do we connect what we’re doing for clients to the clients that we want to work with? And so, I’m, at this point, trying to learn more of, like.
454 00:40:42.720 ⇒ 00:40:45.190 Luke Scorziell: what are we doing for clients?
455 00:40:45.590 ⇒ 00:40:51.740 Luke Scorziell: Because I think that’ll… like, it’s hard to… hard to talk about, Yeah.
456 00:40:51.740 ⇒ 00:40:58.400 Awaish Kumar: We have… we also have a list of same, like, offers, we also have a list of… Case studies.
457 00:40:58.800 ⇒ 00:41:04.210 Awaish Kumar: And in that notion. And where we are, like, kind of…
458 00:41:04.530 ⇒ 00:41:13.350 Awaish Kumar: putting in, like, what we are doing for Eden, for example, what I’m doing for Element, and they are like, okay, I’m using,
459 00:41:13.580 ⇒ 00:41:22.979 Awaish Kumar: Snowflake retail data to help them with some dashboarding. This is one of, kind of, case studies how we help them optimize their
460 00:41:23.370 ⇒ 00:41:27.799 Awaish Kumar: time to insight for retail data, and then…
461 00:41:28.430 ⇒ 00:41:31.219 Awaish Kumar: Then that… you can connect it to some offer.
462 00:41:32.960 ⇒ 00:41:39.980 Luke Scorziell: Yeah. Okay. Yeah, maybe if you can…
463 00:41:42.940 ⇒ 00:41:48.509 Luke Scorziell: Yeah, maybe anything that you guys think would be helpful to share with me, because I think, too, I’m trying to learn the notion.
464 00:41:48.740 ⇒ 00:41:54.100 Luke Scorziell: Would be… Would be great, and then maybe we can check in again.
465 00:41:55.050 ⇒ 00:41:55.500 Samuel Roberts: Yeah.
466 00:41:55.500 ⇒ 00:41:56.250 Luke Scorziell: For kind of review.
467 00:41:56.780 ⇒ 00:42:02.180 Samuel Roberts: Yeah, I mean, the other side of the AI stuff is also, like, a lot of the internal work that we’ve done on the platform.
468 00:42:03.040 ⇒ 00:42:03.390 Luke Scorziell: for, like…
469 00:42:03.390 ⇒ 00:42:08.379 Samuel Roberts: this Zoom meeting is gonna get ingested and added there. So that was sort of how things…
470 00:42:08.680 ⇒ 00:42:12.350 Samuel Roberts: came about, if that makes sense, on the AI, like, automation side.
471 00:42:12.460 ⇒ 00:42:16.240 Samuel Roberts: So there’s, there’s stuff there that we’ve…
472 00:42:16.570 ⇒ 00:42:22.479 Samuel Roberts: done, we can do for others. I’m not sure exactly, you know, but even that’s, like, it’s been a little bespoke to us, so it’s not…
473 00:42:23.590 ⇒ 00:42:24.950 Luke Scorziell: Yeah. Perfect.
474 00:42:24.950 ⇒ 00:42:26.179 Samuel Roberts: For everyone, so…
475 00:42:28.430 ⇒ 00:42:29.380 Luke Scorziell: Okay.
476 00:42:30.540 ⇒ 00:42:31.749 Luke Scorziell: I think that’s…
477 00:42:32.580 ⇒ 00:42:37.589 Luke Scorziell: all I have for right now, and then maybe this is a good, like, stepping-off point for,
478 00:42:38.740 ⇒ 00:42:41.390 Luke Scorziell: Reading more of the,
479 00:42:43.200 ⇒ 00:42:54.420 Luke Scorziell: the Figma, and then… yeah, I think anything that y’all think would be helpful, and if you’re open to kind of, like, throwing together just, like, a, hey, here are a few ideas of what we could offer, and then sending it my way, I think that would be… that would be helpful.
480 00:42:55.860 ⇒ 00:42:58.660 Awaish Kumar: So I sent a list of new studies.
481 00:42:58.940 ⇒ 00:43:00.369 Awaish Kumar: In the chat?
482 00:43:00.760 ⇒ 00:43:01.270 Luke Scorziell: Oh, yeah.
483 00:43:01.270 ⇒ 00:43:07.249 Awaish Kumar: Huh, but I can’t… Yeah, we can also look… like, that’s what I wanted to…
484 00:43:07.560 ⇒ 00:43:18.620 Awaish Kumar: hear from you, like, as a next step, like, maybe you can start a Notion page where we can write down our ideas of offers, then we can work together to refine those.
485 00:43:18.910 ⇒ 00:43:21.839 Luke Scorziell: Yeah, yeah, that’d be great. So,
486 00:43:22.650 ⇒ 00:43:25.559 Luke Scorziell: Well, I know I can do that, and then…
487 00:43:28.640 ⇒ 00:43:36.779 Luke Scorziell: Would you… do you guys think that you’re… like, this group is the best group of stakeholders, at least, for this, or are there…
488 00:43:37.480 ⇒ 00:43:38.840 Luke Scorziell: Like, it depends how…
489 00:43:38.840 ⇒ 00:43:43.170 Awaish Kumar: you want to do that? Like, I… I think everybody…
490 00:43:43.480 ⇒ 00:43:48.779 Awaish Kumar: I’m open to, like, keeping everybody there, like, Everyone has their own ideas.
491 00:43:49.370 ⇒ 00:44:00.590 Awaish Kumar: Yeah. Like, the data team, like, I’m not working on every client, right? People are doing some specific things. They might have some case studies idea.
492 00:44:00.590 ⇒ 00:44:01.040 Samuel Roberts: Hmm.
493 00:44:01.040 ⇒ 00:44:08.840 Awaish Kumar: Or they… that can be turned into an offer. So we can have everyone for meetings, like, where we want to brainstorm.
494 00:44:09.080 ⇒ 00:44:14.249 Awaish Kumar: But they’re gonna… but then, once we have that, we can work in… in our…
495 00:44:14.560 ⇒ 00:44:20.199 Awaish Kumar: Group to, basically, how to refine those, or package those, or then…
496 00:44:20.340 ⇒ 00:44:24.810 Awaish Kumar: Basically, what tool stack you need, we can basically answer all those questions.
497 00:44:25.370 ⇒ 00:44:26.609 Luke Scorziell: Okay. Okay.
498 00:44:26.750 ⇒ 00:44:33.250 Luke Scorziell: Yeah, sweet. So, well, it seems… I mean, this is kind of a… big…
499 00:44:34.470 ⇒ 00:44:38.400 Luke Scorziell: thing that we’re tackling, so I think it, you know, it’s not all gonna come together tomorrow.
500 00:44:38.400 ⇒ 00:44:38.990 Samuel Roberts: Yeah.
501 00:44:39.290 ⇒ 00:44:43.390 Luke Scorziell: But yeah, I’ll reach out, I’ll read through this, reach out.
502 00:44:43.610 ⇒ 00:44:48.809 Luke Scorziell: Talk to Robert and Tom, and then, yeah, see what, what makes the most sense, so…
503 00:44:48.950 ⇒ 00:44:51.009 Samuel Roberts: Thank you guys both for making so much time.
504 00:44:51.550 ⇒ 00:44:52.469 Samuel Roberts: Yeah, no problem.
505 00:44:52.570 ⇒ 00:44:59.759 Luke Scorziell: Sorry, a little more helpful right now, but yeah. No, all good, and I’ll send you… if I have questions and stuff, I’ll send you a loom. Maybe I’ll go through this, and then I can…
506 00:44:59.760 ⇒ 00:45:00.250 Samuel Roberts: Yeah.
507 00:45:00.290 ⇒ 00:45:05.280 Luke Scorziell: You guys can, like, give me feedback on, like, yeah, what I’m seeing, so…
508 00:45:05.280 ⇒ 00:45:05.940 Samuel Roberts: Cool.
509 00:45:07.290 ⇒ 00:45:09.189 Awaish Kumar: Okay. But yeah, okay.
510 00:45:09.360 ⇒ 00:45:10.140 Samuel Roberts: Alrighty.
511 00:45:10.140 ⇒ 00:45:11.510 Luke Scorziell: Alright, thank you.
512 00:45:11.770 ⇒ 00:45:12.890 Samuel Roberts: Yeah, talk to you later.
513 00:45:12.890 ⇒ 00:45:13.530 Luke Scorziell: But…