Meeting Title: Brainforge x Lilo Social Project Sync Date: 2026-02-11 Meeting participants: Pranav, Luke Scorziell
WEBVTT
1 00:00:15.970 ⇒ 00:00:17.630 Luke Scorziell: Hey, Pranav, how’s it going?
2 00:00:18.150 ⇒ 00:00:20.030 Pranav: Hey, I’m good, how are you doing?
3 00:00:20.260 ⇒ 00:00:23.360 Luke Scorziell: Good, good to meet you.
4 00:00:23.560 ⇒ 00:00:24.490 Luke Scorziell: Chatting.
5 00:00:25.900 ⇒ 00:00:30.000 Pranav: Yeah, I’ve been seeing you in, like, different meetings, but we haven’t gotten to talk one-on-one.
6 00:00:30.500 ⇒ 00:00:35.269 Luke Scorziell: Yeah, yeah, yeah, so… Where are you calling from? Where are you coming from?
7 00:00:35.830 ⇒ 00:00:37.709 Pranav: I’m in, Austin, Texas right now.
8 00:00:38.260 ⇒ 00:00:40.120 Luke Scorziell: Oh, cool, okay, nice.
9 00:00:40.470 ⇒ 00:00:41.240 Luke Scorziell: Yeah.
10 00:00:41.240 ⇒ 00:00:46.679 Pranav: Every once in a while, like, me, Utam, and Shaysu will, like, link up at, like, a WeWork nearby.
11 00:00:47.110 ⇒ 00:00:49.110 Luke Scorziell: Okay, yeah, yeah, I guess I’ve seen that.
12 00:00:49.200 ⇒ 00:00:50.040 Pranav: Yeah.
13 00:00:50.870 ⇒ 00:00:56.049 Luke Scorziell: So, dang, that’s dope. And, you’ve… how… when did…
14 00:00:56.450 ⇒ 00:00:58.890 Luke Scorziell: Were you recent at Brandforge, or when did you…
15 00:00:59.070 ⇒ 00:01:04.310 Pranav: It’s pretty recent. Yeah, I started beginning of December. I think December, like, 9th or 10th was my first day.
16 00:01:04.760 ⇒ 00:01:12.980 Luke Scorziell: Okay, okay, cool. What about you? I did consulting in December, so I think I probably started around the same time.
17 00:01:13.180 ⇒ 00:01:13.880 Pranav: Okay.
18 00:01:13.880 ⇒ 00:01:21.819 Luke Scorziell: And then, like, full-time started in a… January, 1st, so…
19 00:01:21.820 ⇒ 00:01:25.119 Pranav: Gotcha. Yeah, I think it was around the same for me. Like, started, like…
20 00:01:25.560 ⇒ 00:01:35.410 Pranav: 20 hours at first, and I think… I’m not sure if this is, like, for everybody, but… started, like, with a 2-week, kind of, like, trial period, seeing how, like, the rest of the team fits around you, like…
21 00:01:35.680 ⇒ 00:01:36.650 Pranav: just kind of…
22 00:01:36.970 ⇒ 00:01:45.569 Pranav: just, they’re just seeing, like, how I would perform a little bit, I think. And then, yeah, starting Jan 1st, it was like, okay, fully ramping, 40 hours a week, go, go, go.
23 00:01:45.840 ⇒ 00:01:47.570 Luke Scorziell: Yeah, yeah.
24 00:01:47.860 ⇒ 00:01:55.919 Luke Scorziell: No, it’s crazy. So, it’s fun, though. And you’re primarily on the AI team, or what’s been, like, your main focus?
25 00:01:56.260 ⇒ 00:01:59.469 Pranav: Yeah, it’s really just been on the AI team.
26 00:01:59.670 ⇒ 00:02:05.359 Pranav: It’s, funny that you reached out to me today, and I think Utam, I was just talking to him yesterday.
27 00:02:05.660 ⇒ 00:02:14.410 Pranav: Because he was like, dude, yeah, you should get more involved in, like, a little bit of the sales stuff, since I’m… I started off as just, like.
28 00:02:14.580 ⇒ 00:02:20.419 Pranav: you know, just kind of… well, in December, we didn’t have this whole concept of, like, EP and CSO and S,
29 00:02:21.840 ⇒ 00:02:41.649 Pranav: And so I just kind of started off as just, like, just like an IC. And then, like, I went into the EP role beginning of Jan, and then as Utam was like, oh, maybe you can start taking off, like, taking some of the CSO stuff. So then I took on CSO, and, like, a kind of a next progression feels like, you know, CSOs are, like.
30 00:02:42.110 ⇒ 00:02:45.449 Pranav: On the delivery team, probably, like, the most…
31 00:02:45.720 ⇒ 00:02:51.470 Pranav: Like, close to, or at least, like, the most aligned with, like, helping the sales team as well.
32 00:02:51.990 ⇒ 00:02:52.399 Luke Scorziell: I’ve been working.
33 00:02:52.780 ⇒ 00:02:57.270 Pranav: And so, it just feels like a cool fit, and it’s something I’m interested in, so it’s just, like, it was perfect.
34 00:02:57.270 ⇒ 00:02:57.940 Luke Scorziell: Yeah.
35 00:02:58.120 ⇒ 00:02:58.480 Pranav: Yeah.
36 00:02:58.480 ⇒ 00:03:02.210 Luke Scorziell: Yeah, that’s cool. No, I know, he’s mentioned that, like, you’d be a good,
37 00:03:03.090 ⇒ 00:03:06.490 Luke Scorziell: Like, I don’t know how… if… did you talk to Greg at all?
38 00:03:07.260 ⇒ 00:03:13.100 Pranav: I talked to Greg a little bit. He’s in, he’s another CSO, so he’s, like, in our, like, CSO weekly meeting, so…
39 00:03:13.220 ⇒ 00:03:22.260 Luke Scorziell: Yeah, yeah, yeah. So he… he’s been helping out with, like, some content on the data side, and so… Sick. I knew Tom was, like,
40 00:03:23.340 ⇒ 00:03:26.679 Luke Scorziell: Yeah, I just thought it would be,
41 00:03:29.100 ⇒ 00:03:34.210 Luke Scorziell: like, you’d be the good, like, the Greg, you could be the Greg on the AI side, so…
42 00:03:34.210 ⇒ 00:03:34.670 Pranav: I like it.
43 00:03:34.670 ⇒ 00:03:39.459 Luke Scorziell: Yeah, I think, like, I’m finding, too, at the CSOs, they’re, like.
44 00:03:39.630 ⇒ 00:03:43.019 Luke Scorziell: You know, the ones like owning client outcomes, and…
45 00:03:43.280 ⇒ 00:03:49.609 Luke Scorziell: And doing, like, organizing a lot of the work, so I found them, like, to be very helpful in…
46 00:03:49.740 ⇒ 00:03:52.299 Luke Scorziell: Asking questions about, I mean, even just, like.
47 00:03:52.490 ⇒ 00:03:57.290 Luke Scorziell: us talking about Lilo, I feel like it was, like, pretty helpful.
48 00:03:57.540 ⇒ 00:03:57.860 Pranav: Yeah.
49 00:03:58.000 ⇒ 00:04:06.960 Luke Scorziell: Just to hear, like, your perspective, and obviously you’re owning it. I’m curious, too, just for my own knowledge, like, what is the EP role? How is that different than the CSO role?
50 00:04:07.460 ⇒ 00:04:08.850 Pranav: Yeah, so…
51 00:04:09.050 ⇒ 00:04:21.329 Pranav: how I think about, like, CSO and EP, and technically SL, but I don’t feel as strongly with that, is, like, CSO and EP, they kind of split, like, the project management, and so having, like, a…
52 00:04:21.740 ⇒ 00:04:24.829 Pranav: Like, say if we… like, the saving, like, kind of…
53 00:04:25.150 ⇒ 00:04:36.269 Pranav: company that, you know, Brainforge is, like, we could split it up as just, like, engineers who only focus on, like, pushing tickets and, like, executing on those and pushing code, and then…
54 00:04:36.340 ⇒ 00:04:46.500 Pranav: The project, or the, yeah, the project manager, basically, they kind of own, okay, client relationship, as well as managing, like, the timeline of things.
55 00:04:46.500 ⇒ 00:04:57.559 Pranav: So, now we have two different roles for that. Like, the client management is, like, happening by the CSO. EPs are just, like, pushing, like, the linear tickets, making sure we’re still on track for, like.
56 00:04:57.600 ⇒ 00:05:04.729 Pranav: reaching certain milestones. They push back on the CSOs, because, like, the CSOs are always going to want to make the client happy.
57 00:05:04.790 ⇒ 00:05:10.789 Pranav: The EP’s always gonna be kind of on a, like, Tell us what’s realistic.
58 00:05:11.150 ⇒ 00:05:19.909 Pranav: you know, it’s not really, like, I’m trying to get everybody to work 100 hours, I would never do that. But it’s just, like, it’s a cool checks and balances thing, that…
59 00:05:20.490 ⇒ 00:05:35.139 Pranav: now, not just one person has to just, like, internally, like, figure out. It’s, like, cool that, like, okay, I can talk… I can have one perspective on things, and then as a CSO, and then Casey, who’s the EP, he has another perspective.
60 00:05:36.000 ⇒ 00:05:42.619 Luke Scorziell: Yeah, so they’re, like, the… like, you’re, like, the one casting the vision a little bit, and then the EP is the one that’s, like.
61 00:05:43.480 ⇒ 00:05:46.589 Luke Scorziell: Slow down, or speed up, or that type of thing, like…
62 00:05:46.840 ⇒ 00:05:54.219 Pranav: Little bit, yeah. It’s like… yeah, I think that’s, like, part of it. That’s kind of where I want to get to, where I can start, like…
63 00:05:54.760 ⇒ 00:05:59.320 Pranav: Talking to the client and start, helping them with…
64 00:05:59.900 ⇒ 00:06:11.280 Pranav: defining the product itself, because sometimes the client, like, they have a vision and they know what their problem is, but the product itself that they come up with isn’t always the best.
65 00:06:11.440 ⇒ 00:06:16.040 Pranav: Even though they might think, like, okay, this is what we need to, like, solve our problem, like…
66 00:06:16.380 ⇒ 00:06:19.850 Pranav: I think where we… what we can do that will really, like.
67 00:06:20.170 ⇒ 00:06:40.150 Pranav: be, like, a huge selling factor is, like, we’re even better at solving your problems than, like, you can think of. Like, the features that you think you need, like, we’re like, no, like, the AI, like, world right now is, like, you could do this, which would solve your problems even better, be more integrated into your current workflows, like, all those different things. So…
68 00:06:40.280 ⇒ 00:06:46.349 Luke Scorziell: And then at that point, they’re just, like, they just tell us our problems, and they’re just like, trust us to, like, build the best thing possible.
69 00:06:47.320 ⇒ 00:06:48.650 Luke Scorziell: Yeah, that’s cool.
70 00:06:48.780 ⇒ 00:06:49.440 Pranav: Yeah.
71 00:06:50.630 ⇒ 00:06:57.510 Luke Scorziell: Yeah, no, okay, that’s, that’s super dope. It’s interesting, just because I’m, like.
72 00:06:57.960 ⇒ 00:07:08.060 Luke Scorziell: more on the internal team, I guess, and, like, less on the delivery side, so… Yeah. A lot of the stuff that I feel like is more intuitive to everyone else, it’s like, I’m kind of…
73 00:07:08.540 ⇒ 00:07:12.130 Luke Scorziell: Like… Learning, piece by piece.
74 00:07:12.130 ⇒ 00:07:14.360 Pranav: I just learned from being in the weeds, you know?
75 00:07:15.130 ⇒ 00:07:17.459 Pranav: I’m just putting out fires, and I’m just like…
76 00:07:17.640 ⇒ 00:07:20.390 Pranav: And then sometimes we’re just, like, working on something, and I’m like…
77 00:07:20.680 ⇒ 00:07:25.439 Pranav: We’re gonna just, like… this is gonna be deprecated in, like, 3 weeks, why are we spending time on this right now?
78 00:07:25.640 ⇒ 00:07:26.500 Luke Scorziell: Oh, my God.
79 00:07:26.500 ⇒ 00:07:27.319 Pranav: So it’s like…
80 00:07:27.490 ⇒ 00:07:36.829 Pranav: Yeah, so basically, but you need to put in, like, that 3 weeks of effort of, like, doing some, like, feature that’s just like, why are we doing this? And, like, at least that’s how I learn, you know, like…
81 00:07:36.950 ⇒ 00:07:40.020 Pranav: Just by being thrown into something, and then just, like, figuring it out.
82 00:07:40.180 ⇒ 00:07:47.549 Luke Scorziell: Yeah, no, same. It’s like, I’ve never really worked on data and AI marketing before, and the last two months have been, like, just…
83 00:07:47.840 ⇒ 00:07:52.519 Luke Scorziell: Drinking out of a fire hose, but I also, like, love it, because I’m, like, learning more.
84 00:07:52.680 ⇒ 00:07:58.420 Luke Scorziell: I feel like than I, like, ever have. So, let me pull up…
85 00:07:58.780 ⇒ 00:08:03.910 Luke Scorziell: Just gonna pull up what you wrote. Thank you for doing that, that was, like, super clutch.
86 00:08:04.490 ⇒ 00:08:05.130 Pranav: Yeah.
87 00:08:06.810 ⇒ 00:08:08.220 Luke Scorziell: On both of those.
88 00:08:08.500 ⇒ 00:08:10.970 Luke Scorziell: Because, yeah, I mean, basically, like, kind of…
89 00:08:11.410 ⇒ 00:08:16.579 Luke Scorziell: I have no clue, like, with either of these calls, they could be, like.
90 00:08:17.340 ⇒ 00:08:23.940 Luke Scorziell: just completely… I’m… well, one, I’m not trying to, like, make it all about a pitch, I guess.
91 00:08:25.720 ⇒ 00:08:29.490 Luke Scorziell: But then… They could be, like.
92 00:08:32.510 ⇒ 00:08:37.600 Luke Scorziell: Interested, not interested, they’re both pretty big agencies, like 200 to 500 employees.
93 00:08:37.880 ⇒ 00:08:39.039 Luke Scorziell: Oh, well.
94 00:08:39.659 ⇒ 00:08:47.360 Luke Scorziell: And… so that either they could… they could be interested in themselves, or I’m kind of hoping, like, maybe they could refer me also to, like, smaller…
95 00:08:47.530 ⇒ 00:08:49.979 Luke Scorziell: Agencies, if they know any.
96 00:08:50.460 ⇒ 00:08:57.279 Luke Scorziell: But, yeah, I think it’s just, like, this is a part of my network that I’m connected with, and, like, might as well just try to, like.
97 00:08:58.100 ⇒ 00:09:03.419 Luke Scorziell: replicate a little bit of what we’re doing with Lilo, and the way I see it is…
98 00:09:03.680 ⇒ 00:09:11.249 Luke Scorziell: Like, starting to do some… Like, content going out from my account.
99 00:09:11.630 ⇒ 00:09:17.140 Luke Scorziell: On the agency side, and then launching, like, a sales campaign on the agency side.
100 00:09:17.250 ⇒ 00:09:23.129 Luke Scorziell: And… so, so, yeah, that’s, I guess, like.
101 00:09:23.290 ⇒ 00:09:26.920 Luke Scorziell: generally where I’m thinking. And…
102 00:09:27.890 ⇒ 00:09:32.300 Luke Scorziell: This is all assuming, I guess, this is, like, a good service to offer, and these are, like, profitable clients.
103 00:09:32.910 ⇒ 00:09:33.440 Pranav: ship.
104 00:09:33.800 ⇒ 00:09:35.210 Luke Scorziell: And not, not just, like.
105 00:09:35.400 ⇒ 00:09:39.889 Luke Scorziell: like, a waste of time, which it doesn’t sound like it is at all.
106 00:09:40.130 ⇒ 00:09:46.809 Luke Scorziell: But, you know, you can shoot me down on stuff if you’re like, I don’t know, that’s, like, the best idea.
107 00:09:47.560 ⇒ 00:09:52.730 Luke Scorziell: But, yeah, so I don’t know if you want to, like, walk me through a little bit of, like, what…
108 00:09:53.030 ⇒ 00:09:59.530 Luke Scorziell: you’ve done with Lilo. I know we could, like, kind of go through that… the markdown file that you sent.
109 00:10:00.290 ⇒ 00:10:00.660 Pranav: Yeah.
110 00:10:00.660 ⇒ 00:10:01.740 Luke Scorziell: I mean…
111 00:10:01.740 ⇒ 00:10:09.140 Pranav: What I could also do, too, is, like, for some, like, maybe a different approach of, like, explaining it, like, I can kind of show you the product right now.
112 00:10:09.140 ⇒ 00:10:13.560 Luke Scorziell: That’s always so much more helpful. Yeah, I love that.
113 00:10:13.560 ⇒ 00:10:14.710 Pranav: Let’s, let’s do that.
114 00:10:15.300 ⇒ 00:10:16.889 Pranav: Okay, give me one sec, I’ll pull it up.
115 00:10:16.890 ⇒ 00:10:18.520 Luke Scorziell: Talking about this stuff, you’re always like.
116 00:10:18.690 ⇒ 00:10:22.200 Luke Scorziell: I have no clue what that means, and then you see it, and you’re like, oh, that makes sense.
117 00:10:22.460 ⇒ 00:10:23.409 Pranav: Yeah, yeah.
118 00:10:31.670 ⇒ 00:10:37.589 Pranav: Here, yeah, let me just… Yeah, actually, okay, I’ll share my screen.
119 00:10:38.760 ⇒ 00:10:39.460 Luke Scorziell: Okay.
120 00:10:44.500 ⇒ 00:10:50.849 Pranav: Okay… Okay, so you can see my screen, right? It says Lilo Social in the top left? Okay.
121 00:10:50.960 ⇒ 00:10:56.990 Pranav: Yeah, so this is… yeah, we’re in just the dev environment, so it’s, like, it’s not showing all of the…
122 00:10:57.660 ⇒ 00:10:59.800 Pranav: Like, their production brands and stuff.
123 00:10:59.980 ⇒ 00:11:06.600 Pranav: But… This is just kind of like the homepage of the Stitch platform, is what we’re calling it.
124 00:11:07.100 ⇒ 00:11:16.689 Pranav: each one of these cards in the center correspond to, like, an individual brand that Lilo Social is, I guess, partnered with.
125 00:11:17.240 ⇒ 00:11:17.910 Luke Scorziell: Iran.
126 00:11:18.240 ⇒ 00:11:24.850 Pranav: like, the… the marketing, advertising, all that stuff for them. So…
127 00:11:25.280 ⇒ 00:11:32.769 Pranav: Yeah, we have the functionality to add a brand, whatever. There’s a few different things on the left, so if I click on one of these…
128 00:11:33.080 ⇒ 00:11:37.490 Pranav: let’s say brands You’ll first see, like, this new chat thing appear.
129 00:11:37.680 ⇒ 00:11:39.579 Luke Scorziell: It’s like, like, new chat.
130 00:11:39.810 ⇒ 00:11:42.780 Pranav: And then you’ll see all of these, like, sliders here.
131 00:11:42.910 ⇒ 00:11:50.210 Pranav: These are the… when I mentioned, like, 4 MCPs, that’s kind of, like, The technical, kind of.
132 00:11:50.320 ⇒ 00:11:59.030 Pranav: jargon of just, like, essentially, we’re able to read the data of their Klaviyo email campaigns, their meta ads,
133 00:11:59.140 ⇒ 00:12:08.930 Pranav: data, Shopify revenue, Google Ads, all of that data is queryable from, like, natural language. So I can ask it, like.
134 00:12:09.160 ⇒ 00:12:26.490 Pranav: what were… what was the performance of our meta ads campaign from last week? And it’ll use that… that natural language, then connect… hit the MCP server, pull in the corresponding data to answer that question, and then format it in a way that’s, like, ingestible in, like, a chat interface.
135 00:12:27.990 ⇒ 00:12:31.680 Luke Scorziell: So you could ask it, like, out of the four…
136 00:12:31.940 ⇒ 00:12:36.769 Luke Scorziell: you know, between Google Ads, Klaviyo, Meta, and Shopify, which…
137 00:12:37.220 ⇒ 00:12:42.260 Luke Scorziell: I had, like, the highest, like, ROAS last week, or what is, like, the…
138 00:12:42.930 ⇒ 00:12:46.250 Luke Scorziell: Yeah, you could do that. Apples to oranges, or… yeah, what do you…
139 00:12:46.810 ⇒ 00:12:57.650 Pranav: You could definitely do that. You can even ask, like, because you probably have campaigns in both Meta and Google, like, this is how, like, Lilo’s using it. They have, like, a…
140 00:12:58.230 ⇒ 00:13:03.850 Pranav: they have a report that gets generated from the data from Google Ads and Meta combined.
141 00:13:05.980 ⇒ 00:13:07.080 Pranav: So…
142 00:13:07.080 ⇒ 00:13:07.710 Luke Scorziell: Huh.
143 00:13:07.880 ⇒ 00:13:12.020 Pranav: Yeah, so, actually, I can show you what that looks like, too.
144 00:13:12.820 ⇒ 00:13:14.340 Pranav: Yeah, and forecasting.
145 00:13:15.800 ⇒ 00:13:21.409 Pranav: So, we have this whole dashboard here that shows some… I’m not sure if…
146 00:13:21.510 ⇒ 00:13:23.500 Pranav: This is blocking it at all, but…
147 00:13:23.500 ⇒ 00:13:25.239 Luke Scorziell: Yeah, I can see, that’s okay.
148 00:13:25.420 ⇒ 00:13:29.010 Pranav: Perfect, yeah, so there’s, like, goals that we can put here.
149 00:13:29.300 ⇒ 00:13:31.680 Pranav: To…
150 00:13:32.050 ⇒ 00:13:40.569 Pranav: Actually, yeah, let me step back. This is basically just, like, a calculator, so then they can do a better job forecasting for the upcoming months for… okay.
151 00:13:41.150 ⇒ 00:13:42.809 Luke Scorziell: And this can be per client?
152 00:13:43.350 ⇒ 00:13:44.740 Pranav: Yeah, this is done per client.
153 00:13:44.740 ⇒ 00:13:46.640 Luke Scorziell: Or ISI per, like, brand?
154 00:13:46.640 ⇒ 00:13:47.539 Pranav: Per brand, yep.
155 00:13:47.740 ⇒ 00:13:48.939 Luke Scorziell: Oh, wow, okay.
156 00:13:49.140 ⇒ 00:13:49.700 Pranav: Yup.
157 00:13:52.060 ⇒ 00:13:54.719 Pranav: And you can have multiple per brand based on, like.
158 00:13:54.930 ⇒ 00:14:00.009 Pranav: You know, how many different, like, goals you want to set,
159 00:14:00.260 ⇒ 00:14:05.600 Pranav: Of course, for different months, you’ll have different ones as well. But yeah, so…
160 00:14:06.340 ⇒ 00:14:10.769 Pranav: there’s, like, different figures here. They’re… they’re not specifically saying, like.
161 00:14:11.240 ⇒ 00:14:15.420 Pranav: where it’s coming from, like, but it… this meta ads thing, like…
162 00:14:15.840 ⇒ 00:14:30.839 Pranav: for right now, we’re only bringing… the connections are only… sorry, this ad spend thing is only pulling from meta ads and Google ads, but in the future, like, we want to be able to support brands that are also using, like, you know, other vehicles for advertising, like TikTok.
163 00:14:31.010 ⇒ 00:14:41.519 Pranav: Anywhere else, right? And so, the idea with this is, like, okay, this is still going to be a relevant feature across, like.
164 00:14:41.640 ⇒ 00:14:51.389 Pranav: based on whatever type of, integrations, whatever ad platforms are using. Because of how our backend is, set up.
165 00:14:51.890 ⇒ 00:15:00.859 Pranav: Like, the data warehouse will bring in all this… all the data from all these different places, and then it will then do the necessary calculations to then…
166 00:15:00.970 ⇒ 00:15:02.649 Pranav: Create these fields here.
167 00:15:02.870 ⇒ 00:15:03.820 Luke Scorziell: So…
168 00:15:04.050 ⇒ 00:15:12.529 Pranav: Yeah, so basically, yeah, on a per-brand basis, whatever connections they have, we’re able to populate, like, dashboards like this. There’s, like, a few different ones.
169 00:15:14.290 ⇒ 00:15:22.319 Pranav: But, yeah, this is a cool thing. This is, like, what I would say… this is, like, the forecasting feature that, even you mentioned when you first messaged me.
170 00:15:22.320 ⇒ 00:15:23.290 Luke Scorziell: Yeah. So…
171 00:15:23.720 ⇒ 00:15:29.889 Pranav: I think this is super cool. They said that this is something that saves them 15 hours per week.
172 00:15:30.560 ⇒ 00:15:32.340 Pranav: Per brand? Yeah.
173 00:15:33.170 ⇒ 00:15:41.269 Pranav: Wow. And now it’s literally, you know, the data’s already in there. What they have to do is just, like, input certain values here.
174 00:15:42.010 ⇒ 00:15:43.450 Pranav: And then just save it.
175 00:15:44.730 ⇒ 00:15:47.210 Luke Scorziell: And this is unique.
176 00:15:47.650 ⇒ 00:15:56.949 Luke Scorziell: Or Brain… like, this is unique that Brainforge can do this because it’s combining both our, like, data expertise and the AI expertise? Like, is that… is that fair to say?
177 00:15:57.400 ⇒ 00:16:02.250 Pranav: I think that’s fair to say. Yeah, yeah.
178 00:16:02.250 ⇒ 00:16:09.140 Luke Scorziell: Because we have that warehouse that we’re building that’s bringing all the information into one spot, and then… and then…
179 00:16:09.560 ⇒ 00:16:15.929 Luke Scorziell: Do the MCPs work on top of that, or how does… do the MCPs bring the information into the warehouse?
180 00:16:16.710 ⇒ 00:16:18.000 Pranav: Yeah, so…
181 00:16:20.050 ⇒ 00:16:27.380 Pranav: How the MCPs work is they actually have access to the same information, but this is a very…
182 00:16:28.480 ⇒ 00:16:33.389 Pranav: like, dense page in terms of, like, the data that needs to get pulled in, and so… Yeah.
183 00:16:33.680 ⇒ 00:16:43.320 Pranav: pulling it via MCP would be very slow, and could just put a lot of strain on our servers. So, what we do instead is we have a data warehouse that pulls in on, like, a
184 00:16:43.630 ⇒ 00:16:53.299 Pranav: scheduled, like, every day. We could set that to, like, every hour if we needed to, just so, like, that we’re just not using too much space. But it’s like, yeah, we’ve…
185 00:16:53.470 ⇒ 00:17:00.879 Pranav: We’ve connected with all of these different, like, platforms, and we’ve done it in a way that, like, it’s…
186 00:17:01.280 ⇒ 00:17:12.769 Pranav: It can support, like, CPU-intensive things, like a dashboard, and then also, like, quick things that maybe…
187 00:17:13.310 ⇒ 00:17:17.099 Pranav: Are gonna happen more, like, frequently via, like, a chat.
188 00:17:17.400 ⇒ 00:17:20.870 Pranav: So, that’s kind of where the MCP and, like, the data warehouses, like.
189 00:17:20.970 ⇒ 00:17:24.159 Pranav: they, like, shine. Like, they shine in different areas.
190 00:17:24.410 ⇒ 00:17:29.639 Luke Scorziell: Yeah. And then, so, cause… with the MCP is that…
191 00:17:30.030 ⇒ 00:17:39.140 Luke Scorziell: more, like, if you’re, like, wanting real-time analytics, then you could ask, like, the MCP what’s happening, versus… but you could, in theory, also chat with the warehouse.
192 00:17:39.410 ⇒ 00:17:40.210 Luke Scorziell: Dada?
193 00:17:41.970 ⇒ 00:17:42.520 Luke Scorziell: No.
194 00:17:44.740 ⇒ 00:17:52.039 Pranav: I think it’s not as necessary to chat with the warehouse data, just because the MCP will be able to
195 00:17:52.560 ⇒ 00:17:58.570 Pranav: They’re all accessing the same data, but yeah, to your point, like, the data warehouse won’t be synced on a, like.
196 00:17:59.020 ⇒ 00:18:18.649 Pranav: isn’t doing, like, an API request every time I load this page. You know, we already have this data in our data warehouse, and so it was probably just loaded up that morning, or, like, an hour before, and so that’s fine for, like, the needs of a forecasting tool, because the day-to-day things aren’t that important, you kind of want to just see things in aggregate.
197 00:18:18.970 ⇒ 00:18:24.270 Pranav: But for an MCP tool, like, maybe you need to know, like, okay, where are we at today?
198 00:18:25.010 ⇒ 00:18:25.660 Luke Scorziell: Huh.
199 00:18:25.920 ⇒ 00:18:26.300 Pranav: Yeah.
200 00:18:26.520 ⇒ 00:18:36.169 Luke Scorziell: And… so… well, this is cool, because this is… this is more on, like, the growth and, like, media manager side, right? Of, like.
201 00:18:36.560 ⇒ 00:18:43.239 Luke Scorziell: Someone who’s… but then you’ve… we’ve also done, like, a lot of work, on the creative, like, the creative briefs, and then…
202 00:18:43.590 ⇒ 00:18:52.869 Luke Scorziell: I don’t know exactly what Lilo’s doing as far as, like, do they actually create the design and ads that they’re running to, or is it…
203 00:18:53.090 ⇒ 00:18:57.380 Pranav: Yeah, so there’s this, like, ad machine thing.
204 00:18:57.580 ⇒ 00:18:58.940 Pranav: And so…
205 00:19:00.260 ⇒ 00:19:08.240 Pranav: Let me actually… because I didn’t work on this, so I don’t know the best way to demo this. I can send you a loom that Casey made.
206 00:19:08.380 ⇒ 00:19:10.859 Pranav: On this.
207 00:19:12.450 ⇒ 00:19:19.320 Pranav: But yeah, like, I know, like, essentially what it’s supposed to be is just, like, yeah, you upload a product photo, and then you can get…
208 00:19:20.190 ⇒ 00:19:26.110 Pranav: more… Product photos to match that, basically, using, like.
209 00:19:26.270 ⇒ 00:19:30.690 Pranav: Nano Banana, which I believe is, like, a Google tool?
210 00:19:30.870 ⇒ 00:19:32.699 Pranav: Yeah. Yeah.
211 00:19:33.160 ⇒ 00:19:37.740 Pranav: But… yeah, it’s… it has, like, a good system prompt that, like.
212 00:19:37.880 ⇒ 00:19:45.340 Pranav: was curated by, Lilo Social, that we just used, and…
213 00:19:45.820 ⇒ 00:19:47.450 Pranav: Yeah, I wonder if I can…
214 00:19:49.760 ⇒ 00:19:54.199 Pranav: Maybe I can just find that loom for you, and we can just, like, watch it real quick, right?
215 00:19:54.200 ⇒ 00:19:55.289 Luke Scorziell: That’d be interesting.
216 00:19:55.540 ⇒ 00:19:56.210 Pranav: Yeah.
217 00:19:57.660 ⇒ 00:19:58.680 Luke Scorziell: Where’s.
218 00:20:36.640 ⇒ 00:20:42.500 Pranav: Maybe what I can do is, I can just see if they’ve used the tool in production, and I can just show you, like, the output.
219 00:20:42.910 ⇒ 00:20:44.450 Luke Scorziell: Oh, yeah, that would work, yeah.
220 00:21:05.690 ⇒ 00:21:07.610 Pranav: Okay, interesting.
221 00:21:08.350 ⇒ 00:21:10.060 Pranav: Not sure what they were trying here.
222 00:21:14.490 ⇒ 00:21:15.170 Luke Scorziell: Huh.
223 00:21:16.390 ⇒ 00:21:17.370 Pranav: Yeah.
224 00:21:18.550 ⇒ 00:21:24.720 Pranav: 3 campaigns and Archive… Yeah.
225 00:21:25.490 ⇒ 00:21:31.050 Pranav: Yeah, to probably get you more clarity on this, I’ll find you that loom, or…
226 00:21:31.770 ⇒ 00:21:35.379 Pranav: like, you can even message Casey to, like, kind of walk you through it.
227 00:21:36.730 ⇒ 00:21:43.959 Pranav: But, yeah, it sounds like that’s gonna be, like, a big thing that you mentioned as well to the, like, during your meetings, right?
228 00:21:44.710 ⇒ 00:21:52.389 Luke Scorziell: Yeah, I mean, I think it just depends on the agency, like, what their priorities are, and if it’s more of, like, a growth,
229 00:21:53.200 ⇒ 00:21:56.309 Luke Scorziell: I mean, all of these things save time, right? I think, so, like.
230 00:21:56.310 ⇒ 00:21:56.650 Pranav: Yep.
231 00:21:56.650 ⇒ 00:22:04.409 Luke Scorziell: generating briefs, like, that’s… that’s great. And then, I think, like, probably the main thing that I would assume, just knowing, like.
232 00:22:04.770 ⇒ 00:22:10.889 Luke Scorziell: probably being a little bit more on the creative side myself, and then… but also really liking to use AI as, like.
233 00:22:11.390 ⇒ 00:22:14.490 Luke Scorziell: You’re just… Spending a lot of time
234 00:22:15.520 ⇒ 00:22:17.920 Luke Scorziell: Kind of saying the same things to…
235 00:22:18.140 ⇒ 00:22:22.040 Luke Scorziell: whatever LLM you’re using, in order to try to get it to have the same
236 00:22:22.420 ⇒ 00:22:28.410 Luke Scorziell: Output, whereas, like, a system like this is helpful because it’s already Has, like, a built-in, like.
237 00:22:29.130 ⇒ 00:22:36.819 Luke Scorziell: parameters for, like, what you want to prompt it with, like, what your brand guidelines are, like, I would assume, like, you can have, like.
238 00:22:37.350 ⇒ 00:22:47.939 Luke Scorziell: layer it in with different, like, brands that you’re working with, like, what their brand guidelines are, and how you should write to them. So, like, all that stuff is super,
239 00:22:48.610 ⇒ 00:22:51.179 Luke Scorziell: Like, I think it’s super powerful.
240 00:22:51.180 ⇒ 00:22:51.790 Pranav: Yes.
241 00:22:51.900 ⇒ 00:22:53.890 Pranav: So, I think what would be…
242 00:22:54.000 ⇒ 00:22:56.920 Pranav: really… I think it would be, honestly, a good…
243 00:22:57.270 ⇒ 00:23:11.719 Pranav: and I can maybe do this and let you know, or if you want to go directly to Casey, just, like, knowing how configurable this is, like, how much of this is a black box, how much of it is, like, can the brand dictate the type of content that’s created, like, via, like, a prompt, or via, like…
244 00:23:12.480 ⇒ 00:23:18.779 Pranav: whatever other inputs, like, the Nano Banana tool takes. But, yeah, I did find this, this loom.
245 00:23:19.090 ⇒ 00:23:20.510 Pranav: That Casey took.
246 00:23:20.650 ⇒ 00:23:21.620 Pranav: So…
247 00:23:22.130 ⇒ 00:23:23.159 Luke Scorziell: Oh, cool, yeah.
248 00:23:23.310 ⇒ 00:23:24.030 Pranav: Yeah.
249 00:23:36.360 ⇒ 00:23:39.189 Luke Scorziell: I don’t know if there’s volume, but I can’t hear anything.
250 00:23:39.440 ⇒ 00:23:40.789 Pranav: Oh, you can’t hear it? Okay.
251 00:23:40.790 ⇒ 00:23:41.440 Luke Scorziell: No.
252 00:23:46.560 ⇒ 00:23:51.920 Pranav: Not sure how to… Here, I’ll just let you, maybe… I’ll just send.
253 00:23:51.920 ⇒ 00:23:54.269 Luke Scorziell: Yeah, if you just send it to me, because then…
254 00:23:55.760 ⇒ 00:24:00.129 Luke Scorziell: I guess, like, what I’d be curious to chat more with you two about.
255 00:24:00.370 ⇒ 00:24:01.040 Pranav: Yeah.
256 00:24:01.170 ⇒ 00:24:07.269 Luke Scorziell: It’s just, like, how do you think we could package… I mean, obviously, I know you sent me, kind of, the, like, case study notes.
257 00:24:07.570 ⇒ 00:24:11.879 Luke Scorziell: And then… Like, what we could build.
258 00:24:12.810 ⇒ 00:24:19.080 Luke Scorziell: so, like, what do you think is a good way to, like.
259 00:24:19.350 ⇒ 00:24:27.610 Luke Scorziell: go get in with agencies in doing this. I mean, like, there’s the platform audit that you sent, like, quick wins.
260 00:24:27.810 ⇒ 00:24:32.640 Luke Scorziell: But I’d be curious, like, what pain points you’re,
261 00:24:33.120 ⇒ 00:24:36.459 Luke Scorziell: like, hearing when you’re on the phone with them, yeah.
262 00:24:37.390 ⇒ 00:24:41.389 Luke Scorziell: Yeah, and maybe we could just walk through that a little bit together, like, what a pilot program.
263 00:24:41.390 ⇒ 00:24:41.850 Pranav: Yeah.
264 00:24:41.850 ⇒ 00:24:42.390 Luke Scorziell: like…
265 00:24:42.960 ⇒ 00:24:44.639 Pranav: I would say if I, like…
266 00:24:45.460 ⇒ 00:24:58.870 Pranav: if I had to guess, because I wasn’t around when they, like, kind of first signed a Lilo, but I feel like it’s a pretty confident guess, is that they just realized with scale, they were paying a lot more to these SaaS tools to actually, like, execute.
267 00:24:59.340 ⇒ 00:25:06.100 Pranav: So, I think there was one metric that I found, it was, like, what is it, like, yeah, 600 per brand per month.
268 00:25:06.230 ⇒ 00:25:19.510 Pranav: And that’s, you know, of course scales, like, as they add more brands, and like, also, when you’re dependent on a third party, like a SaaS tool, I mean, you can’t drive features to be exactly what you need.
269 00:25:19.660 ⇒ 00:25:24.339 Pranav: So, you know, like, you’re paying so much, and that’s only gonna…
270 00:25:24.570 ⇒ 00:25:28.110 Pranav: Grow as, like, you grow, and there’s nothing you can do about it.
271 00:25:28.550 ⇒ 00:25:37.880 Pranav: And so… by… having, like, the Stitch platform, they’re able to get that data directly, and… it doesn’t…
272 00:25:38.440 ⇒ 00:25:39.790 Pranav: It’ll just, like…
273 00:25:40.230 ⇒ 00:25:49.759 Pranav: they obviously don’t have to be paying per rand per month, right? It’s a… it’s a lot different, like, when, like, you look at, like, how much they’re paying in tokens. So…
274 00:25:50.040 ⇒ 00:25:59.950 Pranav: That’s another huge, like… I feel like that’s the biggest thing that we could ask, is be like, okay, what, like, SaaS tools are you dependent on currently to, like, execute on…
275 00:26:00.410 ⇒ 00:26:03.199 Pranav: your… for your business, you know? And…
276 00:26:03.200 ⇒ 00:26:03.980 Luke Scorziell: Yeah.
277 00:26:03.980 ⇒ 00:26:07.020 Pranav: I think there’ll probably be a lot of commonality there with, like.
278 00:26:07.200 ⇒ 00:26:13.149 Pranav: with, like, what they’re doing versus what Lilo’s doing, like… I can show you…
279 00:26:13.770 ⇒ 00:26:16.810 Pranav: Actually, yeah, what… I don’t have access to Orca.
280 00:26:18.750 ⇒ 00:26:19.310 Luke Scorziell: Hmm.
281 00:26:19.850 ⇒ 00:26:22.380 Pranav: But… Yeah, like…
282 00:26:23.680 ⇒ 00:26:30.290 Pranav: basically, like, Orca is essentially trying to do what that forecasting dashboard that I showed you is doing now.
283 00:26:30.560 ⇒ 00:26:31.210 Luke Scorziell: Yeah, yeah.
284 00:26:31.210 ⇒ 00:26:32.279 Pranav: Same thing, yeah.
285 00:26:32.530 ⇒ 00:26:37.079 Luke Scorziell: So it’s kind of the difference of, like, a generic SaaS product that…
286 00:26:37.530 ⇒ 00:26:47.600 Luke Scorziell: You can never push your own features on, or, like, if you want custom-built things, it’s, like, probably not gonna happen unless you get a random update, versus, like, they could work with us.
287 00:26:48.060 ⇒ 00:26:50.850 Luke Scorziell: Be a lot cheaper, and then we can also help them push
288 00:26:51.120 ⇒ 00:26:56.380 Luke Scorziell: Like, specific, features that they… they need in their agency?
289 00:26:56.780 ⇒ 00:27:03.849 Pranav: Yeah, I think that’s a big thing. And then another thing is, too, is, like, we’ve done it once, so we also have, like, that…
290 00:27:04.110 ⇒ 00:27:07.019 Pranav: A little bit more of, like, that domain expertise.
291 00:27:07.350 ⇒ 00:27:15.510 Pranav: I’m coming to just, like, a bunch of devs that have no… have nothing to do with, like, you know, or have no insight to how marketing agencies work.
292 00:27:15.870 ⇒ 00:27:23.130 Pranav: We kind of do some work with them, so, like, we can, you know, probably, like, the ramp-up time, there’ll be less, like.
293 00:27:23.350 ⇒ 00:27:29.539 Pranav: Like, confusion between, like, What they’re trying to build versus, like, what we understand it to be.
294 00:27:29.770 ⇒ 00:27:36.400 Pranav: So, we can even… I feel like that’s another drive, like, another… Positive to working with us.
295 00:27:36.750 ⇒ 00:27:37.150 Luke Scorziell: Yeah.
296 00:27:39.060 ⇒ 00:27:46.540 Pranav: I also think, like… with these SaaS tools as well, like, their development…
297 00:27:46.700 ⇒ 00:27:50.849 Pranav: Pipeline, or their development process, if you think about it, like…
298 00:27:52.050 ⇒ 00:28:03.900 Pranav: they’re trying to stay, like, up-to-date with the latest and greatest of AI, but for them, they need to be able to test it and develop staging, and then push it to production, and it has to…
299 00:28:04.400 ⇒ 00:28:13.350 Pranav: they have to really stress test it, because, like, they have potentially, like, hundreds or thousands of clients, depending on the tool.
300 00:28:13.350 ⇒ 00:28:13.870 Luke Scorziell: Yeah.
301 00:28:13.870 ⇒ 00:28:19.469 Pranav: And so that just… all that… all those stages just slow and slow and slow, like, they’re…
302 00:28:20.150 ⇒ 00:28:30.040 Pranav: the output of, like, what they’re doing on a weekly basis. So, you know, they can’t stay up to date with, like, the latest and greatest of AI, and that’s
303 00:28:30.170 ⇒ 00:28:42.220 Pranav: it’s not something that’s, like, not changing every month, you know? Like, every month there’s a new thing that we can, like, think about adding, and so… by working with us, they would also have that same…
304 00:28:42.420 ⇒ 00:28:47.899 Pranav: benefit, right? Because, like, we’re all aware, just, like, from all of our other projects, like.
305 00:28:48.050 ⇒ 00:28:53.460 Pranav: Okay, what’s the best way to do this? Using the… State-of-the-art tools right now.
306 00:28:53.780 ⇒ 00:28:54.940 Luke Scorziell: And.
307 00:28:55.840 ⇒ 00:29:02.390 Pranav: We’re… the only place we’re applying that is for our clients, so they… they’re, like, the sole benefiters of that.
308 00:29:02.580 ⇒ 00:29:03.550 Pranav: So…
309 00:29:03.950 ⇒ 00:29:04.510 Luke Scorziell: Yeah.
310 00:29:04.800 ⇒ 00:29:19.409 Luke Scorziell: Which is, like, a huge value that AI brings, is that you’re not really dependent on, like, SaaS as it used to be anymore, right? Now you can just kind of build your own software. So it’s like, we’re like a partner that’s helping you build the custom software for your own business.
311 00:29:19.800 ⇒ 00:29:21.450 Pranav: Yeah, exactly.
312 00:29:22.560 ⇒ 00:29:31.609 Luke Scorziell: And then… As fara… what do you think about, like, Pricing and initial, like.
313 00:29:33.840 ⇒ 00:29:38.809 Luke Scorziell: like, obviously there’s the initial getting in, but then, like, I’d be curious, too, with Lilo, like.
314 00:29:39.120 ⇒ 00:29:41.360 Luke Scorziell: It sounds like we’re saving them.
315 00:29:41.730 ⇒ 00:29:44.350 Luke Scorziell: Tens of thousands of dollars,
316 00:29:45.140 ⇒ 00:29:50.379 Luke Scorziell: But yeah, how do you think about the value that we’re then bringing, or we could then bring to agencies?
317 00:29:52.030 ⇒ 00:30:05.830 Pranav: How I kind of think of these is, like, yeah, like, where are we… over time, like, where are we saving money for y’all? Like, you know, we’re obviously charging more than 600 per brand per month.
318 00:30:06.270 ⇒ 00:30:12.889 Pranav: like, at first, right? Or you’re probably… you’re not gonna see those, like, savings in month one.
319 00:30:13.080 ⇒ 00:30:16.609 Pranav: But… probably, like, the way that we do this is, like.
320 00:30:16.650 ⇒ 00:30:34.190 Pranav: could be, like, similar to how we did it for Lilo, is that… that Phase 1 can be super short. Let’s find, like, the most painful thing for y’all, and then we can just show them that we can execute on that, like, super well. So then in Phase 2, like, they can expand the scope of work, give us a few, like, more hours so we can…
321 00:30:34.190 ⇒ 00:30:38.239 Pranav: You know, build something that maybe is a little bit more intricate, and then…
322 00:30:38.500 ⇒ 00:30:49.540 Pranav: So for, like… and then going forward, bigger things. So, that’s kind of what we did with Lilo, like, I’m looking at, like, the hours that we put in for Phase 1, I think it’s… it was…
323 00:30:49.940 ⇒ 00:30:56.890 Pranav: I think it ended up being, like, 80 to, like, 120 hours or something like that. So, like… and that’s me as, like, the only…
324 00:30:57.410 ⇒ 00:31:01.809 Pranav: I think it was only… yeah, it was like 2-3 weeks, basically, of dev time.
325 00:31:01.810 ⇒ 00:31:02.350 Luke Scorziell: Huh.
326 00:31:02.350 ⇒ 00:31:08.410 Pranav: So it’s like, yeah, we can ship something that’s useful for you in 2-3 weeks. Like, that seems like a pretty…
327 00:31:09.880 ⇒ 00:31:14.440 Pranav: That seems like a pretty attractive offer, to me. Yeah.
328 00:31:14.810 ⇒ 00:31:18.440 Pranav: And that’s something that we feel confident doing here, so…
329 00:31:18.550 ⇒ 00:31:19.390 Luke Scorziell: Yeah.
330 00:31:19.560 ⇒ 00:31:24.250 Luke Scorziell: Yeah, well, let me share… maybe I can share my screen.
331 00:31:25.320 ⇒ 00:31:32.070 Luke Scorziell: Because I went through… That one’s… Really assured her, sir.
332 00:31:33.750 ⇒ 00:31:38.260 Luke Scorziell: Okay, I don’t know, we’ll see how this goes.
333 00:31:42.560 ⇒ 00:31:46.730 Luke Scorziell: What are we seeing right now? I got this one.
334 00:31:48.440 ⇒ 00:31:51.490 Luke Scorziell: No, this is the wrong one.
335 00:31:56.110 ⇒ 00:31:57.430 Luke Scorziell: Let me reshare.
336 00:32:01.220 ⇒ 00:32:02.180 Luke Scorziell: Because, like.
337 00:32:20.520 ⇒ 00:32:22.689 Luke Scorziell: I’ll just show one screen.
338 00:32:28.140 ⇒ 00:32:30.350 Luke Scorziell: Oh, okay.
339 00:32:54.660 ⇒ 00:32:57.809 Luke Scorziell: Yeah, so I was just looking at, like, some of the stuff, because they…
340 00:33:01.440 ⇒ 00:33:11.020 Luke Scorziell: Eisenberg posted this whole, article, basically, about how they’re, like, using AI.
341 00:33:11.280 ⇒ 00:33:15.630 Luke Scorziell: So I just asked Cursor to do, like.
342 00:33:16.510 ⇒ 00:33:22.180 Luke Scorziell: kind of a SWOT analysis, I guess. And,
343 00:33:24.070 ⇒ 00:33:26.129 Luke Scorziell: Yeah, I think it’d be interesting to, like.
344 00:33:28.340 ⇒ 00:33:31.129 Luke Scorziell: I don’t know if you can read this, or… I don’t know, like that.
345 00:33:32.040 ⇒ 00:33:35.580 Luke Scorziell: There we go. That was a little bit… There you go.
346 00:33:38.020 ⇒ 00:33:41.840 Luke Scorziell: Like, a couple things that came up from that article is, like.
347 00:33:46.790 ⇒ 00:34:00.920 Luke Scorziell: It seemed like they’re using AI a lot for just, like, kind of one-off random things, like the con… so concepting research, like, blank page problems, versus, like, I think, like, the email generator is interesting, because we’ve streamlined it to where they can, like, create briefs.
348 00:34:01.290 ⇒ 00:34:02.510 Luke Scorziell: Right off the bat.
349 00:34:02.930 ⇒ 00:34:08.340 Luke Scorziell: then the same thing, I think. I don’t know, you can kind of tell me, but she talked about in this article, like, they’re doing…
350 00:34:08.860 ⇒ 00:34:15.609 Luke Scorziell: Reporting, but it… It’s unclear, like, whether or not their reporting is coming from
351 00:34:16.480 ⇒ 00:34:22.289 Luke Scorziell: Yeah, like, kind of a combined data warehouse that, like, similar to what we’ve built, or if it’s just that they’re…
352 00:34:22.630 ⇒ 00:34:27.110 Luke Scorziell: Still having to kind of manually, all the data?
353 00:34:27.650 ⇒ 00:34:30.219 Luke Scorziell: And then…
354 00:34:30.889 ⇒ 00:34:31.519 Pranav: Yeah.
355 00:34:32.110 ⇒ 00:34:33.139 Luke Scorziell: See…
356 00:34:33.530 ⇒ 00:34:36.610 Pranav: So, on that, like, yeah, that point you just mentioned.
357 00:34:36.770 ⇒ 00:34:45.379 Pranav: We built a data warehouse because we knew just the scale of things at Lilo. Like, if their scale is, like, you know, just a few brands,
358 00:34:45.880 ⇒ 00:34:53.050 Pranav: Probably doesn’t make sense to, like, have a data warehouse, but actually… but you mentioned how, like, they’re a pretty big agency, they’re probably working with, like.
359 00:34:53.420 ⇒ 00:34:55.959 Luke Scorziell: Quite a few, like, clients.
360 00:34:55.960 ⇒ 00:35:03.570 Pranav: So, yeah, they would probably want a data warehouse. If they don’t have one, then it’s like, they’re probably suffering from the…
361 00:35:03.750 ⇒ 00:35:05.710 Pranav: The negatives of not having one, so…
362 00:35:06.380 ⇒ 00:35:07.330 Luke Scorziell: Okay.
363 00:35:07.330 ⇒ 00:35:07.690 Pranav: Yeah.
364 00:35:07.690 ⇒ 00:35:12.770 Luke Scorziell: Yeah, it’s interesting that they’re probably…
365 00:35:13.010 ⇒ 00:35:17.920 Luke Scorziell: I would assume they’re working with a lot of clients. Sometimes with these bigger agencies, they’re…
366 00:35:18.510 ⇒ 00:35:23.610 Luke Scorziell: They have fewer clients, but very large projects on each client.
367 00:35:23.610 ⇒ 00:35:24.910 Pranav: Yeah.
368 00:35:25.090 ⇒ 00:35:31.520 Luke Scorziell: So, I don’t know if they’d be working with, like, over 100 clients, or if it would more be, like, they have, like.
369 00:35:32.090 ⇒ 00:35:36.830 Luke Scorziell: 5 or 6 clients that are each paying them, like, a million dollars a month or something crazy.
370 00:35:36.830 ⇒ 00:35:37.280 Pranav: Right, right.
371 00:35:37.930 ⇒ 00:35:38.500 Pranav: Yeah.
372 00:35:39.210 ⇒ 00:35:42.509 Luke Scorziell: So, okay, that’s interesting then. And then…
373 00:35:43.130 ⇒ 00:35:46.580 Luke Scorziell: I mean, I can just send you that article, too, if it’s, like, interesting.
374 00:35:49.540 ⇒ 00:35:50.359 Pranav: Yeah, sure.
375 00:35:50.690 ⇒ 00:35:53.610 Luke Scorziell: To you, cause then…
376 00:35:56.120 ⇒ 00:35:57.570 Pranav: Is there, yeah, is there, like…
377 00:35:57.570 ⇒ 00:35:58.280 Luke Scorziell: Or…
378 00:35:58.500 ⇒ 00:36:03.160 Pranav: Let me know if there’s anywhere else… anywhere else I can help for, like, these meetings tomorrow.
379 00:36:05.230 ⇒ 00:36:05.980 Pranav: So, like, yeah, I can.
380 00:36:05.980 ⇒ 00:36:15.940 Luke Scorziell: Yeah, I guess, like, I’m… So this is the… Or a little bit.
381 00:36:23.290 ⇒ 00:36:27.559 Luke Scorziell: like, they basically went through… I mean, this is kind of like gold, honestly, in a lot of ways, like…
382 00:36:27.860 ⇒ 00:36:34.080 Luke Scorziell: they kind of went through and, like,
383 00:36:34.640 ⇒ 00:36:37.189 Luke Scorziell: had, I don’t know, everyone at their different…
384 00:36:43.310 ⇒ 00:36:54.950 Luke Scorziell: Like, different departments, kind of talk about, like, What they’re doing, so… Like…
385 00:36:55.560 ⇒ 00:36:59.160 Luke Scorziell: Just each person kind of gave, you know, like, they’re using…
386 00:36:59.350 ⇒ 00:37:09.469 Luke Scorziell: AI to visualize ideas during pitches, I think the, like, most… like, most relevant is gonna be… this is the lady that I’m meeting with, so…
387 00:37:10.480 ⇒ 00:37:14.490 Luke Scorziell: like…
388 00:37:18.080 ⇒ 00:37:25.240 Luke Scorziell: Like, they’re using social intelligence partners, so they’re probably… they’re doing a lot of, like, social listening on…
389 00:37:25.600 ⇒ 00:37:28.589 Luke Scorziell: Which is probably also somewhere where they’re paying SaaS costs.
390 00:37:29.460 ⇒ 00:37:37.590 Luke Scorziell: Then it looks like they’re building out some of their own proprietary tools. Like, I sent you the job postings, it looks like they’re hiring for people, too.
391 00:37:37.960 ⇒ 00:37:47.079 Luke Scorziell: It looks like, okay, they probably do have real-time reporting, predictive analytics, data processing, and sophisticated…
392 00:37:47.250 ⇒ 00:37:55.200 Luke Scorziell: So, like, I don’t know… I don’t know if they’re, like, too advanced, maybe? Or if they’re still in a spot where we… you feel like maybe we could be helpful to them.
393 00:37:57.080 ⇒ 00:38:02.340 Pranav: I feel like we could be helpful to them. I don’t know what too advanced looks like, honestly, like…
394 00:38:03.930 ⇒ 00:38:05.260 Pranav: There’s… so there’s…
395 00:38:05.830 ⇒ 00:38:24.179 Pranav: they’re not building their own, like, AI model, which would require, like, you probably need, like, PhDs or something like that, like, to build. People are deep in the weeds of, like, building these things. Even now, there’s tools where you can, like, build simple models with just, like, anybody could do it.
396 00:38:24.510 ⇒ 00:38:26.970 Pranav: But… I don’t think…
397 00:38:27.800 ⇒ 00:38:36.460 Pranav: I don’t think there’s a lot of companies that are, like, that much in the weeds, where, like, we couldn’t help them. I would say we could definitely, like, still help them. I think it’s really just about…
398 00:38:36.830 ⇒ 00:38:52.409 Pranav: understanding just the landscape of, like, what’s possible right now and what’s not, and then having, like, enough technical talent to, like, fill in the gap from, like, okay, A, I can do this, what can it do? And then those two things together, like, just filling in the gap, is,
399 00:38:52.840 ⇒ 00:38:54.619 Pranav: Is basically all you need.
400 00:38:55.580 ⇒ 00:38:58.949 Luke Scorziell: Yeah, well, it’s interesting, so, like, down here, they talk about
401 00:38:59.540 ⇒ 00:39:05.430 Luke Scorziell: Focus group research, something that they’re wanting to do, and…
402 00:39:08.360 ⇒ 00:39:15.190 Luke Scorziell: Intrigued by the prospects of these tools echoing sentiments and concerns expressed by focus group participants given the exam.
403 00:39:16.300 ⇒ 00:39:18.850 Luke Scorziell: So I think they’re, like, trying to…
404 00:39:19.160 ⇒ 00:39:21.529 Luke Scorziell: And I’ve seen this before, where…
405 00:39:21.770 ⇒ 00:39:26.589 Luke Scorziell: Ad agencies will do, like, focus groups that are pretty expensive to try to figure out.
406 00:39:26.900 ⇒ 00:39:29.390 Luke Scorziell: What people are thinking about certain products.
407 00:39:29.510 ⇒ 00:39:35.090 Luke Scorziell: And then… But then, with, like, Reddit.
408 00:39:35.340 ⇒ 00:39:40.040 Luke Scorziell: a lot of people are building off of Reddit, and then you can do, like, kind of, like, social listening.
409 00:39:40.450 ⇒ 00:39:45.540 Luke Scorziell: Through, like, what people are posting and talking about.
410 00:39:46.480 ⇒ 00:39:50.319 Luke Scorziell: like, on platforms like Reddit or Twitter, or, I mean, I guess, like.
411 00:39:50.930 ⇒ 00:39:59.450 Luke Scorziell: Facebook, Instagram, maybe, a little bit, but I know, like, those… those two are more commonly,
412 00:40:00.490 ⇒ 00:40:04.070 Luke Scorziell: Yeah, more commonly you see that. So, I don’t know, like…
413 00:40:04.470 ⇒ 00:40:10.660 Luke Scorziell: I guess I’m just showing this to you for, like, sake of… if it sparks any ideas.
414 00:40:10.660 ⇒ 00:40:14.159 Pranav: Yeah, I mean, I think that’s, like, a great idea, right? Like…
415 00:40:14.300 ⇒ 00:40:16.870 Pranav: That’s kind of… and on that note, like.
416 00:40:17.110 ⇒ 00:40:29.099 Pranav: that’s kind of where we definitely, like, we’re really good about asking the clients a lot of questions to understand, like, okay, where do we need to get their expertise? There’s certain things where, like, maybe…
417 00:40:29.720 ⇒ 00:40:38.609 Pranav: we, like, we handle, like, the technical part, like, how do we integrate AI in the best way to, like, get the insights that you need? But, like, first we need to understand, like.
418 00:40:39.120 ⇒ 00:40:44.440 Pranav: What is, like, what is, like, the best output look?
419 00:40:44.730 ⇒ 00:40:50.009 Pranav: And where can we… where can we get, like, that source of truth data?
420 00:40:50.420 ⇒ 00:41:01.730 Pranav: And so, yeah, like, yeah, building something on top of, like, certain Reddit groups, like, that’s, like, a super cool idea that would come from, like, the client, and then we would just, like, execute on that. So…
421 00:41:02.270 ⇒ 00:41:08.560 Pranav: There’s not a lot of things that I’ve heard where it’s like, once we hear the idea, that I’m like, oh, we can’t do that. You know?
422 00:41:08.560 ⇒ 00:41:09.450 Luke Scorziell: Like, it’s… Yeah.
423 00:41:09.450 ⇒ 00:41:18.470 Pranav: We’re kind of in, like, a cool spot right now where it’s, like, everything seems like it’s, like, possible in some way, like, especially where it’s, like, just pulling data from, like, different…
424 00:41:18.580 ⇒ 00:41:22.650 Pranav: If you can just understand, like, how you… Pulled that data in.
425 00:41:22.770 ⇒ 00:41:25.850 Pranav: From, like, all these different places.
426 00:41:26.510 ⇒ 00:41:30.890 Pranav: Or, like, what tools you can use to, like, Best do that, like…
427 00:41:31.450 ⇒ 00:41:38.130 Pranav: it’s… everything seems kind of possible. It’s kind of… it’s always just like, oh, yeah, we can do it, we can do it, we can do it.
428 00:41:38.490 ⇒ 00:41:44.890 Luke Scorziell: Yeah. Well, so then is that kind of just, like… is it… I wonder if it’s just, like, getting them to dream a little bit and see…
429 00:41:45.110 ⇒ 00:41:54.120 Luke Scorziell: like, you know, what are the areas that you feel like you want, like, help with, and, like, is there any way that we could collaborate? Because I like the idea of, like, hey, and…
430 00:41:54.360 ⇒ 00:41:59.540 Luke Scorziell: in two weeks, we could just get you something. Two to three weeks, we could get you something that, like.
431 00:41:59.660 ⇒ 00:42:04.719 Luke Scorziell: You feel like… you know, like, makes a difference. Yeah.
432 00:42:04.920 ⇒ 00:42:12.780 Luke Scorziell: And then we can… and then we can go from there, if you want to keep working together, or something. Because I think that’d be, like, a pretty interesting offer of, like.
433 00:42:14.730 ⇒ 00:42:20.179 Luke Scorziell: Yeah, like, maybe even talking to either of them, it’s like, oh yeah, like, what, what,
434 00:42:21.020 ⇒ 00:42:34.970 Luke Scorziell: what are they… what’s the current pain point that, like, you wish that you could, like, solve, like, and nothing’s too… too hard to solve? And then they say, oh, like, it’s this, that, and the other, and then…
435 00:42:35.140 ⇒ 00:42:37.940 Luke Scorziell: was like, well, what if I could get you, like, a…
436 00:42:38.130 ⇒ 00:42:43.730 Luke Scorziell: Little pitch on, like, here’s what we could do, and, like, we could get you, like, a proof of concept in 3 weeks.
437 00:42:45.310 ⇒ 00:42:49.309 Luke Scorziell: Yeah. Like, is that something you’d be interested in? I don’t know, like, maybe that’s just the way to go about it.
438 00:42:49.950 ⇒ 00:42:54.020 Pranav: I love that. Yeah, I think that’s the best way to go about it. And it’s like…
439 00:42:54.280 ⇒ 00:42:58.850 Pranav: Even better if we can, like, ask them questions to, like, know what the true, like.
440 00:42:59.090 ⇒ 00:43:04.999 Pranav: problem that they’re having is, and then we can let the… like, and if… because sometimes I feel like they might…
441 00:43:05.680 ⇒ 00:43:14.120 Pranav: like, when I just, like, talk to friends, right, I’ve never really been in, like, formal, like, meetings like this, but, like, when I’m talking to friends, they kind of, like.
442 00:43:14.510 ⇒ 00:43:20.980 Pranav: Talk about what they can build based on what they understand the solutions could be.
443 00:43:21.160 ⇒ 00:43:27.149 Pranav: So, like, they just know that, okay, this is, like, the… they feel like they know this is the arsenal of, like.
444 00:43:27.350 ⇒ 00:43:43.570 Pranav: tools out there to actually solve this problem that I have. And so, they may not even, like, bring up certain problems they have, because they just feel internally that there isn’t a solution for it. And so, that should be, like, on us to decide. Like, can we solve it or not? So…
445 00:43:43.800 ⇒ 00:43:48.999 Pranav: they may come in and be like, oh yeah, we’ve heard of, like, other people using AI to solve X, Y, and Z.
446 00:43:49.150 ⇒ 00:43:51.400 Pranav: But if we go in and ask them, like.
447 00:43:51.760 ⇒ 00:43:58.490 Pranav: okay, screw, like, what other people are using AI to solve, like, what do you guys want solved? Whether you’ve seen other people solve it or not.
448 00:43:59.910 ⇒ 00:44:14.490 Pranav: And then, yeah, that could potentially bring up, like, different things. And then if we’re like, oh yeah, we can solve it, we’ve done it for this client, they’ll be like, oh, shoot, you guys already did that? We didn’t even know this was possible. Because I feel like they don’t have a pretty good understanding if they’re coming…
449 00:44:14.610 ⇒ 00:44:21.459 Pranav: not all the time, if they’re coming to… like, we’re the people that, like, our specialty is, like, knowing what AI can do.
450 00:44:22.040 ⇒ 00:44:22.750 Luke Scorziell: Yeah.
451 00:44:23.190 ⇒ 00:44:24.230 Pranav: So…
452 00:44:24.540 ⇒ 00:44:31.310 Pranav: I think it’s a fair assumption to know, like, to assume that they don’t know as much as us on, like, what AI can do, and so we’re kind of, like.
453 00:44:31.820 ⇒ 00:44:42.969 Pranav: teaching them, like, okay, AI can do this, and then the next step is executing on that, like, not just we’re just saying, oh, yes, yes, yes, but, like, in two to three weeks, we will show you that AI can do this for sure.
454 00:44:43.510 ⇒ 00:44:48.870 Luke Scorziell: Yeah. Okay. Yeah, that’s dope. Because I think, like,
455 00:44:50.460 ⇒ 00:44:57.799 Luke Scorziell: even for me, it’s like, before I started at Brainforge, I would say I was, like, pretty… like, a hobbyist, I guess, like…
456 00:44:57.800 ⇒ 00:44:59.020 Pranav: Yeah. In the AI, like…
457 00:44:59.030 ⇒ 00:45:03.139 Luke Scorziell: fooled around a lot with Lovable, like, Vibe-coded different apps, all this different stuff.
458 00:45:03.320 ⇒ 00:45:07.989 Luke Scorziell: Yeah. But learning things like… like, like,
459 00:45:08.090 ⇒ 00:45:26.779 Luke Scorziell: just, I guess, data warehouse, like, the data stuff and, like, how that works and how your data can be structured, like, gives me one window in, but then also knowing that, like, there’s, like, the context layer and, like, knowledge bases that you can build, and, like, just using Cursor for me has been, like, a huge level up to where I’m like, oh, this would be so cool to have, like, a…
460 00:45:27.220 ⇒ 00:45:37.170 Luke Scorziell: you know, if you just had a different data, or, like, reposit… I don’t know if it’d need to be a repository, but, like, different folders and knowledge bases for each client, and then you just go in and you say, like.
461 00:45:37.360 ⇒ 00:45:49.029 Luke Scorziell: tell me the latest on this, like, that, you know, that’s, like, whoa, like, something that, like, I didn’t even know was possible, that then opens up in my mind a whole other layer of, like.
462 00:45:50.060 ⇒ 00:45:51.739 Luke Scorziell: Problems that could be solved.
463 00:45:52.590 ⇒ 00:45:53.160 Pranav: Right.
464 00:45:53.600 ⇒ 00:46:00.070 Pranav: Yeah, there’s actually, like, on that note, too, like, what would be another good question to ask is, like.
465 00:46:00.100 ⇒ 00:46:13.719 Pranav: where is your experience with AI, like, lacking? And you can just be like, what do you feel like ChatGPT is, like, not good at? Like, you’re probably using it for a lot of things, probably helping you out with a lot, like, what do you feel like the output is, like, just not there yet?
466 00:46:13.860 ⇒ 00:46:21.640 Pranav: And you know, I feel like, yeah, knowledge bases solve a lot of those issues, because it’s like, you have your own source of truth now.
467 00:46:21.770 ⇒ 00:46:29.720 Pranav: And it’s not just using the internet as a source of truth, and then that’s sometimes on really niche topics, or, like, maybe…
468 00:46:29.830 ⇒ 00:46:31.390 Pranav: Topics where, like.
469 00:46:31.920 ⇒ 00:46:43.439 Pranav: you feel like you have a different opinion than just, like, the mass, like, population. Like, ChatGPT is just not gonna give you answers in the format that you like, or give you the output that you want. So…
470 00:46:43.910 ⇒ 00:46:51.400 Pranav: Yeah. If you, like, that could be another end to, like, where do you feel like it’s lacking right now for you? And then we can be like, oh, yeah, like, we’ve had other people that, like.
471 00:46:51.540 ⇒ 00:47:00.549 Pranav: describe, like, similar problems, but, like, yeah, we solved it by doing, you know, knowledge bases, building, like, RAG apps.
472 00:47:00.720 ⇒ 00:47:02.740 Pranav: Yeah.
473 00:47:03.520 ⇒ 00:47:05.150 Luke Scorziell: Yeah, okay, sweet.
474 00:47:06.610 ⇒ 00:47:09.730 Pranav: There’s one other thing… oh, there’s one other thing I wanted to say, too.
475 00:47:09.850 ⇒ 00:47:22.639 Pranav: another huge, like, value add-up is, like, we can also build way faster now because of AI. And so what, like, older consulting firms, like, even a year ago would, like, offer.
476 00:47:22.790 ⇒ 00:47:25.470 Pranav: maybe, like, yeah, before AI, like.
477 00:47:26.240 ⇒ 00:47:30.489 Pranav: what would take them 3 months to build? Like, we can build in 3 weeks.
478 00:47:31.980 ⇒ 00:47:32.650 Pranav: So…
479 00:47:32.650 ⇒ 00:47:33.330 Luke Scorziell: Huh.
480 00:47:33.330 ⇒ 00:47:36.600 Pranav: They also just, like, might have, like,
481 00:47:37.580 ⇒ 00:47:42.839 Pranav: Assumption about how long something takes based on, like, their…
482 00:47:43.090 ⇒ 00:47:51.119 Pranav: relationships with, like, consulting firms in the past that have, like, quoted them for, like, okay, for this feature, it takes this much, but we can really, like.
483 00:47:51.310 ⇒ 00:47:54.909 Pranav: reduce that by, like, 75%, I feel.
484 00:47:55.820 ⇒ 00:47:56.430 Luke Scorziell: Yeah.
485 00:47:56.430 ⇒ 00:47:57.300 Pranav: Yeah. Huh.
486 00:48:02.150 ⇒ 00:48:06.649 Luke Scorziell: Yeah, that makes… because, yeah, we’re moving quicker than, like.
487 00:48:07.420 ⇒ 00:48:25.890 Luke Scorziell: It’s never been easier and never been faster to be able to do things, and then also, like, to pivot, and, like, if we build something and it’s like, oh, this isn’t working perfectly, or how we want it to, then it’s like… it’s not like we spent a year building that, and then now we have to, like… it’s like, okay, we spent a few months, or spent a few weeks,
488 00:48:26.530 ⇒ 00:48:34.129 Luke Scorziell: on something. Yeah, okay, this… I think this is really helpful, because then, in my mind, I can start to think about, like.
489 00:48:35.410 ⇒ 00:48:45.249 Luke Scorziell: I mean, we’ll see it, like, again, I haven’t also been on any of these… I, like, I don’t know if I’ll get on the call, and it’s just, like, they’ll be totally uninterested, or if they’ll have a little bit of interest. But.
490 00:48:45.760 ⇒ 00:48:58.889 Pranav: I assume it’s just, like, a numbers game too, right? Like, you just never know, like, some people are just gonna be completely closed off to it, or just, like, it’s not a good fit, but, like, I feel like if we just explain this for, like, you know, 30 people, like, some people are gonna bite.
491 00:48:59.550 ⇒ 00:49:05.309 Luke Scorziell: Yeah, well, cause… so this is kind of the next thing that I’m thinking, is, I can start.
492 00:49:05.510 ⇒ 00:49:11.110 Luke Scorziell: Launching this as, like, a campaign that we run, and then if you’re down to also, like.
493 00:49:12.260 ⇒ 00:49:23.639 Luke Scorziell: hop on calls, like, Utam was like, oh yeah, you and Pranav should just get on calls, like, together, and chat through it with people, so… Yeah.
494 00:49:24.150 ⇒ 00:49:26.810 Luke Scorziell: So, okay, well, I mean, I’ll do that, and I’ll…
495 00:49:27.330 ⇒ 00:49:29.260 Luke Scorziell: I’m just gonna start writing stuff.
496 00:49:29.460 ⇒ 00:49:31.900 Luke Scorziell: For LinkedIn, and just start…
497 00:49:32.300 ⇒ 00:49:37.149 Luke Scorziell: like, I’ll basically just think of, like, okay, I’m meeting with this person tomorrow, and then I’m just gonna think, like.
498 00:49:37.980 ⇒ 00:49:45.359 Luke Scorziell: what is… what is the post that I would like her to read before I meet with her? And then I’m gonna write it, I think, and then post it, and then I’ll just probably send it to her.
499 00:49:45.560 ⇒ 00:49:48.530 Luke Scorziell: Before, and be like, hey, can you, you know, just…
500 00:49:48.730 ⇒ 00:49:54.940 Luke Scorziell: Feel free to read this if you want before we meet. And then, yeah, and then I’ll start…
501 00:49:55.340 ⇒ 00:50:00.160 Luke Scorziell: Launching… doing more outreach to try to connect with other agency founders.
502 00:50:00.370 ⇒ 00:50:03.570 Luke Scorziell: And then…
503 00:50:04.760 ⇒ 00:50:10.289 Luke Scorziell: Yeah, we can just see where it goes. I mean, like, you know, and hopefully…
504 00:50:10.700 ⇒ 00:50:17.970 Luke Scorziell: I mean, if nothing else, we’ll learn from it, so… It’ll also be interesting with Lilo, too, if you’re… if they have any other, like.
505 00:50:18.340 ⇒ 00:50:22.630 Luke Scorziell: Companies that they could, like, refer us to, or…
506 00:50:23.760 ⇒ 00:50:25.370 Luke Scorziell: That’s what I work with, and they do have similar problems.
507 00:50:25.370 ⇒ 00:50:30.930 Pranav: Because, like, we’re having a conversation, Utam and I, with, like, the Lilo team.
508 00:50:31.040 ⇒ 00:50:34.400 Pranav: And it’s gonna be more so just, like, talking about, like, the future.
509 00:50:34.400 ⇒ 00:50:37.580 Luke Scorziell: And so, yeah, I will add that to my notes to, like, ask them.
510 00:50:38.240 ⇒ 00:50:46.929 Luke Scorziell: Yeah, I think just being like, hey, like, you know, we’re seeing these are, like, pretty common struggles in this space, and we’re looking to do more work with agencies, and…
511 00:50:47.200 ⇒ 00:50:49.439 Luke Scorziell: Part of that is we’re, like, piloting this, like.
512 00:50:49.910 ⇒ 00:50:53.509 Luke Scorziell: 2-3 week, like, launch program or whatever.
513 00:50:53.820 ⇒ 00:50:56.950 Luke Scorziell: Do you know anyone that, like, could…
514 00:50:57.470 ⇒ 00:51:01.170 Luke Scorziell: Do you know anyone that you think might be interested?
515 00:51:01.440 ⇒ 00:51:05.389 Luke Scorziell: Which I’ve always found, too, it’s always interesting with, clients.
516 00:51:06.360 ⇒ 00:51:07.870 Luke Scorziell: Because when you ask…
517 00:51:08.000 ⇒ 00:51:15.940 Luke Scorziell: directly, like, hey, would you be interested in this? Oftentimes, people just close off immediately, and they’re like, oh, I’m not interested in that.
518 00:51:16.040 ⇒ 00:51:32.079 Luke Scorziell: But then if you’re, like, you, like, describe whatever really cool thing you’re gonna do, and then you say, do you know anyone that would be interested in that? Then a lot of the time, what they’ll say is, like, well, we would be interested in that. So I’ve found that to be kind of a hack when you’re selling,
519 00:51:32.080 ⇒ 00:51:32.719 Pranav: Phone number.
520 00:51:32.920 ⇒ 00:51:42.100 Luke Scorziell: So often, they’re like, you say, like, oh, yeah, you know, I’m just looking for… like, would you be interested in doing this? And they’re like, no, no, I’m not interested. But then you’re like, oh.
521 00:51:42.430 ⇒ 00:51:49.229 Luke Scorziell: Yeah, I saw you had, like, a friend that was doing this, would you be interested? Would they be, you know… so it’s different, but…
522 00:51:50.850 ⇒ 00:51:56.660 Pranav: Yeah, that makes sense. I think people just have, like, an aversion to being sold to, you know?
523 00:51:57.340 ⇒ 00:52:03.719 Luke Scorziell: Yeah, but then they have so much FOMO that then it’s like… Exactly. I don’t want to miss out on that if other people are doing that, so…
524 00:52:03.720 ⇒ 00:52:04.640 Pranav: Yeah, exactly.
525 00:52:04.640 ⇒ 00:52:05.430 Luke Scorziell: Yeah, yeah.
526 00:52:06.810 ⇒ 00:52:16.190 Luke Scorziell: Cool. Yeah, well, let’s keep chatting. I don’t know if you have any other thoughts or questions, but I’ll kind of take what we did here and then try to put it together into something.
527 00:52:16.600 ⇒ 00:52:21.390 Pranav: Perfect, yeah, no, I’m, all set for today, I think I gotta hop soon, but… Boom.
528 00:52:21.500 ⇒ 00:52:37.339 Pranav: yeah, let’s keep in touch, like, let me know, where I can help next, like, yeah, for the calls or whatever, like, we can talk about how we want those to go down, like, I assume, like, you’d probably run them mostly, and I can be there to just, like, ask or answer certain questions, but yeah.
529 00:52:37.710 ⇒ 00:52:40.300 Luke Scorziell: Yeah, yeah, yeah, that’d be great. So.
530 00:52:40.390 ⇒ 00:52:41.740 Pranav: Cool. Cool.
531 00:52:41.810 ⇒ 00:52:50.470 Luke Scorziell: Yeah, I’ll start pushing, and I’ll keep you… keep you updated, and let you know how it’s going, so… and then feel free to send me updates, too, on, like, how stuff’s going with Lilo, if there’s, like.
532 00:52:50.640 ⇒ 00:52:52.829 Luke Scorziell: How that conversation goes and whatnot, too.
533 00:52:53.210 ⇒ 00:52:54.720 Pranav: Yeah, definitely, I will do.
534 00:52:55.730 ⇒ 00:52:58.130 Luke Scorziell: Sweet. Alright, I’ll talk to you soon.
535 00:52:58.370 ⇒ 00:52:59.410 Pranav: Yeah, talk soon. See ya.
536 00:52:59.410 ⇒ 00:53:00.170 Luke Scorziell: Heh.