Meeting Title: Uttam Kumaran’s Personal Meeting Room Date: 2025-04-11 Meeting participants: Annie Yu, Uttam Kumaran
WEBVTT
1 00:00:14.050 ⇒ 00:00:23.120 Uttam Kumaran: Gee, look, it’s a real m 1 quality video. I remember this was not that much better. Let’s see what the difference is.
2 00:00:27.410 ⇒ 00:00:36.739 Uttam Kumaran: Alright, yeah, it’s actually mine’s really grainy. Mine’s very grainy. Hi, Hi.
3 00:00:36.740 ⇒ 00:00:37.400 Annie Yu: Keep going.
4 00:00:39.700 ⇒ 00:00:41.760 Uttam Kumaran: Okay.
5 00:00:46.231 ⇒ 00:00:57.069 Uttam Kumaran: okay, let’s so we’re talking about messaging. I guess I can set the stage a little bit. Let me just pull up
6 00:00:57.780 ⇒ 00:00:59.519 Uttam Kumaran: our messaging stuff.
7 00:01:10.620 ⇒ 00:01:14.146 Uttam Kumaran: And then I have one other thing.
8 00:01:33.420 ⇒ 00:01:34.180 Uttam Kumaran: Okay.
9 00:01:38.883 ⇒ 00:01:39.926 Uttam Kumaran: So
10 00:01:43.910 ⇒ 00:01:48.000 Uttam Kumaran: so we’re talking about how we
11 00:01:48.550 ⇒ 00:01:54.809 Uttam Kumaran: talk about Brainforge. Maybe I’ll kind of explain a little bit about
12 00:01:57.020 ⇒ 00:02:04.800 Uttam Kumaran: like the advice that we’ve gotten from people on positioning. But there’s like sort of 2 ways of thinking about it. One is
13 00:02:04.950 ⇒ 00:02:27.995 Uttam Kumaran: we actually don’t want our messaging to resonate with everybody. We want our, the really, the core audience that has our problem when they hear what we have to say to be like, I have that problem. This is a. The problem we’re dealing with is a pretty classic problem. Where we claim to say, we’ll do any data, we’ll do any. AI right? And
14 00:02:28.994 ⇒ 00:02:33.725 Uttam Kumaran: okay, okay, yeah, we’ll do any data. We’ll do any AI and
15 00:02:35.450 ⇒ 00:02:54.370 Uttam Kumaran: we then cast the net. That’s so wide. But then, when people hear about hey, like, I have it like, what do you guys do? We do data? And AI, they’re like, Oh, well, do I have that problem? But then, yeah, exactly, it’s not 1. 1st of all, it’s not a we’re not explaining. Like we solve a problem. We’re saying, this is what we do. So commonly
16 00:02:54.700 ⇒ 00:02:59.667 Uttam Kumaran: people talk about positioning like capabilities. They talk about
17 00:03:00.800 ⇒ 00:03:08.489 Uttam Kumaran: discipline. And then they talk about industry. Right? For example, they talk about. We do X service
18 00:03:08.640 ⇒ 00:03:20.130 Uttam Kumaran: for Y discipline in in Z industry, an example of this is like we do data analytics for revenue teams
19 00:03:20.190 ⇒ 00:03:34.199 Uttam Kumaran: in home services businesses. Yeah. So really like, what the advice that we’ve gotten is that our positioning should resonate there should be like 50 to a hundred companies that, like our positioning hits
20 00:03:34.200 ⇒ 00:03:52.299 Uttam Kumaran: for us, we thought like at the moment, it seems pretty narrow. And so really, what we’re thinking about is more aligning, like our messaging around our capabilities. And then the disciplines that we want. And so what you’re gonna see in this document is a bit of information about
21 00:03:52.300 ⇒ 00:04:08.780 Uttam Kumaran: like how we discuss Brain Forge. But kind of the other way, I think about our messaging, and the positioning is when you describe Brainforge to someone. What did they then take away and then explain to the next person. Right? So you know, when you think about
22 00:04:09.561 ⇒ 00:04:22.900 Uttam Kumaran: when you think about like a fine dining restaurant, all you think about is like, I’m gonna get great service and great food. So when you describe it to someone, that’s how you think. So we want to try to leave people with the core nugget of like what we do.
23 00:04:23.050 ⇒ 00:04:27.249 Uttam Kumaran: I would say, this is where I struggle a lot because
24 00:04:28.050 ⇒ 00:04:57.709 Uttam Kumaran: I talk a lot about what we do and not as much about the problem we’re solving. And we sell commonly to Cmo. Ceos, head of growth. Who they may not know that they have like a data problem. I know what Snowflake is. They don’t know what these tools are, but they do know that. Hey? I’m like running my business on spreadsheets. I can’t tell what like sales is across my business. So part of this exercise is thinking about, like what are what our outstated purposes.
25 00:04:57.900 ⇒ 00:05:15.390 Uttam Kumaran: What do we try to do as a brand? And then how do we speak like, how do we speak about Brainforge? So it’s a little bit. It’s a little bit esoteric if you haven’t gone through like a positioning or messaging sort of thing. But for me, I think it’s helpful to talk to you both, because
26 00:05:15.390 ⇒ 00:05:40.370 Uttam Kumaran: you’ve now come into the company you’ve seen, not only how probably through the interview process we describe it, but also what you do on a day to day, right? And so I think it’d be helpful even before we start any of this, even to just hear from you guys like how you describe. Yeah, yeah, Annie, do you want to start? Or I can start? Essentially, I want us to do is that we will explain it to
27 00:05:40.370 ⇒ 00:05:45.960 Uttam Kumaran: as if I’m explaining to some someone new of what I do for this company. Essentially.
28 00:05:46.050 ⇒ 00:05:59.150 Uttam Kumaran: So I think what I told example of what I told the V, the distributor. Vp, yesterday I was like, we are a data and an AI company.
29 00:05:59.660 ⇒ 00:06:15.040 Uttam Kumaran: And that was like, we work with a lot of clients like you actually because he’s a food Asian food distributor. So it’s like a lot of these companies. They have all these data sources, you know, like suppliers, manufacturers supply chain, and
30 00:06:15.040 ⇒ 00:06:39.570 Uttam Kumaran: it’s very messy. So what we do is that we we organize this for you, and then we help make the process a lot cleaner, and when I was describing I feel like I wasn’t hitting a lot of the key points, and I can tell he was not interested. I could tell he was like. So that’s my understanding of what we do is that we make their make it more organized.
31 00:06:39.570 ⇒ 00:06:48.089 Uttam Kumaran: But even when you explain it you can tell that it’s like it’s not hitting like. If you were talking to yourself, would you be like, Damn! I want to. I need that.
32 00:06:48.170 ⇒ 00:07:13.089 Uttam Kumaran: You might be like, Oh, that sounds like too advanced for me, right? Or like, maybe I don’t like. Is that my number one problem? So part of our job is somewhat to explain, but also to to think about, how does data affect that distributor? Right? So for him, this is where the messaging guidelines we’re thinking about for someone who’s the Vp of this distributor. What problems do they have?
33 00:07:13.310 ⇒ 00:07:25.330 Uttam Kumaran: And then how do we? How do we align our capabilities, which is data? And AI to his discipline. Yeah, you know. For example, do you have a hard time understanding like who your top
34 00:07:25.330 ⇒ 00:07:47.099 Uttam Kumaran: clients are? You may say, like, yeah, so we’re not. I didn’t even ask questions. I was dumping on him. And the key part of sales is letting them talk. Yeah. And so this is something I have to learn as well, which is just like you, really great salespeople they will. They’ll come in and just say like, so like, Tell me, what? What? How’s your business doing? Well, we’re dealing with this this.
35 00:07:47.100 ⇒ 00:07:50.830 Uttam Kumaran: Oh, so tell me more about this problem. Great, great. So have you guys tried to solve that.
36 00:07:50.830 ⇒ 00:08:19.189 Uttam Kumaran: Well, we tried this and this and like, how much have you spent so far? Okay, and how important is that to you? Oh, it’s really important. Well, what if I told you for this price? I could solve that problem today? This is triggering my sales trauma. No, but that’s so. That is how like that’s like, that’s how great sales they back you into like admitting and then being like. Now that you admitted it, here’s like the solution. But it was hard. It was very hard for me. I don’t know, Annie, if you’ve if you’ve had to explain
37 00:08:19.230 ⇒ 00:08:26.880 Uttam Kumaran: what you do or what the company does recently like. How did you imagine I’m your grandma? What would you tell me.
38 00:08:27.170 ⇒ 00:08:33.310 Annie Yu: I I did get asked about like my new job, and also like
39 00:08:34.169 ⇒ 00:08:38.869 Annie Yu: turn down some like recruiters approaching me, and kind of explain
40 00:08:39.179 ⇒ 00:08:47.297 Annie Yu: to them like what I do now with the team. I think I’m I’m gonna share like 2 things. But this is one of that. And
41 00:08:47.650 ⇒ 00:09:12.309 Annie Yu: I remember, I’m just saying, like, this team is like a full on data team, we do projects ranging from data engineering. So from the upstream to downstream data visualization and also to AI products. So I I think that’s 1 thing. That I’ve been telling people. But when if we talk about like
42 00:09:12.590 ⇒ 00:09:41.579 Annie Yu: trying to resonate with the right audience, I think, for us. And this is just my thought. I think if we talk about like a startup, they have their own product, so they can be very explicit on, like what they are trying to solve. With the Api, or like a voc platform. But for us it’s we are a team without like providing a specific product. So I think we can almost like segment
43 00:09:42.600 ⇒ 00:09:55.940 Annie Yu: our audience with, like the targeted key question we can solve for them. So like we could have one for marketing and one for supply chain. I feel like.
44 00:09:56.640 ⇒ 00:09:59.320 Annie Yu: just because we don’t have that product. We could
45 00:10:00.270 ⇒ 00:10:03.429 Annie Yu: lose, focus and be like too broad.
46 00:10:03.430 ⇒ 00:10:28.300 Uttam Kumaran: Yeah, but also, and you’re so, right also gives us the ability to mold to our audience. Because if we’re just a product, then we really can’t be for everyone, but we technically can. But we can also have a very targeted messaging of, we actually just do this for customer service representatives, and we can give them all the case studies on that, and we don’t have to talk about the other part. Yes, that’s exactly right in that
47 00:10:28.430 ⇒ 00:10:49.420 Uttam Kumaran: part of the thing we’re deciding on. And a lot of people said, you need to pick an industry that you’re in. But part of it is like, look, I think we’re gonna accept. We’re gonna both accept work from certain industries. We’re also gonna say no to certain to certain domains. For example, we’ve been talking about trying to say no to anything around customer service and anything around
48 00:10:49.420 ⇒ 00:11:11.529 Uttam Kumaran: like intense marketing measurement. The reason being is in marketing measurement. It’s sort of like never ending every day. There’s sort of like something they want to pull a lever here. They don’t want to measure. Those guys aren’t really data driven. They’re just sort of like, almost like day trading like. And so that’s 1 piece on the customer service side. It’s not a revenue center for the business.
49 00:11:11.700 ⇒ 00:11:30.410 Uttam Kumaran: meaning like it’s like a cost. And then what you measure, how much calls every one of your customer service is making. And then then what like, you know. So for us, what we, what we decided on is like in in all the business we want to focus. If we were to say.
50 00:11:30.640 ⇒ 00:11:34.200 Uttam Kumaran: if we’re forced to say, you have to go one area. We’re like, we want to help
51 00:11:34.310 ⇒ 00:11:37.079 Uttam Kumaran: growing companies grow faster.
52 00:11:37.250 ⇒ 00:12:06.710 Uttam Kumaran: Right? And so this is sort of where we lean into like our messaging, and I have 2 documents in front here, one of which Robert worked on. It has things like our purpose, our promise, our style and voice the other document on the left. Here I worked with Ivana, who is a friend of mine who runs a design agency. She does many of these like messaging exercises with companies like ours, like highly technical founders
53 00:12:06.710 ⇒ 00:12:14.639 Uttam Kumaran: who have a hard time explaining, like in English, like what they do for people. And so she goes through a series of questions and then sort of builds
54 00:12:14.770 ⇒ 00:12:15.840 Uttam Kumaran: like.
55 00:12:16.240 ⇒ 00:12:32.980 Uttam Kumaran: like what she heard about what we do and the problems we solve. So I almost want to start with the left side because we’re here. Yes, I’m on zoom, it’s in the yeah, it’s in the Q 2 chat, and then I’ll I’ll share this. I’ll share this with you both.
56 00:12:36.920 ⇒ 00:12:40.420 Uttam Kumaran: Oh, actually, it’s gonna ask me to share. Okay, you may just have to.
57 00:12:40.560 ⇒ 00:12:46.609 Uttam Kumaran: Robert says, run until 3 pm, we need more time. Okay, that’s fine. So I’m just gonna ask,
58 00:12:53.770 ⇒ 00:13:07.410 Uttam Kumaran: yeah, just like, yeah, that’s fine. But if you want to join the Zoom Meeting. You can see this, or if I join the audio, would you just turn your audio off and mute your audio?
59 00:13:09.320 ⇒ 00:13:12.349 Uttam Kumaran: So what we’re seeing on the left here is basically like.
60 00:13:12.900 ⇒ 00:13:25.769 Uttam Kumaran: what is Brainforge. Okay? Cool. Brainforge sets up the data infrastructure companies need to understand what’s happening in their business, then builds chat based tools inside slack, so teams can get answers instantly without digging through dashboards.
61 00:13:26.180 ⇒ 00:13:30.630 Uttam Kumaran: Another area is like, Know what’s happening in your business without pulling a report.
62 00:13:30.950 ⇒ 00:13:53.850 Uttam Kumaran: Another thing I sort of thought of was like, we eliminate dashboards. We end spreadsheet chaos, and we put your insights on autopilot. It may seem like, you know, for me as an engineer, I’m like, Oh, that’s like, not really what we do like. We do way more than that like, why are you simplifying it? But I also think about if I was going to like a CEO and I was in an elevator and I had 2 min to basically explain
63 00:13:53.950 ⇒ 00:14:04.860 Uttam Kumaran: what we do. Maybe this is the get best 1st thing. And then if they want to get more technical, we can get more technical if they’re like cool, I get it. I do have all these spreadsheets like, I wish I had a solution right?
64 00:14:05.435 ⇒ 00:14:32.450 Uttam Kumaran: So I’ll walk through this, and then would love to get your guys thoughts. So mission and vision. So our mission is to design and deliver systems that turn messy manual tasks, like pulling data, writing reports, searching for answers into clear, fast outputs. Your team can act on our vision is to give ambitious businesses the clarity tools and support to grow right. And this is this is key. Because we’re talking about ambitious businesses.
65 00:14:32.510 ⇒ 00:15:00.099 Uttam Kumaran: We’re talking about giving them clarity tools and support. And then we’re talking about them growing. We’re not talking about them staying flat. We’re not talking to them cutting costs because they’re failing. So already we’re like eliminating some audience. And we’re we’ve given the things like we give ambitious businesses. Then you’re like, I’m an ambitious business, right? So it makes you feel like we are talking to you. We just say, we give businesses like, Okay, but we give ambitious meaning. We don’t work for like
66 00:15:00.100 ⇒ 00:15:18.710 Uttam Kumaran: we don’t work for loser companies. But then, what is what happens for the person on their side. They’re like, yeah, I’m an ambitious business like, okay, cool. I fit. So maybe I’ll stop there. I’ll just talk about that also on the left side, you know this is what Robert wrote, which is to ensure every. So
67 00:15:19.050 ⇒ 00:15:29.919 Uttam Kumaran: this is like we streamline your stack, unlock insights, operationalize growth. We focus on leveling up your team to use data. But just hearing these
68 00:15:30.090 ⇒ 00:15:32.220 Uttam Kumaran: like, how does it make
69 00:15:32.420 ⇒ 00:16:01.789 Uttam Kumaran: y’all feel? Does any one of these resonate more than the other any like questions. Yeah, I’m conflicted because on one end, when we talked about it specifically about oh, data infrastructure. See? Now that you flip to thinking of, not like in our day to day. But you’re like you have 2 min. It’s sort of you’re like, Damn! We totally shouldn’t talk about that at all. No, it just like, okay, blah blah blah technical term technical term.
70 00:16:03.810 ⇒ 00:16:10.040 Uttam Kumaran: Yeah. But then, when we go high level of like, Oh, we help them grow.
71 00:16:10.070 ⇒ 00:16:20.549 Uttam Kumaran: And then, as a person like, I don’t have a complete CEO perspective. But even though even as a day to day team member. I’m like, that’s so vague.
72 00:16:20.630 ⇒ 00:16:23.009 Uttam Kumaran: It’s so hard.
73 00:16:23.930 ⇒ 00:16:25.350 Uttam Kumaran: Yeah, I hear you.
74 00:16:27.110 ⇒ 00:16:40.350 Annie Yu: Yeah, I I think, oh, yeah, I think I would agree on that. And I think maybe for me, what really would caught my attention. Maybe here on the left side would be that, knowing what’s happening in your business without pulling a single report and.
75 00:16:41.026 ⇒ 00:16:42.379 Uttam Kumaran: Like that.
76 00:16:42.380 ⇒ 00:16:58.140 Annie Yu: I think, cause I think I I would really want us not to get too broad, even though we can do like a broad range of things. And I feel like with that, we have to have a
77 00:16:59.840 ⇒ 00:17:04.309 Annie Yu: almost like a key question or so it doesn’t have to be a question, but something
78 00:17:04.550 ⇒ 00:17:06.499 Annie Yu: that people can remember.
79 00:17:06.500 ⇒ 00:17:14.790 Uttam Kumaran: Yes, yeah, you’re right. It’s like, if you were to, if like, if we were to talk for 2 min like, Okay, see ya, what do they remember? Right? So part of this?
80 00:17:14.940 ⇒ 00:17:22.669 Uttam Kumaran: It’s sort of like turning on a marketing hat where it’s like we are we are selling. Where do our customers end after like working with us for 9 months
81 00:17:22.880 ⇒ 00:17:52.360 Uttam Kumaran: like we’re selling the end States. We’re talking about like what the optimal state is. We’re trying to get them to admit that there is a pain. There is going to be some people here where example, if you have a great data team, if you have great insights. It’s not going to work, but we know the majority of companies don’t have that right. And so I agree in that. It needs to be short, it needs to stick, and we need to say it a hundred 1,000 times right, just like in our meeting where I go through the principles, or I go through the clients.
82 00:17:52.660 ⇒ 00:17:54.990 Uttam Kumaran: Whatever we decide here is like.
83 00:17:55.650 ⇒ 00:18:01.079 Uttam Kumaran: you’ll see. I’ll just hammer it every time we talk about the business. We need to mention that, like
84 00:18:01.210 ⇒ 00:18:06.729 Uttam Kumaran: our goal is that we help. You know what’s happening in your business without pulling a single report.
85 00:18:06.880 ⇒ 00:18:07.710 Annie Yu: Right
86 00:18:08.540 ⇒ 00:18:14.579 Uttam Kumaran: You know. That’s that’s how like catchphrase-y, like almost it should be.
87 00:18:15.190 ⇒ 00:18:23.059 Annie Yu: Yeah. And I think one thing that I’ve noticed about this team is, I think we are like, we move really fast. And I
88 00:18:23.690 ⇒ 00:18:26.279 Annie Yu: like just looking at my past roles
89 00:18:26.510 ⇒ 00:18:52.940 Annie Yu: being, let’s say, like Nike, Nike is such a like big company. Their data is messy. And whenever, as an end user of a table. I want to do some like modeling change. I have to go through lots of tickets and for them to like. Finally, get to our ticket to work on it. And I think that’s something I’m betting like in bigger company. That’s gonna be an issue just because there’s so many layers and bureaucracy.
90 00:18:53.275 ⇒ 00:19:01.989 Annie Yu: But but with with our team within, our team can do that all without like going through all the layers, I feel like that’s 1 thing
91 00:19:02.360 ⇒ 00:19:04.869 Annie Yu: I will be like attracted to.
92 00:19:04.870 ⇒ 00:19:05.423 Uttam Kumaran: Yeah,
93 00:19:06.980 ⇒ 00:19:16.490 Uttam Kumaran: you know what cause? You know your document? Robert knows his document, can we like start a new one, just well, that’s the thing I wanted to show, both because
94 00:19:16.740 ⇒ 00:19:33.160 Uttam Kumaran: this is something that someone external like. She knows me. She’s a friend of mine, but she’s never. She’s also like dude. I never can explain what you do. I just say, you guys do data and AI stuff. Then I’m like, gonna cry, I’m like, you know me for like years like, how do you not know what we’re doing? Talk all the time?
95 00:19:33.160 ⇒ 00:19:35.482 Annie Yu: I don’t even know what my my partner does.
96 00:19:35.740 ⇒ 00:20:01.320 Uttam Kumaran: Yeah, you know. And then it’s like, and then we have what we write on here. But even when we look at the right thing. It’s like, that’s more about like what we do not like the impact we make right? And so yeah, and a lot and a lot of the things we want to talk about is like, what when we leave people like, what do they? Where do they go? So it’s less about like we use data we use. AI. It’s like
97 00:20:01.430 ⇒ 00:20:24.139 Uttam Kumaran: you. When you at the end of the day with us, we’re going to help you make more money. Right? Yeah. What we bring to the table is data and AI and insights and clean up your dashboard. Blah blah blah! But like you want to grow, do you want to grow revenue. Yes or no. Okay? Then, like, let us give us a shot at doing that for you. Yeah. Why don’t we? Let’s play. Okay, let’s
98 00:20:24.500 ⇒ 00:20:26.839 Uttam Kumaran: some one of us played a client.
99 00:20:27.010 ⇒ 00:20:45.090 Uttam Kumaran: Annie, you play a client, impress her. No, but but like I would. But this is but this is where it’s like we. If we just say, Hey, you want to grow revenue, then it’s it’s pretty broad. Everyone’s there. So how do we think of something that has elements of both. That is unique. Right? It’s not just like.
100 00:20:45.300 ⇒ 00:20:59.830 Uttam Kumaran: Oh, do you have like human beings? Well, you need our payroll software. Okay, there’s 10 of those right for us. It’s like I want them to have never heard a a phrase or a pitch that’s like that’s like we’re helping ambitious business
101 00:21:00.000 ⇒ 00:21:00.700 Uttam Kumaran: like
102 00:21:01.620 ⇒ 00:21:30.950 Uttam Kumaran: find insights faster. But something like that I like. I kind of like the I like that one. I think we need more of them to. It’s sort of it’s sort of like gives some and be like helps them business business like you. You’re like, oh, like me. And then they’re like, then here’s like exactly what we do, I kind of. And then also, you know, the best way to think about this is if you go to the Brainforge website right. Now think about what we should what we should put. We should put
103 00:21:31.210 ⇒ 00:21:55.380 Uttam Kumaran: like right here, because what we decide, I’m just gonna literally ask the team to put here. I’m just gonna say, delete all this garbage and just write it here. I feel like transform raw data to astral insights is every single. Oh, that’s so cool. It’s every single thing that a chat Gpt will output. Yeah, yeah, no. I probably wrote, I probably wrote this with Chatgpt. I probably wrote that. Yeah, I don’t know. Okay, so what’s
104 00:21:55.380 ⇒ 00:22:01.749 Uttam Kumaran: so unique about us, Annie, you mentioned, we’re fast. We’re small. You mentioned. We help
105 00:22:01.790 ⇒ 00:22:06.000 Uttam Kumaran: help them do things without getting into the mess like
106 00:22:06.150 ⇒ 00:22:08.510 Uttam Kumaran: what is so unique about us?
107 00:22:09.680 ⇒ 00:22:18.321 Uttam Kumaran: Why do people remember you when you talk to so many of your friends? Why do they even remember what you do? Yeah, I mean, part of like,
108 00:22:18.850 ⇒ 00:22:45.649 Uttam Kumaran: part of what I also mentioned here is like, what is our personality? She wrote this right? So we’re sharp. No, Fluff, no filler, clear communication. We’re grounded, we’re outcome, driven. And we’re insightful right? And then so kind of wrote like, what is their personality? They’re sharp, they’re grounded. Outcome, driven. Insightful tone of voice is like clear, not clever, right? Like
109 00:22:45.890 ⇒ 00:22:48.029 Uttam Kumaran: no jargon, no buzzwords.
110 00:22:48.070 ⇒ 00:23:13.010 Uttam Kumaran: Instead of saying, we leverage intelligent automation to optimize workflows. We help you stop wasting time digging for answers. Right? Like, that’s okay. Right? Another one helpful, not salesy. Instead of our cutting edge. AI delivers unprecedented Roi. Yeah, it seems like a joke once already. It’s like you’ll know what’s working, what’s not and where to focus. I feel like I would have wrote the 1st one I did. I probably wrote I mean she probably I
111 00:23:13.010 ⇒ 00:23:23.009 Uttam Kumaran: probably said this, and she was like, no, this is probably what you said and like. This is what you should say. So it’s like what you actually mean. And then also like, it’s calm, not cold.
112 00:23:23.010 ⇒ 00:23:24.040 Uttam Kumaran: right
113 00:23:24.040 ⇒ 00:23:41.529 Uttam Kumaran: instead of here’s your output. And this is also what I say, instead of being like, here’s your dashboard. Here’s what we found, and here’s what we recommend you do right. So we go one step further than like, just like, here’s a dashboard. Yes, like.
114 00:23:41.840 ⇒ 00:23:46.879 Uttam Kumaran: And this is where, like we have. So we were playing around with a couple of things which is like
115 00:23:49.040 ⇒ 00:24:07.730 Uttam Kumaran: like I said, one thing I wrote is like you can’t scale chaos. Brainforge replaces manual reporting slack fire drills random dashboards with tools that give your answers in seconds. But this is like the second layer. It still sounds a bit salesy, though it does sound a bit salesy. I really like what she wrote.
116 00:24:07.930 ⇒ 00:24:13.450 Uttam Kumaran: Brainforge helps growth stage companies replace manual chaos with smart systems.
117 00:24:13.760 ⇒ 00:24:30.169 Uttam Kumaran: One of the things I always tell people is like, if they like. One thing we got forced as part of like this accelerator we’re into to basically say very crisply, like, in like one sentence, what we do. And we basically nailed it as like, we help companies make more decisions faster.
118 00:24:30.400 ⇒ 00:24:44.607 Uttam Kumaran: Yeah, my Linkedin tagline is right. Now, I help teams do more with less. But then some people ask me, okay, what kind of decisions like? Decisions are so broad. We all make a lot of. And I was like, Okay, that’s like, okay, like, you know.
119 00:24:46.170 ⇒ 00:24:52.389 Uttam Kumaran: So I think if we’re like, if anything, if we’re if we’re if we’re good with like something. Then maybe we anchor around
120 00:24:52.690 ⇒ 00:24:57.940 Uttam Kumaran: this vision and like, I kind of do like the personality piece like, I don’t know if you guys think
121 00:24:58.690 ⇒ 00:25:01.509 Uttam Kumaran: any any differently.
122 00:25:05.760 ⇒ 00:25:12.340 Uttam Kumaran: I think my lack of alignment is like, what exactly are we?
123 00:25:12.480 ⇒ 00:25:17.880 Uttam Kumaran: What is our benefit? What are we doing? So this. So so this
124 00:25:18.510 ⇒ 00:25:37.020 Uttam Kumaran: in order to write this. This is what I then go, and all of our documents. The way we write about the company that we speak about the company starts from this framework. Oh, I was like, Okay, I was like, what are we doing? We do so many things. So it’s so hard to hammer down.
125 00:25:37.800 ⇒ 00:25:47.259 Uttam Kumaran: But I would say, don’t think about it. Like as a okay, we need to list all our capabilities. Think about a high level like, how do we impact businesses
126 00:25:47.540 ⇒ 00:25:54.990 Uttam Kumaran: like, if you were to think about your if you were to think about ABC at the end of the day, what’s like? What is the goal.
127 00:25:56.010 ⇒ 00:25:57.419 Uttam Kumaran: the goals for them.
128 00:26:04.970 ⇒ 00:26:12.909 Uttam Kumaran: we empower your Csrs, so you can.
129 00:26:13.450 ⇒ 00:26:21.890 Uttam Kumaran: Your team does more. And then your company gets more essentially. Yeah, yeah.
130 00:26:21.890 ⇒ 00:26:24.390 Annie Yu: I think, on the flip side that would be like
131 00:26:24.670 ⇒ 00:26:30.460 Annie Yu: reduce cost for you. Right? Reduce costs on your like labor, or whatever.
132 00:26:31.920 ⇒ 00:26:49.869 Uttam Kumaran: But at the same time this is where, like commonly as part of call center stuff, people are like, how do we cut 10 people. But instead, I said, How do we turn your like customer service? Yeah, like a little bit more time like, How do we turn your customer service reps from
133 00:26:49.870 ⇒ 00:27:15.249 Uttam Kumaran: dealing with problems to selling more services. That’s why we had them do the oh, by the ways where we turn them all into salespeople, right? We turn them into revenue machines, not just the fact that like Oh, we can now automate your whole thing with AI, and you fire 20 people like the goal is not like firing. That’s like such a tired. Take about this for me, I think about like, how do you get your customer? Service reps
134 00:27:15.400 ⇒ 00:27:25.670 Uttam Kumaran: to genuinely make a connection with your customers, which is their competitive advantage, and then bring in more revenue to the door. Right?
135 00:27:26.470 ⇒ 00:27:39.379 Uttam Kumaran: That’s how we do. It is, of course, through what we’re capable of. But yeah, but we don’t want to explain it in the 1st line. But, like, yeah, if I was talking to CEO, and he’s like, so what do you do for us? I’m like, Oh, yeah, we’re helping your Csr sell more.
136 00:27:40.070 ⇒ 00:27:44.309 Uttam Kumaran: We’re helping your students bring more money through the door. Yeah, so
137 00:27:44.460 ⇒ 00:27:50.520 Uttam Kumaran: we are helping specific target group, not just your business specific target group.
138 00:27:51.360 ⇒ 00:28:10.779 Uttam Kumaran: Then we sort of go through our capabilities. So how would that? Because because all the messaging is supposed to do again? Think of it as like a billboard, right? Another way to think about this is, if you. If you had to get a billboard and fill it with something that compels someone to remember our business, what would you write
139 00:28:11.210 ⇒ 00:28:15.239 Uttam Kumaran: like we would? We wouldn’t write. We design and deliver systems. Blah! Blah, right? No, no
140 00:28:16.830 ⇒ 00:28:28.399 Uttam Kumaran: like think about when you’re in sf, or if you’re in la, you see, like you see, like dash, you see, like billboards for tech software, anything that says anything jargon no one thing. But if it says like we save you money now
141 00:28:28.520 ⇒ 00:28:30.880 Uttam Kumaran: you’re like, Oh, what is that?
142 00:28:31.100 ⇒ 00:28:32.699 Uttam Kumaran: So it’s like, that’s the hook.
143 00:28:33.590 ⇒ 00:28:34.390 Uttam Kumaran: Yeah.
144 00:28:38.399 ⇒ 00:28:40.599 Uttam Kumaran: Earn money, every call.
145 00:28:40.710 ⇒ 00:28:46.509 Uttam Kumaran: yeah, or something like some. But this is where it’s like we help, we help ambitious businesses
146 00:28:46.790 ⇒ 00:28:54.899 Uttam Kumaran: grow faster. That’s like, Oh, like, I wonder what they do or like. That’s me like. I wonder what they do. I feel like we help
147 00:28:55.500 ⇒ 00:29:01.119 Uttam Kumaran: like, I’m thinking about the billboard, right? It’s probably a slightly different messaging. If it’s a billboard, then it’s
148 00:29:01.360 ⇒ 00:29:10.240 Uttam Kumaran: it’s not we help. It’s for them. Yeah, like they have a want, that line triggers their want. So it’s not even we do this. It was like.
149 00:29:10.680 ⇒ 00:29:11.880 Uttam Kumaran: say, like
150 00:29:12.420 ⇒ 00:29:22.140 Uttam Kumaran: my example, earn money, every call they’re like, Oh, I want that rather than I. We help ambitious business grow. It’s like I’m listening to. You talk instead of this, is it?
151 00:29:23.030 ⇒ 00:29:39.460 Uttam Kumaran: I think what I watched on the marketing piece was that you have to say people’s thoughts and wants out loud. Yeah, exactly exactly right. That’s the gap we have right now. I feel like because I don’t, because I’m I don’t really buy. I don’t buy from us
152 00:29:39.710 ⇒ 00:29:44.760 Uttam Kumaran: right? And so it’s hard for me, and we will do more testing where we’ll talk to our customers. But.
153 00:29:45.220 ⇒ 00:29:49.040 Uttam Kumaran: like again, it’s just like, How do we want to hook people
154 00:29:49.290 ⇒ 00:29:56.250 Uttam Kumaran: like, how do we want people to say exactly what you mean, which is like? Are you an ambitious business? And do you want to grow more.
155 00:29:58.430 ⇒ 00:29:59.979 Uttam Kumaran: Tanya? What do you think.
156 00:30:01.480 ⇒ 00:30:06.680 Annie Yu: I still don’t like. I’m being very honest. I I just don’t like
157 00:30:06.990 ⇒ 00:30:12.619 Annie Yu: focusing on like we can help you grow more that just doesn’t like. And anyone can say that.
158 00:30:12.620 ⇒ 00:30:13.280 Uttam Kumaran: Yeah.
159 00:30:13.280 ⇒ 00:30:21.150 Annie Yu: And and I feel like if I’m a business. I I think I still want that like.
160 00:30:21.150 ⇒ 00:30:21.920 Uttam Kumaran: The data piece.
161 00:30:21.920 ⇒ 00:30:32.769 Annie Yu: Mention a case, a use case that could like happen to me like, do you have too many skews that you don’t know which to cut, or I I don’t even have.
162 00:30:32.770 ⇒ 00:30:35.090 Uttam Kumaran: Do you think it’s closer to like
163 00:30:35.974 ⇒ 00:30:38.980 Uttam Kumaran: do you think it’s closer to like this like
164 00:30:40.160 ⇒ 00:30:45.189 Uttam Kumaran: like we, we replace all this manual report. You think it’s something we have to like mention like
165 00:30:46.590 ⇒ 00:30:58.529 Uttam Kumaran: like this. This part is like closer to that, because I like the ambitious business I’m with you. I kind of don’t like this second piece, not the grow part. This grows overused.
166 00:30:59.110 ⇒ 00:31:00.500 Annie Yu: Yeah.
167 00:31:01.985 ⇒ 00:31:02.700 Uttam Kumaran: Yeah.
168 00:31:04.260 ⇒ 00:31:17.700 Uttam Kumaran: And you say, say, it’s just let’s use the skew case. How would you make that on a billboard that were like clickbait title that makes people want to. What would that be?
169 00:31:21.990 ⇒ 00:31:26.490 Annie Yu: Know which skew to invest and divest.
170 00:31:28.695 ⇒ 00:31:36.922 Uttam Kumaran: Yeah, okay, I’m gonna buy a billboard now every day.
171 00:31:39.810 ⇒ 00:31:45.600 Uttam Kumaran: Okay, do, are you guys, do you guys like Vibe with like the ambitious businesses piece.
172 00:31:45.900 ⇒ 00:31:59.070 Uttam Kumaran: because to give you to give you the, to give you less of the marketing companies that can afford us are the ones that are growing like in historically, the companies that have churned or have struggled to like
173 00:31:59.400 ⇒ 00:32:05.779 Uttam Kumaran: been like nervous about the money are all the ones that are like stag. They’re stagnating, or they’re like shrinking.
174 00:32:05.970 ⇒ 00:32:08.040 Uttam Kumaran: And so I like that. We
175 00:32:08.890 ⇒ 00:32:24.430 Uttam Kumaran: we talk about enabling the ambitious ones and everybody’s, and that, like everybody in business, is ambitious, and so I kind of like that piece. I do agree that I don’t know what the answer is, but the second half of it is like kind of challenging like, what
176 00:32:24.960 ⇒ 00:32:37.879 Uttam Kumaran: like, what exactly, are we? Do we do or or like? What specific problem do we hit? And maybe it is the fact that like, we need to have different ones for for different people. Right? Like, we have.
177 00:32:38.080 ⇒ 00:32:46.780 Uttam Kumaran: okay, what do we tell to the operator? Maybe, what do we tell to the technical person. Maybe it is persona based. Maybe it is industry based.
178 00:32:48.590 ⇒ 00:33:01.369 Uttam Kumaran: I still do think about like the tagline of the of the the 1st thing on the on the business, like, when you go to slack.com. They have a key thing there. When you go to salesforce they have a key headline like, what would ours?
179 00:33:01.500 ⇒ 00:33:03.679 Uttam Kumaran: What would our thing be?
180 00:33:08.790 ⇒ 00:33:36.020 Uttam Kumaran: I feel like their tagline is not even that specific. It’s not because you go as a user. I go into the say, like Jira, I go into their website. And I, I directly go filter under this specific team that I’m on. Yeah. So I don’t. I don’t care because their tagline is generic. But I care that. Okay, I’m gonna use this for my team or the daily work that I do. You’re gonna benefit me.
181 00:33:36.560 ⇒ 00:33:38.868 Uttam Kumaran: So we can probably just
182 00:33:39.540 ⇒ 00:33:46.530 Uttam Kumaran: hammer down each use case because each user knows they know who they are. So they’re gonna you look at the parts that
183 00:33:46.690 ⇒ 00:33:53.810 Uttam Kumaran: speaking out their needs out loud. Right? So we have some like industry based
184 00:33:54.550 ⇒ 00:34:14.810 Uttam Kumaran: ideas, right? You know, like the consulting, consulting websites. They have. Yeah, yeah, by industry, or like, by team type, you’re a so this is where it’s like we would, we would show through case studies. Okay, here’s a home service example. Here’s how we’ve done, how we’ve done this. But even for us again.
185 00:34:15.210 ⇒ 00:34:33.040 Uttam Kumaran: it would be tough for me to say, great. We’re we’re a data company. We work with home service businesses, e-comm sas healthcare like there’s no story. There’s no like cohesive story there. So the alternative is for us to say, we only do one thing, we we only do data analytics
186 00:34:33.230 ⇒ 00:34:46.489 Uttam Kumaran: for fast growing Saas businesses. Then the other clients will say, oh, great, not for me. It may not resonate with them. That’s the that is the that is like as far specific as we could go.
187 00:34:47.320 ⇒ 00:34:51.719 Uttam Kumaran: The most broad is where we are now, where it’s like we’re just transformed data into insights.
188 00:34:53.560 ⇒ 00:34:58.949 Uttam Kumaran: Landing somewhere, I would say closer to the Sas. Example is where we want to be.
189 00:35:00.850 ⇒ 00:35:02.570 Uttam Kumaran: Annie. What do you think.
190 00:35:04.810 ⇒ 00:35:09.920 Annie Yu: Let me know if this this just an example. I I’m not sure if this is
191 00:35:10.830 ⇒ 00:35:18.780 Annie Yu: aligned with what we do, but I’m trying to think like a more like catchy, but also like resonating
192 00:35:19.450 ⇒ 00:35:21.189 Annie Yu: with broader audience.
193 00:35:26.720 ⇒ 00:35:27.560 Annie Yu: like.
194 00:35:27.710 ⇒ 00:35:33.029 Annie Yu: I’m thinking what I was thinking like, I think we do like end to end
195 00:35:34.800 ⇒ 00:35:40.219 Annie Yu: analytics. I’m not even sure if that’s the right word and we can like turn
196 00:35:40.930 ⇒ 00:35:48.309 Annie Yu: also don’t know the right word, like spreadsheets or manual work into one unified view, or answer.
197 00:35:50.550 ⇒ 00:36:01.080 Uttam Kumaran: Yeah. So I think we’ve aligned like, what I’m hearing is like, we’re full. It’s end to end. So there’s something about like not shying away from the from
198 00:36:01.220 ⇒ 00:36:09.332 Uttam Kumaran: whatever problem. Right? Yeah, there’s another piece that’s like speed. That’s like, how concise we are.
199 00:36:10.120 ⇒ 00:36:14.180 Uttam Kumaran: there’s another piece about this like anti spreadsheets
200 00:36:14.460 ⇒ 00:36:28.769 Uttam Kumaran: right like. And I say spreadsheets a lot, because for the executive everybody in business knows like what a spreadsheet is, and everybody at every company. Even yesterday? I asked. They all have, like
201 00:36:28.930 ⇒ 00:36:30.460 Uttam Kumaran: a hundred spreadsheets.
202 00:36:30.890 ⇒ 00:36:38.719 Uttam Kumaran: Even in our business there is spreadsheets, right? Not that much, I’ve made sure, but there is a lot of like random spreadsheets.
203 00:36:39.230 ⇒ 00:36:47.299 Uttam Kumaran: and it’s like a problem everybody deals with. So you can catch them like that. Right? So I do like that. I do like something about
204 00:36:48.030 ⇒ 00:37:04.439 Uttam Kumaran: end to end super fast, we solid one. And then the AI piece which we’ve been sort of thinking about is like your AI is only good enough as the context you have
205 00:37:05.090 ⇒ 00:37:12.749 Uttam Kumaran: it fits into. I’m taking very visually. I’m like, end to end. We funnel down to one thing. AI stick down here.
206 00:37:13.260 ⇒ 00:37:33.960 Uttam Kumaran: Yeah. So that’s how we marry like? Because otherwise, before I was like we do, we’re doing AI, we’re doing data like, what’s the link until this? Until the ABC project, I didn’t really see in my mind how it all married. But ultimately, like, it’s because we have the data. We have the feedback loop right? And because we have the clean call logs with a clean
207 00:37:34.220 ⇒ 00:37:45.700 Uttam Kumaran: chat logs. We can then make modifications, and the AI gets better. And then now the AI is a co-pilot, right. But we couldn’t have done that. We can’t do that until we had the data in one place.
208 00:37:45.840 ⇒ 00:37:50.009 Uttam Kumaran: Right? So there is something about like, if we were to go.
209 00:37:50.550 ⇒ 00:38:02.859 Uttam Kumaran: Okay, cool. We, we wrinkle of your spreadsheets. We go end to end. We get it all in one place, and then for me, I commonly say we like activate it, or we help your team. We empower your team to activate it with AI
210 00:38:03.170 ⇒ 00:38:08.489 Uttam Kumaran: like something like that. And that’s this is where, like, we don’t have to say AI,
211 00:38:08.600 ⇒ 00:38:34.480 Uttam Kumaran: but it could be something about empowering your team. I’m thinking about a very visual example of okay. You have spreadsheets scattered all over the floor. Right? We do everything. So we pick everything up. You sort of consolidate it into one rope, and then the rope spirals upwards with AI and data, because there’s a feedback loop. Yes, that’s kind of what I’m thinking, because right now they’re manually picking up trash on the floor. And then we
212 00:38:35.240 ⇒ 00:38:54.980 Uttam Kumaran: we recycle the trash. Yeah, yeah, we recycle that trash. Yeah, recycle your trash would be the thing we recycle. We are your data. We are your database management. That’s pretty cool. I see my, I see my visuals now.
213 00:38:55.470 ⇒ 00:39:20.879 Uttam Kumaran: but it’s where I was like. I think we have to, even if it’s like something like that, something that stands out. This is where for me, I’m out of answers. So all I know is like what we do. And this is where it’s like. Part of it is thinking like what sticks and what gets people to say like, Wait, could you go deeper on that like? What do you mean? And not in a way they’re like confused. They’re more like, Oh, like, yeah, for sure. Like, how does that? How does that work
214 00:39:21.230 ⇒ 00:39:24.901 Uttam Kumaran: tell me about it like, let’s go chat.
215 00:39:25.490 ⇒ 00:39:38.560 Uttam Kumaran: I want some. I want a little bit of curiosity. We don’t have to tell the full story. You want to draw them in till then explore the website. Further, sign up for a meeting.
216 00:39:38.740 ⇒ 00:39:47.979 Uttam Kumaran: So we don’t need to lay out all our cards, but it does need to. It does need to like, resonate. It needs to be like, Damn! That’s me like
217 00:39:48.950 ⇒ 00:39:53.859 Uttam Kumaran: what is a damn that’s me. Damn! I really do
218 00:39:54.580 ⇒ 00:39:57.660 Uttam Kumaran: look through all these spreadsheets and
219 00:39:57.840 ⇒ 00:40:16.520 Uttam Kumaran: ask, send out 60 emails asking for answers. What is a huge damn? That’s me, Annie. What do you think from your previous works. What is a damn? That’s so. Me, when what is something that will make you say that? Who told you to? Who has worked at other companies.
220 00:40:21.372 ⇒ 00:40:22.940 Annie Yu: I I think.
221 00:40:24.560 ⇒ 00:40:32.089 Annie Yu: really, is that like data is all over the place. And we lots of the time people don’t have that unified.
222 00:40:32.920 ⇒ 00:40:43.119 Annie Yu: like centralized, unified view, to make and to know, I think, to know, like the data is accurate just because they are all sitting in different places.
223 00:40:44.231 ⇒ 00:40:48.470 Annie Yu: And that also slows down like the decision making.
224 00:40:48.770 ⇒ 00:41:02.469 Uttam Kumaran: So that’s again. That’s I think that’s where you logically got to the point I did, which is like the decision making gets slow. And if your decision making gets slow, then your iteration cycles get slow, then it takes you 4 months to like, react to anything
225 00:41:02.640 ⇒ 00:41:03.560 Uttam Kumaran: right.
226 00:41:03.690 ⇒ 00:41:12.210 Uttam Kumaran: and life and business moves every day. So for me, I sort of took exactly what you said, and then I arrived at like
227 00:41:12.500 ⇒ 00:41:15.060 Uttam Kumaran: we help companies make more decisions.
228 00:41:15.708 ⇒ 00:41:30.589 Uttam Kumaran: But this is where I think I feel like that’s still what we do. Yeah, I want to go a step back to what is the to put what Annie just said in a story of pain, what is that story of pain like?
229 00:41:32.250 ⇒ 00:41:45.770 Uttam Kumaran: Do you have to go through 20 approvals and 4 months, and back and forth just to click a number? Or do you have to go through 60 spreadsheets
230 00:41:46.390 ⇒ 00:42:13.279 Uttam Kumaran: to just to end up at? You don’t even know if that’s the right number. You arrive at 10. Yeah, it’s exactly the problem. I think we need to spell out that problem rather than just say the solution before. They even know. That’s a problem. Because our customers don’t know. That’s a problem. Yeah? Or they have to. Yeah, we’re like, you know, when you read a good book. And then you’re like, Read a line you’re like, Damn! That’s exactly like I couldn’t. I didn’t have the words
231 00:42:13.390 ⇒ 00:42:31.319 Uttam Kumaran: to write it like that, you know, like you read a good self-help book or something, and it’s like, Damn! I didn’t. I couldn’t have put the words together. But that’s exactly like how I feel. That’s what we need to do for them, and maybe we can come up with a couple and then test it out like I can run it by our.
232 00:42:31.610 ⇒ 00:42:32.010 Annie Yu: Wow!
233 00:42:32.010 ⇒ 00:42:32.520 Uttam Kumaran: There isn’t.
234 00:42:33.920 ⇒ 00:42:56.339 Uttam Kumaran: I think we do have a couple of things we have like the full stack date, and then I know they’re probably waiting for us. We can go. We have the full stack data team, something around. Starting with a question. We have the no bureaucracy, no layers like speed. We have this like anti spreadsheet movement. We have like something around like unification verification.
235 00:42:58.620 ⇒ 00:43:03.319 Uttam Kumaran: Yeah, I think maybe we can start with like that. And then.
236 00:43:03.320 ⇒ 00:43:04.130 Annie Yu: Thanks, bye.
237 00:43:04.130 ⇒ 00:43:24.490 Uttam Kumaran: Yeah, start with those elements. I will show I’m going to shove this whole meeting into AI and ask it to come up with, can we also run? Run it through? Ivana? Yeah, so I will. Her messaging. Her wording is so much more down to earth than all of what we’re saying verification. And all these like classier words, yeah, yeah.
238 00:43:25.310 ⇒ 00:43:54.689 Uttam Kumaran: like, it should make sense to a 5 year old, exactly the 5 year old would have the same problem. Yeah, they would have a problem of, I have all these toys, or I have all these stuff that I don’t know how to put like, where is it? What should I buy next? Yeah, it should be the same. How should I categorize? Because this this is a core human pain of organization? So a grandma, a kid, someone, non data should all have that problem. And we should.
239 00:43:54.860 ⇒ 00:43:56.200 Uttam Kumaran: you know, as such.
240 00:43:56.400 ⇒ 00:44:06.460 Annie Yu: I love that. And I think that resonates with me like I was thinking like, Okay, if this is like a slogan, for, like anyone who’s a living thing that would be like.
241 00:44:06.580 ⇒ 00:44:12.929 Annie Yu: Life’s too short. You you shouldn’t have be like spending time on this and that, you know.
242 00:44:15.350 ⇒ 00:44:19.319 Annie Yu: and let us do that for you. Or so, you know.
243 00:44:20.620 ⇒ 00:44:23.860 Annie Yu: Yeah, like, life’s too short like, organize your shit, or something like that.
244 00:44:24.280 ⇒ 00:44:28.309 Uttam Kumaran: Ad of like of like Legos, and it gets like back into something
245 00:44:28.910 ⇒ 00:44:35.409 Uttam Kumaran: different spreadsheets. And then we have a video and or just a analogy of Legos that you said, and just
246 00:44:36.010 ⇒ 00:44:51.760 Uttam Kumaran: they all like morph into like a perfect architecture. I know the trash analogy. Now, this is something that yeah, I don’t I. This is beyond my capability to do to draw that. But no, they are very. They’ll yeah. They need a challenge because I have them.
247 00:44:51.760 ⇒ 00:45:06.680 Uttam Kumaran: They’re building Pdfs. Now, there’s AI generators. I saw so many videos. They’re so good. Now you generate a photo in the journey and plug it through a video. AI App does everything for you. It’s crazy. I like the life’s too short. Do something, or like
248 00:45:06.760 ⇒ 00:45:08.620 Uttam Kumaran: business is hard.
249 00:45:09.290 ⇒ 00:45:12.260 Uttam Kumaran: We’ll help you get organized like you already.
250 00:45:12.260 ⇒ 00:45:19.420 Annie Yu: I remember seeing I think a person makes 35,000 decisions a day. I’m not sure.
251 00:45:19.420 ⇒ 00:45:20.140 Uttam Kumaran: Yeah.
252 00:45:20.140 ⇒ 00:45:22.370 Annie Yu: Yeah, like that can feel.
253 00:45:22.370 ⇒ 00:45:23.860 Uttam Kumaran: Only have 4.
254 00:45:23.860 ⇒ 00:45:24.200 Annie Yu: Soon.
255 00:45:24.200 ⇒ 00:45:37.280 Uttam Kumaran: Active time per day. Don’t waste it on this. Don’t waste it on spreads. Don’t waste it on like bad spreads. Exactly. Every minute of yours is worth like a thousand bucks.
256 00:45:37.540 ⇒ 00:45:42.989 Uttam Kumaran: Your spreadsheet, every spreadsheet you look at is taking you 10 like a thousand.
257 00:45:43.384 ⇒ 00:45:44.569 Annie Yu: Do the math.
258 00:45:44.570 ⇒ 00:45:46.970 Uttam Kumaran: Maybe we should say, like ambitious.
259 00:45:47.170 ⇒ 00:45:58.239 Uttam Kumaran: like ambitious companies make. I don’t know. I like it, or we should say, like ambitious executives, or like great executives, make 500 decisions a day
260 00:45:58.430 ⇒ 00:45:59.370 Uttam Kumaran: like.
261 00:46:00.130 ⇒ 00:46:09.230 Uttam Kumaran: let’s help you get that. I don’t know. Like something like that. I like that, though. That’s usually like, you know, there’s something in marketing where they like. Say, statistic, like
262 00:46:09.230 ⇒ 00:46:38.329 Uttam Kumaran: one in 35 adults like, yeah, die from this thing. You should take our vitamin. So you don’t die. They’re like, Damn, that’s me. Yeah. Struck them with a statistic and number they haven’t seen before or like. You only have 8 HA day, or you only have 2 h of like actual, actual, active time per day. Yeah, like, it’s something limiting, something that makes you feel like, oh, my God, I’m like, I gotta like that needs to be way. More of that. That’s why I also said, like every hour of yours is worth this much time.
263 00:46:38.700 ⇒ 00:46:39.890 Uttam Kumaran: Yeah.
264 00:46:39.930 ⇒ 00:47:08.540 Uttam Kumaran: right? Like, make a starking make the number something a concept, a concept. I was reading. I was writing like my Linkedin strategy. I like, what makes virality is something they haven’t thought about before. Yeah. And then they’ll click and like it. Yeah, and that’s the part of like, oh, you only have 2 HA day. You only have your every or change and ask a mindset of Okay, or we just make it up. The average executive sits in like spreadsheets like
265 00:47:08.560 ⇒ 00:47:13.759 Uttam Kumaran: 20 HA week. Yeah, like, don’t let that be you
266 00:47:14.700 ⇒ 00:47:36.170 Uttam Kumaran: that hits. I will make up a number. I don’t even care. We’ll find like, even if you want to have some source. No, we’ll do a study. We’ll publish it, and we’ll cite it. Sample size of like one honesty. Don’t know.
267 00:47:37.380 ⇒ 00:47:37.950 Uttam Kumaran: Okay.
268 00:47:37.950 ⇒ 00:47:44.950 Annie Yu: Also one more question is there like, How how did you come up with Brandforge? Is there any story about that?
269 00:47:45.243 ⇒ 00:47:52.879 Uttam Kumaran: Yeah, the story is kind of lame. The story is kind of lame, but I feel like the the a lot of people have been like
270 00:47:53.110 ⇒ 00:48:16.760 Uttam Kumaran: looked at it and been like, Oh, that makes sense. And I’m like, Oh, there was no story because I just we when I 1st started the company. I met these 2 guys in Austin. We were all gonna like, start something together, something like a consultancy where we did like technology work. We met at a coffee shop, and then someone was like, we need to add a name to this after like a few weeks of meeting, and I’m like you can name whatever you want like. That’s I said, the least of my problems.
271 00:48:16.760 ⇒ 00:48:45.739 Uttam Kumaran: And then they were like, what if we name a brain forge? I’m like cool good. So there was like, really, no, there’s like, not much. But if you think about it, there is like a for me. It ended up evolving towards like, there’s a forge where, like we forge ideas, there’s like a collective brain. We forge your data spreadsheets. We put everything together into a new tool. But I think that the nice thing about not having an origin story is like we can make it up. So if you want to make. If you do think about a nice like
272 00:48:46.190 ⇒ 00:48:57.310 Uttam Kumaran: like the company started, and we the name came from this, we could just literally write that story and like, do it. So let’s come up with that. And then Brian can write the story.
273 00:48:57.530 ⇒ 00:48:58.280 Uttam Kumaran: Yeah.
274 00:48:58.500 ⇒ 00:49:02.550 Uttam Kumaran: Oh, sorry not, Brian, right? The story got it.
275 00:49:02.550 ⇒ 00:49:04.070 Annie Yu: Yeah, or with, yeah.
276 00:49:04.820 ⇒ 00:49:06.230 Uttam Kumaran: Have a good idea again. It’s.
277 00:49:06.230 ⇒ 00:49:09.910 Annie Yu: Like not your typical consulting firm, or whatever.
278 00:49:10.370 ⇒ 00:49:26.629 Uttam Kumaran: Yeah, no, these are all gold. Actually, you guys should sit with them. I’m not. I’m useless, not your typical consultancy is kind of gas. That’s kind of great.
279 00:49:26.630 ⇒ 00:49:28.640 Annie Yu: Yeah, like, that’s why I use on my dating app.
280 00:49:28.640 ⇒ 00:49:37.189 Uttam Kumaran: Or it should be like your your your data consultancy sucks like that is such a clickbait title.
281 00:49:37.190 ⇒ 00:50:01.649 Uttam Kumaran: Here’s why your data. Here’s 10 or top 10 reasons. Your data consulting sucks one, they charge you a million bucks. Yeah, yeah. And then you’re still in spreadsheets. Yes, you take you take a year to do anything your money goes goes from. I mean, you know, we can ab test this on the site like we can. We can just use 5 of these.
282 00:50:01.690 ⇒ 00:50:04.249 Uttam Kumaran: I’ll a B test. How many people
283 00:50:04.340 ⇒ 00:50:16.930 Uttam Kumaran: like what the session duration is, and then how many people click on to something else, and like Ryan would like to do that. Yeah. Well, then, I then I mean, Annie, you’re happy. You have to help with the data for post hoc, but
284 00:50:17.040 ⇒ 00:50:24.410 Uttam Kumaran: but we’ll we’ll have. We’ll have a lean run the Ab. Test and then internal team performance dashboard.
285 00:50:24.800 ⇒ 00:50:31.449 Uttam Kumaran: Okay, cool. All right. I’m happy. I think we’ll jump into, we’ll jump back into the other meeting. Annie.
286 00:50:31.450 ⇒ 00:50:33.570 Annie Yu: Alright! I’ll hop off.
287 00:50:34.007 ⇒ 00:50:34.882 Uttam Kumaran: Thank you.