Meeting Title: Friday Brainforge Demos & Retro Date: 2025-11-14 Meeting participants: Awaish Kumar, Samuel Roberts, Mustafa Raja, Gabriel Lam, Demilade Agboola, Joseph Good, Uttam Kumaran, Rico Rejoso, Hannah Wang, Amber Lin, Casie Aviles, Henry Zhao, Ryan Brosas, Robert Tseng
WEBVTT
1 00:02:22.270 ⇒ 00:02:23.670 Uttam Kumaran: Hello?
2 00:02:27.150 ⇒ 00:02:28.020 Joseph Good: And…
3 00:02:28.020 ⇒ 00:02:29.930 Uttam Kumaran: Yo, good to see ya!
4 00:02:30.680 ⇒ 00:02:32.090 Joseph Good: I know, how’s it going?
5 00:02:32.560 ⇒ 00:02:33.360 Uttam Kumaran: Good.
6 00:02:34.340 ⇒ 00:02:37.129 Joseph Good: The dog in the back. What’s, his or her name?
7 00:02:38.130 ⇒ 00:02:39.640 Uttam Kumaran: His name is Finn.
8 00:02:40.670 ⇒ 00:02:41.460 Joseph Good: Nice, bro.
9 00:02:41.690 ⇒ 00:02:42.719 Joseph Good: He’s chillin’ right there.
10 00:02:42.720 ⇒ 00:02:51.070 Uttam Kumaran: There’s people working on the house, so he’s surprisingly calm, like, not too, yeah, he’s a…
11 00:02:51.750 ⇒ 00:02:54.259 Uttam Kumaran: Just relax mode today. Like, every day.
12 00:02:54.620 ⇒ 00:02:55.250 Uttam Kumaran: Right.
13 00:02:57.340 ⇒ 00:02:59.030 Uttam Kumaran: Now he’s saying to people outside.
14 00:02:59.380 ⇒ 00:03:06.039 Hannah Wang: I have a dog next to me. I’m dog sitting, but she’s, like, just buried in…
15 00:03:06.040 ⇒ 00:03:07.030 Uttam Kumaran: Aww.
16 00:03:08.370 ⇒ 00:03:09.100 Hannah Wang: Yay!
17 00:03:09.100 ⇒ 00:03:10.609 Uttam Kumaran: That’s how I’m trying to be.
18 00:03:11.220 ⇒ 00:03:11.930 Hannah Wang: Yay!
19 00:03:13.440 ⇒ 00:03:16.430 Uttam Kumaran: Nice. Wait, is that the… that’s the usual dog, right?
20 00:03:17.260 ⇒ 00:03:18.180 Hannah Wang: I’m dog sitting.
21 00:03:18.180 ⇒ 00:03:18.839 Uttam Kumaran: That’s it.
22 00:03:19.320 ⇒ 00:03:22.249 Uttam Kumaran: I thought you were dog-setting her last time, yeah.
23 00:03:22.250 ⇒ 00:03:23.240 Hannah Wang: Yeah, yeah.
24 00:03:28.090 ⇒ 00:03:29.000 Uttam Kumaran: Nice.
25 00:04:44.000 ⇒ 00:04:46.299 Uttam Kumaran: Okay, should we get started?
26 00:04:50.820 ⇒ 00:04:53.970 Uttam Kumaran: Maybe Rico, do you… do you want me to,
27 00:04:54.090 ⇒ 00:04:58.249 Uttam Kumaran: the lead, or do you want to, or Henry, are you leading the first part?
28 00:05:00.730 ⇒ 00:05:02.079 Henry Zhao: Yes.
29 00:05:02.560 ⇒ 00:05:03.220 Henry Zhao: Okay.
30 00:05:04.230 ⇒ 00:05:06.279 Henry Zhao: I will share my screen.
31 00:05:06.530 ⇒ 00:05:08.140 Henry Zhao: One second, prepare it.
32 00:05:09.830 ⇒ 00:05:11.270 Henry Zhao: Where is it?
33 00:05:11.460 ⇒ 00:05:12.220 Henry Zhao: Cool.
34 00:05:18.960 ⇒ 00:05:22.920 Uttam Kumaran: Mustafa, I know you have a camera. Same with Casey, I know you have a camera.
35 00:05:24.890 ⇒ 00:05:26.889 Robert Tseng: I don’t think I’ve ever seen Mustafa.
36 00:05:28.810 ⇒ 00:05:32.310 Uttam Kumaran: I know, and it’s like, I don’t know why.
37 00:05:32.850 ⇒ 00:05:34.859 Uttam Kumaran: But, we need a reveal.
38 00:05:35.890 ⇒ 00:05:38.979 Uttam Kumaran: I’ve seen Mustafa. We talked.
39 00:05:43.790 ⇒ 00:05:45.859 Henry Zhao: How do I present on Google Flights?
40 00:05:46.500 ⇒ 00:05:49.169 Uttam Kumaran: Click on the right button next to the slideshow.
41 00:05:50.300 ⇒ 00:05:54.230 Uttam Kumaran: the arrow, and then click on Presentation Display Options.
42 00:05:56.090 ⇒ 00:05:57.850 Henry Zhao: Okay, that works.
43 00:05:57.850 ⇒ 00:05:58.480 Uttam Kumaran: Yeah.
44 00:05:59.000 ⇒ 00:06:09.100 Henry Zhao: Alright guys, so today we’re gonna start with an icebreaker, then we’ll go on to new team members, exec updates, and then the usual sales, operations, marketing, and then shoutouts.
45 00:06:10.090 ⇒ 00:06:10.630 Uttam Kumaran: Just a year.
46 00:06:10.630 ⇒ 00:06:12.970 Henry Zhao: Today’s icebreaker? Yeah.
47 00:06:13.200 ⇒ 00:06:21.539 Henry Zhao: So today’s icebreaker, we’re going to be doing team debate, so I’ve put in some, like, very heated topics for us to discuss as a team.
48 00:06:21.540 ⇒ 00:06:36.760 Henry Zhao: And vote in Mentimeter to see, kind of, how we feel about these, these topics, and also to get to know each other a little bit better, beyond the where are you from, and what are your hobbies, okay? So, just to practice… oh, what happened? One second.
49 00:06:37.000 ⇒ 00:06:42.230 Uttam Kumaran: Refresher. I deleted some, because I didn’t think… I thought there was, like, so many, but I’ll, you could do all of them, because I…
50 00:06:42.230 ⇒ 00:06:43.740 Henry Zhao: They’re fast, though, so…
51 00:06:43.930 ⇒ 00:06:46.730 Uttam Kumaran: Okay, yeah, just refresh your thing, yeah, it’s back.
52 00:06:52.330 ⇒ 00:06:55.209 Henry Zhao: Okay, so the first one is a practice round, right? So…
53 00:06:56.270 ⇒ 00:07:04.550 Henry Zhao: how do you guys present the… pronounce the following words? I just want to hear people tell me if it’s GIF, is it GIF? How do you guys pronounce it, and why?
54 00:07:06.960 ⇒ 00:07:07.910 Demilade Agboola: It’s Jiff.
55 00:07:10.260 ⇒ 00:07:11.040 Uttam Kumaran: if…
56 00:07:11.230 ⇒ 00:07:12.289 Demilade Agboola: It’s just.
57 00:07:12.290 ⇒ 00:07:15.390 Samuel Roberts: Chief is the peanut butter.
58 00:07:15.670 ⇒ 00:07:16.830 Samuel Roberts: No, it’s GIF.
59 00:07:19.990 ⇒ 00:07:20.639 Samuel Roberts: Definitely a gift.
60 00:07:23.350 ⇒ 00:07:28.230 Henry Zhao: Alright, so now I’d like you guys to go to this Mentimeter that I have,
61 00:07:30.390 ⇒ 00:07:33.219 Henry Zhao: So, this is the QR code, join that.
62 00:07:33.910 ⇒ 00:07:34.970 Samuel Roberts: Cool, okay.
63 00:07:40.620 ⇒ 00:07:45.980 Henry Zhao: And I’m gonna try to put this on the… Here’s the code.
64 00:07:50.340 ⇒ 00:07:52.170 Robert Tseng: I think the QR code works… I’m sure you didn’t get it.
65 00:07:54.090 ⇒ 00:07:54.780 Henry Zhao: Doesn’t?
66 00:07:56.010 ⇒ 00:07:56.629 Robert Tseng: It works, it works.
67 00:07:56.630 ⇒ 00:07:56.980 Samuel Roberts: Yeah, yeah.
68 00:07:56.980 ⇒ 00:07:57.869 Robert Tseng: Yeah, yeah, got it.
69 00:08:00.140 ⇒ 00:08:03.410 Robert Tseng: Oh, oh, I have very strong opinions about this.
70 00:08:05.970 ⇒ 00:08:07.190 Robert Tseng: I disagree.
71 00:08:07.190 ⇒ 00:08:09.650 Samuel Roberts: No opinion to a very strong opinion, yeah.
72 00:08:09.650 ⇒ 00:08:12.680 Uttam Kumaran: I couldn’t have less of an opinion. I…
73 00:08:12.680 ⇒ 00:08:13.529 Samuel Roberts: That’s how I used to be.
74 00:08:13.530 ⇒ 00:08:16.589 Uttam Kumaran: Not spend any amount of time thinking about this.
75 00:08:17.290 ⇒ 00:08:18.859 Robert Tseng: No, no, this matters so much.
76 00:08:18.860 ⇒ 00:08:19.809 Demilade Agboola: I think we care code works.
77 00:08:19.810 ⇒ 00:08:21.919 Uttam Kumaran: Oh my god.
78 00:08:27.410 ⇒ 00:08:29.860 Henry Zhao: Alright, so let’s hear some debates,
79 00:08:30.440 ⇒ 00:08:33.469 Henry Zhao: What do you guys think is the correct toilet paper orientation?
80 00:08:35.440 ⇒ 00:08:36.039 Robert Tseng: Definitely.
81 00:08:36.049 ⇒ 00:08:36.879 Henry Zhao: Albert, you have…
82 00:08:37.059 ⇒ 00:08:38.539 Joseph Good: Yeah, certainly, yeah.
83 00:08:38.539 ⇒ 00:08:39.199 Robert Tseng: Yeah.
84 00:08:39.740 ⇒ 00:08:41.209 Henry Zhao: I agree that it’s A.
85 00:08:41.729 ⇒ 00:08:47.110 Robert Tseng: You can’t do B. If you do B and you pull from B, like, you have to, like, pull more in order to get.
86 00:08:47.110 ⇒ 00:08:54.490 Uttam Kumaran: But how do you get tension to tear it? How do you get the tension? If you pull A down, you just keep pulling, there’s no way to, like, get tension.
87 00:08:55.180 ⇒ 00:08:56.940 Robert Tseng: What do you mean? There’s attention on being.
88 00:08:57.360 ⇒ 00:08:59.250 Robert Tseng: I have to do a little riffing, yeah.
89 00:09:00.680 ⇒ 00:09:02.810 Uttam Kumaran: So my point is, like, after you pull…
90 00:09:02.810 ⇒ 00:09:07.250 Robert Tseng: After you pull on B, when it goes back, it’ll just be, like, too much will be dangled.
91 00:09:07.250 ⇒ 00:09:07.760 Henry Zhao: Yeah.
92 00:09:07.760 ⇒ 00:09:14.040 Robert Tseng: And you risk getting to the point where it, like, kind of keeps going, and you, like, completely just lose it.
93 00:09:14.670 ⇒ 00:09:21.240 Uttam Kumaran: Are you, like, a Tom and Jerry film? Like, what is going on in your bathroom where, like, the…
94 00:09:21.760 ⇒ 00:09:24.970 Uttam Kumaran: Spoil here is going everywhere, you’re… it’s like, what?
95 00:09:24.970 ⇒ 00:09:28.129 Robert Tseng: It does if you, if you do, if you do B.
96 00:09:28.130 ⇒ 00:09:28.570 Samuel Roberts: I, I…
97 00:09:28.570 ⇒ 00:09:29.310 Robert Tseng: I will say.
98 00:09:29.310 ⇒ 00:09:36.700 Uttam Kumaran: You really just, like, you really just tear that, like, you really just… That’s funny.
99 00:09:36.700 ⇒ 00:09:37.730 Henry Zhao: Every single bite.
100 00:09:37.730 ⇒ 00:09:39.929 Robert Tseng: Hi, bro, but double ply doesn’t tear like that.
101 00:09:40.060 ⇒ 00:09:45.360 Uttam Kumaran: No, dude, I’m… I’m using… I’m using that Costco Charmin! We upgraded.
102 00:09:46.160 ⇒ 00:09:46.800 Robert Tseng: Okay, okay.
103 00:09:46.800 ⇒ 00:09:51.590 Uttam Kumaran: That’s… that’s after, yeah, that’s after this Brainforge money all came in.
104 00:09:52.810 ⇒ 00:09:53.280 Samuel Roberts: Hey.
105 00:09:53.280 ⇒ 00:09:55.750 Uttam Kumaran: AKA no money.
106 00:09:56.650 ⇒ 00:10:00.899 Henry Zhao: Alright, the next one’s a little bit less controversial. Do you brush your teeth before or after breakfast?
107 00:10:04.770 ⇒ 00:10:08.399 Henry Zhao: So for the previous one, everyone is in favor of A, except for, apparently, Uta.
108 00:10:10.100 ⇒ 00:10:12.380 Uttam Kumaran: I just… I just take, take the other side.
109 00:10:19.130 ⇒ 00:10:24.199 Uttam Kumaran: Yeah, what does breakfast mean, truly? I don’t even know… I don’t even know about that.
110 00:10:24.470 ⇒ 00:10:26.279 Demilade Agboola: I mean, if you don’t have breakfast, then…
111 00:10:26.740 ⇒ 00:10:29.710 Henry Zhao: So we’re pretty, pretty, pretty even anyway.
112 00:10:29.710 ⇒ 00:10:30.550 Robert Tseng: Someone just told me about…
113 00:10:30.550 ⇒ 00:10:31.329 Henry Zhao: other than before.
114 00:10:31.330 ⇒ 00:10:33.739 Robert Tseng: That’s a no-brainer. What the heck?
115 00:10:34.420 ⇒ 00:10:36.870 Uttam Kumaran: What do you do, Robert? Are you after breakfast?
116 00:10:37.250 ⇒ 00:10:38.459 Robert Tseng: Yeah, after breakfast.
117 00:10:39.010 ⇒ 00:10:40.440 Uttam Kumaran: Bro, what?
118 00:10:43.970 ⇒ 00:10:46.039 Robert Tseng: Dude, the whole point of brushing your teeth…
119 00:10:46.230 ⇒ 00:10:49.650 Robert Tseng: Is to remineralize your teeth after you eat.
120 00:10:51.300 ⇒ 00:10:52.740 Uttam Kumaran: I don’t know.
121 00:10:52.740 ⇒ 00:10:53.330 Demilade Agboola: You’re small.
122 00:10:53.330 ⇒ 00:10:54.390 Uttam Kumaran: I don’t know what that…
123 00:10:54.630 ⇒ 00:11:03.719 Uttam Kumaran: I don’t know what those big words mean, like, look, I just… all I know is I wake up, and I brush my teeth, and then I go do my day. I’m not, like, back…
124 00:11:05.060 ⇒ 00:11:07.780 Uttam Kumaran: Not like going back. It’s refreshing.
125 00:11:08.010 ⇒ 00:11:10.889 Henry Zhao: But then orange juice tastes terrible after you brush your teeth.
126 00:11:11.190 ⇒ 00:11:12.110 Robert Tseng: Yes.
127 00:11:12.110 ⇒ 00:11:12.620 Uttam Kumaran: Yeah.
128 00:11:12.620 ⇒ 00:11:13.830 Robert Tseng: Also, yeah.
129 00:11:14.240 ⇒ 00:11:15.330 Uttam Kumaran: No, but I’m not…
130 00:11:15.330 ⇒ 00:11:18.620 Demilade Agboola: No, I wouldn’t know, actually.
131 00:11:19.230 ⇒ 00:11:24.019 Demilade Agboola: I wouldn’t know that it tastes terrible, because I always just brush my teeth and then drink orange, and it feels good to me.
132 00:11:24.410 ⇒ 00:11:25.300 Demilade Agboola: See?
133 00:11:25.530 ⇒ 00:11:26.810 Uttam Kumaran: Yeah, you get a little bent down.
134 00:11:26.810 ⇒ 00:11:31.769 Robert Tseng: It’s spic Try drinking orange juice before you brush tomorrow. It might change your life.
135 00:11:33.330 ⇒ 00:11:39.169 Demilade Agboola: It just feels like you’re swallowing, like, the nasty saliva.
136 00:11:40.760 ⇒ 00:11:41.949 Demilade Agboola: That’s my issue.
137 00:11:43.080 ⇒ 00:11:44.080 Robert Tseng: Oh, I see, I see.
138 00:11:44.080 ⇒ 00:11:44.690 Henry Zhao: history.
139 00:11:46.990 ⇒ 00:11:54.800 Henry Zhao: Alright, the next question is, when you are on a plane, are you a window seat person or an aisle seat person? And I left middle in there, in case you just love to meet people.
140 00:11:54.800 ⇒ 00:11:55.360 Robert Tseng: Yep.
141 00:11:55.360 ⇒ 00:11:56.179 Gabriel Lam: If you’re a recycode.
142 00:11:56.180 ⇒ 00:11:58.200 Samuel Roberts: I gotta think. Yeah, it is chaotic.
143 00:11:59.070 ⇒ 00:11:59.750 Robert Tseng: Yeah.
144 00:12:02.840 ⇒ 00:12:08.700 Samuel Roberts: Now, this one, I understand the reasons people have other reasons, but I definitely am very strong about this one.
145 00:12:10.010 ⇒ 00:12:13.780 Henry Zhao: But I think it’s interesting, even in a small team like us, we have such differing opinions.
146 00:12:14.760 ⇒ 00:12:15.420 Samuel Roberts: True.
147 00:12:19.430 ⇒ 00:12:20.730 Henry Zhao: Oh, wow, two people’s more than…
148 00:12:20.730 ⇒ 00:12:22.039 Robert Tseng: Who’s the middle? Who’s the other middle person?
149 00:12:22.040 ⇒ 00:12:22.910 Joseph Good: trails.
150 00:12:22.910 ⇒ 00:12:24.849 Uttam Kumaran: I need to hear the middle people.
151 00:12:24.850 ⇒ 00:12:26.360 Samuel Roberts: Other middle person?
152 00:12:27.430 ⇒ 00:12:30.010 Robert Tseng: Yeah, yeah, who’s the other middle person? You can go first.
153 00:12:32.200 ⇒ 00:12:33.370 Uttam Kumaran: You’re on trial.
154 00:12:33.370 ⇒ 00:12:38.520 Mustafa Raja: Yeah, the other… The other one would be me, but I haven’t been on a plane, so yeah.
155 00:12:38.840 ⇒ 00:12:40.010 Mustafa Raja: I just selected minutes.
156 00:12:40.010 ⇒ 00:12:42.560 Uttam Kumaran: Oh, you have no idea.
157 00:12:42.560 ⇒ 00:12:43.880 Samuel Roberts: He doesn’t know better yet.
158 00:12:43.880 ⇒ 00:12:44.670 Uttam Kumaran: We’ll get you…
159 00:12:44.670 ⇒ 00:12:48.540 Joseph Good: When we do the meetup, we’ll get you the middle, you’ll under… you’ll realize.
160 00:12:49.100 ⇒ 00:12:49.570 Samuel Roberts: Yeah.
161 00:12:49.570 ⇒ 00:12:51.230 Uttam Kumaran: laundry experience.
162 00:12:51.230 ⇒ 00:12:51.850 Robert Tseng: Yeah.
163 00:12:52.550 ⇒ 00:12:53.700 Uttam Kumaran: Who else is middle?
164 00:12:53.950 ⇒ 00:12:55.200 Robert Tseng: I, I put middle.
165 00:12:55.860 ⇒ 00:12:57.449 Uttam Kumaran: Oh, okay, let’s hear it.
166 00:12:58.060 ⇒ 00:13:03.049 Robert Tseng: Well, my point is, once you’re married, it doesn’t matter what you prefer, you’ll always be in the middle.
167 00:13:03.340 ⇒ 00:13:04.130 Robert Tseng: Yeah.
168 00:13:04.130 ⇒ 00:13:07.089 Samuel Roberts: That’s, yeah, that’s very slicing.
169 00:13:10.630 ⇒ 00:13:15.340 Henry Zhao: Alright, the next one I don’t think is in Mentimeter, so are you guys Team iPhone or Team Android?
170 00:13:19.330 ⇒ 00:13:31.920 Demilade Agboola: I’m Team Apple, but Team Android, if that makes any sense. Like, if it was, like, the Apple ecosystem, I think the Apple ecosystem is as an ecosystem, yes. But if it was just simply down to the phones themselves.
171 00:13:32.150 ⇒ 00:13:34.849 Demilade Agboola: as they are, I think I’ll be Team Android.
172 00:13:35.560 ⇒ 00:13:47.739 Henry Zhao: Okay. And then this one I really want to know, what does your notifications count look more like? So when you open up your email, is it always clean, no one reads, gotta make sure I checked all my emails, or do you have too many notifications to count?
173 00:13:48.160 ⇒ 00:13:50.350 Henry Zhao: I know which one Robert is, I’ve seen his Zimba.
174 00:13:57.460 ⇒ 00:14:00.419 Uttam Kumaran: I don’t even know where my… where’s my mail? Hold on, let me see.
175 00:14:02.900 ⇒ 00:14:10.610 Uttam Kumaran: Yo, but guys, it’s so brutal, like, I’m… I actually… growing up, in my whole life, I’m, like, mostly inbox zero, but…
176 00:14:11.280 ⇒ 00:14:13.510 Uttam Kumaran: I just can’t, like, do this…
177 00:14:14.010 ⇒ 00:14:16.940 Uttam Kumaran: Business and figure it out, like, it’s just so hard.
178 00:14:17.990 ⇒ 00:14:21.280 Henry Zhao: How do you know what’s actually unread and what’s not if you just have so many notifications?
179 00:14:21.280 ⇒ 00:14:24.159 Uttam Kumaran: You don’t. That’s… you assume that you need to know that.
180 00:14:24.590 ⇒ 00:14:25.260 Samuel Roberts: It’s crazy.
181 00:14:25.260 ⇒ 00:14:29.159 Uttam Kumaran: I wake up, and I do my best, Henry, and that’s all I can do.
182 00:14:31.950 ⇒ 00:14:36.130 Uttam Kumaran: If something is important, we’ll get another email about it.
183 00:14:36.130 ⇒ 00:14:38.170 Samuel Roberts: That’s a… yeah, that’s a good, yeah.
184 00:14:40.280 ⇒ 00:14:46.460 Henry Zhao: Alright, so now we have a little, what’d you rather? Would you rather lose the ability to use copy and paste for the rest of your life.
185 00:14:46.840 ⇒ 00:14:47.170 Robert Tseng: No.
186 00:14:47.170 ⇒ 00:14:48.459 Henry Zhao: ability to type up two hands for the rest.
187 00:14:48.460 ⇒ 00:14:49.470 Samuel Roberts: Whoa…
188 00:14:49.470 ⇒ 00:14:50.930 Henry Zhao: You gotta do everything with one hand.
189 00:14:51.880 ⇒ 00:14:53.779 Henry Zhao: Or you gotta type everything out.
190 00:14:54.120 ⇒ 00:15:00.059 Uttam Kumaran: Wait, lose the ability to type with two hands for the rest of your… Oh, so you can only type one hand?
191 00:15:00.880 ⇒ 00:15:02.699 Samuel Roberts: So what would we rather lose?
192 00:15:03.990 ⇒ 00:15:06.739 Uttam Kumaran: Well, what about… hey, I’ve been using Whisper to talk.
193 00:15:06.740 ⇒ 00:15:07.130 Samuel Roberts: Yeah.
194 00:15:07.130 ⇒ 00:15:08.330 Uttam Kumaran: We’re still allowed to use that.
195 00:15:09.320 ⇒ 00:15:10.980 Henry Zhao: Yeah, but you just can’t copy and paste them.
196 00:15:10.980 ⇒ 00:15:11.900 Uttam Kumaran: Hi, Speech.
197 00:15:13.580 ⇒ 00:15:15.850 Uttam Kumaran: Anything, even, like, images and stuff.
198 00:15:16.670 ⇒ 00:15:18.030 Uttam Kumaran: I mean, how do we…
199 00:15:18.030 ⇒ 00:15:22.449 Samuel Roberts: Good point, it’s a good point. Yeah, exactly. Copy and paste, I think, is way more important.
200 00:15:23.930 ⇒ 00:15:27.690 Samuel Roberts: There are people out there that only have one hand and figure it out. I don’t think anyone’s figures it out.
201 00:15:27.920 ⇒ 00:15:28.600 Samuel Roberts: his.
202 00:15:29.760 ⇒ 00:15:30.480 Demilade Agboola: Exactly.
203 00:15:31.160 ⇒ 00:15:34.429 Henry Zhao: Okay. I think some people would rather lose copy and paste. Okay, cool.
204 00:15:35.460 ⇒ 00:15:43.180 Henry Zhao: Alright, and then the last question is, would you rather receive a one-time… this is not sponsored by UTAM, this is not happening, but would you rather receive a one-time $1 million.
205 00:15:43.180 ⇒ 00:15:44.060 Uttam Kumaran: I would love to.
206 00:15:44.060 ⇒ 00:15:54.389 Henry Zhao: or get 100K extra every year for the rest of your uninterrupted career. So think about it. You have to work 10 years to get that million, but you could work more than that. If you work less than that, you get less than the million.
207 00:15:56.110 ⇒ 00:16:00.509 Uttam Kumaran: Yeah, let’s… let’s hear… I want to hear from Gabe, Joe, what you guys think.
208 00:16:02.380 ⇒ 00:16:05.830 Demilade Agboola: Is it 10 million? Like, in anti, it’s 10 million, not 1 million, right?
209 00:16:05.830 ⇒ 00:16:07.210 Samuel Roberts: I’m here, yeah.
210 00:16:08.790 ⇒ 00:16:10.640 Demilade Agboola: Is this gonna retire if you want?
211 00:16:10.980 ⇒ 00:16:12.990 Henry Zhao: It’s supposed to be 1 million or 10 million?
212 00:16:12.990 ⇒ 00:16:14.970 Robert Tseng: Let’s say 1 million. 10 million is a little.
213 00:16:14.970 ⇒ 00:16:15.370 Uttam Kumaran: Oh, man.
214 00:16:15.370 ⇒ 00:16:16.000 Robert Tseng: Takes too long.
215 00:16:16.000 ⇒ 00:16:16.520 Henry Zhao: Oh, yes.
216 00:16:16.520 ⇒ 00:16:18.150 Robert Tseng: I think that one’s gonna be working that long.
217 00:16:18.610 ⇒ 00:16:21.079 Henry Zhao: No, 1 million, sorry. 1 million, not 10 million, sorry.
218 00:16:21.620 ⇒ 00:16:22.320 Demilade Agboola: Gotcha.
219 00:16:22.320 ⇒ 00:16:22.950 Henry Zhao: Good catch.
220 00:16:24.890 ⇒ 00:16:28.149 Joseph Good: Definitely 10 million, just put it in the market, compound over.
221 00:16:28.150 ⇒ 00:16:30.170 Gabriel Lam: Power of compound interest.
222 00:16:30.170 ⇒ 00:16:30.860 Joseph Good: value of money.
223 00:16:30.860 ⇒ 00:16:31.920 Samuel Roberts: Time value of money.
224 00:16:31.920 ⇒ 00:16:32.490 Demilade Agboola: Never mind.
225 00:16:34.460 ⇒ 00:16:35.889 Henry Zhao: Oh, interesting, I thought marketing…
226 00:16:35.890 ⇒ 00:16:37.240 Uttam Kumaran: on this call.
227 00:16:37.660 ⇒ 00:16:39.699 Uttam Kumaran: Too many smart people.
228 00:16:40.080 ⇒ 00:16:48.930 Demilade Agboola: So if the 100K a year requires you to actually work, and you never could tell what could happen. You could, you know, get diagnosed.
229 00:16:48.930 ⇒ 00:16:50.890 Samuel Roberts: I lose copy-paste, I’ll never be able to do that.
230 00:16:53.110 ⇒ 00:16:53.720 Henry Zhao: Cool.
231 00:16:53.820 ⇒ 00:16:59.289 Henry Zhao: Alright, then I’m just gonna do a quick lab share on, kind of, the attribution work we’ve been doing for Eden. This’ll be pretty quick.
232 00:16:59.420 ⇒ 00:17:05.009 Henry Zhao: But basically, we’ve used a tool called Segment to basically log all of the…
233 00:17:05.300 ⇒ 00:17:08.510 Henry Zhao: user visits to the Eden pages.
234 00:17:08.540 ⇒ 00:17:19.199 Henry Zhao: So, Eden, for those of you that are not familiar, has intake forms, where if you are interested in one of their medicines, you would fill out a form that has multiple questions that you get to from either an ad.
235 00:17:19.200 ⇒ 00:17:30.609 Henry Zhao: or an email, or from a organic social, things like that. And Eden wants to know, out of the people filling out the forms and then purchasing medication, where are they coming from?
236 00:17:30.610 ⇒ 00:17:41.259 Henry Zhao: Right? So, initially, we’ve only really looked at last touch attribution, which means who is the source of the customer that actually converted into a paying customer?
237 00:17:41.560 ⇒ 00:17:44.329 Henry Zhao: Right? So, you can think of, like, if you were buying a car.
238 00:17:44.730 ⇒ 00:18:02.800 Henry Zhao: You probably see an ad on TV for a Ford, or whatever, or a Toyota, then you do some research on Google, and then you go to the car dealership, and you can talk to a salesman, and then you finally buy the car. Who should get the credit for that sale? Should it be the car salesman? Should it be the Google search? Should it be the ad that you saw on TV?
239 00:18:03.520 ⇒ 00:18:06.829 Henry Zhao: That’s where I think attribution really,
240 00:18:06.990 ⇒ 00:18:16.859 Henry Zhao: It has that business impact to help marketers understand, out of all these touchpoints, what is actually contributing to the revenue, so that we can either invest more, invest less.
241 00:18:17.150 ⇒ 00:18:28.130 Henry Zhao: Or maybe change up our marketing strategy. So that’s what this diagram is showing, is, like, there’s a lot of different touchpoints that you go through, and segment basically logs all the touchpoints, at least, that we can track.
242 00:18:28.430 ⇒ 00:18:30.290 Henry Zhao: And that puts them together into a table.
243 00:18:30.930 ⇒ 00:18:35.700 Henry Zhao: So I was gonna do a live demo, but I think we’re kind of out of time, so I’m just gonna go straight.
244 00:18:35.700 ⇒ 00:18:39.170 Uttam Kumaran: No, you can go, go ahead, go ahead and, yeah, go ahead and do the demo, that’s fine.
245 00:18:40.030 ⇒ 00:18:56.380 Henry Zhao: So, if I go to, like, the intake on this link, you’re gonna see there’s a bunch of different, things in the URL, right? So you have the main URL, and then you have a question mark, and then you get all of the metadata, right? So you have coupon information, and then you have what we call UTMs.
246 00:18:56.380 ⇒ 00:19:08.029 Henry Zhao: For those that are not familiar. And then the main UTMs we look at are Source, Medium, and Campaign. So it tells us, are they from Facebook, or are they from Google, are they from a social campaign, or are they from a paid campaign?
247 00:19:08.180 ⇒ 00:19:10.919 Henry Zhao: And then what is the actual name of the campaign, right?
248 00:19:11.210 ⇒ 00:19:17.439 Henry Zhao: So all of this gets tracked in segment, and the way I like to debug it is I have this,
249 00:19:18.320 ⇒ 00:19:34.229 Henry Zhao: Chrome extension that basically looks at what’s getting tracked in segment, which really helps me debug and helps me make sure the tracking is properly working. And so you can see here that a page was loaded at whatever time, and then the UTM source is Henry Tests Order Completed, and then this is the promo code.
250 00:19:34.380 ⇒ 00:19:41.810 Henry Zhao: So that when the data comes into my database, I can look for this UTM source, I can look at the timestamp, and I can look for the promo code or whatever.
251 00:19:42.760 ⇒ 00:19:47.460 Henry Zhao: And then I’ll fill it out, and then other things will fire. So, as I fill things out.
252 00:19:47.850 ⇒ 00:19:49.589 Henry Zhao: This will continue to update.
253 00:19:50.070 ⇒ 00:20:08.769 Henry Zhao: Okay? And then, basically, eventually, I would write some code that basically looks at all of the UTMs, look at it over time, and kind of organize it into a table that says each customer and each order came from these UTMs, they came in on these dates, so what was the first touch that they came from, and what was the last touch?
254 00:20:08.930 ⇒ 00:20:12.809 Henry Zhao: And then we can do a bunch of different analysis, so that’s what I want to show here.
255 00:20:13.660 ⇒ 00:20:16.730 Henry Zhao: There’s a bunch of different models that you can look at for attribution.
256 00:20:17.720 ⇒ 00:20:23.260 Henry Zhao: So… Again, I don’t know how to do this.
257 00:20:24.450 ⇒ 00:20:25.260 Henry Zhao: Okay.
258 00:20:25.490 ⇒ 00:20:38.059 Henry Zhao: So we have last click, which is like, let’s attribute all of the credit to whatever converted you, right? So in the car example, the salesman gets all the credit. I don’t care where this person found out about Toyota, the salesperson made the sale, he gets the credit.
259 00:20:38.520 ⇒ 00:20:52.140 Henry Zhao: First click is, who was the first person that made us aware? So this is more awareness-type things, right? So, in the car example, the TV ad would get all the credit, right? Because the person would not have even known about Toyota if he hadn’t seen that TV ad. He wouldn’t have gone to the dealership.
260 00:20:52.290 ⇒ 00:20:55.300 Henry Zhao: Linear just gives every touchpoint equal credit.
261 00:20:55.420 ⇒ 00:21:03.319 Henry Zhao: Time decay says, I want to give more credit to people that converted, or more credit to people that gave awareness, but then decrease that credit over time.
262 00:21:03.780 ⇒ 00:21:11.200 Henry Zhao: U-shaped says, let me give most credit to the first and last, so person that gave awareness, person that converted, and then the ones in the middle kind of…
263 00:21:11.660 ⇒ 00:21:16.490 Henry Zhao: like, kind of complemented that marketing value, so we’ll give them a little bit of credit.
264 00:21:16.750 ⇒ 00:21:35.190 Henry Zhao: And then you have some other things, like W-shaped, and then data-driven. Data-driven is usually used in more complex sales funnels, so things where you’re buying a more complex product, where you need to generate awareness, then you need to nurture those leads, then you need to give them demos, then you need to send them mail, like…
265 00:21:35.190 ⇒ 00:21:39.260 Henry Zhao: You gotta figure out what is the weight that you want to give to each touchpoint or each channel.
266 00:21:39.540 ⇒ 00:21:50.229 Henry Zhao: So, this is why attribution is a kind of a long, tedious project, and why companies are willing to pay a lot of money to have an attribution model and data in place.
267 00:21:51.900 ⇒ 00:21:56.529 Henry Zhao: And Hannah, you’re gonna be doing a case study on this soon, so… any questions, just let me know.
268 00:21:59.740 ⇒ 00:22:03.659 Henry Zhao: Alright, any questions on the lab share, before I turn it over to Uten?
269 00:22:05.210 ⇒ 00:22:11.669 Amber Lin: Why did they pick First Touch? I know Eden wants First Touch, like, why do they pick that over the other things?
270 00:22:12.060 ⇒ 00:22:29.269 Henry Zhao: So Stuart, and Cutter want First Touch because they want to know what’s actually generating the awareness. And Facebook is more meant to generate awareness, so if Stuart wants to maximize his Facebook campaigns, he wants to maximize for people that got awareness from Facebook, and not necessarily who converted.
271 00:22:31.420 ⇒ 00:22:48.210 Henry Zhao: Because let’s say that Facebook is not converting a lot of people, because it’s not likely that you’re gonna see one GLP1 ad on Facebook, and you’re gonna be like, alright, let me buy it. You’re gonna probably shop around first, so Facebook is not likely to convert a lot, and so if you optimize on conversions, it’s gonna be really expensive, because not a lot of people are converting from Facebook.
272 00:22:48.780 ⇒ 00:22:53.509 Henry Zhao: So you’d rather optimize on awareness, and then it’ll be cheaper, and you can have more volume there.
273 00:22:54.850 ⇒ 00:22:56.069 Henry Zhao: But that’s a great question.
274 00:23:04.110 ⇒ 00:23:06.589 Amber Lin: Oh, Tom, you’re muted if you were talking.
275 00:23:07.410 ⇒ 00:23:09.190 Uttam Kumaran: I had another question in the chat.
276 00:23:10.190 ⇒ 00:23:12.620 Henry Zhao: Are you talking about the marketing mix model?
277 00:23:13.670 ⇒ 00:23:14.739 Uttam Kumaran: Yeah, what is that?
278 00:23:15.980 ⇒ 00:23:18.969 Henry Zhao: Where did you see that? It’s basically just, like, the mix of their marketing channels.
279 00:23:18.970 ⇒ 00:23:22.870 Uttam Kumaran: Someone said it in a meeting earlier, and I was like, yeah, totally, we could do that.
280 00:23:25.610 ⇒ 00:23:26.790 Henry Zhao: I think they’re exploring.
281 00:23:26.790 ⇒ 00:23:31.370 Uttam Kumaran: Can you just explain it to me in basic terms? I think I understand what it is, but can you just explain to me?
282 00:23:32.660 ⇒ 00:23:36.620 Henry Zhao: Does somebody else want to explain it? I don’t really know how to explain it in basic terms. It’s just like the…
283 00:23:36.950 ⇒ 00:23:41.360 Henry Zhao: Where do you, like, delegate your marketing dollars out of all the marketing platforms you have?
284 00:23:42.870 ⇒ 00:23:46.090 Uttam Kumaran: So it’s more about, like, spend allocation.
285 00:23:47.060 ⇒ 00:23:49.430 Henry Zhao: I think so. Somebody wanna correct me if I’m wrong?
286 00:23:50.330 ⇒ 00:23:58.999 Robert Tseng: Yeah, it’s about spend allegation, but it’s not at the order level. You’re aggregating spend by channel, so you can’t actually
287 00:24:00.570 ⇒ 00:24:20.099 Robert Tseng: you can’t drill down at the user… at, like, the customer or order level. So, for a while, it was just kind of a way to, look at how, like, different channels, like, if you increase spend on Facebook, does it eat into, like, Google or whatever? So it kind of captures some, like.
288 00:24:20.610 ⇒ 00:24:35.180 Robert Tseng: level of the cannibalization across channels, but, I think people realize that it’s also just a pretty limited view, because you’re not really getting very specific at an audience. It’s always just going to be at a channel level.
289 00:24:37.850 ⇒ 00:24:41.139 Uttam Kumaran: And is that, like, that’s not what Northbeam and Triple Whale do?
290 00:24:41.850 ⇒ 00:24:45.549 Robert Tseng: That is what North Bean does. That was, like, their claim to fame.
291 00:24:46.020 ⇒ 00:24:48.270 Robert Tseng: Yeah.
292 00:24:48.640 ⇒ 00:24:55.500 Robert Tseng: But, I guess… I think MMM has kind of fallen out of, like.
293 00:24:56.720 ⇒ 00:25:08.480 Robert Tseng: practice lately, like, it’s not… like, maybe, like, 3, 4 years ago, it was, like, all the hype, but now personalization is all the hype, so people aren’t really looking at channel versus channel comparisons as closely.
294 00:25:09.790 ⇒ 00:25:11.240 Uttam Kumaran: Oh, okay, interesting.
295 00:25:11.980 ⇒ 00:25:12.600 Robert Tseng: Yeah.
296 00:25:15.410 ⇒ 00:25:16.130 Uttam Kumaran: Okay.
297 00:25:16.640 ⇒ 00:25:17.200 Uttam Kumaran: Cool.
298 00:25:17.200 ⇒ 00:25:18.050 Henry Zhao: Cool, I’ll turn it over to.
299 00:25:18.050 ⇒ 00:25:21.849 Uttam Kumaran: Yeah, I can take it from here.
300 00:25:24.230 ⇒ 00:25:28.720 Uttam Kumaran: Yeah, I guess I’ll just… we just have a couple things,
301 00:25:42.900 ⇒ 00:25:50.870 Uttam Kumaran: Cool. So yeah, I mean, I think, kind of like, well, it feels like it’s been… I mean, it’s been 2 weeks since we chatted, but I feel like it’s been…
302 00:25:51.440 ⇒ 00:26:06.240 Uttam Kumaran: there’s a lot that’s, like, that’s changing. I mean, one is, like, I think we… even in just the last two weeks, we had 2 more clients start. We have a few clients that are at the finish line. Oh, I didn’t even put CES on this list. And so for the folks that are new.
303 00:26:06.290 ⇒ 00:26:14.780 Uttam Kumaran: you know, to give framing, like, I think we, like, maybe every two weeks or a month have, like, one or two new clients, and…
304 00:26:14.860 ⇒ 00:26:20.349 Uttam Kumaran: In the last 2 weeks, Where, like, it seems like it’s almost maybe 4 or 5.
305 00:26:20.450 ⇒ 00:26:36.740 Uttam Kumaran: So things are, like, accelerating really fast. To give also a lot of context, because I remember when we were doing, like, one of these every month or two, like, just one deal every month or two, let alone,
306 00:26:36.920 ⇒ 00:26:41.699 Uttam Kumaran: 4 or 5 in a week, and several that are, like, still in progress, so…
307 00:26:41.890 ⇒ 00:26:47.779 Uttam Kumaran: I think it’s just, like, a huge shout out to, like, everybody on sales. Like, this isn’t…
308 00:26:47.960 ⇒ 00:27:03.580 Uttam Kumaran: just continuing to, like, put muscle into stuff, which, like, we always do, we always just, like, try our best, but everything from how we’re positioning ourselves, especially the case study, especially the decks, the speed at which we can produce those has, like, helped.
309 00:27:03.690 ⇒ 00:27:09.680 Uttam Kumaran: I would say… Where there is still a gap is a lot of these kind of came in
310 00:27:09.820 ⇒ 00:27:12.250 Uttam Kumaran: Sort of…
311 00:27:12.520 ⇒ 00:27:24.499 Uttam Kumaran: without too much, like, forcefulness, meaning it’s not… this may not continue to happen. And so I think there’s a huge push that I know, Robert, you’re leading on, like, top of funnel.
312 00:27:24.630 ⇒ 00:27:31.049 Uttam Kumaran: work that, I think, you know, Joe, you’re involved in, and a bunch of the folks, that still needs to happen.
313 00:27:32.970 ⇒ 00:27:42.229 Uttam Kumaran: And I think that’s where we kind of control our own destiny. Like, a lot of these just came in just because we’ve been playing in the market for a while, which is great, but it’s… they’re, like…
314 00:27:42.230 ⇒ 00:27:54.300 Uttam Kumaran: I don’t… I don’t know. I think we want to kind of make sure that we know where we’re going, so there’s a lot to be done there, and get smarter in how we’re achieving. I think the other piece, is also just having, like, more well-defined
315 00:27:54.300 ⇒ 00:28:08.450 Uttam Kumaran: you know, offers. We’re going into each of these clients and starting to offer more similar things, like a discovery sprint or omni-channel analysis, and I think that’s something that also
316 00:28:08.820 ⇒ 00:28:12.469 Uttam Kumaran: For the sales folks on this team to continue to, like.
317 00:28:12.660 ⇒ 00:28:26.429 Uttam Kumaran: refine is how… when we get an opportunity, how do we match them to an offer? Because the faster that we can match folks to an offer, the faster that I can work with engineering to make sure that we deliver that cheaper and faster.
318 00:28:26.570 ⇒ 00:28:34.799 Uttam Kumaran: The more, like, bespoke work we have, the harder it is to, like, get any benefits of scale. And so that’s something that, like.
319 00:28:34.900 ⇒ 00:28:37.939 Uttam Kumaran: I think we’ve made some good progress on, but still, I feel like…
320 00:28:38.230 ⇒ 00:28:42.119 Uttam Kumaran: Robert is really the only one, like, kind of really driving that, and so…
321 00:28:42.470 ⇒ 00:28:46.550 Uttam Kumaran: that’s where I would like us to sort of start to get, you know, a lot more…
322 00:28:47.040 ⇒ 00:28:51.519 Uttam Kumaran: lean on. I’ve also provided a lot of feedback on, on decks.
323 00:28:51.550 ⇒ 00:29:02.250 Uttam Kumaran: we present all of our materials very, very often, and so I think the speed at which we can make updates to those is also very important. Right now, we have this sort of AI and data
324 00:29:02.250 ⇒ 00:29:15.640 Uttam Kumaran: we’re gonna go one step deeper on having decks, most likely decks focused on offers, and decks focused on industries. And so it’s just gonna get deeper. So I think for Hannah, for you, and, like, design, is to think about, like.
325 00:29:15.700 ⇒ 00:29:20.090 Uttam Kumaran: Also, systems and, like, what support you need to, like, maintain that.
326 00:29:22.200 ⇒ 00:29:36.179 Uttam Kumaran: there are… there’s certainly opportunities where we want to go into a client meeting and be sure that we’re talking to them and to their problem, versus, like, broadly. But additionally, one thing that we’re learning as we’re… we’re trying to continue to sell bigger deals is
327 00:29:36.680 ⇒ 00:29:52.740 Uttam Kumaran: we just have to change the way we’re pitching, you know, to bigger customers. They’re less focused on, like, pace and immediate outcome, and more focused on thoroughness and having a long-term partner. And so, I think also, Robert, we probably need to have
328 00:29:53.020 ⇒ 00:29:57.089 Uttam Kumaran: SMB and Enterprise SKUs on, like, DEX, or at least, like.
329 00:29:57.290 ⇒ 00:30:14.840 Uttam Kumaran: some way to, like, supplement those slides. I mean, in an ideal world, we have slides for every deal, right? Now, I think we’re reusing a lot, so that’s sort of what I want to get to, is, like, before going into a sales meeting, we have a deck prepared that is matching exactly, like, what the
330 00:30:15.120 ⇒ 00:30:22.420 Uttam Kumaran: what our go-to-market is for them. So, just, like, learnings from That.
331 00:30:22.640 ⇒ 00:30:31.790 Uttam Kumaran: I think we’re getting better on operations. I think Rico has been leading a lot of that. Lauren is coming in to lead some of that. I’ll kind of share a little bit of updates from them.
332 00:30:31.790 ⇒ 00:30:44.150 Uttam Kumaran: It’s getting smoother, I think still we just have a lot to do, so we’re… we’re gonna be looking for more support on the operations side. I mean, this is… operations is, like, the backbone of a lot of different things that happen here, like…
333 00:30:44.270 ⇒ 00:30:49.530 Uttam Kumaran: There’s sales operations, finance, legal, client,
334 00:30:50.180 ⇒ 00:30:53.959 Uttam Kumaran: So it’s sort of like something that I think Rico and I have just been…
335 00:30:54.210 ⇒ 00:31:07.719 Uttam Kumaran: leading for a while, and we’re gonna try to bring in some more support. And then, yeah, I don’t know if everybody has met Joe, but maybe, Joe, if you want to give a quick introduction to everybody, and then also would love to hear about, like.
336 00:31:07.800 ⇒ 00:31:18.200 Uttam Kumaran: what you’ve been working on, and yeah, if you can shout out anything that, you know, I think for the broader team may not, again, have a lot of visibility into the work that’s going in on the sales side, so I would love
337 00:31:18.450 ⇒ 00:31:19.610 Uttam Kumaran: Do you want to share?
338 00:31:20.250 ⇒ 00:31:26.979 Joseph Good: Yeah, absolutely. Hey everyone, just joined Brainforge a week or two ago. I’ve been working most closely with
339 00:31:26.990 ⇒ 00:31:46.450 Joseph Good: Robert on all things GTM, and kind of like what Uten was saying, building out, top of funnel. My experience is mostly in, like, RevOps and GTM engineering. It came from AirOps, which is a Series A company. Actually just raised their B today, I saw on LinkedIn, which was… which was cool, but they’re in kind of, like, the AI SEO space.
340 00:31:46.450 ⇒ 00:31:48.920 Joseph Good: We’re, yeah, working with Robert on…
341 00:31:49.050 ⇒ 00:32:06.819 Joseph Good: driving more top of funnel, and then also just getting a little bit more visibility into, our funnel right now, and kind of conversion rates, and how folks are progressing down funnel, any leakage that’s happening, and then ways that we can tighten that up to support all the awesome work that Robert and
342 00:32:06.820 ⇒ 00:32:13.200 Joseph Good: Ryan and Hannah and the rest of the team have been putting in so far. And then I would say the second piece of that is just…
343 00:32:13.330 ⇒ 00:32:30.000 Joseph Good: figuring out how to talk about our AI product offering, in a more accessible way for folks. There’s a lot of noise happening in the market right now with AI transformation and AI engineering, there’s just a lot of stuff going on. I think folks are
344 00:32:30.060 ⇒ 00:32:47.259 Joseph Good: reaching a saturation point of, like, okay, who’s kind of the source of truth here? So, figuring out where we can position ourselves as a leader in that space, and really, like, package the team, the awesome work that the AI team is doing in a way that’s digestible to clients, and, yeah.
345 00:32:47.360 ⇒ 00:32:50.449 Joseph Good: Clear… clear value prop for those folks as well, so…
346 00:32:51.340 ⇒ 00:33:10.770 Uttam Kumaran: Yeah, give me a sense of, like, you’ve been here two weeks, like, would love to hear, sort of your observance of, like, what kind of work we do, and how that’s, like, how maybe that… what you knew before coming in versus now, and, like, yeah, just, like, curious. I’m always curious on, like, when new people join.
347 00:33:11.250 ⇒ 00:33:13.580 Uttam Kumaran: What they see,
348 00:33:13.710 ⇒ 00:33:19.929 Uttam Kumaran: It is we do, or the clients, and, like, how you guys are framing the narrative around it, so…
349 00:33:21.120 ⇒ 00:33:28.350 Joseph Good: Yeah, I think I was… I’m new to, kind of, the data world a little bit, so it was helpful to get up to speed,
350 00:33:28.350 ⇒ 00:33:42.020 Joseph Good: with sort of the data infra, like, V1 stack, it seems like we’re setting up for Hedra and a few other folks. I’m still learning, like, how to speak to the pain points that folks are experiencing in that space, but it seems like that’s
351 00:33:42.070 ⇒ 00:33:46.079 Joseph Good: kind of our bread and butter at this point. I…
352 00:33:46.440 ⇒ 00:33:57.770 Joseph Good: from my understanding, we’re doing AI work for, like, ABC, pool parts, and then Interlude, and I think those are kind of the main ones right now. I…
353 00:33:58.010 ⇒ 00:34:03.579 Joseph Good: I think, like, from the website prior to joining the team,
354 00:34:04.100 ⇒ 00:34:08.050 Joseph Good: I know there was the workshops, I kind of poked around a little bit. I think…
355 00:34:09.110 ⇒ 00:34:15.280 Joseph Good: it was a little… it could be, like, flushed out more, probably, like, what specifically the AI product offering is.
356 00:34:15.280 ⇒ 00:34:17.460 Uttam Kumaran: 1 million percent, yeah.
357 00:34:17.460 ⇒ 00:34:19.830 Joseph Good: Yeah, which is probably… The website is, like, me…
358 00:34:19.830 ⇒ 00:34:29.770 Uttam Kumaran: Hannah’s, like, stepchild, so we, like, tried. We did a really good job. I think it just is so much that we’re doing that we even, I think.
359 00:34:29.980 ⇒ 00:34:42.099 Uttam Kumaran: we still are always trying to figure out, like, finish out services, and I mean, it’s all blocked by me for the most part, so… but yeah, you’re… I think it’s… I’m glad that you’re seeing it now from the outside and inside, you know?
360 00:34:42.690 ⇒ 00:34:43.310 Uttam Kumaran: Yeah.
361 00:34:43.310 ⇒ 00:34:48.300 Joseph Good: Yeah, and I think, like, the… the data stuff is probably Ring’s…
362 00:34:48.530 ⇒ 00:34:55.390 Joseph Good: Or it’s very clear for folks, like, okay, it’s what the pain point is, what the solution is, but learning to speak a little bit more about
363 00:34:55.800 ⇒ 00:35:07.109 Joseph Good: Probably there’s an education piece of the AI side of things. There’s probably folks who are just like, okay, let me just get everyone using ChatGPT in my business, and there’s sort of, like, an enablement piece, and then there’s probably some…
364 00:35:07.250 ⇒ 00:35:09.680 Joseph Good: Another component of that, which is, like.
365 00:35:09.830 ⇒ 00:35:22.320 Joseph Good: folks who are AI, or they’ve tasted AI a little bit, and now they want to, like, build a more robust solution, so learning how to, like, speak about those two different things. But that’s… yeah, that’s what I’ll be continuing to work on and whatnot, so…
366 00:35:24.030 ⇒ 00:35:27.799 Uttam Kumaran: Cool, yeah, I mean, everything is sort of up for grabs and up for change, like.
367 00:35:27.920 ⇒ 00:35:30.570 Uttam Kumaran: I would say most of what’s been done has been done
368 00:35:30.930 ⇒ 00:35:37.239 Uttam Kumaran: with a lot of focus, but, like, with a lot of speed, and so I’m really happy that we have folks like you joining that are, like.
369 00:35:37.530 ⇒ 00:35:40.640 Uttam Kumaran: Really thinking hard about it, and seeing it from, like.
370 00:35:40.870 ⇒ 00:35:54.159 Uttam Kumaran: other experiences. But again, a lot of what the objections that we’re handling and things like that are all in our emails and in the Zoom meetings and the platform, so I think what’s rare for a company
371 00:35:54.470 ⇒ 00:36:04.159 Uttam Kumaran: like ours is to just, like, have all that data available. So one thing I’m hopeful for is that we start to, yeah, we build out, really, core flows that are tied to objections.
372 00:36:04.210 ⇒ 00:36:24.090 Uttam Kumaran: And, I mean, what you’re seeing now is that regardless of the fact that we didn’t get all these things right, people are buying from us, which is a great thing, right? Which means, even though we’re not, like, wrapping exactly what it is we do and why, people still are getting it, so it’s only gonna go up from here.
373 00:36:24.300 ⇒ 00:36:27.709 Uttam Kumaran: You know? So, great. And then, yeah, I don’t know,
374 00:36:27.980 ⇒ 00:36:30.439 Uttam Kumaran: I don’t know, Gabe, I would love to…
375 00:36:30.760 ⇒ 00:36:41.850 Uttam Kumaran: I don’t know if you’ve talked to… if you’ve said hi to everyone, but would love for you to give a little intro, and then maybe I can hand it to you to sort of,
376 00:36:42.080 ⇒ 00:36:55.720 Uttam Kumaran: maybe give a little bit of the, goals for the AI team. I think maybe I’ll just set the stage a little bit. For everyone that’s interacted with our AI team, one, it’s like, I just think it’s, like, amazing that we have
377 00:36:55.980 ⇒ 00:37:02.619 Uttam Kumaran: even resources internally to be able to dedicate towards making our processes better. I think… I… I would…
378 00:37:02.770 ⇒ 00:37:07.369 Uttam Kumaran: I would be surprised if anyone who joined Brainforge thought that they would
379 00:37:07.630 ⇒ 00:37:21.929 Uttam Kumaran: be this AI-enabled, and, like, I’m particularly thinking about the folks on sales and design, on… and I’ve been really proud to see that those folks have been adopting AI the most. And one thing that we’ve always looked to do with the AI team
380 00:37:21.930 ⇒ 00:37:28.619 Uttam Kumaran: You know, it’s… even when it was just, me and one person, is to just try to enable our company to
381 00:37:28.620 ⇒ 00:37:41.560 Uttam Kumaran: to just, you know, spend more time with clients, and spend more time with prospects, and less time on things that can be automated. And I think it’s rare for a company our size to be doing this much in AI. In fact, I don’t…
382 00:37:41.590 ⇒ 00:37:43.250 Uttam Kumaran: I continued to…
383 00:37:43.310 ⇒ 00:37:59.409 Uttam Kumaran: get told that, like, it’s actually not the… not… it’s not the case in… in any company, like, that’s happening this way. And we heard that feedback from a lot of people, and I know folks that have been in engineering teams, you guys also know that it’s… this is kind of a rare thing.
384 00:37:59.530 ⇒ 00:38:16.430 Uttam Kumaran: But a lot of this, like, we just need way more organization on. I think we’ve done a good job at setting the platform, but I’m really glad Gabe’s joining, to kind of start to push out features and sets of features in a more organized manner, in more of a product development sort of way.
385 00:38:16.450 ⇒ 00:38:25.250 Uttam Kumaran: sprint fashion. And so I’ve given him and the team very tough timelines to hit on really,
386 00:38:25.360 ⇒ 00:38:41.500 Uttam Kumaran: cohesive sets of features that directly attack, our OKRs, attack in a good way, meaning trying to get there faster, or get there at all. And they’ve really, I think, like, nailed it in the last, like, two, three weeks that we’ve been doing this. So maybe, Gabe, I can hand it to you to kind of
387 00:38:41.660 ⇒ 00:38:45.090 Uttam Kumaran: would love for you to share a little reflection, also, like, Joe, on…
388 00:38:45.310 ⇒ 00:38:52.219 Uttam Kumaran: What you thought before, what you kind of think now, and then, feel free to… feel free to take the demo on.
389 00:38:52.780 ⇒ 00:39:01.350 Gabriel Lam: Yeah, thank you. Great to meet everyone. I’m Gabe. I… I come from a very different background. I’m trained as an architect.
390 00:39:01.530 ⇒ 00:39:07.989 Gabriel Lam: And so, I have a very different set of, domain knowledge, but during that time, I came across
391 00:39:08.360 ⇒ 00:39:11.369 Gabriel Lam: how AI and how…
392 00:39:11.660 ⇒ 00:39:28.470 Gabriel Lam: SaaS products are really trying to be implemented in that space, and so I come from a very large, slow, sort of rigid enterprise background, so the velocity here is very, very different, and I think it’s been a big breath of fresh air. I also come in with a sort of, like, large-scale
393 00:39:28.520 ⇒ 00:39:32.380 Gabriel Lam: Like, these are all the different checkpoints that typically are hit,
394 00:39:32.680 ⇒ 00:39:40.009 Gabriel Lam: And so, I think joining a team like this has been awesome just to see, yeah, how strategically we’re able to not only
395 00:39:40.180 ⇒ 00:39:53.280 Gabriel Lam: accelerate our own productivity, but also the way in which clients are able to see, like, hey, we’re seeing these, results, and we’re able to see these offerings. I think that’s, like Joe was saying, definitely something that can be…
396 00:39:53.610 ⇒ 00:39:58.510 Gabriel Lam: More… streamlined,
397 00:39:58.750 ⇒ 00:40:17.319 Gabriel Lam: I think I had a similar reflection or experience looking at the website, and I’m like, oh, like, what exactly is it that they’re doing, and how does that… how does someone in the market, or how does a potential lead actually read those offerings and be like, I see, you know, these are the things that we can take along versus,
398 00:40:18.200 ⇒ 00:40:20.800 Gabriel Lam: Like, what exactly it is that our pain points are.
399 00:40:21.070 ⇒ 00:40:25.779 Gabriel Lam: So I think I saw that the internal AI team is also sort of a testing ground.
400 00:40:26.160 ⇒ 00:40:28.039 Gabriel Lam: As a way for us to…
401 00:40:28.490 ⇒ 00:40:34.779 Gabriel Lam: use the processes that we’ve built out ourselves as a way to demonstrate and become case studies again. For…
402 00:40:35.140 ⇒ 00:40:50.119 Gabriel Lam: Yeah, for… to nurture our existing leads or to get new ones. And on the topic of case studies, I can dive into the demo. So, we have been working pretty quickly. I’m used to very long sprint times, and so when Utong’s like, yeah, let’s try to get these things out in a week.
403 00:40:50.260 ⇒ 00:41:06.329 Gabriel Lam: And to see it be accomplished has been… has been cool. So to preface, one of the things that has been a pain point at Brainforge has been just the backlog of case study materials. As I’m sure everyone knows, like, you finish a project.
404 00:41:06.590 ⇒ 00:41:11.509 Gabriel Lam: and any of these success stories take some time to then…
405 00:41:12.090 ⇒ 00:41:20.790 Gabriel Lam: be distributed out to either existing or new leads, and in talking with the marketing team, especially Hannah, a big pain point has been the interviewing part.
406 00:41:20.960 ⇒ 00:41:26.160 Gabriel Lam: And so I’m sure you guys have, you know, scheduled interviews and had to go through, you know.
407 00:41:26.450 ⇒ 00:41:39.359 Gabriel Lam: these half an hour, calls, as well as the schedule and find time, in a way that we wanted to do that was to really allow that to happen asynchronously. Not everyone works in U.S. time,
408 00:41:39.580 ⇒ 00:41:46.590 Gabriel Lam: And so, yeah, this is something that we hope that people will begin using. It’ll become something a little more commonplace, and people are a little more…
409 00:41:46.750 ⇒ 00:41:49.160 Gabriel Lam: Used to the idea of
410 00:41:49.540 ⇒ 00:42:01.099 Gabriel Lam: talking to an AI agent to get these things out, and that the result that we get, the generated copy, would just be something that would augment what the marketing and design team would be able to do.
411 00:42:01.350 ⇒ 00:42:03.660 Gabriel Lam: So I can quickly share screen.
412 00:42:03.980 ⇒ 00:42:05.670 Gabriel Lam: If I can find it.
413 00:42:07.070 ⇒ 00:42:07.780 Gabriel Lam: Hmm…
414 00:42:15.600 ⇒ 00:42:19.270 Gabriel Lam: So, this… is…
415 00:42:20.000 ⇒ 00:42:36.920 Gabriel Lam: a dashboard of all the existing case studies that are open. There’s a lot of tests, because we are deep in the midst of testing to make sure all the features are running. This is the screen that the marketing team would see, as you’re able to filter through clients.
416 00:42:37.090 ⇒ 00:42:38.110 Gabriel Lam: projects.
417 00:42:38.360 ⇒ 00:42:43.830 Gabriel Lam: And statuses. And so this is… there… we have updates pushed.
418 00:42:43.970 ⇒ 00:42:46.899 Gabriel Lam: But… We’re hoping that…
419 00:42:47.600 ⇒ 00:42:53.980 Gabriel Lam: Once you need to figure out what projects you’re doing, what clients you’re doing, that will be quickly sorted. For the rest of us.
420 00:42:54.120 ⇒ 00:42:55.900 Gabriel Lam: Sam has thankfully
421 00:42:57.190 ⇒ 00:43:04.980 Gabriel Lam: is about to push out a feature to get Slack notifications, so you’re gonna receive a Slack notification when one of these are built.
422 00:43:05.230 ⇒ 00:43:08.529 Gabriel Lam: And you’re able to open a link. And so a link would look like
423 00:43:08.880 ⇒ 00:43:12.659 Gabriel Lam: So, let me… sorry, let me step back. For the marketing team,
424 00:43:15.730 ⇒ 00:43:19.709 Gabriel Lam: You might do something like this, where we’re gonna do the client, this is a…
425 00:43:19.950 ⇒ 00:43:24.599 Gabriel Lam: You can write a description about it, and this will help the agent
426 00:43:26.690 ⇒ 00:43:33.359 Gabriel Lam: get some context on what needs to happen, or what the… what the interview was actually about. I’m just gonna put my name, and…
427 00:43:34.130 ⇒ 00:43:35.320 Gabriel Lam: we’ve also…
428 00:43:35.430 ⇒ 00:43:46.190 Gabriel Lam: This is also undergoing some updates that we’re gonna push out before the end of the day, as a way to distinguish between a draft, which you might be working on for now, versus awaiting interviews.
429 00:43:46.510 ⇒ 00:43:58.809 Gabriel Lam: Once we create an interview, the link, a Slack notification will be sent out to you, in which you will be able to click from there and open the interview. You’re gonna see a page like this, and…
430 00:43:58.980 ⇒ 00:44:02.280 Gabriel Lam: All you have to do as an interviewee is
431 00:44:03.770 ⇒ 00:44:06.759 Gabriel Lam: Click Start Interview. So if you guys can hear what’s going on.
432 00:44:07.100 ⇒ 00:44:09.570 Gabriel Lam: Let me know. If not…
433 00:44:10.170 ⇒ 00:44:13.930 Gabriel Lam: Yeah, just… just someone voice out, like, I can’t hear anything.
434 00:44:17.190 ⇒ 00:44:18.310 Audio shared by Gabriel Lam: Hi, Gabriel Lamb.
435 00:44:18.650 ⇒ 00:44:22.730 Audio shared by Gabriel Lam: It’s great to meet you. I’m here to learn more about the Case Study Assistant Project.
436 00:44:22.730 ⇒ 00:44:35.380 Gabriel Lam: This is a project that we have spent this week on. One of our pain points has been just the speed in which we are trying to get case studies out. There’s been a big backlog, and the current process has been
437 00:44:35.780 ⇒ 00:44:40.560 Gabriel Lam: quite slow. A lot of… hands-on scheduling.
438 00:44:42.250 ⇒ 00:44:46.580 Gabriel Lam: Yeah, a lot of the back and forth that we have with the different team members.
439 00:44:46.810 ⇒ 00:44:54.599 Gabriel Lam: just takes time, and so we are trying to implement an AI agent to really speed up, allow this to happen asynchronously. We’re able to get.
440 00:44:58.120 ⇒ 00:45:03.519 Audio shared by Gabriel Lam: More scheduling done without so much manual effort. Makes sense. How long has this project been in development so far?
441 00:45:03.520 ⇒ 00:45:09.269 Gabriel Lam: Yeah, so we started on Monday, it’s a team of Sam, Casey, Mustafa, Utam, and I, and…
442 00:45:09.740 ⇒ 00:45:12.180 Gabriel Lam: Yeah, we’re hoping to get this tested, and…
443 00:45:12.370 ⇒ 00:45:18.180 Gabriel Lam: See what results we can get, how quickly we can get these interview, interviews done and the transfers sent out.
444 00:45:19.330 ⇒ 00:45:22.410 Gabriel Lam: And, yeah, I can end the interview here. Thank you very much.
445 00:45:24.150 ⇒ 00:45:27.389 Audio shared by Gabriel Lam: Thanks for your time. Let me know if you need anything else down the line.
446 00:45:30.600 ⇒ 00:45:32.339 Robert Tseng: Can we make it sound like Hannah?
447 00:45:33.780 ⇒ 00:45:36.060 Gabriel Lam: Is that something we can add later? No, thank you.
448 00:45:36.060 ⇒ 00:45:36.639 Robert Tseng: I’m just kidding.
449 00:45:36.640 ⇒ 00:45:39.949 Gabriel Lam: We’re just trying to get this out the door and get people using it.
450 00:45:40.390 ⇒ 00:45:46.000 Gabriel Lam: once this is done, and you can have multiple people do it, you can pause and start.
451 00:45:46.390 ⇒ 00:45:55.310 Gabriel Lam: And I’m doing this quite quickly. I’m sure, Hannah, you know it’s a lot more prescriptive usually, but we also wanted to give you the flexibility of, like.
452 00:45:55.420 ⇒ 00:45:59.780 Gabriel Lam: Sometimes you just want to get all the information out at the same time, so this is able to capture both.
453 00:46:00.140 ⇒ 00:46:05.689 Gabriel Lam: We did another example with Sam and I, so you are able to have multiple interviewees.
454 00:46:05.890 ⇒ 00:46:07.860 Gabriel Lam: Which will give you a little more context.
455 00:46:08.540 ⇒ 00:46:11.549 Gabriel Lam: Once that’s done, once all the…
456 00:46:11.750 ⇒ 00:46:16.720 Gabriel Lam: interviewees have added, it will automatically generate a… Case?
457 00:46:16.920 ⇒ 00:46:19.569 Gabriel Lam: Study copy out of it.
458 00:46:19.940 ⇒ 00:46:25.799 Gabriel Lam: If you want to do it beforehand, say only Sam and I have done it, you can also just click Generate.
459 00:46:26.350 ⇒ 00:46:36.289 Gabriel Lam: full case study here. We also gave two sub-options, as we want to maybe refine these things, either for industry or client or,
460 00:46:37.190 ⇒ 00:46:41.420 Gabriel Lam: or specific… You know, strategic callouts, whether it’s for
461 00:46:41.600 ⇒ 00:46:53.369 Gabriel Lam: enterprise or SMBs. And yeah, so this is what we get. You get everything that you typically would have in one of these case studies, from context to solutions to tools to results.
462 00:46:53.570 ⇒ 00:47:10.049 Gabriel Lam: And, you know, these are very short interviews, you know, 4 or 5 minutes. We’re just hoping to have a demo out so people are aware that this can be something that Tap goes on. And, yeah, I think once people start working on it, once people start using it, it’ll be a great thing to…
463 00:47:10.240 ⇒ 00:47:27.369 Gabriel Lam: To really supercharge how quickly we get things out, and also to save Hannah time, which is really the goal here. So, yeah, I’m hoping that this in itself will become a case study, and then we’re able to get a lot of these out. So that, you know, whenever you guys are on sales calls, lead calls.
464 00:47:27.830 ⇒ 00:47:31.509 Gabriel Lam: You know, you can pull something out a lot more frequently instead of trying to find things.
465 00:47:33.810 ⇒ 00:47:40.709 Uttam Kumaran: Nice. I guess my question is for the main user, Hannah. You feel like this is, like, a good spot for you to start assigning to folks?
466 00:47:42.630 ⇒ 00:47:45.290 Hannah Wang: I… I think so. I mean, this definitely…
467 00:47:45.650 ⇒ 00:47:58.439 Hannah Wang: solves, like, one of the bottlenecks we had, but I guess, like, the step prior to this, like, actually finding out what case studies to build out, like, I talked… I know, Gabe, I talked to you about that, just, like.
468 00:48:00.020 ⇒ 00:48:05.590 Hannah Wang: Yeah, well, anyway, yes, the short answer is yes, I have a lot of thoughts, so sorry if it’s…
469 00:48:05.770 ⇒ 00:48:07.799 Hannah Wang: I’m not making sense, but…
470 00:48:08.120 ⇒ 00:48:17.130 Uttam Kumaran: So that’s something definitely, like, in our Monday delivery meetings, we can… we are spending time talking about what successes did we have last week.
471 00:48:17.350 ⇒ 00:48:21.119 Uttam Kumaran: I think previously, because we have so much in the backlog.
472 00:48:21.190 ⇒ 00:48:23.520 Uttam Kumaran: I was kind of like, okay…
473 00:48:23.580 ⇒ 00:48:43.399 Uttam Kumaran: I don’t know, I guess, like, we still have other priorities, but now that we have this, we can go ahead next week and kind of create that, like, master list of things we want to get out, and we could do an exercise of kind of going through. I mean, I’m telling you, it’s just going to be so much, but if we want to create a master list of all those, I don’t think that’ll be too hard to do.
474 00:48:43.500 ⇒ 00:48:51.230 Uttam Kumaran: It’s actually this piece that I thought was, like, more complicated, because I can… we could totally tell you the client, the work stream, who’s working on it.
475 00:48:51.560 ⇒ 00:48:55.039 Uttam Kumaran: That’s kind of, like, all that’s needed, right, to go start to create these.
476 00:48:55.040 ⇒ 00:48:56.050 Hannah Wang: requests.
477 00:48:56.780 ⇒ 00:48:58.950 Hannah Wang: Yeah, definitely, I…
478 00:48:59.130 ⇒ 00:49:09.470 Hannah Wang: Yeah, I need to dig around more, and I think I have, like, a couple other questions, but Gabe, we can talk… take that offline. But yeah, I want to use this, like.
479 00:49:09.820 ⇒ 00:49:20.340 Hannah Wang: Because I… yeah, I’m sure all of you are sick of meeting with me for 30 minutes, and I think this is a lot faster. So yeah, awesome. Thank you guys.
480 00:49:23.640 ⇒ 00:49:24.490 Uttam Kumaran: Awesome.
481 00:49:24.870 ⇒ 00:49:40.039 Uttam Kumaran: Cool. I guess, maybe we could spend… I just wanted to give a quick shout-out to a couple people. One to Amber on a lot of the analysis work. I think she’s been really, really, crushing it there.
482 00:49:40.250 ⇒ 00:49:45.920 Uttam Kumaran: It’s been really, really helpful. I think to the whole, Eden team.
483 00:49:46.400 ⇒ 00:49:56.759 Uttam Kumaran: We secured a great renewal, and we, you know, had a great… I would say the first call where we’re not… first, like, ELT call where we’re not, like, super… we’re not on our heels.
484 00:49:56.780 ⇒ 00:50:12.119 Uttam Kumaran: You know, we’re actually, like, prescribing forward motion, which is really great. You know, previously, we used to come into all those calls worried. I think this call, we came in a lot more confident, and of course, like, the hard work starts now, but a lot different posture than before, so…
485 00:50:12.180 ⇒ 00:50:17.200 Uttam Kumaran: It’s also our biggest client, and the client truly where we’re, like, integrated into their business, so…
486 00:50:17.990 ⇒ 00:50:23.900 Uttam Kumaran: I appreciate, you know, all the support there. And then, yeah, I think overall,
487 00:50:24.130 ⇒ 00:50:39.300 Uttam Kumaran: for… for all the folks on the delivery side, like, everybody’s been playing, help on a lot of different clients, like, just coming in and, like, randomly when you get pinged, and being a really, really great sport about it. This is not…
488 00:50:39.400 ⇒ 00:50:59.009 Uttam Kumaran: how life is gonna be forever. I know it is, like, kind of chaotic, but as you can tell from the slides, it’s not… it’s not a problem of, like, we’re going down. It’s actually just a problem of, like, going up, and we are… we are going to be working on, you know, processes and policies as we, like, scale up.
489 00:50:59.170 ⇒ 00:51:02.110 Uttam Kumaran: larger, and I think that’s just something that,
490 00:51:02.240 ⇒ 00:51:09.489 Uttam Kumaran: You know, if you can… if you all can just be patient with us, but also give me… give me feedback as we go, that would be great.
491 00:51:10.920 ⇒ 00:51:27.830 Uttam Kumaran: you know, I think one last thing I want to share, and this is a lot of the hard work from Rico and from Lauren, is we’re starting to do some new, like, operations policies. And so, kind of a couple things. I’ll send out the survey, after this meeting, and then we can sort of
492 00:51:27.930 ⇒ 00:51:45.859 Uttam Kumaran: Hopefully everybody can kind of finish it up, either if you have a couple minutes today, or early next week. But we’re going to send and start doing these team pulse surveys. You know, I think we’re getting to the point of business where I’m not able to meet everybody, as often as possible, and sort of ask for structured feedback, which…
493 00:51:45.930 ⇒ 00:52:01.240 Uttam Kumaran: that’s all I would… I would love to do that. This is not something I want to automate, but, we have to do something about getting feedback from people in a more structured manner, so I’ll be sending out a little bit of a survey. This is just helping us establish, you know, making sure that we
494 00:52:01.310 ⇒ 00:52:07.120 Uttam Kumaran: are addressing issues, and this is anonymous, so… and I actually… I won’t even be able to see
495 00:52:07.300 ⇒ 00:52:25.929 Uttam Kumaran: who submitted what, Lauren will be able to, and then she’ll kind of help, like, aggregate results. So, please just be as honest as possible. It’s just gonna help us get better. Donuts, I was supposed to do a donut with Zoran and Mustafa this week, and I dropped the ball because I said I would schedule it, which…
496 00:52:25.930 ⇒ 00:52:38.819 Uttam Kumaran: if I say I’m gonna schedule something, it’s never gonna happen, and so the donuts thing is in Slack, so it basically pairs people up for virtual coffees. Did anyone get a chance to do it this week?
497 00:52:39.050 ⇒ 00:52:42.840 Uttam Kumaran: Hannah did? Sam did?
498 00:52:43.160 ⇒ 00:52:44.470 Samuel Roberts: Yeah, the two of us.
499 00:52:45.450 ⇒ 00:52:47.389 Uttam Kumaran: How did it go? What’d you guys think?
500 00:52:49.650 ⇒ 00:52:55.929 Hannah Wang: I thought it was good. Yeah. Both of you are, like, both of you are like…
501 00:52:55.930 ⇒ 00:52:57.690 Uttam Kumaran: I don’t know, I’m trying to…
502 00:52:57.690 ⇒ 00:53:00.320 Samuel Roberts: We were both like this is the first one we’ve done, so it was kind of like…
503 00:53:00.320 ⇒ 00:53:02.000 Uttam Kumaran: Yeah. Okay, okay, cool.
504 00:53:02.000 ⇒ 00:53:04.760 Samuel Roberts: Yeah, it was nice to just, yeah, get to know each other and talk at times.
505 00:53:04.760 ⇒ 00:53:13.640 Uttam Kumaran: Did you guys feel like it’s, like, worthwhile? I mean, like, I would like everybody to do it. I’ll send a reminder. I know we just kind of turned it on, but,
506 00:53:13.920 ⇒ 00:53:15.840 Uttam Kumaran: Yeah, what did you guys think?
507 00:53:17.750 ⇒ 00:53:23.529 Hannah Wang: I mean, I personally like getting to know people outside of just work talk,
508 00:53:23.700 ⇒ 00:53:41.909 Hannah Wang: So I… I thought it’s worth… worthwhile. I think what might be helpful is just having, like, a set of questions that people can ask each other. I’m… I feel like I’m naturally okay at asking questions, so it was… and Sam is too, but maybe for people who are more shy, that might be helpful, but… yeah.
509 00:53:42.530 ⇒ 00:53:44.580 Samuel Roberts: Yeah, I think there is some stuff in there that it was, like.
510 00:53:44.750 ⇒ 00:53:48.779 Samuel Roberts: Favorite this and that, and it had some things that hopefully we can lean on if we need to.
511 00:53:49.280 ⇒ 00:53:51.269 Uttam Kumaran: Okay. Looks like a good tool. Cool.
512 00:53:52.110 ⇒ 00:54:00.410 Uttam Kumaran: Item 3, and this is probably for the LA folks. Joe, you’re in LA, right? So we now have 3 people in LA. I told,
513 00:54:00.920 ⇒ 00:54:13.299 Uttam Kumaran: I asked Lauren to draft a little bit of, like, an in-person meeting, like, co-working policy, so I’ll also send this. It just basically outlines, like, one, we would love for you guys to get in person if you’d like to.
514 00:54:13.360 ⇒ 00:54:21.239 Uttam Kumaran: And, like, it kind of outlines, like, budget and, like, what we’d be able to support. But certainly, I think I’m very jealous that there’s 3 people
515 00:54:21.580 ⇒ 00:54:33.919 Uttam Kumaran: I guess it’s not really driving distance, like, I don’t know LA at all, but, like, whatever, you get what I mean? There’s 3 people within an area, and you guys should… if you guys are down, you should totally meet and work together, and we’d love to sort of pave for that.
516 00:54:34.390 ⇒ 00:54:38.830 Uttam Kumaran: Gabe, are you in… where are you? Are you in LA, or are you in Boston?
517 00:54:39.740 ⇒ 00:54:40.340 Gabriel Lam: Yeah.
518 00:54:40.600 ⇒ 00:54:49.160 Uttam Kumaran: Okay, okay, okay. All right, so everybody else is sort of on their own. But yeah, so take a look at that, and
519 00:54:49.250 ⇒ 00:55:01.990 Uttam Kumaran: Yeah, and if you end up flying somewhere where there’s another person at Brain Forge and you guys want to go do stuff, like, it sort of highlights that. Similarly for the folks in the Philippines, if you guys end up beating with each other or want to, like, love to sponsor that.
520 00:55:02.250 ⇒ 00:55:10.069 Uttam Kumaran: And then the last thing, and I know we have a couple of, sort of, meetings to go through, but we also drafted a little bit of, like, a Zoom etiquette,
521 00:55:10.360 ⇒ 00:55:24.129 Uttam Kumaran: Notion Doc, I think this just helps starting to set some standards for how we come across with clients and, like, our expectations. This is something that, like, we want to work with everybody on, but I know it starts by us just putting something on paper.
522 00:55:24.130 ⇒ 00:55:30.809 Uttam Kumaran: But for the folks that join us and continue to join us, you know, I don’t want it to be a question on, like, what the minimum standards are.
523 00:55:30.810 ⇒ 00:55:43.359 Uttam Kumaran: And again, like, as we start to go sell, you know, 100,000 a month deals, there’s expectations from our clients on what they expect from us. And we all are smart enough to deliver the work.
524 00:55:43.360 ⇒ 00:55:55.660 Uttam Kumaran: But perception matters, and perception is important, and so we just wanted to highlight that there. So I’ll send these. We’re starting to put more stuff like this out, and so would love feedback.
525 00:55:55.670 ⇒ 00:56:02.720 Uttam Kumaran: You know, and this is just gonna be the way that we sort of judge each other and, you know, make sure that our clients are getting a
526 00:56:02.770 ⇒ 00:56:15.360 Uttam Kumaran: a great experience. A lot of it will be obvious, but also, again, if you’re working directly with clients, I think it’s important to come across in a certain way, especially spending their hard… hard-earned money on us, so…
527 00:56:15.800 ⇒ 00:56:18.020 Uttam Kumaran: Cool.
528 00:56:18.300 ⇒ 00:56:24.649 Uttam Kumaran: I think that’s… all I had. Anything else we wanted to cover?
529 00:56:30.150 ⇒ 00:56:30.970 Uttam Kumaran: Cool.
530 00:56:31.880 ⇒ 00:56:32.720 Uttam Kumaran: Okay.
531 00:56:33.000 ⇒ 00:56:38.609 Uttam Kumaran: If not, then I’ll talk to everyone later, and maybe, Henry, do you want to chat briefly about next meeting?
532 00:56:41.260 ⇒ 00:56:43.619 Uttam Kumaran: Okay. Thanks, everyone, appreciate it.
533 00:56:46.340 ⇒ 00:56:46.960 Demilade Agboola: Thank you.
534 00:56:47.430 ⇒ 00:56:48.360 Demilade Agboola: Bye.
535 00:56:48.970 ⇒ 00:56:49.670 Awaish Kumar: Right.
536 00:56:55.330 ⇒ 00:57:00.620 Uttam Kumaran: Yeah, I just want to make sure you’re prepared, or however I can… I know we only have a few minutes, but…
537 00:57:01.510 ⇒ 00:57:02.300 Amber Lin: Yeah.
538 00:57:02.680 ⇒ 00:57:03.520 Uttam Kumaran: Yeah, I’m prepared.
539 00:57:03.520 ⇒ 00:57:05.079 Amber Lin: Is this, like, a cake? Is this like a c.
540 00:57:05.080 ⇒ 00:57:08.429 Uttam Kumaran: Kickoff for a work stream, or, like, what is it exactly?
541 00:57:08.770 ⇒ 00:57:20.439 Henry Zhao: So, about 4 weeks ago, we met with the pharmacy team to, like, figure out what their needs were. So they asked us to, like, automate a few of their reports, they asked us to help them with forecasting.
542 00:57:20.440 ⇒ 00:57:31.539 Uttam Kumaran: And so we did some of that work, but it kind of never got finished, because we ended up having to do the attribution stuff, and then Brad ended up traveling for a while, and also we needed some data from BASC that we never got.
543 00:57:31.540 ⇒ 00:57:32.820 Henry Zhao: from Basquiat.
544 00:57:33.230 ⇒ 00:57:40.880 Henry Zhao: So today is just kind of, like, regrouping and just saying, alright, where did we leave off last time? What still needs to be done? And just kind of make a plan for that.
545 00:57:42.060 ⇒ 00:57:42.650 Uttam Kumaran: Okay.
546 00:57:43.610 ⇒ 00:57:46.300 Henry Zhao: So I know now, like, that’s not gonna… Yeah, go ahead.
547 00:57:46.780 ⇒ 00:57:47.599 Uttam Kumaran: Go, go, go.
548 00:57:48.110 ⇒ 00:58:04.529 Henry Zhao: So I know now BASC is not gonna give us this data at any, like, short-term deadline, and also Remo is still delayed, so today the discussion is more gonna… that’s gonna be, like, we need to ask BASS for this, but, like, if BASC is not gonna give this to us, what is the alternate solution that works for what we want to do in the meantime?
549 00:58:05.070 ⇒ 00:58:19.819 Henry Zhao: And then I’m also going to be focusing on not just accepting automation tasks, but, like, these are your goals, which Robert already sent me today. What can we actually do in terms of analysis and, like, additional new… net new work that can help you get to those goals, instead of us just being, like.
550 00:58:19.940 ⇒ 00:58:24.019 Henry Zhao: Assembly line people that just automate things that they already have, and doesn’t actually drive the needle.
551 00:58:26.200 ⇒ 00:58:29.269 Uttam Kumaran: And then fourth, like, I guess one question I have is, like.
552 00:58:29.320 ⇒ 00:58:48.969 Uttam Kumaran: For, like, Zoran’s workstream, right, I’m having him sort of put together a bit of a roadmap. I would like to kind of put together, like, an internal Gantt chart on, like, what we’re doing. How often are you meeting with these folks, and is it something that you’re squarely driving? Like, is it similar to kind of Zoran’s workstream? I would kind of just want to, like.
553 00:58:49.470 ⇒ 00:59:02.349 Uttam Kumaran: talk about expectations, about, like, okay, if you’re gonna go in this meeting, you’re gonna come out with a bunch of things, how are… how am I supporting you in, like, keeping that organized and making sure that we can report on our progress there?
554 00:59:03.430 ⇒ 00:59:17.950 Henry Zhao: Yeah, that’s a good point. So, we haven’t met regularly yet, we only met that one time, but starting today, he wants to meet weekly, so today, I think we should define the goals. I’ll put that in the same outline doc that Zoran has, and then share a loom with you, just like he did.
555 00:59:18.220 ⇒ 00:59:24.319 Henry Zhao: I’ll make a Gantt chart, and then we can discuss as a team, like, does that make sense? Do the deadlines make sense, and go from there.
556 00:59:25.270 ⇒ 00:59:29.760 Uttam Kumaran: Okay, I would like, if Amber’s okay with it, I’d like her to just listen in. Do you care if she’s…
557 00:59:29.760 ⇒ 00:59:31.910 Henry Zhao: She’s invited to it, yeah. She’s invited to it.
558 00:59:31.910 ⇒ 00:59:32.990 Uttam Kumaran: Okay, so let me…
559 00:59:33.450 ⇒ 00:59:37.069 Henry Zhao: But she declined it. She said, out of office. I don’t know if she’s actually out of office.
560 00:59:38.270 ⇒ 00:59:51.089 Uttam Kumaran: Yeah, I think it’s helpful for any of these calls where you’re, like, one-on-one with a big crew, just to have someone else from our team, because you can… she’ll take notes, and then you can also rely on her to… I would say either… I would suggest her or Rico,
561 00:59:51.230 ⇒ 00:59:53.469 Uttam Kumaran: So, take one of them with you, for sure.
562 00:59:54.080 ⇒ 01:00:00.750 Uttam Kumaran: Yeah, okay, so I… I messaged her, see what she says. Okay. But yeah, let me know how it goes, and then, yeah, kind of like…
563 01:00:00.890 ⇒ 01:00:09.090 Uttam Kumaran: do your best to just get as much info as possible, and then we can work together on a roadmap. Because I would really like us to report out on.
564 01:00:09.090 ⇒ 01:00:12.029 Henry Zhao: Both of those work streams really significantly, plus.
565 01:00:12.030 ⇒ 01:00:24.740 Uttam Kumaran: the maintenance, and then also, we’re gonna start to loop in some of the analysts to work on the opportunistic stuff, so… Yeah. As long as you and Zoran are really comfortable, we’re gonna work on the AI piece a little bit, and then we’re gonna talk about, like, future, so…
566 01:00:25.600 ⇒ 01:00:34.499 Henry Zhao: Yeah, I’m excited that this call, we’re finally going to be starting to talk about net new work and analysis, so that we can drive the business forward, instead of up until this point, which is just maintenance and…
567 01:00:34.610 ⇒ 01:00:37.319 Henry Zhao: Doing tasks and closing tickets, like you said, so…
568 01:00:37.320 ⇒ 01:00:52.610 Uttam Kumaran: Yeah, make sure also to, like, make sure they book the weekly, so before you end the call, just ask, like, hey, can I… can I put the… okay, so it’s… it’s there. Okay, cool. All right, great. So then on Monday, I think what we’ll do is I’ll maybe book a little bit of a larger planning session just on Eden.
569 01:00:52.690 ⇒ 01:00:59.220 Uttam Kumaran: So we can talk through both the work streams, and then we can also… I mean, we have a couple work streams that we’re gonna basically kick off on Monday, so…
570 01:00:59.820 ⇒ 01:01:00.470 Henry Zhao: Okay.
571 01:01:01.520 ⇒ 01:01:03.639 Uttam Kumaran: Okay, dude. Alright, thank you, let me know how it goes.
572 01:01:04.140 ⇒ 01:01:04.990 Henry Zhao: Alright, thank you.
573 01:01:05.460 ⇒ 01:01:06.000 Uttam Kumaran: Okay, thanks.
574 01:01:06.000 ⇒ 01:01:06.680 Henry Zhao: study.