Meeting Title: Friday Brainforge Demos & Retro Date: 2025-07-11 Meeting participants: Luke Daque, Rico Rejoso, Uttam Kumaran, Raymund Verzosa, Hannah Wang, Ryan Brosas, Miguel de Veyra, Amber Lin, Caio Velasco, Annie Yu, Casie Aviles, Demilade Agboola, Robert Tseng, Mustafa Raja, Awaish Kumar, Anne
WEBVTT
1 00:02:12.050 ⇒ 00:02:12.979 Uttam Kumaran: Hey, guys.
2 00:02:17.210 ⇒ 00:02:18.259 Rico Rejoso: How are you, Tom?
3 00:02:18.910 ⇒ 00:02:19.918 Uttam Kumaran: Hey! How are you?
4 00:02:20.760 ⇒ 00:02:21.730 Rico Rejoso: Good.
5 00:02:34.820 ⇒ 00:02:35.510 Rico Rejoso: Okay.
6 00:02:53.670 ⇒ 00:02:54.760 Amber Lin: Hi.
7 00:02:54.930 ⇒ 00:02:55.980 Uttam Kumaran: Hi! Good morning!
8 00:02:56.460 ⇒ 00:03:04.382 Amber Lin: Good morning. Our team just joined from a stand up, so all the urban standards folks will be here soon. Hopefully.
9 00:03:16.220 ⇒ 00:03:17.400 Miguel de Veyra: Everyone, morning.
10 00:04:10.270 ⇒ 00:04:12.050 Uttam Kumaran: Okay, we can probably get started.
11 00:04:16.540 ⇒ 00:04:22.190 Hannah Wang: E, get my screen organized and then
12 00:04:22.540 ⇒ 00:04:27.710 Hannah Wang: share cause. I have so many things open all the time.
13 00:04:38.020 ⇒ 00:04:39.430 Hannah Wang: I’m sorry.
14 00:04:52.953 ⇒ 00:04:58.729 Hannah Wang: Okay, so we have a lot on the agenda.
15 00:04:59.470 ⇒ 00:05:01.771 Hannah Wang: So we’ll just get started. So
16 00:05:02.260 ⇒ 00:05:23.460 Hannah Wang: for the icebreaker, unfortunately, you’re gonna have to turn on your camera, please, because we’re gonna do charades. So I am going to private message one person to act out a word or a phrase. It won’t be super hard, hopefully, but
17 00:05:24.590 ⇒ 00:05:32.609 Hannah Wang: yeah, so no words, no sounds, no props. Just your actions. And then others have to guess. So
18 00:05:34.870 ⇒ 00:05:36.870 Hannah Wang: does anyone want to?
19 00:05:37.260 ⇒ 00:05:43.185 Hannah Wang: Your camera’s broken? No, get it fixed.
20 00:05:44.100 ⇒ 00:05:49.990 Hannah Wang: does anyone want to volunteer? If not, I’m just gonna choose someone.
21 00:05:50.150 ⇒ 00:05:51.180 Hannah Wang: So
22 00:05:58.330 ⇒ 00:06:01.484 Hannah Wang: no one wants to go. Okay,
23 00:06:02.710 ⇒ 00:06:08.069 Hannah Wang: we’ll start with whoever’s camera is on. So I’m gonna choose amber.
24 00:06:08.580 ⇒ 00:06:09.560 Amber Lin: No.
25 00:06:09.920 ⇒ 00:06:10.400 Uttam Kumaran: It’s okay.
26 00:06:10.410 ⇒ 00:06:11.750 Hannah Wang: That difficult?
27 00:06:13.300 ⇒ 00:06:21.190 Hannah Wang: Oh, I can’t. Private message people anymore. Wasn’t that like a thing in slack?
28 00:06:21.450 ⇒ 00:06:23.569 Hannah Wang: I don’t see like the.
29 00:06:23.570 ⇒ 00:06:25.939 Amber Lin: Oh, just message me in slack. It’s okay.
30 00:06:25.940 ⇒ 00:06:30.540 Hannah Wang: Oh, okay, I’ll give you a really easy one to start.
31 00:06:30.830 ⇒ 00:06:31.720 Amber Lin: Okay.
32 00:06:31.970 ⇒ 00:06:33.180 Hannah Wang: I sent it now.
33 00:06:34.000 ⇒ 00:06:35.370 Amber Lin: Yeah, let me go see?
34 00:06:44.340 ⇒ 00:06:49.869 Amber Lin: okay, so I need this to see my whole face.
35 00:06:51.910 ⇒ 00:06:53.600 Amber Lin: Oh.
36 00:06:56.190 ⇒ 00:06:57.949 Robert Tseng: Brushing your teeth.
37 00:06:59.000 ⇒ 00:06:59.780 Hannah Wang: Very good.
38 00:06:59.780 ⇒ 00:07:02.020 Uttam Kumaran: That was it. Come on.
39 00:07:02.020 ⇒ 00:07:05.119 Hannah Wang: I started easy. I started easy. Okay,
40 00:07:06.860 ⇒ 00:07:08.750 Amber Lin: I’m glad I went first, st then.
41 00:07:08.750 ⇒ 00:07:09.720 Hannah Wang: Yeah, okay.
42 00:07:10.080 ⇒ 00:07:12.760 Hannah Wang: So I’m gonna choose.
43 00:07:13.360 ⇒ 00:07:23.809 Hannah Wang: Okay, let’s do, Luke. So I’m gonna message you on slack I just sent it.
44 00:07:33.620 ⇒ 00:07:37.079 Luke Daque: I, how do I do this? I need to stand up so I’m.
45 00:07:37.420 ⇒ 00:07:38.100 Uttam Kumaran: Standing.
46 00:07:38.490 ⇒ 00:07:39.275 Luke Daque: So.
47 00:07:42.070 ⇒ 00:07:43.780 Robert Tseng: Vacuuming the floor.
48 00:07:44.580 ⇒ 00:07:47.880 Uttam Kumaran: Elevator, shovel.
49 00:07:48.920 ⇒ 00:07:50.250 Amber Lin: Wait, what.
50 00:07:50.460 ⇒ 00:07:52.100 Robert Tseng: Sweeping the floor.
51 00:07:52.260 ⇒ 00:07:53.309 Uttam Kumaran: We’ve been.
52 00:07:53.310 ⇒ 00:07:56.549 Amber Lin: Wait, luffy I man.
53 00:07:56.550 ⇒ 00:07:57.750 Luke Daque: I’m gone! Man gone!
54 00:07:57.750 ⇒ 00:07:58.909 Uttam Kumaran: This is great.
55 00:08:00.520 ⇒ 00:08:02.030 Luke Daque: Imagine I’m walking with him.
56 00:08:02.710 ⇒ 00:08:03.550 Hannah Wang: You can’t use one.
57 00:08:05.170 ⇒ 00:08:05.710 Amber Lin: Okay.
58 00:08:06.870 ⇒ 00:08:08.390 Uttam Kumaran: The dog is walking, the dog.
59 00:08:08.390 ⇒ 00:08:09.540 Hannah Wang: Yeah. Dog.
60 00:08:09.540 ⇒ 00:08:12.581 Uttam Kumaran: You said. Imagine I’m walking.
61 00:08:16.030 ⇒ 00:08:17.820 Luke Daque: That was cheating, I guess.
62 00:08:19.840 ⇒ 00:08:25.799 Hannah Wang: Okay. So these aren’t all gonna be verbs. Okay. So some of them will be nouns. So just keep that in. Mind.
63 00:08:27.210 ⇒ 00:08:30.730 Hannah Wang: Okay, I’m gonna choose Casey.
64 00:08:33.090 ⇒ 00:08:35.569 Hannah Wang: Let me message you on slack.
65 00:08:39.630 ⇒ 00:08:40.089 Hannah Wang: Okay.
66 00:08:46.020 ⇒ 00:08:47.480 Casie Aviles: Am I gonna do this
67 00:08:56.210 ⇒ 00:08:57.780 Miguel de Veyra: Rocka by Baby.
68 00:08:58.010 ⇒ 00:08:58.760 Robert Tseng: Yeah.
69 00:08:58.760 ⇒ 00:09:01.090 Uttam Kumaran: One military.
70 00:09:01.090 ⇒ 00:09:01.935 Luke Daque: Guitar,
71 00:09:03.020 ⇒ 00:09:03.619 Uttam Kumaran: It’s hard.
72 00:09:03.620 ⇒ 00:09:04.530 Luke Daque: Guitarist.
73 00:09:04.530 ⇒ 00:09:05.380 Uttam Kumaran: Saxophone.
74 00:09:05.380 ⇒ 00:09:06.200 Miguel de Veyra: Swinging.
75 00:09:06.200 ⇒ 00:09:07.510 Luke Daque: Father.
76 00:09:09.630 ⇒ 00:09:11.700 Robert Tseng: Nursing, nursing a Child.
77 00:09:11.700 ⇒ 00:09:12.460 Rico Rejoso: Dancing.
78 00:09:12.460 ⇒ 00:09:13.909 Uttam Kumaran: Swing, dancing, swing, dancing.
79 00:09:15.460 ⇒ 00:09:16.340 Luke Daque: The baby to sleep.
80 00:09:16.340 ⇒ 00:09:16.840 Rico Rejoso: One rig.
81 00:09:18.260 ⇒ 00:09:19.460 Uttam Kumaran: One word.
82 00:09:20.230 ⇒ 00:09:21.020 Amber Lin: 13.
83 00:09:21.020 ⇒ 00:09:21.450 Uttam Kumaran: Keep on!
84 00:09:21.450 ⇒ 00:09:22.799 Amber Lin: Chopping, a child.
85 00:09:22.800 ⇒ 00:09:23.900 Hannah Wang: Goes really, close.
86 00:09:24.954 ⇒ 00:09:25.599 Amber Lin: Move.
87 00:09:25.600 ⇒ 00:09:27.130 Hannah Wang: Was really really close with.
88 00:09:29.770 ⇒ 00:09:31.970 Uttam Kumaran: La la cradle.
89 00:09:33.260 ⇒ 00:09:33.640 Rico Rejoso: Terry.
90 00:09:33.640 ⇒ 00:09:34.400 Uttam Kumaran: Yeah, I don’t remember what.
91 00:09:34.400 ⇒ 00:09:35.040 Robert Tseng: Waiting.
92 00:09:36.080 ⇒ 00:09:41.440 Hannah Wang: That’s okay. I’ll give it to him, he said. Father, and the word is parent. So.
93 00:09:43.790 ⇒ 00:09:44.869 Rico Rejoso: Your job.
94 00:09:45.940 ⇒ 00:09:48.036 Casie Aviles: That was the syllables.
95 00:09:49.210 ⇒ 00:09:51.749 Robert Tseng: Rocking the child to chopping him.
96 00:09:52.030 ⇒ 00:09:55.910 Uttam Kumaran: That was the chop. Why, where does the chop come into play?
97 00:09:56.900 ⇒ 00:09:57.719 Robert Tseng: Bad bad shot.
98 00:09:57.720 ⇒ 00:10:00.209 Amber Lin: When I hear parenting, I’m just like.
99 00:10:00.640 ⇒ 00:10:02.315 Uttam Kumaran: I hear. I just like.
100 00:10:05.780 ⇒ 00:10:08.320 Hannah Wang: Okay, I’m gonna choose, Robert.
101 00:10:08.660 ⇒ 00:10:09.060 Robert Tseng: Okay.
102 00:10:09.060 ⇒ 00:10:12.040 Hannah Wang: And I sent in slack.
103 00:10:14.710 ⇒ 00:10:15.909 Robert Tseng: Oh, that’s too easy!
104 00:10:20.370 ⇒ 00:10:21.860 Hannah Wang: What’s your phone?
105 00:10:21.860 ⇒ 00:10:23.699 Robert Tseng: What? Why not? Isn’t it? Probably.
106 00:10:23.700 ⇒ 00:10:24.550 Hannah Wang: What?
107 00:10:24.550 ⇒ 00:10:26.400 Robert Tseng: No props to that. Okay? Fine. Well.
108 00:10:26.400 ⇒ 00:10:28.050 Uttam Kumaran: Trophy is way too easy.
109 00:10:28.050 ⇒ 00:10:28.909 Robert Tseng: This is that.
110 00:10:29.310 ⇒ 00:10:30.270 Hannah Wang: Okay.
111 00:10:30.680 ⇒ 00:10:31.410 Robert Tseng: We’re good.
112 00:10:33.780 ⇒ 00:10:34.770 Uttam Kumaran: Another, one.
113 00:10:35.280 ⇒ 00:10:36.000 Hannah Wang: Do another, one.
114 00:10:36.000 ⇒ 00:10:38.670 Amber Lin: Give him a harder one.
115 00:10:38.670 ⇒ 00:10:39.900 Hannah Wang: Another one.
116 00:10:40.560 ⇒ 00:10:45.610 Hannah Wang: This was kind of easy, too. I chose easy ones. I need to think of harder ones actually.
117 00:10:50.960 ⇒ 00:10:51.890 Luke Daque: Cooking.
118 00:10:52.720 ⇒ 00:10:53.999 Uttam Kumaran: Walk, surf, ride.
119 00:10:54.690 ⇒ 00:10:55.300 Luke Daque: Drumming.
120 00:10:55.300 ⇒ 00:10:58.490 Robert Tseng: The it doesn’t count. His answer doesn’t count.
121 00:10:59.240 ⇒ 00:11:00.850 Hannah Wang: They didn’t say it.
122 00:11:01.820 ⇒ 00:11:02.510 Luke Daque: Chef.
123 00:11:02.910 ⇒ 00:11:04.320 Uttam Kumaran: Yeah, yeah.
124 00:11:04.320 ⇒ 00:11:05.090 Hannah Wang: Catch up.
125 00:11:05.430 ⇒ 00:11:07.930 Hannah Wang: Okay, wait. I need to choose harder.
126 00:11:08.120 ⇒ 00:11:16.510 Hannah Wang: harder verbs or like harder things. Okay, let’s just do a call.
127 00:11:16.510 ⇒ 00:11:19.319 Robert Tseng: I’ll I’ll give you ideas as you choose, as he gets up as you choose.
128 00:11:19.320 ⇒ 00:11:24.160 Hannah Wang: Oh, okay, yeah, Robert, okay, time next. So you you message him something.
129 00:11:27.310 ⇒ 00:11:29.490 Uttam Kumaran: No, Robert’s gonna do something advanced.
130 00:11:29.900 ⇒ 00:11:30.600 Robert Tseng: Yeah.
131 00:11:32.900 ⇒ 00:11:35.410 Luke Daque: Data modeling like that like, how do you.
132 00:11:42.490 ⇒ 00:11:44.329 Robert Tseng: Oh, okay, I got a perfect one.
133 00:11:53.250 ⇒ 00:11:55.840 Uttam Kumaran: Dude. It can be like 5 sentences.
134 00:11:58.220 ⇒ 00:11:59.300 Uttam Kumaran: Oh, my God!
135 00:12:02.670 ⇒ 00:12:03.500 Uttam Kumaran: Hmm!
136 00:12:05.250 ⇒ 00:12:08.459 Robert Tseng: Okay, I’ll try. I’ll try to simplify it. But that’s that’s the scenario.
137 00:12:11.020 ⇒ 00:12:11.700 Uttam Kumaran: Okay.
138 00:12:11.700 ⇒ 00:12:13.810 Robert Tseng: Okay, okay, let’s just simplify this in.
139 00:12:15.240 ⇒ 00:12:20.340 Uttam Kumaran: Okay, do do, yeah, do tiny bit simpler. I’ll try.
140 00:12:25.100 ⇒ 00:12:26.290 Uttam Kumaran: Okay. Okay. Good.
141 00:12:29.120 ⇒ 00:12:30.010 Hannah Wang: 2 words.
142 00:12:30.010 ⇒ 00:12:30.839 Rico Rejoso: 2 words.
143 00:12:34.430 ⇒ 00:12:39.710 Hannah Wang: Eating, eating, conversation, people, talking.
144 00:12:39.710 ⇒ 00:12:40.170 Demilade Agboola: You know.
145 00:12:40.170 ⇒ 00:12:41.230 Hannah Wang: Eating noodles.
146 00:12:42.620 ⇒ 00:12:43.620 Luke Daque: Feeding.
147 00:12:44.080 ⇒ 00:12:45.250 Amber Lin: A date.
148 00:12:45.877 ⇒ 00:12:47.100 Amber Lin: Oh, I’m like.
149 00:12:47.100 ⇒ 00:12:48.110 Hannah Wang: Maneuver.
150 00:12:48.950 ⇒ 00:12:49.590 Rico Rejoso: Whoa!
151 00:12:51.440 ⇒ 00:12:52.919 Robert Tseng: It’s really good. Good job.
152 00:12:56.300 ⇒ 00:12:59.000 Miguel de Veyra: Is it the hybrid maneuver? Is that how you say it.
153 00:13:00.910 ⇒ 00:13:03.550 Hannah Wang: Choking on Noodles, choking on food.
154 00:13:03.550 ⇒ 00:13:04.190 Uttam Kumaran: Yes.
155 00:13:04.830 ⇒ 00:13:05.180 Robert Tseng: Yeah, but.
156 00:13:06.540 ⇒ 00:13:07.390 Uttam Kumaran: Noodles.
157 00:13:09.420 ⇒ 00:13:26.956 Uttam Kumaran: Yeah, choking on food. Well, the the original one was, you’re at a sales coffee meetup and the other guy starts choking. So I was like, and then like sign the check or something, and like.
158 00:13:28.850 ⇒ 00:13:35.659 Hannah Wang: Okay, I I have another one that maybe we can try. So demalade. I’m gonna message you
159 00:13:36.470 ⇒ 00:13:41.400 Hannah Wang: and you can try acting that out.
160 00:13:48.230 ⇒ 00:13:52.699 Demilade Agboola: Okay, I need to stand up for this one.
161 00:14:01.980 ⇒ 00:14:03.230 Robert Tseng: Backflip! Oh.
162 00:14:04.260 ⇒ 00:14:04.850 Luke Daque: Slipping.
163 00:14:04.850 ⇒ 00:14:07.240 Robert Tseng: Slips, slips on the.
164 00:14:07.240 ⇒ 00:14:07.600 Rico Rejoso: No.
165 00:14:07.600 ⇒ 00:14:08.620 Uttam Kumaran: On the banana peel.
166 00:14:08.620 ⇒ 00:14:09.550 Robert Tseng: It’s not a banana peel.
167 00:14:09.550 ⇒ 00:14:16.559 Hannah Wang: Yeah. Well, mine are too easy. Okay, I give up Robert. You can do one more and choose someone.
168 00:14:18.300 ⇒ 00:14:22.370 Robert Tseng: I mean, didn’t we? I guess the only other person with a camera on is so.
169 00:14:22.370 ⇒ 00:14:24.240 Hannah Wang: No. Choose someone with their camera.
170 00:14:25.146 ⇒ 00:14:26.219 Hannah Wang: Okay? Fine.
171 00:14:27.680 ⇒ 00:14:29.269 Uttam Kumaran: They do. Hannah. Last one.
172 00:14:29.830 ⇒ 00:14:30.520 Robert Tseng: Okay.
173 00:14:49.770 ⇒ 00:14:51.790 Hannah Wang: Oh, okay.
174 00:14:56.090 ⇒ 00:14:56.465 Hannah Wang: it’s
175 00:15:05.480 ⇒ 00:15:05.970 Hannah Wang: oh.
176 00:15:06.145 ⇒ 00:15:08.080 Robert Tseng: I can give you a different one, if that’s too hard.
177 00:15:08.080 ⇒ 00:15:10.199 Hannah Wang: This is too hard. Yeah.
178 00:15:10.200 ⇒ 00:15:10.910 Robert Tseng: What?
179 00:15:11.040 ⇒ 00:15:12.010 Robert Tseng: Okay? Fine.
180 00:15:12.010 ⇒ 00:15:14.030 Hannah Wang: Then you you act it out.
181 00:15:14.890 ⇒ 00:15:18.769 Robert Tseng: What the let’s do. A second one. Okay, fine, fine. Whatever I’ll do, I’ll do it.
182 00:15:21.790 ⇒ 00:15:25.160 Uttam Kumaran: Roller, coaster, dentist, dermatologist.
183 00:15:25.160 ⇒ 00:15:25.950 Amber Lin: Sure.
184 00:15:26.120 ⇒ 00:15:29.880 Uttam Kumaran: Laser, therapy, acupuncture.
185 00:15:32.730 ⇒ 00:15:33.360 Amber Lin: Huh!
186 00:15:33.360 ⇒ 00:15:37.030 Uttam Kumaran: Needles, tattoo.
187 00:15:37.030 ⇒ 00:15:38.000 Amber Lin: 3, rd eye.
188 00:15:38.680 ⇒ 00:15:39.450 Robert Tseng: Tattoo face.
189 00:15:41.460 ⇒ 00:15:47.759 Robert Tseng: Yeah, I mean, acupuncture was like close. But it wasn’t exactly that. So it’s like pimple pimple would have been good, too.
190 00:15:47.870 ⇒ 00:15:48.560 Hannah Wang: Okay.
191 00:15:49.670 ⇒ 00:15:52.470 Robert Tseng: All right, popping a pimple that would have been that would be good.
192 00:15:52.470 ⇒ 00:15:53.150 Uttam Kumaran: Oh!
193 00:15:55.380 ⇒ 00:16:01.390 Hannah Wang: Alright. I feel like we should do this ice rigger every week. It’s so entertaining. Anyway.
194 00:16:01.590 ⇒ 00:16:10.260 Hannah Wang: Okay, so no lab share. So yeah, I guess, Utam, you can introduce our new, our new team members.
195 00:16:10.260 ⇒ 00:16:27.189 Uttam Kumaran: Yeah. So I wanted to introduce 2 people. Is Rico here? Yeah. So I want to introduce Rico. And then Vishnu, I don’t think is on the call. But, Rico, if you want to say Hi to everybody, you’ve probably interacted with almost everybody so far, but would love for you to just say, Hi! And
196 00:16:28.340 ⇒ 00:16:31.149 Uttam Kumaran: to share a little bit about yourself and
197 00:16:31.832 ⇒ 00:16:33.529 Uttam Kumaran: sort of what you’re working on.
198 00:16:37.680 ⇒ 00:16:39.030 Rico Rejoso: Oh, can you hear me, guys?
199 00:16:39.720 ⇒ 00:16:40.290 Uttam Kumaran: Yes.
200 00:16:40.460 ⇒ 00:16:45.069 Rico Rejoso: I’m sorry I don’t have my camera on a lot. I mean, a lot happened earlier. I’m sorry.
201 00:16:45.670 ⇒ 00:16:57.031 Rico Rejoso: So yes, I’m your new operations lead. My name is Rico Rejo, and I am from the Philippines. Right now. We’re focusing mainly on improving our operations by
202 00:16:57.850 ⇒ 00:17:04.690 Rico Rejoso: with the help of utham and amber from the project management side, Hannah and Aisha as well.
203 00:17:04.859 ⇒ 00:17:10.809 Rico Rejoso: So we look forward to further improving our process, and me helping you out in every possible way, so we that I can.
204 00:17:13.190 ⇒ 00:17:16.000 Uttam Kumaran: Cool, so really, really thankful to have.
205 00:17:16.450 ⇒ 00:17:17.325 Rico Rejoso: Rico
206 00:17:18.359 ⇒ 00:17:22.580 Uttam Kumaran: We’re we’re working on a lot of procedures internally to smooth things out. So very happy.
207 00:17:22.944 ⇒ 00:17:29.739 Uttam Kumaran: And then Vishnu, who I think, maybe will be. I don’t think he was able to make it today, but it’s part of our intern team along with Abigail.
208 00:17:30.149 ⇒ 00:17:40.640 Uttam Kumaran: So 2 of our interns that are working on a lot of things related to data. So they’ll probably reach out. I know they’re partnering with a few other people internally but happy that we get to
209 00:17:41.040 ⇒ 00:17:45.130 Uttam Kumaran: you know, have interns and you know. Give some opportunity back.
210 00:17:45.930 ⇒ 00:17:49.790 Uttam Kumaran: So yeah. And maybe Hannah, I can share on my side because I’m gonna go to the
211 00:17:50.400 ⇒ 00:17:53.369 Uttam Kumaran: lash duh, whatever thing right after this.
212 00:17:56.130 ⇒ 00:18:01.989 Uttam Kumaran: Cool. So issue this. Yeah. So I just wanted to talk a little bit about
213 00:18:02.600 ⇒ 00:18:05.370 Uttam Kumaran: some accomplishments for June.
214 00:18:05.898 ⇒ 00:18:08.049 Uttam Kumaran: I think we’re getting sort of better at
215 00:18:08.360 ⇒ 00:18:27.670 Uttam Kumaran: closing out the month a little bit faster. Looking forward to next month. When this is done, you know, within the 1st few days. But we got a lot. We got a lot done last month. We signed our highest per hour client, you know, in our team’s history we did 5 new proof of concepts
216 00:18:28.280 ⇒ 00:18:47.079 Uttam Kumaran: or audits. We have a new brand identity. We have a new workshop service offering Alex and amber work pretty diligently on establishing our project, management, office, and sort of all the procedures which, if you’re on any team. You’re probably now getting sort of the benefit of that organization.
217 00:18:47.599 ⇒ 00:19:14.640 Uttam Kumaran: We have an established partnership motion now. So, partnering with vendors, with referral partners. And with a few others which is really really great. We sort of have delivered, I think, like sort of the 1st set of functionality, for, like our internal platform, which will be sort of sharing a lot about just in the next few slides which you know, I know a lot of folks on the team are using this and finding value. And
218 00:19:15.125 ⇒ 00:19:22.500 Uttam Kumaran: it’s really, really exciting. And I’m I’m you know, this is just the beginning of a lot of that work and sort of why, we sort of have our own
219 00:19:22.900 ⇒ 00:19:51.269 Uttam Kumaran: you know, internal efficiency engine that allows people to skip the boring part of of this job and and continue to deliver for clients. We established an automated marketing event tracker that’s helping the marketing team find events for the team to attend. And then we also saw increases in both the users on the website and page views on the website. So a lot of great work on the SEO side and the blog side, and just generally overall traffic.
220 00:19:51.812 ⇒ 00:19:54.470 Uttam Kumaran: To all of our sort of socials.
221 00:19:57.370 ⇒ 00:20:06.329 Uttam Kumaran: in terms of next month goals. So these are just a couple this month. So we want to migrate sales team from notion to Hubspot.
222 00:20:06.670 ⇒ 00:20:19.270 Uttam Kumaran: We’re attempting to establish a marketing campaign framework which we’ll share. Basically, now that we are able to execute several marketing activities, whether it’s sending Linkedin email events.
223 00:20:19.728 ⇒ 00:20:45.359 Uttam Kumaran: You know, meeting people in person case studies. We have all these activities. And so we want to start to bundle them up and measure their impact on. You know, our business. We’re looking to hire. We have sort of open. Jd’s out for a mid level. Pm. And a Pm. Coordinator. So we’ll talk about that a little bit later. But if anyone has anyone in their network that they would like to refer in, please do
224 00:20:45.849 ⇒ 00:20:59.249 Uttam Kumaran: we’ll probably have finalized our Q. 3 okrs. I think this next week it’s sort of blocked by me. But the Pm. Team finished up our version of sort of our delivery okrs, and so be able to sort of finalize that
225 00:21:00.550 ⇒ 00:21:16.140 Uttam Kumaran: assigning 3 referral partners. So we have a couple that are in process right now, referral partners are just folks that want to refer business to us, and we want to give them all the resources necessary to do that and give them a financial incentive to do that. So excited for that.
226 00:21:16.290 ⇒ 00:21:34.340 Uttam Kumaran: We we’re also gonna finish our website redesign and finding sort of a Cdp expert. I know. We have Henry on the team. That’s sort of trialing in this motion as well, but ideally again, trying to find someone that can match Robert’s expertise at the Cdp product analytics level.
227 00:21:34.630 ⇒ 00:21:37.829 Uttam Kumaran: and of course, start to take that work on from him.
228 00:21:40.490 ⇒ 00:21:45.270 Uttam Kumaran: So a little bit of sales pipeline and then, Rob, we’re happy if you want to add anything here.
229 00:21:45.780 ⇒ 00:21:55.239 Uttam Kumaran: So we have some really great momentum. We have, you know, 4 new clients that we sign I feel like we. Maybe I’m probably missing
230 00:21:55.460 ⇒ 00:21:58.919 Uttam Kumaran: one or 2. I think I may be missing. Read me on the left.
231 00:21:59.819 ⇒ 00:22:05.879 Uttam Kumaran: We had one client churn matter more. And I can talk about that just after going through things
232 00:22:06.426 ⇒ 00:22:21.100 Uttam Kumaran: existing clients. So we sign pool parts to a minimum retainer as well as Eden health. We expanded by another 10 k. Per month. To in order to take on the new Emr work. And we’re continuing to take on some segment work ourselves.
233 00:22:21.505 ⇒ 00:22:26.730 Uttam Kumaran: We have several new clients in pipeline, and a lot of the ones on the left are actually referrals.
234 00:22:27.120 ⇒ 00:22:54.488 Uttam Kumaran: I wanna shout out to Kyle. He referred, number 4, which is, do be cars to Dubai based, I think. It’s a car rental service. And then we got several referrals from one of our clients off the record. Which is Craig folds. He referred. Sustain provenance and the last 2 which we’re really really grateful for. I think we have several other leads that we’re sort of working on, but those are just some I wanted to highlight
235 00:22:55.470 ⇒ 00:23:03.550 Uttam Kumaran: we’re signing some new partnerships. So Brian, who used who kind of works worked, has worked in and out of the company on the data side.
236 00:23:04.026 ⇒ 00:23:07.130 Uttam Kumaran: We’re signing him as a referral partner, and we have a couple more teed up.
237 00:23:07.680 ⇒ 00:23:31.620 Uttam Kumaran: And it’s not only just signing an agreement. We’re actually gonna put them into ideally at least a 1 month sort of cycle where we meet with them, share our latest service, offering and materials, and then sort of work with them to get leads. We’re also signing Metaplan and mother duck as a referral partner. Just because we’re gonna start to implement them potentially moving some clients off of Snowflake to mother duck.
238 00:23:32.041 ⇒ 00:23:40.470 Uttam Kumaran: It’s cheaper. And they’re they’re much better partner to work with. It seems like, and have much more. You know, provenance on
239 00:23:41.690 ⇒ 00:23:48.960 Uttam Kumaran: socials and things like that so excited for that partnership. And then, yeah, we have some great partner referred leads and
240 00:23:49.320 ⇒ 00:23:59.540 Uttam Kumaran: still doing well on Linkedin. I think this is just my Linkedin, but I know this month we actually activated Robert’s Linkedin a lot more. I know amber. Kyle and Mustafa
241 00:23:59.920 ⇒ 00:24:21.094 Uttam Kumaran: are posting things. So I’m looking forward to hopefully this week. Sort of getting all the stats for everybody in one place. And then on the churn client matter. More so. Matter more was is a startup company. So they are just building their 1st product. The the lead came in through me. It’s a connection of mine. Who’s the CEO?
242 00:24:21.870 ⇒ 00:24:43.890 Uttam Kumaran: I would say across the board. We’re really trying not to work with startup companies. They are our Icp, which is like our ideal customer profile primarily because of some of the issues that we saw on matter more, which is, they don’t have a clear roadmap. We don’t have a clear partner, and there’s a high chance they just turn or change so I’m actually not too like
243 00:24:44.030 ⇒ 00:25:07.475 Uttam Kumaran: I’m bum. It’s money, but I’m actually not like there wasn’t much in our control that we could have done here. I think we did a great job at delivering for them. In fact, the feedback that we got from from everybody on their team was really really positive and we did some really interesting work around synthetic data. Hr, related data. So Annie Luke, amber really shout out there to you guys?
244 00:25:08.060 ⇒ 00:25:11.140 Uttam Kumaran: so yeah, overall, we’re gonna get a great testimonial out of them, and
245 00:25:11.470 ⇒ 00:25:18.069 Uttam Kumaran: Matthew continues to be a great sort of partner and advocate for our company, so I don’t. I don’t really consider this?
246 00:25:18.944 ⇒ 00:25:22.475 Uttam Kumaran: you know, I would say, what do they call like a
247 00:25:24.680 ⇒ 00:25:36.089 Uttam Kumaran: expected attrition, or or like we’re attributing the attrition to something that we did. I think this is sort of what we expected from this client, anyways, but we delivered a lot. There’s a good chance they bring us back at some point. So
248 00:25:39.180 ⇒ 00:25:42.950 Uttam Kumaran: yeah, I guess anything else, Robert. We want to add here.
249 00:25:46.400 ⇒ 00:25:50.410 Robert Tseng: Yeah, no. I think this is a. This is a good snapshot.
250 00:25:51.280 ⇒ 00:25:52.776 Robert Tseng: Yeah. I mean, there’s
251 00:25:53.970 ⇒ 00:25:56.419 Robert Tseng: What can we? What else can we say here.
252 00:25:56.630 ⇒ 00:26:08.569 Robert Tseng: no, I think this is, yeah. This is a good recap of like, what’s happened the past 2 weeks. Yeah. So it’s clearly a lot. A lot has moved. And I think that reading this out on a 2 week cadence is probably better. Just because, like.
253 00:26:08.820 ⇒ 00:26:15.480 Robert Tseng: yeah, we don’t want to make announcements too early, like, we wanna like share things when they’ve been signed. And like, yeah, this is, this is.
254 00:26:15.600 ⇒ 00:26:18.160 Robert Tseng: yeah. But I think you know we we can.
255 00:26:18.960 ⇒ 00:26:23.999 Robert Tseng: This is just a good good way to keep- keep the everyone else updated.
256 00:26:24.710 ⇒ 00:26:25.370 Uttam Kumaran: Cool.
257 00:26:28.110 ⇒ 00:26:40.289 Uttam Kumaran: Yeah, and then maybe I’ll I’ll pass it to Rico if you wanna just chat briefly, or if you want to share your screen about some of the new sops that are in progress, and then anything around foxify.
258 00:26:41.560 ⇒ 00:26:43.630 Rico Rejoso: Oh, yes, thank you for that.
259 00:26:43.910 ⇒ 00:26:47.939 Rico Rejoso: So on the operation side. Sorry. Let me share my screen.
260 00:27:01.850 ⇒ 00:27:30.029 Rico Rejoso: Alright. So from the operation side. As to Tom mentioned earlier that we are working on some procedures and documenting everything. So right now, we’re focusing and further improving our client. When it comes to onboarding and off boarding client, and also allocation of time and employees second would be the employee on how we auto hire the process that we have to make it smooth and easier transition from you know, from
261 00:27:30.250 ⇒ 00:27:55.550 Rico Rejoso: the Operations department, from onboarding to each department, and lastly, is for the tools. So you want to make sure that the proper tools that are, let’s say, the needed tools for each department or each employee will be provided to each through requests. And you know, still, as I mentioned as I mentioned, this is a work in progress. So there were. There will be suggestions and changes that will be made
262 00:27:56.140 ⇒ 00:28:01.483 Rico Rejoso: throughout this one. So we are open. If you guys wanted to. Rick wanted to.
263 00:28:01.880 ⇒ 00:28:12.590 Rico Rejoso: suggest anything on your end, which you think will be much easier much easier for you guys. So that’s for the sop, we’ll further update this one and keep you guys posted.
264 00:28:13.440 ⇒ 00:28:28.067 Rico Rejoso: Next would be the clock if I hours. So for this one it’s just a quick announcement for everyone, as you all know, and has you sign on your contract that we will be needing you guys to log your hours? Accordingly, much preferably on a daily basis since.
265 00:28:28.810 ⇒ 00:28:52.340 Rico Rejoso: the clock, if I hours is a big thing, if you try to. Look at it. A bigger, bigger picture, because it’s affecting the operations, the project management, and the and the finance side. So you want to make sure that you are properly logging your hours so we won’t be encountering any problem. Furthermore, we are coming up with a new plan, and how we can monitor this one. But again, due diligence guys make sure that you’re logging your hours properly. And accordingly.
266 00:28:52.440 ⇒ 00:29:00.049 Rico Rejoso: if you’re expected to work 40 or 20 HA week, we want to make sure we want to see those hours reflected on your clockify.
267 00:29:00.430 ⇒ 00:29:02.309 Rico Rejoso: That’s just on my end. Thank you. Guys.
268 00:29:04.020 ⇒ 00:29:10.619 Uttam Kumaran: That’s your top cool. So let me. I can share again.
269 00:29:14.315 ⇒ 00:29:15.160 Uttam Kumaran: Marketing.
270 00:29:18.180 ⇒ 00:29:20.688 Hannah Wang: Oh, I didn’t know I was going here. But okay,
271 00:29:23.070 ⇒ 00:29:30.600 Hannah Wang: this is just my raw thoughts. I did not prepare. So my bad. But basically, I guess the screenshot of
272 00:29:32.620 ⇒ 00:29:37.979 Hannah Wang: that Google sheet is basically a bunch of campaigns. So campaigns are just.
273 00:29:38.090 ⇒ 00:29:47.320 Hannah Wang: I guess, set initiatives that we have to. Kinda it’s like a time box initiative that we want just to make sure that we
274 00:29:47.630 ⇒ 00:29:56.100 Hannah Wang: track everything and make sure that all the assets and all the Linkedin posts and all the videos that we put out are actually effective. And
275 00:29:57.250 ⇒ 00:29:59.390 Hannah Wang: oh, yeah, being used.
276 00:29:59.977 ⇒ 00:30:06.970 Hannah Wang: And I think the goal of this, I think, before. We were kind of in the weeds of like cranking out
277 00:30:07.230 ⇒ 00:30:30.809 Hannah Wang: a ton of assets, and I think we just didn’t have structure around it. But now that we have, like a lot of one, pagers and decks and videos in place, I think we want to find a way to track everything. So as your time kind of scrolls to the right, you can see that we’re going to track primarily impressions, likes, leads added. How many emails were sent. How many replies we got?
278 00:30:31.184 ⇒ 00:30:49.529 Hannah Wang: Ultimately, we want to track meetings booked and I guess finally, the clients that we get from a certain campaign. So for example, where’s the if you look at Row 8 Q. 3. Brain forge sessions? I don’t know if you guys know this, but we’re
279 00:30:49.530 ⇒ 00:31:11.300 Hannah Wang: basically Utam and Robert are basically interviewing top leaders and other companies or people that data experts or AI experts. And we’re taking that zoom recording or in person interview. And we’re making a Youtube video out of it. And with that, we’re kind of repurposing it to linkedin Youtube shorts.
280 00:31:11.300 ⇒ 00:31:25.160 Hannah Wang: So that’s a lot of effort that we’re putting in from the marketing side. And we just want to see that that’s actually working. And if it is, then we keep going. If it’s not working. Then we stop and kind of move our efforts elsewhere.
281 00:31:25.665 ⇒ 00:31:27.059 Hannah Wang: So yeah, that
282 00:31:27.430 ⇒ 00:31:39.520 Hannah Wang: is currently our Youtube page. We have a lot of videos. There are a decent number of views, I guess, for the very 1st video we put out, which is the one with default, how top go-to-market.
283 00:31:39.520 ⇒ 00:31:41.810 Uttam Kumaran: He’s got a thousand. That’s pretty good.
284 00:31:41.810 ⇒ 00:31:53.400 Hannah Wang: Oh, yeah, that somehow went kind of viral. So that was from an event we did with Lori, who’s from operating. He’s the founder of operating dot app.
285 00:31:53.852 ⇒ 00:32:06.270 Hannah Wang: So yeah, we’re cranking out a lot of content. But like, like, I said, we just want to find a way to track everything so that we know that is actually working. And ultimately that we get clients from this.
286 00:32:06.778 ⇒ 00:32:35.819 Hannah Wang: So every campaign kind of has a leader, and an owner. Just someone who kind of knows the full cycle of everything. So, as you can see, I’m kind of in charge of like the videos, and also a lot of the partnerships that we have. Ryan is kind of leading, like recruiting and awareness. Or I feel like Rico is also doing recruiting. Sid is kind of leading like the sales. Heavy initiatives. So
287 00:32:35.900 ⇒ 00:32:44.909 Hannah Wang: yeah, there’s a lot of moving pieces. And I think we just have, like a structure and a foundation. Now. So we’re just tracking everything and seeing what works.
288 00:32:48.090 ⇒ 00:32:48.760 Uttam Kumaran: Cool.
289 00:32:53.620 ⇒ 00:32:55.080 Uttam Kumaran: Pmo. Amber.
290 00:32:55.310 ⇒ 00:33:06.049 Amber Lin: Yeah, I’ll take over this. I had I yesterday. We had 2 slides for the current clients.
291 00:33:06.580 ⇒ 00:33:09.489 Amber Lin: do we? Still, I can’t find that anymore.
292 00:33:10.330 ⇒ 00:33:12.429 Uttam Kumaran: 2 slides for the current. Oh,
293 00:33:12.780 ⇒ 00:33:13.150 Amber Lin: Yeah.
294 00:33:13.150 ⇒ 00:33:15.359 Uttam Kumaran: Remove it. I guess I wasn’t gonna go through.
295 00:33:15.950 ⇒ 00:33:23.550 Amber Lin: Okay, that’s okay. I’ll give a 2 min overview of all of them.
296 00:33:23.920 ⇒ 00:33:27.460 Amber Lin: So actually, let me share my screen.
297 00:33:37.060 ⇒ 00:33:40.339 Amber Lin: So for the current clients.
298 00:33:40.936 ⇒ 00:33:49.929 Amber Lin: Which went over that matter more churned. So in terms of project management.
299 00:33:50.930 ⇒ 00:34:05.760 Amber Lin: I currently manage ABC in urban stems adjustments we’re making internally, is that I’m going to be helping Robert with project management on Eden. And also
300 00:34:06.110 ⇒ 00:34:08.700 Amber Lin: I’ll show you guys in a second.
301 00:34:09.210 ⇒ 00:34:28.139 Amber Lin: the newly established Pmo office, which right now is currently me and our advisor Alex will help with current clients newly coming in clients, and how they transition and also internal projects.
302 00:34:28.510 ⇒ 00:34:52.790 Amber Lin: So also, recently, an update is that we’re helping the marketing team with project management, and also for now we are helping leveling Rico up to do some project management for the internal teams, mainly marketing and a bit of operations as well. So I,
303 00:34:52.929 ⇒ 00:34:55.109 Amber Lin: me and the Pmo. Will help
304 00:34:55.827 ⇒ 00:35:11.050 Amber Lin: all the internal projects and other projects that need support and help to make sure that you are organized, and you have all the information you need to deliver successfully.
305 00:35:11.940 ⇒ 00:35:18.870 Amber Lin: And I just want to do a quick overview of the Pmo so
306 00:35:19.010 ⇒ 00:35:22.912 Amber Lin: overall how this impacts you is that
307 00:35:24.050 ⇒ 00:35:48.720 Amber Lin: well, let’s start here before I did not want to follow anything that was set on a Pm. Textbook. I took courses before I started being a Pm. And then I was like, you know what? They’re all textbook. They don’t make sense. They’re just cliche. And I didn’t. I didn’t use it. So I just winged it. And then what happened was that
308 00:35:49.420 ⇒ 00:36:01.050 Amber Lin: I realized that textbooks was there for a reason, and they are sweat and tears of people who have failed because they didn’t follow them.
309 00:36:01.360 ⇒ 00:36:07.489 Amber Lin: So all the stuff that we implement is just so that
310 00:36:07.780 ⇒ 00:36:19.500 Amber Lin: we avoid the very unfortunate consequences of not doing that. And a quick example is that if we do not do grooming together
311 00:36:19.790 ⇒ 00:36:21.549 Amber Lin: during the cycle.
312 00:36:23.210 ⇒ 00:36:33.630 Amber Lin: The team does not even know what this ticket is, and then we spend more hours explaining what this is. Should we move it out, should it still be in here?
313 00:36:33.770 ⇒ 00:36:56.140 Amber Lin: And my teams, I think, for urban stems especially, we felt the benefit of having a grooming session and stuff similar to that is why we established the Pmo. Is not that we just have rituals and processes. It’s so that the team has what it needs to not fail, and of course to succeed.
314 00:36:57.690 ⇒ 00:36:59.010 Amber Lin: And so
315 00:36:59.200 ⇒ 00:37:18.640 Amber Lin: we would want your pro your feedback on the overall Pmo structure which I will share in our team channel and then for the teams that I am managing. I will share a team charter, and then we can discuss how we want to move forward and what we want to improve on.
316 00:37:19.350 ⇒ 00:37:40.589 Amber Lin: And lastly, just again we cover. We’ll cover, climb projects. We’ll cover internal teams and we’ll make sure that we are prepared for new projects coming in from sales and project stats turned and leaving us. So we’ll look over the entire process and make sure everybody is prepared.
317 00:37:40.760 ⇒ 00:37:44.750 Amber Lin: So that’s a quick announcement on the Pmo. Side.
318 00:37:45.690 ⇒ 00:37:56.990 Uttam Kumaran: Yeah, I’m particularly, really excited for this. We’re as you saw this past month, we are basically on boarded one new sort of client in one fashion or another every week.
319 00:37:57.661 ⇒ 00:38:14.439 Uttam Kumaran: And this is just gonna get crazier. And so I’m really really thankful that Amber and Alex who’s our advisor for our Pm. Team took the time to do this. Because we’re gonna need process for how fast sales is gonna go
320 00:38:15.220 ⇒ 00:38:17.679 Uttam Kumaran: and ultimately like
321 00:38:18.260 ⇒ 00:38:23.700 Uttam Kumaran: saying No, to revenue is is what will have to happen if we don’t have internal process.
322 00:38:24.269 ⇒ 00:38:31.929 Uttam Kumaran: To take on these these clients. Additionally, every new Pm. That joins will benefit from this work as well as everybody on their teams.
323 00:38:32.757 ⇒ 00:38:35.740 Uttam Kumaran: So this is actually incredibly helpful.
324 00:38:37.650 ⇒ 00:38:50.250 Amber Lin: Oh, yeah. One thing I didn’t mention is that I think I did is to help create processes that people can learn project management. Even they, even if they didn’t have experience before.
325 00:38:55.220 ⇒ 00:38:56.150 Uttam Kumaran: Awesome.
326 00:38:56.150 ⇒ 00:38:58.250 Amber Lin: Who’s next as Demos.
327 00:38:59.450 ⇒ 00:39:05.830 Uttam Kumaran: So Demos are next think we wanted to demo the AI platform.
328 00:39:06.240 ⇒ 00:39:12.449 Uttam Kumaran: and then I think this isn’t here to Demo Dax or pipelines, but I think we have a couple of things on the ad side that I can probably just
329 00:39:13.370 ⇒ 00:39:23.960 Uttam Kumaran: steal the thunder on. So this is all a lot of great work by Casey Miguel Awaii, Luke,
330 00:39:25.320 ⇒ 00:39:35.270 Uttam Kumaran: and Mustafa. I know a lot of you guys have interacted with this team over the past few months. But I feel like in the past 3 weeks or so, quite a bit of stuff
331 00:39:35.510 ⇒ 00:39:44.030 Uttam Kumaran: has now come to the surface and and been made online. I think, particularly, we had a great establishment of a relationship between design and the AI team.
332 00:39:44.615 ⇒ 00:40:11.804 Uttam Kumaran: Additionally, the AI team is doing a great job of meeting individuals understanding like what tools they’re using. And really, this hub that we built. You know, we’re not a product company. But really, in going to look in the market for something that could help us become more efficient. There wasn’t anything that existed. And so what we do for clients. We, you know, we continue to do for ourselves, which is, you know, innovate. And so I just wanted to kind of share what’s new in in the platform
333 00:40:12.190 ⇒ 00:40:16.069 Uttam Kumaran: today, so I’ll be going through a couple of these.
334 00:40:16.310 ⇒ 00:40:28.499 Uttam Kumaran: But to kind of start off, you can now go to platform dot brainforgeai so no longer going to the demo site. You can still go. It’ll just redirect you. And if you go into here
335 00:40:28.950 ⇒ 00:40:34.550 Uttam Kumaran: there is a prompt to log in with Google. So we added, Sso login,
336 00:40:35.540 ⇒ 00:40:37.589 Uttam Kumaran: I have a thousand logins.
337 00:40:38.126 ⇒ 00:40:51.189 Uttam Kumaran: And so once you’re in here, you kind of see our our latest dashboard. So what you’ll see is all the benefit of working with a design team, and that there’s Logos. Things look nice. And you’ll see basically all of our meetings, as well as some light analytics about
338 00:40:51.350 ⇒ 00:41:03.820 Uttam Kumaran: meetings and meetings this week. Participants. On the left side you’ll see a couple of different sections, clients, agents, and demos as well as other actions and departments coming soon.
339 00:41:04.800 ⇒ 00:41:22.480 Uttam Kumaran: So just to start off, I’ll just sort of outline what you see here on this main dashboard screen. So you’re gonna see all of our meetings. You can also go in here and and update dates. For, for example, I want to go. Look at just the meetings between 5, 11 and 5 5.
340 00:41:23.077 ⇒ 00:41:31.119 Uttam Kumaran: Right? So you can just go find meetings as you need to. You can also search. So if I was gonna search for like a meeting
341 00:41:31.810 ⇒ 00:41:37.110 Uttam Kumaran: had with pool parts the other day, I was going to search for pool. And here’s this AI regroup
342 00:41:37.840 ⇒ 00:41:55.489 Uttam Kumaran: on the right side. This is our typical brain for Gpt. It’s straight on just who we are as a business. So you can ask it basic questions. It’s pretty much, very similar to a to open AI 4 0, eventually, this is gonna end up being almost like a router agent. Where
343 00:41:55.490 ⇒ 00:42:12.199 Uttam Kumaran: once you talk to Brain Forge Gpt, you’ll be able to say, Hey, I want to go. I want. I have a question about this client. It will route you to the right place, or it will leverage those resources to give you information. So you can basically start your journey here. If we just click into one of these meetings.
344 00:42:13.750 ⇒ 00:42:23.906 Uttam Kumaran: what you’ll be presented here is the title of the meeting. Participants. And actually a video recording I don’t know if
345 00:42:24.540 ⇒ 00:42:26.229 Uttam Kumaran: I think I had my video off
346 00:42:27.740 ⇒ 00:42:32.440 Uttam Kumaran: because I was sick. But of course, just a video player. You can.
347 00:42:32.720 ⇒ 00:42:47.829 Uttam Kumaran: But you can start talking directly to the transcript right here, so very similar. If you use granola or fireflies. We also, you know, generate summaries. Have the full transcript that you can copy and paste wherever you want to take it.
348 00:42:48.526 ⇒ 00:42:58.900 Uttam Kumaran: And of course chat with the meeting Transcript pretty directly. Here we do have a feature that you can go here and click, create linear tickets. This will create tickets based on a meeting transcript.
349 00:42:59.347 ⇒ 00:43:05.450 Uttam Kumaran: And then I’ll give you a really helpful interface to actually go create them in directly linear. We also added this helpful
350 00:43:05.740 ⇒ 00:43:23.840 Uttam Kumaran: email summary, which I think Hannah, Robert. Anyone on sales or on Pm. Will will find this really helpful. You can actually select the type of email you want to send, whether it’s sales or project management. And then, for example, if you click sales, we have now, like 6 or 7 different templates.
351 00:43:24.412 ⇒ 00:43:30.580 Uttam Kumaran: For project management. We only have one but we can add as many as you want. Now.
352 00:43:30.580 ⇒ 00:43:33.419 Amber Lin: Because I only need one. It’s okay.
353 00:43:33.820 ⇒ 00:43:56.369 Uttam Kumaran: For now. So I think what what we’ll end up doing is depending on the types of communications that go out to anybody, whether it’s a new lead. Whether it’s an existing client, whether it’s a partner, we will start to generate those email templates, emailing and sending slack updates is sort of what a lot of us spend a majority of our day on and is extremely
354 00:43:56.540 ⇒ 00:44:07.029 Uttam Kumaran: boring to do. And typically we get we procrastinate on it. At least, I’m talking about me. And so having this is really really helpful for anybody to go produce these and get this out to a client.
355 00:44:07.519 ⇒ 00:44:17.079 Uttam Kumaran: One simple email summarizing what we did could be the way we stand out as as a consultancy over others. Also slack summary. So you can also generate
356 00:44:17.420 ⇒ 00:44:20.911 Uttam Kumaran: basically the same thing except tailored towards
357 00:44:22.120 ⇒ 00:44:32.670 Uttam Kumaran: towards slack and so I would say, this is just sort of a meeting specific view the additional stuff. Let me just make sure. So design, yeah, meeting name automation.
358 00:44:34.160 ⇒ 00:44:35.989 Uttam Kumaran: So for example, here’s like a
359 00:44:36.320 ⇒ 00:44:40.259 Uttam Kumaran: quick thing of like, here’s a markdown based thing that you can copy paste into into slack
360 00:44:41.011 ⇒ 00:44:56.450 Uttam Kumaran: so if you have email summaries or slack summary templates that you want us to use, you just tell us. Now you can add those pretty easy. The other thing, is commonly meeting. Names aren’t great. Sometimes they’re just like this person, plus this person talk
361 00:44:56.550 ⇒ 00:44:57.400 Uttam Kumaran: or like.
362 00:44:57.780 ⇒ 00:45:13.590 Uttam Kumaran: stand up right? And so we have AI that actually looks at the existing meeting name. And if it’s not descriptive enough, it updates it. So you will actually be able to see the right. You actually just see descriptive meeting names instead of just like
363 00:45:13.820 ⇒ 00:45:20.157 Uttam Kumaran: stand up or like sync or like whatever. So that’s a really like nifty feature.
364 00:45:21.190 ⇒ 00:45:23.129 Uttam Kumaran: the other thing on the left side.
365 00:45:23.756 ⇒ 00:45:36.539 Uttam Kumaran: You can also search so you can search by topic. This is just searching through the trans through the title and the summary we’re gonna add transcript search. It’s coming up probably next sprint or the sprint after
366 00:45:36.833 ⇒ 00:46:02.040 Uttam Kumaran: of course, there’s only a lot of transcripts, so it’ll be probably a different environment. But ideally, you could search anytime. We’ve mentioned a tool or anything, and that way for whatever your use cases on the left. We also have AI agents and and Demos. I’ll probably leave the AI agents just for last. But I’ll go through the Demos. These are demos that we typically put in front of clients. So this is a demo of contextual, which is an AI Api that we use.
367 00:46:02.751 ⇒ 00:46:05.190 Uttam Kumaran: Let’s say I want to ask it a question.
368 00:46:05.645 ⇒ 00:46:20.289 Uttam Kumaran: I actually, we actually demo this to contextual yesterday or the day before, showing them like a proof of concept that we delivered, and additionally, as part of sales calls, I will be putting this in front of clients. As you see, this Demo is pulling
369 00:46:20.400 ⇒ 00:46:23.740 Uttam Kumaran: information from Pdfs showing highlights.
370 00:46:24.790 ⇒ 00:46:31.079 Uttam Kumaran: Really, really nice. We also have demos of our other agents like our customer support agent.
371 00:46:31.190 ⇒ 00:46:38.769 Uttam Kumaran: our lead researcher agent, our training knowledge training agent, record analysis in our patient record search.
372 00:46:38.890 ⇒ 00:46:49.969 Uttam Kumaran: The AI team develops these as we meet with clients, and we kind of make these available now. So anybody who’s in a sales call or wants to see some of these demos, you can see them directly right here. Pretty pretty.
373 00:46:50.490 ⇒ 00:46:52.319 Uttam Kumaran: you know, easy to come. Do that.
374 00:46:53.490 ⇒ 00:47:00.606 Uttam Kumaran: The other thing I wanted to share is the AI agent. So this is something that we just shipped this morning.
375 00:47:01.620 ⇒ 00:47:21.369 Uttam Kumaran: One of the things that we have in notion is this thing called prompt library. These are helpful prompts that other people in the company, including myself, are developing. So I use Chat Gpt, commonly, locally, and I develop prompts that I use to help me edit notes create sows, create project plans.
376 00:47:21.630 ⇒ 00:47:25.739 Uttam Kumaran: send emails just like anything I do. Just try to speed it up.
377 00:47:25.830 ⇒ 00:47:55.480 Uttam Kumaran: And our goal is to try to make this accessible by anybody on the team. And so we started off with just a couple here. The marketing interview prompt a prompt improver, and sow creator and a proofreader. And so what you can do here is you can go into here and I know. Ryan sometimes asks questions from everybody about case studies. And so you can go in here. Say, I want to talk about a case study where I implemented Dbt for a client.
378 00:47:56.170 ⇒ 00:47:58.470 Uttam Kumaran: This prompt is really great.
379 00:47:59.140 ⇒ 00:48:04.589 Uttam Kumaran: in that you could just tell it, hey? I want just a handful of nuggets. And
380 00:48:04.910 ⇒ 00:48:09.560 Uttam Kumaran: that it’ll actually interview you about sort of the topic.
381 00:48:11.180 ⇒ 00:48:25.469 Uttam Kumaran: and yeah, so we have several of these prompts that we can start to use. There’s also a prompt inner improver. If you have a prompt that you’re like. This isn’t good enough. This prompt helps you do that. We also have an sow creator prompt that I use really. Often, I put in transcripts here.
382 00:48:25.590 ⇒ 00:48:28.549 Uttam Kumaran: And then just a general proofreader editor prompt.
383 00:48:28.700 ⇒ 00:48:39.170 Uttam Kumaran: So one of the things that we’re gonna do on top of these is actually show the history. So you’ll be able to see on the right side everybody in the company that’s using these and sort of what they asked, what were the outputs
384 00:48:39.920 ⇒ 00:48:45.723 Uttam Kumaran: again? Just making some of these things that we found helpful for individuals more available?
385 00:48:46.520 ⇒ 00:49:04.929 Uttam Kumaran: on the left side. Here also you can go and manage these agents. So, for example, if you want to add one, it’s completely dynamic. So you don’t need to interact with the AI team to do this, you can go and create agents directly. Here, for example, the marketing interviewer prompt. Here’s the actual prompt that that’s it’s using. You can go edit this
386 00:49:05.635 ⇒ 00:49:08.994 Uttam Kumaran: change a category, and these these will course get more
387 00:49:10.190 ⇒ 00:49:19.670 Uttam Kumaran: kind of get get more dense with options as we go. So typically so anybody now can go add agents and sort of if you want to leverage or share this with the team, you can add them here
388 00:49:20.252 ⇒ 00:49:42.097 Uttam Kumaran: additionally, as part of our operations team. You can now add clients directly in here. So when you go to add client, you can add a new client. Name any aliases. The linear teams and automatically meetings that meetings will get automatically categorized to this client. Slack channels would automatically get ingested and linear tickets will automatically get made available to
389 00:49:42.980 ⇒ 00:49:55.149 Uttam Kumaran: to the agent, and you’ll be able to create linear tickets in these teams. So this will be a crucial step for the operations team to sort of like, get this going for every additional client. I know we’re already missing some, so we can get some some of those
390 00:49:55.420 ⇒ 00:49:59.010 Uttam Kumaran: going, and then I guess the last thing I wanted to share.
391 00:49:59.740 ⇒ 00:50:04.141 Uttam Kumaran: I believe, is Mustafa. If you want to just share briefly the
392 00:50:04.630 ⇒ 00:50:08.859 Uttam Kumaran: the linear ticket sort of automation that you’re working on.
393 00:50:09.660 ⇒ 00:50:11.580 Mustafa Raja: Yeah, let me share my screen.
394 00:50:25.560 ⇒ 00:50:28.440 Mustafa Raja: Yeah. So what what we are doing is,
395 00:50:29.030 ⇒ 00:50:42.130 Mustafa Raja: we have this linear tickets thing. But it’s attached to a certain meeting. Only right? So now, the idea is that we have a standalone linear tickets, generator, generator agent.
396 00:50:42.620 ⇒ 00:50:59.410 Mustafa Raja: That anyone can input any text, and based on the that text, it will detect the teams that. You want to generate tickets for. For. Also the signees, and so and so so let’s let’s quickly test it.
397 00:51:00.360 ⇒ 00:51:06.310 Mustafa Raja: I’ll pick this pick this summary of this ticket. Oh, sorry! The transcript of this ticket, and
398 00:51:06.510 ⇒ 00:51:08.550 Mustafa Raja: I’ll just over here generate tickets
399 00:51:08.850 ⇒ 00:51:14.680 Mustafa Raja: and what it will do. It will read this transcript and create tickets accordingly.
400 00:51:15.540 ⇒ 00:51:23.100 Mustafa Raja: So the idea of this is a standalone agent that does not depend on a particular. What’s it called
401 00:51:23.620 ⇒ 00:51:24.470 Mustafa Raja: meeting?
402 00:51:24.720 ⇒ 00:51:27.269 Mustafa Raja: Yeah, that’s pretty much it for this. Thank you.
403 00:51:28.630 ⇒ 00:51:34.600 Uttam Kumaran: Yeah. So creating tickets after meetings is a huge time suck for anybody that’s
404 00:51:35.060 ⇒ 00:51:52.581 Uttam Kumaran: in any sort of project, management or leadership capacity here. And this is like one way we’re trying to attack that pretty head on. So anytime, you have even a transcript or even just notes. You can go into here and create tickets pretty easily. We’re also building a couple of other really helpful
405 00:51:53.070 ⇒ 00:51:58.270 Uttam Kumaran: things directly into linear, which I want to share. Briefly,
406 00:51:59.350 ⇒ 00:52:01.979 Uttam Kumaran: let me just find. And a good example of this.
407 00:52:03.050 ⇒ 00:52:06.100 Uttam Kumaran: Oh, yay.
408 00:52:10.810 ⇒ 00:52:12.420 Uttam Kumaran: one second!
409 00:52:27.910 ⇒ 00:52:30.520 Uttam Kumaran: Where do we have a test? One.
410 00:52:38.200 ⇒ 00:53:05.429 Uttam Kumaran: So Miguel, on the team is actually working on another linear automation which is basically going to be like an auto groomer or basically help our Pm team understand what tickets need grooming and assist in easily grooming tickets. Once you make a quick one, so you can go into linear now on any ticket. And just type in literally the word groom, and there’s an AI, our automation spot. We’ll come back with a verdict
411 00:53:05.550 ⇒ 00:53:09.849 Uttam Kumaran: and sort of give some suggestions on why, the ticket isn’t great.
412 00:53:10.040 ⇒ 00:53:37.489 Uttam Kumaran: This is like a very simple, prompt proof of concept. But couple of things we want to do here, one we want you to be able to create. If you’re in a meeting, and you’re like quickly, I need to create a ticket with a couple of bullets. I want you to quickly be able to take those couple of bullets and turn them into a full fledged ticket with a story acceptance criteria. Second, we want to easily be able to see what tickets are active or in the current cycle that need grooming and ideally have something in the ambient that’s in the background, helping our Pm’s
413 00:53:37.930 ⇒ 00:53:41.280 Uttam Kumaran: focus their time, because, of course.
414 00:53:41.430 ⇒ 00:53:50.740 Uttam Kumaran: our engineers ability to execute the tickets is really determined by you know, the quality of them. So this is something we’re also working on.
415 00:53:51.443 ⇒ 00:53:59.520 Uttam Kumaran: The last thing I’ll probably share as room.
416 00:54:00.250 ⇒ 00:54:03.920 Uttam Kumaran: Let’s see if I have this sign in
417 00:54:05.780 ⇒ 00:54:10.010 Uttam Kumaran: cool. So this is something that Luke is working on, as well as several other things.
418 00:54:10.393 ⇒ 00:54:19.479 Uttam Kumaran: As part of this project. We’re calling Project Leverage, where we’re starting to look at our slack messages, linear tickets and calendar analytics, looking at sort of our efficiency as a team.
419 00:54:19.805 ⇒ 00:54:45.680 Uttam Kumaran: And so we’ve successfully loaded in all of our slack messages via s. 3 into rail. And so we’ll be doing some helpful analytics starting to look at. You know, peak periods and basically understanding like, how do we communicate more effectively. Also, this will be helpful to understand. If we are successfully able to delegate from Robert and I out to the team and sort of looking at how the amount of times we’re responding or tagging, getting tagged.
420 00:54:45.992 ⇒ 00:54:59.840 Uttam Kumaran: Are working. And so we’ll also look at sort of messages by team and also ideally try to find ways where we can correlate this to client success. So very, very excited. This is just like completely bare bones. But we’re also going to be linking in the
421 00:54:59.880 ⇒ 00:55:04.580 Uttam Kumaran: sort of analytics from calendars and our analytics from linear tickets as well, so
422 00:55:04.790 ⇒ 00:55:09.580 Uttam Kumaran: wanted to shout out to Luke for getting this all already.
423 00:55:11.990 ⇒ 00:55:26.600 Uttam Kumaran: and then the last thing is, we’re also going to be adding, analytics for the AI Hub. So you’re you’ll be everybody in the team will be able to see who’s using the AI Hub, what they’re doing, how often things are being used. And we’re going to use that as a team to sort of measure our performance.
424 00:55:27.435 ⇒ 00:55:31.870 Uttam Kumaran: The new event scrapers. So I’ll just quickly breeze through this. I know we have
425 00:55:31.990 ⇒ 00:55:37.399 Uttam Kumaran: not, not, not not a ton of time. Well, in our marketing hub we actually have now
426 00:55:38.480 ⇒ 00:56:02.400 Uttam Kumaran: And a raw scraped events tab. This is an event scraper that’s actually scraping for events across New York and Austin. For events that match our criteria. So you can see here there’s about 52 some of these as soon as you know, in the next few days, some of these in the next, you know, September, August, October. And so this is now a scraper that’s going to be run.
427 00:56:02.770 ⇒ 00:56:08.080 Uttam Kumaran: I think, every few days which scrapes Luma Meetup, Linkedin and just general web.
428 00:56:08.730 ⇒ 00:56:11.629 Uttam Kumaran: One thing on the marketing team is we want 60% of our
429 00:56:12.050 ⇒ 00:56:32.629 Uttam Kumaran: budget and energy budget to be going to events. Events are really high roi activity for us to be able to go meet people and speak, and so easily. We took this and built a little bit of a quick view for Robert and I to go check. Hey? I’m interested in in these events. I would love the marketing team to go find a way if I can attend, and if I can speak.
430 00:56:32.830 ⇒ 00:56:43.939 Uttam Kumaran: And so ideally. We’re gonna try to go to a few events every every week, and then we can easily expand this now to any city in the world and allow other people to to go to events which is huge.
431 00:56:44.347 ⇒ 00:56:52.679 Uttam Kumaran: So this used to be a process that takes an hour or 2 by, you know. Just me to just find in Austin. And now it’s completely on rails.
432 00:56:52.800 ⇒ 00:56:54.160 Uttam Kumaran: which is really really great.
433 00:56:56.600 ⇒ 00:56:59.159 Uttam Kumaran: And then intern team is not here.
434 00:56:59.901 ⇒ 00:57:13.239 Uttam Kumaran: And then recruiting. So yes, we are. We are recruiting right now for a mid level. Pm. A Pm. Coordinator and an AI tech lead mid level Pm. Is ideally someone with Pmp. Certification. Pmp, is the
435 00:57:13.660 ⇒ 00:57:18.200 Uttam Kumaran: project management something criteria. I forgot.
436 00:57:18.200 ⇒ 00:57:18.840 Amber Lin: Call.
437 00:57:18.840 ⇒ 00:57:29.859 Uttam Kumaran: Professional project manager professional. Yes, we hope they are professional. And it’s like it’s like kind of like the bar exam for project managers.
438 00:57:30.828 ⇒ 00:57:49.549 Uttam Kumaran: so we’re looking for a Pm ideally, with past client experience. I’m doing some interviews here in Austin today. Actually, we’re also looking for a Pm. Coordinator. Ideally, this person could be junior, or someone looking to sort of become a Pm. In their career, or just has a few years experience being a Pm, this person would basically support the entire project management team
439 00:57:49.550 ⇒ 00:58:03.398 Uttam Kumaran: and the processes they’re working on. And then finally, we’re looking for like an AI tech lead. I’m also sort of interviewing some people here. Ideally, this is someone who can assist me and sort of building out the roadmap for the AI team. Supporting them, you know,
440 00:58:03.860 ⇒ 00:58:11.769 Uttam Kumaran: more rigorously. And then yeah, sort of at least taking over and sort of the day to day project management there.
441 00:58:12.222 ⇒ 00:58:18.710 Uttam Kumaran: But also again taking over as technically for our clients, so coming on with us as solutions architect when we go pitch clients
442 00:58:19.211 ⇒ 00:58:24.958 Uttam Kumaran: and, you know, acting as like sort of the lead when when we take on new clients in order to
443 00:58:25.320 ⇒ 00:58:26.680 Uttam Kumaran: deliver solutions.
444 00:58:27.134 ⇒ 00:58:37.730 Uttam Kumaran: And then both of these. You can actually see if you go to Rainforge. If you’re if you’re thinking about sending this to anybody, please feel free. You can actually go here and go to the Careers page and
445 00:58:38.030 ⇒ 00:58:41.389 Uttam Kumaran: see our open rules, and you can shoot this over to anybody.
446 00:58:43.990 ⇒ 00:58:48.570 Uttam Kumaran: Or you can. Just if you know somebody in your life that’s qualified. You can just literally
447 00:58:48.740 ⇒ 00:58:52.430 Uttam Kumaran: connect me with over email or overtax, or whatever works.
448 00:58:54.180 ⇒ 00:59:00.900 Uttam Kumaran: Okay? And then shout outs, I know we have few minutes left. Does anyone have any
449 00:59:01.500 ⇒ 00:59:02.979 Uttam Kumaran: shout outs this week?
450 00:59:08.630 ⇒ 00:59:15.760 Hannah Wang: I can shout out the AI team, I think because I’m working more closely with you guys just seeing like you bring
451 00:59:16.390 ⇒ 00:59:20.379 Hannah Wang: the designs that Anne and I made to life and
452 00:59:20.650 ⇒ 00:59:33.920 Hannah Wang: adding even more stuff that I wasn’t even aware of from like seeing today’s demo. It’s like really cool that you guys can crank it out, and that you guys are so quick to deliver. So thank you.
453 00:59:44.010 ⇒ 00:59:47.530 Amber Lin: Anyone else shout out, I have a shout out.
454 00:59:48.300 ⇒ 00:59:48.800 Uttam Kumaran: Of course.
455 00:59:48.800 ⇒ 00:59:58.500 Amber Lin: So the matter, more clean team has closed. So I wanted to thank everybody for working on that project.
456 00:59:59.079 ⇒ 01:00:21.709 Amber Lin: We closed it, and they were very happy. So I would consider that a great success, and also we will be. We’ll look at as a project. Retro. We’ll look at our profitability, and how our efficiency on this client together. I already think we have done a really good job
457 01:00:21.930 ⇒ 01:00:42.080 Amber Lin: in terms of that. And I think we grew a lot throughout the project process, from how we interact with the clients in the beginning to to the end. There was a very big change. So thank you. To Luke, Annie and Aish on this project. And I think we really did a good job.
458 01:00:46.260 ⇒ 01:00:54.350 Uttam Kumaran: Yeah, I wanted to shout out, probably the entire Eden team. Eden is now our largest client, and a great case study of how
459 01:00:54.880 ⇒ 01:01:20.230 Uttam Kumaran: we can actually support clients of that size and that capacity like for me when I when I’m in a call with with a client, and I get confidence and try to like, you know. Go big, I think about them and sort of the work we’re doing there. So it’s it’s definitely not easy. And I know the client is not easy. But I think the money we’re bringing in is a reflection of our impact there, and we’re continuing to
460 01:01:20.250 ⇒ 01:01:26.359 Uttam Kumaran: to sort of layer on more. It’s also allowing us to go after more specialized roles
461 01:01:26.430 ⇒ 01:01:29.049 Uttam Kumaran: and bring on some really amazing talent.
462 01:01:29.760 ⇒ 01:01:33.000 Uttam Kumaran: And that’s a huge anchor client of ours. So,
463 01:01:33.520 ⇒ 01:01:35.739 Uttam Kumaran: yeah, really, really, great job. Everyone.
464 01:01:42.527 ⇒ 01:02:03.759 Annie Yu: I have one more so for me. Shout out to Luke, and these are for 2 different projects, but there are not just this week, but a lot of moments where I like ask for a huddle. And after that I’m like, thank God, I reach out to them because they’re like just so knowledgeable and helpful. So thank you. I really appreciate having you guys as my teammates.
465 01:02:06.860 ⇒ 01:02:12.249 Uttam Kumaran: Call your teammates. Yeah, I feel like we. I sort of get into this a lot where
466 01:02:12.370 ⇒ 01:02:16.360 Uttam Kumaran: sort of trying to build something. And I’m like I just should just call someone real quick.
467 01:02:16.530 ⇒ 01:02:23.989 Uttam Kumaran: And so I know we’re if we’re in office it’d be easy to just turn around and do that, but a slack huddle is your friend.
468 01:02:24.930 ⇒ 01:02:25.470 Uttam Kumaran: So
469 01:02:25.710 ⇒ 01:02:33.130 Uttam Kumaran: give it a good go and and and call your team. I think everybody’s now really tuned in, and I’m seeing great collaboration on slack
470 01:02:33.939 ⇒ 01:02:38.679 Uttam Kumaran: before, I think, even just a few months ago it was very silent in a lot of channels, and
471 01:02:38.810 ⇒ 01:02:42.249 Uttam Kumaran: sort of only kicked up when there was issues. And now I think there’s a lot of
472 01:02:42.410 ⇒ 01:02:44.249 Uttam Kumaran: chatting and collaboration.
473 01:02:45.000 ⇒ 01:02:49.020 Uttam Kumaran: So I really think, want to thank everybody for making an effort, you know.
474 01:02:54.310 ⇒ 01:03:01.560 Uttam Kumaran: And then, yeah, I guess my, I’ll make a shout out also to the AI team. You know, we’ve been working really diligently for a few months now on
475 01:03:01.710 ⇒ 01:03:09.000 Uttam Kumaran: a lot of the behind the scenes of what you saw today, like ingesting all that data building these systems that aren’t easy to build.
476 01:03:09.810 ⇒ 01:03:35.500 Uttam Kumaran: explaining to the team how to build some of these for our business. And then also, the AI team is now going and interacting directly with our team like this is really, you know, part of the reason we’re we’re investing time on the side, right? All the feet folks on those team are working on clients. And so part of the reason we’re investing in it is for our entire team. And so really, when we talk about like, why Brainforge is differentiated. It’s that type of effort that’s
477 01:03:36.010 ⇒ 01:03:50.760 Uttam Kumaran: gonna really allow us to to shine. And when I go talk to advisors. And when I explain why we’re different. It’s 1 of the key things that I explain in that we we have our own internal automations team, and we’re continue continuing to attack
478 01:03:50.900 ⇒ 01:03:59.929 Uttam Kumaran: sort of the boring parts of this business and giving everyone tools, you know. So this is just like kind of the beginning of of where that’s gonna be
479 01:04:00.590 ⇒ 01:04:05.010 Uttam Kumaran: but I use it every day, and I’m I’m like way more efficient.
480 01:04:05.180 ⇒ 01:04:09.710 Uttam Kumaran: Then, you know, I would have been without it. So I’m super super grateful.
481 01:04:14.130 ⇒ 01:04:14.620 Uttam Kumaran: cool.
482 01:04:15.160 ⇒ 01:04:16.779 Amber Lin: One last thing.
483 01:04:18.570 ⇒ 01:04:27.850 Amber Lin: oh, Kyle is here. So I was gonna say for the urban stems team. I feel like we’ve become a much more
484 01:04:28.536 ⇒ 01:04:55.100 Amber Lin: self organizing team for the past few. Through the past few sprints. I think we iterated a lot on a lot of how we work together. And thank you, Kyle, for going through the deprecations work I know at at a certain point it has become a quite a grueling task, and I’m glad that you’re able to start on some new work with revenue, and
485 01:04:55.420 ⇒ 01:05:14.149 Amber Lin: I want to thank Dem Lade and Kyle for taking a lot of ownership on urban stems. And I feel like, I can really trust you guys when on this project of where we’re going, what we, what we need to do, and it feels like a lot
486 01:05:14.450 ⇒ 01:05:27.190 Amber Lin: more of a collaborative effort. And I don’t have to pretend to lead the roadmap when I don’t really know technically, what’s going on. So I really appreciate our collaborative environment. There.
487 01:05:30.070 ⇒ 01:05:42.509 Caio Velasco: Thank you, Amber, I totally agree. It’s been really really nice to work and to learn along the way as well. And the deprecation work was definitely huge and still going on. I just actually finished to
488 01:05:42.630 ⇒ 01:05:48.109 Caio Velasco: copy almost 2,000 tables to the archive scheme literally just now.
489 01:05:48.809 ⇒ 01:05:56.990 Caio Velasco: And yeah, working with you, and then has also been quite helpful. And I’m learning a lot. So I’m on board.
490 01:05:59.190 ⇒ 01:06:12.830 Uttam Kumaran: Yeah, there’s that’s just just the work we did in the last month. And a half guys is like a huge case study. There’s not some. There are companies like Accenture and Ibm and cognizant that they would take 6 months just to do that.
491 01:06:13.190 ⇒ 01:06:16.950 Uttam Kumaran: you know, and they would charge 10 times what we’re charging right? So
492 01:06:17.270 ⇒ 01:06:41.829 Uttam Kumaran: I I don’t mean that to say that like we’re cheap and that we were like. But what I mean to say is like that quality of work is is what’s gonna allow us to stand out because when when these guys go and try to compete or find someone else. They’re gonna realize how impactful our work is, and so Super super excited us. Doing that, too, allows us to build a roadmap for the next sort of large deprecation or or
493 01:06:42.199 ⇒ 01:06:53.649 Uttam Kumaran: migration that we do. And so the next one gets way easier, and there’s no, there’s not many companies that have done that size of a of a deprecation just to share. So yeah, awesome job.
494 01:06:58.570 ⇒ 01:06:59.420 Uttam Kumaran: Cool.
495 01:07:00.310 ⇒ 01:07:03.218 Uttam Kumaran: Okay. Well, if nothing else.
496 01:07:04.390 ⇒ 01:07:06.690 Uttam Kumaran: yeah, I guess we’ll close it out. We’ll be back
497 01:07:07.241 ⇒ 01:07:12.169 Uttam Kumaran: altogether in another 2 weeks. If anyone has any questions, please. DM, me.
498 01:07:13.190 ⇒ 01:07:19.700 Uttam Kumaran: and yeah, please give us go try to log into the platform and try to break stuff and try to work with AI and stuff.
499 01:07:21.080 ⇒ 01:07:22.210 Uttam Kumaran: Let us know what you think.
500 01:07:25.150 ⇒ 01:07:27.110 Uttam Kumaran: Okay, thanks. Everyone.
501 01:07:29.080 ⇒ 01:07:30.010 Uttam Kumaran: Talk soon.