Meeting Title: Weekly Managers Meeting Date: 2025-05-20 Meeting participants: Uttam Kumaran, Amber Lin, Hannah Wang, Robert Tseng, Awaish Kumar
WEBVTT
1 00:00:40.360 ⇒ 00:00:42.989 Hannah Wang: Hello! New background.
2 00:00:43.430 ⇒ 00:00:45.397 Amber Lin: I’m at autumn’s house.
3 00:00:46.070 ⇒ 00:00:47.500 Hannah Wang: That’s crazy.
4 00:00:47.500 ⇒ 00:00:49.153 Amber Lin: I know.
5 00:00:49.980 ⇒ 00:00:51.290 Uttam Kumaran: Crazy.
6 00:00:51.290 ⇒ 00:00:55.780 Amber Lin: And now next time I’ll take the meeting and in some spots.
7 00:00:55.970 ⇒ 00:00:59.490 Uttam Kumaran: Yes, please take take my job with you.
8 00:01:03.110 ⇒ 00:01:11.599 Amber Lin: Okay, I don’t know when Robert’s gonna join. We’re just gonna be packing. So let’s run this meeting and what I’m gonna just chime in with his thoughts.
9 00:01:11.790 ⇒ 00:01:18.347 Amber Lin: But I will share my screen, and we can run through the initiatives. I apologize. A lot of them are a little bit duplicated because
10 00:01:18.820 ⇒ 00:01:27.120 Amber Lin: I will blame Chatgpt for that. But I could have grouped through that so sorry if it took a bit of time. Let’s go through the.
11 00:01:28.920 ⇒ 00:01:31.819 Amber Lin: let me share my screen, and we can look at them together.
12 00:01:56.790 ⇒ 00:02:01.470 Amber Lin: So I’m just taking Roberts as an example.
13 00:02:02.380 ⇒ 00:02:06.000 Amber Lin: We have effort, and we have impact.
14 00:02:06.170 ⇒ 00:02:10.850 Amber Lin: And suppose, when we have high effort and
15 00:02:11.800 ⇒ 00:02:18.339 Amber Lin: high impact, those are more of long term things that
16 00:02:18.590 ⇒ 00:02:26.650 Amber Lin: we should get started on not necessarily quick wins per se, but my things, we should.
17 00:02:27.600 ⇒ 00:02:38.730 Amber Lin: Where is it? Things we should keep doing? And yeah, over here thinking of.
18 00:02:41.080 ⇒ 00:02:45.120 Amber Lin: And then if it’s low impact and low
19 00:02:45.340 ⇒ 00:02:54.109 Amber Lin: low impact, high effort. We’re probably not gonna do low impact, low effort, maybe.
20 00:02:55.420 ⇒ 00:02:58.040 Amber Lin: And if it’s high impact.
21 00:02:58.250 ⇒ 00:03:01.900 Amber Lin: low effort, it’s definitely something we want to do. And we want to do it
22 00:03:02.100 ⇒ 00:03:04.810 Amber Lin: really soon, like it will be a quick win.
23 00:03:05.120 ⇒ 00:03:07.680 Amber Lin: So going back
24 00:03:14.330 ⇒ 00:03:15.305 Amber Lin: oh.
25 00:03:22.400 ⇒ 00:03:26.230 Amber Lin: how do I make it? So that we look at everybody’s different votings.
26 00:03:30.940 ⇒ 00:03:32.237 Hannah Wang: I don’t know.
27 00:03:33.920 ⇒ 00:03:39.400 Hannah Wang: You can probably just take like the higher
28 00:03:39.930 ⇒ 00:03:42.659 Hannah Wang: ones, and then compare everyone’s answer.
29 00:03:42.970 ⇒ 00:03:55.710 Uttam Kumaran: Like. The other thing you can do is like you can put Robert. You can add a new column called name. But Robert, for all of these, and then put it all into one sheet, and then you can segment on name.
30 00:03:56.180 ⇒ 00:03:57.809 Amber Lin: Yeah, I agree.
31 00:04:19.740 ⇒ 00:04:22.209 Uttam Kumaran: Yeah, we’ll just double click that thing. It’ll.
32 00:04:22.210 ⇒ 00:04:23.456 Amber Lin: I tried
33 00:04:24.080 ⇒ 00:04:25.500 Uttam Kumaran: Alright. Cool. Yeah. Yeah.
34 00:04:25.500 ⇒ 00:04:26.060 Amber Lin: Hmm!
35 00:04:26.450 ⇒ 00:04:27.360 Amber Lin: Go ahead!
36 00:04:50.590 ⇒ 00:04:52.899 Hannah Wang: Well, I didn’t know decimals were possible.
37 00:04:53.240 ⇒ 00:04:55.298 Amber Lin: I didn’t know that either.
38 00:05:00.640 ⇒ 00:05:01.869 Uttam Kumaran: Yeah, I didn’t know either.
39 00:05:20.875 ⇒ 00:05:22.180 Amber Lin: there it goes!
40 00:05:48.470 ⇒ 00:05:51.470 Hannah Wang: I will make another sheet for utam.
41 00:05:52.580 ⇒ 00:05:54.540 Amber Lin: Hmm, okay, appreciate that.
42 00:06:13.750 ⇒ 00:06:16.479 Hannah Wang: Well, why is it decimal?
43 00:06:16.670 ⇒ 00:06:18.499 Hannah Wang: That’s kind of weird.
44 00:06:37.460 ⇒ 00:06:40.569 Amber Lin: Oh, my gosh! Do you see the squirrel behind me?
45 00:06:43.700 ⇒ 00:06:44.700 Hannah Wang: No.
46 00:06:46.590 ⇒ 00:06:50.760 Amber Lin: That guy is on on the window.
47 00:06:52.960 ⇒ 00:06:56.170 Hannah Wang: Is that a bird? Feeder on the tree?
48 00:06:56.170 ⇒ 00:06:59.159 Amber Lin: It is, it is. It’s the following tree. It’s like
49 00:06:59.330 ⇒ 00:07:00.939 Amber Lin: it’s on the window. It’s stuck.
50 00:07:00.940 ⇒ 00:07:01.949 Hannah Wang: Oh! Oh! Oh!
51 00:07:01.950 ⇒ 00:07:02.770 Amber Lin: Yeah.
52 00:07:04.900 ⇒ 00:07:05.680 Robert Tseng: Hello!
53 00:07:06.600 ⇒ 00:07:09.690 Amber Lin: Bye, so we have.
54 00:07:10.900 ⇒ 00:07:13.617 Amber Lin: Oh, Robert, I’m in Uton’s couch.
55 00:07:14.780 ⇒ 00:07:24.989 Robert Tseng: Yeah, I saw the video, the photo you took of him in the lead. That was like it. It’s it belongs on like a music video, like thumbnail or something.
56 00:07:26.140 ⇒ 00:07:26.530 Amber Lin: Hmm.
57 00:07:26.530 ⇒ 00:07:28.479 Hannah Wang: Why are they not smiling?
58 00:07:28.660 ⇒ 00:07:30.350 Hannah Wang: Why do they look so.
59 00:07:30.350 ⇒ 00:07:37.120 Uttam Kumaran: That was, that was a reference. Shot like I and I was. I was at the event, and then I forgot to send it to Ryan
60 00:07:37.347 ⇒ 00:07:38.029 Uttam Kumaran: like, and also
61 00:07:38.030 ⇒ 00:07:44.316 Uttam Kumaran: like shit, he, I know he’s gonna get pissed like out if he doesn’t get to post the pre meeting the pre event.
62 00:07:45.510 ⇒ 00:07:45.950 Uttam Kumaran: Yeah. Post.
63 00:07:45.950 ⇒ 00:07:47.359 Robert Tseng: Is it at your house?
64 00:07:47.970 ⇒ 00:07:54.089 Uttam Kumaran: No, yeah, just like we I, we me and believe, did like a little interview right before.
65 00:07:54.090 ⇒ 00:07:55.430 Robert Tseng: Sick. Yeah.
66 00:07:55.690 ⇒ 00:08:01.159 Uttam Kumaran: He wanted some stuff for their case study, and I was like, let’s just do something, and we’ll put something up.
67 00:08:01.854 ⇒ 00:08:07.635 Uttam Kumaran: And so we just the lighting was just good, like, right there. So
68 00:08:08.300 ⇒ 00:08:18.403 Uttam Kumaran: but yeah, that was like a random photo, like, I don’t think that like that wasn’t that we were just like waiting to talk. Or we we just said something. And I think we were like, Okay, let’s do that again.
69 00:08:19.120 ⇒ 00:08:24.106 Uttam Kumaran: So it does. Yeah, I think I thought it came out pretty cool.
70 00:08:24.490 ⇒ 00:08:28.769 Robert Tseng: Yeah, no, the candid is great. Yeah, it didn’t look. It didn’t look that. It didn’t look staged.
71 00:08:29.950 ⇒ 00:08:30.650 Uttam Kumaran: It.
72 00:08:31.570 ⇒ 00:08:35.049 Amber Lin: Amber. Uton. I added a sheet for Uton, cool.
73 00:08:35.049 ⇒ 00:08:40.279 Hannah Wang: But I’m wondering if I copy paste it wrong, because his also has decimals, so I don’t.
74 00:08:40.280 ⇒ 00:08:43.090 Amber Lin: That’s fine. We’ll we’ll deal with that
75 00:08:45.340 ⇒ 00:08:54.239 Amber Lin: I was wanting to. Hmm! I’ll probably do a column where it’s like differences. Call them.
76 00:08:54.470 ⇒ 00:08:57.510 Uttam Kumaran: Yeah, you can do. Yeah, something like that.
77 00:08:59.220 ⇒ 00:09:01.260 Amber Lin: I know the formula.
78 00:09:08.440 ⇒ 00:09:09.240 Amber Lin: Hmm!
79 00:09:21.260 ⇒ 00:09:23.749 Amber Lin: Give me one second. I’ve seen Gpt.
80 00:09:24.180 ⇒ 00:09:26.660 Amber Lin: Sorry I should have done this before the meeting.
81 00:09:31.460 ⇒ 00:09:36.185 Uttam Kumaran: I guess, while we’re waiting. So, Rob, are we? Are we kicking off the
82 00:09:36.900 ⇒ 00:09:38.439 Uttam Kumaran: Read me next week.
83 00:09:38.890 ⇒ 00:09:46.740 Robert Tseng: Yeah, supposedly. I mean, I told, I told Phoebe I could come in. Yeah, I’m going to the knit office.
84 00:09:47.450 ⇒ 00:09:50.530 Robert Tseng: Let’s go yeah. Tomorrow or Thursday.
85 00:09:51.360 ⇒ 00:09:53.109 Robert Tseng: I need to confirm with him.
86 00:09:56.730 ⇒ 00:09:59.440 Robert Tseng: Yeah, it’s great that they both have New York offices. So.
87 00:10:00.140 ⇒ 00:10:00.860 Uttam Kumaran: Watch.
88 00:10:22.780 ⇒ 00:10:23.720 Hannah Wang: Can we talk about?
89 00:10:23.720 ⇒ 00:10:25.650 Hannah Wang: Sorry? Go ahead.
90 00:10:25.650 ⇒ 00:10:28.405 Hannah Wang: This is not important. You can go ahead.
91 00:10:31.948 ⇒ 00:10:38.709 Amber Lin: Mostly. I just wanted to do a roundup of each of us. How we’re doing as I am doing this
92 00:10:39.070 ⇒ 00:10:40.610 Amber Lin: doing this calculation.
93 00:10:43.650 ⇒ 00:10:58.000 Uttam Kumaran: I can go first, st I think. You know, I mainly tried to set up marketing and AI team to kind of push through. So the biggest things that I’m hopeful for is that Halim knocks out the demo page.
94 00:10:58.230 ⇒ 00:11:02.657 Uttam Kumaran: The designs are ready, and that Miguel comes through with the
95 00:11:03.250 ⇒ 00:11:06.541 Uttam Kumaran: the Iframes. So that’s 1 thing.
96 00:11:07.530 ⇒ 00:11:13.339 Uttam Kumaran: on the marketing side. Yeah, I feel like we’re I’m gonna get a little bit of downtime. And I’m gonna start to build out the assembly
97 00:11:13.780 ⇒ 00:11:18.807 Uttam Kumaran: like cadence for the next few weeks.
98 00:11:19.830 ⇒ 00:11:24.389 Uttam Kumaran: and then I yeah, I just got invoices out and we got a bunch of contracts settled.
99 00:11:25.400 ⇒ 00:11:36.329 Uttam Kumaran: So that’s been the the biggest thing for me the last 2 weeks, I think, while I’m out I just told anyone who has questions to escalate to Amber and Marianne first, st and then sort of from there. So.
100 00:11:36.660 ⇒ 00:11:37.250 Amber Lin: Hmm.
101 00:11:41.100 ⇒ 00:11:42.190 Uttam Kumaran: Probably it for me.
102 00:11:42.870 ⇒ 00:11:43.610 Amber Lin: Okay
103 00:11:48.230 ⇒ 00:11:51.340 Amber Lin: a wish. How about you? What’s up on your side?
104 00:11:54.140 ⇒ 00:11:59.850 Awaish Kumar: You know, all good. There’s been progress.
105 00:12:00.130 ⇒ 00:12:07.540 Awaish Kumar: Yeah, we have almost about to finish all the all the documentation work.
106 00:12:08.680 ⇒ 00:12:14.050 Awaish Kumar: and which which I’m working on to put in S. 3, which can be further read by.
107 00:12:14.150 ⇒ 00:12:15.909 Awaish Kumar: I think our AI bots
108 00:12:17.860 ⇒ 00:12:25.250 Awaish Kumar: And apart from that Meta plane for Eden. It’s kind of done. We.
109 00:12:25.470 ⇒ 00:12:30.140 Awaish Kumar: We have added customer custom tests into it. So we are like
110 00:12:30.390 ⇒ 00:12:32.480 Awaish Kumar: all the tests we had in DVD test.
111 00:12:32.830 ⇒ 00:12:39.079 Awaish Kumar: We also have those in meta plane. We can now easily compare, like how Meta plane is going to.
112 00:12:39.640 ⇒ 00:12:44.460 Awaish Kumar: how like, how we are going to have a advantage by using metaplane.
113 00:12:45.245 ⇒ 00:12:49.610 Awaish Kumar: We are going to roll out for
114 00:12:49.900 ⇒ 00:12:55.159 Awaish Kumar: other clients as well, and I’ve in doing that. I have tried to
115 00:12:55.960 ⇒ 00:13:05.710 Awaish Kumar: assign those tasks to everyone in the team like the questions to the, to this work, so that everybody get a on boarded to this tool
116 00:13:05.900 ⇒ 00:13:06.830 Awaish Kumar: as well.
117 00:13:07.230 ⇒ 00:13:12.060 Awaish Kumar: And yeah. So I’ve have some tasks
118 00:13:12.440 ⇒ 00:13:16.739 Awaish Kumar: on the event side as well, and I think, like we are.
119 00:13:17.590 ⇒ 00:13:23.540 Awaish Kumar: Robert can tell more, but we are. I think we are going going from that side as well.
120 00:13:26.800 ⇒ 00:13:32.239 Awaish Kumar: Yeah. Like, I had one on one today with basically any. And Kyle
121 00:13:33.270 ⇒ 00:13:35.990 Awaish Kumar: with Annie everything was good. She was.
122 00:13:36.210 ⇒ 00:13:37.640 Awaish Kumar: She has the
123 00:13:39.030 ⇒ 00:13:48.129 Awaish Kumar: like. She said, like she wanted. She mentioned to Amber that she wants to be on multiple projects, but
124 00:13:48.420 ⇒ 00:13:54.749 Awaish Kumar: she’s now like she’s like. What she mentioned is that she don’t want to get burned out. So
125 00:13:54.910 ⇒ 00:13:59.079 Awaish Kumar: yeah, we we just have to see like she gets the proper workload.
126 00:13:59.570 ⇒ 00:14:04.139 Awaish Kumar: But apart from that all good Kai was happy with the.
127 00:14:04.300 ⇒ 00:14:10.259 Awaish Kumar: with the thing that we have assigned him to our burstance, but he mentioned that he has not got anything so far
128 00:14:10.420 ⇒ 00:14:12.040 Awaish Kumar: to do.
129 00:14:12.040 ⇒ 00:14:15.190 Amber Lin: Yeah, we still need to sign. So.
130 00:14:15.961 ⇒ 00:14:17.620 Uttam Kumaran: Yeah, I don’t know. I’m
131 00:14:17.910 ⇒ 00:14:23.209 Uttam Kumaran: it was supposed to be done Friday. They’re just taking another day or 2, so I’ll I’m gonna ping today.
132 00:14:23.450 ⇒ 00:14:25.839 Uttam Kumaran: But yeah, thanks for playing defense.
133 00:14:27.920 ⇒ 00:14:33.200 Awaish Kumar: Yeah. So from from here, him is, was everything was okay as well.
134 00:14:33.450 ⇒ 00:14:34.050 Uttam Kumaran: Okay.
135 00:14:37.320 ⇒ 00:14:43.819 Uttam Kumaran: great. You wanna do you? Wanna do? You wanna go ahead and call the Meta plane sales and get a quote.
136 00:14:45.760 ⇒ 00:14:46.740 Awaish Kumar: Okay. Yeah.
137 00:14:46.740 ⇒ 00:14:48.959 Uttam Kumaran: Or whenever whenever you feel comfortable. Yeah.
138 00:14:51.500 ⇒ 00:14:53.499 Awaish Kumar: Yeah, I think we are
139 00:14:54.250 ⇒ 00:15:00.280 Awaish Kumar: okay with like understanding the pricing structure. Now, I can like in this week, try to
140 00:15:00.900 ⇒ 00:15:04.190 Awaish Kumar: get connected to someone who might apply.
141 00:15:06.050 ⇒ 00:15:09.689 Uttam Kumaran: I would also mention to them that we’re going to bring them to into a bunch of clients like.
142 00:15:09.910 ⇒ 00:15:14.130 Uttam Kumaran: for example, I don’t think for for Eden. I think we may cover the cost.
143 00:15:14.260 ⇒ 00:15:21.949 Uttam Kumaran: but for urban stems I’m going to ask them to buy it. So when you talk to them, ask them about discounts for our customers.
144 00:15:24.700 ⇒ 00:15:28.020 Uttam Kumaran: Just mention that we’re an agency and see what they say. But okay, great.
145 00:15:28.890 ⇒ 00:15:29.910 Awaish Kumar: Okay. Sure. Sure.
146 00:15:37.180 ⇒ 00:15:40.490 Amber Lin: Awesome. Hannah, anything from anything from you?
147 00:15:44.870 ⇒ 00:15:46.940 Hannah Wang: Yeah, I guess.
148 00:15:47.720 ⇒ 00:15:49.130 Hannah Wang: Just rolling out
149 00:15:49.270 ⇒ 00:16:00.010 Hannah Wang: more emotional stuff like interviews. I don’t know if I lost the thread or anything, but I don’t know if you want to schedule anything with
150 00:16:00.230 ⇒ 00:16:03.059 Hannah Wang: vicinity or superposition.
151 00:16:03.060 ⇒ 00:16:04.020 Uttam Kumaran: I do?
152 00:16:04.020 ⇒ 00:16:05.130 Hannah Wang: Or next.
153 00:16:05.980 ⇒ 00:16:07.309 Hannah Wang: Week or something.
154 00:16:08.310 ⇒ 00:16:13.329 Uttam Kumaran: Okay, I can get that on the books. Do you care which one.
155 00:16:15.386 ⇒ 00:16:19.539 Hannah Wang: I don’t have a good judgment, I mean, I I know you wanted to.
156 00:16:20.128 ⇒ 00:16:29.530 Hannah Wang: What’s the word prioritize, based on the 3 kinda tiers like the follow account the topic alignment, and about the last one, so.
157 00:16:30.069 ⇒ 00:16:31.149 Uttam Kumaran: Yeah. Okay.
158 00:16:31.650 ⇒ 00:16:40.669 Hannah Wang: That’s up to you, I will say next week. I am like on the road for basically the whole week. So I’m just gonna have to figure out how to
159 00:16:41.660 ⇒ 00:16:49.191 Hannah Wang: not look at my laptop and get carsick. So yeah, we we’ll we’ll figure that out.
160 00:16:50.320 ⇒ 00:16:55.249 Uttam Kumaran: Yeah. So I think I may. So I think, superposition guys and
161 00:16:56.380 ⇒ 00:16:59.439 Uttam Kumaran: Dallas and this guy. So I’ll go see them in person.
162 00:16:59.810 ⇒ 00:17:00.290 Hannah Wang: Hmm.
163 00:17:00.290 ⇒ 00:17:04.277 Uttam Kumaran: So let me let me text them and get something booked.
164 00:17:05.160 ⇒ 00:17:11.099 Uttam Kumaran: The other thing is, does anyone here have anybody that we should meet in Chicago.
165 00:17:12.410 ⇒ 00:17:15.010 Amber Lin: I’m going the following weekend.
166 00:17:15.420 ⇒ 00:17:16.239 Robert Tseng: Oh, yeah.
167 00:17:16.349 ⇒ 00:17:17.859 Uttam Kumaran: Okay, I think anyone.
168 00:17:17.869 ⇒ 00:17:18.439 Amber Lin: Okay.
169 00:17:18.780 ⇒ 00:17:19.390 Robert Tseng: I don’t. I don’t.
170 00:17:19.720 ⇒ 00:17:20.420 Robert Tseng: Chicago.
171 00:17:20.839 ⇒ 00:17:30.229 Amber Lin: Yeah. Also be in DC for a bit, new York for a bit, and then Chicago for a bit. So anyone you want me to go meet there. I can also do that.
172 00:17:30.950 ⇒ 00:17:31.460 Robert Tseng: Sure.
173 00:17:33.500 ⇒ 00:17:38.350 Uttam Kumaran: Yeah, I’ll be there, Robert. I’ll be there Thursday night until Sunday.
174 00:17:38.560 ⇒ 00:17:42.640 Uttam Kumaran: so I can definitely go Friday and meet anyone.
175 00:17:43.150 ⇒ 00:17:43.840 Robert Tseng: Okay.
176 00:17:47.660 ⇒ 00:17:53.749 Uttam Kumaran: But okay, so I’ll light up, so I’ll light up one of them, if not both of them, for this next week, Hannah, and then we can close it out.
177 00:17:54.160 ⇒ 00:18:02.250 Uttam Kumaran: The other thing is, yeah. The the video editor is good. So I think he’s gonna come on and take that off of Brian’s plate and
178 00:18:02.550 ⇒ 00:18:03.130 Amber Lin: Good.
179 00:18:03.650 ⇒ 00:18:06.420 Uttam Kumaran: Yeah. And it’s someone that Ryan worked with before and
180 00:18:06.680 ⇒ 00:18:11.020 Uttam Kumaran: was solid like, I watched his like portfolio reel and stuff. So.
181 00:18:11.020 ⇒ 00:18:24.470 Hannah Wang: Okay, for the in person. Interviews like, Are you good with like setting up all the gear and getting like cameras and like audio stuff. Maybe Mickey can help you. Not sure.
182 00:18:24.880 ⇒ 00:18:32.799 Uttam Kumaran: Yeah. So I guess what I’m gonna do is I’m I’m gonna get it. I’ll have a tripod for my iphone. And then I am either going to rent
183 00:18:34.660 ⇒ 00:18:38.580 Uttam Kumaran: I’m gonna either rent the little like wireless mics.
184 00:18:38.810 ⇒ 00:18:42.950 Uttam Kumaran: Or if you guys have a recommendation and it I can buy one.
185 00:18:42.950 ⇒ 00:18:47.679 Amber Lin: Yeah, what I have. It would work. It was 30 bucks, and there’s a there’s.
186 00:18:47.680 ⇒ 00:18:50.740 Uttam Kumaran: Oh, let’s go! I thought that was like super expensive.
187 00:18:50.740 ⇒ 00:18:57.329 Amber Lin: Yeah, my friend in cinema like she she does film. She recommended to me.
188 00:18:57.600 ⇒ 00:19:02.490 Uttam Kumaran: Okay, then I’ll just get that and then tripod. And then, if I can rent one other camera.
189 00:19:02.680 ⇒ 00:19:06.929 Uttam Kumaran: I will and then we should be good. I’ll I’ll I’ll just
190 00:19:07.460 ⇒ 00:19:10.670 Uttam Kumaran: I’ll get it to work. We’ll just go to a We work and record it, or something.
191 00:19:10.670 ⇒ 00:19:17.140 Hannah Wang: Yeah, let me know when you get that scheduled. So we can do like the Speaker spotlight post. Assuming that they’re okay with
192 00:19:17.330 ⇒ 00:19:17.970 Hannah Wang: that.
193 00:19:17.970 ⇒ 00:19:20.361 Uttam Kumaran: Yeah, they’ll they’ll they both want the promotion.
194 00:19:20.660 ⇒ 00:19:32.679 Hannah Wang: Okay, okay, cool. Other than that, I feel like there’s not a lot of like events happening. So that’s why we’re having all the interviews lined up. And then we’re starting work on
195 00:19:32.880 ⇒ 00:19:37.824 Hannah Wang: revamping some parts of the website. But that’s I feel like a huge overhaul.
196 00:19:38.570 ⇒ 00:19:40.370 Hannah Wang: Yeah. So I guess my question, guys.
197 00:19:40.630 ⇒ 00:19:42.609 Uttam Kumaran: My question, for that is like.
198 00:19:43.010 ⇒ 00:19:50.129 Uttam Kumaran: Do you want me involved at all like I would prefer not to be.
199 00:19:50.270 ⇒ 00:19:54.079 Uttam Kumaran: and just sort of give you the ownership, and like, of course, like I can.
200 00:19:54.550 ⇒ 00:19:57.859 Uttam Kumaran: I can like critique anything, but I feel like I’m
201 00:19:58.110 ⇒ 00:20:03.325 Uttam Kumaran: it’s pretty obvious what needs to get better, and I don’t think I’m adding, like tons of alpha, like
202 00:20:04.360 ⇒ 00:20:10.109 Uttam Kumaran: sort of on that. I think that your message was great, and I and the tickets are fine. I think you know exactly what to do.
203 00:20:11.120 ⇒ 00:20:23.230 Hannah Wang: Yeah, I’ll just like once Haleem pushes everything to staging. You can just look at it. And then I feel like that’s kind of what we’re doing, anyway, with all the landing page stuff. So I can handle the website.
204 00:20:23.230 ⇒ 00:20:24.470 Uttam Kumaran: Exactly. Yeah, yeah.
205 00:20:24.800 ⇒ 00:20:25.910 Hannah Wang: That’s not a huge.
206 00:20:25.910 ⇒ 00:20:31.707 Uttam Kumaran: I think the only thing I’ll put pressure on is just like, get as many videos or faces
207 00:20:32.650 ⇒ 00:20:33.869 Uttam Kumaran: up there as possible. That’d be my.
208 00:20:33.870 ⇒ 00:20:40.109 Hannah Wang: Yeah, we’ll probably try to like embed a bunch of videos. And if you could record the.
209 00:20:40.110 ⇒ 00:20:40.850 Uttam Kumaran: Yes, yes.
210 00:20:40.850 ⇒ 00:20:45.669 Hannah Wang: Yeah, that’d be awesome.
211 00:20:45.670 ⇒ 00:20:48.960 Uttam Kumaran: No, no, no, no! I will do this. No, I will do it, I will do it.
212 00:20:48.960 ⇒ 00:20:49.370 Hannah Wang: Okay.
213 00:20:49.370 ⇒ 00:20:50.489 Uttam Kumaran: Good reminder.
214 00:20:51.602 ⇒ 00:20:54.910 Hannah Wang: Other than that nothing on my end.
215 00:20:57.253 ⇒ 00:21:05.149 Amber Lin: For me. This week’s gonna be pretty ABC heavy because we do need to drive some usage there. Gonna go in.
216 00:21:05.390 ⇒ 00:21:06.990 Amber Lin: Yeah. Yeah. I wish.
217 00:21:09.540 ⇒ 00:21:14.879 Awaish Kumar: Sorry you can go ahead. I was just wanted to ask something about certification lab.
218 00:21:15.300 ⇒ 00:21:15.870 Amber Lin: Hmm.
219 00:21:17.810 ⇒ 00:21:23.660 Awaish Kumar: So it was like, after the post, I think, like, apart from data, people
220 00:21:23.850 ⇒ 00:21:30.080 Awaish Kumar: like to mention like AI team can also do it, but I don’t think anyone have maybe seen it. Or
221 00:21:30.656 ⇒ 00:21:35.750 Awaish Kumar: should I tag them separately, or just like how we want to.
222 00:21:38.130 ⇒ 00:21:39.150 Uttam Kumaran: I would.
223 00:21:40.830 ⇒ 00:21:44.879 Uttam Kumaran: I would just tag them one more time just to confirm.
224 00:21:45.000 ⇒ 00:21:50.039 Uttam Kumaran: If not, then let’s move forward. Yeah, they don’t necessarily have to do it.
225 00:21:51.470 ⇒ 00:21:52.210 Awaish Kumar: Okay.
226 00:21:52.480 ⇒ 00:21:54.349 Uttam Kumaran: I think they probably just missed it.
227 00:21:56.180 ⇒ 00:21:57.780 Awaish Kumar: Sure I will just ask them.
228 00:21:58.120 ⇒ 00:22:02.819 Amber Lin: Yeah, where are you sending it? And is it in a data team channel or.
229 00:22:03.380 ⇒ 00:22:04.950 Awaish Kumar: It’s a brain food stream, channel.
230 00:22:09.490 ⇒ 00:22:14.719 Amber Lin: And then, if not, we can also ask in a Friday meeting, but that might be too late already.
231 00:22:16.860 ⇒ 00:22:19.679 Awaish Kumar: Yeah, yeah, sure, I can just ask.
232 00:22:21.010 ⇒ 00:22:24.540 Amber Lin: Okay, on my side, I’m going to
233 00:22:26.260 ⇒ 00:22:28.740 Awaish Kumar: Do you have slack groups like
234 00:22:28.900 ⇒ 00:22:32.480 Awaish Kumar: like data team AI team. So you can just the device.
235 00:22:32.480 ⇒ 00:22:36.750 Uttam Kumaran: Good idea. I haven’t set it up yet. No, you should feel free.
236 00:22:37.820 ⇒ 00:22:41.179 Uttam Kumaran: I don’t think you need to be an admin to to do it.
237 00:22:42.260 ⇒ 00:22:44.210 Awaish Kumar: Okay, then I can create that.
238 00:22:46.940 ⇒ 00:22:49.690 Amber Lin: Oh, Hannah, do we have anyone hosting this Friday?
239 00:22:50.931 ⇒ 00:23:01.040 Hannah Wang: Yes, hosting, and then I’ll ask someone else for next Friday. I don’t think I’ll be able to make it this Friday so hopefully. Nothing explodes. But yeah.
240 00:23:01.040 ⇒ 00:23:01.480 Hannah Wang: I’ll be there.
241 00:23:01.480 ⇒ 00:23:02.730 Amber Lin: Fine. Okay. Sweet.
242 00:23:03.130 ⇒ 00:23:05.039 Uttam Kumaran: What’s gonna explode.
243 00:23:05.040 ⇒ 00:23:07.179 Hannah Wang: Oh, you never know. I feel like when.
244 00:23:07.180 ⇒ 00:23:09.399 Uttam Kumaran: Dude. Don’t freak me out. I’m getting stressed.
245 00:23:09.400 ⇒ 00:23:13.009 Hannah Wang: Oh, oh, just kidding. Nothing is gonna explode for sure.
246 00:23:13.010 ⇒ 00:23:14.979 Amber Lin: Both Robert and Utam’s not gonna be here.
247 00:23:14.980 ⇒ 00:23:17.650 Uttam Kumaran: Rayna’s more stressed than I am. Dude.
248 00:23:17.650 ⇒ 00:23:21.339 Hannah Wang: I’m a stressed person. You should know that I’m a highly anxious individual, so.
249 00:23:21.870 ⇒ 00:23:24.335 Amber Lin: Take everything I say with a grain of salt.
250 00:23:24.610 ⇒ 00:23:27.959 Uttam Kumaran: I’m just bummed. I got to miss the lab share. I’ll watch a recording.
251 00:23:29.250 ⇒ 00:23:29.920 Hannah Wang: Yeah.
252 00:23:30.520 ⇒ 00:23:31.430 Amber Lin: Okay.
253 00:23:34.710 ⇒ 00:23:38.786 Amber Lin: Hi, Jess, cool.
254 00:23:40.000 ⇒ 00:23:41.210 Amber Lin: I’m good.
255 00:23:43.820 ⇒ 00:23:50.620 Amber Lin: Yeah. On my side. I’m gonna go into ABC this week in person. And then the other projects are
256 00:23:50.820 ⇒ 00:23:58.839 Amber Lin: going well, I need to catch up from yesterday, cause we were. I was on the plane, and we were at the event the whole day, but other than that.
257 00:23:59.350 ⇒ 00:24:10.938 Amber Lin: I think they’re like they’re all right. I think once I catch up I’ll feel better about them. But right now I’m like there’s pending doom. Kinda I feel about these things until I catch up.
258 00:24:11.400 ⇒ 00:24:12.180 Hannah Wang: Yeah.
259 00:24:12.770 ⇒ 00:24:18.180 Amber Lin: Okay, let me share my screen. It’s ready, and
260 00:24:18.340 ⇒ 00:24:21.269 Amber Lin: mostly just want us to look at the disagreements we have.
261 00:24:21.780 ⇒ 00:24:33.670 Amber Lin: I did a range, and I did a standard deviation. So this score is the sum of these 2 standard deviations. So differences in effort and differences in impact.
262 00:24:33.850 ⇒ 00:24:38.280 Amber Lin: And and we ranked it by
263 00:24:38.540 ⇒ 00:24:49.950 Amber Lin: the disagreement score, and say, we can see for the 1st one of we have a big disagreement somewhere
264 00:24:50.290 ⇒ 00:24:53.249 Amber Lin: here, mostly, I I guess, mostly from me.
265 00:24:54.600 ⇒ 00:24:55.526 Hannah Wang: And me.
266 00:24:55.990 ⇒ 00:24:57.060 Amber Lin: Yeah. Yeah.
267 00:25:00.141 ⇒ 00:25:04.190 Amber Lin: I guess if I’m the differentiator, I’ll say, why it is. I think
268 00:25:04.650 ⇒ 00:25:09.970 Amber Lin: it. It’s it’s a lot of effort to get people to adopt things
269 00:25:10.260 ⇒ 00:25:23.910 Amber Lin: because I was trying to, maybe because I was a person who’s interview every single person. And I was like, Wow, this is taking more than I thought it would be, because we can set policies, but they they don’t get adopted that easily.
270 00:25:24.330 ⇒ 00:25:30.500 Amber Lin: But I also think they’ll have a huge impact of enabling people to
271 00:25:31.280 ⇒ 00:25:34.349 Amber Lin: use AI more often. That’s my take.
272 00:25:37.270 ⇒ 00:25:50.866 Hannah Wang: Yeah, same. That’s why I thought it was a higher effort. Just because even with small things like the culture stuff, no one’s really doing it. So I feel like it’s just hard to get people on board to things.
273 00:25:51.780 ⇒ 00:25:52.620 Hannah Wang: yeah.
274 00:25:52.850 ⇒ 00:25:55.459 Amber Lin: Yeah. What do you guys think those who ranked it?
275 00:25:56.804 ⇒ 00:25:57.609 Amber Lin: Lower.
276 00:26:05.800 ⇒ 00:26:09.529 Awaish Kumar: What I understood from talking to people is that
277 00:26:10.390 ⇒ 00:26:15.010 Awaish Kumar: like when they are comfortable enough, they can try
278 00:26:15.574 ⇒ 00:26:19.709 Awaish Kumar: like, if we ask them to try something have a pressure
279 00:26:20.270 ⇒ 00:26:22.739 Awaish Kumar: like they. They are going to face some.
280 00:26:23.390 ⇒ 00:26:24.080 Uttam Kumaran: I agree.
281 00:26:24.080 ⇒ 00:26:33.460 Awaish Kumar: Implications. Then they they don’t try to adopt that, or maybe push it away. Otherwise we just encourage them to use it
282 00:26:33.640 ⇒ 00:26:35.040 Awaish Kumar: without any
283 00:26:37.460 ⇒ 00:26:40.189 Awaish Kumar: Let’s say consequences. Then they they might try.
284 00:26:40.790 ⇒ 00:26:42.909 Awaish Kumar: and if they love it they they will adopt it.
285 00:26:43.900 ⇒ 00:26:44.710 Amber Lin: I see.
286 00:26:47.625 ⇒ 00:27:16.950 Amber Lin: I think the main thing that we have different views on is how like adoption works of how we like me and Hannah trying to get people adopted. We’ve seen that it’s actually like people will say they love it, but they don’t, really. They don’t really do anything about it, and so, like, as you said, those who use it will continue to use it. But those who don’t are the ones whose kind of hard to
287 00:27:17.060 ⇒ 00:27:18.779 Amber Lin: push past with.
288 00:27:20.106 ⇒ 00:27:27.039 Amber Lin: Also, I have a I know here a few ones Robert put like a 1 1 on those that repeated
289 00:27:27.660 ⇒ 00:27:33.649 Amber Lin: cause I was just trying to see like, maybe some of these are because we have duplicates.
290 00:27:37.630 ⇒ 00:27:40.860 Robert Tseng: Oh, yeah, I put once for where I already saw it come up before.
291 00:27:40.860 ⇒ 00:27:46.790 Amber Lin: Yeah, okay, that’s great. Helps us. Helps us know with duplicates as well. That’s really helpful.
292 00:27:48.500 ⇒ 00:27:51.299 Amber Lin: It’s a great next point.
293 00:27:51.450 ⇒ 00:27:52.612 Amber Lin: Oh, please mute.
294 00:27:53.130 ⇒ 00:27:53.800 Robert Tseng: Yeah.
295 00:27:54.220 ⇒ 00:28:06.429 Robert Tseng: I mean, you could. You could filter out anything for me on like that’s that’s equal that that. Well, I don’t know. It’s the conditional logic. By what but like, when column C and column H equals one.
296 00:28:07.340 ⇒ 00:28:20.779 Robert Tseng: you could ignore those. But I I don’t know. That might be a little bit too much to do. I don’t. I don’t think you can do it off of a filter. You probably would need to create a another, you know I’ll I’ll make. I’ll make the the field while you’re talking.
297 00:28:21.190 ⇒ 00:28:29.219 Amber Lin: Okay, let’s look at personality and communication mapping.
298 00:28:29.720 ⇒ 00:28:36.270 Amber Lin: I think Utam put me and Hannah, me and Utam put a high impact.
299 00:28:36.560 ⇒ 00:28:47.060 Amber Lin: and I have submitted high effort. So I’ll talk. It’s like we need to.
300 00:28:47.390 ⇒ 00:28:54.330 Amber Lin: I guess the mapping part takes us to make everybody do a personality test, have it documented.
301 00:28:54.440 ⇒ 00:29:05.540 Amber Lin: And then I think I was thinking too far ahead. It was like, Okay, implementing that type of communication needs a lot of also needs a lot of thought. So that was my approach.
302 00:29:06.060 ⇒ 00:29:09.709 Amber Lin: But do we? I think.
303 00:29:09.710 ⇒ 00:29:11.609 Uttam Kumaran: What did I put as a score for that.
304 00:29:12.147 ⇒ 00:29:19.130 Amber Lin: You said, effort is 1.5, and impact is 4.5.
305 00:29:20.120 ⇒ 00:29:33.330 Uttam Kumaran: Yeah, I guess what I meant was like, I don’t think it’s that hard for us to spend 5 min on every person and sort of be like this person needs this. This person needs that. I don’t know what we do with that, though, like I was more like.
306 00:29:34.160 ⇒ 00:29:38.389 Uttam Kumaran: I think it’s pretty easy to write that it’ll take us 30 min to go across the company and write that down
307 00:29:38.770 ⇒ 00:29:41.390 Uttam Kumaran: like how we action on that I’m not sure.
308 00:29:43.300 ⇒ 00:29:50.730 Amber Lin: I agree, and that makes sense. What about the people who put low and low? Or how do you? How do you feel about this.
309 00:30:06.470 ⇒ 00:30:07.870 Hannah Wang: Who put low and low.
310 00:30:10.330 ⇒ 00:30:11.620 Amber Lin: That would be.
311 00:30:11.620 ⇒ 00:30:12.730 Hannah Wang: Oh, okay.
312 00:30:13.230 ⇒ 00:30:14.269 Amber Lin: That would be.
313 00:30:14.270 ⇒ 00:30:14.920 Robert Tseng: Me!
314 00:30:16.710 ⇒ 00:30:19.340 Amber Lin: And Robert. Yes, he.
315 00:30:19.900 ⇒ 00:30:20.480 Robert Tseng: Oh, yeah.
316 00:30:20.480 ⇒ 00:30:28.539 Robert Tseng: yeah, I mean, I thought, it’s just like you send. Send them a couple of tests to go and and fill out. People can take. You know we we can curate.
317 00:30:29.100 ⇒ 00:30:37.239 Robert Tseng: I mean to me this is like a team like a team. Building exercise would make you take the Mbti make you take the Enneagram strength finder, you know we just.
318 00:30:37.240 ⇒ 00:30:41.969 Uttam Kumaran: See you. What? Okay? So you know, way more about this. Yeah, I feel like, if if you’re.
319 00:30:41.970 ⇒ 00:30:52.190 Amber Lin: Okay, I think to to say that I think I I all I should adjust my like estimate. I think this should be like low effort.
320 00:30:53.560 ⇒ 00:30:55.929 Hannah Wang: Probably low effort on our end in.
321 00:30:55.930 ⇒ 00:31:01.199 Robert Tseng: Yeah, let’s have to execute it like we just, I mean high effort on people to sit down
322 00:31:01.660 ⇒ 00:31:10.280 Robert Tseng: to take it. But that’s fine. That’s that’s how I framed everything here. It was like, how much effort is it for us to like? Do something about it?
323 00:31:10.280 ⇒ 00:31:12.330 Amber Lin: Oh, I see! See! I see!
324 00:31:12.330 ⇒ 00:31:14.030 Robert Tseng: Seems a little like it’s a low effort.
325 00:31:14.220 ⇒ 00:31:21.529 Amber Lin: I see that I see. I think that’s the difference about how I ranked a lot of things because I was thinking of how does it take the company to do this, and the.
326 00:31:21.530 ⇒ 00:31:22.150 Hannah Wang: Yeah, me, too.
327 00:31:22.150 ⇒ 00:31:29.480 Amber Lin: I mean, the personality test takes a while. Okay, that’s a really good differentiator to to know when we scores.
328 00:31:29.620 ⇒ 00:31:32.279 Amber Lin: I filtered out all the things that I consider to be duplicate.
329 00:31:32.280 ⇒ 00:31:36.149 Amber Lin: Thank you. That helps things make a lot more sense.
330 00:31:36.150 ⇒ 00:31:36.760 Robert Tseng: Yeah.
331 00:31:37.256 ⇒ 00:31:39.740 Amber Lin: Okay, let me try. And
332 00:31:40.430 ⇒ 00:31:48.330 Amber Lin: all of these are ranked properly. Right. Next one would be minimal viable ticket standard.
333 00:31:50.370 ⇒ 00:31:53.640 Amber Lin: I think it seems like with this one.
334 00:31:54.620 ⇒ 00:32:04.570 Amber Lin: It’s mostly, I think this was, this was one and one oops.
335 00:32:09.270 ⇒ 00:32:12.470 Amber Lin: Yeah, I think this one we had a
336 00:32:12.580 ⇒ 00:32:15.309 Amber Lin: utam said this would take a lot of effort
337 00:32:15.640 ⇒ 00:32:18.830 Amber Lin: to have a minimal viable context ticket standard.
338 00:32:19.630 ⇒ 00:32:21.689 Amber Lin: I mean, that’s the main differentiator.
339 00:32:22.630 ⇒ 00:32:25.859 Uttam Kumaran: But again I didn’t. This is where it’s like I’m more of like.
340 00:32:26.560 ⇒ 00:32:30.469 Uttam Kumaran: It’s where it will take us like. I can tell you what’s a good ticket?
341 00:32:30.710 ⇒ 00:32:36.029 Uttam Kumaran: But for everybody who writes tickets to write good tickets, it’s hard.
342 00:32:37.600 ⇒ 00:32:40.610 Amber Lin: I agree. I think that’s also why I put.
343 00:32:40.610 ⇒ 00:32:42.060 Uttam Kumaran: And I guess what I’m saying.
344 00:32:42.490 ⇒ 00:32:48.899 Uttam Kumaran: Does it matter like this would be a question for the team? I think every I mean I,
345 00:32:49.340 ⇒ 00:32:52.960 Uttam Kumaran: and more like I think we should have great tickets.
346 00:32:53.150 ⇒ 00:32:58.250 Uttam Kumaran: I know it takes a long time that should be a good onus to push somehow to use AI for that stuff.
347 00:32:58.660 ⇒ 00:33:07.589 Uttam Kumaran: But like there’s in engineering, it’s just like layers and layers of loss from the client through pm. Through the engineer and backup.
348 00:33:08.490 ⇒ 00:33:14.480 Uttam Kumaran: It’s having bad contacts is not, is a huge reason for that.
349 00:33:14.980 ⇒ 00:33:15.700 Amber Lin: Students.
350 00:33:15.700 ⇒ 00:33:17.559 Uttam Kumaran: You know, so I don’t know.
351 00:33:18.800 ⇒ 00:33:22.959 Amber Lin: I see, I agree. Wait! I think this might have been a.
352 00:33:24.710 ⇒ 00:33:27.419 Uttam Kumaran: It’s hard to do. It’s it takes a long time.
353 00:33:28.110 ⇒ 00:33:29.000 Amber Lin: I see
354 00:33:34.170 ⇒ 00:33:34.840 Amber Lin: cool
355 00:33:35.160 ⇒ 00:33:47.380 Amber Lin: next one track project versus non-project time said, it will take a very, very little, Robert said. It will take a long time. And Ute I think
356 00:33:48.670 ⇒ 00:33:54.230 Amber Lin: I think we also have disagreements on the impact of tracking project versus non-project time.
357 00:33:58.040 ⇒ 00:34:01.239 Uttam Kumaran: I thought, tracking is easy, because people are tracking their hours.
358 00:34:05.340 ⇒ 00:34:06.570 Uttam Kumaran: What? Like right?
359 00:34:07.860 ⇒ 00:34:10.249 Amber Lin: That’s true. That’s true.
360 00:34:10.250 ⇒ 00:34:13.060 Hannah Wang: Like the way people track is also different.
361 00:34:13.080 ⇒ 00:34:18.229 Amber Lin: That’s true. Yeah. Oh, I guess making it very standardized, Robert. What do you think.
362 00:34:19.899 ⇒ 00:34:26.369 Robert Tseng: I suppose I was thinking from the perspective of yeah, I mean, I guess
363 00:34:27.069 ⇒ 00:34:33.759 Robert Tseng: hours for the contractors is one thing, but like. I don’t track my out. I don’t track my time. Maybe I should be.
364 00:34:34.389 ⇒ 00:34:34.944 Robert Tseng: and
365 00:34:35.500 ⇒ 00:34:36.559 Uttam Kumaran: Yeah, oh, I agree.
366 00:34:36.560 ⇒ 00:34:38.460 Robert Tseng: This is more of like a question of like.
367 00:34:38.730 ⇒ 00:34:42.040 Robert Tseng: what’s our true utilization, you know, like.
368 00:34:42.565 ⇒ 00:34:43.090 Amber Lin: Okay.
369 00:34:43.090 ⇒ 00:34:47.109 Robert Tseng: Kind of this team is mostly non billable.
370 00:34:47.350 ⇒ 00:34:53.339 Robert Tseng: and we kind of just at least, at least I would say we tough. And I kind of just throw ourselves at whatever.
371 00:34:53.340 ⇒ 00:34:55.870 Uttam Kumaran: The that’s the goal, like, I don’t think we should
372 00:34:56.170 ⇒ 00:34:58.839 Uttam Kumaran: necessarily have a lot of our time
373 00:34:59.480 ⇒ 00:35:03.229 Uttam Kumaran: as billable. I guess if you’re saying it. There definitely is some now.
374 00:35:03.710 ⇒ 00:35:11.669 Uttam Kumaran: I could tell you like it’s probably like it’s probably 10 h for me, right? So worth measuring. I’m not sure.
375 00:35:11.930 ⇒ 00:35:12.610 Robert Tseng: Yeah.
376 00:35:14.690 ⇒ 00:35:15.029 Uttam Kumaran: Trying to get.
377 00:35:15.030 ⇒ 00:35:18.940 Robert Tseng: So as far as like non project time, it’s like, I don’t know to me that
378 00:35:19.070 ⇒ 00:35:24.870 Robert Tseng: that differentiation was like, okay, for me, project is like client project. Non project is what like
379 00:35:25.070 ⇒ 00:35:27.520 Robert Tseng: rainforest time like I don’t know. I think like.
380 00:35:27.520 ⇒ 00:35:36.630 Uttam Kumaran: But like for us, do we want to measure like I’m spending this much time on sales? I would say a good alternative. Here is like my calendar is pretty reflective of my time.
381 00:35:37.190 ⇒ 00:35:41.370 Uttam Kumaran: so I’m sure, like we could just use the calendar data to figure that out. But.
382 00:35:42.630 ⇒ 00:35:45.569 Uttam Kumaran: I don’t know how helpful it is for us to
383 00:35:46.160 ⇒ 00:35:55.560 Uttam Kumaran: kind of know? Like I’m gonna if I’m not working, I’m gonna sell, and if I’m not selling, I’m gonna work on this stuff. So I don’t know kind of like
384 00:35:55.940 ⇒ 00:35:59.049 Uttam Kumaran: for the management team. I’m not sure how important it is.
385 00:35:59.470 ⇒ 00:36:02.409 Robert Tseng: Yeah, I mean, I put it as pretty low back. My opinion.
386 00:36:02.410 ⇒ 00:36:11.300 Amber Lin: I see, I see. I agree that it’s not not as impactful for the management team. I think
387 00:36:11.800 ⇒ 00:36:17.659 Amber Lin: what I put as high impact was mostly for our engineers and individual contributors
388 00:36:17.800 ⇒ 00:36:37.810 Amber Lin: that I want to see. How much time are they spending, actually working on client work, or like how much of meeting like random meeting times is taking it up for them. Or just to get an I I think I put a high high impact because I’m lost of how things are distributed like, I want to know.
389 00:36:38.080 ⇒ 00:36:38.690 Amber Lin: That’s it.
390 00:36:38.960 ⇒ 00:36:51.450 Robert Tseng: Yeah, I feel like our team doesn’t meet enough outside of like, our stand ups and stuff. But yeah, I’m not. I don’t. I don’t feel like I’m I mean, maybe that’s just a feeling. But I I feel like people don’t really need that much outside of
391 00:36:51.640 ⇒ 00:36:52.920 Robert Tseng: what we’ve organized.
392 00:36:54.240 ⇒ 00:37:06.610 Amber Lin: I see. So this would just help give us and clarity from data, I would say, like lower impact.
393 00:37:07.890 ⇒ 00:37:08.860 Amber Lin: great.
394 00:37:09.420 ⇒ 00:37:19.600 Amber Lin: Next one is test the Linkedin upwork and Linkedin outbound.
395 00:37:20.380 ⇒ 00:37:27.647 Amber Lin: I, me and Hannah put, okay, okay, great robbery and a ways agrees on a lot of things.
396 00:37:28.030 ⇒ 00:37:29.860 Uttam Kumaran: What is this one? What was this one.
397 00:37:31.059 ⇒ 00:37:34.170 Robert Tseng: And I are like the same person. We vote the same on everything.
398 00:37:34.590 ⇒ 00:37:36.069 Uttam Kumaran: You guys should be besties.
399 00:37:36.810 ⇒ 00:37:39.639 Hannah Wang: Amber. I feel like you, and I also vote similarly.
400 00:37:39.640 ⇒ 00:37:42.380 Amber Lin: I know it’s so funny.
401 00:37:42.380 ⇒ 00:37:48.899 Uttam Kumaran: I just I just chose randomly, just to cause issues. No, wait. What was what’s this one? Again?
402 00:37:49.090 ⇒ 00:37:56.900 Amber Lin: Linkedin and upwork outbound. I I guess it’s more like a B testing, said low impact and low effort.
403 00:37:58.750 ⇒ 00:38:03.359 Amber Lin: But I think Robert is the one doing the work. So I think this is more.
404 00:38:03.360 ⇒ 00:38:11.729 Robert Tseng: It’s like manual adjustments that I make. Like, you know, I’m I’m touching stuff like almost every day. And then, you know, we just we just bought gig radar. So like, you know, we’re
405 00:38:12.310 ⇒ 00:38:15.480 Robert Tseng: are like making investments. And I to me, I think this is
406 00:38:16.010 ⇒ 00:38:21.420 Robert Tseng: like a big part of our go to market motion is outbound. So I consider a high effort.
407 00:38:21.770 ⇒ 00:38:26.809 Robert Tseng: And then, I guess I didn’t make it the highest impact. Because,
408 00:38:29.790 ⇒ 00:38:34.110 Robert Tseng: I yeah, I think that I what I’ve noticed is that
409 00:38:34.520 ⇒ 00:38:52.650 Robert Tseng: in inbound leads, like they’re they’re less price sensitive than the outbound one. So like, you know, as much as we want to test it and grow this pipeline, like, I think, all the other marketing stuff that we do to get like a warm intro referrals, etc. like that’s that’s been getting us like better margin. Better, better.
410 00:38:52.770 ⇒ 00:38:53.290 Robert Tseng: better.
411 00:38:53.290 ⇒ 00:38:59.609 Uttam Kumaran: I was also like, my point was that Ab testing implies like, we’re literally doing a B testing like
412 00:39:00.010 ⇒ 00:39:03.259 Uttam Kumaran: changing it and constantly improving. We we already do that.
413 00:39:03.490 ⇒ 00:39:09.179 Uttam Kumaran: So I’m like, we’re gonna run literal like A B tests. I was like that. Seems overkill.
414 00:39:09.730 ⇒ 00:39:10.650 Uttam Kumaran: Okay.
415 00:39:10.950 ⇒ 00:39:11.300 Amber Lin: Okay.
416 00:39:11.300 ⇒ 00:39:13.040 Robert Tseng: Yeah, yeah, yeah, no. I, I.
417 00:39:13.040 ⇒ 00:39:20.579 Uttam Kumaran: I could go the other way on that. But that’s what I was like. I don’t think it’s worth running like we do basically that already.
418 00:39:20.980 ⇒ 00:39:24.579 Amber Lin: Yeah. And I think I would probably take
419 00:39:25.370 ⇒ 00:39:34.979 Amber Lin: like, I think, effort wise, it’ll still be like a 4 or 5 and impact maybe around like a like a like a 3 or 4.
420 00:39:34.980 ⇒ 00:39:35.620 Robert Tseng: Sure.
421 00:39:35.900 ⇒ 00:39:36.470 Amber Lin: Yeah.
422 00:39:36.470 ⇒ 00:39:40.309 Robert Tseng: Yeah, maybe this is too much. I I like overreacted to it because I I.
423 00:39:40.310 ⇒ 00:39:47.799 Amber Lin: It’s what you do like when it comes to Pm, stuff I overreact so normal.
424 00:39:48.870 ⇒ 00:39:53.749 Uttam Kumaran: I think at some point we will maybe test the sales things. I think right now.
425 00:39:54.170 ⇒ 00:39:59.299 Uttam Kumaran: we’re just like ramping up the amount of activities. And we do. We do, MoD, just like you said it.
426 00:39:59.420 ⇒ 00:40:01.839 Uttam Kumaran: We do update things every day.
427 00:40:01.840 ⇒ 00:40:11.680 Robert Tseng: Yeah, yeah, we’re not formally AV testing, like Erickson did that before when he was working with us, he would test like 3 email sequences, or I was just like I don’t know.
428 00:40:11.680 ⇒ 00:40:13.360 Uttam Kumaran: The email was sort of dead.
429 00:40:13.360 ⇒ 00:40:14.360 Robert Tseng: It doesn’t really make.
430 00:40:14.360 ⇒ 00:40:18.229 Uttam Kumaran: We’re gonna have the upwork. We’re gonna have gig radar. We’re gonna have
431 00:40:18.722 ⇒ 00:40:23.619 Uttam Kumaran: hey reach. And we have a lot of data on, hey? Reach now where I think we can start to do that.
432 00:40:23.740 ⇒ 00:40:24.450 Robert Tseng: Yeah.
433 00:40:24.450 ⇒ 00:40:25.290 Uttam Kumaran: Yeah.
434 00:40:32.360 ⇒ 00:40:49.649 Amber Lin: Okay, next one. Similar to the personality communication mapping. How I work profiles and directory. I think my, I’m me and Hannah are are the ones who ranked it as higher effort. Mostly because probably we were thinking about how much it takes for it.
435 00:40:50.320 ⇒ 00:40:54.680 Amber Lin: Our employees, our team, to do them. So I think we’re.
436 00:40:54.680 ⇒ 00:40:55.340 Hannah Wang: Yeah.
437 00:40:55.620 ⇒ 00:41:04.940 Amber Lin: Yeah, I think we can like, we know what differentiates for the effort. But for the impact, seems like
438 00:41:05.900 ⇒ 00:41:08.220 Amber Lin: we have 2 2 groups here.
439 00:41:08.490 ⇒ 00:41:09.029 Amber Lin: Don’t forget.
440 00:41:09.754 ⇒ 00:41:13.375 Amber Lin: No, that does not matter.
441 00:41:14.100 ⇒ 00:41:28.909 Uttam Kumaran: No, I well, I kind of know. I mean I now that I read it again. It just depends on the day for me, because sometimes I’m like yo. This isn’t like that important. I think it’s easy to do, though, like it’s not that hard to do.
442 00:41:29.456 ⇒ 00:41:34.580 Uttam Kumaran: I think the other thing which is understanding people’s personalities is probably more impactful.
443 00:41:35.983 ⇒ 00:41:45.379 Uttam Kumaran: This is kind of sometimes for showing companies like executives just like to be like, I don’t like to be talked this way and stuff like that, so I don’t know. Can’t really do much about that.
444 00:41:49.450 ⇒ 00:41:50.180 Amber Lin: Okay.
445 00:41:50.180 ⇒ 00:41:56.480 Robert Tseng: Yeah, I just put it low effort and it’s like a nice nice to nice to know, but I don’t.
446 00:41:57.190 ⇒ 00:41:57.810 Amber Lin: For you.
447 00:41:59.210 ⇒ 00:42:03.670 Uttam Kumaran: That before sending messages I send messages, messages.
448 00:42:03.670 ⇒ 00:42:08.289 Amber Lin: That’s true. That is, that is true. Like in practicality, like in practice.
449 00:42:08.440 ⇒ 00:42:09.689 Amber Lin: We might not.
450 00:42:09.890 ⇒ 00:42:14.560 Uttam Kumaran: I think the personality test covers this like. If we can get that, then I could have it in my head.
451 00:42:14.960 ⇒ 00:42:20.780 Uttam Kumaran: But, like I’m as fast as my fingers can move. I’m sending messages so.
452 00:42:20.780 ⇒ 00:42:22.600 Amber Lin: Yeah, that’s a good point.
453 00:42:36.240 ⇒ 00:42:56.059 Amber Lin: I guess this would be kind of like a roundup at the end of each week. We say, Okay, how? How are you talking to this person? Should you change anything about it for next week? And then it’s more of like a retro thing, but not for each and every single message. I think I don’t think we’ll use that in every every message.
454 00:42:58.140 ⇒ 00:43:03.839 Amber Lin: So I would say, like all over the effort. Lower the impact of like a 3
455 00:43:03.960 ⇒ 00:43:06.019 Amber Lin: ish, 2 or 3
456 00:43:11.180 ⇒ 00:43:14.269 Amber Lin: next one about the data platform.
457 00:43:15.135 ⇒ 00:43:26.140 Amber Lin: Seems like we have goodam. You put for data platform standardization as a 1.5
458 00:43:30.650 ⇒ 00:43:35.870 Amber Lin: wait 1.5 on which which score which method, data, platform standardization.
459 00:43:35.870 ⇒ 00:43:39.070 Amber Lin: No, no, but effort or impact, like which effort.
460 00:43:40.110 ⇒ 00:43:40.900 Uttam Kumaran: And then what did I put.
461 00:43:40.900 ⇒ 00:43:42.775 Amber Lin: Very and low impact. Honestly.
462 00:43:43.150 ⇒ 00:43:45.889 Uttam Kumaran: Yeah, I mean, I don’t think this is like, if we do this.
463 00:43:46.430 ⇒ 00:43:52.670 Uttam Kumaran: if we don’t do this, we can still make it like it’s not the most impactful thing. It’s a really, really nice to have.
464 00:43:52.780 ⇒ 00:44:01.119 Uttam Kumaran: and it is helpful. But it’s not like. For example, if we got 5 more clients, we would drop that work. We’ve got work on client work.
465 00:44:01.120 ⇒ 00:44:01.580 Amber Lin: Hmm.
466 00:44:02.040 ⇒ 00:44:06.120 Uttam Kumaran: So we’re working on it in a moment now, because we have free time.
467 00:44:06.810 ⇒ 00:44:07.600 Amber Lin: Hmm.
468 00:44:08.290 ⇒ 00:44:09.659 Uttam Kumaran: Is my opinion.
469 00:44:10.610 ⇒ 00:44:13.590 Amber Lin: Does it really take that low of an effort? Do you think?
470 00:44:14.340 ⇒ 00:44:18.310 Amber Lin: I guess that’s where you want to waste? You guys disagree on how much effort this should take.
471 00:44:18.480 ⇒ 00:44:21.890 Uttam Kumaran: I mean, I guess. Yeah, I guess. Ask, Aisha, what do you think.
472 00:44:23.820 ⇒ 00:44:27.385 Awaish Kumar: I like. It’s a kind of like
473 00:44:28.290 ⇒ 00:44:32.226 Awaish Kumar: a full time work like you have real tasks. And
474 00:44:33.760 ⇒ 00:44:36.630 Awaish Kumar: and it’s ongoing project. It’s like
475 00:44:37.170 ⇒ 00:44:44.220 Awaish Kumar: we cannot just finish it off in a moment’s time. So think it requires the effort.
476 00:44:44.370 ⇒ 00:44:46.050 Awaish Kumar: Build a data platform.
477 00:44:47.820 ⇒ 00:44:49.750 Uttam Kumaran: I guess you’re right. I think maybe my
478 00:44:49.990 ⇒ 00:44:55.390 Uttam Kumaran: score should have been more of like a 3, although I just don’t think it’s the hardest thing that we’ve ever done.
479 00:44:56.390 ⇒ 00:45:01.340 Uttam Kumaran: Like, yeah, I I agree that. Yeah, it’s probably full time amount of work.
480 00:45:01.340 ⇒ 00:45:02.280 Awaish Kumar: I’m digging today.
481 00:45:03.440 ⇒ 00:45:18.060 Uttam Kumaran: It’s yeah, it’s it’s like again, I would. I would say, like, Look, we can. We’ve had clients before without that, and we will continue to. So in terms of like impact, I don’t think it’s the most important thing. I think effort is probably closer to 3,
482 00:45:18.600 ⇒ 00:45:25.869 Uttam Kumaran: like I think it’s I think it. It will give us better scalability over time, like we will probably keep people because it’s things are cleaner.
483 00:45:26.567 ⇒ 00:45:32.399 Uttam Kumaran: And it isn’t. It isn’t like it is. It’s important, I would say. Client work, though, is is the number one.
484 00:45:34.400 ⇒ 00:45:37.600 Amber Lin: Hmm, so impact.
485 00:45:37.780 ⇒ 00:45:50.080 Uttam Kumaran: Like this goes more to retention, and like cost of goods, like reducing our improving our margins, improving our retention. But, like for me, the number. One thing is growing sales right now.
486 00:45:50.240 ⇒ 00:45:52.399 Uttam Kumaran: and getting new clients in the door. So.
487 00:45:52.400 ⇒ 00:45:53.140 Amber Lin: Okay.
488 00:45:53.140 ⇒ 00:45:55.110 Uttam Kumaran: That’s how I so I’m sort of judging.
489 00:45:55.110 ⇒ 00:46:02.802 Amber Lin: I see I still kind of see that see effort. I see as if we were to do it. How much effort would it take
490 00:46:03.220 ⇒ 00:46:19.259 Amber Lin: and I think you were viewing it as in we’re just doing it in our free time. I think maybe your impact and effort kind of mixed up a little bit, I do think, for impact. It would be closer to like a 3 rather than
491 00:46:19.960 ⇒ 00:46:21.429 Amber Lin: like maybe.
492 00:46:21.430 ⇒ 00:46:22.910 Uttam Kumaran: We can live at a 4.
493 00:46:23.300 ⇒ 00:46:24.850 Amber Lin: Yeah, cool.
494 00:46:25.280 ⇒ 00:46:26.890 Amber Lin: Okay, I like that. Okay.
495 00:46:27.270 ⇒ 00:46:46.419 Amber Lin: this, really, I think this is really helpful because it helps us view all these things that we’re already doing like we are doing, putting a lot of time and data platform work. And this helps us value. Okay, where where is this? And will we drop it, or can we drop it when other things come like, where does this rank in our priorities? I think that’s really helpful.
496 00:46:47.320 ⇒ 00:46:52.768 Amber Lin: Next up is case study pipelines to see
497 00:46:53.740 ⇒ 00:47:05.309 Amber Lin: Oh, I ranked it as high. I just don’t know much that what goes into it, so you can disregard it, and Utam said, is really low. In fact.
498 00:47:05.920 ⇒ 00:47:07.969 Amber Lin: you’ll have to talk again with Tommy.
499 00:47:08.440 ⇒ 00:47:12.550 Amber Lin: We’re talking about case study creation, pipeline impact.
500 00:47:15.130 ⇒ 00:47:17.619 Uttam Kumaran: Like we already have. We already have this.
501 00:47:17.890 ⇒ 00:47:19.200 Amber Lin: Oh, okay.
502 00:47:19.470 ⇒ 00:47:21.999 Amber Lin: But is it that unimpactful.
503 00:47:23.310 ⇒ 00:47:31.030 Uttam Kumaran: No, I just don’t know, like we already have this pretty set, like we’re doing this right, like we have a pipeline of them. Our backlog is filled with them.
504 00:47:31.766 ⇒ 00:47:35.769 Uttam Kumaran: Like Hannah. Hannah knows we have, like 4 or 5 that are in the backlog.
505 00:47:36.130 ⇒ 00:47:40.549 Uttam Kumaran: We push 5 out in one week. We could. If we did it tomorrow we could push another 3 out, so.
506 00:47:40.550 ⇒ 00:47:44.500 Amber Lin: I see, I see. So this effort should be low, because it’s already done.
507 00:47:45.500 ⇒ 00:47:46.250 Uttam Kumaran: Yeah.
508 00:47:50.830 ⇒ 00:47:53.790 Uttam Kumaran: right? I don’t know, Hannah. What do you think? I I think like, if we
509 00:47:54.530 ⇒ 00:47:58.630 Uttam Kumaran: focus, we could do a bunch more like whenever we want. Now that we have sort of a process.
510 00:47:59.340 ⇒ 00:48:04.090 Hannah Wang: Yeah, I rated the slow effort because we already have a template kind of.
511 00:48:04.350 ⇒ 00:48:06.710 Amber Lin: The high impact. I would say.
512 00:48:07.510 ⇒ 00:48:09.860 Hannah Wang: I think so. Cause I I mean
513 00:48:10.090 ⇒ 00:48:13.340 Hannah Wang: for a while. Here we were focused on
514 00:48:13.780 ⇒ 00:48:20.840 Hannah Wang: needing case studies. So I for sales. So I just assumed that it was impactful to
515 00:48:20.960 ⇒ 00:48:22.370 Hannah Wang: show what we do.
516 00:48:22.910 ⇒ 00:48:23.630 Amber Lin: Oh.
517 00:48:27.050 ⇒ 00:48:29.159 Amber Lin: cool. I’ll keep it high and fast.
518 00:48:31.500 ⇒ 00:48:36.059 Amber Lin: Okay, next one mid-level management structure.
519 00:48:37.214 ⇒ 00:48:46.299 Amber Lin: Okay, I’m the outlier again. I think it’s creating that structure is easier, just helping implement or having this group
520 00:48:46.430 ⇒ 00:48:53.169 Amber Lin: getting used to the these new responsibilities like that will take time. But I think we already have it set up
521 00:48:53.380 ⇒ 00:49:00.429 Amber Lin: so similar to the one above. Like it, it’s in progress. So what’s left is not too much.
522 00:49:00.540 ⇒ 00:49:08.200 Amber Lin: And Uton put impact at 3, I think the rest of us have it 4 and above.
523 00:49:09.000 ⇒ 00:49:09.820 Amber Lin: So
524 00:49:14.230 ⇒ 00:49:14.840 Amber Lin: Utah.
525 00:49:14.840 ⇒ 00:49:15.260 Hannah Wang: Just.
526 00:49:16.230 ⇒ 00:49:19.139 Amber Lin: He’s talking so.
527 00:49:21.350 ⇒ 00:49:23.540 Uttam Kumaran: Wait. I’m sorry. Can you say that one more time? Just.
528 00:49:23.540 ⇒ 00:49:32.709 Amber Lin: Mid level management. I think we disagree on the impact. You put a 3 and we put 4 and above. So what are your thoughts on that.
529 00:49:33.630 ⇒ 00:49:38.860 Uttam Kumaran: Yeah, I thought this was important. I I just feel like we’ve sort of done this.
530 00:49:40.310 ⇒ 00:49:52.419 Amber Lin: Oh, I mean, like impact. Wise, I think, cause we’re gonna use the impact to rank the priorities of different things that we do. And it helps me. Dedicate. Where do I want to allocate my energy as well? So.
531 00:49:52.420 ⇒ 00:49:59.470 Uttam Kumaran: I think it’s a high impact. I think. Really the general theme of a lot of my stuff was like, I rank anything that’s touching sales
532 00:49:59.710 ⇒ 00:50:11.719 Uttam Kumaran: as like 5. And then I was sort of dog everything else. So I didn’t. I wasn’t like kind to the ones that weren’t sales related. I like wanted to basically make sure that we could make some decisions because
533 00:50:12.000 ⇒ 00:50:14.829 Uttam Kumaran: we’re not gonna be able to take on all of these. So.
534 00:50:14.830 ⇒ 00:50:16.720 Amber Lin: Yeah, I agree.
535 00:50:16.720 ⇒ 00:50:25.059 Uttam Kumaran: Like. The difference between a 4 and 5 is close, but 3 and 5 is like pretty clear, if it’s like, you know. So if it’s not a high priority, I put it at 3.
536 00:50:25.430 ⇒ 00:50:29.710 Uttam Kumaran: Alright. I basically did like mostly one threes and fives. At least, I tried.
537 00:50:29.710 ⇒ 00:50:32.150 Amber Lin: Yeah, that’s that’s a good. That’s a good level.
538 00:50:32.550 ⇒ 00:50:33.330 Amber Lin: Okay?
539 00:50:33.830 ⇒ 00:50:38.720 Uttam Kumaran: Because 4, you know how they say like, pick a number between one and 10 like 7. It’s like kind of a cop answer.
540 00:50:39.226 ⇒ 00:50:40.240 Amber Lin: I agree.
541 00:50:43.580 ⇒ 00:50:48.270 Amber Lin: Ai agents think,
542 00:50:52.900 ⇒ 00:51:10.500 Amber Lin: Robert. And awaish have it at medium effort around like A, 3, and the rest of us have well, like 4.5 ish. Then, in terms of impact, Hannah and Utum says, low impact. So.
543 00:51:13.160 ⇒ 00:51:16.670 Hannah Wang: What’s in the parentheses to get generator meeting.
544 00:51:16.670 ⇒ 00:51:20.460 Amber Lin: Like just General AI ages. We’re doing internally.
545 00:51:20.790 ⇒ 00:51:21.320 Hannah Wang: Not just.
546 00:51:21.320 ⇒ 00:51:22.330 Amber Lin: Sorry.
547 00:51:23.770 ⇒ 00:51:29.989 Hannah Wang: It’s okay. I think I put it at low effort or low impact, because
548 00:51:30.710 ⇒ 00:51:32.790 Hannah Wang: not a lot of people use.
549 00:51:33.880 ⇒ 00:51:37.950 Hannah Wang: Right now, anyway, so I don’t know if it’ll be adopted.
550 00:51:50.980 ⇒ 00:51:58.760 Amber Lin: I assume the same logic for autumn on this, probably is like this is not directly.
551 00:51:58.760 ⇒ 00:52:06.620 Uttam Kumaran: I mean, I’m this is like again, this is probably what I’m really closest to. So my answers are gonna bias towards it being harder and more impactful. And if.
552 00:52:06.620 ⇒ 00:52:10.079 Amber Lin: Well, you put a 2 on the impact.
553 00:52:11.050 ⇒ 00:52:16.539 Uttam Kumaran: Yeah, I I, this is where I’m like, I don’t know. I I in, based on what our
554 00:52:16.770 ⇒ 00:52:21.789 Uttam Kumaran: history has been. It’s not like immediately apparent where this is, gonna be impactful.
555 00:52:23.890 ⇒ 00:52:28.110 Uttam Kumaran: Like, we’re trying to make this a reality right now. But
556 00:52:28.910 ⇒ 00:52:39.109 Uttam Kumaran: it’s hard. We’re not. I think we’re gonna have to change a couple of the ways we do things like instead of waiting for people to use. AI, the AI is gonna automatically comment on things and sort of
557 00:52:39.360 ⇒ 00:52:43.630 Uttam Kumaran: reach out to people proactively. So my issue.
558 00:52:43.630 ⇒ 00:52:45.730 Uttam Kumaran: But also, yeah.
559 00:52:45.730 ⇒ 00:53:11.100 Robert Tseng: With with it. Is that like, the stuff that we’ve been building? It’s it’s either like a replacement to a consumer app that we already use. So like, I think the meeting summarization tools is like a good example. It’s a good integration to have in slack, because, like Granola doesn’t immediately post in slack. But if I have a granola meeting no, I’m still gonna look at the granola. And then also, for, like the ticket generator like, I end up just like drafting.
560 00:53:11.160 ⇒ 00:53:29.920 Robert Tseng: I mean, I rely on our ticket templates and then, like, I’ll just run it through. Gpt, anyway. So I think there’s like certain enhancements. I think the example that I’m kind of always thinking about is like the whole account, based like event, triggered like lead targeting thing. That’s something that no generic tool out there is going to be able to do for you.
561 00:53:29.920 ⇒ 00:53:30.250 Amber Lin: For us.
562 00:53:30.250 ⇒ 00:53:57.569 Robert Tseng: So once we start to like, own these in on specific use cases, I think it’ll become more useful for for me personally. And I mean, I think I had a call with a with like A with a Vc for fun or like earlier today. I mean, I dropped a note in slack. We talked so just shout out, there you should go and read it. But like the granola there they were, just they weren’t asking me to build like generic stuff like they’re like, Oh, yeah, this is like our use case. For the obviously, people want something that’s more.
563 00:53:57.760 ⇒ 00:54:13.069 Robert Tseng: The people are interested in talking about custom tools. So like it’s I think that’s where, like the the like. It can’t. We can’t raise the floor we have to like, you know, Max, we have to increase the ceiling. I think that’s kind of what it will take to get these.
564 00:54:13.070 ⇒ 00:54:14.790 Robert Tseng: It was to be adopted.
565 00:54:16.320 ⇒ 00:54:28.310 Uttam Kumaran: Yeah, I agree, like the Zoom agent is something that has been sitting there for a while. And then we started working on ABC, so we didn’t do much development like when ABC was ramping. And now we’re back to doing bunch of stuff on sales. So I also agree that
566 00:54:28.620 ⇒ 00:54:42.559 Uttam Kumaran: there’s a couple of things we just did, and then. Now, Granola is there. We can make a distinction whether it’s worth the 15 bucks to do that, or to do this or to do notion that sort of stuff I’m fine with. The other thing is just like I want there to be Gpts and slack, and the agents for the clients. I think
567 00:54:42.800 ⇒ 00:54:53.200 Uttam Kumaran: that’s just nice to have like to be there. It’s it’s actually pretty low cost as well. The next immediate thing is everything towards sales. So all the stuff Miguel’s working on for Demos is all related to sales.
568 00:54:53.650 ⇒ 00:54:56.890 Uttam Kumaran: Mustafa is going to take on all of the sales go to market
569 00:54:57.640 ⇒ 00:55:06.710 Uttam Kumaran: tasks basically single handedly. So lead list building. So from clay through Apollo to hey? Reach back to Hubspot, he’s going to take on
570 00:55:07.510 ⇒ 00:55:10.919 Uttam Kumaran: and then we’ll sort of level up from there.
571 00:55:11.874 ⇒ 00:55:28.346 Uttam Kumaran: The other piece is like, you’re right in that. There’s things that are novel that I want to do that are beyond. For example, we have all these Zoom Meetings and it’s really hard to chat with them. For example, even the granola chat with meeting sucks like I pull the transcript out and put into Gpt.
572 00:55:28.630 ⇒ 00:55:28.980 Robert Tseng: Yeah.
573 00:55:28.980 ⇒ 00:55:38.339 Uttam Kumaran: I’m gonna have Miguel in like 2 days, just like Vibe Code, like a little ui that will have a list of all the meetings. You can click on the meeting you want and chat with the Transcript.
574 00:55:38.871 ⇒ 00:55:43.210 Uttam Kumaran: That would be huge, because I do that in Gpt every day.
575 00:55:43.703 ⇒ 00:55:47.069 Uttam Kumaran: Right. So those are the the next steps we’re
576 00:55:47.340 ⇒ 00:55:50.659 Uttam Kumaran: it’s just speed at this point. You know.
577 00:55:51.030 ⇒ 00:55:51.760 Amber Lin: Hmm.
578 00:55:53.790 ⇒ 00:56:11.270 Uttam Kumaran: But this is where, like the reason why I’m starting to put a lot of ideas in the chat is, I want people to come up with some of these like, take a look at the workflows you’re using. And and we can do a similar like effort impact thing on, hey? If you’re doing something in custom, Gpt, that you want to move either into slack or you want like a simple Ui built, we can do that.
579 00:56:11.840 ⇒ 00:56:17.590 Uttam Kumaran: Now that I’m doing a lot of sales, I have like a hundred ideas about things that would save me so much time.
580 00:56:23.780 ⇒ 00:56:44.419 Amber Lin: cool. So looking back, I think this is this single initiative covers a lot of things and hence, why we’re kind of conflicted on different areas. And also, I think, what Rob brought up about a lot of things not being immediately impactful or just very generic. Where
581 00:56:45.190 ⇒ 00:56:54.099 Amber Lin: or say, what is it the buy versus build conflict is also there of like, should we be spending time
582 00:56:54.230 ⇒ 00:57:04.160 Amber Lin: on these when there’s something that we’re gonna use? We’re gonna use other things anyways like, is it? Is it that impactful to spend this much time.
583 00:57:04.470 ⇒ 00:57:09.769 Uttam Kumaran: Yeah. But like, so Zoom Meetings, we haven’t touched Zoom Meetings in 4 months, like, there’s no no updates. That logic
584 00:57:10.000 ⇒ 00:57:12.249 Uttam Kumaran: that’s been done. Just it just works.
585 00:57:12.560 ⇒ 00:57:18.710 Uttam Kumaran: So we can take it from there and like, basically improve it, or just leave it. So
586 00:57:18.960 ⇒ 00:57:29.279 Uttam Kumaran: so that’s some of the things that I think we released. Now we are going to use granola, and so we don’t have a sort of a policy like I don’t care if you use granola. It’s our thing is not immediately good enough
587 00:57:29.750 ⇒ 00:57:37.660 Uttam Kumaran: so we can get everyone. Granola. Then keep using that. Sometimes it’s helpful, because I’ll be just quickly check slack. See how all the stand ups went.
588 00:57:38.310 ⇒ 00:57:46.739 Robert Tseng: Yeah, that’s where that’s useful for me to just like, get a full check on, like, I think the fact that it puts it in slack makes it easy for me to go and check on meetings.
589 00:57:47.720 ⇒ 00:58:03.189 Uttam Kumaran: Yeah, I think there is a v, 2, v. 3 of that. That would be better. For example, like, here’s a good example, Robert, if we have, we tag all the meetings as sales operations imagine, you can take in all the sales meetings and ask, Hey, give me a like score. All these.
590 00:58:03.490 ⇒ 00:58:04.760 Robert Tseng: Yeah, yeah.
591 00:58:04.760 ⇒ 00:58:07.479 Uttam Kumaran: That’s some that’s some crazy right like that’s awesome.
592 00:58:07.480 ⇒ 00:58:14.799 Robert Tseng: Once we can do stuff across meetings rather than just like, yeah, I mean, the granola is obviously limited to only the transcript. It has so.
593 00:58:15.600 ⇒ 00:58:18.770 Uttam Kumaran: So can you note that Amber just like Cross meeting.
594 00:58:27.120 ⇒ 00:58:33.270 Amber Lin: I think the reason why I put it also as high impact is because it I was thinking about
595 00:58:33.380 ⇒ 00:58:47.110 Amber Lin: in terms of sales when we talk about company. This is a lot of times what gets people very excited when they hear our what our company is. Of all this AI stuff or new things we’re doing.
596 00:58:47.380 ⇒ 00:58:54.389 Amber Lin: And I look at it through that lens of, okay, this is, gonna get us more attention.
597 00:59:03.970 ⇒ 00:59:16.149 Amber Lin: Okay? And I think this AI agent probably needs further breakdown. What kind of agents and we can. We can come back and we can think about them. But it it’s great to get a pulse on
598 00:59:16.340 ⇒ 00:59:21.009 Amber Lin: each of these things. Does anyone need to jump? I need? I know we’re a bit over.
599 00:59:23.920 ⇒ 00:59:27.460 Robert Tseng: I have a 2 30, but I’m good for another 15 min.
600 00:59:27.830 ⇒ 00:59:31.150 Amber Lin: Okay, sounds good and
601 00:59:35.240 ⇒ 00:59:48.910 Amber Lin: great. This, yeah, let’s talk about this recurring revenue. Now I think of it. From what I heard from yesterday. I guess it’s less important to have recurring revenue. I think this is a
602 00:59:49.830 ⇒ 00:59:56.870 Amber Lin: duplicate from somewhere, but I just want to hear what everybody thinks
603 01:00:02.450 ⇒ 01:00:04.160 Amber Lin: growing revenue.
604 01:00:06.080 ⇒ 01:00:09.859 Robert Tseng: I just didn’t respond to that like I just left it blank. So
605 01:00:10.660 ⇒ 01:00:12.990 Robert Tseng: I have nothing. I have nothing to say about this.
606 01:00:14.360 ⇒ 01:00:18.800 Amber Lin: I think it’s somewhere oops.
607 01:00:19.060 ⇒ 01:00:24.579 Uttam Kumaran: What was still on ship 20% of revenue to recurring. I mean, where did I put? I probably put like, yeah, the.
608 01:00:25.290 ⇒ 01:00:29.030 Amber Lin: You put low effort and low impact.
609 01:00:29.650 ⇒ 01:00:31.899 Uttam Kumaran: Well, like we’re already did it. We’re already doing that.
610 01:00:35.430 ⇒ 01:00:40.369 Uttam Kumaran: 20% of revenue is occurring. We’re already, like most of our clients.
611 01:00:40.370 ⇒ 01:00:44.839 Robert Tseng: Most of our yeah. Most of our revenue is recurring. We don’t really have that much like.
612 01:00:46.150 ⇒ 01:00:50.619 Uttam Kumaran: And all of our new clients were pushing towards recurring. So that’s why I was like, we’re already kind of doing this.
613 01:00:54.300 ⇒ 01:00:58.250 Amber Lin: Great sounds. Good. So already, doing.
614 01:01:01.850 ⇒ 01:01:10.469 Amber Lin: Okay, sounds good product, size, internal delivery tools. I think we
615 01:01:10.620 ⇒ 01:01:14.929 Amber Lin: most of us are in like medium to high effort range.
616 01:01:15.100 ⇒ 01:01:23.989 Amber Lin: And then I guess, yeah, we have slightly disagreements on whether it’s medium or high effort, and whether it’s medium or high
617 01:01:24.150 ⇒ 01:01:25.470 Amber Lin: impact
618 01:01:27.430 ⇒ 01:01:43.839 Amber Lin: based on like product sizing will take a bit of effort. But I think when it comes to impact. I remember the conversation I heard from mutton yesterday of like, do we really want to make products? If we’re gonna still be a service company is what I was thinking about.
619 01:01:45.550 ⇒ 01:01:47.790 Uttam Kumaran: Yeah, that was my feedback was like.
620 01:01:48.880 ⇒ 01:01:55.090 Uttam Kumaran: I don’t know how much like we, we’re not gonna be going to market with these as products from my mind. So
621 01:01:55.200 ⇒ 01:01:59.660 Uttam Kumaran: the work we’re doing on the AI team and the data platform team is
622 01:01:59.890 ⇒ 01:02:03.730 Uttam Kumaran: that right? We’re taking our internal processes. We’re creating structure around it.
623 01:02:05.680 ⇒ 01:02:12.119 Uttam Kumaran: like, I would say, the AI team is is the closest to this, where we’re like building clay workflows or stuff for ourselves, and then we go out and sell it.
624 01:02:12.800 ⇒ 01:02:17.599 Uttam Kumaran: But it’s still like we’re doing it as a service, right like we’re not necessarily selling it as a product.
625 01:02:26.570 ⇒ 01:02:30.209 Awaish Kumar: And it’s like, should we do it? Because, like
626 01:02:30.500 ⇒ 01:02:35.110 Awaish Kumar: we already have bots like they can be added in slack, perhaps.
627 01:02:35.390 ⇒ 01:02:37.710 Awaish Kumar: and it can be sold right.
628 01:02:43.160 ⇒ 01:02:50.309 Robert Tseng: I think the difference between selling services and products products like you can’t. They have people always look for features, but like you.
629 01:02:50.430 ⇒ 01:03:09.279 Robert Tseng: in in a sense like you, it has to. You have to round out this entire workflow, and it often gets too bloated. You sell a package of features together. And that’s what we’re charging people for. I think, like with services like the we get more flexibility because we only build what we use. And then.
630 01:03:09.460 ⇒ 01:03:14.390 Robert Tseng: if anything, how it helps the sales motion, and it gets us to a place where we can talk about
631 01:03:15.165 ⇒ 01:03:38.770 Robert Tseng: like, pretty broadly like, hey, let’s talk. If you know what biggest problem the company is trying to solve is and go to market. Here are a couple of examples of different agents that we’ve built to help help us, and we can use it as talking points to like, figure out like what they’re more interested in. So like the fund I talked to today. It was less about lead research. It was more about follow ups they want to like, do automation on the follow up side?
632 01:03:39.091 ⇒ 01:03:55.460 Robert Tseng: And it was like, Okay, like we, I was able to like, have a conversation about that. But yeah, I don’t. I’m not promising them like, Hey, I have a demo for to show you. And it’s just more like cool like this is how we’ve how we’ve thought about this problem ourselves. And I I think if anything, it’s it’s like, I think
633 01:03:55.880 ⇒ 01:04:01.199 Robert Tseng: people are not wanting to always have new products, right? Because it just like being.
634 01:04:01.680 ⇒ 01:04:17.450 Robert Tseng: If it’s it’s bloated. You you it’s another ui to manage. Yeah, they they would rather just be like, Oh, cool. Okay. So you’ve had this problem you’ve solved before. You have a low code, no code way of doing it, or, like, you understand, like the thing that we’re trying to solve great like. That’s another like.
635 01:04:17.610 ⇒ 01:04:33.069 Robert Tseng: you know, level of trust that we win with a prospect to show them that like, hey, we can be like your AI partner to go and build some stuff out for you. But anyway, like, that’s that’s how I think that’s how I’ve been thinking about talking about AI. In these, in these sales calls.
636 01:04:33.070 ⇒ 01:04:33.880 Amber Lin: Hmm.
637 01:04:34.370 ⇒ 01:04:45.359 Amber Lin: yeah, I think this item is actually a very important part for us to talk about, because it’s really what the nature of our business is right and drawing boundaries of, okay, we don’t wanna
638 01:04:45.940 ⇒ 01:04:52.379 Amber Lin: derail that far into. Okay, we’re going to become a product company. So I think this is, there’s a great that we’re having this conversation.
639 01:04:54.280 ⇒ 01:04:59.060 Amber Lin: And so do we. So I guess we all agree that.
640 01:04:59.320 ⇒ 01:05:05.439 Uttam Kumaran: I agree with that? Yeah, I I think like, that’s what we’ve been doing. I I think I guess I was more of like.
641 01:05:06.080 ⇒ 01:05:09.149 Uttam Kumaran: I don’t want us to come across like we’re selling a product.
642 01:05:09.150 ⇒ 01:05:09.470 Robert Tseng: Yeah.
643 01:05:09.900 ⇒ 01:05:15.190 Uttam Kumaran: We’re like maintaining. I want us to get paid for all those hours. Basically. So.
644 01:05:15.670 ⇒ 01:05:23.159 Amber Lin: Say that it’s so impactful. It’s just we need to think about the impact in a different way, because we can think about it.
645 01:05:23.160 ⇒ 01:05:26.950 Uttam Kumaran: I see it as an impact to like our our cost of fulfill. For example.
646 01:05:26.950 ⇒ 01:05:27.320 Amber Lin: Huh!
647 01:05:27.320 ⇒ 01:05:34.890 Uttam Kumaran: Like. Now that we’ve done the ABC. Thing, we can go to any home services, and like we can probably do that within half the time, charge the same price
648 01:05:36.280 ⇒ 01:05:43.009 Uttam Kumaran: right and like, maintain it for the same amount of time, without having to pay for how much we pay to develop the solution.
649 01:05:43.210 ⇒ 01:05:54.450 Uttam Kumaran: Same thing with some of our internal clay stuff as soon as we do it for ourselves, I’m gonna throw it on the demo site, and then we can sell that, and the next time we do it we do it for cheap. We don’t charge on. We charge sort of almost on like a
650 01:05:54.550 ⇒ 01:06:01.219 Uttam Kumaran: productized service basis where you get it’s like usage based, or you charge based on access
651 01:06:01.650 ⇒ 01:06:03.039 Uttam Kumaran: charging for our hours.
652 01:06:04.950 ⇒ 01:06:05.779 Amber Lin: But this set.
653 01:06:05.780 ⇒ 01:06:06.509 Uttam Kumaran: I think we’re okay.
654 01:06:06.510 ⇒ 01:06:14.799 Amber Lin: Still like like service. That may maybe involves products. But we still, we’re not gonna
655 01:06:15.340 ⇒ 01:06:19.590 Amber Lin: sell these as singular standing products.
656 01:06:19.890 ⇒ 01:06:33.010 Amber Lin: Think that’s I think it’s still pretty impactful. It’s just I think we were misaligned on what the impact would be, because I was also before I was thinking, Oh, we can make some money selling these. But that’s not like, I don’t think that’s our point.
657 01:06:34.600 ⇒ 01:06:35.450 Amber Lin: Great.
658 01:06:37.471 ⇒ 01:06:59.929 Amber Lin: Okay, 7 min left, and then we’ll finish this meeting succession succession planning and leadership training. I think Robert and a put low effort. Rest of us pretty high. And Hannah and Utam put low impact. So
659 01:07:00.260 ⇒ 01:07:05.940 Amber Lin: let’s hear from Robert, and wish first, st how do you think this would be? How can we make it
660 01:07:06.260 ⇒ 01:07:07.090 Amber Lin: lower effort.
661 01:07:07.710 ⇒ 01:07:08.330 Awaish Kumar: Bye.
662 01:07:08.960 ⇒ 01:07:13.989 Awaish Kumar: Leadership training means like people who are here can get training on like.
663 01:07:14.330 ⇒ 01:07:20.220 Awaish Kumar: it will be easier for us to get to get some resources and things like that to
664 01:07:21.140 ⇒ 01:07:22.590 Awaish Kumar: to get started.
665 01:07:22.720 ⇒ 01:07:29.120 Awaish Kumar: But I think, like obviously it, it will take time to learn. But I think it’s easier to execute on.
666 01:07:29.480 ⇒ 01:07:30.820 Amber Lin: Oh, I see! I see!
667 01:07:30.820 ⇒ 01:07:32.910 Awaish Kumar: This is in things available.
668 01:07:33.140 ⇒ 01:07:45.550 Amber Lin: So you’re thinking about making that available. And then you’re not really talking about the time it takes for the people to actually learn those skills. I think that’s the main difference of why we ranked it so differently.
669 01:07:48.190 ⇒ 01:07:55.859 Robert Tseng: Yeah, I’d probably say the same thing as a wish. It’s like to get to get started on. This is not to to me. You know, it’s just like.
670 01:07:56.950 ⇒ 01:07:58.330 Robert Tseng: not like
671 01:07:59.200 ⇒ 01:08:10.559 Robert Tseng: we. This this meeting is is part of our part of that, like it didn’t, didn’t take us that long to figure out how to create middle management and to get some of the stuff going. Yeah.
672 01:08:11.350 ⇒ 01:08:19.269 Amber Lin: I agree, so similar to like the personality test, like the implementation of people actually learning it and becoming better at it, will take some time.
673 01:08:19.279 ⇒ 01:08:22.269 Robert Tseng: Yeah, that’s that’s more on them. And like, yeah, yeah.
674 01:08:22.921 ⇒ 01:08:31.419 Amber Lin: Okay, and in terms of in terms of impact. I was mostly just thinking in terms of
675 01:08:31.760 ⇒ 01:08:40.290 Amber Lin: time, one of time leverage for Utam and Robert, and 2 of
676 01:08:40.529 ⇒ 01:08:48.800 Amber Lin: like, how the company is organized, that we have more guardrails, and we have more organization in the company. That’s why I put it as high impact.
677 01:08:50.710 ⇒ 01:08:53.469 Amber Lin: I guess Hannah and Utam like, what do you guys think.
678 01:08:57.069 ⇒ 01:09:00.439 Hannah Wang: Trying to remember why I put a 3
679 01:09:03.039 ⇒ 01:09:07.199 Hannah Wang: maybe my thought was like, Oh, not everyone is. Gonna wanna do
680 01:09:07.329 ⇒ 01:09:14.699 Hannah Wang: like be part of the leadership team. So, like how many people are, gonna be on board with this.
681 01:09:14.910 ⇒ 01:09:19.343 Uttam Kumaran: It for me. It just wasn’t like immediately related to sales. So I didn’t rate it a 5.
682 01:09:20.850 ⇒ 01:09:35.140 Uttam Kumaran: If that’s gonna be my reasons for the most part, if I like, was like, this is good, and I we should do this, but not it’s not sales. Then I rated it 3. If I said we shouldn’t do this, maybe like in 2 quarters, I rated it like a 1.
683 01:09:35.140 ⇒ 01:09:36.100 Uttam Kumaran: Okay, okay.
684 01:09:36.109 ⇒ 01:09:43.189 Amber Lin: Want to really like affirm that I’m rooting just for a couple, because I feel like we’re gonna boil down to only being able to take a couple. So yeah.
685 01:09:43.189 ⇒ 01:09:45.039 Uttam Kumaran: Yeah. Yeah. Totally.
686 01:09:45.199 ⇒ 01:09:52.179 Amber Lin: Okay, 4 min left works awesome.
687 01:09:54.549 ⇒ 01:10:01.389 Amber Lin: I guess. Launching workshops and pay discovery, probably low effort. Because I guess we’re already doing it.
688 01:10:01.569 ⇒ 01:10:03.739 Amber Lin: And then impact.
689 01:10:04.509 ⇒ 01:10:07.409 Amber Lin: I guess again from Utam, like, why do you think
690 01:10:07.599 ⇒ 01:10:10.379 Amber Lin: workshops and pay discovery was low impact.
691 01:10:14.330 ⇒ 01:10:15.650 Uttam Kumaran: We’re already doing this.
692 01:10:15.850 ⇒ 01:10:20.440 Amber Lin: Okay, okay? Makes sense. I’m gonna skip this one.
693 01:10:20.440 ⇒ 01:10:24.530 Uttam Kumaran: We’re doing paid discovery. We’re gonna start doing workshops. It’s natural.
694 01:10:27.100 ⇒ 01:10:27.570 Amber Lin: You’re right.
695 01:10:27.570 ⇒ 01:10:30.900 Uttam Kumaran: In fact, it like sort of fell the workshop thing sort of fell into our my lap like.
696 01:10:30.900 ⇒ 01:10:34.290 Amber Lin: Oh, I see. Okay, okay. I guess you’re listening.
697 01:10:34.290 ⇒ 01:10:39.509 Uttam Kumaran: And this other guy, David, is gonna be the one like sort of leading it for us, and we’ll try to take something on the top. So it’s.
698 01:10:39.510 ⇒ 01:10:40.820 Amber Lin: Oh!
699 01:10:41.220 ⇒ 01:10:43.529 Uttam Kumaran: It’s a win-win. It works.
700 01:10:43.530 ⇒ 01:10:44.590 Amber Lin: I don’t like that.
701 01:10:45.109 ⇒ 01:11:00.089 Amber Lin: I put weekly status update as a high impact, just because I’m a Pm, and that’s mostly what I do so we can. We can jump over that I wanna talk about in the last 3 min. Talk about the go to market system. I think that’s pretty important.
702 01:11:01.450 ⇒ 01:11:09.750 Amber Lin: I think all of us agree, is pretty like medium, at least medium to high effort. And then.
703 01:11:10.810 ⇒ 01:11:16.199 Amber Lin: oh, Utam, you put low impact on the go-to-market system. Explain yourself.
704 01:11:17.720 ⇒ 01:11:20.349 Uttam Kumaran: I thought we already kind of have this.
705 01:11:20.350 ⇒ 01:11:28.460 Amber Lin: Okay, I see we we, I think we just you just understand. We just understand the impact ranking in a different way. I got you
706 01:11:29.300 ⇒ 01:11:32.480 Amber Lin: so and.
707 01:11:33.200 ⇒ 01:11:38.170 Uttam Kumaran: Or like, I mean, I rated it. A 3 like this can blow for me is is a 1.
708 01:11:38.820 ⇒ 01:11:41.079 Uttam Kumaran: If I rated it 3, I was like, yeah, like.
709 01:11:41.690 ⇒ 01:11:43.520 Amber Lin: 2 won’t impact.
710 01:11:44.800 ⇒ 01:11:57.880 Uttam Kumaran: But 3 on effort, meaning, we kind of already had it 2 on impact. Yeah, I don’t think this is the most impactful thing like we already have a. We know how to. We know what we’re trying to sell, and we know how to sell it. So we just need more at bats. That’s what I feel.
711 01:11:58.463 ⇒ 01:11:59.629 Amber Lin: Let’s see.
712 01:12:04.310 ⇒ 01:12:06.759 Uttam Kumaran: Like, I mean, I swear, yeah, go ahead. Go ahead.
713 01:12:07.860 ⇒ 01:12:10.770 Awaish Kumar: Yeah. But it says, automated process for doing that.
714 01:12:11.750 ⇒ 01:12:14.700 Uttam Kumaran: Oh, automate outbound, go to market.
715 01:12:14.960 ⇒ 01:12:20.039 Uttam Kumaran: Yeah, I I again that. And now if I read it, yeah, I mean, I don’t think we.
716 01:12:20.720 ⇒ 01:12:29.526 Uttam Kumaran: This is like the the most important thing, like we still, between me and Robert, we still have time to take more stuff where we are doing some of this.
717 01:12:30.660 ⇒ 01:12:34.169 Uttam Kumaran: you know, I think what is more impactful is a lot of the top of funnel stuff. And
718 01:12:34.940 ⇒ 01:12:38.250 Uttam Kumaran: we’re already doing some of the lead automation things like that.
719 01:12:40.750 ⇒ 01:12:45.670 Amber Lin: And what part of this we’re already doing, I would say.
720 01:12:46.790 ⇒ 01:12:49.010 Amber Lin: And great
721 01:12:49.610 ⇒ 01:12:57.919 Amber Lin: one last thing, Prof. Mostly on profitability of tracking profitability, maybe putting that into a report or dashboard.
722 01:12:59.780 ⇒ 01:13:08.269 Amber Lin: I think mostly, Robert. In a way, if you guys think this is low impact and low effort.
723 01:13:08.380 ⇒ 01:13:10.500 Amber Lin: So I wanted to hear from you guys.
724 01:13:14.582 ⇒ 01:13:28.489 Robert Tseng: I mean, I feel like it’s just a matter of getting. I mean, it’s a it’s a data problem. You just like you, you know which? Yeah, we just gotta build, build the table. And then once they have the table, we can visualize it.
725 01:13:28.910 ⇒ 01:13:33.510 Amber Lin: I agree, I agree, yeah, cool
726 01:13:45.290 ⇒ 01:13:51.070 Amber Lin: think that’s all for today, I, will, clean this up i’ll look at everybody’s responses.
727 01:13:53.580 ⇒ 01:14:06.739 Amber Lin: and then we’ll see. We’ll spot a few that we want to take on, and we’ll do a last round to just. We’ll just do Async to see. Okay, are these the things that we want to take on? And then that should be good.
728 01:14:08.840 ⇒ 01:14:12.040 Hannah Wang: Thanks, Amber, for finding this tool.
729 01:14:12.040 ⇒ 01:14:12.490 Awaish Kumar: Thank you.
730 01:14:12.490 ⇒ 01:14:16.019 Hannah Wang: On fig jam. It’s pretty cool. Didn’t know it was a thing.
731 01:14:16.830 ⇒ 01:14:18.200 Amber Lin: Me neither. That’s pretty cool.
732 01:14:21.080 ⇒ 01:14:22.170 Hannah Wang: Alrighty!
733 01:14:22.170 ⇒ 01:14:24.230 Robert Tseng: Yeah, it’s good exercise, all right.
734 01:14:24.230 ⇒ 01:14:24.770 Amber Lin: Yeah.
735 01:14:24.770 ⇒ 01:14:25.209 Robert Tseng: Thanks, all.
736 01:14:26.520 ⇒ 01:14:27.500 Hannah Wang: Bye.
737 01:14:27.730 ⇒ 01:14:28.730 Awaish Kumar: You, bye.