Meeting Title: Amber Lin’s Personal Meeting Room Date: 2025-04-07 Meeting participants: Uttam Kumaran, Amber Lin
WEBVTT
1 00:00:26.940 ⇒ 00:00:27.590 Uttam Kumaran: Hey!
2 00:00:28.040 ⇒ 00:00:29.210 Uttam Kumaran: Hi!
3 00:00:29.230 ⇒ 00:00:30.060 Uttam Kumaran: Hi! How are you?
4 00:00:30.060 ⇒ 00:00:33.441 Amber Lin: Twice I kept it under 5 min. I was like, no, I gotta go. I gotta go.
5 00:00:33.640 ⇒ 00:00:34.760 Uttam Kumaran: No, no, that’s fine.
6 00:00:34.760 ⇒ 00:00:35.699 Amber Lin: Yeah, that’s fine.
7 00:00:36.890 ⇒ 00:00:42.850 Uttam Kumaran: I’m sorry I know the Alexander call extended, seemed a bit rushed. But let me tell you what the play is there so.
8 00:00:42.850 ⇒ 00:00:43.630 Amber Lin: Okay. Okay.
9 00:00:43.630 ⇒ 00:00:46.300 Uttam Kumaran: He’s a so you heard of like Bridgewater?
10 00:00:47.330 ⇒ 00:00:47.989 Uttam Kumaran: Very, very.
11 00:00:47.990 ⇒ 00:00:49.980 Amber Lin: It’s a big big hedge fund right.
12 00:00:49.980 ⇒ 00:00:54.239 Uttam Kumaran: It’s like probably the most one of the most famous hedge funds right now.
13 00:00:54.600 ⇒ 00:00:55.460 Amber Lin: Day.
14 00:00:55.460 ⇒ 00:00:57.020 Uttam Kumaran: Yeah, they’re just.
15 00:00:57.020 ⇒ 00:00:57.590 Amber Lin: To him.
16 00:00:57.590 ⇒ 00:00:59.070 Uttam Kumaran: Do you know Ray Dalio.
17 00:00:59.520 ⇒ 00:01:01.699 Amber Lin: Yeah, I love his book.
18 00:01:01.700 ⇒ 00:01:03.790 Uttam Kumaran: Ray dahlias. He started Bridgewater.
19 00:01:06.462 ⇒ 00:01:08.299 Uttam Kumaran: And yeah, so.
20 00:01:08.300 ⇒ 00:01:08.740 Amber Lin: Okay.
21 00:01:08.740 ⇒ 00:01:25.799 Uttam Kumaran: Yeah, Kyle, Kyle introduced me to him. And yeah, he’s just like he’s just like 5 steps ahead of us and is like pretty senior. But again, like for me. One thing I think a lot about in this business is like just getting friends as many places as possible, so.
22 00:01:25.930 ⇒ 00:01:32.980 Amber Lin: I know, like, even if this call doesn’t lead to anything we did, you pay him for the call.
23 00:01:32.980 ⇒ 00:01:34.150 Uttam Kumaran: No, no, no.
24 00:01:34.150 ⇒ 00:01:34.810 Amber Lin: What?
25 00:01:35.150 ⇒ 00:01:38.600 Uttam Kumaran: These are just, these are just, these are just friends we make along the way.
26 00:01:38.600 ⇒ 00:01:44.179 Amber Lin: That is insane, because people would charge like $500 for that 30 min.
27 00:01:44.180 ⇒ 00:01:47.009 Uttam Kumaran: Yeah, but you can’t put a price on friendship, you know.
28 00:01:47.140 ⇒ 00:01:52.019 Amber Lin: Oh, my God, okay, okay. Now that I hear that
29 00:01:52.210 ⇒ 00:01:58.000 Amber Lin: because I I kind of wanted to rush a call because I really thought you paid him, and I was like, why are we spending so much time.
30 00:01:58.000 ⇒ 00:02:00.590 Uttam Kumaran: Oh, really no! No! No! No!
31 00:02:00.590 ⇒ 00:02:01.230 Amber Lin: Okay.
32 00:02:01.230 ⇒ 00:02:19.730 Uttam Kumaran: Maybe I should have. Maybe I should have said in the beginning that no, no, we’re not paying, and you know for for me. I think you know, I meet a lot of different people, and so I try to find out in what way we can help them. And how can they be up for us? There’s no like I would I would if if I had a limited money in the world, I would. I would ask him to just come, join, and like.
33 00:02:19.850 ⇒ 00:02:28.960 Uttam Kumaran: just lead a part of the business. But like, I also think that a lot of what we’re we need right now is really like boots on the ground type stuff. And so.
34 00:02:28.960 ⇒ 00:02:29.490 Amber Lin: Yeah.
35 00:02:29.490 ⇒ 00:02:29.840 Uttam Kumaran: And the other.
36 00:02:29.840 ⇒ 00:02:31.240 Amber Lin: Very high, level.
37 00:02:31.240 ⇒ 00:02:32.090 Uttam Kumaran: Yeah, and also like.
38 00:02:32.090 ⇒ 00:02:32.820 Amber Lin: Great insight.
39 00:02:33.030 ⇒ 00:02:56.369 Uttam Kumaran: And also we have we, you, you and Akash are there and like there’s no need. I don’t think we have a immediate need for more people. For me more is like just guidance, and so similarly to like having Patrick on the AI side, having, like Vishnu on the operation side, having Ivana on design, like, I just want us to have someone, because I don’t know all the answers like
40 00:02:56.370 ⇒ 00:03:08.399 Uttam Kumaran: we are. We’re we’re not at the point where I’m like running out of answers. But in a lot of these situations we will reach the point where we’ll all start to have to go read to go find out how to run
41 00:03:08.500 ⇒ 00:03:11.929 Uttam Kumaran: our Pmo like how to run an org like the Pm office?
42 00:03:12.968 ⇒ 00:03:29.989 Uttam Kumaran: So I want us to have someone else that we can rely on to answer those questions. And so what I’ll what I’ll probably end up doing is just asking if he can just be available, maybe once a month, or like, basically just available over email or slack. And basically just help us as like, be an advisor.
43 00:03:30.830 ⇒ 00:03:35.450 Amber Lin: I know that is so awesome. I really like your approach of every function, needs a coach.
44 00:03:35.680 ⇒ 00:03:41.520 Uttam Kumaran: Yeah, because I’m that person right now. And I will run out of answers soon, like, yeah, okay.
45 00:03:41.520 ⇒ 00:03:50.080 Amber Lin: Only also have 7 years of working. But these people have been working for 2030 years, and they have access to other people who have
46 00:03:50.080 ⇒ 00:03:50.770 Amber Lin: yes.
47 00:03:50.770 ⇒ 00:04:00.330 Amber Lin: 40, 50 years, and is at the top of the field. So now that I know that he is helping out for free, because I also reached out to him on Linkedin.
48 00:04:00.330 ⇒ 00:04:00.920 Uttam Kumaran: Oh, great!
49 00:04:00.920 ⇒ 00:04:04.229 Amber Lin: I will maintain this relationship like, Yeah, no, no.
50 00:04:04.230 ⇒ 00:04:05.420 Amber Lin: 3. I will.
51 00:04:05.420 ⇒ 00:04:07.530 Uttam Kumaran: Such a. He’s such a nice guy, and even.
52 00:04:07.530 ⇒ 00:04:08.380 Amber Lin: It’s so nice.
53 00:04:08.380 ⇒ 00:04:15.010 Uttam Kumaran: Even if we pay him like this is for me. It’s like, I just want everybody to have access to the experts.
54 00:04:15.370 ⇒ 00:04:28.919 Uttam Kumaran: Right? And for us like these are things that I don’t want to like. There’s no reason for us to try to do this our own way, like we should find out what the best people are doing. And then think about what is the 20% like of the brain forge spin here?
55 00:04:29.470 ⇒ 00:04:31.710 Uttam Kumaran: That’s that’s that’s mainly it, you know.
56 00:04:32.460 ⇒ 00:04:34.920 Amber Lin: Totally. I have a question. So
57 00:04:35.240 ⇒ 00:04:36.750 Amber Lin: what can we do for him?
58 00:04:37.590 ⇒ 00:04:53.739 Uttam Kumaran: Yeah, this is where, like, I’m trying to. Well, that’s why I’m trying to. Sometimes it’s hard to understand, like what cause some people they want to come work for us, and it’s that’s gonna be dependent on like money. And like, if it’s the right time. So they’re they’re for some folks. They have that for some folks. They just wanna they want to.
59 00:04:53.770 ⇒ 00:05:12.880 Uttam Kumaran: Maybe just like be a resource. And they want to make a little bit of money on the side, but they sort of like doing this like I think he really does like speaking with startups and doing this sort of stuff. He’s also, like Pmp certified. That’s like the big project management certificate. It takes like, really, really hard to get and like.
60 00:05:12.880 ⇒ 00:05:19.990 Amber Lin: I think you can get it already as long as you learn it. You already have the 3 or say years of experience. Managing teams.
61 00:05:19.990 ⇒ 00:05:24.930 Uttam Kumaran: Yeah, I don’t. Wanna. I don’t care about. I don’t care about getting anything.
62 00:05:24.930 ⇒ 00:05:25.760 Amber Lin: Okay.
63 00:05:26.037 ⇒ 00:05:42.409 Uttam Kumaran: Yeah, I I don’t for me, but it’s more about like if if our team wants to get it. But also it’s just like he. He’s done a lot of the things like the textbook way, and so he can show us like what the North Star is, and then we can figure out what’s possible for us right?
64 00:05:43.030 ⇒ 00:05:57.250 Uttam Kumaran: Otherwise, if you, if you, if you learn from me, I’m always gonna tell you the shortcut way, because that’s all. All I know is like how to get the job done in less time, not like the 100% like, if we were to go to like Facebook, and how do they run.
65 00:05:57.250 ⇒ 00:06:00.280 Amber Lin: You did sound very textbook, but that’s every big company.
66 00:06:00.280 ⇒ 00:06:18.479 Uttam Kumaran: That’s fine. It’s that, you know, that’s like that should be. Our north star is like, that’s what you know. We wanna act like, okay, how does Mckenzie run project? Manage their project management office? Right? So so that’s so. That’s how we utilize him. I’ll send him a note and sort of see like what an arrangement we can get. I mean, I just wanna make him available as a
67 00:06:18.680 ⇒ 00:06:20.284 Uttam Kumaran: as a resource.
68 00:06:20.820 ⇒ 00:06:33.529 Amber Lin: 2 things came across to my mind. 1. 1st of all, we should totally have the marketing function boost him, because any visible people like visibility, and it’s a way to publicly.
69 00:06:33.530 ⇒ 00:06:37.689 Uttam Kumaran: Well, you know what we should do is like we should just have him. I should do a webinar with him.
70 00:06:37.690 ⇒ 00:06:38.100 Amber Lin: Yes.
71 00:06:38.100 ⇒ 00:06:38.800 Uttam Kumaran: View him and.
72 00:06:38.800 ⇒ 00:06:47.239 Amber Lin: Yes, a webinar today we we should post something that we took away from this meeting like just, I can just post that I can just post it on Linkedin.
73 00:06:47.240 ⇒ 00:06:50.160 Uttam Kumaran: Let me check, because I don’t know whether his company will like.
74 00:06:50.610 ⇒ 00:06:51.930 Uttam Kumaran: Sometimes people are.
75 00:06:51.930 ⇒ 00:06:57.559 Amber Lin: No! Oh, I see! I think I can just frame it as like a personal connection. It was like, Thank you for coaching me.
76 00:06:57.913 ⇒ 00:06:58.620 Amber Lin: Perfect, perfect.
77 00:06:58.620 ⇒ 00:06:59.909 Uttam Kumaran: Me. Inside.
78 00:06:59.910 ⇒ 00:07:00.290 Uttam Kumaran: Yeah.
79 00:07:00.290 ⇒ 00:07:00.890 Amber Lin: That.
80 00:07:00.890 ⇒ 00:07:01.540 Uttam Kumaran: Okay. Okay.
81 00:07:01.540 ⇒ 00:07:13.239 Amber Lin: And then cause it’s it’s helping the connection and also ask for a webinar. I think that will make him more interested any any automations. And the second thing is like.
82 00:07:13.640 ⇒ 00:07:15.990 Amber Lin: because we’re doing all these automations that
83 00:07:16.150 ⇒ 00:07:20.000 Amber Lin: can we help you? Can we suggest some tools for you?
84 00:07:20.430 ⇒ 00:07:25.013 Amber Lin: Oh, my God, I’m drinking soda. I’m burping so much that’s so fine. Don’t worry.
85 00:07:25.340 ⇒ 00:07:26.926 Amber Lin: And then
86 00:07:27.970 ⇒ 00:07:35.541 Amber Lin: Like all of these automation tools of workflow shortcuts that maybe will save him time. I don’t know like.
87 00:07:35.940 ⇒ 00:07:44.539 Uttam Kumaran: I’ll figure out, yeah, you’re totally right in that one I want to do co-marketing with. Like all we have a, we have a, we actually have, like a bunch of these people that are like.
88 00:07:44.690 ⇒ 00:07:54.929 Uttam Kumaran: totally, just want to help the business. And so for me, it’s like, without money. How can I pay them or like with lower money? So co-marketing, I think, is super great. So I’ll ask him if he’s down to
89 00:07:54.930 ⇒ 00:07:55.390 Uttam Kumaran: yeah.
90 00:07:55.390 ⇒ 00:07:58.530 Uttam Kumaran: The biggest thing is, I’m just gonna see if he wants to be available in slack and
91 00:07:58.730 ⇒ 00:08:02.760 Uttam Kumaran: and like he can, Bill, or whatever. Just tell me, like what you need to do for that, you know.
92 00:08:03.900 ⇒ 00:08:14.189 Amber Lin: and also it’s also, what kind of questions can we use them? For because, like today of oh, what do you? I almost asked the question of, Oh, what do you put in a planning document? But it’s like.
93 00:08:14.190 ⇒ 00:08:14.780 Uttam Kumaran: Yes.
94 00:08:14.780 ⇒ 00:08:17.029 Amber Lin: The answer of what I search online.
95 00:08:17.260 ⇒ 00:08:23.170 Uttam Kumaran: No, no, but but this, your question is like, Hey, your question should be, I searched online. This is what it told me.
96 00:08:23.170 ⇒ 00:08:23.730 Amber Lin: Anymore.
97 00:08:23.730 ⇒ 00:08:25.189 Uttam Kumaran: Like? Would you add anything.
98 00:08:25.190 ⇒ 00:08:27.689 Amber Lin: I see I see that’s a much better way to frame it.
99 00:08:27.690 ⇒ 00:08:33.260 Uttam Kumaran: Yeah, because you get you get him to give you like the like. Okay, he’s done this so many times like
100 00:08:33.520 ⇒ 00:08:36.579 Uttam Kumaran: what what is like the edge right like. What else should I.
101 00:08:36.580 ⇒ 00:08:40.244 Amber Lin: Thank you here. If I if you were to write the perfect document I see
102 00:08:40.929 ⇒ 00:08:43.029 Uttam Kumaran: Yeah, that’s probably a good way of thinking about it.
103 00:08:46.160 ⇒ 00:08:50.470 Amber Lin: Cool. Let me write down in my own linear, too.
104 00:08:50.670 ⇒ 00:08:53.570 Amber Lin: Post something about about that.
105 00:09:04.700 ⇒ 00:09:10.020 Amber Lin: cool. Yeah, back to what we’re talking about.
106 00:09:10.380 ⇒ 00:09:13.690 Amber Lin: I think we wanted to work on the yeah.
107 00:09:13.690 ⇒ 00:09:14.700 Uttam Kumaran: Oh yes!
108 00:09:14.700 ⇒ 00:09:18.030 Amber Lin: Phase 2 document and also the Snowflake tech tips.
109 00:09:18.350 ⇒ 00:09:23.229 Amber Lin: Do you wanna delegate that to someone else? Or do you have time to do that?
110 00:09:23.230 ⇒ 00:09:28.000 Uttam Kumaran: Yeah. So the phase 2 documentation, the phase, 2 plan. Maybe I’m I just want to make.
111 00:09:28.000 ⇒ 00:09:34.070 Uttam Kumaran: I missed making some lunch real quick. But maybe you want to take. You want to take some notes. I can just tell you sort of what I’m thinking.
112 00:09:34.070 ⇒ 00:09:38.190 Amber Lin: Well, I’m on Granola, so all what you say will just be go.
113 00:09:38.190 ⇒ 00:09:39.050 Uttam Kumaran: Oh, okay. Cool.
114 00:09:39.050 ⇒ 00:09:40.219 Amber Lin: To meeting notes.
115 00:09:40.220 ⇒ 00:09:40.830 Uttam Kumaran: Okay, so it’s.
116 00:09:41.165 ⇒ 00:09:41.500 Amber Lin: Everything.
117 00:09:41.500 ⇒ 00:09:48.239 Uttam Kumaran: So so then here’s like, here’s sort of what I’m thinking. So they gave me. And in those in those spreadsheets that they gave us
118 00:09:48.730 ⇒ 00:09:53.710 Uttam Kumaran: told us, basically, how many people are, how many calls. People are taking.
119 00:09:53.710 ⇒ 00:09:54.160 Amber Lin: Okay.
120 00:09:54.458 ⇒ 00:10:08.790 Uttam Kumaran: Per day. Right now. They also said how many calls overall the pest org takes per month. And then, lastly, they also shared what their goals were like. They basically want right now, people are taking like 60 calls per day. They want it to be more like.
121 00:10:10.320 ⇒ 00:10:20.789 Uttam Kumaran: yeah, or like 90 or something, or whatever. Right? So for me, like my thing is like I need to figure out. So here’s sort of what I talked to Scott about is like we need to do it based on like tiered pricing. Basically.
122 00:10:21.180 ⇒ 00:10:22.749 Uttam Kumaran: Say we have 3 tiers
123 00:10:22.990 ⇒ 00:10:28.950 Uttam Kumaran: on the 1st tier, we should make our, we should make our 50% margins.
124 00:10:30.440 ⇒ 00:10:35.670 Uttam Kumaran: Right? So this is where it’s like from from your side. What’s helpful is like, can you give me a sense of like
125 00:10:37.310 ⇒ 00:10:42.930 Uttam Kumaran: how much like everybody’s budget included, like how much money you think.
126 00:10:43.060 ⇒ 00:10:47.490 Uttam Kumaran: if it would take to sort of sustain the project per month, bye.
127 00:10:47.490 ⇒ 00:11:06.510 Amber Lin: This is, unless major things come up all the updates of minor things. I’m trying to del like we had the conversation Friday. I’m trying to delegate all of that to the client. Right? So all the updates is there, Yvette, Janice or Shannon? Suggest the updates?
128 00:11:06.905 ⇒ 00:11:18.580 Amber Lin: And then you’ve approved some that’s not on us. So what we need is to point out those errors to them. This can be automated down the line right now. We’re sort of going through the errors
129 00:11:18.922 ⇒ 00:11:41.520 Amber Lin: in the morning and telling Denise to update them because she doesn’t really look at them that much. So that’s that any other updates. Essentially, just minor tweaks in the central document. That takes no time. We get that done in the meeting. So that’s okay. Only there’s like Batch updates on the bots behavior. That probably takes Casey around like 2 to 3 h
130 00:11:41.520 ⇒ 00:11:50.499 Amber Lin: per week is my estimate. So that says it, it probably would be sustained really? Well, over the months and
131 00:11:50.580 ⇒ 00:11:51.600 Amber Lin: the
132 00:11:51.970 ⇒ 00:12:00.150 Amber Lin: down the line, there’ll be a lot less of catching up. And only a few of okay, we have this new thing. Let’s push it out.
133 00:12:00.310 ⇒ 00:12:00.909 Amber Lin: So I.
134 00:12:00.910 ⇒ 00:12:01.290 Uttam Kumaran: Okay.
135 00:12:01.290 ⇒ 00:12:09.010 Amber Lin: I know meetings. Immediate updates and plus like a batch update each week.
136 00:12:10.170 ⇒ 00:12:13.130 Amber Lin: Probably, like probably like 10 or 10.
137 00:12:13.610 ⇒ 00:12:17.440 Uttam Kumaran: Okay. So then let’s say, yeah. So let’s say, 10 h.
138 00:12:19.100 ⇒ 00:12:23.920 Uttam Kumaran: I would say, if we just, I think it’s fair to just say it’s anywhere from 10 to 20.
139 00:12:24.370 ⇒ 00:12:28.540 Uttam Kumaran: 20 ha week. What is 20 HA week
140 00:12:29.200 ⇒ 00:12:32.070 Uttam Kumaran: for a month at billable rate of like 200.
141 00:12:33.240 ⇒ 00:12:36.469 Amber Lin: Huh! 20 HA week!
142 00:12:36.960 ⇒ 00:12:40.539 Uttam Kumaran: Yeah, like, what is 20 times 4 times 200
143 00:12:41.260 ⇒ 00:12:43.570 Uttam Kumaran: or yeah, 20 times 4 times 200.
144 00:12:43.780 ⇒ 00:12:46.249 Amber Lin: My mine is blanking out. Okay.
145 00:12:46.250 ⇒ 00:12:48.209 Uttam Kumaran: Wait! No, no use your calculator. Use your Cal.
146 00:12:48.210 ⇒ 00:12:50.730 Amber Lin: I know that’s 16 K.
147 00:12:51.450 ⇒ 00:12:52.130 Uttam Kumaran: Okay,
148 00:12:54.110 ⇒ 00:12:59.890 Uttam Kumaran: cool. So then, what I’m gonna try to do is basically the the math we need to do is figure out
149 00:12:59.990 ⇒ 00:13:03.300 Uttam Kumaran: how many if we were to charge on a per call basis.
150 00:13:03.300 ⇒ 00:13:03.730 Amber Lin: Okay.
151 00:13:03.730 ⇒ 00:13:09.720 Uttam Kumaran: What do we need to charge per call, so that we make at least 15 KA month.
152 00:13:12.380 ⇒ 00:13:13.480 Uttam Kumaran: Do you see what I mean?
153 00:13:13.480 ⇒ 00:13:16.669 Amber Lin: I see. Let me pull up my calculator again.
154 00:13:16.670 ⇒ 00:13:33.569 Uttam Kumaran: So this is where I’m not. I’m not 100% sure what that is. We’re basically, we want to say, cool, we they use. But this is the thing. It’s like, we wanna make sure that they’re that we only comp them for the calls that they’re using AI on. So we’re saying for every call that that AI is used.
155 00:13:33.700 ⇒ 00:13:38.430 Uttam Kumaran: We’re going to charge X amount.
156 00:13:38.620 ⇒ 00:13:44.070 Uttam Kumaran: And then we have 3 tiers on the lowest tier. We want to aim to make 15 KA month.
157 00:13:44.540 ⇒ 00:13:51.099 Amber Lin: Okay. So right now, they, you say they do. 60 calls a day in the pest division.
158 00:13:51.320 ⇒ 00:13:53.810 Uttam Kumaran: No, they do. 60 calls per person.
159 00:13:54.540 ⇒ 00:13:55.570 Amber Lin: So this is where, like
160 00:13:55.570 ⇒ 00:14:06.530 Amber Lin: per person, and then times 25, right? So 30 days, 25 agents, 60 calls per day. Right? So that’s 45 K. Calls.
161 00:14:07.861 ⇒ 00:14:16.170 Uttam Kumaran: Yeah, that’s that’s well, not not yeah. I would. I would have to check what weekends is like. But we also have this data from David. But let’s just assume that. Yeah, sure.
162 00:14:16.170 ⇒ 00:14:22.874 Amber Lin: Let’s just assume that we’ll we’ll just say that. And then let’s say, this sounds like consulting
163 00:14:23.870 ⇒ 00:14:30.180 Amber Lin: 15 K. Divided by 45 k, that’s around 33 cents per call.
164 00:14:30.180 ⇒ 00:14:32.710 Uttam Kumaran: But this is, if every single call.
165 00:14:33.300 ⇒ 00:14:34.919 Amber Lin: Yes, they use AI.
166 00:14:35.130 ⇒ 00:14:39.420 Uttam Kumaran: So let’s start with. First, st let’s assume that quarter of the calls. They’re gonna use AI.
167 00:14:41.020 ⇒ 00:14:43.799 Amber Lin: Okay, if all.
168 00:14:43.800 ⇒ 00:14:47.020 Uttam Kumaran: So so do 25% of the 15. Yeah.
169 00:14:47.020 ⇒ 00:14:55.760 Amber Lin: We do 50 K as a base assumption. Let’s say we aim for aim for 15 k per month.
170 00:14:55.870 ⇒ 00:15:01.320 Amber Lin: If 100% AI usage.
171 00:15:01.440 ⇒ 00:15:08.369 Amber Lin: That’s gonna be 33 cents per call. If we say only 25% AI usage.
172 00:15:08.830 ⇒ 00:15:14.800 Amber Lin: Alright, that’s gonna be paint driveway.
173 00:15:21.170 ⇒ 00:15:23.459 Amber Lin: Well, how much we want to charge right.
174 00:15:25.420 ⇒ 00:15:30.449 Uttam Kumaran: Yeah. So let’s say, let’s say we’re gonna let’s say we’re gonna aim on the low end to make 15 grand a month.
175 00:15:32.950 ⇒ 00:15:33.410 Amber Lin: So that.
176 00:15:33.410 ⇒ 00:15:34.669 Uttam Kumaran: Which means, yeah.
177 00:15:34.670 ⇒ 00:15:38.990 Amber Lin: 1, $21 20 cents per call.
178 00:15:39.240 ⇒ 00:15:40.900 Amber Lin: That might be a lot.
179 00:15:41.120 ⇒ 00:15:42.319 Uttam Kumaran: Dollar per call.
180 00:15:42.530 ⇒ 00:15:44.030 Amber Lin: Yeah. Dollar per call.
181 00:15:44.030 ⇒ 00:15:45.689 Uttam Kumaran: So dollar per call
182 00:15:45.960 ⇒ 00:15:51.900 Uttam Kumaran: to start with sounds good. And then basically, what we do is as it goes up the the
183 00:15:52.580 ⇒ 00:15:56.569 Uttam Kumaran: like, basically as it goes up we start to lower the per per price right?
184 00:15:57.950 ⇒ 00:15:59.100 Amber Lin: I’m just wondering if.
185 00:15:59.100 ⇒ 00:15:59.500 Uttam Kumaran: Isn’t that.
186 00:15:59.500 ⇒ 00:15:59.930 Amber Lin: Consult.
187 00:15:59.930 ⇒ 00:16:00.250 Uttam Kumaran: Things.
188 00:16:00.250 ⇒ 00:16:08.869 Amber Lin: So yeah, I’m just wondering if you can sell a dollar per call like, how’s there? Would they be receptive to that.
189 00:16:10.000 ⇒ 00:16:13.479 Uttam Kumaran: I I mean, okay, so like, why don’t you play?
190 00:16:13.600 ⇒ 00:16:16.530 Uttam Kumaran: Why don’t you play Matt Burns being like.
191 00:16:16.530 ⇒ 00:16:17.050 Amber Lin: Okay.
192 00:16:17.050 ⇒ 00:16:38.590 Uttam Kumaran: We’re not. Gonna we’re not gonna go with this. So for me, the math is your your average customers may be spending like I think I got heard. It was like 200 to $300 a year. Like! What is the risk of you losing that customer? Is it worth a dollar just to make sure that call gets resolved immediately.
193 00:16:38.740 ⇒ 00:16:41.149 Uttam Kumaran: Right? That’s what I would say is that
194 00:16:41.480 ⇒ 00:16:49.050 Uttam Kumaran: for me it seems well worth it that for your customers to not get a phone call back or not be put on hold, for.
195 00:16:49.230 ⇒ 00:16:53.550 Uttam Kumaran: you know 32 min to 2 days. Is that worth a dollar?
196 00:16:56.950 ⇒ 00:17:03.310 Amber Lin: I see, well, the problem here is that
197 00:17:03.700 ⇒ 00:17:10.139 Amber Lin: we don’t know how you’re gonna save the cost right right, now, this is so tentative.
198 00:17:10.890 ⇒ 00:17:14.379 Amber Lin: I haven’t seen the proof that this is gonna happen.
199 00:17:14.810 ⇒ 00:17:21.349 Amber Lin: I would want to start lower because you haven’t proved to me that you are going to save the customer.
200 00:17:22.130 ⇒ 00:17:29.089 Uttam Kumaran: Okay, that’s a fair point. Then what we could do is we could come down a little lower. But then we would want to.
201 00:17:29.360 ⇒ 00:17:34.730 Uttam Kumaran: We would basically want to renegotiate sooner than end of Q. 2.
202 00:17:37.780 ⇒ 00:17:44.130 Uttam Kumaran: Cause, if we’re starting to. Yeah, that’s what I would suggest is like right now, we’re set to renegotiate at the end of June.
203 00:17:45.470 ⇒ 00:17:50.720 Uttam Kumaran: I would, I would suggest. We we renegotiate at the end of May
204 00:17:51.950 ⇒ 00:17:57.539 Uttam Kumaran: and then and then we basically can can show the estimate of how what we’re impact. Our impact is.
205 00:17:57.900 ⇒ 00:18:04.420 Amber Lin: Alright, that sounds pretty good. What about the Base plus commission plan that they proposed earlier?
206 00:18:05.020 ⇒ 00:18:07.710 Amber Lin: Yeah, so we per call, but have a.
207 00:18:07.710 ⇒ 00:18:11.619 Uttam Kumaran: Yeah, so we we were, we were planning on doing so we were planning on
208 00:18:12.180 ⇒ 00:18:25.959 Uttam Kumaran: so base meaning, yeah. So right now, we basically, we said is, let’s get a deal over the line. And then in, when we come into Q. 2, we will have a lot of data on like, what? How do we actually impact? And then we can make a different deal, which is like.
209 00:18:26.590 ⇒ 00:18:29.410 Uttam Kumaran: Okay, now that we know, like.
210 00:18:29.590 ⇒ 00:18:41.199 Uttam Kumaran: basically, we know that we’re we’re spending like this much, you know, you know, that we’ve saved your customers this much. Now, we wanna maybe charge based on like only the ones that we get a specific upsell on, or we want a percentage of the upsell
211 00:18:42.020 ⇒ 00:18:43.049 Uttam Kumaran: things like that.
212 00:18:43.870 ⇒ 00:18:51.739 Amber Lin: Yeah, totally. We need data to prove that. So that will be a key key goal for this next
213 00:18:52.476 ⇒ 00:19:02.293 Amber Lin: q, 2. I’m essentially this spring. I’m telling honey, this is the most important thing we need right now, because last Friday you weren’t there. But they asked, How do we?
214 00:19:02.910 ⇒ 00:19:19.369 Amber Lin: How do we track the upsells? So okay, we’re gonna we’re gonna figure out how many upsells were brought up per conversation. And then in the dashboard, I’m asking Annie to okay. What are the type of upsells? And how frequently were they brought up.
215 00:19:19.370 ⇒ 00:19:19.840 Uttam Kumaran: Yeah.
216 00:19:19.840 ⇒ 00:19:26.170 Amber Lin: So when we have that, we can then connect it to their upsell data.
217 00:19:26.310 ⇒ 00:19:51.400 Amber Lin: So those 2 things are the most important right now, we really need the data to prove this, because as a business owner, I don’t. I don’t think I want to give you a dollar per call. What I don’t know what your call even does. I mean, they like us. So I think they’ll probably still give us 10 k, just like right now of development, because we’re also doing the trainer trainer. But I don’t know if you want this as a separate deal.
218 00:19:51.400 ⇒ 00:19:55.510 Uttam Kumaran: Well, yeah, that’s why I’m basically what I’m gonna say is like, look.
219 00:19:55.810 ⇒ 00:20:24.199 Uttam Kumaran: we we want to do this in a fair way we’re doing. We’re gonna be doing trainer bot, we’re gonna be helping on the data side like I can start to separate all those things up as individual line items and charge, you are typically hourly. But instead, if if we’re just gonna help the business, and you guys are just down to do this as a on a on this sort of like usage basis that would cover all those costs. Because if look, if we end up making 15 or 20 K. We’ll do all this, we’ll do all those things. Those things are easy.
220 00:20:26.410 ⇒ 00:20:54.299 Amber Lin: What I think is that at least until the end of May, before we renegotiate, we probably want, say, a 10 K base that covers the trainer bot. And maybe a little bit of data probably doesn’t the data stuff. I don’t know how fast it’s gonna take off. So I don’t think those 2 things take a long time, but it sounds more reasonable to the client. Okay, we paid 10 K. Now we can pay 10 K. And then plus
221 00:20:54.763 ⇒ 00:21:02.399 Amber Lin: usage per call. Right? So then we can say, we will gonna charge you less per call. And we can still.
222 00:21:02.400 ⇒ 00:21:05.970 Amber Lin: yeah, that. So that’s a great. That’s a great plan. BI would say.
223 00:21:06.759 ⇒ 00:21:12.060 Amber Lin: Let me see if 5 K divided by 20% AI usage. Let me see how much that is.
224 00:21:12.060 ⇒ 00:21:15.790 Uttam Kumaran: Okay, you’re totally right in that. If they’re like yo, we don’t want to bite on that. I would say, cool.
225 00:21:15.790 ⇒ 00:21:16.180 Amber Lin: Don’t you.
226 00:21:16.180 ⇒ 00:21:17.639 Uttam Kumaran: Some sort of flat fee.
227 00:21:18.320 ⇒ 00:21:24.879 Amber Lin: Okay, 5 k for 20%. AI usage is 40 cents per call. I think that’s decent.
228 00:21:25.870 ⇒ 00:21:28.320 Uttam Kumaran: Okay, then I, so okay,
229 00:21:30.200 ⇒ 00:21:42.759 Uttam Kumaran: so you’re saying, we either do a dollar per call minimum. But see, this is the thing like, so for between 20 and 50%, what if we reduced it to like 80 cents? And then, for the rest, we did. We did at 50 cents, right
230 00:21:43.000 ⇒ 00:21:45.409 Uttam Kumaran: cause. I don’t. I want them to use it more
231 00:21:46.780 ⇒ 00:21:51.929 Uttam Kumaran: so, meaning like as they use them more incrementally goes down. And there’s a Max cap of like 20 K.
232 00:21:52.290 ⇒ 00:21:57.300 Amber Lin: Yeah, yeah, so that’s.
233 00:21:57.300 ⇒ 00:21:57.959 Uttam Kumaran: See what I mean.
234 00:21:58.090 ⇒ 00:22:00.890 Amber Lin: Yeah. So we could say at
235 00:22:01.040 ⇒ 00:22:17.750 Amber Lin: 2025% usage. It’s maybe like 80 cents per call at 50% usage. It’s like certain for certain cents per call, and 100% usage will be like 30 cents per call, like, are you thinking about that?
236 00:22:17.960 ⇒ 00:22:22.639 Uttam Kumaran: It’s it’s more like 0 to 25. Is this 25 to
237 00:22:23.560 ⇒ 00:22:26.000 Uttam Kumaran: 25 to 75 is this? And then.
238 00:22:26.000 ⇒ 00:22:27.080 Amber Lin: Hmm.
239 00:22:27.950 ⇒ 00:22:30.420 Uttam Kumaran: Well, it’s honestly, but it’s not percentage. It’s like.
240 00:22:30.670 ⇒ 00:22:38.150 Uttam Kumaran: Okay, you’re taking 60 calls. So whatever we said 4, they taking 45,000 calls right. So whatever 25
241 00:22:38.440 ⇒ 00:22:41.900 Uttam Kumaran: 0 to 15,000 calls is a dollar per call.
242 00:22:42.540 ⇒ 00:22:43.069 Amber Lin: After that.
243 00:22:43.070 ⇒ 00:22:48.620 Uttam Kumaran: That it’s 50 cents. After that there’s 25 cents, and then there’s a Max cap of 20 K.
244 00:22:53.630 ⇒ 00:22:56.839 Amber Lin: Max. Cap. Of 20 K. Means.
245 00:22:57.048 ⇒ 00:23:01.420 Uttam Kumaran: Or like I don’t know. I just said it. I just said 20 K. But like it could be something else.
246 00:23:04.110 ⇒ 00:23:10.869 Amber Lin: So you’re not talking about the base 10 k plus the other parts. You’re just saying, totally, completely usage based.
247 00:23:11.380 ⇒ 00:23:12.100 Uttam Kumaran: Yeah.
248 00:23:12.320 ⇒ 00:23:12.770 Amber Lin: Okay.
249 00:23:12.770 ⇒ 00:23:20.429 Uttam Kumaran: That’s that’s like, that’s like, Plan A, if they’re like, not down for that, then I would say, cool. Well, we need to cover our our engineering costs.
250 00:23:20.940 ⇒ 00:23:23.540 Uttam Kumaran: So we could do 5 K base.
251 00:23:23.800 ⇒ 00:23:28.420 Uttam Kumaran: and then we’ll we’ll we’d lower all of the usage costs.
252 00:23:28.610 ⇒ 00:23:30.490 Amber Lin: By some factor.
253 00:23:31.150 ⇒ 00:23:31.950 Amber Lin: Yeah.
254 00:23:32.630 ⇒ 00:23:40.130 Uttam Kumaran: And this is where, like I, I wanna have, I basically want to come to the table with, like both of those options. And then they can tell us like.
255 00:23:40.790 ⇒ 00:23:42.140 Amber Lin: Yeah. Totally.
256 00:23:42.140 ⇒ 00:23:43.369 Uttam Kumaran: What they want to do.
257 00:23:43.764 ⇒ 00:23:51.660 Amber Lin: Is there a plan? C, where you split up all the different tasks like the trainer? Bot the data stuff.
258 00:23:51.960 ⇒ 00:23:55.489 Uttam Kumaran: Yeah, other other plan. C is basically like.
259 00:23:55.910 ⇒ 00:23:59.559 Uttam Kumaran: I’ll tell them, like, Look, we’re, we’re building the 2 agents.
260 00:24:00.030 ⇒ 00:24:05.669 Uttam Kumaran: We’re helping with some stuff on the data side. We want to continue exploring, expanding this to the other teams.
261 00:24:05.940 ⇒ 00:24:13.780 Uttam Kumaran: We can also begin to carve those out and price those individually. It’s gonna you’re it’s gonna end up
262 00:24:14.130 ⇒ 00:24:26.500 Uttam Kumaran: like I would. I’m gonna basically tell them like we charge 200 an hour. So this is like, gonna be the most the more fair option for you to look for. It’s just for us to kind of consider that as part of this work
263 00:24:27.330 ⇒ 00:24:39.939 Uttam Kumaran: cause can I tell you can. I tell you why? And from my, from my perspective, I want recurring billing based on usage. Because then we can start to consider that as like guaranteed future, basically guaranteed.
264 00:24:40.090 ⇒ 00:24:40.580 Amber Lin: Totally.
265 00:24:40.580 ⇒ 00:24:42.840 Uttam Kumaran: It’s Mrr, yeah, right?
266 00:24:42.840 ⇒ 00:24:47.289 Amber Lin: This is where like it hides, how much we actually need to do honestly.
267 00:24:47.290 ⇒ 00:24:47.970 Uttam Kumaran: Yes.
268 00:24:47.970 ⇒ 00:24:54.009 Amber Lin: There’s not a lot. And we talked about. We talked internally about the trainer. Bot!
269 00:24:54.160 ⇒ 00:24:56.850 Amber Lin: We’re we’re done like, no, I know.
270 00:24:56.850 ⇒ 00:25:00.799 Amber Lin: Want to make it hard. We’re just gonna keep it with the Google docs.
271 00:25:00.930 ⇒ 00:25:04.059 Amber Lin: There’s nothing much we need to do.
272 00:25:04.690 ⇒ 00:25:07.820 Uttam Kumaran: No, I totally agree. I mean, I that’s why I’m saying is like.
273 00:25:08.120 ⇒ 00:25:11.520 Uttam Kumaran: it’s either gotta be plan A or plan B, like, I really don’t want to.
274 00:25:11.520 ⇒ 00:25:37.359 Amber Lin: Why don’t we just not suggest plan? C, like, just don’t do it. If they ask, we say, Hey, this is the breakdown. This is probably what it’s gonna look like. If we mentioned 2,200 per hour, they’re probably like, Oh, my goodness! So they probably won’t want to hear plan, C, unless you want to use it as an anchor. I think you can use it as a base case anchor of hey? It’s $200 per hour, or you can have the whole package. I don’t know
275 00:25:37.590 ⇒ 00:25:38.710 Amber Lin: technology.
276 00:25:38.980 ⇒ 00:25:43.849 Uttam Kumaran: Yeah, I’m I’m let’s go with. Just let’s just go with saying the option A option B.
277 00:25:44.890 ⇒ 00:25:56.199 Uttam Kumaran: I’m gonna say we prefer option a, because that allows us to sort of go work on any part of the country you need us, and then we will come back and we can renegotiate, based on the success we’re seeing.
278 00:25:57.150 ⇒ 00:26:02.529 Amber Lin: Yeah. The the only concern I have with plan A is that we might not earn enough
279 00:26:03.000 ⇒ 00:26:05.340 Amber Lin: for the next month.
280 00:26:05.340 ⇒ 00:26:07.260 Uttam Kumaran: No, but this is where I’m saying is that
281 00:26:08.050 ⇒ 00:26:10.630 Uttam Kumaran: but like 25% usage.
282 00:26:10.770 ⇒ 00:26:18.729 Uttam Kumaran: I don’t know right? That seems like pretty fair of a thing to just aim for, and that covers our costs that that covers our cost plus 50. So basically like.
283 00:26:18.730 ⇒ 00:26:22.360 Amber Lin: 25% uses at a dollar per call. Right?
284 00:26:22.360 ⇒ 00:26:26.319 Uttam Kumaran: Yeah, so that puts us at like 15 grand, right?
285 00:26:28.990 ⇒ 00:26:30.909 Uttam Kumaran: Or like somewhere close to that.
286 00:26:31.614 ⇒ 00:26:38.699 Amber Lin: If they have, if we have 25% from calls, I’m just concerned that if we say if we
287 00:26:38.820 ⇒ 00:26:54.410 Amber Lin: do it per usage, and it’s a dollar per call. I’m a little bit scared that it might motivate them to use it less, because for them they’ll think, oh, every call I make. I’m incurring in a cost where I can just search it manually. That’s 1 of my concerns
288 00:26:56.140 ⇒ 00:26:58.259 Amber Lin: like, do you think is a valid concern?
289 00:26:58.470 ⇒ 00:27:02.869 Uttam Kumaran: I I hear the concern. I guess my point is that, like
290 00:27:03.720 ⇒ 00:27:07.541 Uttam Kumaran: it, the goal of using it is so that your calls are.
291 00:27:08.310 ⇒ 00:27:10.430 Uttam Kumaran: get resolved on the 1st call.
292 00:27:10.590 ⇒ 00:27:21.070 Uttam Kumaran: Right? Like, that’s our. That’s our. That’s our anchor point. So you cannot use it. But then you’re gonna be like there. They have this problem. They have a problem today. The problem today is that
293 00:27:21.390 ⇒ 00:27:23.440 Uttam Kumaran: their current solution is not good.
294 00:27:24.840 ⇒ 00:27:30.700 Uttam Kumaran: So that’s what we’re offering. It’s like. And and of course, like you have to pay for that like, what option, what option do they have.
295 00:27:32.730 ⇒ 00:27:33.889 Amber Lin: That’s true.
296 00:27:34.270 ⇒ 00:27:39.639 Amber Lin: So I guess this is just more of a thing that we keep within the execs and don’t tell
297 00:27:40.320 ⇒ 00:27:45.430 Amber Lin: of course don’t tell any of the Csrs, and maybe don’t even tell Shannon or Grace.
298 00:27:45.430 ⇒ 00:27:50.970 Uttam Kumaran: Yeah, but I guess it’s for them. It’s like again, for example, the company bought them headsets. What’s.
299 00:27:50.970 ⇒ 00:27:51.890 Amber Lin: Let me tell you.
300 00:27:51.890 ⇒ 00:27:57.160 Uttam Kumaran: What is the cost of a headset right like? Was that a good work? Was that a good spend of like
301 00:27:57.930 ⇒ 00:27:59.579 Uttam Kumaran: 2030 bucks, you know.
302 00:27:59.580 ⇒ 00:28:00.060 Amber Lin: Absolutely.
303 00:28:00.060 ⇒ 00:28:01.670 Uttam Kumaran: That’s what we’re arguing. I’m not. I’m saying.
304 00:28:02.350 ⇒ 00:28:03.969 Uttam Kumaran: I think a dollar is cheap.
305 00:28:06.810 ⇒ 00:28:15.409 Uttam Kumaran: Right like I think a dollar is super cheap, like if you don’t lose half your customers where your customers are way happier. What’s a dollar right.
306 00:28:15.550 ⇒ 00:28:20.170 Amber Lin: I see. I guess I just don’t. I didn’t really have an idea of how.
307 00:28:20.170 ⇒ 00:28:23.990 Uttam Kumaran: This is where, like, yeah, this is where don’t consider like a dollar out of your pocket
308 00:28:24.100 ⇒ 00:28:32.249 Uttam Kumaran: or my pocket. Consider it like for for a company like ABC, that’s making several 100 million dollars, and they’re investing in hiring more Csrs who are
309 00:28:32.550 ⇒ 00:28:35.879 Uttam Kumaran: like, maybe like 50 60 KA year.
310 00:28:36.190 ⇒ 00:28:38.429 Uttam Kumaran: and they have to invest 6 months in training.
311 00:28:38.980 ⇒ 00:28:43.270 Uttam Kumaran: That’s who we’re like. That’s the cost that we’re finding for them, you know.
312 00:28:45.690 ⇒ 00:28:46.470 Amber Lin: Yeah.
313 00:28:47.230 ⇒ 00:28:50.749 Uttam Kumaran: Then it seems like pennies.
314 00:28:53.500 ⇒ 00:28:57.820 Amber Lin: I think we just need to ramp up our financial modeling.
315 00:28:58.363 ⇒ 00:29:05.780 Amber Lin: Do a bit of that. If we have their data, I can look a bit more into how to do that.
316 00:29:05.780 ⇒ 00:29:12.029 Uttam Kumaran: The the guidance that Scott told me was like, look in the 1st tier. Make sure you’re making your margin back.
317 00:29:12.410 ⇒ 00:29:13.090 Amber Lin: Hmm.
318 00:29:13.550 ⇒ 00:29:15.800 Uttam Kumaran: And then the rest is sort of growth.
319 00:29:15.960 ⇒ 00:29:18.100 Uttam Kumaran: So that’s what I think we do. We just say like.
320 00:29:18.300 ⇒ 00:29:20.709 Uttam Kumaran: and for between 0 to 25%.
321 00:29:21.000 ⇒ 00:29:22.350 Uttam Kumaran: It’s this cost.
322 00:29:22.610 ⇒ 00:29:27.040 Uttam Kumaran: And then after that, it’s it’s this. And then we have a Max cap of like 20 KA month
323 00:29:27.360 ⇒ 00:29:29.489 Uttam Kumaran: or 25 KA month, basically.
324 00:29:31.450 ⇒ 00:29:33.030 Uttam Kumaran: But I think that seems pretty fair.
325 00:29:33.170 ⇒ 00:29:34.856 Amber Lin: Yeah, cool.
326 00:29:35.990 ⇒ 00:29:46.769 Amber Lin: here. I don’t know if you’ll have time to flesh it out. I could go look at the call data they sent us. Essentially, I hope they filter by past. I haven’t done any.
327 00:29:46.770 ⇒ 00:29:47.859 Uttam Kumaran: They did. Yeah.
328 00:29:48.150 ⇒ 00:30:03.740 Amber Lin: But I’ll see like, get the actual numbers and maybe make an Excel model off of that. It’ll make our pricing a lot more easier, because right now you’re eyeballing. Oh, mass, cap of 20 K or 25 K. I do think we need more data on.
329 00:30:03.740 ⇒ 00:30:06.000 Uttam Kumaran: Yeah, maybe just throw it in Google sheets.
330 00:30:06.000 ⇒ 00:30:06.320 Amber Lin: Yeah.
331 00:30:06.320 ⇒ 00:30:17.199 Uttam Kumaran: And then, if you want to send me this granola, we can put this in. We can basically just throw this into a notion like summary, and then I can send it to Scott to basically be like, what do you think.
332 00:30:17.200 ⇒ 00:30:18.650 Amber Lin: Hmm. Okay. Cool.
333 00:30:19.160 ⇒ 00:30:20.320 Amber Lin: Sounds good.
334 00:30:21.400 ⇒ 00:30:22.350 Amber Lin: Yeah.
335 00:30:23.050 ⇒ 00:30:24.150 Uttam Kumaran: Okay, that’s all.
336 00:30:26.030 ⇒ 00:30:26.610 Uttam Kumaran: Okay.
337 00:30:27.110 ⇒ 00:30:31.249 Amber Lin: That’s for ABC for the snowflake stuff. Do you have time to work on that today?
338 00:30:31.250 ⇒ 00:30:34.700 Amber Lin: Yeah, I do. I just got up. I just got in super late yesterday.
339 00:30:34.700 ⇒ 00:30:35.110 Amber Lin: Oh.
340 00:30:35.110 ⇒ 00:30:36.040 Uttam Kumaran: That’s where I am.
341 00:30:36.360 ⇒ 00:30:38.210 Uttam Kumaran: So I I
342 00:30:39.286 ⇒ 00:30:48.559 Amber Lin: That’s not too urgent, because they also haven’t replied to me, multiple size, David is just busy. So I think you have some more time.
343 00:30:48.800 ⇒ 00:30:53.370 Uttam Kumaran: Okay, yeah, I have time. Today, I’m just like finishing up my last meetings. And then.
344 00:30:53.720 ⇒ 00:30:58.380 Amber Lin: Yeah. Great. Okay, David, join our channel in 7 Am. This morning. So.
345 00:30:58.580 ⇒ 00:30:59.720 Uttam Kumaran: Okay. Nice.
346 00:30:59.720 ⇒ 00:31:12.060 Amber Lin: But he will be. He will be more active, based on that. And also since our meeting Friday I was doing some head down time, because I was analyzing where I spent my time last week.
347 00:31:12.060 ⇒ 00:31:12.770 Uttam Kumaran: Okay.
348 00:31:12.770 ⇒ 00:31:33.310 Amber Lin: It’s like, Okay, I I copy and pasted my clock. If I logs, I’m glad I logged it detailed enough. So I was like, okay, what type of task is this? What can be automated, based on what we talked about last Friday? I’ll share the doc with you. I found that really interesting, and one of the things I’m already starting to do is that I do. Wanna
349 00:31:33.310 ⇒ 00:31:42.820 Amber Lin: tag the clock 5 HI don’t know if I should share those tags. Apparently it’s public to everyone as a workspace. I might just do it in notion.
350 00:31:42.880 ⇒ 00:31:51.829 Amber Lin: And Tag is as okay. What state is this? Because you sent the 5 stages scaling leadership. Right? Of, okay? Who? Who am I doing.
351 00:31:53.260 ⇒ 00:31:59.809 Amber Lin: Can this be scaled? Who? What state is it in right? Because eventually all of it
352 00:31:59.950 ⇒ 00:32:04.290 Amber Lin: should be? Most of it should be at stage 4 or 5. Right.
353 00:32:04.290 ⇒ 00:32:04.740 Uttam Kumaran: Yeah.
354 00:32:04.740 ⇒ 00:32:11.630 Amber Lin: Want everything in stage one or even stage 0 where a lot of it. I’m doing it on my own. Nobody’s watching.
355 00:32:11.630 ⇒ 00:32:26.149 Uttam Kumaran: No, this is actually super super great. I wonder if I mean, I want to use this, too, because right now it’s a it’s an absolute mess. Even today, though I woke up, I had to do something financial. I’m like, okay. I should. I should loop in Marianne before I do that myself.
356 00:32:26.310 ⇒ 00:32:26.840 Amber Lin: Yeah.
357 00:32:26.840 ⇒ 00:32:35.769 Uttam Kumaran: But even yeah, I think I guess, thinking about our time, and like how much of it is spent solo versus with someone else versus just watching someone else like just the 1st 3 stages.
358 00:32:35.770 ⇒ 00:32:42.220 Amber Lin: Totally. I will send you that document. I I did it, and I was like, Oh, this is interesting. So I wanted to bring it up.
359 00:32:42.220 ⇒ 00:32:48.950 Uttam Kumaran: You know, the other thing. Other thing I’m gonna try to start doing is basically I, I was using my Google calendar as like a to do list for a while.
360 00:32:49.610 ⇒ 00:32:55.119 Uttam Kumaran: because basically, you could just put in because my a lot of my time is meetings. So you can just put in like what you’re working on.
361 00:32:55.250 ⇒ 00:32:58.339 Amber Lin: That way. I’m I may just export that, and then.
362 00:32:58.580 ⇒ 00:33:00.830 Uttam Kumaran: I use that to put into clockify anyways.
363 00:33:00.830 ⇒ 00:33:05.310 Amber Lin: Yeah, clock if I has the calendar view also.
364 00:33:05.520 ⇒ 00:33:11.820 Amber Lin: But I can. Didn’t you already install the extension from clockify like.
365 00:33:11.820 ⇒ 00:33:12.370 Uttam Kumaran: I did.
366 00:33:12.370 ⇒ 00:33:13.750 Amber Lin: Automatically add.
367 00:33:13.960 ⇒ 00:33:17.480 Uttam Kumaran: It doesn’t automatically add those. It just gives you an overlay to, then do it.
368 00:33:17.480 ⇒ 00:33:18.990 Amber Lin: Oh, I see!
369 00:33:18.990 ⇒ 00:33:19.760 Uttam Kumaran: Yeah.
370 00:33:20.180 ⇒ 00:33:21.210 Amber Lin: Let’s see.
371 00:33:21.512 ⇒ 00:33:23.930 Uttam Kumaran: Okay, but let me give that a shot.
372 00:33:24.590 ⇒ 00:33:36.600 Uttam Kumaran: And yeah, I just had, like an insane morning, just got everything through. We sent out 3 proposals for new clients, and then we have one client who we used to work with that’s like potentially coming back. Can you hear me.
373 00:33:36.600 ⇒ 00:33:37.470 Amber Lin: Stella.
374 00:33:37.660 ⇒ 00:33:39.090 Uttam Kumaran: Yeah. Stella, Stella, yeah.
375 00:33:42.090 ⇒ 00:33:46.640 Amber Lin: I think I have more space to. Pm. One more client is my take.
376 00:33:46.640 ⇒ 00:33:49.629 Uttam Kumaran: Yeah, I think I think either Stella or urban stems.
377 00:33:49.630 ⇒ 00:33:50.260 Amber Lin: Okay.
378 00:33:51.700 ⇒ 00:33:56.380 Uttam Kumaran: Yeah. I mean, either one sell is sell. It is maybe pretty easy. I don’t know.
379 00:33:57.100 ⇒ 00:34:00.499 Amber Lin: Both of them doesn’t sound too hard based on what you said.
380 00:34:00.500 ⇒ 00:34:06.099 Uttam Kumaran: The urban stem. One is going to become a little bit more. I don’t know. Again. I think they’re both probably similar difficulty.
381 00:34:06.610 ⇒ 00:34:10.810 Amber Lin: Yeah, yeah. Okay, start to onboard. Me.
382 00:34:11.219 ⇒ 00:34:21.959 Amber Lin: that would be helpful. And also I wanted to check in on the I know you said. They sent it out last Friday. When should I expect the payment to come in.
383 00:34:22.687 ⇒ 00:34:25.259 Uttam Kumaran: I will double check on that today.
384 00:34:25.260 ⇒ 00:34:25.960 Amber Lin: Okay, so.
385 00:34:26.329 ⇒ 00:34:30.999 Uttam Kumaran: And then I have 30 min blocked later to finish up contract stuff.
386 00:34:31.000 ⇒ 00:34:35.920 Amber Lin: Great. There’s that, you know the payment for the when I was on hourly.
387 00:34:35.929 ⇒ 00:34:36.509 Uttam Kumaran: Yes.
388 00:34:36.510 ⇒ 00:34:45.009 Amber Lin: I assume the since I know our full time contract is not there yet, but if we do pay on a bi-weekly basis, that’s probably last
389 00:34:45.230 ⇒ 00:34:48.449 Amber Lin: end of last week as a bi-weekly, bi-weekly period.
390 00:34:48.650 ⇒ 00:34:54.940 Amber Lin: Okay, yeah, let me get that. Let me figure that all out this afternoon. And then also, if they don’t, if they didn’t submit it, I’ll go submit it right now.
391 00:34:54.949 ⇒ 00:34:56.089 Amber Lin: Sounds good.
392 00:34:56.090 ⇒ 00:34:56.730 Uttam Kumaran: Okay.
393 00:34:56.730 ⇒ 00:35:00.639 Amber Lin: I just wanna bring it up, that’s all from my side. I’ll send you the summary from Granola.
394 00:35:00.640 ⇒ 00:35:01.712 Uttam Kumaran: Okay. Thank you.
395 00:35:02.480 ⇒ 00:35:03.400 Amber Lin: Bye.
396 00:35:03.400 ⇒ 00:35:04.060 Uttam Kumaran: Bye.