Meeting Title: Amber Lin’s Personal Meeting Room Date: 2025-05-01 Meeting participants: Uttam Kumaran, Amber Lin
WEBVTT
1 00:06:03.250 ⇒ 00:06:04.320 Uttam Kumaran: Hello!
2 00:06:04.830 ⇒ 00:06:05.810 Amber Lin: Hi.
3 00:06:05.810 ⇒ 00:06:07.340 Uttam Kumaran: Hi! Good morning!
4 00:06:07.510 ⇒ 00:06:09.490 Amber Lin: Spreadsheet company.
5 00:06:12.650 ⇒ 00:06:23.320 Amber Lin: let me send you that spreadsheet. Honestly, what I feel right now is that kind of don’t know where to start, but you might feel the same way.
6 00:06:23.500 ⇒ 00:06:28.659 Uttam Kumaran: Yeah. And I think as part of this, I also think we could build some sort of Gpt to have basically help us.
7 00:06:28.970 ⇒ 00:06:30.740 Amber Lin: Yeah. Totally.
8 00:06:31.270 ⇒ 00:06:36.589 Uttam Kumaran: Take in the prompt, create the the thing, and, like you can have access to all of our case studies and.
9 00:06:36.590 ⇒ 00:06:37.460 Amber Lin: Yeah.
10 00:06:37.460 ⇒ 00:06:38.210 Uttam Kumaran: Yeah.
11 00:06:39.430 ⇒ 00:06:46.459 Amber Lin: Okay. I shared the spreadsheet and the chat. I’m still filling it out. So just look at the threads for now.
12 00:06:47.655 ⇒ 00:06:53.340 Amber Lin: But I guess a good place to start. We could do for.
13 00:06:53.450 ⇒ 00:06:56.059 Uttam Kumaran: Like the AI strategy once.
14 00:06:56.370 ⇒ 00:07:04.620 Uttam Kumaran: Yeah. So I guess a couple of things. One is like, yeah, we can. I guess I just clicked on apply. And we just the only thing we really have to do is fill out a case study and then fill out the price.
15 00:07:05.151 ⇒ 00:07:08.340 Amber Lin: We don’t have enough connects, though.
16 00:07:08.340 ⇒ 00:07:14.290 Uttam Kumaran: Yeah, I looked to connect for 15 cents to connect. So that’s fine, like, I don’t know just we’ll buy them.
17 00:07:15.360 ⇒ 00:07:22.000 Amber Lin: Fif, like most of them, are like 15 an x per application. I believe.
18 00:07:22.580 ⇒ 00:07:27.680 Uttam Kumaran: Yeah, I think someone, one of them is 20. So that’s what maybe we can list them all, put the amount of connects.
19 00:07:28.240 ⇒ 00:07:34.590 Uttam Kumaran: And then basically, we can connect or it’s 15 cents a connect. So we can also put a column that’s like the price.
20 00:07:34.590 ⇒ 00:07:36.129 Amber Lin: Hmm, yeah, yeah, like.
21 00:07:36.130 ⇒ 00:07:39.989 Uttam Kumaran: And then they also have, like the leaderboard of like, how many
22 00:07:40.210 ⇒ 00:07:43.079 Uttam Kumaran: connects to get onto like the top spot.
23 00:07:43.080 ⇒ 00:07:44.700 Amber Lin: Hmm.
24 00:07:45.860 ⇒ 00:07:47.799 Uttam Kumaran: For example, you could. You can like.
25 00:07:47.960 ⇒ 00:07:51.920 Uttam Kumaran: bid 40 connects, and you’ll get like the 1st spot. Basically.
26 00:07:53.280 ⇒ 00:07:54.920 Amber Lin: I see, I see.
27 00:08:00.440 ⇒ 00:08:04.139 Amber Lin: Oh, let me to the
28 00:08:16.690 ⇒ 00:08:18.249 Amber Lin: think. Too bad!
29 00:08:19.920 ⇒ 00:08:20.900 Amber Lin: Cool!
30 00:08:21.358 ⇒ 00:08:28.549 Amber Lin: Why don’t we just start with one? Do you want to share screen. Like as you do it. I’m filling out the spreadsheet, but I’ll just look at it.
31 00:08:28.970 ⇒ 00:08:30.470 Uttam Kumaran: Yeah, give me a sec.
32 00:08:33.710 ⇒ 00:08:34.530 Uttam Kumaran: Yeah.
33 00:08:52.930 ⇒ 00:08:56.869 Uttam Kumaran: there’s a pretty good chance we close this clay client, by the way, that I talked to this morning.
34 00:08:56.870 ⇒ 00:08:57.700 Amber Lin: Hmm.
35 00:08:59.882 ⇒ 00:09:02.239 Uttam Kumaran: I think he’s gonna get back to me. And
36 00:09:02.500 ⇒ 00:09:09.280 Uttam Kumaran: I mean, maybe gets back to me by tomorrow. But then I’m gonna try and close them by Monday or Tuesday. Here in person.
37 00:09:09.710 ⇒ 00:09:16.210 Amber Lin: Awesome. What is the? And we’re gonna talk to the person tomorrow, right?
38 00:09:16.420 ⇒ 00:09:17.410 Amber Lin: Craig.
39 00:09:20.570 ⇒ 00:09:26.176 Uttam Kumaran: Yes, I emailed him, asking him if that time works. I don’t know, did he?
40 00:09:27.470 ⇒ 00:09:32.539 Uttam Kumaran: I guess I need. I I asked him like, Hey does tomorrow work? I can send him a text basically saying, this tomorrow work.
41 00:10:15.920 ⇒ 00:10:19.889 Amber Lin: A lot of these are AI,
42 00:10:20.100 ⇒ 00:10:22.589 Amber Lin: and a lot of them are AI consulting.
43 00:10:23.540 ⇒ 00:10:24.490 Uttam Kumaran: Oh, great. Okay.
44 00:10:24.490 ⇒ 00:10:34.549 Amber Lin: A lot of strategy, and we’re in a really really good spot if we have a lot of our like internal stuff at least posted, or at least out somewhere.
45 00:10:34.550 ⇒ 00:10:38.530 Uttam Kumaran: Yeah, boost our chances a lot. So that
46 00:10:38.530 ⇒ 00:10:43.650 Uttam Kumaran: we’re gonna we can just start to link to as many case studies as possible. And they should have all the case studies done.
47 00:10:43.650 ⇒ 00:10:44.640 Amber Lin: Yeah.
48 00:10:44.640 ⇒ 00:10:45.640 Uttam Kumaran: By tomorrow night.
49 00:10:45.640 ⇒ 00:10:48.390 Amber Lin: For the AI ones like.
50 00:10:48.390 ⇒ 00:10:48.970 Uttam Kumaran: Yeah.
51 00:10:48.970 ⇒ 00:10:49.630 Amber Lin: Stuff.
52 00:10:49.950 ⇒ 00:10:51.360 Uttam Kumaran: Oh, not the internal stuff.
53 00:10:51.360 ⇒ 00:10:58.840 Amber Lin: Even done, but I think we have a good enough vision, and we have some like we. Our internal agents are pretty good, and.
54 00:10:58.840 ⇒ 00:11:00.020 Uttam Kumaran: No, that’s the thing you just
55 00:11:00.020 ⇒ 00:11:02.789 Uttam Kumaran: like. Turn them into case studies like.
56 00:11:03.580 ⇒ 00:11:04.880 Amber Lin: Okay, so.
57 00:11:05.970 ⇒ 00:11:08.649 Uttam Kumaran: Like we just have to record basically.
58 00:11:09.730 ⇒ 00:11:20.240 Amber Lin: Okay, so to write, I’ll do what to do. Create case study for internal of agents.
59 00:12:09.480 ⇒ 00:12:10.380 Amber Lin: Yeah.
60 00:12:11.983 ⇒ 00:12:20.700 Amber Lin: If you see it in everything I posted in the Fraser and the Google sheets now, and a lot of them are AI strategy.
61 00:12:21.110 ⇒ 00:12:23.060 Uttam Kumaran: Can you link me the Google Sheet.
62 00:12:23.350 ⇒ 00:12:27.190 Amber Lin: Yeah, I’ll send it. It was in sales, but I’ll send it to you.
63 00:12:27.190 ⇒ 00:12:27.940 Uttam Kumaran: Okay.
64 00:12:41.160 ⇒ 00:12:43.001 Uttam Kumaran: do we have any like,
65 00:12:43.670 ⇒ 00:12:44.160 Amber Lin: Hello!
66 00:12:44.160 ⇒ 00:12:48.070 Uttam Kumaran: Guidelines on like how to write a great like upward case study, or like.
67 00:12:49.880 ⇒ 00:12:53.279 Amber Lin: Maybe Robert has any documentation.
68 00:12:54.620 ⇒ 00:13:00.070 Amber Lin: That would be the only thing that if we have anything that would, that would be Robert.
69 00:13:00.070 ⇒ 00:13:00.820 Uttam Kumaran: Okay.
70 00:13:08.280 ⇒ 00:13:08.950 Uttam Kumaran: Okay.
71 00:13:23.520 ⇒ 00:13:28.009 Amber Lin: Oh, yeah, let me just look at the phone.
72 00:13:28.010 ⇒ 00:13:29.890 Amber Lin: Scrape these.
73 00:13:30.020 ⇒ 00:13:33.530 Amber Lin: I really don’t want to fill out this stuff one by one.
74 00:13:33.970 ⇒ 00:13:37.520 Uttam Kumaran: I think, do a couple, and then I think, we can ask
75 00:13:38.080 ⇒ 00:13:40.210 Uttam Kumaran: AI team to figure out how to scrape this.
76 00:13:40.210 ⇒ 00:13:41.190 Amber Lin: Yeah.
77 00:13:52.170 ⇒ 00:13:59.329 Uttam Kumaran: So I guess like should like. Let’s put a let’s put something on the right here, which is more of like.
78 00:13:59.330 ⇒ 00:13:59.880 Amber Lin: Hmm.
79 00:14:03.050 ⇒ 00:14:03.540 Amber Lin: Yeah, just.
80 00:14:03.540 ⇒ 00:14:06.490 Uttam Kumaran: Let’s do it like fit.
81 00:14:07.640 ⇒ 00:14:13.890 Uttam Kumaran: Why or why not that way? I can just write some stuff here.
82 00:14:18.116 ⇒ 00:14:20.970 Uttam Kumaran: I’m just gonna move to my own laptop.
83 00:14:58.770 ⇒ 00:15:02.610 Amber Lin: I’ll pick a random AI one and just go through the
84 00:15:02.800 ⇒ 00:15:04.810 Amber Lin: try and go through the process.
85 00:15:05.020 ⇒ 00:15:05.770 Uttam Kumaran: Okay.
86 00:15:06.280 ⇒ 00:15:11.800 Amber Lin: Chat AI expert, for I mean, there’s the 1st one is like a
87 00:15:12.940 ⇒ 00:15:17.149 Amber Lin: AI product like Private AI assistant that.
88 00:15:17.830 ⇒ 00:15:20.660 Uttam Kumaran: Yeah, that one is like the scope is too big.
89 00:15:21.580 ⇒ 00:15:22.540 Amber Lin: Is it?
90 00:15:23.490 ⇒ 00:15:25.540 Amber Lin: For 7 K. Is too big.
91 00:15:27.420 ⇒ 00:15:28.252 Uttam Kumaran: Well, let me
92 00:15:30.050 ⇒ 00:15:34.240 Amber Lin: Oh, and it also wants like development stuff.
93 00:15:34.510 ⇒ 00:15:36.410 Amber Lin: So nuance front end.
94 00:15:38.790 ⇒ 00:15:40.140 Amber Lin: Is that true?
95 00:15:40.690 ⇒ 00:15:42.660 Uttam Kumaran: Yeah, let me pull it up. Okay.
96 00:15:42.660 ⇒ 00:15:43.280 Amber Lin: Good job
97 00:15:50.220 ⇒ 00:15:54.259 Amber Lin: are they outsourcing their company? Core products.
98 00:15:55.220 ⇒ 00:15:56.020 Uttam Kumaran: Yeah.
99 00:15:56.190 ⇒ 00:16:03.069 Amber Lin: Why would I go through that company? If I’m developing all their products? I would just sell it on my own.
100 00:16:06.210 ⇒ 00:16:09.919 Uttam Kumaran: Well, that’s the thing. Some of these, like I can already tell, are gonna be like.
101 00:16:13.640 ⇒ 00:16:14.799 Uttam Kumaran: yeah, I don’t know.
102 00:16:16.290 ⇒ 00:16:18.040 Uttam Kumaran: So okay, let me look at this one.
103 00:16:18.040 ⇒ 00:16:20.330 Amber Lin: It looks like just their big idea.
104 00:17:05.540 ⇒ 00:17:07.520 Uttam Kumaran: Yeah, there’s a couple of things that
105 00:17:09.810 ⇒ 00:17:13.090 Uttam Kumaran: stand out to me. Bootstrapping, lean and fast.
106 00:17:13.770 ⇒ 00:17:15.690 Uttam Kumaran: Huge scope.
107 00:17:21.819 ⇒ 00:17:24.180 Uttam Kumaran: 5 k, like.
108 00:17:35.240 ⇒ 00:17:38.740 Uttam Kumaran: So if it’s beyond automation. Yeah, okay.
109 00:17:39.550 ⇒ 00:17:42.869 Uttam Kumaran: so that’s that. Let me look at the next one.
110 00:18:16.310 ⇒ 00:18:18.619 Amber Lin: Yeah, some of them just don’t look
111 00:18:18.750 ⇒ 00:18:25.700 Amber Lin: if the next one that I add a senior tech partner and AI customer insights analytic platform, fixed price of
112 00:18:26.120 ⇒ 00:18:28.279 Amber Lin: 1.5 K.
113 00:18:30.320 ⇒ 00:18:32.510 Amber Lin: Seems really cheap.
114 00:18:34.980 ⇒ 00:18:36.356 Uttam Kumaran: So this one is
115 00:18:40.020 ⇒ 00:18:46.170 Uttam Kumaran: AI expert for Chatgpt Company consultation. We’re a small company. Assets to mass amount of data.
116 00:18:47.280 ⇒ 00:18:53.109 Uttam Kumaran: Come and spend one to 3 h for some leaders learn about the company, and we can like this person who’ll tackle information, essential questions.
117 00:18:53.880 ⇒ 00:18:57.659 Amber Lin: That’s what you do get paid for that pretty good.
118 00:19:09.630 ⇒ 00:19:11.810 Uttam Kumaran: Okay, this one’s yeah. I think this is a fit.
119 00:19:12.260 ⇒ 00:19:13.090 Amber Lin: Yeah.
120 00:19:13.090 ⇒ 00:19:18.830 Uttam Kumaran: And then, if I was to say why, it’s a fit
121 00:19:21.620 ⇒ 00:19:27.709 Uttam Kumaran: one. They’re looking for high level strategic help. It seems like it’s at the Executive level. First, st
122 00:19:27.820 ⇒ 00:19:34.729 Uttam Kumaran: they wanna they want help on procurement and then sort of strategy. And then they want to move to implementation. So that sounds.
123 00:19:34.730 ⇒ 00:19:38.399 Amber Lin: Yeah, seems like there’s a space definitely to sell more.
124 00:19:39.096 ⇒ 00:19:43.380 Amber Lin: We’re talking with people more in the exact level.
125 00:19:57.950 ⇒ 00:20:01.350 Amber Lin: Some of these postings pay very little.
126 00:20:01.630 ⇒ 00:20:02.450 Uttam Kumaran: Yeah.
127 00:20:18.225 ⇒ 00:20:31.030 Amber Lin: I’ll add one there. I think that’s kind of what I’m doing already. They want a training plan which, if I could, I’m like today. I finally have time. I’m just gonna do it for us.
128 00:20:31.420 ⇒ 00:20:34.940 Amber Lin: Honestly, you can just send it to them like.
129 00:20:35.190 ⇒ 00:20:41.150 Uttam Kumaran: Auto. So yeah, let me look at this next one. AI automation technical partner needed for pricing, consulting firm.
130 00:20:41.680 ⇒ 00:20:46.840 Uttam Kumaran: fractional pricing advisors, seeing experts. Hi developer to build custom tools.
131 00:20:47.160 ⇒ 00:20:55.060 Uttam Kumaran: Blah, blah, blah, blah, blah. Previous work, Aiml portfolio of completed technical projects.
132 00:20:55.580 ⇒ 00:20:57.319 Uttam Kumaran: Initial 3 months.
133 00:21:00.490 ⇒ 00:21:04.789 Uttam Kumaran: I’m way willing to pay higher than most. Yeah, this is fine. This is great.
134 00:21:07.080 ⇒ 00:21:09.700 Uttam Kumaran: Yes. And then the reason why
135 00:21:14.020 ⇒ 00:21:18.440 Uttam Kumaran: I like this one because they’re willing to pay higher.
136 00:21:19.240 ⇒ 00:21:23.759 Uttam Kumaran: It’s another consulting firm. So they’re used to working with other consultants.
137 00:21:23.760 ⇒ 00:21:24.260 Amber Lin: Okay.
138 00:21:24.260 ⇒ 00:21:26.650 Uttam Kumaran: We fit all the criteria?
139 00:21:33.370 ⇒ 00:21:36.909 Uttam Kumaran: Yeah. And it’s it’s it’s a Us specific. So we win.
140 00:21:40.720 ⇒ 00:21:42.770 Uttam Kumaran: Okay, next one.
141 00:21:43.050 ⇒ 00:22:00.680 Uttam Kumaran: So one thing I’m doing is, I’m just I’m gonna write, why or why not? In each of the categories I’m basically narrating like I’m I’m talking to whisper, and it’s writing it? But then we can shove all this into AI and have have it basically create a couple of Gpts. One is like a qualification Gpt.
142 00:22:00.680 ⇒ 00:22:01.530 Amber Lin: Oh no!
143 00:22:02.130 ⇒ 00:22:05.289 Uttam Kumaran: So you can take the entire page and then basically have it qualified.
144 00:22:05.860 ⇒ 00:22:09.230 Uttam Kumaran: The second piece is, then we’ll work on like how to write the case, study.
145 00:22:11.140 ⇒ 00:22:15.298 Amber Lin: Damn! You’re really thinking processes. This is so cool.
146 00:22:20.430 ⇒ 00:22:26.200 Uttam Kumaran: I would like to speak to a expert finance analytics space, how to position myself for the coming trend.
147 00:22:32.040 ⇒ 00:22:33.879 Uttam Kumaran: Yeah, I mean, this seems fine.
148 00:22:34.370 ⇒ 00:22:42.590 Uttam Kumaran: Why, this seems fine, although the budget seems really small, and it’s just
149 00:22:42.740 ⇒ 00:22:57.670 Uttam Kumaran: like sort of speaking to someone. This is where we excel, probably beyond a lot of other firms, and that we’re not just here to develop. We actually do talk to Ceos and executives all the time about how to do this sort of stuff. What tools to use? Why, to use.
150 00:22:58.110 ⇒ 00:23:04.749 Uttam Kumaran: you know. And then, anecdotally, I’m talking to people every day that really love speaking with me about this stuff. So I, I think we’re.
151 00:23:04.870 ⇒ 00:23:06.529 Uttam Kumaran: this is a good one to take on.
152 00:23:11.330 ⇒ 00:23:12.979 Uttam Kumaran: Okay, next one.
153 00:23:19.247 ⇒ 00:23:24.949 Uttam Kumaran: we’re seeking a detail oriented data analyst with expert level experience in Google sheets.
154 00:23:27.144 ⇒ 00:23:34.440 Uttam Kumaran: You’re working with our team to learn a dashboard using survey data implementing formulas.
155 00:23:34.950 ⇒ 00:23:37.060 Uttam Kumaran: They’ve hired a lot. Looks like
156 00:23:55.350 ⇒ 00:24:01.329 Uttam Kumaran: they want a short, 1 min video with the recent work that displays complex survey report.
157 00:24:25.180 ⇒ 00:24:29.929 Uttam Kumaran: I mean, this one honestly looks like they’re they don’t. They haven’t done anything like
158 00:24:30.800 ⇒ 00:24:34.329 Uttam Kumaran: at any price that’s like in our range at all. So I’m gonna say, no.
159 00:24:34.720 ⇒ 00:24:35.510 Amber Lin: Okay.
160 00:24:36.280 ⇒ 00:24:41.570 Uttam Kumaran: And for the reason, I’m just gonna say the price
161 00:24:42.040 ⇒ 00:24:48.459 Uttam Kumaran: that I’m seeing on all their past engagements are like within the basically a 10 to $15 an hour, range.
162 00:24:48.460 ⇒ 00:24:49.260 Amber Lin: What?
163 00:24:51.420 ⇒ 00:25:02.269 Uttam Kumaran: and it looks like they’ve spent a lot of money, but I mean, not really a ton of money, but they’ve their average billable rate is 1364 an hour. So there’s like no way they’re gonna be open to us.
164 00:25:02.660 ⇒ 00:25:03.550 Amber Lin: I see.
165 00:25:03.550 ⇒ 00:25:09.599 Uttam Kumaran: Oh, okay, let’s look at the next one.
166 00:25:15.230 ⇒ 00:25:16.150 Uttam Kumaran: Alright.
167 00:25:16.940 ⇒ 00:25:24.390 Uttam Kumaran: We’re looking for segment integration specialists help us one time setup. We identify customer data sources. We’re not hiring for general analytics.
168 00:25:25.840 ⇒ 00:25:31.919 Uttam Kumaran: We’re not hiring for analytics or event tracking. It’s about getting the data coming right. Identify all customer sources.
169 00:25:34.300 ⇒ 00:25:38.090 Uttam Kumaran: fixed scope milestone based 2 weeks of work.
170 00:25:39.970 ⇒ 00:25:42.340 Uttam Kumaran: 80 to a hundred an hour.
171 00:25:56.430 ⇒ 00:26:03.360 Uttam Kumaran: I mean, sure. But like this, like they really they just want like 2 weeks of work from us like.
172 00:26:05.300 ⇒ 00:26:06.450 Uttam Kumaran: and I’m kind of like.
173 00:26:10.490 ⇒ 00:26:13.280 Uttam Kumaran: probably, where I defer to Robert and ask him what we should do.
174 00:26:14.930 ⇒ 00:26:17.929 Amber Lin: Okay? And we can add Robert in spreadsheets as well.
175 00:26:29.786 ⇒ 00:26:31.859 Uttam Kumaran: Okay, let’s look at the next one
176 00:26:40.680 ⇒ 00:26:49.300 Uttam Kumaran: or data analyst from seeing someone mix modeling blah blah blah blah blah
177 00:27:25.010 ⇒ 00:27:37.889 Amber Lin: Oh, there is one that I just added. It’s a Meta base to Hubspot integration, mostly just for Hubspot Apis. And that is exactly what we’re trying to figure out for our own like, go to market project.
178 00:27:39.090 ⇒ 00:27:40.940 Amber Lin: Yeah, that could be something.
179 00:27:59.470 ⇒ 00:28:00.120 Uttam Kumaran: One.
180 00:28:03.560 ⇒ 00:28:11.040 Uttam Kumaran: We’re seeking expert database for product design team creating impactful campaign Roi dashboards.
181 00:28:19.100 ⇒ 00:28:24.389 Uttam Kumaran: The deliverables will be fully fidelity figma design files. Yeah, I mean.
182 00:28:26.240 ⇒ 00:28:28.570 Uttam Kumaran: I’ve built this. But like, we’re
183 00:28:28.850 ⇒ 00:28:32.390 Uttam Kumaran: yeah, like, I don’t know. They need like a designer.
184 00:28:32.870 ⇒ 00:28:33.760 Amber Lin: Oh!
185 00:28:36.820 ⇒ 00:28:45.051 Uttam Kumaran: Or a designer role, we can definitely help with strategy
186 00:28:58.500 ⇒ 00:28:59.190 Uttam Kumaran: one.
187 00:29:04.630 ⇒ 00:29:06.395 Uttam Kumaran: So this Google sheet, can we?
188 00:29:07.400 ⇒ 00:29:13.719 Uttam Kumaran: clean it up a little bit? Can we start to sort this by the highest hourly?
189 00:29:14.180 ⇒ 00:29:23.179 Uttam Kumaran: And then also, sort of we can start using this as a waitress to start to triage basically.
190 00:29:25.010 ⇒ 00:29:31.879 Amber Lin: Sure, I mean hourly, there’s a range, and occasionally some of them are fixed price.
191 00:29:32.170 ⇒ 00:29:37.269 Amber Lin: So there will be a question on how to sort based on that.
192 00:29:38.100 ⇒ 00:29:42.150 Amber Lin: So I could just I could also separate the fixed to hourly.
193 00:29:42.150 ⇒ 00:29:46.840 Uttam Kumaran: If it’s fixed, then can. There’s a duration right.
194 00:29:49.630 ⇒ 00:29:51.540 Uttam Kumaran: Oh, but again I don’t know, I guess.
195 00:29:54.140 ⇒ 00:30:05.479 Amber Lin: Yeah, I think I’ll just Mark, I’ll like, do a conditional formatting. I’ll highlight the ones that’s bigger than that has an hourly or fixed rate higher than a certain range.
196 00:30:07.640 ⇒ 00:30:09.550 Uttam Kumaran: Okay, yeah, let’s just start with a hundred.
197 00:30:33.540 ⇒ 00:30:39.079 Amber Lin: Found another AI strategy, one.
198 00:30:58.980 ⇒ 00:31:00.980 Uttam Kumaran: For this one, I’ll say
199 00:31:04.000 ⇒ 00:31:21.939 Uttam Kumaran: the scope that they mentioned is super super thin and meaning, like they didn’t write a lot. But it’s also, like they’ll basically want like couple of different roles. I think it’s worth applying. And ideally, we come in and just do strategy on an hourly basis. Seems like they probably don’t know where to begin here at all, so.
200 00:31:21.940 ⇒ 00:31:22.790 Amber Lin: Yeah.
201 00:31:27.870 ⇒ 00:31:29.290 Uttam Kumaran: Next one
202 00:31:34.150 ⇒ 00:31:45.849 Uttam Kumaran: AI training and content. We’re taking Expert AI to put tailoring guidance. Help me understand, Master Ideal candidate, we’ll have. Yeah, you don’t need any of that. This rules will involve structure, training, providing resources.
203 00:31:46.310 ⇒ 00:31:48.420 Uttam Kumaran: We would love to hear from you.
204 00:31:52.330 ⇒ 00:31:54.409 Uttam Kumaran: There’s no budget, though, right.
205 00:31:54.640 ⇒ 00:31:59.260 Amber Lin: Yeah, a lot of them don’t have budget and it that’s confusing.
206 00:31:59.810 ⇒ 00:32:04.239 Amber Lin: I was asking Robert how to deal with those, but I just added it, regardless.
207 00:32:04.490 ⇒ 00:32:08.800 Uttam Kumaran: I mean, I would say yes, and then I would say.
208 00:32:09.560 ⇒ 00:32:19.580 Uttam Kumaran: we can definitely do training and work also have partners that can execute.
209 00:32:19.780 ⇒ 00:32:25.260 Uttam Kumaran: Yes, oh, that’s fine. Let’s talk about this next one.
210 00:32:52.110 ⇒ 00:32:52.730 Amber Lin: Whoa!
211 00:33:14.480 ⇒ 00:33:16.410 Uttam Kumaran: The budget on this one is too low.
212 00:33:16.540 ⇒ 00:33:17.220 Amber Lin: Hmm.
213 00:33:22.940 ⇒ 00:33:25.799 Uttam Kumaran: But I mean we can do the work. It’s just a budget is too low.
214 00:33:26.350 ⇒ 00:33:31.449 Amber Lin: Yeah, you think we don’t have a chance to expand it.
215 00:33:34.040 ⇒ 00:33:36.999 Uttam Kumaran: 75 to 150 is tough.
216 00:33:37.760 ⇒ 00:33:38.410 Amber Lin: Hmm!
217 00:33:39.370 ⇒ 00:33:45.159 Amber Lin: Some of them go really low. I think we’re running out of ones that’s in our range.
218 00:33:45.350 ⇒ 00:33:47.370 Amber Lin: All the other ones are.
219 00:33:49.770 ⇒ 00:33:50.570 Amber Lin: Oh.
220 00:33:55.610 ⇒ 00:33:58.100 Uttam Kumaran: So maybe we should have a 2 sorts of fits
221 00:33:59.360 ⇒ 00:34:03.329 Uttam Kumaran: like we have a price fit, and like a scope fit.
222 00:34:04.890 ⇒ 00:34:07.170 Uttam Kumaran: Right, and this is a scope.
223 00:34:11.060 ⇒ 00:34:17.659 Uttam Kumaran: So then I would say, this is no, and then this is yes, this is now.
224 00:34:21.850 ⇒ 00:34:24.329 Uttam Kumaran: These don’t have prices.
225 00:34:27.100 ⇒ 00:34:28.159 Uttam Kumaran: Yes.
226 00:34:32.409 ⇒ 00:34:33.100 Uttam Kumaran: Okay.
227 00:35:05.340 ⇒ 00:35:07.209 Uttam Kumaran: Here, let me look at this next one
228 00:35:11.020 ⇒ 00:35:18.220 Uttam Kumaran: metabase to hubspot data sync. We need a skilled developer to create integration between metabase and Hubspot
229 00:35:18.880 ⇒ 00:35:20.650 Uttam Kumaran: set up integration.
230 00:35:31.130 ⇒ 00:35:35.629 Uttam Kumaran: I mean at at the high end. Sure, that’s a price fit. Yes.
231 00:35:36.390 ⇒ 00:35:38.529 Uttam Kumaran: So let’s see something
232 00:35:43.590 ⇒ 00:35:48.190 Uttam Kumaran: and Crm to bi integration.
233 00:35:55.040 ⇒ 00:35:58.500 Uttam Kumaran: Another thing we could probably add here is also like,
234 00:36:01.390 ⇒ 00:36:04.370 Uttam Kumaran: it’s like, we basically want to match everyone with the case. Study.
235 00:36:04.940 ⇒ 00:36:05.820 Amber Lin: Yeah.
236 00:36:05.820 ⇒ 00:36:14.660 Uttam Kumaran: Right? So matching case study, we should just sort of like, try to. Yeah, I don’t know. Basically.
237 00:36:15.200 ⇒ 00:36:16.890 Uttam Kumaran: somehow. Link that here.
238 00:36:23.810 ⇒ 00:36:24.730 Uttam Kumaran: Okay.
239 00:36:40.223 ⇒ 00:36:41.430 Uttam Kumaran: next one.
240 00:37:01.610 ⇒ 00:37:03.590 Uttam Kumaran: They want someone in Asia for this.
241 00:37:04.800 ⇒ 00:37:06.360 Amber Lin: Oh, I see!
242 00:37:06.360 ⇒ 00:37:09.440 Uttam Kumaran: Preferred qualifications. Location? But
243 00:37:15.400 ⇒ 00:37:17.779 Uttam Kumaran: I mean, I don’t know. This is sort of this is
244 00:37:21.150 ⇒ 00:37:22.100 Uttam Kumaran: yes,
245 00:37:27.270 ⇒ 00:37:28.580 Uttam Kumaran: architectures.
246 00:37:40.290 ⇒ 00:37:51.429 Uttam Kumaran: I’m just gonna move the pricing to here, and then that way I can freeze this room
247 00:37:52.600 ⇒ 00:37:54.430 Uttam Kumaran: and freeze this column.
248 00:38:03.270 ⇒ 00:38:04.770 Uttam Kumaran: Okay, next one.
249 00:38:11.550 ⇒ 00:38:16.099 Uttam Kumaran: So you could say, Nope, you could add a 3rd option for price fit like no price or.
250 00:38:16.710 ⇒ 00:38:25.730 Amber Lin: Yeah. Have a in the price dropdown. There’s a no info in the price type column. H, right now.
251 00:38:32.040 ⇒ 00:38:36.080 Uttam Kumaran: Yes, that’s see.
252 00:38:46.610 ⇒ 00:38:51.420 Uttam Kumaran: And can you create a dropdown for the yeah for the case study?
253 00:38:51.580 ⇒ 00:38:59.839 Uttam Kumaran: Or maybe it’s like, I don’t know materials. And let’s just do. Let’s just list a couple of the clients we’ve had. So we have, like
254 00:39:00.280 ⇒ 00:39:09.860 Uttam Kumaran: Stack, Blitz, ABC, we just couldn’t list a bunch of the ones that we have case studies. For
255 00:39:12.930 ⇒ 00:39:15.489 Uttam Kumaran: today, we use panda power cause, not
256 00:39:16.800 ⇒ 00:39:23.500 Uttam Kumaran: load flow analysis of distribution systems. We, some of these customers are power factory save changes.
257 00:39:42.420 ⇒ 00:39:44.540 Uttam Kumaran: Okay? And then last one.
258 00:39:50.980 ⇒ 00:39:53.709 Uttam Kumaran: I mean, honestly, we should. We could probably
259 00:39:53.820 ⇒ 00:39:57.000 Uttam Kumaran: end up building a clay table for this, too.
260 00:39:57.450 ⇒ 00:39:59.979 Amber Lin: I agree that would be.
261 00:40:00.550 ⇒ 00:40:04.189 Uttam Kumaran: Because, you know, and Clay, you can use AI for all these columns right? It’s like so sick.
262 00:40:04.190 ⇒ 00:40:04.880 Amber Lin: Yeah.
263 00:40:06.010 ⇒ 00:40:07.339 Uttam Kumaran: Clay is legit.
264 00:40:07.340 ⇒ 00:40:10.400 Amber Lin: I do not want to build these out one by one.
265 00:40:10.400 ⇒ 00:40:13.099 Uttam Kumaran: No, no, no, no! I just wanna do once so that we can handle.
266 00:40:13.100 ⇒ 00:40:16.169 Amber Lin: Yeah, we’ll figure out what we need. This is helpful.
267 00:40:20.320 ⇒ 00:40:23.343 Amber Lin: So for case studies.
268 00:40:26.750 ⇒ 00:40:34.249 Amber Lin: I have stack Blitz, ABC, I remember we had to buy the cocoa. I had pool parts, urban stems. What else.
269 00:40:37.790 ⇒ 00:40:39.070 Amber Lin: Previous ones, as well.
270 00:40:39.650 ⇒ 00:40:47.760 Uttam Kumaran: You can just list like anonymous Amazon anonymous revenue optimization.
271 00:40:49.540 ⇒ 00:40:57.150 Uttam Kumaran: You could put Stella source pool parts.
272 00:41:00.370 ⇒ 00:41:01.990 Uttam Kumaran: I think that’s probably it, for now.
273 00:41:01.990 ⇒ 00:41:04.310 Amber Lin: Am I missing any current clients.
274 00:41:04.510 ⇒ 00:41:06.860 Uttam Kumaran: No, we just don’t have case studies for them now.
275 00:41:06.860 ⇒ 00:41:08.839 Amber Lin: Oh, I see!
276 00:41:09.450 ⇒ 00:41:13.519 Uttam Kumaran: I mean, you could list all the clients. But like we just don’t have case studies for every single.
277 00:41:13.520 ⇒ 00:41:15.590 Amber Lin: I see cool.
278 00:41:48.620 ⇒ 00:41:51.030 Amber Lin: Oh, we have a meeting. We should.
279 00:41:56.280 ⇒ 00:41:57.900 Uttam Kumaran: Alright, I’m gonna hop on there.
280 00:41:57.900 ⇒ 00:41:59.840 Uttam Kumaran: Okay, okay.