Meeting Title: Team-Meeting-9-27 Date: 2024-09-27 Meeting participants: Patrick Trainer, Nicolas Sucari, Joshuadeveyra, Ryan Brosas, Ericson Dalusong, Uttam Kumaran
WEBVTT
1 00:00:09.530 ⇒ 00:00:10.670 Roy Christian Piñon: Young man.
2 00:00:24.060 ⇒ 00:00:25.080 Roy Christian Piñon: hey? Y’all.
3 00:00:29.570 ⇒ 00:00:30.500 Uttam Kumaran: Hey, everyone.
4 00:00:31.620 ⇒ 00:00:32.420 Roy Christian Piñon: Hi, Tom.
5 00:00:32.820 ⇒ 00:00:33.550 Uttam Kumaran: Hey!
6 00:00:34.650 ⇒ 00:00:35.600 Roy Christian Piñon: How are you?
7 00:00:35.830 ⇒ 00:00:36.730 Roy Christian Piñon: Right?
8 00:00:39.170 ⇒ 00:00:40.160 Roy Christian Piñon: It’s Friday.
9 00:00:41.490 ⇒ 00:00:42.080 Roy Christian Piñon: yeah.
10 00:00:42.080 ⇒ 00:00:45.600 Uttam Kumaran: I’m excited to today should be a pretty chill day, so.
11 00:00:52.660 ⇒ 00:00:53.540 joshuadeveyra: Good morning, everyone.
12 00:00:54.410 ⇒ 00:00:55.330 Roy Christian Piñon: Morning.
13 00:01:02.840 ⇒ 00:01:03.710 Ryan Brosas: Good morning!
14 00:01:04.110 ⇒ 00:01:05.230 Uttam Kumaran: Hey! Good morning!
15 00:01:07.020 ⇒ 00:01:08.999 Ericson Dalusong: Good morning, guys. Nice to meet you.
16 00:01:09.710 ⇒ 00:01:10.770 Roy Christian Piñon: Eric’s son.
17 00:01:11.090 ⇒ 00:01:12.229 Uttam Kumaran: Glad to have you.
18 00:01:15.850 ⇒ 00:01:19.999 Uttam Kumaran: I think we’re waiting for Patrick. I know Ryan’s not joining
19 00:01:21.270 ⇒ 00:01:22.010 Uttam Kumaran: cool.
20 00:01:23.710 ⇒ 00:01:28.490 Uttam Kumaran: awesome. Okay. I think we get started. I think maybe we’re waiting for like one or 2 more people. But
21 00:01:28.778 ⇒ 00:01:39.651 Uttam Kumaran: I guess I’ll kick things off with a bunch of stuff to talk about. So yeah, I think we have a a couple of new people on the call, so I’ll do some intros as we get to.
22 00:01:40.080 ⇒ 00:01:52.000 Uttam Kumaran: everybody’s. You know. Section. I sent a little bit of an agenda in the team channel. But yeah, maybe we can just start off maybe, Nico, if you want to talk a little bit about
23 00:01:52.330 ⇒ 00:02:01.871 Uttam Kumaran: like, how’s it going with existing clients? So that, aware of like who we’re working with, and you know how it’s going, and then I’ll talk a little bit about
24 00:02:02.330 ⇒ 00:02:06.179 Uttam Kumaran: sort of kick off some stuff around sales, and then we can kind of work our way down the list.
25 00:02:07.480 ⇒ 00:02:16.879 Nicolas Sucari: Okay, yeah, so nice to meet you all guys, new ones. We’ve we’ve been working this week with 3 different clients. 1st of all pull parts.
26 00:02:16.900 ⇒ 00:02:24.999 Nicolas Sucari: We’ve been working with Ryan. It’s not in the meeting on a lot of different updates in the dashboards that we are sharing with them in real.
27 00:02:25.070 ⇒ 00:02:49.079 Nicolas Sucari: We were doing some investigations on some metrics that were kind of odd of what they were expecting and trying to identify all of the numbers of conversions from different sources that they are using, we got to a good result. We are still trying to work on some stuff to to have the correct numbers, but they are using the dashboards, and they are liking what we are producing there.
28 00:02:49.080 ⇒ 00:03:09.334 Nicolas Sucari: So yeah, it’s really interesting. For the marketing side. Also for shipping. We’re we’re having some issues with one of our partners called Unis. We’re also trying to get some data from them that we need in order to get all of the shipping all of the shipments from different warehouses, and finally create these
29 00:03:09.690 ⇒ 00:03:30.719 Nicolas Sucari: warehouse performance dashboards that we wanna give the client. And then we are doing some analysis on all of the products that they have in the different sources. We’re trying to review all of the different sources. Get all of the skills list and try to compare each of them to have the same information across all of all of them.
30 00:03:30.995 ⇒ 00:03:48.349 Nicolas Sucari: We just started to work on that. We’re gonna keep investigating a little bit on all of the information that we have for each skew and try to have like the same across all of them. We shared everything with the clients. And now we’re expecting a little bit of feedback. But yeah, we’ll keep working on that for pool parts.
31 00:03:48.836 ⇒ 00:04:10.929 Nicolas Sucari: For then we have vitacoco. We’ve created this agent that scrapes their target product pages to see if they have the product in stock or or it’s out of stock. And we we got a list of a lot of different stores target stores that we want to scrape, and we are checking.
32 00:04:11.241 ⇒ 00:04:30.260 Nicolas Sucari: For the if the product is in stock or not, and we are giving them a spreadsheet. With that information Miguel was working on that automation, and they are liking it. So yeah, we Miguel, maybe we should send that in a team’s post for them for the team so that they can take. Take a look at what we
33 00:04:30.590 ⇒ 00:04:49.669 Nicolas Sucari: accomplished. And yeah, we have a meeting on with them on Monday. We will share some more updates there, and we will gather new requests, maybe. And finally, we have Javi coffee. Brian is working on that client. We just started like 2 weeks ago. We are just integrating on syncing all of the data from shopify and Amazon
34 00:04:49.670 ⇒ 00:05:04.940 Nicolas Sucari: into our snowflake tables. And we are. We’re starting to do some modeling there so that we can have, like some final tables and start working on real and producing the dashboards. But so far so good with all of the clients, a lot of
35 00:05:04.960 ⇒ 00:05:10.410 Nicolas Sucari: movement on the 3 of them. I’ll leave Hpi to me all then to explain.
36 00:05:10.800 ⇒ 00:05:14.035 Nicolas Sucari: but that’s what we’ve been up to with.
37 00:05:14.560 ⇒ 00:05:16.609 Nicolas Sucari: with the clients. Yep.
38 00:05:17.930 ⇒ 00:05:18.570 Uttam Kumaran: Cool
39 00:05:18.820 ⇒ 00:05:20.900 Uttam Kumaran: any questions
40 00:05:21.110 ⇒ 00:05:22.200 Uttam Kumaran: there.
41 00:05:24.930 ⇒ 00:05:28.780 Uttam Kumaran: cool. So I’ll kind of share a little bit of
42 00:05:29.590 ⇒ 00:05:32.967 Uttam Kumaran: the clients that we’re just starting to.
43 00:05:33.680 ⇒ 00:05:35.870 Uttam Kumaran: You know, we have some meetings booked with.
44 00:05:35.890 ⇒ 00:05:37.476 Uttam Kumaran: So today,
45 00:05:38.300 ⇒ 00:05:42.170 Uttam Kumaran: I have a meeting book with this company called Mammoth Labs.
46 00:05:42.603 ⇒ 00:05:46.230 Uttam Kumaran: And I’ll show you kind of who they are.
47 00:05:47.602 ⇒ 00:05:53.567 Uttam Kumaran: I need to do some research later today. But it looks like they are a
48 00:05:54.450 ⇒ 00:05:57.880 Uttam Kumaran: they are a data company kind of focused on
49 00:05:57.900 ⇒ 00:05:59.900 Uttam Kumaran: Cpg.
50 00:06:00.374 ⇒ 00:06:06.229 Uttam Kumaran: so they have like some sort of platform that I think like Cpg companies can plug into
51 00:06:06.716 ⇒ 00:06:13.870 Uttam Kumaran: and then they provide like data. I haven’t checked in exactly on like what their product is. But
52 00:06:14.572 ⇒ 00:06:16.469 Uttam Kumaran: we’ll see. Usually
53 00:06:16.630 ⇒ 00:06:19.420 Uttam Kumaran: they need us for some reason, so we’ll find that out today.
54 00:06:19.920 ⇒ 00:06:21.680 Uttam Kumaran: The other company actually
55 00:06:22.500 ⇒ 00:06:25.389 Uttam Kumaran: Erickson, you you referred us. That was the
56 00:06:26.214 ⇒ 00:06:30.775 Uttam Kumaran: the one of the manufacturing companies. Where they work in. They work work in
57 00:06:31.280 ⇒ 00:06:34.211 Uttam Kumaran: industrials. I think the company is called
58 00:06:37.290 ⇒ 00:06:38.056 Uttam Kumaran: It’s called
59 00:06:40.380 ⇒ 00:06:42.620 Uttam Kumaran: Let me try to.
60 00:06:44.576 ⇒ 00:06:46.480 Uttam Kumaran: So it’s this company.
61 00:06:49.730 ⇒ 00:06:55.654 Uttam Kumaran: this is company Aviva. They’re like A, it’s like an Australian based company.
62 00:06:56.460 ⇒ 00:07:01.829 Uttam Kumaran: or maybe a global company that does a variety of stuff in industrials.
63 00:07:03.340 ⇒ 00:07:12.440 Uttam Kumaran: so erickson had a meeting with them, I think, related to his business. And then they needed some data folks so already appreciate you
64 00:07:12.830 ⇒ 00:07:18.010 Uttam Kumaran: plugging us. And so really excited to talk to these guys next week.
65 00:07:19.150 ⇒ 00:07:22.179 Ericson Dalusong: That’s amazing. Thank you for that. Update Utah.
66 00:07:22.460 ⇒ 00:07:23.419 Uttam Kumaran: Yeah,
67 00:07:24.660 ⇒ 00:07:35.539 Uttam Kumaran: so let’s see what else I think those are the major 2 clients. We are probably gonna continue to work with Robert at Bungo insights on another client that’s gonna come our way.
68 00:07:35.984 ⇒ 00:07:38.976 Uttam Kumaran: So I’m gonna get some insight into that
69 00:07:39.370 ⇒ 00:07:45.179 Uttam Kumaran: We’ve done a lot of work with Robert. So far we worked with Sella with him, Jabby. We’re working with their team on.
70 00:07:45.190 ⇒ 00:07:53.560 Uttam Kumaran: So I’m really happy that we get to continue to work with them on stuff, the other update on the real side. So I met with Sid, who is the head of sales at real
71 00:07:53.913 ⇒ 00:08:18.176 Uttam Kumaran: I know I mentioned a few weeks ago that we’re going to begin to to try to do some work within their team. And so that’s progressing. Their team was out like, basically they’ve been at all these conferences that were going on sort of across the world the last few weeks. So I’ve kind of just been reminding them and pushing them. So we had a good conversation yesterday. Basically, they have a client that’s
72 00:08:18.480 ⇒ 00:08:34.300 Uttam Kumaran: that’s just getting started with real. But the problem that they’re having is that their data, models and stuff aren’t in a good place. So real is kind of struggling to show the value of their product. And so they want to bring us in to start for like 10 h on that client.
73 00:08:34.623 ⇒ 00:08:54.690 Uttam Kumaran: I just got the name of the client and can send that over. We don’t have anything signed, or a start date or anything but ideally, it’ll probably be 10 h of like data modeling. And then kind of like, I pro, probably honestly, just exactly what we do for normal clients just coming coming through row. And they’re really
74 00:08:54.690 ⇒ 00:09:09.719 Uttam Kumaran: they. Their goal is actually to allow us to sell our services direct to the client. Which is great, you know, and I think we’ve done some great work with real and we had a great post to go out this week about their product. So I’m excited again. I think
75 00:09:09.990 ⇒ 00:09:30.550 Uttam Kumaran: it’s taking just about as long as I thought it would. So it’s just like week after week, trying to remind them and and push folks so that’s really good. I think I want to kind of transition. Next to talking a little bit about other stuff on the sales side, I think. Erickson, if you want to maybe give a brief intro and then also.
76 00:09:30.810 ⇒ 00:09:52.439 Uttam Kumaran: if you, I think everybody you know on the call, is is pretty data forward and and understands data. Well. So I think even just jumping right in and showing what you what you did in that loom video, which is just like one of the clay tables. I think it’s really really cool. I think Patrick me, you and and Nico will kind of understand the parts of it we’ve been talking about for weeks about
77 00:09:52.860 ⇒ 00:09:54.050 Uttam Kumaran: how to actually
78 00:09:54.150 ⇒ 00:10:03.279 Uttam Kumaran: like, take action on the lead scoring and all those different campaign filters, and finally, to see it in one place in action has been really cool. So yeah, take it away.
79 00:10:04.950 ⇒ 00:10:12.830 Ericson Dalusong: Sounds good. Thank you so much for them. And before we, before I get started sharing the the clay table, I just want to say
80 00:10:13.376 ⇒ 00:10:21.240 Ericson Dalusong: nice to meet you all, and I’ll be spearheading our outbound Legion campaign for brain porch.
81 00:10:21.290 ⇒ 00:10:24.979 Ericson Dalusong: So for the lead for the outbound Legion side.
82 00:10:25.416 ⇒ 00:10:35.229 Ericson Dalusong: We are at 90% completion when it comes to setting up what we need to kick. Start on Monday.
83 00:10:35.680 ⇒ 00:10:44.680 Ericson Dalusong: And you can find all the the resources related to our outbound marketing here in
84 00:10:46.074 ⇒ 00:10:52.500 Ericson Dalusong: in this notion document. So we’ve I’ve completed here. I’ve added, all the
85 00:10:53.028 ⇒ 00:10:57.690 Ericson Dalusong: the resources, like, how many emails are we sending?
86 00:10:58.419 ⇒ 00:11:02.150 Ericson Dalusong: Are we going to use to send hold emails
87 00:11:02.670 ⇒ 00:11:07.390 Ericson Dalusong: code, email sequences or code email copy that we are going to use.
88 00:11:07.560 ⇒ 00:11:12.099 Ericson Dalusong: And some of the feedback. Regarding our
89 00:11:12.960 ⇒ 00:11:17.209 Ericson Dalusong: regarding the improvement that that we need to make on our website.
90 00:11:17.610 ⇒ 00:11:22.809 Ericson Dalusong: So yeah, I’m just, gonna you know, go over to play
91 00:11:22.890 ⇒ 00:11:28.269 Ericson Dalusong: and share with you the the things that my team. And I have been
92 00:11:28.788 ⇒ 00:11:35.019 Ericson Dalusong: working on for for the past 5 days. So can you guys confirm if you can see my screen?
93 00:11:37.690 ⇒ 00:11:38.619 Ericson Dalusong: Yes, yes.
94 00:11:40.230 ⇒ 00:11:45.809 Ericson Dalusong: So yeah. We built a couple of tables here. But I’m gonna show you
95 00:11:46.345 ⇒ 00:11:50.259 Ericson Dalusong: a sample one here. So here’s an example of
96 00:11:50.320 ⇒ 00:11:58.910 Ericson Dalusong: a campaign that we are going to launch next next week. So this is a list of Stella source
97 00:11:59.350 ⇒ 00:12:00.680 Ericson Dalusong: look alike.
98 00:12:00.750 ⇒ 00:12:06.359 Ericson Dalusong: So for this process we’ve used a tool called ocean dot I/O,
99 00:12:07.048 ⇒ 00:12:10.559 Ericson Dalusong: it’s a tool that can find similar
100 00:12:11.115 ⇒ 00:12:16.379 Ericson Dalusong: companies of our best customer, which I believe Stella source in this case.
101 00:12:16.500 ⇒ 00:12:20.550 Ericson Dalusong: So these are all the the information that we
102 00:12:20.570 ⇒ 00:12:24.479 Ericson Dalusong: we’re able to extract. So we’re able to extract the
103 00:12:24.890 ⇒ 00:12:26.469 Ericson Dalusong: the basic
104 00:12:27.500 ⇒ 00:12:30.500 Ericson Dalusong: data like company size domain
105 00:12:31.220 ⇒ 00:12:33.480 Ericson Dalusong: company linking profile and
106 00:12:33.890 ⇒ 00:12:35.580 Ericson Dalusong: other information here
107 00:12:35.980 ⇒ 00:12:40.650 Ericson Dalusong: and then, since revenue is, you know, our
108 00:12:41.186 ⇒ 00:12:43.620 Ericson Dalusong: main filter for qualifying the
109 00:12:43.750 ⇒ 00:12:45.542 Ericson Dalusong: the the leads.
110 00:12:46.220 ⇒ 00:12:50.289 Ericson Dalusong: we’ve used a combination of different data providers to
111 00:12:50.680 ⇒ 00:12:56.629 Ericson Dalusong: to get the revenue of each of this company. So these are all the revenue
112 00:12:58.830 ⇒ 00:13:01.559 Ericson Dalusong: annual revenue of this company.
113 00:13:01.760 ⇒ 00:13:06.260 Ericson Dalusong: It’s not in dollar format, because, you know.
114 00:13:06.400 ⇒ 00:13:08.730 Ericson Dalusong: it’s easier for us to
115 00:13:08.930 ⇒ 00:13:12.179 Ericson Dalusong: to score them in this format. So
116 00:13:12.550 ⇒ 00:13:17.169 Ericson Dalusong: basically, we are scoring them based on the
117 00:13:17.627 ⇒ 00:13:21.560 Ericson Dalusong: the revenue range. So, for example, if they fall.
118 00:13:21.700 ⇒ 00:13:25.629 Ericson Dalusong: or if if a company has a revenue of
119 00:13:25.700 ⇒ 00:13:28.120 Ericson Dalusong: 8 million dollars to
120 00:13:31.386 ⇒ 00:13:36.149 Ericson Dalusong: nearly 12 million dollars. It’s gonna be scored as 25, which is
121 00:13:36.170 ⇒ 00:13:37.540 Ericson Dalusong: the highest score.
122 00:13:38.140 ⇒ 00:13:39.610 Ericson Dalusong: So
123 00:13:39.960 ⇒ 00:13:42.530 Ericson Dalusong: these are all the other scoring criteria.
124 00:13:46.150 ⇒ 00:13:49.974 Ericson Dalusong: And we are scoring
125 00:13:51.000 ⇒ 00:13:53.780 Ericson Dalusong: companies with 3 million to 5 million
126 00:13:54.532 ⇒ 00:13:58.269 Ericson Dalusong: dollars annual revenue as 15 and
127 00:13:59.050 ⇒ 00:14:01.889 Ericson Dalusong: others are lower than that, just like this one.
128 00:14:04.090 ⇒ 00:14:11.239 Ericson Dalusong: And then we also extracted the the company tech stack. So
129 00:14:11.440 ⇒ 00:14:20.120 Ericson Dalusong: we are also stalling them by keywords. So, for example, if some of these keywords are present, for example, if they are using sub.
130 00:14:20.270 ⇒ 00:14:23.139 Ericson Dalusong: They’re gonna be scored as 25.
131 00:14:23.200 ⇒ 00:14:24.460 Ericson Dalusong: And if
132 00:14:25.230 ⇒ 00:14:28.960 Ericson Dalusong: these keywords are not present in their tech stack.
133 00:14:29.000 ⇒ 00:14:35.359 Ericson Dalusong: they’re gonna be scored as 0. That’s why you’re seeing some of these companies have a score of 0,
134 00:14:36.990 ⇒ 00:14:41.050 Ericson Dalusong: and then in this column you are going to see
135 00:14:41.080 ⇒ 00:14:43.460 Ericson Dalusong: an enrichment we’re in.
136 00:14:43.470 ⇒ 00:14:51.400 Ericson Dalusong: We are searching the the decision makers using using Apollo.
137 00:14:51.460 ⇒ 00:14:52.960 Ericson Dalusong: So
138 00:14:53.940 ⇒ 00:14:58.069 Ericson Dalusong: for for this specific low low number one.
139 00:14:58.280 ⇒ 00:15:03.419 Ericson Dalusong: this means that we were able to find 4 people or 4 decision makers in this company.
140 00:15:04.060 ⇒ 00:15:05.980 Ericson Dalusong: So this is one.
141 00:15:07.410 ⇒ 00:15:11.010 Ericson Dalusong: and then this is the second one.
142 00:15:11.920 ⇒ 00:15:19.450 Ericson Dalusong: So for the, for the contacts, we’re putting it in a different table which I’m going to to share with you later.
143 00:15:19.830 ⇒ 00:15:30.539 Ericson Dalusong: And one thing, though you might have noticed here that some of the the contacts are located in Australia. So we’re going to refine this, because the other day
144 00:15:30.620 ⇒ 00:15:34.679 Ericson Dalusong: I got a confirmation from you with them that we’re only going to be
145 00:15:34.730 ⇒ 00:15:37.820 Ericson Dalusong: targeting people that are located in the Us. Right?
146 00:15:39.770 ⇒ 00:15:40.260 Ericson Dalusong: For sure.
147 00:15:42.240 ⇒ 00:15:42.850 Ericson Dalusong: Yeah.
148 00:15:43.310 ⇒ 00:15:54.100 Ericson Dalusong: So yeah, this is also the feed score by title. And we are getting the average total lead score for each of the criteria just to.
149 00:15:54.140 ⇒ 00:15:56.560 Ericson Dalusong: you know, gauge the
150 00:15:57.280 ⇒ 00:16:00.639 Ericson Dalusong: the quality of the the lead.
151 00:16:00.990 ⇒ 00:16:06.060 Ericson Dalusong: So we can also make some adjustments with the score. This is just the
152 00:16:06.150 ⇒ 00:16:09.092 Ericson Dalusong: these are just the scores that you know.
153 00:16:09.520 ⇒ 00:16:11.460 Ericson Dalusong: we’re able to add just to.
154 00:16:11.610 ⇒ 00:16:14.640 Ericson Dalusong: you know identify which one have.
155 00:16:14.860 ⇒ 00:16:16.780 Ericson Dalusong: which one is the better lead, and
156 00:16:17.350 ⇒ 00:16:19.408 Ericson Dalusong: which one is, you know,
157 00:16:20.020 ⇒ 00:16:21.190 Ericson Dalusong: not.
158 00:16:22.480 ⇒ 00:16:25.759 Ericson Dalusong: And then this is how it looks like
159 00:16:26.730 ⇒ 00:16:32.839 Ericson Dalusong: in in the other table. So basically, all of the contacts that we’ve extracted from
160 00:16:33.040 ⇒ 00:16:35.610 Ericson Dalusong: from the table that I shared with you earlier.
161 00:16:35.900 ⇒ 00:16:41.769 Ericson Dalusong: They they have been added in this table. So these are all the contacts
162 00:16:41.880 ⇒ 00:16:45.990 Ericson Dalusong: that we’re able to extract from those companies that we’ve qualified.
163 00:16:46.450 ⇒ 00:16:52.919 Ericson Dalusong: And then, what we’ve done is that we’ve used a combination of data providers to
164 00:16:53.200 ⇒ 00:16:57.610 Ericson Dalusong: just basically get the the email addresses of these people.
165 00:16:58.510 ⇒ 00:16:59.830 Ericson Dalusong: And
166 00:17:00.443 ⇒ 00:17:03.059 Ericson Dalusong: right here, you’re gonna see
167 00:17:03.570 ⇒ 00:17:08.819 Ericson Dalusong: the other information that we’ve we’ve extracted and we’re gonna be using them for
168 00:17:09.099 ⇒ 00:17:11.569 Ericson Dalusong: purcode email personalization.
169 00:17:12.349 ⇒ 00:17:14.300 Ericson Dalusong: So would it be okay with
170 00:17:14.380 ⇒ 00:17:20.210 Ericson Dalusong: with you, Utam and team? If I’m going to share an example of how the email is gonna look like.
171 00:17:20.704 ⇒ 00:17:21.199 Uttam Kumaran: Yeah.
172 00:17:21.520 ⇒ 00:17:22.450 Uttam Kumaran: please.
173 00:17:23.200 ⇒ 00:17:28.790 Ericson Dalusong: So we’ve set up the campaigns on instantly. And here’s an example. So
174 00:17:29.390 ⇒ 00:17:34.649 Ericson Dalusong: once the lead gets added to this campaign, this is how the email is gonna look like.
175 00:17:35.570 ⇒ 00:17:37.000 Ericson Dalusong: So
176 00:17:38.420 ⇒ 00:17:42.960 Ericson Dalusong: this is the the 1st line. This is the case. Study end.
177 00:17:43.050 ⇒ 00:17:45.180 Ericson Dalusong: This is the call to action.
178 00:17:45.420 ⇒ 00:17:53.579 Ericson Dalusong: Of course, this is not yet final. I’m still gonna need to get your feedback on the copy. Utahman team.
179 00:17:54.646 ⇒ 00:17:58.569 Ericson Dalusong: I just presented this to you so that you guys would
180 00:17:59.328 ⇒ 00:18:01.062 Ericson Dalusong: would have like
181 00:18:01.840 ⇒ 00:18:06.199 Ericson Dalusong: I would let would picture out how it is it going to look like on the prospects end?
182 00:18:07.730 ⇒ 00:18:11.279 Ericson Dalusong: And then here’s for email Step 2.
183 00:18:11.810 ⇒ 00:18:18.520 Ericson Dalusong: So basically, we’re just mentioning the title. And how long have they been in the company? That’s how we’re personalizing the
184 00:18:18.610 ⇒ 00:18:19.620 Ericson Dalusong: the email?
185 00:18:19.990 ⇒ 00:18:25.629 Ericson Dalusong: And then here’s an example of email Step 3. So basically for email step 3,
186 00:18:25.890 ⇒ 00:18:28.219 Ericson Dalusong: my idea here is that
187 00:18:28.450 ⇒ 00:18:31.810 Ericson Dalusong: you know, to have AI like that.
188 00:18:32.130 ⇒ 00:18:33.230 Ericson Dalusong: the full
189 00:18:33.790 ⇒ 00:18:38.549 Ericson Dalusong: email sequence for our email copy for Step 3. So this is how it looks like.
190 00:18:41.930 ⇒ 00:18:49.019 Ericson Dalusong: So, yeah, we are still yet to finalize the clay tables that
191 00:18:49.040 ⇒ 00:18:55.769 Ericson Dalusong: we’re building. This is just one of the one of a couple of tables that we are
192 00:18:56.414 ⇒ 00:19:02.589 Ericson Dalusong: working on, and on Monday. I’ll I’ll give you another update.
193 00:19:02.660 ⇒ 00:19:10.459 Ericson Dalusong: and hopefully by, you know, before before noon by Monday we’ll be able to launch some of these campaigns.
194 00:19:11.820 ⇒ 00:19:18.770 Uttam Kumaran: Great. So the biggest thing we’ll be looking at is like we’re gonna be looking at number of emails sent the open rates.
195 00:19:18.810 ⇒ 00:19:21.339 Uttam Kumaran: And then also the booking rates.
196 00:19:22.070 ⇒ 00:19:22.740 Uttam Kumaran: So
197 00:19:22.830 ⇒ 00:19:23.840 Uttam Kumaran: you know.
198 00:19:24.100 ⇒ 00:19:40.369 Uttam Kumaran: And instantly, right now we did one campaign. So I kind of have a little bit of a baseline. I know that campaign did really well on open rates, but we didn’t get much inbound, so it’ll be interesting to see like how we measure all that and you know, additionally, one of the things as we start to
199 00:19:40.500 ⇒ 00:19:48.499 Uttam Kumaran: send people to the site and stuff, I’ll I’ll we’ll be looking a little bit at the website traffic. And we can use that to loop more people into funnels.
200 00:19:48.899 ⇒ 00:19:52.049 Uttam Kumaran: So yeah, that’s great. If there’s any questions about
201 00:19:52.180 ⇒ 00:19:56.010 Uttam Kumaran: sales outbound like, please feel free to ask or ask in slack
202 00:19:56.590 ⇒ 00:19:59.840 Uttam Kumaran: but yeah, I’m excited to meet on Monday and like finalize that.
203 00:19:59.870 ⇒ 00:20:08.179 Uttam Kumaran: I think. Ryan, you’re on the call. Do you wanna just share a little bit about the content stuff that we did this week? I think we had 2.
204 00:20:08.622 ⇒ 00:20:12.829 Uttam Kumaran: Really good, you know pieces go out really glad. So.
205 00:20:14.940 ⇒ 00:20:25.970 Ryan Brosas: Hello, guys, so I’m just going to share my screen. So again, so just to like introduce myself. My name is Ryan. I’m the social media manager. And you know, I’m copywriter.
206 00:20:25.990 ⇒ 00:20:28.679 Ryan Brosas: So let me share my screen.
207 00:20:29.190 ⇒ 00:20:31.049 Ryan Brosas: Okay, so
208 00:20:37.080 ⇒ 00:20:40.889 Ryan Brosas: so this is the buffer the social media.
209 00:20:42.010 ⇒ 00:20:46.140 Ryan Brosas: posting or analyze software that we are currently using.
210 00:20:46.230 ⇒ 00:20:52.369 Ryan Brosas: So currently, I’m disposting manually because it’s giving me too much control.
211 00:20:52.510 ⇒ 00:20:59.580 Ryan Brosas: So the 1st post that we have that we posted on our social media in and on Linkedin.
212 00:21:00.149 ⇒ 00:21:08.929 Ryan Brosas: We got like a good good impression likes and comments that this is we posted this on September 25, th
213 00:21:09.070 ⇒ 00:21:17.099 Ryan Brosas: and we’ve got like a good impression like 219. And you know, engagement rate of like 71% with point 23.
214 00:21:17.847 ⇒ 00:21:18.862 Ryan Brosas: I think.
215 00:21:20.113 ⇒ 00:21:29.620 Ryan Brosas: the shout out from real really do like it like boost our engagement rate or our in our reach or in impression.
216 00:21:29.800 ⇒ 00:21:49.910 Ryan Brosas: And that, all that also help us to for for this today, today, content, help us to, you know, to to improve our reach and imp our impression as of like posting a new content on on our on Linkedin account.
217 00:21:50.628 ⇒ 00:21:53.560 Ryan Brosas: This is just 2 h.
218 00:21:53.800 ⇒ 00:21:55.050 Ryan Brosas: and we’ve got
219 00:21:55.400 ⇒ 00:22:08.239 Ryan Brosas: 587 impression. This is really a good impression and reach. And also, I’m just going to like, you know, taking care of like engagement to have to have this reach
220 00:22:08.480 ⇒ 00:22:10.380 Ryan Brosas: more improve
221 00:22:10.720 ⇒ 00:22:17.890 Ryan Brosas: and hopefully that we could, you know, replicate those this result to our future content.
222 00:22:17.960 ⇒ 00:22:19.106 Ryan Brosas: And yeah,
223 00:22:20.360 ⇒ 00:22:25.779 Ryan Brosas: I think that’s all for now, because this is the we got like the 2 content right
224 00:22:25.860 ⇒ 00:22:34.220 Ryan Brosas: way this week, and I hope to have, you know, you know, a 3 content next week, and you know for further on, 5 contents later on.
225 00:22:34.310 ⇒ 00:22:38.430 Ryan Brosas: And I think that’s all. Thank you, for you know, listening, and, you know, show
226 00:22:38.610 ⇒ 00:22:41.420 Ryan Brosas: taking interest on this, and have a great day.
227 00:22:44.210 ⇒ 00:22:44.940 Uttam Kumaran: Thanks.
228 00:22:46.020 ⇒ 00:22:51.675 Uttam Kumaran: yeah. So I’m excited that we finally are getting to kind of like, publish content outwards, I think.
229 00:22:54.050 ⇒ 00:23:14.370 Uttam Kumaran: the main thing that we want to show. And there’s kind of like 2 strategies we’re working through. One is there’s going to be content. That comes through the company. This is gonna be like from the company blog or the company Linkedin Page. It’s going to be like updates about our work or our people or our clients, or things. We like each of us right on behalf of the company, and there’s going to be
230 00:23:14.370 ⇒ 00:23:24.750 Uttam Kumaran: stuff from each of us individually. The one thing that you know, I read a lot of content on the Internet and there’s a diff. I wanna make
231 00:23:24.770 ⇒ 00:23:42.630 Uttam Kumaran: the ability to have a different voice for each. Right. The company content will be more like evergreen. It’ll just be, you know, more professional it’ll have like it’ll be on the blog on the site, of course, on the company page may reference other companies, and then we have our individual voices right?
232 00:23:42.630 ⇒ 00:23:58.919 Uttam Kumaran: Which is what I really like. Love is that I can go in and repost something and give my 2 cents. And you talk from your voice right? It’s not all super prescriptive. I think that’s the one thing that we’re gonna continue to push is that we have less prescriptive content.
233 00:23:58.920 ⇒ 00:24:26.753 Uttam Kumaran: You know, as, like all of us are, you know leaders in one way in the company. When you write content, you want it to come from your voice and your perspective. Otherwise it’s just gonna nobody’s gonna click on it. Nobody’s gonna like it. And so I’m really excited. You know, that we we’re getting a lot of great you know, analytics just like in this 1st week. But again, I think it’s we’re not. We’re gonna try not to water it down. We’re actually gonna try to continue to have it be
234 00:24:27.720 ⇒ 00:24:43.957 Uttam Kumaran: you know, from my voice, and and then ideally, what I would love is for folks in the company to be able to share their own perspective on stuff that we post or, again, once we start getting in the habit of writing content and having, like this sort of editing.
235 00:24:44.350 ⇒ 00:24:51.990 Uttam Kumaran: you know, process and illustration process. Everyone will be able to write blogs. So I’m really really excited. For that
236 00:24:53.242 ⇒ 00:25:05.090 Uttam Kumaran: cool, I think moving on. Roy, you’re on the call. If you wanted to give a brief intro and kind of share a couple of things about what you’ll be taking care of and owning that would be
237 00:25:06.026 ⇒ 00:25:07.179 Uttam Kumaran: awesome. Yeah.
238 00:25:07.180 ⇒ 00:25:10.959 Roy Christian Piñon: Definitely sorry I’m kind of in the shadow, cause I have.
239 00:25:11.604 ⇒ 00:25:18.085 Roy Christian Piñon: Somebody doesn’t want any light, but I hope you’re able to see my face a bit earlier. So as far as
240 00:25:18.470 ⇒ 00:25:28.010 Roy Christian Piñon: the Tom and I’s agreement. So I’m kind of more on a phone 1st person. So I’ve always been dealing with a lot of
241 00:25:28.070 ⇒ 00:25:37.470 Roy Christian Piñon: cold calling and love. The intelligence they actually get is by talking to people having conversations. And I think that’s kind of the
242 00:25:37.500 ⇒ 00:25:43.106 Roy Christian Piñon: kind of the backbone of where we’re always going to come from, because it’s always
243 00:25:43.630 ⇒ 00:25:45.250 Roy Christian Piñon: from my understanding.
244 00:25:45.300 ⇒ 00:25:51.189 Roy Christian Piñon: we’re a custom. We’re a custom solution. So it’s always a different voice for different companies.
245 00:25:51.200 ⇒ 00:25:54.560 Roy Christian Piñon: and we have to make, or at least
246 00:25:54.840 ⇒ 00:25:59.370 Roy Christian Piñon: we just gotta try to open conversations as much as possible.
247 00:25:59.390 ⇒ 00:26:09.580 Roy Christian Piñon: I was considering a couple of strategies that would try to engage. So whether I was thinking it might be like Linkedin events, or
248 00:26:09.990 ⇒ 00:26:21.267 Roy Christian Piñon: something that would trigger specific, like very niche topics that we actually solve for these specific companies. I think that’s kind of
249 00:26:22.060 ⇒ 00:26:24.597 Roy Christian Piñon: how I’m I’m seeing it because it’s
250 00:26:25.160 ⇒ 00:26:27.130 Roy Christian Piñon: it’s more targeted.
251 00:26:27.160 ⇒ 00:26:31.660 Roy Christian Piñon: So even though we have specific personas we’re we’re working with.
252 00:26:31.950 ⇒ 00:26:38.529 Roy Christian Piñon: it’s it’s always going to be different for each company. So I guess that’s kind of the strength that brain Forge
253 00:26:38.600 ⇒ 00:26:44.789 Roy Christian Piñon: has subconsciously given me. That’s kind of how I’ve understood
254 00:26:45.262 ⇒ 00:26:47.739 Roy Christian Piñon: from all the materials I’ve read.
255 00:26:47.760 ⇒ 00:26:54.609 Roy Christian Piñon: so I I guess the part that I was looking to. Really, just get.
256 00:26:54.680 ⇒ 00:27:00.889 Roy Christian Piñon: There’s a bit more stories like actual stories from kind of before, during and after.
257 00:27:01.000 ⇒ 00:27:04.479 Roy Christian Piñon: How how Brain Forge has improved
258 00:27:04.630 ⇒ 00:27:07.560 Roy Christian Piñon: their processes and
259 00:27:07.620 ⇒ 00:27:09.689 Roy Christian Piñon: the adoption that they’re
260 00:27:09.900 ⇒ 00:27:11.020 Roy Christian Piñon: their
261 00:27:11.730 ⇒ 00:27:18.570 Roy Christian Piñon: applying the technology we’re we’re giving them. And it’s from those stories that would
262 00:27:18.590 ⇒ 00:27:20.690 Roy Christian Piñon: help me sell
263 00:27:20.840 ⇒ 00:27:31.230 Roy Christian Piñon: to future, to at least to the future companies we’re looking to work with. But you know, mostly it’s gonna be data driven companies. But yes,
264 00:27:31.720 ⇒ 00:27:32.919 Roy Christian Piñon: it’s kind of my
265 00:27:33.020 ⇒ 00:27:34.790 Roy Christian Piñon: where I’m coming from.
266 00:27:34.910 ⇒ 00:27:36.909 Roy Christian Piñon: Yeah, let me show my face.
267 00:27:37.426 ⇒ 00:28:03.100 Uttam Kumaran: Yeah, the one. The one thing I the one thing I loved about talking with Roy, and we’ve been talking for a few weeks. Is that you know I’ve I went and consulted a lot of my friends that work in like b 2 b sales and selling, you know, selling solutions that are, you know, in our price range that are in our like sort of take about one to 3 months to close. You know, higher touch, custom, solutions, and all of them, you know, told me that
268 00:28:03.100 ⇒ 00:28:21.600 Uttam Kumaran: you know you can do content. You can do email and Linkedin. But the last kind of still, the thing that’s really a huge part is actually calling and meeting people. And so when Roy mentioned that, you know, that’s something that he was really focused on. And, in fact, that was what he was like, you know, an expert in
269 00:28:21.904 ⇒ 00:28:45.969 Uttam Kumaran: I thought it really helped us like round out our sales strategy. As I put in. You know the the channel earlier this week. We have been primarily a referral and word of mouth business. A lot of that has come through like old friends of mine people I’ve met. The the nice thing is that’s great like we haven’t. We haven’t spent anything on sales? The problem with that is, it’s very unreliable.
270 00:28:46.277 ⇒ 00:29:07.180 Uttam Kumaran: And we have been stuck in this like limbo of like one to 3 clients for a while. So one of the investments in, you know, the team that’s here, and all these, you know, great folks. And adding more folks. The team on the sales side is expanding our different sales channels. Right? So we’re gonna continue to have word of mouth and referrals that
271 00:29:07.210 ⇒ 00:29:29.279 Uttam Kumaran: doesn’t stop. I’m still meeting people and still have friends. So that’s gonna happen. I still hope that you know that’ll come from everybody who has ability, and we’ll set up some incentive programs to promote that. I think the second thing is we want to have cold outbound right. We want to have outbound that goes to clients that have are like the clients we work with have problems that we can solve.
272 00:29:29.280 ⇒ 00:29:44.489 Uttam Kumaran: and that’ll be all the stuff that Erickson is kind of setting up and part- part of that is also gonna be, you know, calling. It’s gonna be talking to people and one of the things that I liked about Roy’s process is, you know, I was someone. I’m someone who’s like, Okay, what are?
273 00:29:44.490 ⇒ 00:30:02.950 Uttam Kumaran: What’s the talk track like? What is this. And you know, he was like, Hey, this is gonna take time to learn what people’s problems are and really spend time with them on the phone, understand? And especially for some of these low to medium data industries that we’re going after like manufacturing. These are phone call businesses. Right? They wanna hear people talk
274 00:30:03.305 ⇒ 00:30:23.240 Uttam Kumaran: and that’s where I think, compared to other data agencies. We’re really gonna win, right? We’re not a company that’s like in New York, in San Francisco. We only email. And like you, come to us like I really am. And I really am passionate about and impressed by our long term relationships with clients.
275 00:30:23.512 ⇒ 00:30:43.437 Uttam Kumaran: and all of those clients, no matter what happens, like whether they they no longer need us, they will go to some other company and end up meeting us. And so these are just relationships that help us in one way or another. So we’re in the relationship building business. So I’m very excited to kind of have Roy start in a smaller capacity, and then, you know, kind of as we grow start to
276 00:30:43.820 ⇒ 00:31:02.810 Uttam Kumaran: you know, build. So I think, between the last piece also. You know, I mentioned 2 different channels is content. Right? So this is content that you know Ryan has really owned in terms of the Content schedule. But it’s also stories from all of us. So I think a lot of the stories from me and Patrick, on the data side
277 00:31:02.810 ⇒ 00:31:18.660 Uttam Kumaran: are gonna be really helpful, I think, from Nico on like the project management, and like actual, what the client stories are is, gonna be super helpful. And then anyone that joins the team, you know, who’s actually delivering client solutions has a story to tell. So our goal is to get that out, have our voice on it, have our opinions in it.
278 00:31:18.899 ⇒ 00:31:33.029 Uttam Kumaran: And, you know, kind of like to share people what we do, and you can already see that people are interested and people are liking that sort of stuff. So I wanna make sure that continue to make that a channel. And we’re gonna start to see splits between all these in terms of how we’re getting clients.
279 00:31:33.640 ⇒ 00:31:39.369 Uttam Kumaran: So super excited for that, I know we’re kind of at time, but I had a couple of other things I guess
280 00:31:39.630 ⇒ 00:31:42.219 Uttam Kumaran: we wanted to go through really quickly.
281 00:31:42.770 ⇒ 00:31:55.050 Uttam Kumaran: we talked a little bit about the chat. Gpt, Pr, I think maybe Miguel, the the Hpi lease assist is kind of like a bigger thing. So maybe we do another meeting on Monday about that.
282 00:31:55.330 ⇒ 00:31:56.670 joshuadeveyra: Yeah, yeah, probably.
283 00:31:56.670 ⇒ 00:32:03.030 Uttam Kumaran: Cool and then maybe, Patrick, I guess I wanted to give you a little bit of the floor to talk about
284 00:32:03.450 ⇒ 00:32:07.100 Uttam Kumaran: like your ideas on the engineering and kind of like
285 00:32:07.502 ⇒ 00:32:26.589 Uttam Kumaran: how the cake is baked like manufacturing process. Side. I kind of won’t speak more than I know we have a meeting after that, but I kind of want we haven’t talked at all about today, about the actual solutions we do. You know, which is a huge part of like. Why, we’re all here, so I’ll kind of let you speak a little bit on that right.
286 00:32:27.820 ⇒ 00:32:33.540 Patrick Trainer: and just so I’ll keep it like super high level. Because it does like go
287 00:32:33.740 ⇒ 00:32:52.220 Patrick Trainer: pretty deep into the weeds. And I could probably take up an hour or 2 talking about it, but just the overall kind of like framework for it. And the way that I’m thinking about it is like, how does data work in the context of like Brainforges business?
288 00:32:52.410 ⇒ 00:33:09.470 Patrick Trainer: And if you think about it, it’s unlike a traditional business where you kind of have, like one data platform, or like one data project that then proliferates throughout the entire business. We have a bunch of
289 00:33:10.270 ⇒ 00:33:13.770 Patrick Trainer: basically like those templates that are being
290 00:33:14.660 ⇒ 00:33:18.340 Patrick Trainer: made each every time with with every single client.
291 00:33:18.560 ⇒ 00:33:33.390 Patrick Trainer: And so what does that mean for like, what are the problem areas that that we have. It’s we’ve got multiple data projects. We’ve got a bunch of like fragmented management tools.
292 00:33:33.460 ⇒ 00:33:45.579 Patrick Trainer: Growth of the data is unpredictable. We don’t really know. At the beginning, like what our assets are so like, what schemas are running, what tables are running all of the
293 00:33:45.590 ⇒ 00:33:48.300 Patrick Trainer: kind of like assets that provide that
294 00:33:49.400 ⇒ 00:33:52.040 Patrick Trainer: inconsistent metadata between
295 00:33:52.659 ⇒ 00:34:02.249 Patrick Trainer: projects as well. There’s complexities that have to do with data access and like all of the our back stuff managing that
296 00:34:02.360 ⇒ 00:34:07.810 Patrick Trainer: quality is an issue, and that can be inconsistent across projects
297 00:34:08.456 ⇒ 00:34:13.560 Patrick Trainer: and then just like relative management of that
298 00:34:13.911 ⇒ 00:34:22.460 Patrick Trainer: and then we have like slow data, and we gotta know when like things are breaking. So there’s kind of like 3 tranches there.
299 00:34:22.550 ⇒ 00:34:39.799 Patrick Trainer: So we I have like the way that keeping it high level like we need to plan for like having a centralized control plane which then begets like a sort of like scalable infrastructure, so that we can support multiple projects without
300 00:34:39.800 ⇒ 00:35:03.660 Patrick Trainer: losing our minds or having like a 1 to one relationship with like engineer to project so that we could have, like one engineer to multiple projects. And so we got to be able to scale that with that we need a good catalog of one our data assets. So what’s actually running in production? But then, also the metadata that
301 00:35:03.840 ⇒ 00:35:14.065 Patrick Trainer: relates to all of those assets, we have, to be sure, on our access control, especially as it relates to multiple clients.
302 00:35:14.610 ⇒ 00:35:20.279 Patrick Trainer: with that like, we don’t want gaps there. I I mean, I’m sure Udem, you’re
303 00:35:20.290 ⇒ 00:35:22.682 Patrick Trainer: the lawyers and everything have
304 00:35:23.860 ⇒ 00:35:24.460 Uttam Kumaran: Yeah.
305 00:35:24.620 ⇒ 00:35:34.939 Patrick Trainer: Provision for like risks and data breach. But, like it’s the likelihood of that is is low. But it’s still something that we need to to plan about
306 00:35:35.509 ⇒ 00:35:45.919 Patrick Trainer: and then with our like overall data quality framework? We’ve got like that rules engine. How are we monitoring it? And then how do we remediate that?
307 00:35:46.120 ⇒ 00:35:54.863 Patrick Trainer: And so given all of these kind of like problem areas and solutions to it? It’s all going to help us kind of craft this
308 00:35:55.400 ⇒ 00:36:08.169 Patrick Trainer: overall kind of like. I mean, really control plane of our infrastructure and really help us like, serve multiple clients in a scalable way.
309 00:36:09.980 ⇒ 00:36:15.700 Uttam Kumaran: Yeah, on on that piece, I think. 2 things like one our goal, you know. We are.
310 00:36:15.720 ⇒ 00:36:36.019 Uttam Kumaran: of course, like agency consultancy, you know, however, you describe it. But I talk about us like a data analytics company. The the thing is, we are a company with multiple bosses. But, however, a shared workforce and there are shared learnings from each of those that every new client we get benefits from all that
311 00:36:36.020 ⇒ 00:36:59.220 Uttam Kumaran: benefits from our speed our folks, and it works on both sides right? The better our process, the better clients we get, the better we can deliver for them. The more clients we get the better our process the better engineers we can get, because the one thing about great engineers is they don’t want to work in a chaotic environment. And so there’s a couple of things. So one is this whole framework on, how do we support? And Patrick mentioned leverage? Right?
312 00:36:59.250 ⇒ 00:37:17.100 Uttam Kumaran: My goal was never. For like, okay, we have like one person per client, we’re we’re trying to hire people that have the ability to switch contacts and to to kind of work on multiple clients. Not only does that help us out financially, it keeps the team really, really strong, right? And the the way we’re gonna do that is
313 00:37:17.100 ⇒ 00:37:31.009 Uttam Kumaran: through methods like this? Right? How do we build scalable processes that allow us to go from one data engineer to 2 clients, to one data engineer to 4 clients. Right? Additionally, a lot of the stuff under this is actually a lot of the AI stuff that we’re working on right.
314 00:37:31.010 ⇒ 00:37:55.729 Uttam Kumaran: And you can think about this on the operation side, on the sales side, but also on the engineering side. How do we trim off the things that are? You know, really the tax on the quality of life for the data engineers and the analytics engineers and allow them to focus just on the work and therefore scaling their time up right? My goal is never to, you know I’m not interested in running a big organization just for the sake of
315 00:37:55.740 ⇒ 00:38:03.810 Uttam Kumaran: consultancies are always huge. I wanna maximize for our clients. And I know that like through processes like this, we can get the best people
316 00:38:03.860 ⇒ 00:38:16.070 Uttam Kumaran: that we can also upskill people to the level of quality that we need and then deliver consistently. The faster we can show results, the faster we can deliver, and the more quality those are like, we’re
317 00:38:16.070 ⇒ 00:38:34.479 Uttam Kumaran: we’re gonna be just fine, right? We’ve been doing all that already. But I do think that when we look left to right in our industry. We’re not gonna see ideas like this. We’re gonna see traditional people run traditional consultancies with the same model, with the same margins, but with the same sort of issues
318 00:38:34.880 ⇒ 00:38:37.330 Uttam Kumaran: like, we have a different strategy here. So
319 00:38:37.380 ⇒ 00:38:40.660 Uttam Kumaran: I’m really excited for all the stuff that Patrick’s working on
320 00:38:42.750 ⇒ 00:38:49.879 Uttam Kumaran: cool. I think that’s that basically rounds out most of our stuff, I think. Monday. We’ll we’ll try a little bit plan for next week.
321 00:38:50.171 ⇒ 00:39:01.490 Uttam Kumaran: But it’s been an awesome week so far. I’m really really excited. I know we have a couple of new folks on the team who have been ramping up, and Roy excited to to have you on and introduce you to everybody as well.
322 00:39:01.884 ⇒ 00:39:20.939 Uttam Kumaran: So I’m hoping that everybody can meet everybody and feel free to schedule meetings, and I’ll try to facilitate as much as possible. One thing that I I’ve been trying to do is meet one on one with everybody on the team. Once a week at least. You know I highly encourage everybody on the team to try to do that. It doesn’t have to be every week but once a month, or
323 00:39:20.940 ⇒ 00:39:33.419 Uttam Kumaran: once every 2 weeks, like I happen to have been a lot of meetings, but I know a lot of other people are in heads down, work in their own domains. So meeting with other people and talking about what we’re building is the way this place improves, and you kind of learn
324 00:39:33.470 ⇒ 00:39:52.780 Uttam Kumaran: like we don’t want siloed. We don’t want Ryan on the content side to never talk to Patrick on engineering to never talk like this is a team. Everybody has overlap on everybody. And so that’s the way there’s never gonna be someone who’s like there’s there’s never someone who’s sitting in an isolated manner.
325 00:39:52.780 ⇒ 00:40:05.449 Uttam Kumaran: who can’t learn or can’t improve their workflow from the perspective. These meetings we’re gonna have, you know twice a week. But you can only get through so much and 40 min. So encourage you guys to all chat
326 00:40:06.560 ⇒ 00:40:13.459 Uttam Kumaran: cool. The only thing we can go through is website stuff. But hopefully, we can go through that on Monday and kind of share the latest. But yeah, if nothing else, I think we’ll
327 00:40:13.500 ⇒ 00:40:19.499 Uttam Kumaran: I’m chatting with, I guess, Nico and Pat, we can jump on the other meeting, and then everybody else. I’ll talk to you on slack.
328 00:40:20.550 ⇒ 00:40:21.770 Patrick Trainer: Cool. Cool. See? You all.
329 00:40:22.290 ⇒ 00:40:22.929 Patrick Trainer: Thanks, guys.
330 00:40:22.930 ⇒ 00:40:23.600 joshuadeveyra: You guys, bye-bye.
331 00:40:23.600 ⇒ 00:40:25.130 Nicolas Sucari: Thanks. Have a nice weekend.
332 00:40:25.200 ⇒ 00:40:26.210 Nicolas Sucari: Bye, bye.