Meeting Title: Internal Design Review Date: 2025-03-18 Meeting participants: Aakash Tandel, Luke Daque, Uttam Kumaran, Amber Lin, Demilade Agboola, Hannah Wang
WEBVTT
1 00:00:53.530 ⇒ 00:00:55.089 Hannah Wang: Hey! How are you doing
2 00:00:59.390 ⇒ 00:01:00.869 Aakash Tandel: Hey? I’m doing well, how about you?
3 00:01:02.050 ⇒ 00:01:05.449 Hannah Wang: I’m hanging in there.
4 00:01:06.039 ⇒ 00:01:13.829 Hannah Wang: Let’s wait a little bit. Some people said. Yes, I don’t know who King was that was interesting.
5 00:01:14.010 ⇒ 00:01:16.380 Hannah Wang: The person that came in and left
6 00:01:16.830 ⇒ 00:01:18.912 Aakash Tandel: Oh, I was not paying attention
7 00:01:19.260 ⇒ 00:01:20.330 Hannah Wang: Oh, no worries.
8 00:01:22.780 ⇒ 00:01:23.590 Hannah Wang: Okay.
9 00:01:24.110 ⇒ 00:01:32.699 Hannah Wang: I think Nico is gonna join maybe not. Not sure. Also, if Utah it’s gonna join or not.
10 00:01:32.960 ⇒ 00:01:34.020 Hannah Wang: Hey? There!
11 00:01:37.910 ⇒ 00:01:41.535 Aakash Tandel: I am a Chelsea fan. So
12 00:01:42.460 ⇒ 00:01:49.630 Aakash Tandel: against you. But I saw, yeah, I saw the game. This weekend was not fun. But you guys are doing well.
13 00:01:50.688 ⇒ 00:01:55.019 Demilade Agboola: Not doing as well as I would have liked us to, to be honest, but
14 00:01:56.060 ⇒ 00:01:58.827 Demilade Agboola: I’ll take a win
15 00:01:59.540 ⇒ 00:02:01.228 Aakash Tandel: That’s fair wins win
16 00:02:01.650 ⇒ 00:02:02.896 Demilade Agboola: Yeah, yeah.
17 00:02:03.970 ⇒ 00:02:05.859 Demilade Agboola: But how long have you been? A Chelsea fan?
18 00:02:07.930 ⇒ 00:02:28.290 Aakash Tandel: I started watching Premier League like in like high school. So the late aughts so like 2,006, 2,007. But then I basically didn’t watch like much Chelsea until like 2017. So there’s a good decade of like, basically kind of being like, oh, I’m kind of affiliated here. But yeah, it was mostly after 2017
19 00:02:29.230 ⇒ 00:02:35.120 Demilade Agboola: Yeah. So I I became an arsenal fan, like maybe oh, 2, 0, 3
20 00:02:35.290 ⇒ 00:02:39.670 Demilade Agboola: But my uncle, my dad’s elder brother wasn’t was a Chelsea fan.
21 00:02:39.770 ⇒ 00:02:47.429 Demilade Agboola: and we used to banter ourselves all the time. So like that to me is one of my childhood memories about like football and stuff. So
22 00:02:47.430 ⇒ 00:02:47.770 Aakash Tandel: Nice
23 00:02:48.026 ⇒ 00:02:53.929 Demilade Agboola: My dad, my dad himself would just watch football for the fun of it. He didn’t really support anything which isn’t fun, too.
24 00:02:54.204 ⇒ 00:02:54.479 Demilade Agboola: like.
25 00:02:54.480 ⇒ 00:03:02.570 Demilade Agboola: I like the ability to be able to troll someone have bad days. My dad just was just neutral to everything like all that in one call.
26 00:03:02.940 ⇒ 00:03:14.700 Demilade Agboola: I guess that’s 1 way to look at it. But no, definitely. Not so. Yeah, I’ve been a huge arsenal fan. I actually went to watch some arsenal games last year, probably one of the best highlights of my of my life. To be honest
27 00:03:15.140 ⇒ 00:03:20.680 Demilade Agboola: cause. It was like my 1st games I ever watched there. I mean it was. It was beautiful we won. It was the
28 00:03:21.270 ⇒ 00:03:28.239 Demilade Agboola: one was the Crystal Palace game, 1 5 0. The other one was the united game, we won 2 0. So it was pretty good.
29 00:03:28.660 ⇒ 00:03:29.710 Demilade Agboola: Yeah, yeah.
30 00:03:29.710 ⇒ 00:03:30.951 Aakash Tandel: It’s a good track record
31 00:03:31.420 ⇒ 00:03:38.760 Demilade Agboola: Yeah, maybe I should make it my full time job, you know, just like games. Maybe that might be what they need
32 00:03:38.760 ⇒ 00:03:39.820 Aakash Tandel: Yeah, yeah.
33 00:03:40.340 ⇒ 00:03:47.925 Aakash Tandel: yeah, it’s definitely fun to go to games over there. It’s just such a different atmosphere. I don’t know if you’ve gone to Mls games, but it’s not the same vibe
34 00:03:48.400 ⇒ 00:04:16.970 Demilade Agboola: I haven’t gone to Mls. I watched a couple of games on TV, and you can even hear from TV that it’s just different. But, like I really love how it is. In, like the Premier League, where, like teams, have different chants for different scorelines, for different players, for different situations for different teams like different opponents like, it’s just. There’s just so much going on. And it’s just beautiful to hear, like 60,000 people screaming the same sort of song. It’s beautiful. It’s really beautiful.
35 00:04:17.310 ⇒ 00:04:18.380 Aakash Tandel: Yeah, definitely.
36 00:04:19.950 ⇒ 00:04:45.345 Hannah Wang: Sorry I like can’t participate in this conversation. I like, I’m not yeah super into well, my husband’s into like American football and basketball. So all the American sports, basically. But yeah, before him, I was also neutral to like any team. So I kind of. But now I’m like bandwagon on with him. He’s from Ohio, so he supports, like all the Ohio teams.
37 00:04:46.120 ⇒ 00:04:46.940 Hannah Wang: sorry
38 00:04:47.120 ⇒ 00:04:48.699 Demilade Agboola: Like the cows for basketball, right
39 00:04:48.700 ⇒ 00:05:12.420 Hannah Wang: Yes, yeah. The cabs. I’m like shocked that they’re doing so well this season. Actually, there’s a game in La right now against tonight. It’s against the clippers. And we’re like debating if we should go or not like, get the nosebleed tickets, pay like 20 bucks per person, and just go because we live super close to so far, I think that’s where it is, or one of the stadiums. So
40 00:05:12.570 ⇒ 00:05:18.950 Hannah Wang: crypto, yeah. Yeah. So I don’t know it’s like I’ve never been to a basketball game. So
41 00:05:19.340 ⇒ 00:05:27.590 Hannah Wang: we’ll see. But anyway, yeah, thanks for joining I know that.
42 00:05:28.100 ⇒ 00:05:53.689 Hannah Wang: Yeah. Design is kind of like its own kind of bubble. But I just feel like it’d be helpful to get feedback from people that are not designers who are not super technical. Although I do have like a bit of a technical background, but not like data. Or AI related and also, yeah, the same pair of eyes have been looking at the designs for a long time. So yeah.
43 00:05:54.347 ⇒ 00:06:12.420 Hannah Wang: let’s see, I’m not sure the best way to do this. Not sure if you guys had a chance to go through the documents and, like have written notes, or if I should just share my screen and go through each of the documents. That you guys have like a preference
44 00:06:14.430 ⇒ 00:06:18.140 Aakash Tandel: I think for me the latter is probably helpful. I haven’t spent a lot of time with them.
45 00:06:18.890 ⇒ 00:06:19.500 Demilade Agboola: Dan.
46 00:06:19.900 ⇒ 00:06:22.550 Hannah Wang: Alrighty. Let’s do that.
47 00:06:22.960 ⇒ 00:06:24.633 Hannah Wang: Okay, so
48 00:06:26.430 ⇒ 00:06:36.150 Hannah Wang: so these, this is the figma file. This is kind of the sales assets that we created. So like the one pagers. So there’s around
49 00:06:37.380 ⇒ 00:06:43.460 Hannah Wang: 4. I guess this one counts, too. But yeah, there’s 4 or 5 1 pages, and also
50 00:06:43.570 ⇒ 00:07:08.519 Hannah Wang: the capability stack. I know it’s a lot of content. But I think maybe yeah, any type of feedback is helpful. So I’ll just start with the 1st page here. This is kind of for, like the general services that Brainforge offers so data and AI and also like strategy, in terms of how to help clients, and roadmapping and stuff like that. So I guess yeah, initial impression. Like.
51 00:07:08.910 ⇒ 00:07:19.179 Hannah Wang: if you were a client or a potential lead. Looking at this, would you be like enticed to learn more. If you kind of got this from us.
52 00:07:24.419 ⇒ 00:07:28.380 Demilade Agboola: Yes, but I I also feel like there’s too much
53 00:07:28.990 ⇒ 00:07:31.019 Demilade Agboola: going on. Maybe it’s just me.
54 00:07:31.270 ⇒ 00:07:33.190 Demilade Agboola: I feel like there’s too many words.
55 00:07:37.600 ⇒ 00:07:49.260 Demilade Agboola: I’m trying to figure out where exactly because I try. I’m trying to like not just say what’s wrong, but like, see where I could improve it again, I’m not designing, but just like, okay. So like the numbers being like Bold. So
56 00:07:49.640 ⇒ 00:07:56.770 Demilade Agboola: We have. Our success is really bold. I like the services I’m
57 00:07:57.370 ⇒ 00:08:03.309 Demilade Agboola: I don’t know. I think the charts might be best without the thing on the left
58 00:08:04.250 ⇒ 00:08:09.620 Demilade Agboola: Like felt like the words on the left like this, yeah, like the boulder chart going. It’s just like, Hey.
59 00:08:09.970 ⇒ 00:08:15.210 Demilade Agboola: I think it’s something that could be said on a pitch like or or could be said when you’re talking to them.
60 00:08:15.980 ⇒ 00:08:18.309 Demilade Agboola: I don’t know or like it could be
61 00:08:18.560 ⇒ 00:08:25.179 Demilade Agboola: implied. I don’t know. It just feels like there are too many words love reading to do. I don’t know if that is.
62 00:08:25.330 ⇒ 00:08:26.210 Demilade Agboola: you know.
63 00:08:26.390 ⇒ 00:08:32.140 Demilade Agboola: But, like I said, it’s it’s it’s great. It looks really great. The it’s very. The alignment is really good.
64 00:08:33.299 ⇒ 00:08:37.970 Demilade Agboola: I’m just just thinking about like the like, how much reading is in it. That’s it.
65 00:08:39.200 ⇒ 00:08:42.480 Hannah Wang: Yeah, yeah, that’s a fair point.
66 00:08:43.230 ⇒ 00:08:53.800 Hannah Wang: yeah, I’m not sure like, at what point like, sales uses this like kind of one pager. But my kind of general idea is that one pagers.
67 00:08:54.060 ⇒ 00:09:10.150 Hannah Wang: It’s okay for it to have a little bit of text, just because, yeah, when you send it to someone. I think it’s already, if we hand it to them at this stage, like, I feel like they’re already interested in knowing a little bit more about Brainforge, and already have a bit of context. I feel like
68 00:09:11.120 ⇒ 00:09:18.939 Hannah Wang: they would be willing to read through it versus, for example, like a capabilities deck which is very visual, heavy.
69 00:09:19.516 ⇒ 00:09:24.650 Hannah Wang: Like we can. Yeah. Utam was mentioning how the capabilities that
70 00:09:25.430 ⇒ 00:09:40.249 Hannah Wang: can be tailored to people who are more visual and kind of the one pagers can be tailored to someone who doesn’t mind reading through a ton of text. But I hear you, yeah, like all the previous iterations that we’ve had. It was very
71 00:09:41.370 ⇒ 00:09:48.199 Hannah Wang: yeah, like, text, heavy. So we did try to like, cut down on the text. So yeah, you can see
72 00:09:48.810 ⇒ 00:10:04.800 Hannah Wang: there is a lot of text. And we try to like trim it down even more. But I do feel like that’s the general consensus. I hear. It’s like, Oh, it’s it’s a lot of text, or it just with any document or kind of sales, asset or design asset.
73 00:10:05.273 ⇒ 00:10:09.010 Hannah Wang: So yeah, I’ll keep that in mind for future iterations.
74 00:10:13.363 ⇒ 00:10:26.066 Aakash Tandel: I’m I’m looking at the cards and kind of pulling up the like. The 30% success, the 10 million. I like those as like high level items. I think
75 00:10:26.860 ⇒ 00:10:31.479 Aakash Tandel: the text below might offer more context. So I’m trying to figure out a way to like
76 00:10:33.290 ⇒ 00:10:42.389 Aakash Tandel: highlight that a little bit more, because right now, 30%, 10,010,000 don’t mean a lot unless I guess I dive into it a little bit more. But that might be fine
77 00:10:44.370 ⇒ 00:10:47.209 Hannah Wang: Yeah, because this is really tiny. So people
78 00:10:47.560 ⇒ 00:10:54.009 Hannah Wang: like, at a 1st glance, you might not really look at it and be confused as to what those numbers indicate.
79 00:10:57.020 ⇒ 00:10:58.110 Hannah Wang: Yeah.
80 00:11:03.700 ⇒ 00:11:09.260 Hannah Wang: oh, that’s a lot of words. But I guess, does the copy
81 00:11:09.840 ⇒ 00:11:15.749 Hannah Wang: make sense? Like the words? And does it like kinda
82 00:11:15.850 ⇒ 00:11:21.700 Hannah Wang: is it clear what we do and what we offer to clients
83 00:11:27.480 ⇒ 00:11:29.860 Demilade Agboola: Yeah. So I mean, the services seems clear.
84 00:11:31.670 ⇒ 00:11:38.140 Demilade Agboola: In that. Yeah, it’s quite clear like what the different things are.
85 00:11:41.070 ⇒ 00:11:44.529 Demilade Agboola: I don’t know if figma does it. But is it possible to?
86 00:11:47.600 ⇒ 00:11:56.799 Demilade Agboola: okay, not my bad. I was thinking of something else. But like, yeah, I was thinking, like the numbers. I was wondering, especially like the tanky lines of an optimized sequel cleaned up.
87 00:11:57.180 ⇒ 00:11:58.709 Demilade Agboola: I’m wondering if
88 00:12:01.350 ⇒ 00:12:06.560 Demilade Agboola: there’s potentially another like business achievement. I know this is not a street. You this is just copy
89 00:12:06.980 ⇒ 00:12:07.900 Demilade Agboola: but like
90 00:12:08.490 ⇒ 00:12:16.049 Demilade Agboola: I don’t know does like it’s him at the end of the year might go, hey? We’ve saved a hundred 1 million dollars. We’ve saved 30% of our, you know.
91 00:12:16.689 ⇒ 00:12:22.799 Demilade Agboola: Total cost. But will they necessarily say we save 10 K lines of unoptimized SQL. Cleaned up. Code.
92 00:12:23.551 ⇒ 00:12:31.139 Demilade Agboola: I don’t think so. So. That’s kind of like. Is there a better metric we could put, or something that could, you know, be more hard hitting
93 00:12:33.170 ⇒ 00:12:43.470 Hannah Wang: Yeah, I mean, that’s a question, for I guess you tell you’re lurking. But that’d be something. I know we change these metrics a lot over the past couple of iterations. But
94 00:12:44.070 ⇒ 00:12:53.279 Hannah Wang: I think we just like settled on this for now, but probably probably like at the end of the year, or, as the business grows, there’d probably be more hard hitting
95 00:12:53.420 ⇒ 00:12:59.029 Hannah Wang: ones that we can replace it with. But I think, for now this is kind of what we settled on.
96 00:12:59.773 ⇒ 00:13:04.640 Hannah Wang: Not sure, Tom, if you wanna pitch in your thoughts.
97 00:13:07.730 ⇒ 00:13:09.855 Hannah Wang: it’s okay, if not, all right.
98 00:13:11.262 ⇒ 00:13:12.367 Hannah Wang: Let’s see.
99 00:13:14.940 ⇒ 00:13:33.750 Hannah Wang: Yeah. So this one pager. I think we try to just recycle as much design as we can. So you’ll see that. Yeah, this type of this top heading part is the same, and the successes are the same. So this is kind of diving into
100 00:13:34.070 ⇒ 00:13:47.099 Hannah Wang: more specifically what the services are. Because data AI strategy are more like general. But this is kind of more specific. So again, I’m gonna kind of pose the same questions, is it clear
101 00:13:47.540 ⇒ 00:13:51.120 Hannah Wang: what we offer, is it?
102 00:13:51.890 ⇒ 00:14:00.019 Hannah Wang: Yeah, is it just? Is it clear? Or is it too much wording? Is it too much copy? Should we just add more diagrams?
103 00:14:02.350 ⇒ 00:14:05.529 Hannah Wang: Yeah, I can send this link
104 00:14:06.090 ⇒ 00:14:09.949 Hannah Wang: in the chat as well. If you wanna poke around
105 00:14:10.860 ⇒ 00:14:14.719 Aakash Tandel: I think it’s pretty clear to me, I think
106 00:14:15.190 ⇒ 00:14:18.070 Aakash Tandel: what I would be interested in is
107 00:14:18.210 ⇒ 00:14:37.710 Aakash Tandel: like, who are we sending these to specifically, and then maybe tailoring them a little bit to either vertical or service offering type, like if we have specifically a lead that’s looking for product analytics and strategy. Maybe we can blow that out a little bit and just come up with like 3 different things. There. That type of thing
108 00:14:45.480 ⇒ 00:14:51.110 Hannah Wang: Yeah, I think that’s what Robert, he like requested.
109 00:14:51.510 ⇒ 00:14:55.118 Hannah Wang: like a another one pager.
110 00:14:56.180 ⇒ 00:15:19.569 Hannah Wang: that’s yeah kind of exactly what you were talking about where there’s specific pool types of clients that we’re gonna send things to. And he was just saying, Oh, maybe we can do like a test. As to if a more like funneled down specific version is more impactful. For potential leads rather than a more general service offering. So
111 00:15:20.350 ⇒ 00:15:21.870 Hannah Wang: yeah, definitely.
112 00:15:21.990 ⇒ 00:15:28.249 Hannah Wang: we’ll just keep iterating on that and just creating more. One pagers that we can send to specific
113 00:15:28.460 ⇒ 00:15:29.830 Hannah Wang: groups of people.
114 00:15:31.630 ⇒ 00:15:33.878 Hannah Wang: Yeah, I think that’s like a good
115 00:15:34.900 ⇒ 00:15:37.339 Hannah Wang: kind of approach to take moving forward
116 00:15:39.420 ⇒ 00:15:39.810 Demilade Agboola: See, the
117 00:15:39.810 ⇒ 00:15:40.240 Hannah Wang: Yeah.
118 00:15:40.240 ⇒ 00:15:48.359 Demilade Agboola: The other stuff is, I really like that? The, there’s like the small tabs where you put what the actual things imply
119 00:15:48.550 ⇒ 00:15:55.099 Demilade Agboola: like. Maybe customer segmentation. I think that’s really helpful. Catches the eye quite quickly.
120 00:15:55.390 ⇒ 00:15:55.890 Hannah Wang: Right?
121 00:15:56.670 ⇒ 00:15:59.909 Hannah Wang: Yeah, cause it’s pretty eye catching. And it’s like a lot of
122 00:16:01.670 ⇒ 00:16:08.230 Hannah Wang: yeah specific things that clients might be looking for. So that is good.
123 00:16:08.580 ⇒ 00:16:12.780 Hannah Wang: Hi, Luke! Sorry to call you out, but we’re just reviewing
124 00:16:12.880 ⇒ 00:16:15.629 Hannah Wang: these one pagers, so if you have any feedback.
125 00:16:16.036 ⇒ 00:16:22.160 Hannah Wang: whether it be the look or the porting of stuff. If something is confusing, feel free to let me know
126 00:16:22.390 ⇒ 00:16:23.700 Hannah Wang: or put it in the chat.
127 00:16:24.910 ⇒ 00:16:26.756 Hannah Wang: Alrighty.
128 00:16:28.788 ⇒ 00:16:37.499 Hannah Wang: even more specific. Yeah, so this is a 1 pager for specifically the data services that we offer.
129 00:16:40.040 ⇒ 00:16:48.389 Hannah Wang: yeah, this is a lot more text heavy. But like, I said, I think the type of audience that we’re sending this to. They don’t mind reading through it.
130 00:16:51.150 ⇒ 00:16:53.499 Hannah Wang: But yeah, any thoughts on this.
131 00:17:00.370 ⇒ 00:17:02.879 Hannah Wang: or even like the types of
132 00:17:03.270 ⇒ 00:17:11.820 Hannah Wang: yeah, services quote unquote that we have. If it can be more impactful like the title. Or if we should focus on something else.
133 00:17:18.869 ⇒ 00:17:21.309 Aakash Tandel: I guess these look good to me. I think the
134 00:17:22.129 ⇒ 00:17:28.949 Aakash Tandel: I guess services are gonna kind of come down to what? What product offerings we want to offer, and these these look right to me
135 00:17:30.690 ⇒ 00:17:40.229 Demilade Agboola: Yeah, it’s also interesting. We use the snow. The only thing I was this looks great. The only thing I wanted to just add or point out was that we use snowflakes, icon.
136 00:17:40.480 ⇒ 00:17:43.219 Demilade Agboola: But we didn’t use Dvc’s icon. I think. Maybe pretend
137 00:17:43.220 ⇒ 00:17:43.960 Hannah Wang: Oh!
138 00:17:43.960 ⇒ 00:17:46.090 Demilade Agboola: Be something to
139 00:17:51.450 ⇒ 00:17:58.870 Hannah Wang: That’s a good call out. I think that’ll make it stand out even more, too. Right? Cause it’s a pretty iconic logo that they have
140 00:17:59.710 ⇒ 00:18:08.709 Luke Daque: Yeah. But yeah, but if we do that, though, it’s gonna be a different color, right? This is like, it’s not in the temp theme like snowflake even, is like
141 00:18:08.910 ⇒ 00:18:09.940 Luke Daque: pretty often
142 00:18:09.940 ⇒ 00:18:15.178 Hannah Wang: Blue at this. Yeah, yeah, I don’t really know, like the
143 00:18:17.400 ⇒ 00:18:29.490 Hannah Wang: like the legal things behind, like changing the color of the logo, because I know that we in the capabilities that we make everything white, but that’s just to kind of match like the theme of
144 00:18:29.840 ⇒ 00:18:37.350 Hannah Wang: the deck. So it’s totally possible to change it to green or something like that to match our color scheme. But
145 00:18:38.370 ⇒ 00:18:42.960 Hannah Wang: yeah, maybe that’s the reason why we didn’t do that I can also ask Ann
146 00:18:45.600 ⇒ 00:18:46.389 Demilade Agboola: In this case.
147 00:18:46.830 ⇒ 00:18:49.159 Demilade Agboola: I just noticed it. So I was pointing up
148 00:18:49.380 ⇒ 00:18:58.361 Hannah Wang: Yeah. But I I mean, I do like the idea of using the logo cause we already use it for Snowflake, anyway.
149 00:18:59.480 ⇒ 00:19:01.938 Hannah Wang: but yeah, I’ll keep that in mind.
150 00:19:04.660 ⇒ 00:19:13.039 Hannah Wang: Okay, yeah. And then I don’t know.
151 00:19:13.940 ⇒ 00:19:20.910 Hannah Wang: Yeah, I know Miguel isn’t here. But he, yeah, the AI services. One pager is still working.
152 00:19:21.370 ⇒ 00:19:26.160 Hannah Wang: Progress. Oh, actually.
153 00:19:26.160 ⇒ 00:19:35.221 Demilade Agboola: Also sorry. Just another thing. I wanted to call out, and I was kind of on my screen. So that’s why it came to my mind is the language of the copy of the previous
154 00:19:35.840 ⇒ 00:19:43.559 Demilade Agboola: thing you just showed. It keeps switching between like we, and just like the verb and what it’s doing. So supercharger.
155 00:19:44.210 ⇒ 00:19:54.349 Demilade Agboola: And we use Dvt, then slow down slowed data this, and then it goes. We go beyond so just being able to have that consistency in language will be helpful
156 00:19:55.170 ⇒ 00:19:59.809 Uttam Kumaran: Yeah, it’s like, it’s similar to like resumes where they say use action words to start. Like.
157 00:20:00.090 ⇒ 00:20:03.779 Uttam Kumaran: I agree, probably we should just agree on like how we’re gonna start each of these
158 00:20:06.750 ⇒ 00:20:13.289 Hannah Wang: We can agree on it. Now, should we use action words? Or should we start with like a we type of thing
159 00:20:17.850 ⇒ 00:20:23.920 Uttam Kumaran: I don’t know. I’m not strong. I I this is one of the few things where I have no opinions on so
160 00:20:23.920 ⇒ 00:20:24.900 Hannah Wang: Okay.
161 00:20:27.730 ⇒ 00:20:29.680 Uttam Kumaran: I feel like actually practice. I don’t know
162 00:20:31.110 ⇒ 00:20:34.215 Hannah Wang: I guess we can. I can ask Ryan, too.
163 00:20:45.680 ⇒ 00:20:47.289 Hannah Wang: and I just feel like
164 00:20:47.920 ⇒ 00:20:55.529 Hannah Wang: if we start with. We like, all the sentences will start with we but if we start with action words it’ll change it up.
165 00:20:55.650 ⇒ 00:21:00.910 Hannah Wang: So I I have. I think my vote is for action words. But how
166 00:21:01.160 ⇒ 00:21:03.740 Hannah Wang: stick around? Ask Ryan as well
167 00:21:04.170 ⇒ 00:21:14.839 Aakash Tandel: I’m looking at some of my previous company’s service pages and they have a mix. So we might be okay with having a little bit of a mix. But yeah.
168 00:21:16.660 ⇒ 00:21:17.380 Luke Daque: Okay.
169 00:21:17.380 ⇒ 00:21:18.250 Hannah Wang: Good to know.
170 00:21:21.910 ⇒ 00:21:35.249 Hannah Wang: Yeah. So for the AI one pagers Miguel. It’s late for all of you in the Philippines. So, Luke. I don’t know how you’re here, but he mentioned that maybe we should use
171 00:21:36.060 ⇒ 00:21:51.950 Hannah Wang: metrics that are not, that are AI related, not data related. And that was a fair point tailoring it there. So I did ask him for that, but I think that probably got off his radar, so I’ll follow up with him, and then change it
172 00:21:56.570 ⇒ 00:22:11.619 Hannah Wang: alright for the one pagers, or sorry the pricing page. This is a on hold. I think Ann and I and Tom. We’re trying to think of a good way to kind of format our pricing so, as you know, we
173 00:22:11.620 ⇒ 00:22:22.770 Uttam Kumaran: It’s good to get. Yeah, it’s probably good to get feedback from this crew. Like we have AI services, we have data services. But basically, the way we’re doing things is we have an audit
174 00:22:22.950 ⇒ 00:22:34.975 Uttam Kumaran: piece where we come in and sort of give you like the recommendation. This is probably similar to like a lot of comp service companies offer like a workshop, or like a some sort of like
175 00:22:35.800 ⇒ 00:22:37.125 Uttam Kumaran: what do they call like?
176 00:22:38.630 ⇒ 00:22:53.830 Uttam Kumaran: Just some they they call like 10 different things. But it’s basically like, it’s like a 1 week or 2 week thing where we come in. And we just tell you what we’ll do if if we’re to sign on as a client implementation and for deploy team, really for for up for if I was, if I’m playing like
177 00:22:54.340 ⇒ 00:23:14.730 Uttam Kumaran: head of like finance. For me. It’s just like we just want 2 plans, and we, our goal is to move everybody to the biggest plan. But ideally, we want to have the middle plan be something different than the 3rd one, which is the middle plan, is almost just like purely implementation. And the the 3rd plan is almost like what we’re doing for Eden, which is like
178 00:23:14.960 ⇒ 00:23:19.980 Uttam Kumaran: both like executive help and development, and like strategy.
179 00:23:21.420 ⇒ 00:23:26.289 Uttam Kumaran: So we could use your help on like both. How this looks
180 00:23:26.420 ⇒ 00:23:32.511 Uttam Kumaran: right? Like, how we convey, we do that across data and AI, but also what’s in the bullets?
181 00:23:33.470 ⇒ 00:23:34.210 Uttam Kumaran: yeah.
182 00:23:37.420 ⇒ 00:23:38.960 Hannah Wang: Because previously. Oh, sorry!
183 00:23:38.960 ⇒ 00:23:40.200 Aakash Tandel: No go for it. Yeah.
184 00:23:40.520 ⇒ 00:23:43.200 Hannah Wang: Oh, I was just gonna say it looks like a lot, because
185 00:23:43.370 ⇒ 00:23:47.529 Hannah Wang: previously it’s it was like 6 different tiers
186 00:23:47.730 ⇒ 00:23:55.420 Hannah Wang: tiers. But we try to like, combine it, and like sub nest everything. So it is a lot. But yeah, go ahead
187 00:23:55.780 ⇒ 00:24:10.360 Aakash Tandel: Yeah, I was. Gonna say, the the audit I’ve also heard like strategy piece, or just like upfront planning. I think that’s it’s and it’s only 2 weeks. But that typically is like the thing to highlight with clients like, Hey, we need to figure out a a
188 00:24:10.750 ⇒ 00:24:20.455 Aakash Tandel: forward. So this is a really important thing to kind of get our ducks in a row. So yeah, I audit strategy. I think, whatever that is, I think it’s usually pretty helpful.
189 00:24:22.350 ⇒ 00:24:23.659 Aakash Tandel: helpful piece to that. Yeah.
190 00:24:26.180 ⇒ 00:24:32.190 Uttam Kumaran: Yeah, everything is up for debate in terms of how we name, if audits like, if
191 00:24:32.360 ⇒ 00:24:38.950 Uttam Kumaran: to give you I I gave all the verbiage here. And I’m like, it’s gonna be bad. So I wanted this to be like, very clear
192 00:24:39.380 ⇒ 00:24:45.129 Uttam Kumaran: for folks when they see this to be like, oh, I know what an audit is when I think audit. I think tax stuff.
193 00:24:45.770 ⇒ 00:24:49.169 Uttam Kumaran: but that’s because, like, I don’t know other words for this so
194 00:24:49.540 ⇒ 00:24:54.330 Uttam Kumaran: well, it could be a success workshop. It could be success planning, you know. But like.
195 00:24:54.900 ⇒ 00:24:57.370 Uttam Kumaran: yeah, you can change that too.
196 00:25:01.510 ⇒ 00:25:09.320 Demilade Agboola: It could also be something like maybe road mapping or data, North Star, or something of that nature or something whatever
197 00:25:10.040 ⇒ 00:25:11.859 Demilade Agboola: would resonate.
198 00:25:12.867 ⇒ 00:25:18.900 Demilade Agboola: Is this the final one? Or is the one you showed before? Not final, but like the latest version
199 00:25:19.310 ⇒ 00:25:21.332 Hannah Wang: This one is the latest version.
200 00:25:24.880 ⇒ 00:25:37.309 Hannah Wang: Yeah, I feel like, maybe road mapping or like workshop. I don’t know. Cause when I think of workshop I think of, like, Oh, yeah, short, like stint that people like help me assess things
201 00:25:37.420 ⇒ 00:25:38.859 Hannah Wang: in. So
202 00:25:39.210 ⇒ 00:25:46.290 Hannah Wang: maybe, yeah. Cause when I think of audit, I hear taxes. I also think of taxes, too. So yeah.
203 00:25:49.510 ⇒ 00:25:54.260 Demilade Agboola: Okay, kind of funny. If at the end of 2 weeks you just go. So this is how much actually have to pay
204 00:25:56.190 ⇒ 00:25:59.935 Uttam Kumaran: Basically what we do. You know.
205 00:26:09.700 ⇒ 00:26:15.399 Hannah Wang: Hi Amber. I know you just joined, but we’re just going over like the pricing kinda
206 00:26:15.690 ⇒ 00:26:20.970 Hannah Wang: design that we have right now. And if it’s like clear to
207 00:26:21.320 ⇒ 00:26:31.169 Hannah Wang: people like what we offer. So everything is like super up in the air right now in terms of like wording, and also design and stuff. So yeah, we’re just kinda looking through it
208 00:26:31.780 ⇒ 00:26:36.849 Amber Lin: Yeah, I think for me when it 1st comes to eyes that this is a little crowded.
209 00:26:37.080 ⇒ 00:26:41.429 Amber Lin: Yeah thing that comes to me. I think, one, because we have
210 00:26:41.700 ⇒ 00:26:50.690 Amber Lin: many different colors, which becomes a problem of visual overload. We have yellow, green, purple, and other greens.
211 00:26:50.940 ⇒ 00:27:09.060 Amber Lin: and then there’s also frames and frames which can be like I’m trying to focus on where to see. So maybe we could just expand the frame to. You know, the purple or green frames inside, so that it’s full width.
212 00:27:09.610 ⇒ 00:27:19.230 Amber Lin: I don’t know. I just feel like I I know why we’re doing these frames, and it helps navigate. It’s just at 1st glance it may look a little
213 00:27:20.040 ⇒ 00:27:21.200 Amber Lin: crowded
214 00:27:21.420 ⇒ 00:27:22.180 Hannah Wang: Yeah.
215 00:27:22.670 ⇒ 00:27:30.890 Amber Lin: It could be, say, like different tabs of like different cards, stack on each other rather than 2 little frames. I don’t know
216 00:27:31.454 ⇒ 00:27:39.959 Uttam Kumaran: Like the before the before. We had 6 cards right? We had an audit implementation and data team
217 00:27:39.960 ⇒ 00:27:41.080 Amber Lin: Across that
218 00:27:41.080 ⇒ 00:27:42.609 Hannah Wang: And AI! So my
219 00:27:42.610 ⇒ 00:27:43.320 Amber Lin: I see that is
220 00:27:43.320 ⇒ 00:27:44.840 Uttam Kumaran: So just to consolidate
221 00:27:45.306 ⇒ 00:27:51.639 Amber Lin: I agree, I like that, we have all of it in one place, maybe hmm.
222 00:27:52.150 ⇒ 00:27:56.900 Amber Lin: yeah, it is different, how we do data in AI audits.
223 00:27:58.810 ⇒ 00:28:04.520 Uttam Kumaran: Because so one of the fun, the fundamental things like, I’m you know, I’ve tasked.
224 00:28:04.700 ⇒ 00:28:10.250 Uttam Kumaran: We’re gonna task. The marketing team with doing over time is merging the 2 sides of the business
225 00:28:10.610 ⇒ 00:28:12.020 Uttam Kumaran: right overall.
226 00:28:12.350 ⇒ 00:28:27.709 Uttam Kumaran: I think, from the engineering, the operation side, even there there’s some opportunity to do that. But when we come out to customers, how do we? How do we put portray that? We do both at the same level, right, and bridge the gap? Because right now it’s very distinct.
227 00:28:28.590 ⇒ 00:28:33.150 Uttam Kumaran: And so one, this is sort of a push in that direction.
228 00:28:33.850 ⇒ 00:28:35.549 Uttam Kumaran: Yeah.
229 00:28:38.180 ⇒ 00:28:41.790 Hannah Wang: Yeah, because right now, even on our pricing page, there’s like a
230 00:28:42.944 ⇒ 00:28:46.195 Hannah Wang: let me just share my whole screen.
231 00:28:51.229 ⇒ 00:28:56.500 Hannah Wang: yeah, it’s like it just seems very distinct, like AI versus
232 00:28:56.850 ⇒ 00:29:09.220 Hannah Wang: data. But in an ideal world we wouldn’t even have this toggle, and everything would just be like 1 3 cards. Like 3 tiers, kind of like, typically what you see in pricing pages.
233 00:29:09.705 ⇒ 00:29:15.270 Hannah Wang: So yeah, we’re trying to figure out like the best way to to do that, because
234 00:29:15.270 ⇒ 00:29:16.780 Amber Lin: What if we have
235 00:29:17.050 ⇒ 00:29:46.860 Amber Lin: activation upon hover? So when we hover, then we expand certain things, or maybe we have a flip card of when you hover on audit, then basically the first, st the 1st page we see has data and audit, but when you hover on it it flips over and it goes into data. Has this AI audit has this that will make the initial view a lot cleaner, and then those who are interested can always read more
236 00:29:49.100 ⇒ 00:29:56.619 Hannah Wang: Yeah, that that could work for the website. Because obviously, you can interact on a website. But
237 00:29:58.350 ⇒ 00:30:08.219 Hannah Wang: if we try to implement that same strategy here. I think it’s just hard on a deck to do that like a hub, like an interaction of some sort, or even on like a 1 pager.
238 00:30:09.670 ⇒ 00:30:12.740 Amber Lin: Oh, I see, I was thinking about the website. Yeah, for the
239 00:30:12.740 ⇒ 00:30:19.550 Hannah Wang: Yeah, the website that’s like, that’s actually a very good idea. But yeah, for them.
240 00:30:19.550 ⇒ 00:30:29.100 Amber Lin: A little bit hard because I was. I was talking to this other company. I was like, oh, you can do flip cars. They’re like. No, the development team would not like that, because it’s too hard, so I don’t know
241 00:30:29.100 ⇒ 00:30:32.020 Uttam Kumaran: Oh, no, no, not not our company. Helene will
242 00:30:32.020 ⇒ 00:30:32.820 Hannah Wang: Kaleem
243 00:30:33.607 ⇒ 00:30:36.250 Amber Lin: He can’t do anything. Okay? Yeah, yeah.
244 00:30:36.290 ⇒ 00:30:38.720 Uttam Kumaran: Yeah, yeah, yeah.
245 00:30:39.520 ⇒ 00:30:42.049 Uttam Kumaran: You should challenge him. I mean, he’ll do it like
246 00:30:42.050 ⇒ 00:30:43.320 Amber Lin: Oh dear!
247 00:30:43.320 ⇒ 00:30:44.210 Uttam Kumaran: Yeah.
248 00:30:44.400 ⇒ 00:30:52.060 Amber Lin: I mean, when I read this tooling strategy for data, audit features and tool recommendations for AI audit. That’s repetitive.
249 00:30:52.540 ⇒ 00:30:53.560 Amber Lin: So
250 00:30:53.680 ⇒ 00:31:05.539 Amber Lin: I guess we can have less bullet points. But I suppose what the thing that we’re aiming for is to have data and AI audit separate in the deck is that our goal
251 00:31:09.350 ⇒ 00:31:10.510 Hannah Wang: Hmm.
252 00:31:11.120 ⇒ 00:31:12.009 Uttam Kumaran: I can’t walk
253 00:31:12.010 ⇒ 00:31:12.680 Luke Daque: Just
254 00:31:12.680 ⇒ 00:31:21.819 Uttam Kumaran: Yeah. Well, so the so the kind of the way we could do this is one i i kind of want one cohesive we could do. We could do also pages
255 00:31:22.140 ⇒ 00:31:23.839 Uttam Kumaran: where it it split up
256 00:31:24.550 ⇒ 00:31:30.729 Uttam Kumaran: but for the site. And if like, if I was to send this to anybody and be like. This is what we do.
257 00:31:31.070 ⇒ 00:31:35.969 Uttam Kumaran: We want to sort of show this. This isn’t the end of the world to like get it perfect. But
258 00:31:36.400 ⇒ 00:31:42.039 Uttam Kumaran: I do want to start to align what our services are across both, and how we come across
259 00:31:51.080 ⇒ 00:32:02.039 Hannah Wang: Yeah, I mean, cause the easiest way right would be to have 6 of these kind of similar to the website. But that kind of defeats, the purpose of like wanting to merge data. And I, AI together.
260 00:32:05.120 ⇒ 00:32:13.869 Hannah Wang: or make make it clear that it’s like it’s like A, it’s not a super separate type of entity like our company does everything basically
261 00:32:13.870 ⇒ 00:32:18.259 Amber Lin: I mean, can we look at the the other page again?
262 00:32:19.420 ⇒ 00:32:22.820 Amber Lin: No, the one that has data in?
263 00:32:22.820 ⇒ 00:32:23.500 Amber Lin: Yes.
264 00:32:23.500 ⇒ 00:32:41.809 Amber Lin: yeah. So, looking at the middle one. We have slack connect for communications and weekly project meetings. These 2, if we want to make it more concise, these 2 can just be at the top, and then we’ll have 2 separate little cards for AI and data features. And that way
265 00:32:42.090 ⇒ 00:32:43.030 Amber Lin: it’s
266 00:32:43.260 ⇒ 00:32:57.529 Amber Lin: we sort deliver the message that we do do this comprehensively it just if you want data, we have certain things that’s different. So so in that way, we’ll be on track to merging things together
267 00:32:58.860 ⇒ 00:33:05.879 Hannah Wang: Yeah, I did think about that, you know. It’s like repetitive and you could like, take it out and stuff. But
268 00:33:06.520 ⇒ 00:33:11.279 Hannah Wang: like, let me. Just try to quickly. There’s no way to quickly do it, but it just
269 00:33:12.070 ⇒ 00:33:16.709 Hannah Wang: to me like, maybe it’s the way the cards are
270 00:33:17.850 ⇒ 00:33:21.069 Hannah Wang: designed, or something like that. But
271 00:33:21.550 ⇒ 00:33:26.890 Hannah Wang: I don’t know. Does this look any neater or less like chaotic?
272 00:33:28.160 ⇒ 00:33:29.250 Hannah Wang: Let’s see.
273 00:33:33.000 ⇒ 00:33:35.410 Hannah Wang: something like this, right? Is what you were thinking
274 00:33:35.410 ⇒ 00:33:38.010 Amber Lin: Yeah, would this feel?
275 00:33:38.520 ⇒ 00:33:41.870 Amber Lin: Would this feel like we’re offering it?
276 00:33:42.260 ⇒ 00:33:47.539 Amber Lin: It would feel like we’re doing everything since we have bullet points that applies to both of them
277 00:33:48.240 ⇒ 00:33:49.899 Uttam Kumaran: No, I kind of like this.
278 00:33:51.130 ⇒ 00:33:53.729 Uttam Kumaran: I mean what I what I, what I really
279 00:33:54.040 ⇒ 00:33:56.689 Uttam Kumaran: like, what I don’t like is
280 00:33:57.020 ⇒ 00:33:59.710 Uttam Kumaran: all this stuff that I probably helped right
281 00:34:00.126 ⇒ 00:34:00.960 Hannah Wang: You know.
282 00:34:00.960 ⇒ 00:34:04.009 Uttam Kumaran: But I do like sort of
283 00:34:04.350 ⇒ 00:34:09.739 Uttam Kumaran: stacking these up, and then even here we could get stacked up, and
284 00:34:10.489 ⇒ 00:34:12.870 Uttam Kumaran: you know it’s like this is great
285 00:34:13.060 ⇒ 00:34:20.210 Uttam Kumaran: preferred vendor pricing would show up here, and then you can say, plus everything that you get to the left, or something. You know
286 00:34:20.480 ⇒ 00:34:21.190 Amber Lin: Hmm.
287 00:34:26.679 ⇒ 00:34:28.169 Hannah Wang: I see.
288 00:34:29.239 ⇒ 00:34:38.209 Hannah Wang: Okay, yeah, we can work on an iteration where it’s kind of we abstract, or we take out the ones that are in common.
289 00:34:39.149 ⇒ 00:34:42.349 Hannah Wang: And we put it up here. It does look a little bit neater.
290 00:34:42.619 ⇒ 00:34:43.529 Hannah Wang: And
291 00:34:46.379 ⇒ 00:35:02.289 Hannah Wang: yeah, I like, I’ve been staring at this for so long that I know very clearly, like, Oh, this applies to both data and AI. But I’m just wondering from like an outsider’s perspective what they would think. So I guess? Yeah, after we design a new version, we would need input from
292 00:35:02.759 ⇒ 00:35:06.579 Hannah Wang: new eyes. But
293 00:35:10.539 ⇒ 00:35:16.499 Hannah Wang: okay, yeah, any more feedback on kind of the pricing aspect
294 00:35:16.919 ⇒ 00:35:19.429 Hannah Wang: of all our designs and assets.
295 00:35:23.619 ⇒ 00:35:36.649 Hannah Wang: Alrighty and this is just the FAQ, I feel like it’s pretty straightforward. In standard. Yeah, I need to work on the copy here. But
296 00:35:36.859 ⇒ 00:35:39.299 Hannah Wang: for the most part I think it’s fine.
297 00:35:42.902 ⇒ 00:35:48.429 Hannah Wang: Okay, next we can, I guess, go through the
298 00:35:48.919 ⇒ 00:35:52.148 Hannah Wang: deck it’s a lot of slides.
299 00:35:52.929 ⇒ 00:35:56.469 Hannah Wang: so not sure. The best way to go through this with you guys. But
300 00:35:57.569 ⇒ 00:36:00.339 Hannah Wang: yeah, this is more of like the visual
301 00:36:01.029 ⇒ 00:36:06.259 Hannah Wang: asset that we have versus the one. Pagers are more text forward. So
302 00:36:06.629 ⇒ 00:36:08.639 Hannah Wang: yeah, just going through it. It’s just like.
303 00:36:10.829 ⇒ 00:36:16.849 Hannah Wang: yeah, I don’t know the best way to go through this. I don’t wanna just like click through each one of these and then wait for you guys to give
304 00:36:16.959 ⇒ 00:36:20.539 Hannah Wang: feedback. Maybe that is the best way, but
305 00:36:21.129 ⇒ 00:36:24.849 Hannah Wang: anyone have any suggestions on how to. For the last 10 min
306 00:36:24.850 ⇒ 00:36:31.009 Uttam Kumaran: You could. You could you could probably send it to everybody. Say, take 5 min, click through it.
307 00:36:33.670 ⇒ 00:36:35.219 Uttam Kumaran: Then come back.
308 00:36:35.220 ⇒ 00:36:40.760 Uttam Kumaran: Just think about the worst thing that you see in here, or the thing you’re most concerned with, and then let’s discuss. Yeah.
309 00:36:41.390 ⇒ 00:36:47.569 Hannah Wang: Alright. Yeah. I sent the link. So take the next 5 or so minutes, and then can go through it
310 00:36:49.280 ⇒ 00:36:50.649 Uttam Kumaran: Silence is good.
311 00:36:51.760 ⇒ 00:36:53.300 Hannah Wang: I don’t like silence, but
312 00:36:53.549 ⇒ 00:36:58.279 Uttam Kumaran: Oh, I can keep talking, I mean, I can talk about lunch, or whatever you want to talk about.
313 00:36:59.770 ⇒ 00:37:04.479 Hannah Wang: Or I’m like bad. With awkward silences in like a conversation I’d like to fill
314 00:37:04.480 ⇒ 00:37:17.049 Uttam Kumaran: You know, you know, it was really funny. Today we me and Robert, when a sales call, and it’s such a dance because I don’t know like I if you ask me like, I’m pretty honest with like
315 00:37:17.480 ⇒ 00:37:22.379 Uttam Kumaran: how to negotiate stuff, but I’m not like the best at it, so I kind of like defer to Robert. But
316 00:37:22.590 ⇒ 00:37:34.499 Uttam Kumaran: there was like a 15 second like no talking, and it was so painful you would have literally left your computer like it was so well, it was sort of like.
317 00:37:34.930 ⇒ 00:37:40.850 Uttam Kumaran: So let me just go ahead and tell back what we’re hearing. And then we said that, and then it was like nothing.
318 00:37:41.040 ⇒ 00:37:45.929 Uttam Kumaran: And you know there’s like a lot of money on the line. I’m like, this is crazy
319 00:37:47.020 ⇒ 00:37:52.399 Hannah Wang: That’s why I hate silence. It’s any situation. It’s just awkward.
320 00:37:53.750 ⇒ 00:37:57.120 Hannah Wang: for sure. We can test our ability to be quiet
321 00:37:57.715 ⇒ 00:38:00.690 Uttam Kumaran: Don’t have any music, but
322 00:38:02.344 ⇒ 00:38:09.839 Aakash Tandel: One thing that I’m seeing is the the challenge color for that button is like red, and it’s very
323 00:38:10.080 ⇒ 00:38:12.050 Aakash Tandel: sorry on slide like 15
324 00:38:12.360 ⇒ 00:38:12.940 Hannah Wang: Yeah.
325 00:38:13.200 ⇒ 00:38:16.539 Aakash Tandel: It’s very different. I don’t know like it just feels
326 00:38:18.440 ⇒ 00:38:20.710 Hannah Wang: Yeah, it’s too red.
327 00:38:22.060 ⇒ 00:38:33.109 Hannah Wang: Let’s see if this color makes a difference. I mean, we want the challenge to stand out right? So I think that was kind of the point, but let me see if making it a more muted red would be.
328 00:38:34.080 ⇒ 00:38:38.709 Hannah Wang: Oh, it’s not good on a green background. Is that better?
329 00:38:43.271 ⇒ 00:38:46.810 Aakash Tandel: Yeah, I I honestly shouldn’t be making these decisions. But like
330 00:38:47.133 ⇒ 00:38:47.779 Hannah Wang: I just
331 00:38:47.780 ⇒ 00:38:49.699 Aakash Tandel: The other one was very bright.
332 00:38:49.700 ⇒ 00:38:50.300 Hannah Wang: Yeah.
333 00:38:50.450 ⇒ 00:38:52.849 Aakash Tandel: Kind of Christmassy with the green and red. But yeah.
334 00:38:53.284 ⇒ 00:38:59.369 Hannah Wang: Christmas. Yeah. Why do? Why do these colors have to be associated with Christmas?
335 00:39:05.710 ⇒ 00:39:06.200 Hannah Wang: alright!
336 00:39:08.810 ⇒ 00:39:12.310 Aakash Tandel: The other visualization. I’m thinking that might
337 00:39:12.920 ⇒ 00:39:20.560 Aakash Tandel: be. I guess it’s similar to the 5th slide of the maxing. Your data potential is like a data maturity
338 00:39:22.200 ⇒ 00:39:44.580 Aakash Tandel: visualization like, Hey, how far you like? I’m trying to think of how we loop like AI into like the data stuff. And like, if they don’t have like data or a data warehouse and stuff like that, like the AI piece is pretty far off. So showing those 2 things on a like almost like a roadmap. And like, Hey, let’s get your data set up here. So like in the future, we can get you an AI
339 00:39:45.640 ⇒ 00:39:48.339 Aakash Tandel: Ish application involved in your stack
340 00:39:52.820 ⇒ 00:40:02.969 Uttam Kumaran: So something about how do we? How do we make this include AI stuff? Yeah, I don’t know. Probably need your help. Amber on this as well like, are you maximizing your AI potential or
341 00:40:03.170 ⇒ 00:40:07.200 Uttam Kumaran: automation potential? We probably need, like a corollary slide
342 00:40:07.850 ⇒ 00:40:14.680 Amber Lin: Hmm, okay, I’m just going through all of them. Let’s leave a comment there to
343 00:40:14.880 ⇒ 00:40:17.899 Amber Lin: add, okay, we’ve done that
344 00:40:18.330 ⇒ 00:40:19.299 Hannah Wang: I’ll tag you in it.
345 00:40:19.300 ⇒ 00:40:22.119 Hannah Wang: Oh, I am still on slide
346 00:40:23.390 ⇒ 00:40:24.060 Amber Lin: T
347 00:40:32.870 ⇒ 00:40:35.859 Hannah Wang: Yeah, I guess a lot of these are like data data.
348 00:40:36.100 ⇒ 00:40:43.570 Hannah Wang: heavy like, even with this unsure where to begin with data, it’s like we want to include AI in it, too. Somehow.
349 00:40:45.060 ⇒ 00:40:48.780 Luke Daque: I will just ensure where to begin with data. And AI
350 00:40:49.960 ⇒ 00:40:52.240 Hannah Wang: Yeah, it’s possible. It’s just
351 00:40:53.320 ⇒ 00:40:54.310 Luke Daque: Needs to do it like that
352 00:40:55.520 ⇒ 00:40:58.600 Hannah Wang: Maybe let’s see.
353 00:41:03.120 ⇒ 00:41:05.430 Amber Lin: I don’t like the last slide
354 00:41:05.610 ⇒ 00:41:07.980 Amber Lin: with a big B in the back
355 00:41:08.460 ⇒ 00:41:09.669 Hannah Wang: You like it.
356 00:41:09.670 ⇒ 00:41:11.300 Amber Lin: Yeah, I do. Oh.
357 00:41:11.300 ⇒ 00:41:12.500 Hannah Wang: I love that be
358 00:41:12.500 ⇒ 00:41:17.289 Amber Lin: Me, too. It fades in. It’s like it looks really good. Yeah.
359 00:41:18.420 ⇒ 00:41:24.710 Amber Lin: Oh, on slide 2. Are we still 15 experts? I really feel like we’re bigger than that
360 00:41:27.430 ⇒ 00:41:29.409 Uttam Kumaran: Someone tell me I don’t know.
361 00:41:29.740 ⇒ 00:41:32.829 Amber Lin: You tell me I’m asking you
362 00:41:32.830 ⇒ 00:41:37.540 Uttam Kumaran: No, I mean, I these are all just know whatever looks good. Yeah, if it’s 15 or 20
363 00:41:37.540 ⇒ 00:41:39.730 Amber Lin: Yeah, I feel like if they save me like 6
364 00:41:39.730 ⇒ 00:41:42.970 Uttam Kumaran: Team, though, like that’s a weird. It’s like not a round number, you know.
365 00:41:43.540 ⇒ 00:41:45.620 Hannah Wang: I thought, we’re at 20
366 00:41:46.075 ⇒ 00:41:47.439 Uttam Kumaran: Say 2020,
367 00:41:47.860 ⇒ 00:41:48.870 Amber Lin: Say 20,
368 00:41:49.240 ⇒ 00:41:49.899 Hannah Wang: Oh no!
369 00:41:50.212 ⇒ 00:41:54.579 Uttam Kumaran: It just can’t be like a intermediate number, you know. So I just
370 00:41:54.580 ⇒ 00:41:55.639 Amber Lin: I know.
371 00:41:55.640 ⇒ 00:41:58.340 Uttam Kumaran: Maybe when we made this slide 2 weeks ago. Yeah, we were there
372 00:41:58.693 ⇒ 00:42:01.520 Luke Daque: Like 17.32 experts like that
373 00:42:05.060 ⇒ 00:42:09.150 Amber Lin: Okay, how do I resolve this comment?
374 00:42:09.150 ⇒ 00:42:10.739 Hannah Wang: I got it I got it.
375 00:42:10.740 ⇒ 00:42:11.670 Amber Lin: Okay?
376 00:42:13.950 ⇒ 00:42:19.560 Amber Lin: And do we want? I like, I like Utam’s green background photo.
377 00:42:19.720 ⇒ 00:42:24.820 Amber Lin: When I 1st saw it, I was like, it’s so brain force color themed. Now he’s blackable
378 00:42:24.820 ⇒ 00:42:25.510 Uttam Kumaran: One.
379 00:42:25.860 ⇒ 00:42:28.460 Amber Lin: Yeah. Your photo on the second slide
380 00:42:28.830 ⇒ 00:42:31.790 Uttam Kumaran: Oh, yeah, it’s nice. I like, this is, I love. This slide
381 00:42:32.190 ⇒ 00:42:35.740 Uttam Kumaran: shows that like we’ve done so much stuff like it’s kind of crazy
382 00:42:37.270 ⇒ 00:42:40.040 Hannah Wang: Wait, Amber, are you saying you like the colored version more
383 00:42:40.040 ⇒ 00:42:43.460 Amber Lin: I like this color version because his background was green.
384 00:42:45.090 ⇒ 00:42:46.690 Hannah Wang: Oh, yeah.
385 00:42:46.690 ⇒ 00:42:50.087 Uttam Kumaran: No, that’s just real, though I was in front of like I was at a wedding
386 00:42:50.300 ⇒ 00:42:51.040 Hannah Wang: Oh!
387 00:42:51.540 ⇒ 00:42:52.540 Amber Lin: So funny.
388 00:42:52.540 ⇒ 00:42:56.359 Uttam Kumaran: That’s yeah, Amber. We need all we all need headshots soon.
389 00:42:56.360 ⇒ 00:42:59.249 Amber Lin: Yes, yes, yes, I will. I will bring my setup
390 00:42:59.250 ⇒ 00:43:04.270 Uttam Kumaran: There’s some people on the team that really don’t want to send me pictures of like them.
391 00:43:04.270 ⇒ 00:43:05.190 Amber Lin: I know.
392 00:43:05.190 ⇒ 00:43:08.783 Uttam Kumaran: Normal. And I’m like, like Miguel’s photo. He looks like a CIA agent
393 00:43:10.160 ⇒ 00:43:11.030 Demilade Agboola: To be fair, my
394 00:43:12.310 ⇒ 00:43:12.800 Uttam Kumaran: Huh!
395 00:43:12.800 ⇒ 00:43:15.329 Demilade Agboola: My head just is from like, 6 years ago.
396 00:43:15.490 ⇒ 00:43:16.550 Uttam Kumaran: Oh, really
397 00:43:18.600 ⇒ 00:43:25.199 Uttam Kumaran: great dude like your the color you you literally look like an like an artist like an R. And B. Artist.
398 00:43:25.200 ⇒ 00:43:32.698 Demilade Agboola: Yeah, but like you can tell from the hair that it was a really long time ago.
399 00:43:33.140 ⇒ 00:43:34.200 Uttam Kumaran: True.
400 00:43:34.740 ⇒ 00:43:36.289 Demilade Agboola: Yeah, my hair’s much longer now.
401 00:43:39.040 ⇒ 00:43:52.539 Demilade Agboola: but yeah. And even then it was literally a thing of it was like a surprise present for like for me, and that’s that’s how I did it then. I don’t really take pictures that much so
402 00:43:55.700 ⇒ 00:43:59.259 Amber Lin: Or if you come to La, I will take photos for you
403 00:44:00.252 ⇒ 00:44:04.239 Demilade Agboola: See, Pius has invited me to. La! You’re inviting me to la!
404 00:44:04.620 ⇒ 00:44:07.679 Demilade Agboola: Well, Tom has invited me like when, like, whenever
405 00:44:07.680 ⇒ 00:44:13.186 Uttam Kumaran: Everyone can come, stay at my house at any time, whenever, no matter what
406 00:44:14.180 ⇒ 00:44:14.910 Hannah Wang: For everyone.
407 00:44:14.910 ⇒ 00:44:16.559 Demilade Agboola: Destinations of the map
408 00:44:16.790 ⇒ 00:44:32.529 Uttam Kumaran: Yeah, we’ll have to do. I know we’re we. We just cobbled together an LA. Trip for the folks there. So if we make some money this quarter, I’ll try to do something next quarter. We have a lot of people that need to fly in, though, so it’s not cheap
409 00:44:33.270 ⇒ 00:44:35.000 Demilade Agboola: When is the La Hangout
410 00:44:35.530 ⇒ 00:44:38.520 Uttam Kumaran: La Hangout is in 2 2 weeks or 3 weeks
411 00:44:39.360 ⇒ 00:44:40.060 Demilade Agboola: Boy.
412 00:44:40.600 ⇒ 00:44:44.879 Uttam Kumaran: I think it was. Yeah, I would. I wish I could have everyone there, but
413 00:44:45.180 ⇒ 00:44:48.457 Uttam Kumaran: I would have to close the company if I did that right now.
414 00:44:48.710 ⇒ 00:44:52.359 Demilade Agboola: I was asking cause, like I plan to be in the Us. And like
415 00:44:52.360 ⇒ 00:44:53.980 Uttam Kumaran: Yeah, are you here? Yeah.
416 00:44:54.370 ⇒ 00:44:59.447 Demilade Agboola: No, not not yet. But that’s why I said 2 weeks is a bit
417 00:45:00.460 ⇒ 00:45:06.910 Uttam Kumaran: Yeah, well, we’re just there for a weekend. But it’s yeah. It’s I don’t. It depends on how long you’re gonna be here in the States.
418 00:45:08.070 ⇒ 00:45:09.260 Demilade Agboola: Fair enough, fair enough.
419 00:45:12.020 ⇒ 00:45:13.950 Uttam Kumaran: But, dude, we’ll come see you in Malta.
420 00:45:14.860 ⇒ 00:45:16.339 Hannah Wang: Yeah, I wanna go
421 00:45:16.530 ⇒ 00:45:18.440 Uttam Kumaran: Me too how long ago.
422 00:45:20.360 ⇒ 00:45:21.700 Luke Daque: That would be awesome.
423 00:45:22.120 ⇒ 00:45:23.499 Demilade Agboola: Yeah, the weather’s pretty nice.
424 00:45:29.860 ⇒ 00:45:33.150 Hannah Wang: Alright. I know we’re over time. But
425 00:45:34.350 ⇒ 00:45:37.890 Hannah Wang: yeah, I I see comments here and there, but
426 00:45:38.260 ⇒ 00:45:44.813 Hannah Wang: doesn’t seem like there’s anything starkingly terrible. So that’s a good sign.
427 00:45:49.800 ⇒ 00:45:50.910 Aakash Tandel: Overall at the end of the
428 00:45:50.910 ⇒ 00:45:53.120 Demilade Agboola: Well. Done. Yeah. Very well done.
429 00:45:53.120 ⇒ 00:45:54.919 Amber Lin: It looks really pretty
430 00:45:55.440 ⇒ 00:45:56.769 Luke Daque: Yeah, very, pretty.
431 00:45:57.150 ⇒ 00:45:57.670 Hannah Wang: I’m
432 00:45:57.670 ⇒ 00:45:58.130 Luke Daque: I mean
433 00:45:58.130 ⇒ 00:45:59.010 Hannah Wang: I’m glad.
434 00:46:00.930 ⇒ 00:46:06.220 Hannah Wang: Yeah, that’s the feedback we got from one from Vixel right that our slides were like
435 00:46:06.570 ⇒ 00:46:08.189 Hannah Wang: the best. I guess
436 00:46:08.510 ⇒ 00:46:09.290 Uttam Kumaran: Yes.
437 00:46:09.930 ⇒ 00:46:10.540 Hannah Wang: Yeah.
438 00:46:10.870 ⇒ 00:46:20.889 Hannah Wang: So it definitely helps us stand out among other companies. So I think we’ll try to keep working on that raising the bar there
439 00:46:20.890 ⇒ 00:46:29.589 Uttam Kumaran: The next piece here is like we’re gonna start doing. We’re gonna start using the same sort of design language across architecture, diagrams.
440 00:46:29.880 ⇒ 00:46:30.760 Uttam Kumaran: roadmap.
441 00:46:31.130 ⇒ 00:46:34.940 Uttam Kumaran: And like everything we present to clients, I want to sort of have, like
442 00:46:35.080 ⇒ 00:46:46.390 Uttam Kumaran: a very beautiful presentation around. So the next step here, which I think is going to be a challenge is, how do we move like erp diagrams into stuff like this? How do we move bigger architecture diagrams?
443 00:46:46.930 ⇒ 00:46:50.699 Uttam Kumaran: Cause that’s always ends up being the ugliest stuff. So
444 00:46:52.120 ⇒ 00:47:06.290 Hannah Wang: Yeah. And and I like, started on that like a diagram redesign. I mean, it’s very, very low fidelity at this point. But that is something on our radar kind of taking this diagram that we made in fig jam and
445 00:47:06.620 ⇒ 00:47:11.880 Hannah Wang: making it prettier. So we’ll continue working on that as well.
446 00:47:13.490 ⇒ 00:47:20.690 Hannah Wang: okay. Yeah. Feel free to leave any other comments. In any of the
447 00:47:21.440 ⇒ 00:47:27.692 Hannah Wang: any of the assets that we have. But appreciate. You guys looking at this with us?
448 00:47:28.490 ⇒ 00:47:47.529 Hannah Wang: yeah, new pairs of eyes are always always welcome. So I’ll probably bother you guys later. Once there’s new versions of other assets out there but for now I think this is a good starting point for V ones, and then we’ll just make things better. For V twos and onward. So
449 00:47:48.070 ⇒ 00:48:01.699 Hannah Wang: yeah, any more feedback feel free to pm me or put it in the design channel, if you guys are in that. But other than that, I think we’re good to close here. Unless anything else is needed
450 00:48:02.070 ⇒ 00:48:06.190 Hannah Wang: to be talked about. But if not, have a good Tuesday
451 00:48:06.680 ⇒ 00:48:07.330 Uttam Kumaran: Thank you.
452 00:48:07.540 ⇒ 00:48:08.480 Luke Daque: Thanks, Aaron, thanks.
453 00:48:09.100 ⇒ 00:48:09.870 Uttam Kumaran: Okay.
454 00:48:10.260 ⇒ 00:48:10.940 Hannah Wang: Email