Meeting Title: Friday Brainforge Demos & Retro Date: 2025-04-11 Meeting participants: Mariane Cequina, Annie Yu, Luke Daque, Nicolas Sucari, Uttam Kumaran, Amber Lin, Demilade Agboola, Hannah Wang, Miguel De Veyra, Casie Aviles, Awaish Kumar
WEBVTT
1 00:00:18.710 ⇒ 00:00:19.740 Uttam Kumaran: Hey, Ryan?
2 00:00:32.740 ⇒ 00:00:35.060 Uttam Kumaran: I can’t hear you if you’re talking.
3 00:00:35.790 ⇒ 00:00:36.910 Luke Daque: Hello! How about now? Can you.
4 00:00:36.910 ⇒ 00:00:38.540 Uttam Kumaran: Oh, yeah, yeah, it’s working now.
5 00:00:38.670 ⇒ 00:00:39.480 Luke Daque: Yeah. Cool.
6 00:00:39.640 ⇒ 00:00:41.159 Luke Daque: Hello! How’s it going.
7 00:00:41.460 ⇒ 00:00:42.800 Uttam Kumaran: Good? How’s everything?
8 00:00:43.870 ⇒ 00:00:50.060 Luke Daque: Yeah, I think we’re getting good progress in pool parts, at least for the data accuracy.
9 00:00:50.540 ⇒ 00:00:52.549 Luke Daque: So yeah, I think we’re pretty good.
10 00:00:55.230 ⇒ 00:00:57.629 Uttam Kumaran: Yeah, I’ll review the Pr here in a sec.
11 00:00:57.920 ⇒ 00:01:07.150 Luke Daque: Sure there’s still a couple of things we need to confirm with Stephen and
12 00:01:07.710 ⇒ 00:01:14.890 Luke Daque: and stuff or Amazon fees. This is like getting fees from stuff. I don’t. I’m I’m not quite sure.
13 00:01:15.390 ⇒ 00:01:16.450 Uttam Kumaran: Oh, really.
14 00:01:20.760 ⇒ 00:01:27.339 Uttam Kumaran: yeah. And I just I just landed in la, so I’ll be meeting with the team today.
15 00:01:27.840 ⇒ 00:01:28.630 Luke Daque: Nice.
16 00:01:28.920 ⇒ 00:01:30.925 Luke Daque: That’s good. That’s gonna be fun.
17 00:01:38.190 ⇒ 00:01:43.649 Uttam Kumaran: Yeah, so I’m excited. Well, me just me meeting with some of the la folks this weekend.
18 00:01:47.690 ⇒ 00:01:48.430 Luke Daque: Cool.
19 00:02:07.250 ⇒ 00:02:08.050 Uttam Kumaran: Hey! How are you?
20 00:02:09.270 ⇒ 00:02:10.710 Awaish Kumar: I’m good. How are you?
21 00:02:11.030 ⇒ 00:02:11.910 Uttam Kumaran: Good.
22 00:02:12.590 ⇒ 00:02:14.170 Awaish Kumar: How has been your week?
23 00:02:15.210 ⇒ 00:02:17.601 Uttam Kumaran: Week has been really, really great.
24 00:02:19.600 ⇒ 00:02:24.040 Uttam Kumaran: it’s like it’s we did a lot. We did a lot of really great sales stuff this week.
25 00:02:24.210 ⇒ 00:02:27.590 Uttam Kumaran: I spoke at a at a presentation yesterday.
26 00:02:28.280 ⇒ 00:02:31.650 Uttam Kumaran: And we’re gonna get probably 5 or 6 leads out of that.
27 00:02:32.710 ⇒ 00:02:33.390 Luke Daque: It’s.
28 00:02:33.390 ⇒ 00:02:39.889 Uttam Kumaran: And then next week Robert and I are speaking at a conference presenting some of our work with our ABC client.
29 00:02:39.990 ⇒ 00:02:43.389 Uttam Kumaran: And so, yeah, it’s a it’s going. Well.
30 00:02:45.610 ⇒ 00:02:47.529 Awaish Kumar: Hey? That’s that’s exciting!
31 00:02:47.960 ⇒ 00:02:48.560 Uttam Kumaran: Yeah.
32 00:02:48.840 ⇒ 00:02:49.400 Uttam Kumaran: Thank you.
33 00:03:00.530 ⇒ 00:03:05.100 Uttam Kumaran: Yeah. I presented on a little bit of a topic on like
34 00:03:05.600 ⇒ 00:03:08.160 Uttam Kumaran: how to use AI in in consulting
35 00:03:08.605 ⇒ 00:03:23.489 Uttam Kumaran: and it was a bunch of other like consulting leaders. And yeah, feedback was really good. It sort of talked about the different ways that we’re using AI, that we have, like AI agents, and slack and sort of thinking through how to use AI to help. You know, everyone here become more efficient.
36 00:03:23.988 ⇒ 00:03:28.899 Uttam Kumaran: And I, you know a lot of people aren’t using it at all. And so I think people are
37 00:03:29.180 ⇒ 00:03:32.140 Uttam Kumaran: are. We’re excited to hear that. And
38 00:03:32.310 ⇒ 00:03:36.280 Uttam Kumaran: I think some people are going to call us and ask us to help them implement. AI. So
39 00:03:36.430 ⇒ 00:03:46.049 Uttam Kumaran: I’m really excited and talked to a lot of you about data as well. So there were a lot of like people in healthcare and and different sort of businesses. So it was a nice. It was a nice event yesterday.
40 00:04:01.532 ⇒ 00:04:04.960 Uttam Kumaran: Cool. I don’t know amber. I know we’re talking is
41 00:04:05.280 ⇒ 00:04:07.880 Uttam Kumaran: any code in the share, or I don’t know.
42 00:04:27.260 ⇒ 00:04:29.450 Uttam Kumaran: Hi Amber. We can’t hear you if you’re talking.
43 00:04:30.020 ⇒ 00:04:37.339 Amber Lin: Hello! Sorry I realized it didn’t join the audio. I was like, why is nobody talking? What was the question for me?
44 00:04:38.986 ⇒ 00:04:46.923 Uttam Kumaran: I am just getting to my laptop. I was wondering if you were gonna share. If Nico can share and maybe we can just jump right into the AI stuff.
45 00:04:47.180 ⇒ 00:04:47.550 Amber Lin: Sure.
46 00:04:47.550 ⇒ 00:04:52.170 Uttam Kumaran: And then I have a couple of things. I’ll I’ll pop in. I’ll pop in later and and share some stuff.
47 00:04:52.770 ⇒ 00:05:00.580 Amber Lin: Yeah, I mean, Casey, do you want to share? You guys did it? I don’t want to take the credit. So I want you guys to share what you did.
48 00:05:02.110 ⇒ 00:05:04.230 Casie Aviles: Yeah, sure. So yeah.
49 00:05:08.161 ⇒ 00:05:11.680 Casie Aviles: Well, one of the things that we worked on was
50 00:05:12.360 ⇒ 00:05:19.119 Casie Aviles: earlier in this in the week is the Gpt 4 0 endpoint. So we just I think we talked about this last week.
51 00:05:19.660 ⇒ 00:05:22.290 Casie Aviles: So basically, what we did is just
52 00:05:22.670 ⇒ 00:05:28.860 Casie Aviles: have our Api exposed. So yeah, people could. Yeah, you could go here to AI show and tell, and just
53 00:05:29.843 ⇒ 00:05:32.319 Casie Aviles: test it out. So it’s pretty straightforward.
54 00:05:34.126 ⇒ 00:05:36.814 Casie Aviles: Yeah, I mean? Yeah, that’s it for the
55 00:05:37.350 ⇒ 00:05:40.859 Casie Aviles: agent. But I guess another one that we worked on was
56 00:05:42.196 ⇒ 00:05:44.410 Casie Aviles: sorry. Let me just look for that.
57 00:05:44.990 ⇒ 00:05:55.229 Casie Aviles: I think, for Javi, so that this is part of our data roadmap and one of our one of the quick wins that we wanted to work on, which is
58 00:05:55.620 ⇒ 00:06:02.300 Casie Aviles: basically to address the pain point, where sometimes there are some ad hoc tasks or requests from the client that would
59 00:06:03.960 ⇒ 00:06:07.019 Casie Aviles: yeah, that people won’t catch or people would forget. So
60 00:06:07.390 ⇒ 00:06:14.530 Casie Aviles: that’s the whole idea behind this. So we basically, this pulls from data from slack and
61 00:06:14.820 ⇒ 00:06:21.619 Casie Aviles: also Zoom Meetings. So your daily stand up. So I think the
62 00:06:21.860 ⇒ 00:06:26.390 Casie Aviles: this message should be in the client, Java Hub.
63 00:06:26.680 ⇒ 00:06:30.280 Casie Aviles: Oh, wait. Sorry. I mean Glen Javi Coffee Channel. Yeah.
64 00:06:30.960 ⇒ 00:06:38.559 Casie Aviles: But yeah, I guess that’s what we work on. And there’s also something that Miguel worked on, which is
65 00:06:38.900 ⇒ 00:06:41.329 Casie Aviles: the sales. But I don’t, really.
66 00:06:41.700 ⇒ 00:06:45.550 Casie Aviles: I yeah, I don’t have much to demo on that yet.
67 00:06:46.150 ⇒ 00:06:51.840 Amber Lin: Yeah, I can take. I can take that if we go to the bye.
68 00:06:52.230 ⇒ 00:07:03.080 Amber Lin: see if I can find where it is. So essentially what we did for the sales team is that the main pain point was, we have a lot of leads that
69 00:07:03.330 ⇒ 00:07:14.960 Amber Lin: we need to circle back on that have expressed interest in the past or it was just not the right time, but they do might want to work with us. So what we did is
70 00:07:15.200 ⇒ 00:07:30.659 Amber Lin: as a 1st step we got all the information from notion. And now, in slack, we’ll send out a reminder every week to say, Okay, these are the leads that we might wanna circle back on. And once we get more detailed context
71 00:07:31.400 ⇒ 00:07:53.589 Amber Lin: from Robert on what each of the leads are, and also what kind of follow up messages we want to send. We’ll be able to also prompt. Hey? Here’s a few messages you might. You might want to select from, and eventually, as an ultimate phase, we might want to automate sending the messages as well. But for now this is the 1st step in the process.
72 00:07:55.660 ⇒ 00:07:56.770 Amber Lin: Yeah, thank you.
73 00:07:58.590 ⇒ 00:08:01.860 Nicolas Sucari: If anyone wants to see, we’re sending this message.
74 00:08:02.000 ⇒ 00:08:09.230 Nicolas Sucari: And what Amber said is pretty accurate. And yeah, this is getting to Robert and salespeople that need it
75 00:08:14.580 ⇒ 00:08:15.330 Nicolas Sucari: cool.
76 00:08:16.080 ⇒ 00:08:21.499 Nicolas Sucari: This is a chat gpt 4.0 that Casey just shared about that.
77 00:08:21.790 ⇒ 00:08:24.539 Nicolas Sucari: and we have the one for
78 00:08:24.980 ⇒ 00:08:29.620 Nicolas Sucari: Job BI don’t know how to go back on these here.
79 00:08:29.880 ⇒ 00:08:30.650 Nicolas Sucari: Yeah.
80 00:08:34.850 ⇒ 00:08:35.620 Nicolas Sucari: cool
81 00:08:39.640 ⇒ 00:08:41.530 Nicolas Sucari: any other thing.
82 00:08:42.120 ⇒ 00:08:43.110 Nicolas Sucari: Hey? Team.
83 00:08:48.828 ⇒ 00:08:51.940 Demilade Agboola: I have a question about the the model.
84 00:08:53.150 ⇒ 00:09:01.460 Demilade Agboola: if, like, maybe someone is out out of office for like a week and you come back. Can you ask like, Hey, what’s happened over the past week?
85 00:09:02.180 ⇒ 00:09:04.240 Demilade Agboola: Will they be able to give like context.
86 00:09:05.160 ⇒ 00:09:06.910 Casie Aviles: Oh, for the client. Hub.
87 00:09:07.640 ⇒ 00:09:12.329 Casie Aviles: Yeah, that should. You could also tag it and ask it some questions.
88 00:09:15.950 ⇒ 00:09:24.039 Nicolas Sucari: I mean the I think. Yes, you can try that. Casey. The sources for the client hubs is slack.
89 00:09:24.160 ⇒ 00:09:27.189 Nicolas Sucari: I guess, like Github notion, right
90 00:09:27.340 ⇒ 00:09:31.430 Nicolas Sucari: and Snowflake. Those are the sources. Am I correct?
91 00:09:32.431 ⇒ 00:09:37.650 Casie Aviles: Yes, slack and zoom are are zoom transcripts. And yeah, and also the.
92 00:09:38.340 ⇒ 00:09:38.890 Nicolas Sucari: So
93 00:09:38.890 ⇒ 00:10:00.900 Nicolas Sucari: if if you ask that, yeah, the the AI board should be able to check like previous messages that are on slack or meeting transcripts from the previous week and give yeah, an update. We can try that and see how it goes. If you want to go ahead and do it. Just go ahead and share any feedback that you find out.
94 00:10:01.800 ⇒ 00:10:11.279 Demilade Agboola: Okay, it’s pretty. I I mean, I think it’s pretty cool, especially if people like maybe go out of office, or if something has happened, and so on, hasn’t been around for a bit of time.
95 00:10:11.380 ⇒ 00:10:17.260 Demilade Agboola: It’ll allow them to be able to figure out what what has happened quite quickly, so.
96 00:10:23.550 ⇒ 00:10:24.090 Nicolas Sucari: Yep
97 00:10:27.900 ⇒ 00:10:37.909 Nicolas Sucari: cool. What else? I don’t know. I I joined like 2 min late, and what I was already talking, so I didn’t hear about that. But do we have anything else to review today?
98 00:10:39.040 ⇒ 00:10:42.360 Nicolas Sucari: I think there is a lot on the sales side, but I don’t know if Robert is here.
99 00:10:50.280 ⇒ 00:10:51.430 Nicolas Sucari: or anyone is here.
100 00:10:51.690 ⇒ 00:10:53.310 Uttam Kumaran: Yeah. Nico, one second. Hold on.
101 00:10:53.310 ⇒ 00:10:55.070 Nicolas Sucari: Yeah. Sorry. Okay.
102 00:11:14.980 ⇒ 00:11:16.619 Hannah Wang: Hello! Click! On.
103 00:11:16.620 ⇒ 00:11:17.400 Nicolas Sucari: Nice.
104 00:11:17.400 ⇒ 00:11:19.090 Amber Lin: Wow. Okay.
105 00:11:19.090 ⇒ 00:11:20.920 Luke Daque: Oh, wow! Hey, guys.
106 00:11:20.920 ⇒ 00:11:23.649 Hannah Wang: Click on the view on the right.
107 00:11:24.440 ⇒ 00:11:29.130 Hannah Wang: and then go show something. Yeah. There we are.
108 00:11:29.750 ⇒ 00:11:31.060 Hannah Wang: Hi! Everyone.
109 00:11:31.760 ⇒ 00:11:33.060 Amber Lin: Hi.
110 00:11:33.700 ⇒ 00:11:34.760 Demilade Agboola: Hi.
111 00:11:35.730 ⇒ 00:11:39.739 Hannah Wang: Sorry. That’s why I was like I just was in the uber. And then I had to get out of the Uber.
112 00:11:39.940 ⇒ 00:11:44.430 Hannah Wang: and then we ran out of time. But yeah, we’re a couple of us are in la this week
113 00:11:44.570 ⇒ 00:11:50.619 Hannah Wang: to meet up, which is really really nice. And yeah, I guess
114 00:11:50.800 ⇒ 00:11:56.453 Hannah Wang: we can talk a little bit. We spoke a little bit about the AI work. I think the biggest thing amber on
115 00:11:57.710 ⇒ 00:12:02.719 Hannah Wang: some of those pieces is we just wanna do. Did you guys mention the AI office hours stuff that we’re planning?
116 00:12:03.156 ⇒ 00:12:05.560 Hannah Wang: Basically, I think we have a lot of these.
117 00:12:06.330 ⇒ 00:12:07.560 Hannah Wang: Wait, say it again.
118 00:12:07.770 ⇒ 00:12:11.382 Amber Lin: No, not yet. Casey sent out the invite. At least I got it.
119 00:12:11.660 ⇒ 00:12:11.980 Amber Lin: Okay.
120 00:12:12.310 ⇒ 00:12:12.830 Amber Lin: So.
121 00:12:12.830 ⇒ 00:12:36.150 Hannah Wang: Yeah, one of the things that I was telling the AI team about is that we have all these helpful agents in slack. But just we wanna share how to use them and sort of have an open conversation with people who want to test those out. I I read all the zoom. I read all the transcript summaries. It’s just helpful in case I need to do something or miss something, so I find it really helpful.
122 00:12:36.780 ⇒ 00:12:43.130 Hannah Wang: But we also want feedback from everybody on how to make that a lot better. So
123 00:12:43.600 ⇒ 00:12:47.495 Hannah Wang: I think if everyone can make that, that’d be really really great.
124 00:12:48.180 ⇒ 00:12:53.170 Hannah Wang: yeah, I have a couple of things to share. But maybe one item
125 00:12:53.625 ⇒ 00:13:02.090 Hannah Wang: I wanna share is, we actually had an opportunity to do a a presentation yesterday in Austin.
126 00:13:02.550 ⇒ 00:13:06.070 Hannah Wang: And I actually have some pictures. Maybe I can share them.
127 00:13:08.200 ⇒ 00:13:11.030 Hannah Wang: Hannah, can I airdrop these to you? Maybe. Yeah,
128 00:13:15.540 ⇒ 00:13:18.539 Hannah Wang: I’ll just airdrop some photos. But this was like a huge
129 00:13:18.950 ⇒ 00:13:23.169 Hannah Wang: undertaking from the marketing team, from the marketing team.
130 00:13:23.754 ⇒ 00:13:29.140 Hannah Wang: On getting us prepared to do promotions. And let me just grab.
131 00:13:29.990 ⇒ 00:13:32.110 Hannah Wang: Just grab these and they load.
132 00:13:47.210 ⇒ 00:13:55.079 Hannah Wang: Maybe the Wi-fi he just got here. So I look like he just walked in. Sorry. Okay.
133 00:13:55.840 ⇒ 00:14:12.859 Hannah Wang: so we we, we did a presentation in Austin for about like, almost 20 different consulting leaders last night. With the CEO of operating which I know the the operations and project teams, you guys know. So Lori is in Austin.
134 00:14:13.615 ⇒ 00:14:14.270 Hannah Wang: and
135 00:14:14.450 ⇒ 00:14:30.140 Hannah Wang: I I met up with him, and then we we presented. We sort of had everyone to this like wine cellar, and they did a wine tasting. And then he like asked me a bunch of questions about how we use AI. All I did was share, like, basically everything. We just went through where I just talked about sort of the stuff. We’re trying
136 00:14:30.731 ⇒ 00:14:37.579 Hannah Wang: the different tools we’re we’re building and there was like, really, really great reception. I think a lot of people really.
137 00:14:37.984 ⇒ 00:15:01.979 Hannah Wang: We’re interested in in how we do things. And I think, thank you. They were interested in in us, potentially helping them do that which is the number one goal? And so what was really really great about that event in in particular, is we basically walk through like how we think about our team. How we think about becoming like more AI enabled overall. And if I can just share
138 00:15:02.170 ⇒ 00:15:03.540 Hannah Wang: it’s gonna load it all.
139 00:15:05.370 ⇒ 00:15:06.460 Hannah Wang: Let’s see.
140 00:15:13.610 ⇒ 00:15:17.839 Nicolas Sucari: Also, I guess the reception after some bottles of wine is is great. Yeah.
141 00:15:18.850 ⇒ 00:15:20.330 Hannah Wang: Yeah, right.
142 00:15:20.660 ⇒ 00:15:20.990 Nicolas Sucari: Yeah.
143 00:15:20.990 ⇒ 00:15:22.710 Hannah Wang: Say that one more say that one more time.
144 00:15:23.180 ⇒ 00:15:30.550 Nicolas Sucari: That after people after tasting wine and having a couple of bottles, I think the reception is gonna be great right.
145 00:15:30.898 ⇒ 00:15:37.050 Hannah Wang: Yeah, I guess people will listen to anything at that point. That’s a nice strategy. Yes.
146 00:15:37.050 ⇒ 00:15:39.790 Hannah Wang: I can’t get. It’s I can’t get these pictures.
147 00:15:40.130 ⇒ 00:15:44.880 Hannah Wang: you know, quite off my phone, but maybe I’ll I’ll hold them up to the camera. I don’t know. Sorry. My like
148 00:15:45.280 ⇒ 00:15:47.370 Hannah Wang: my thing is, is loading.
149 00:15:49.030 ⇒ 00:15:50.020 Amber Lin: Too.
150 00:15:50.330 ⇒ 00:15:54.840 Hannah Wang: Yeah, it’s just like, just got them sent to me. But like this is this was like
151 00:15:55.300 ⇒ 00:15:56.450 Hannah Wang: the wine cellar. Yeah.
152 00:15:58.050 ⇒ 00:16:00.485 Amber Lin: And so that’s me in the in the
153 00:16:01.050 ⇒ 00:16:02.140 Hannah Wang: In the gray.
154 00:16:02.140 ⇒ 00:16:02.870 Nicolas Sucari: Nice.
155 00:16:02.870 ⇒ 00:16:09.050 Hannah Wang: And I’ll show one more photo. Sorry. I just got these photos sent to me like 10 min ago.
156 00:16:09.914 ⇒ 00:16:16.309 Hannah Wang: But this is a this is like a video of of us talking in front of, like, you know, everybody. And
157 00:16:16.470 ⇒ 00:16:39.049 Hannah Wang: I’m just speaking a little bit about what we do as a company? And these are people that are running like big consulting companies like media creative. And everybody is asking like how to use AI in their business, and it was like a really great discussion, like people are asking a lot of great questions, you know, and the great thing that we found is a lot of what we change about
158 00:16:39.250 ⇒ 00:16:47.239 Hannah Wang: how we think about our business recently has been like how to marry the data and AI pieces. I think the ABC. Client is a really great version of that where
159 00:16:47.280 ⇒ 00:17:10.101 Hannah Wang: we almost deployed the AI and then are doing the data now. But what we’re telling clients. Now, is that what we’ll do is we’ll organize a lot of your data, get it ready, and then make it available to AI agents. That could help, you know, assist actually very similar thing to what we’re doing internally right? We are grabbing our Zoom Meetings. We’re grabbing our slack messages. We’re grabbing our
160 00:17:10.410 ⇒ 00:17:25.909 Hannah Wang: like the github code. We’re grabbing linear tickets. And then we’re making it available to us in the form of like a summary or a question. Answer. Bot, that’s exactly the work that there’s a big data engineering piece. There’s a big data modeling piece. And then there’s finally like this, AI piece.
161 00:17:26.277 ⇒ 00:17:35.149 Hannah Wang: and so in the last 2 weeks, we’ve sort of gotten closer to like, how do we actually have a cohesive story around? Why, we do data, and why we do. AI
162 00:17:36.430 ⇒ 00:17:49.489 Hannah Wang: But I think one of the big things as just part of this event is. It’s not just me going in and talking. That was probably the easiest part. It was actually everything around it, I think. Hannah
163 00:17:49.490 ⇒ 00:18:09.930 Hannah Wang: and and Ryan worked super hard for about a week prior to work with the operating team to get illustrations done to post every 2 days and then like to get everything set up for me to just show up. And then there’s like 2030 people there and then. We also have a white paper. We’re gonna send out to everybody. So we now have.
164 00:18:10.130 ⇒ 00:18:20.449 Hannah Wang: We now have it down where we can basically do one of these promotions probably once every few weeks, and then we’ll get better at doing it, you know, ideally, like once a week.
165 00:18:21.027 ⇒ 00:18:37.559 Hannah Wang: And that means on average, we’re having like a very close connection with like 30 to 50 people a week at minimum, right? And and that’s how we sort of start to really, really get our name out there. And so yeah, that that went super super. Well, yesterday.
166 00:18:41.000 ⇒ 00:18:42.569 Hannah Wang: Any questions?
167 00:18:42.570 ⇒ 00:18:44.519 Amber Lin: Thank you. Marketing team.
168 00:18:45.010 ⇒ 00:18:47.290 Hannah Wang: Yeah, it was really, really helpful.
169 00:18:50.068 ⇒ 00:18:54.460 Hannah Wang: Yeah, I think that’s I think the only other point I had is, I sent a poll
170 00:18:55.042 ⇒ 00:19:09.940 Hannah Wang: this week just about is everybody using cursor? And I know some people responded, but I think my my biggest point is, if you’re if you do have a chance to go to the AI office hours next week. Please do like you’ll you’ll find, like one or 2 things
171 00:19:10.422 ⇒ 00:19:16.049 Hannah Wang: that you can use every day, even personally or professionally, that will help you. Just speed things up.
172 00:19:16.740 ⇒ 00:19:26.030 Hannah Wang: I think additionally, if you, if you aren’t using Chat Gpt, give it a shot this weekend, and I’m starting to send like interesting things I’m doing there, and
173 00:19:26.970 ⇒ 00:19:33.020 Hannah Wang: it’s like getting unbelievably helpful for me and and for sales team for marketing team.
174 00:19:33.498 ⇒ 00:19:35.290 Hannah Wang: So so give it a shot.
175 00:19:35.540 ⇒ 00:19:37.930 Hannah Wang: I don’t know. Did we want to talk about anything like
176 00:19:38.160 ⇒ 00:19:42.909 Hannah Wang: for sales, or like sort of oh, yeah, sure. For the past week. Yeah.
177 00:19:43.430 ⇒ 00:20:07.719 Hannah Wang: Yeah. Well, I guess along with all the kind of events that we’re doing. We we hosted a happy hour yesterday. So that was great. Gotta reconnect with some some folks that I know in la hopefully, there’s some prospects that come out of that. But I think the strategy these past few weeks I know I haven’t been on this demo call in a couple of weeks has just been to focus less on just like
178 00:20:08.000 ⇒ 00:20:23.490 Hannah Wang: hitting, like casting a really wide net and doing huge volume, but being more targeted, doing more account based kind of marketing and sales, and so finding, like specific targets, and trying to go and and meet a lot of the
179 00:20:23.490 ⇒ 00:20:44.720 Hannah Wang: like stakeholders within a particular company to get yeah, to to build that relationship. I would say, like, as far as like the number of proposals we’re sending, we sent out 3 proposals this week we’re still waiting on a couple back. There’s some old clients that are that are that are looking to kick off again. So I mean, we’re expecting like, you know, one of our old clients, Stella, to come back next week.
180 00:20:45.220 ⇒ 00:21:14.390 Hannah Wang: Yeah, I’ve been continuing to. I hopped on a call with yesterday. Yeah, just help them reboot some of the systems that we had set up for them. So yeah, I think a lot of it is kind of more been in the background. And I haven’t really been sharing as frequent updates with the with the broader audience. But yeah, I mean overall. I think it’s it’s been good with the new positioning. We’re doing. We’re having an offsite with. I guess Amber will be joining us like shortly after this call I’ll send you the location of where we’ll be.
181 00:21:14.647 ⇒ 00:21:39.319 Hannah Wang: But yeah, we’re going to be trying to just brainstorm with with the team more broadly, and kind of get everyone ready to kind of talk about like what we do in in a in a slightly different way, heading into like the rest of Q. 2. That we think will set us up for just like more messaging. That resonates more with the prospects that we’re talking to. So more to come on that. Yeah, there’s a lot of a lot of stuff that we’ll we’ll share out, probably in the next week.
182 00:21:39.800 ⇒ 00:21:44.710 Hannah Wang: Great any questions on sales stuff.
183 00:21:47.180 ⇒ 00:22:06.168 Hannah Wang: I think the last piece I’ll mention, maybe before we hop today is yeah, we getting a like, I think, as the core team. We have right now, across data. AI is really the core team that we’re gonna expect to support the next 5 to 10 clients. So I know there’s a lot of questions about
184 00:22:06.600 ⇒ 00:22:35.059 Hannah Wang: allocation. There’s a lot of questions about availability and like where time is being spent. I know there’s questions about Pto about out of office. These are all things that we’re working on right now. And working with our new finance team. And basically deciding on how to do all those things as a company. It’s our sort of 1st time doing that. And so I’m really excited. I think also we should expect that everybody here
185 00:22:35.490 ⇒ 00:22:41.789 Hannah Wang: soon as we start to bring on more clients we’ll have a full plate but also for us, like my.
186 00:22:41.990 ⇒ 00:23:00.169 Hannah Wang: Our interest is not to hire like a hundred people. We want to scale as lean as possible, so we can continue to spend time with everybody, but also invest back and and have a really core data group. And so AI and and using automation, is a key part of it. The other piece is
187 00:23:00.661 ⇒ 00:23:23.729 Hannah Wang: and this is where I think we we did a lot of this, I think, sort of as we’re hustling the last few weeks. It became less. But we’ll start to meet more as like horizontal teams. I think, in particular, like the data platform team, I’ve sort of added all the core data folks there. We’ll talk a little bit how we can spend 5 to 10% of our time on things like documentation. Better tooling, like
188 00:23:24.301 ⇒ 00:23:43.279 Hannah Wang: just ways that we can all use AI in our day to day. Better Pr reviews, observability. So as a data team, now not, you’re not just isolated to one client and sort of can’t make decisions. I think we will start to meet and understand how all clients are going. And then basically think about how we can raise the bar.
189 00:23:43.920 ⇒ 00:23:51.799 Hannah Wang: We’re we’re testing out oasis, working on testing out metaplane. I’ll be talking to Kyle a bit about some documentation items.
190 00:23:52.380 ⇒ 00:24:04.059 Hannah Wang: I’ll sort of be talking to Annie a little bit about a couple of things on setting documentation for how we do analysis and basically setting the guidelines for what good analysis is?
191 00:24:04.494 ⇒ 00:24:22.459 Hannah Wang: And so all of that, I think we’ll kick off next week. The last 2 weeks we really shifted from, I think, especially my time, and I think Robert’s time from being 100 on clients to like this week. And last week I there was stuff that happened like I wasn’t involved in, which was really really great.
192 00:24:22.965 ⇒ 00:24:27.519 Hannah Wang: And so for me, a lot of my time shifted towards operations and marketing.
193 00:24:27.933 ⇒ 00:24:30.659 Hannah Wang: It’s now shifting. Now we’ll start to add on
194 00:24:31.266 ⇒ 00:24:59.090 Hannah Wang: sales, and then we’ll start to do stuff on the data platform side. So all of the work. To like, get client stuff going every week is really helping us think about like big picture stuff. Of course we recruit. We do like one or 2 interviews a day. And we we’re we just try to meet really great people and sort of have them interested in Brain Forge. And when the time comes, so I’ll sort of make a call again. If you have anybody
195 00:24:59.180 ⇒ 00:25:07.050 Hannah Wang: in your world that you think would be interested in working here or wants an opportunity at either AI or data. You can let us know.
196 00:25:07.300 ⇒ 00:25:11.189 Hannah Wang: And then, yeah, we can definitely do, shout outs
197 00:25:12.650 ⇒ 00:25:14.970 Hannah Wang: I don’t know Amber. If you want to kick it off.
198 00:25:16.350 ⇒ 00:25:39.940 Amber Lin: Yes, yes, very excited. So I have a lot of shout outs 1st of all. Thank you, Annie. So much for joining the ABC team. It has made our data stuff so much easier. And the clients were very impressed. Today they’re like, Oh, my God! How did you join this. How did this get connected to that? They were very, very impressed. So.
199 00:25:39.940 ⇒ 00:25:50.189 Hannah Wang: Maybe. Say one piece there, Annie, the the work you’ve done on ABC. What started as just an AI client. They clearly need a lot of help on data as well.
200 00:25:50.490 ⇒ 00:26:08.629 Hannah Wang: And, as you can tell, you’re working with Brian and David like they need a lot of assistance. They’re not a data company. They’re a local home services company right there, like they’ve been in business like 70 years. But they were like incredibly impressed today with the with what we presented on the data side.
201 00:26:09.176 ⇒ 00:26:29.159 Hannah Wang: Especially like measuring. Oh, by the ways like we’ve done things that I thought would take like weeks to get down the line, and we’ve done them so quickly, even with just limited time. And that where we’re gonna see that translate is in this new contract that we’re about to sign with them where we’re gonna try to get, you know, as much money as we can. So that that’s really really helping set the stage for that. So.
202 00:26:33.790 ⇒ 00:26:46.810 Amber Lin: That’s my 1st shout out, I have a few more Luke. Thank you. I wanted to thank you for taking on the pool parts part we. Last week
203 00:26:46.990 ⇒ 00:26:50.869 Amber Lin: one of the execs came to us, and they were like
204 00:26:51.600 ⇒ 00:27:04.520 Amber Lin: pissed. We want to end the contract, and then dived in so as as a firefighting to save that. And now we meet with the stakeholders every week we met with 3 of their
205 00:27:04.740 ⇒ 00:27:08.480 Amber Lin: managers, and then I think
206 00:27:08.610 ⇒ 00:27:25.489 Amber Lin: we have fixed something that just hasn’t been really looked at for the past few months. So a lot of work has went in there. And I really appreciate Lou for taking on and owning the pool parts. The client work, and also looking at
207 00:27:25.600 ⇒ 00:27:32.959 Amber Lin: other parts for the company of How do we stage real? And how do we do things better? So there’s a lot of
208 00:27:33.520 ⇒ 00:27:35.970 Amber Lin: lot of stuff that Luke has been doing.
209 00:27:36.320 ⇒ 00:27:37.980 Amber Lin: So thank you for that.
210 00:27:40.710 ⇒ 00:27:50.369 Amber Lin: Lastly, also more shout outs, thank you, Casey and Miguel the AI team
211 00:27:50.800 ⇒ 00:27:57.309 Amber Lin: spit out so much progress this week as you’ve seen. That was the only demo this week. So it’s all up.
212 00:27:57.310 ⇒ 00:27:57.870 Hannah Wang: Awesome.
213 00:28:00.300 ⇒ 00:28:16.370 Amber Lin: And I know you guys are so busy and even some, sometimes you’re working even when you’re on holiday. That’s mind blowing to me and really pushing out all those updates. And we’re recruiting. And we’re getting more people on the AI team. So
214 00:28:16.640 ⇒ 00:28:21.690 Amber Lin: thank you guys for being there and taking it on.
215 00:28:22.480 ⇒ 00:28:24.270 Amber Lin: That’s 1 of my shout! Outs.
216 00:28:27.690 ⇒ 00:28:29.650 Hannah Wang: Anyone else any shout outs.
217 00:28:30.200 ⇒ 00:28:42.729 Hannah Wang: I’ll give some shout out. Since I haven’t been able to do that the past few weeks. Yeah, I want to shout out, oation them a lot like I know it’s been kind of up and down the past couple of weeks. But I feel like.
218 00:28:42.910 ⇒ 00:28:45.339 Hannah Wang: yeah, you guys are like.
219 00:28:45.970 ⇒ 00:28:53.519 Hannah Wang: yeah, really, really proactive. And when when issues do come up, because, you know, there’s always things that we don’t foresee, just like
220 00:28:53.660 ⇒ 00:29:14.909 Hannah Wang: kind of thinking about next step and kind of make making a solution to kind of patch things. So I feel like, maybe if the clients are not necessarily giving their gratitude every day, like at least I I see it. And yeah, like, I, I feel like, this is a team where we don’t make the same mistake more than once. And so I think, I’m I’m just. I’m just like
221 00:29:15.247 ⇒ 00:29:25.010 Hannah Wang: I think that’s that’s really great. And we’re we’re just keep. We’re gonna keep getting better. I have more and more confidence in our ability to deliver that great work to our clients.
222 00:29:25.810 ⇒ 00:29:30.240 Hannah Wang: Also want to shout out Annie, like totally some issue that stumped like
223 00:29:30.480 ⇒ 00:29:37.210 Hannah Wang: us on, how do we build a separate particular data, viz. Like Component?
224 00:29:37.310 ⇒ 00:30:05.540 Hannah Wang: Apparently she did it like I didn’t even realize she did it without like, did it so quickly. We we’ve been stuck on it for a couple of weeks. So I think, having her just kind of being looped into more clients like she’s really been able to kind of flex her skills. And yeah, I think I’m I’m excited for the stretch project that she’s taking on for one of her clients, and also just like these these unexpected ways where she’s able to really like help help our team get on block so
225 00:30:07.680 ⇒ 00:30:11.960 Hannah Wang: great anyone else.
226 00:30:13.700 ⇒ 00:30:19.009 Annie Yu: Well, yeah, keep charming, showering me with compliments
227 00:30:20.240 ⇒ 00:30:49.710 Annie Yu: that. But I want to say Thank you so much, I think, especially for 1st to Amber and Akash, I think, like yesterday, I ping amber. I was like, Okay, it feels strange going a day without talking to you, because I and I think that’s where I realized, okay, I’m really reliant on, like amber and Akash on like prioritizing my work and having that priorities for each day. So thank you so much. And
228 00:30:49.920 ⇒ 00:31:00.380 Annie Yu: also I think this is for everyone, and like, I think, for Robert, appreciate you for singing that reminder yesterday in a slack. I think
229 00:31:00.720 ⇒ 00:31:02.730 Annie Yu: I really feel that
230 00:31:03.160 ⇒ 00:31:16.609 Annie Yu: there will be ups and downs, and I think I have that confidence that we are like working as a team, and can be candid with each other, and then work working like anything out with a better solution.
231 00:31:19.050 ⇒ 00:31:21.040 Hannah Wang: Yeah, I agree, yeah.
232 00:31:21.630 ⇒ 00:31:35.039 Hannah Wang: yeah, don’t hold it in. Just like, you know, we’re we’re a small team. We can. You can message everyone like, we’re, you know, I think that’s kind of the culture we have here. So yeah, we value all the feedback that you guys give. And yeah, I think
233 00:31:35.200 ⇒ 00:31:37.120 Hannah Wang: I’m I’m glad to hear that you feel
234 00:31:37.420 ⇒ 00:31:40.950 Hannah Wang: we feel like so connected to each other, more so now than before.
235 00:31:46.620 ⇒ 00:31:55.880 Hannah Wang: Cool? Yeah. I guess my last point on that is any other feedback anyone has on process, or
236 00:31:56.110 ⇒ 00:31:58.340 Hannah Wang: the ways that we’re doing. Stuff
237 00:31:58.450 ⇒ 00:32:15.370 Hannah Wang: like my expectation is that it shifts from coming from me and Robert to now it’s coming a lot from the team like these are your projects, these are this is your work. This is your company. If you don’t take the reins of how things are going.
238 00:32:15.941 ⇒ 00:32:20.030 Hannah Wang: And find a way to either. Lead your team, lead yourself
239 00:32:20.780 ⇒ 00:32:30.679 Hannah Wang: it’s it’s not gonna work. And so if you were to do one thing next week is find an opportunity to to be outspoken in a meeting or be outspoken with an opinion
240 00:32:31.380 ⇒ 00:32:53.299 Hannah Wang: like this is a 1 place where, like, as you guys know, we, we change a lot, and we morph pretty easily. And but a lot of those ideas. If they’re coming just from a couple of people, then it doesn’t become your company right, and we don’t move as fast as we could. And so what I’m hoping is that I’m able to set up opportunities
241 00:32:53.792 ⇒ 00:33:08.290 Hannah Wang: either meetings or we’re able to set teams up that can work together in doing that, but exactly like for Annie to reach out to amber for other folks to reach out to each other and solve problems like this isn’t a this isn’t a typical consultancy.
242 00:33:08.664 ⇒ 00:33:32.820 Hannah Wang: Like, we’ve all worked in sort of those in personal sort of ways where you may not be able to ping someone on another team. We definitely want to encourage that. And it’s I would say, this is probably a team where we have marketing folks, working with operations, working with data, working with Pm’s and working with sales like everybody on a lot of projects. It’s taking the entire company. And that’s great. It’s sort of breaking down a lot of barriers
243 00:33:35.980 ⇒ 00:33:40.580 Hannah Wang: cool. I don’t know. Wish if you had, I know you, I saw you unmuted. I don’t know if you had anything.
244 00:33:41.530 ⇒ 00:33:43.424 Awaish Kumar: Yeah, I just wanted to thank
245 00:33:44.587 ⇒ 00:33:49.760 Awaish Kumar: Robert, for for your understanding, and the in helping us face the client
246 00:33:49.930 ⇒ 00:34:02.160 Awaish Kumar: and also Akash like after he he joined, like the things has been a lot smoother, helping with the prioritizing and everything. So yeah, shout out for both of them.
247 00:34:06.380 ⇒ 00:34:10.509 Hannah Wang: Okay, great. Anything else we want to cover.
248 00:34:15.280 ⇒ 00:34:18.475 Hannah Wang: Okay? Awesome. I guess the last thing I’ll mention
249 00:34:19.130 ⇒ 00:34:46.060 Hannah Wang: is, we’re doing more like marketing events, webinars. If anyone here is interested in doing stuff on with their personal Linkedin posting stuff about their work here, about client work they’ve done attending webinars, or even going to conferences. Please like, let me know, or let someone on the marketing team know? We want to. We’re creating an environment where any everybody can show off their work.
250 00:34:46.514 ⇒ 00:34:58.589 Hannah Wang: And on a very personal level. For your personal resume and your you know your personal career. I highly encourage using Brainforge as a vehicle to share. We are getting a lot of eyeballs.
251 00:34:59.129 ⇒ 00:35:10.629 Hannah Wang: If if you start to use this as a vehicle to share. It’s gonna really, really help your career. And so if you if there’s any interest in that, whether it’s blog posts, webinars, videos.
252 00:35:11.105 ⇒ 00:35:27.549 Hannah Wang: we are all ears right now, like I’m the I’m the big content person here, and like it’s not my skill set and I want to encourage anyone that wants to to do that, and needs help with images, or I ideation or editing
253 00:35:28.021 ⇒ 00:35:43.680 Hannah Wang: like. We have all the scaffolding to support that. So there’s a lot. Everybody here is very smart. I’m looking at the grid of people. And all of us have things to say, and you’d be surprised that something you think is very basic. The world thinks is like incredibly advanced
254 00:35:44.062 ⇒ 00:36:00.989 Hannah Wang: and so I’ll start as we get better as a marketing team. I’ll start to remind people that you can take advantage of that resource, and it’s a great win win like our our company wins because we promote ourselves. And again, you, as an individual win because your name is on something that you’ve published to the world. So
255 00:36:01.730 ⇒ 00:36:05.030 Hannah Wang: yeah, if you want to consider that, please let us know
256 00:36:06.910 ⇒ 00:36:10.009 Hannah Wang: cool, that’s all. That’s all I had.
257 00:36:10.825 ⇒ 00:36:19.650 Hannah Wang: Yeah, I’m excited for this weekend to spend time with some folks here in la and again. Hope sometime this year I’ll get a chance to see everybody. In person.
258 00:36:20.433 ⇒ 00:36:23.169 Hannah Wang: And yeah, any questions, please let me know in slack
259 00:36:26.330 ⇒ 00:36:28.149 Hannah Wang: cool. Thank you.
260 00:36:28.960 ⇒ 00:36:31.659 Miguel de Veyra: Bye, bye, guys, enjoy your time. There.
261 00:36:31.660 ⇒ 00:36:32.660 Mariane Cequina: Thank you.
262 00:36:32.660 ⇒ 00:36:33.300 Mariane Cequina: Hello!