Meeting Title: Friday Brainforge Demos & Retro Date: 2025-12-01 Meeting participants: Awaish Kumar, Henry Zhao, Rico Rejoso, Amber Lin, Casie Aviles, Mustafa Raja, Demilade Agboola, Ryan Brosas, Hannah Wang, Holly Condos, Robert Tseng
WEBVTT
1 00:02:48.900 ⇒ 00:02:51.479 Henry Zhao: Hi, Awish, how are you doing? Where’s everybody?
2 00:02:56.880 ⇒ 00:02:59.709 Awaish Kumar: Hello, I’m good, how are you?
3 00:03:00.330 ⇒ 00:03:01.170 Henry Zhao: Good, thanks.
4 00:03:51.680 ⇒ 00:03:52.870 Amber Lin: Hi there.
5 00:03:53.770 ⇒ 00:03:55.950 Amber Lin: Is this meeting still happening?
6 00:03:55.950 ⇒ 00:03:58.850 Henry Zhao: Yes, and I’m looking forward to our slide session.
7 00:04:04.470 ⇒ 00:04:09.910 Amber Lin: Awesome. Do we know if Utum’s back from ABC? Because he’s still on-site.
8 00:04:10.860 ⇒ 00:04:11.720 Henry Zhao: Oh, really?
9 00:04:11.720 ⇒ 00:04:16.630 Amber Lin: Earlier, I know he was on the site, but I don’t know where he’s… where he is now.
10 00:29:08.840 ⇒ 00:29:10.030 Amber Lin: Hi, Robert.
11 00:29:10.210 ⇒ 00:29:11.939 Amber Lin: Do you know if it has come into that?
12 00:29:11.940 ⇒ 00:29:12.940 Robert Tseng: What’s going on here?
13 00:29:13.870 ⇒ 00:29:14.430 Amber Lin: Oh, he’s…
14 00:29:14.430 ⇒ 00:29:15.330 Robert Tseng: still on site.
15 00:29:15.840 ⇒ 00:29:18.470 Amber Lin: Oh, that’s so funny. Okay.
16 00:29:18.730 ⇒ 00:29:24.530 Amber Lin: Well, we started… we didn’t start, because we weren’t sure if he would be coming.
17 00:29:24.670 ⇒ 00:29:29.330 Amber Lin: But we still have some time left. Is there any…
18 00:29:29.450 ⇒ 00:29:35.459 Amber Lin: Key, like, exec stuff we want to go over, any new sales that’s happening we want to announce?
19 00:29:36.320 ⇒ 00:29:42.620 Robert Tseng: I… did not prep for this, but I guess,
20 00:29:43.280 ⇒ 00:29:53.120 Robert Tseng: Yeah, I mean, we have a few things kicking off. Remo closed, so that’s… That’s where we’re,
21 00:29:54.060 ⇒ 00:29:59.700 Robert Tseng: That’s… That’s starting today. And then,
22 00:29:59.950 ⇒ 00:30:09.060 Robert Tseng: Lilo Socials, Lilo Socials started today, Element starting this week, and
23 00:30:10.130 ⇒ 00:30:26.439 Robert Tseng: I guess, like, he rose up for renewal on Wednesday, so… and then Utam’s obviously at ABC, kind of doing discovery there. So, yeah, I mean, we just pretty much have, like, 5 new clients, like, all starting this week, so we’re kind of just swamped on delivery side.
24 00:30:28.590 ⇒ 00:30:34.520 Robert Tseng: Yeah, I guess that’s… that’s a mix of the… I mean, and then as far as, like, new…
25 00:30:34.940 ⇒ 00:30:41.499 Robert Tseng: new deals, I mean, we are… we are continuing to talk to new,
26 00:30:41.640 ⇒ 00:30:55.709 Robert Tseng: to talk… talk to leads. I mean, nothing… nothing noteworthy right now. I think these are all stations for… for next year, in… in Q1 2026. But yeah, I guess that’s… that’s kind of what we’ve been working on.
27 00:30:58.830 ⇒ 00:30:59.485 Amber Lin: Mmm…
28 00:31:12.690 ⇒ 00:31:22.200 Amber Lin: I guess, is there specific demos? Any teams that, I guess specifically the AI team, is there anything we want to demo, and…
29 00:31:22.350 ⇒ 00:31:23.180 Amber Lin: Show.
30 00:31:30.960 ⇒ 00:31:33.219 Casie Aviles: Didn’t really prep for any demos.
31 00:31:34.510 ⇒ 00:31:35.060 Amber Lin: I see.
32 00:31:35.060 ⇒ 00:31:40.020 Casie Aviles: for… Yeah, but we’ve just been working on…
33 00:31:40.860 ⇒ 00:31:52.890 Casie Aviles: Improvements on the platform, specifically the ticket generation, like, after the meetings, and… We’re also, like… planning… on…
34 00:31:53.310 ⇒ 00:31:56.350 Casie Aviles: Doing some migration internally for, like.
35 00:31:58.060 ⇒ 00:32:06.379 Casie Aviles: You know, for our AI agents, so our client hub agents, so we’ll be moving them to code, so before, we were just using
36 00:32:07.370 ⇒ 00:32:12.039 Casie Aviles: And even… So yeah, hopefully, like, this, this will…
37 00:32:12.870 ⇒ 00:32:21.630 Casie Aviles: allow us to, like… and also we’re kind of doing the same thing for ABC, in order to… since anytime we’re running into some limits already, so…
38 00:32:22.140 ⇒ 00:32:30.389 Casie Aviles: yeah, moving it into code should ideally help us, you know, address some observability issues with the AI.
39 00:32:31.180 ⇒ 00:32:33.399 Casie Aviles: Be able to do triage.
40 00:32:33.920 ⇒ 00:32:35.849 Casie Aviles: Faster, so yeah.
41 00:32:36.060 ⇒ 00:32:37.980 Casie Aviles: That’s pretty much it that we have.
42 00:32:40.290 ⇒ 00:32:43.020 Amber Lin: Cool. Guess…
43 00:32:43.540 ⇒ 00:32:53.100 Amber Lin: Then, on the marketing side, is there any new case studies that we’re putting out? In the last week on the delivery meeting, we outlined a few case studies.
44 00:32:53.200 ⇒ 00:32:58.719 Amber Lin: Do we know who to ask for those case studies?
45 00:32:58.870 ⇒ 00:33:02.799 Amber Lin: it’s like, what case studies are lined up.
46 00:33:02.950 ⇒ 00:33:05.420 Amber Lin: I feel like everybody has a few things that…
47 00:33:05.590 ⇒ 00:33:10.139 Amber Lin: can be made into case studies, I just want to know if they’re happening yet.
48 00:33:11.330 ⇒ 00:33:14.150 Hannah Wang: You can…
49 00:33:14.480 ⇒ 00:33:22.639 Hannah Wang: probably message me, and then I would just need to get it prioritized, because I don’t really know which one is…
50 00:33:22.880 ⇒ 00:33:27.270 Hannah Wang: More urgent, or would be the most useful.
51 00:33:27.720 ⇒ 00:33:37.509 Hannah Wang: And then I think with the AI team’s new case study assistant, I wouldn’t need to interview people anymore, it’s just people record their,
52 00:33:38.270 ⇒ 00:33:42.319 Hannah Wang: Just them talking with the assistant, so that should be pretty…
53 00:33:42.480 ⇒ 00:33:46.820 Hannah Wang: Easy. I know you messaged me, a number of case studies.
54 00:33:46.920 ⇒ 00:33:54.550 Hannah Wang: Amber, and then… yeah, there’s, like, a backlog of case studies we have in, linear.
55 00:33:54.710 ⇒ 00:33:57.390 Hannah Wang: But… Yeah, I guess…
56 00:33:57.650 ⇒ 00:34:08.929 Hannah Wang: I would just need help prioritizing it. And I don’t really know who else to ask except Utam and Robert, but if… in terms of priority,
57 00:34:09.830 ⇒ 00:34:14.880 Hannah Wang: So yeah, like, I can… I can have the list, it’s just I don’t know which ones to work on first.
58 00:34:16.000 ⇒ 00:34:19.409 Amber Lin: Cool. Okay.
59 00:34:19.670 ⇒ 00:34:25.409 Amber Lin: I know we talked about it, like, a week ago, so hopefully people know…
60 00:34:25.590 ⇒ 00:34:27.949 Amber Lin: What case studies they have on hand.
61 00:34:28.270 ⇒ 00:34:30.130 Amber Lin: Okay.
62 00:34:32.120 ⇒ 00:34:33.199 Amber Lin: I did…
63 00:34:33.469 ⇒ 00:34:43.919 Amber Lin: Sorry, I did prepare slides, and I know we were just sitting in this room, and I went to do other analysis. I can go over… I have something to present, I have a…
64 00:34:44.070 ⇒ 00:34:54.019 Amber Lin: AI-assisted Analysis Outline. Are people interested in seeing that? I can show you what I’ve been doing and how I make the slides.
65 00:34:55.830 ⇒ 00:34:57.389 Hannah Wang: Yeah, let’s do it.
66 00:34:57.930 ⇒ 00:34:58.560 Amber Lin: Okay.
67 00:35:02.270 ⇒ 00:35:03.060 Amber Lin: Cool.
68 00:35:10.590 ⇒ 00:35:16.260 Amber Lin: So my simple process… is…
69 00:35:16.560 ⇒ 00:35:20.049 Amber Lin: I go from the database to cursor.
70 00:35:20.250 ⇒ 00:35:23.939 Amber Lin: And then from cursor, I build the narrative.
71 00:35:24.130 ⇒ 00:35:32.180 Amber Lin: Then the third, like, the third round, I make some recommendations, and finally put it into slides. And when I…
72 00:35:32.940 ⇒ 00:35:39.350 Amber Lin: Let’s see, okay. So, starting off with cursor… I usually…
73 00:35:39.580 ⇒ 00:35:53.440 Amber Lin: this is the database that we see. I think there’s a lot of tables, there’s a lot of stuff going on, and so it’s… how do we start from there to give out something that
74 00:35:53.550 ⇒ 00:36:00.040 Amber Lin: Clients would read that has… has meaning, or has some insights that’s interesting.
75 00:36:00.840 ⇒ 00:36:09.670 Amber Lin: And so, starting from a database. I connect it. I connect to it via cursor.
76 00:36:09.760 ⇒ 00:36:25.580 Amber Lin: And this step, I think you can either ask our more technical folks for assistance to set up cursor, I needed help there. And then you can search online to connect to DuckDB or connect to Snowflake, depending on
77 00:36:25.910 ⇒ 00:36:28.120 Amber Lin: Where the class database is.
78 00:36:29.060 ⇒ 00:36:48.780 Amber Lin: And then, the second step would be to explore the database, and here’s something really simple you can do it just to tell ChatGPT, tell Cursor, can you explore this database? And then you can find the tables, and you can say, can you explore specific tables?
79 00:36:49.440 ⇒ 00:36:51.280 Amber Lin: And then it will give you
80 00:36:51.530 ⇒ 00:36:58.049 Amber Lin: The different fields, the dimensions, if there’s nulls. So, you can give it a very generic
81 00:36:58.760 ⇒ 00:37:07.499 Amber Lin: Argument, and then something… you might… you will be able to have a broad view of what data you actually have.
82 00:37:08.370 ⇒ 00:37:17.580 Amber Lin: And I think that’s the two preparation steps, and the next part does it get a bit more interesting, which in this part is where we look at
83 00:37:17.720 ⇒ 00:37:37.650 Amber Lin: one dimension of, maybe it’s sales, maybe it’s traffic, and we look at, is there something interesting, or to say if there’s something really big or really small. I think this is for people who have not done analysis before, if, essentially.
84 00:37:37.740 ⇒ 00:37:45.419 Amber Lin: The first step is to see, okay, is there… abnormal patterns. Sometimes.
85 00:37:46.760 ⇒ 00:37:50.689 Amber Lin: Sometimes really big things, like here.
86 00:37:50.940 ⇒ 00:38:05.620 Amber Lin: like, the green part got really big, that’s something interesting. Or if it suddenly dipped really small, that’s interesting. Or even if it’s flat over time, you can ask, okay, why is it…
87 00:38:05.620 ⇒ 00:38:13.620 Amber Lin: flat over time. Say, if a company’s revenue is flat over 3 years, then that’s something we can point out.
88 00:38:14.080 ⇒ 00:38:23.990 Amber Lin: Alright, so in this step, essentially, it’s to see, are there patterns noteworthy patterns? Too big? Too small?
89 00:38:24.200 ⇒ 00:38:26.799 Amber Lin: Or… no change at all.
90 00:38:27.480 ⇒ 00:38:36.259 Amber Lin: So I think that’s a pretty basic rule of thumb you can apply there. You can look at it from one dimension, and then you can compare
91 00:38:36.510 ⇒ 00:38:38.889 Amber Lin: multiple dimensions.
92 00:38:39.150 ⇒ 00:38:41.199 Amber Lin: And put it on the same graph.
93 00:38:41.410 ⇒ 00:38:56.179 Amber Lin: over… over time, you can… and then, to spice it up, you can slice two metrics. You can say, okay, let’s look at product category, and then look at it over time.
94 00:38:56.660 ⇒ 00:39:00.230 Amber Lin: So, I think those are some basic
95 00:39:00.620 ⇒ 00:39:11.889 Amber Lin: approaches to look at the data to see if there’s any patterns. I think that’s a good place to start. And then, I think, once you complete this step with Cursor, you would end up with
96 00:39:12.320 ⇒ 00:39:19.510 Amber Lin: Some graphs, some insights, some numbers, and at that point, it’s,
97 00:39:19.870 ⇒ 00:39:26.029 Amber Lin: how can we make sense of what’s happening? Because if you only have a graph.
98 00:39:26.560 ⇒ 00:39:31.240 Amber Lin: What does that… what does that mean? What does it mean for…
99 00:39:32.150 ⇒ 00:39:37.759 Amber Lin: the client, what can they do with it? Like, why should they… why should they care about
100 00:39:38.650 ⇒ 00:39:41.570 Amber Lin: what you just found, and I think that’s the…
101 00:39:41.900 ⇒ 00:39:51.549 Amber Lin: That’s the key step that I think a lot of us struggle with, is what do we make of… what do we make of the image, or make of the data that we have?
102 00:39:52.600 ⇒ 00:39:53.670 Amber Lin: And…
103 00:39:53.930 ⇒ 00:40:01.130 Amber Lin: From there, I think once we have the data, there’s 3 steps we need to do. So, first is
104 00:40:01.310 ⇒ 00:40:10.710 Amber Lin: What story does it tell? And then, based on that, what recommendations can we do?
105 00:40:10.750 ⇒ 00:40:23.250 Amber Lin: That makes it actionable, and that makes it… makes it so that the client can do something about it. And lastly, we can put it together, because often in this step.
106 00:40:23.250 ⇒ 00:40:36.290 Amber Lin: especially when we use cursor, I think there’s a lot of information that will come out, but our brain can only digest so much, so our job here is to filter it down so that the client can only see
107 00:40:36.370 ⇒ 00:40:39.659 Amber Lin: Like, 3 relevant points instead of 30.
108 00:40:40.690 ⇒ 00:40:48.129 Amber Lin: So… I think here… so the first step is to figure out if we can tell a story.
109 00:40:48.240 ⇒ 00:40:56.030 Amber Lin: And Like, if we just look at this here… What do you think…
110 00:40:56.160 ⇒ 00:40:58.170 Amber Lin: Like, what do you think this…
111 00:40:58.650 ⇒ 00:41:02.619 Amber Lin: this would mean, right? What trends do you see, and…
112 00:41:02.880 ⇒ 00:41:05.210 Amber Lin: what… what might it mean? Can…
113 00:41:05.450 ⇒ 00:41:10.699 Amber Lin: Can I grab someone to tell me about that? I’m gonna point to a participant.
114 00:41:11.330 ⇒ 00:41:14.730 Amber Lin: Can someone volunteer, or I will call names.
115 00:41:19.670 ⇒ 00:41:25.260 Amber Lin: Okay, I will call names. Let’s see…
116 00:41:25.680 ⇒ 00:41:28.860 Amber Lin: Will Mustafa help me here?
117 00:41:32.800 ⇒ 00:41:36.989 Mustafa Raja: Sorry to inconveni, ask the question again?
118 00:41:37.710 ⇒ 00:41:43.299 Amber Lin: What do you… is there a trend that you see from this, and what do you think it would mean?
119 00:41:44.770 ⇒ 00:41:45.910 Mustafa Raja: Let me see…
120 00:42:03.170 ⇒ 00:42:05.540 Mustafa Raja: Hmm… I can’t see it.
121 00:42:27.040 ⇒ 00:42:36.119 Amber Lin: I think the conclusion that I drew back then was that, we saw this blue trend going up, so there was…
122 00:42:36.290 ⇒ 00:42:39.970 Amber Lin: It started pretty small, and then it grew pretty big.
123 00:42:40.050 ⇒ 00:42:56.729 Amber Lin: And then we could say that, okay, as people started to make their second, third, fourth, like, they… as they made more purchases, as they returned to make more purchases, they started… more of them started getting classic cookies.
124 00:42:57.060 ⇒ 00:42:57.950 Mustafa Raja: Instead of before.
125 00:42:57.950 ⇒ 00:43:05.859 Amber Lin: Or when, usually for people’s first order, they generally only, say, like, 20% of them get classic cookies.
126 00:43:05.990 ⇒ 00:43:08.650 Amber Lin: So, that tells us that
127 00:43:09.240 ⇒ 00:43:24.579 Amber Lin: okay, seems like VP customers like to get individual cookies instead of boxes, and but they don’t usually start off with… they usually start off with boxes. So that’s, like.
128 00:43:24.720 ⇒ 00:43:38.410 Amber Lin: That’s our interpretation of why things might… what it is, and then we can think about what the possibilities are of why that happened, of why do people usually get
129 00:43:38.710 ⇒ 00:43:40.800 Amber Lin: Boxes in the first order.
130 00:43:41.510 ⇒ 00:43:49.329 Amber Lin: And that… And with that question, if you look at their website, you’re gonna see that their free delivery
131 00:43:49.580 ⇒ 00:44:05.880 Amber Lin: limit is $20, so then you may make a good guess, is that, okay, maybe the first time I order on a website, I want to get a free delivery. So that’s why people… maybe that’s why people get boxes on their first order.
132 00:44:06.170 ⇒ 00:44:09.220 Amber Lin: But as they return, then…
133 00:44:09.250 ⇒ 00:44:26.319 Amber Lin: They might get more individual cookies because they know what they want, and they don’t want to spend that much, so they might just get the singular things that they like. And that’s the possible why of, this trend happening.
134 00:44:26.900 ⇒ 00:44:35.189 Amber Lin: So, I think that’s where we add these callouts of what we think things are happening, and we add… I add the title.
135 00:44:35.320 ⇒ 00:44:36.150 Amber Lin: There.
136 00:44:37.080 ⇒ 00:44:43.380 Amber Lin: And then the next part is making the recommendation for the clients of, okay, why…
137 00:44:43.700 ⇒ 00:45:02.420 Amber Lin: yes, this is happening, but what can we do about it? What does it mean for me? How can I… essentially, how can I make… how can a client make for money, or stop, losing money in some places? And this… in this scenario.
138 00:45:03.360 ⇒ 00:45:07.410 Amber Lin: Because boxes are higher value.
139 00:45:07.890 ⇒ 00:45:15.880 Amber Lin: the client would want people to order more boxes, right? So that’s one of the house of recommendations we can make.
140 00:45:16.200 ⇒ 00:45:17.489 Holly Condos: Oh, that’s item.
141 00:45:19.110 ⇒ 00:45:20.000 Amber Lin: Hello?
142 00:45:21.220 ⇒ 00:45:23.420 Amber Lin: Okay, I thought someone was talking.
143 00:45:23.650 ⇒ 00:45:27.059 Amber Lin: So we can either help them
144 00:45:27.610 ⇒ 00:45:32.650 Amber Lin: Think of solutions to sell more boxes to the returning customers.
145 00:45:32.710 ⇒ 00:45:47.990 Amber Lin: Or, maybe the trend of people getting more cookie… individual cookies on their return purchases, so when they come back, they like to get more individual cookies. Maybe that’s something we can use to get more people to return.
146 00:45:48.120 ⇒ 00:45:53.800 Amber Lin: So that could tell us what we can potentially market
147 00:45:54.230 ⇒ 00:45:56.950 Amber Lin: To those… to get people to return.
148 00:45:57.160 ⇒ 00:46:05.029 Amber Lin: So those are recommendations based on what we found, and then based on that, what can the clients do to…
149 00:46:05.150 ⇒ 00:46:07.170 Amber Lin: earn more money, essentially.
150 00:46:09.420 ⇒ 00:46:20.040 Amber Lin: Okay, and then the last part is to pick from all the insights something that has a through line of… in this case, I was talking about
151 00:46:20.090 ⇒ 00:46:31.160 Amber Lin: the life cycle, essentially, is when people make their first cookie purchase, and then when they return and make their second, third, fourth. So everything I put here is about
152 00:46:31.160 ⇒ 00:46:42.059 Amber Lin: one thing. And that just helps the client’s brain to digest it together, because this is a lot of information for people to digest, so we gotta pick the ones that
153 00:46:43.170 ⇒ 00:46:58.609 Amber Lin: have something in common, and then tells a progressive story, is that it builds off of each other, and there’s a, because of this, we did that, and because of that, we found out this. So there’s a logical progression between the slides.
154 00:46:58.610 ⇒ 00:47:06.659 Amber Lin: So that someone that’s listening to your story can actually understand that, like, something is happening progressively.
155 00:47:08.180 ⇒ 00:47:15.730 Amber Lin: So that’s the end of my presentation. Any questions about
156 00:47:16.110 ⇒ 00:47:22.689 Amber Lin: how you can do this, or any examples you have questions on, you can always message me as well.
157 00:47:25.910 ⇒ 00:47:28.320 Hannah Wang: Yeah, I know we’re on ti- er…
158 00:47:28.320 ⇒ 00:47:31.020 Amber Lin: We’re… the meeting’s over, but…
159 00:47:31.070 ⇒ 00:47:36.589 Hannah Wang: Yeah, I have time, but I have a question. I’ll message you later.
160 00:47:36.590 ⇒ 00:47:37.210 Amber Lin: Okay.
161 00:47:37.500 ⇒ 00:47:49.759 Amber Lin: Sounds good. I know more and more people are trying to do analysis, so hopefully this helps and this is timely. And always let me know if you have any questions, because I think
162 00:47:49.930 ⇒ 00:47:55.680 Amber Lin: I’ve spent most of my time
163 00:47:55.800 ⇒ 00:47:57.770 Amber Lin: making slides, and I made it.
164 00:47:58.320 ⇒ 00:47:59.730 Amber Lin: A lot of slides.
165 00:47:59.930 ⇒ 00:48:09.809 Amber Lin: So let me know if you have any questions about what to put on them, how to do the story, and then how to do the recommendations.
166 00:48:11.730 ⇒ 00:48:13.559 Amber Lin: Okay, thank you. Your slides are pretty.
167 00:48:13.560 ⇒ 00:48:14.640 Robert Tseng: Pretty good now, Amber.
168 00:48:14.940 ⇒ 00:48:25.709 Amber Lin: Yeah, thank you, Robert. I’ve been… I’ve been learning, so it’s… a lot of my knowledge has came from Robert, so if Robert does not have time, we can think about it together.
169 00:48:25.820 ⇒ 00:48:30.430 Amber Lin: So, another point of resource for everybody.
170 00:48:33.260 ⇒ 00:48:38.639 Amber Lin: All right. Thanks, everybody. Thanks for staying on the call. Thanks for taking time today.
171 00:48:38.640 ⇒ 00:48:39.150 Henry Zhao: Dude.
172 00:48:39.150 ⇒ 00:48:44.300 Amber Lin: We’ll see what Utam has to say after his ABC visit.
173 00:48:45.340 ⇒ 00:48:46.740 Robert Tseng: Yeah. Okay.
174 00:48:46.740 ⇒ 00:48:48.090 Amber Lin: Alright, bye everyone.
175 00:48:48.090 ⇒ 00:48:49.540 Hannah Wang: Oh, bye.