Meeting Title: Brainforge x EdenOS Model Sync Date: 2026-04-02 Meeting participants: Awaish Kumar, Amber Lin, Ashwini Sharma
WEBVTT
1 00:00:52.820 ⇒ 00:00:53.770 Ashwini Sharma: Hello.
2 00:00:54.550 ⇒ 00:00:55.560 Amber Lin: Hello.
3 00:00:58.120 ⇒ 00:01:03.130 Amber Lin: Alright, so I… sent some…
4 00:01:04.750 ⇒ 00:01:09.089 Amber Lin: tables or, I think, topics that it’s been using.
5 00:01:09.240 ⇒ 00:01:19.999 Amber Lin: So those are the 6 tickets. I’m now trying to go through to screenshot the dashboards they’re used in, and trying to trace down, like, the…
6 00:01:20.660 ⇒ 00:01:23.550 Amber Lin: The views and the tables used.
7 00:01:25.160 ⇒ 00:01:26.570 Awaish Kumar: Hi, Amber.
8 00:01:27.160 ⇒ 00:01:28.100 Amber Lin: Hello.
9 00:01:28.420 ⇒ 00:01:37.090 Awaish Kumar: Yeah, I just wanted, the same, so that we can actually, instead of Slacking and waiting.
10 00:01:37.200 ⇒ 00:01:38.780 Awaish Kumar: We can just get started.
11 00:01:39.620 ⇒ 00:01:40.590 Awaish Kumar: Yeah.
12 00:01:40.780 ⇒ 00:01:41.739 Amber Lin: Oh, good.
13 00:01:41.740 ⇒ 00:01:50.960 Awaish Kumar: So… I… yeah, like, we… I already created revenue… cohort revenue retention summary tables yesterday.
14 00:01:51.270 ⇒ 00:01:57.549 Awaish Kumar: When you said I need retention with ad spend, that… I don’t know what…
15 00:01:57.770 ⇒ 00:02:00.060 Awaish Kumar: we were talking about, can… can we…
16 00:02:00.400 ⇒ 00:02:02.590 Awaish Kumar: Can you show me what you were asking for?
17 00:02:03.530 ⇒ 00:02:05.560 Amber Lin: Can you repeat that question?
18 00:02:06.010 ⇒ 00:02:11.790 Awaish Kumar: Yeah, there’s one of the tickets you created, let me show you.
19 00:02:13.270 ⇒ 00:02:15.549 Amber Lin: Like, the channel info?
20 00:02:16.090 ⇒ 00:02:18.939 Awaish Kumar: So, you’ve created a ticket for…
21 00:02:22.410 ⇒ 00:02:26.800 Amber Lin: The retention table with daddy grain, monthly grain.
22 00:02:27.690 ⇒ 00:02:31.280 Amber Lin: Yeah, that’s the one you created, I’m just tracking it.
23 00:02:31.280 ⇒ 00:02:34.009 Awaish Kumar: Yeah, but it does not have the…
24 00:02:37.380 ⇒ 00:02:39.099 Awaish Kumar: It does not have Edison, right?
25 00:02:40.920 ⇒ 00:02:44.320 Amber Lin: Why can’t we have ad spend?
26 00:02:49.840 ⇒ 00:02:55.759 Awaish Kumar: Yeah, it’s more about, revenue numbers, right? We are…
27 00:03:00.620 ⇒ 00:03:05.649 Awaish Kumar: It has, like, first order month, product, the cohort size.
28 00:03:05.860 ⇒ 00:03:08.889 Awaish Kumar: One cent square of store cumulative revenue.
29 00:03:11.440 ⇒ 00:03:17.970 Amber Lin: Yeah, like, I guess I can… maybe I can join in, like, ad spend, but…
30 00:03:18.780 ⇒ 00:03:25.089 Awaish Kumar: But why do we need it? We didn’t have it in our existing models as well.
31 00:03:25.330 ⇒ 00:03:32.330 Amber Lin: I think we do, because I need this to calculate LTV over CAC. Those are…
32 00:03:32.330 ⇒ 00:03:36.059 Awaish Kumar: No, I can’t… like, but… Okay, can you show me what…
33 00:03:36.450 ⇒ 00:03:38.810 Awaish Kumar: dashboard you are looking at, because…
34 00:03:38.810 ⇒ 00:03:39.290 Amber Lin: Yeah.
35 00:03:39.290 ⇒ 00:03:42.499 Awaish Kumar: We should not be getting CAT from this model.
36 00:03:43.630 ⇒ 00:03:45.290 Amber Lin: Sounds good, okay.
37 00:03:47.370 ⇒ 00:03:48.729 Amber Lin: Let me pull it up.
38 00:03:50.410 ⇒ 00:03:57.920 Amber Lin: If we don’t get CAC from this model, where will we be getting it from?
39 00:03:58.060 ⇒ 00:04:00.550 Awaish Kumar: Yeah, if you can show me the dashboard, I might have some.
40 00:04:00.990 ⇒ 00:04:03.310 Awaish Kumar: Clues of where it is coming from.
41 00:04:03.340 ⇒ 00:04:09.750 Amber Lin: Cool, okay. There’s so many different random dashboards.
42 00:04:10.590 ⇒ 00:04:11.680 Amber Lin: One sec…
43 00:04:26.430 ⇒ 00:04:27.860 Awaish Kumar: Okay.
44 00:04:28.570 ⇒ 00:04:33.860 Amber Lin: So, the… These are reorder rates.
45 00:04:35.440 ⇒ 00:04:35.800 Awaish Kumar: Yup.
46 00:04:35.800 ⇒ 00:04:40.860 Amber Lin: And they have ones that’s, like, this uses ad spend and revenue.
47 00:04:40.860 ⇒ 00:04:46.909 Awaish Kumar: Can we… can we click on… These three dots… And go into the worksheet.
48 00:04:47.590 ⇒ 00:04:49.000 Awaish Kumar: And then, yeah.
49 00:04:53.030 ⇒ 00:04:54.180 Awaish Kumar: Okay, cool.
50 00:04:54.180 ⇒ 00:04:59.149 Amber Lin: This is the cohort, this is the one we’re trying to build, right?
51 00:04:59.150 ⇒ 00:04:59.690 Awaish Kumar: Okay.
52 00:05:00.030 ⇒ 00:05:03.280 Amber Lin: And then they have total ad spend.
53 00:05:06.340 ⇒ 00:05:07.110 Awaish Kumar: Hmm.
54 00:05:07.640 ⇒ 00:05:09.239 Awaish Kumar: Can I see the model?
55 00:05:10.660 ⇒ 00:05:11.220 Amber Lin: Yeah.
56 00:05:12.610 ⇒ 00:05:14.020 Awaish Kumar: Shouldn’t…
57 00:05:15.510 ⇒ 00:05:17.680 Amber Lin: Oh, wait.
58 00:05:19.860 ⇒ 00:05:22.229 Awaish Kumar: Model, go to definition.
59 00:05:29.960 ⇒ 00:05:30.530 Awaish Kumar: Good job.
60 00:05:34.500 ⇒ 00:05:36.300 Awaish Kumar: It has total added span.
61 00:05:39.220 ⇒ 00:05:42.620 Amber Lin: Yeah.
62 00:05:43.270 ⇒ 00:05:47.130 Awaish Kumar: Let me look at the model, because…
63 00:05:48.360 ⇒ 00:05:49.919 Awaish Kumar: It shouldn’t…
64 00:05:53.230 ⇒ 00:05:57.200 Amber Lin: I mean, if I can take this from another… I can…
65 00:05:57.420 ⇒ 00:05:58.229 Awaish Kumar: If I can…
66 00:05:58.230 ⇒ 00:06:02.170 Amber Lin: calculate that can work, but then it also relies on, like.
67 00:06:02.300 ⇒ 00:06:15.200 Amber Lin: first order month and first order product, so I’m not sure of how I would attribute, like, I would assign the ad spend if I joined to a different model.
68 00:06:15.440 ⇒ 00:06:17.540 Amber Lin: Like, that’s my only concern.
69 00:06:20.710 ⇒ 00:06:26.140 Awaish Kumar: cohorting base… Let me see how it’s bringing in.
70 00:06:28.440 ⇒ 00:06:30.660 Awaish Kumar: Okay, I just went… oh no.
71 00:06:30.800 ⇒ 00:06:37.690 Awaish Kumar: Okay, okay, we can do… I can edit.
72 00:06:37.820 ⇒ 00:06:42.350 Awaish Kumar: But it is, like, What is happening in…
73 00:06:42.610 ⇒ 00:06:46.360 Awaish Kumar: And there is, when you bring in total spend there.
74 00:06:46.610 ⇒ 00:06:53.830 Awaish Kumar: I will bring it based on the… the… the… month, and the… product, right?
75 00:06:55.100 ⇒ 00:06:58.969 Awaish Kumar: So, when you have same rows for the month and product, like.
76 00:06:59.080 ⇒ 00:07:05.700 Awaish Kumar: we have a column, like, month since first order. So, like, for the same cohort, you might have multiple rows.
77 00:07:05.850 ⇒ 00:07:08.120 Awaish Kumar: But for each of those, the…
78 00:07:08.220 ⇒ 00:07:16.309 Awaish Kumar: same revenue. So, when we are, like, then we have to, like, have some logic… Don’t you just.
79 00:07:16.310 ⇒ 00:07:17.500 Amber Lin: Oh…
80 00:07:17.500 ⇒ 00:07:21.590 Awaish Kumar: You just have to select max total spend instead of some
81 00:07:22.030 ⇒ 00:07:26.990 Awaish Kumar: That way, you can get the… like, ad spend is on a just month and product level.
82 00:07:27.230 ⇒ 00:07:31.619 Awaish Kumar: What the revenue will be for month, product, and month since first order.
83 00:07:32.860 ⇒ 00:07:33.970 Awaish Kumar: So, yeah, go ahead.
84 00:07:35.890 ⇒ 00:07:36.470 Amber Lin: Oh.
85 00:07:36.470 ⇒ 00:07:41.649 Awaish Kumar: be clear on that, but otherwise, yeah, I can adjust my models to include that.
86 00:07:42.030 ⇒ 00:07:45.670 Awaish Kumar: I don’t know why I didn’t add it in the first place.
87 00:07:45.890 ⇒ 00:07:48.820 Amber Lin: I see, I hear, okay, sounds good.
88 00:07:48.970 ⇒ 00:07:56.190 Amber Lin: So that’s the first ticket. Let me copy… Let me link this.
89 00:07:56.600 ⇒ 00:07:58.850 Awaish Kumar: Okay, what else?
90 00:07:59.200 ⇒ 00:08:03.639 Awaish Kumar: Okay, I will fix these models, these two models, and
91 00:08:04.010 ⇒ 00:08:06.170 Awaish Kumar: What else we have, apart from this?
92 00:08:06.370 ⇒ 00:08:09.039 Awaish Kumar: The generalist pen, this one is blocked.
93 00:08:09.220 ⇒ 00:08:13.980 Awaish Kumar: You… can you write the update in the comment?
94 00:08:14.330 ⇒ 00:08:22.599 Awaish Kumar: It is blocked because we, right now, don’t have UTM parameters in the orders that are coming from Eden West.
95 00:08:23.390 ⇒ 00:08:24.410 Amber Lin: Mmm.
96 00:08:25.640 ⇒ 00:08:26.870 Awaish Kumar: And we are…
97 00:08:26.870 ⇒ 00:08:31.689 Amber Lin: This is the other ticket that has… that we’re talking about UTM parameters.
98 00:08:31.990 ⇒ 00:08:32.630 Awaish Kumar: Alright.
99 00:08:33.559 ⇒ 00:08:38.900 Awaish Kumar: So, all the tickets that need UTM parameters, they are blocked, and this one is also blocked.
100 00:08:40.220 ⇒ 00:08:43.539 Amber Lin: Wait, one sec… this is, this is the one for you.
101 00:08:44.280 ⇒ 00:08:46.130 Awaish Kumar: Okay, yes, I need to link.
102 00:08:48.870 ⇒ 00:08:56.589 Amber Lin: And then… I know that… I think not channel spend, but this.
103 00:08:56.590 ⇒ 00:09:00.350 Awaish Kumar: focus on pharmacy SLAs and the…
104 00:09:01.200 ⇒ 00:09:01.890 Amber Lin: Yeah.
105 00:09:02.290 ⇒ 00:09:03.500 Awaish Kumar: But, yeah.
106 00:09:03.820 ⇒ 00:09:08.189 Awaish Kumar: UTM fields, this is also blocked, no UTM fields right now, and…
107 00:09:09.450 ⇒ 00:09:11.730 Amber Lin: So channel spend is also blocked?
108 00:09:11.990 ⇒ 00:09:13.030 Awaish Kumar: Yes, because it’.
109 00:09:13.030 ⇒ 00:09:13.980 Amber Lin: UTR.
110 00:09:13.980 ⇒ 00:09:16.129 Awaish Kumar: Because we need a channel for an order.
111 00:09:16.430 ⇒ 00:09:21.350 Awaish Kumar: In a chat summary, and the channel comes from UTM parameters? UTM parameters?
112 00:09:21.350 ⇒ 00:09:22.710 Amber Lin: -Oh, oh, oh. Okay.
113 00:09:22.710 ⇒ 00:09:26.999 Awaish Kumar: of broken, like, I talked to Greg, sorry, Zoran.
114 00:09:27.340 ⇒ 00:09:33.020 Awaish Kumar: And he mentioned that it is kind of broken right now, but we are going to figure that out.
115 00:09:33.140 ⇒ 00:09:35.909 Amber Lin: Cool, sounds good. So I will update…
116 00:09:36.670 ⇒ 00:09:37.190 Awaish Kumar: God.
117 00:09:37.190 ⇒ 00:09:38.590 Amber Lin: General spend.
118 00:09:39.450 ⇒ 00:09:42.230 Amber Lin: Alright, so that’s also blocked.
119 00:09:44.830 ⇒ 00:09:47.380 Awaish Kumar: Okay, I’ll leave them. What else?
120 00:09:47.850 ⇒ 00:09:51.520 Amber Lin: I’m gonna open Linear. This is too messy.
121 00:09:51.730 ⇒ 00:09:52.760 Amber Lin: Current.
122 00:09:53.540 ⇒ 00:09:54.390 Amber Lin: Okay.
123 00:09:57.340 ⇒ 00:09:58.589 Awaish Kumar: Yeah, this one…
124 00:09:58.590 ⇒ 00:09:59.760 Amber Lin: Order sequence.
125 00:10:00.000 ⇒ 00:10:05.360 Awaish Kumar: Yeah, 1, 2, and the other one, what is that?
126 00:10:06.370 ⇒ 00:10:12.929 Amber Lin: And is this also blocks channel info into, like, what channel these…
127 00:10:13.490 ⇒ 00:10:16.080 Awaish Kumar: Which retention table you are talking about here?
128 00:10:16.080 ⇒ 00:10:19.630 Amber Lin: Great question. Let me pull that up.
129 00:10:27.410 ⇒ 00:10:30.549 Amber Lin: I think that… This one. There’s this one on this.
130 00:10:31.390 ⇒ 00:10:33.379 Amber Lin: Like, this is the…
131 00:10:36.340 ⇒ 00:10:45.290 Amber Lin: like, where it came from… I think we won’t be able to do this if we don’t have what channels they came from. So let me go check.
132 00:10:45.720 ⇒ 00:10:46.430 Awaish Kumar: Which model?
133 00:10:46.430 ⇒ 00:10:47.480 Amber Lin: modeling.
134 00:10:50.590 ⇒ 00:10:54.150 Awaish Kumar: Return on ad spend, we have first order month.
135 00:10:55.410 ⇒ 00:10:58.010 Awaish Kumar: I respond. Do we have revenue?
136 00:10:59.780 ⇒ 00:11:00.970 Awaish Kumar: Okay, yeah.
137 00:11:00.970 ⇒ 00:11:04.020 Amber Lin: These are the same, but ad spend…
138 00:11:04.440 ⇒ 00:11:09.429 Awaish Kumar: Yeah, it is blog, it is… it has revenue, like, that means it is looking for orders.
139 00:11:09.700 ⇒ 00:11:12.220 Awaish Kumar: Came from specific.
140 00:11:12.470 ⇒ 00:11:13.580 Amber Lin: Channels.
141 00:11:14.010 ⇒ 00:11:15.260 Awaish Kumar: And we don’t want to change.
142 00:11:15.760 ⇒ 00:11:17.280 Amber Lin: Let’s say this.
143 00:11:19.270 ⇒ 00:11:22.760 Awaish Kumar: Okay, Ashwini, like, when we are talking about this, you are clear on…
144 00:11:23.030 ⇒ 00:11:26.290 Awaish Kumar: what to do, right? Once they get unblocked.
145 00:11:29.780 ⇒ 00:11:30.740 Awaish Kumar: Ashwini?
146 00:11:32.640 ⇒ 00:11:34.259 Ashwini Sharma: Sorry, I was on mute. Yeah.
147 00:11:35.170 ⇒ 00:11:35.800 Awaish Kumar: Okay.
148 00:11:36.550 ⇒ 00:11:38.060 Awaish Kumar: Yeah, now we all know.
149 00:11:38.260 ⇒ 00:11:38.690 Ashwini Sharma: Hmm.
150 00:11:38.690 ⇒ 00:11:40.690 Awaish Kumar: For the modeling, you have,
151 00:11:42.470 ⇒ 00:11:45.200 Awaish Kumar: So, yeah, we have 1, 2…
152 00:11:47.960 ⇒ 00:11:52.309 Amber Lin: Oh, sorry, back here. We have, essentially, 1 and 2.
153 00:11:52.560 ⇒ 00:11:54.550 Amber Lin: And maybe… I think this is…
154 00:11:54.550 ⇒ 00:11:56.580 Awaish Kumar: These aren’t all of my…
155 00:11:56.920 ⇒ 00:11:59.250 Amber Lin: This is what you already did, right? I’m gonna delete that.
156 00:11:59.250 ⇒ 00:12:06.789 Awaish Kumar: No, no, I didn’t… I created… like, we created, fact transaction is kind of…
157 00:12:06.980 ⇒ 00:12:10.510 Awaish Kumar: item enriched, maybe, that Anshuni already worked on, but…
158 00:12:10.510 ⇒ 00:12:14.489 Amber Lin: Yeah, I think we have similar ones to these.
159 00:12:14.490 ⇒ 00:12:18.899 Awaish Kumar: But we didn’t create this sales data joint monthly LTV codes yet.
160 00:12:18.900 ⇒ 00:12:23.429 Amber Lin: But that’s essentially your, like, retention table, right?
161 00:12:23.740 ⇒ 00:12:27.619 Awaish Kumar: Yeah, but I don’t know why they wanted me to do it separately, like, keep it…
162 00:12:28.840 ⇒ 00:12:32.159 Awaish Kumar: Like, just keep it as low priority. If you need it, you can bump it.
163 00:12:32.910 ⇒ 00:12:37.180 Amber Lin: Cool, because I was able to do the… Dash.
164 00:12:37.420 ⇒ 00:12:49.159 Amber Lin: boards, I think, but without this… I mean, if we have the monthly one, I should be able to have these. I can let you know if I end up needing them, but I’m gonna set them as low.
165 00:12:50.440 ⇒ 00:12:58.220 Amber Lin: And… Low pri-low priority. Those are for me.
166 00:12:58.660 ⇒ 00:13:01.509 Amber Lin: Okay, so essentially these two. These two.
167 00:13:02.330 ⇒ 00:13:09.199 Awaish Kumar: Okay, these are high priority, models. Now, if you need any clarification, you can ask Amber.
168 00:13:09.200 ⇒ 00:13:15.239 Ashwini Sharma: Okay, yeah, yeah, let’s go into one of them, right? The return, sorry, not return, spend or something.
169 00:13:17.670 ⇒ 00:13:19.769 Ashwini Sharma: Sequence, sequence, order sequence, yeah.
170 00:13:19.770 ⇒ 00:13:20.860 Amber Lin: sequence.
171 00:13:21.150 ⇒ 00:13:25.019 Amber Lin: Cool, so this is… I put the dashboard link here.
172 00:13:25.230 ⇒ 00:13:30.390 Amber Lin: And then once we’re here, we can go down to…
173 00:13:31.840 ⇒ 00:13:34.760 Amber Lin: This is the order sequence table.
174 00:13:35.130 ⇒ 00:13:39.410 Amber Lin: I’m gonna go to the model and link it for you.
175 00:13:40.080 ⇒ 00:13:41.970 Amber Lin: Cool, so.
176 00:13:51.030 ⇒ 00:13:54.239 Ashwini Sharma: Order sequence. Order sequence reorder rate.
177 00:13:54.700 ⇒ 00:13:59.159 Amber Lin: This is from order sequence analysis summary. This is the…
178 00:14:00.030 ⇒ 00:14:06.399 Awaish Kumar: Actually, you can also log into Omni using our
179 00:14:06.680 ⇒ 00:14:10.459 Awaish Kumar: edonate at brainforge.ai email, which is in one pass.
180 00:14:10.800 ⇒ 00:14:11.380 Ashwini Sharma: Okay.
181 00:14:12.670 ⇒ 00:14:14.810 Amber Lin: This one uses…
182 00:14:18.000 ⇒ 00:14:21.360 Amber Lin: This is the… There’s a table.
183 00:14:23.180 ⇒ 00:14:25.529 Ashwini Sharma: Cohort subsequent order summary.
184 00:14:25.720 ⇒ 00:14:33.660 Amber Lin: Yeah, so it has essentially what a wish… I think what a wish… And then…
185 00:14:35.740 ⇒ 00:14:42.659 Amber Lin: Like, one order after first, how many distinct customers there are, what’s the percentage of the cohort?
186 00:14:43.920 ⇒ 00:14:49.170 Amber Lin: Like, Awish, do you think we should just add this to your monthly table?
187 00:14:57.600 ⇒ 00:15:07.569 Amber Lin: Because you have months… oh, never mind. Yours is all months after first. This is orders after first. Okay, never mind, this is a separate table. Ignore what I said.
188 00:15:08.310 ⇒ 00:15:09.460 Amber Lin: Okay.
189 00:15:10.190 ⇒ 00:15:15.869 Amber Lin: Cool. This one, and then let’s go to pharmacy SLA.
190 00:15:22.810 ⇒ 00:15:31.020 Amber Lin: So, pharmacy… This is in farm… Fire mops…
191 00:15:32.170 ⇒ 00:15:38.529 Amber Lin: And when do you think we’ll have data coming in? Everything’s just blank right now.
192 00:15:51.620 ⇒ 00:15:56.789 Amber Lin: I wish. Do you know when we’ll have, like, data coming in to Eden OS?
193 00:15:59.110 ⇒ 00:16:08.000 Awaish Kumar: I’m talking about that. I’m concerned about that as well, because I don’t know how this will work out. We are building separate models, and then…
194 00:16:08.250 ⇒ 00:16:11.720 Awaish Kumar: If our… all data doesn’t come in… Yeah.
195 00:16:13.800 ⇒ 00:16:20.150 Awaish Kumar: We might have to… then unify… about these two systems.
196 00:16:20.600 ⇒ 00:16:30.760 Amber Lin: Okay, so I don’t think we should build, like, all of the models right away? I don’t know. Oh, okay, this also comes from order summary.
197 00:16:32.290 ⇒ 00:16:34.719 Amber Lin: So, this pharmacy SLA thing.
198 00:16:35.200 ⇒ 00:16:44.559 Awaish Kumar: Yeah, or a summary, like, let’s… like, Amber, I just want you to… I need your little bit more help here, because,
199 00:16:44.610 ⇒ 00:16:56.279 Awaish Kumar: the current order table in Eden OS has all the dates, like, the dates for when it was shipped, when it was sent to pharmacy, and things like that.
200 00:16:56.640 ⇒ 00:16:57.160 Awaish Kumar: Huh.
201 00:16:57.960 ⇒ 00:16:58.900 Awaish Kumar: Like, it has all these…
202 00:16:58.900 ⇒ 00:17:00.240 Amber Lin: Check the…
203 00:17:00.560 ⇒ 00:17:03.889 Awaish Kumar: Look at that, if you can build something with that table.
204 00:17:04.619 ⇒ 00:17:09.059 Amber Lin: Yeah, I think we should be able to do that, Eden.
205 00:17:09.380 ⇒ 00:17:10.340 Awaish Kumar: Yeah, you know what?
206 00:17:10.349 ⇒ 00:17:11.319 Amber Lin: Yes.
207 00:17:12.399 ⇒ 00:17:18.679 Amber Lin: I just, maybe I just need, like, a little help of modeling to put that into our existing order?
208 00:17:19.510 ⇒ 00:17:24.040 Awaish Kumar: Yeah, it was just there, we missed it. It was faked fact, Eden was…
209 00:17:24.290 ⇒ 00:17:26.809 Awaish Kumar: Order table, not this one, this…
210 00:17:31.590 ⇒ 00:17:33.269 Awaish Kumar: Yeah, oh, yeah.
211 00:17:34.300 ⇒ 00:17:34.690 Amber Lin: Right.
212 00:17:34.820 ⇒ 00:17:38.320 Awaish Kumar: See, there are… Fundates for all of this.
213 00:17:38.320 ⇒ 00:17:38.830 Amber Lin: Okay.
214 00:17:39.500 ⇒ 00:17:42.829 Awaish Kumar: And then maybe we can utilize these dates to…
215 00:17:43.610 ⇒ 00:17:44.530 Amber Lin: Oh, fuck.
216 00:17:44.560 ⇒ 00:17:45.860 Awaish Kumar: Days and things like that.
217 00:17:46.600 ⇒ 00:17:48.159 Amber Lin: Okay, okay, okay.
218 00:17:48.470 ⇒ 00:17:58.549 Amber Lin: Cool. Let me try and work with this. Let me try, and then… Let’s say this is…
219 00:17:59.450 ⇒ 00:18:10.019 Amber Lin: Medium. So, if you can do the order sequence, I can try to… try to see if I can build this, and once we have the order sequence, I’ll build the…
220 00:18:10.210 ⇒ 00:18:10.930 Amber Lin: the other one.
221 00:18:10.930 ⇒ 00:18:13.430 Ashwini Sharma: Sure, yeah, I’ll start on the order sequencing.
222 00:18:13.430 ⇒ 00:18:15.169 Amber Lin: Okay, cool, sounds good.
223 00:18:16.280 ⇒ 00:18:17.210 Ashwini Sharma: Alright.
224 00:18:17.450 ⇒ 00:18:18.240 Amber Lin: Yeah.
225 00:18:18.700 ⇒ 00:18:31.040 Amber Lin: Alright, let’s get started. And, Owish, how many dashboards do we need by the end of week? Like, right now, I think we should be good for top 10, except for everything that’s blocked.
226 00:18:31.990 ⇒ 00:18:34.069 Awaish Kumar: Yeah, that’s… that’s enough, right?
227 00:18:34.830 ⇒ 00:18:35.310 Amber Lin: Yeah.
228 00:18:35.310 ⇒ 00:18:45.039 Awaish Kumar: We were in a rush to show something. I think we now have the understanding of the system, understanding of how models will… how dashboards will be built from this.
229 00:18:45.290 ⇒ 00:18:47.630 Awaish Kumar: We are in a good… Oh.
230 00:18:47.910 ⇒ 00:19:00.420 Amber Lin: Cool, because especially if everything is in staging, when we move to prod, everything’s gonna break. So I don’t want to build too much, and… but… and there’s…
231 00:19:00.420 ⇒ 00:19:07.710 Awaish Kumar: We can, like, now get to a more… like,
232 00:19:08.160 ⇒ 00:19:12.470 Awaish Kumar: Like, what you say, a state where we can think about things, like…
233 00:19:12.900 ⇒ 00:19:18.409 Awaish Kumar: what happened, and how we are going to solve them, instead of building, that we have built out
234 00:19:18.520 ⇒ 00:19:27.030 Awaish Kumar: We can show them that this is based on the staging data, but we need more iterations, and we are going to slow down on this, but what I…
235 00:19:27.030 ⇒ 00:19:27.500 Amber Lin: Oh, cool.
236 00:19:27.500 ⇒ 00:19:34.519 Awaish Kumar: What I’m pushing for is, like, right now, I’m also talking about how the data from BASC will come in into the.
237 00:19:34.520 ⇒ 00:19:36.150 Amber Lin: Oh, okay.
238 00:19:36.150 ⇒ 00:19:37.410 Awaish Kumar: identify it.
239 00:19:37.520 ⇒ 00:19:43.709 Awaish Kumar: So once I have an answer, we might, maybe in a much better position.
240 00:19:43.960 ⇒ 00:19:44.880 Awaish Kumar: Okay.
241 00:19:46.340 ⇒ 00:19:53.900 Amber Lin: Yeah, sounds good. So, I will… like, everything’s gonna look blank, because there’s very little data, but…
242 00:19:53.900 ⇒ 00:19:55.920 Awaish Kumar: Just 40 orders there, so…
243 00:19:55.920 ⇒ 00:19:56.560 Amber Lin: Yeah.
244 00:19:56.560 ⇒ 00:19:57.000 Awaish Kumar: Endo.
245 00:19:57.000 ⇒ 00:20:05.939 Amber Lin: Okay, sounds good. Thank you, appreciate the help and the push. So, Ashreen, just let me know when the model is done.
246 00:20:06.100 ⇒ 00:20:08.149 Amber Lin: And always let me know when you push…
247 00:20:08.150 ⇒ 00:20:08.480 Awaish Kumar: It’s annoying.
248 00:20:08.480 ⇒ 00:20:09.220 Amber Lin: change.
249 00:20:10.370 ⇒ 00:20:14.660 Awaish Kumar: Yeah, I… I will… I have to work… do some work on CTA right now.
250 00:20:14.800 ⇒ 00:20:16.120 Awaish Kumar: So…
251 00:20:17.380 ⇒ 00:20:18.060 Amber Lin: Okay.
252 00:20:18.060 ⇒ 00:20:21.780 Awaish Kumar: Maybe after an hour or something, I will come back to Eden, okay?
253 00:20:21.780 ⇒ 00:20:24.050 Amber Lin: Yeah, okay, sounds good.
254 00:20:25.380 ⇒ 00:20:26.080 Awaish Kumar: Thank you.
255 00:20:26.080 ⇒ 00:20:28.100 Amber Lin: Thanks both. Alright, bye bye.