Meeting Title: Magic Spoon — Brainforge sync Date: 2026-02-13 Meeting participants: Demilade Agboola, Ashwini Sharma, Michael Thorson, Uttam Kumaran, Mary Burke
WEBVTT
1 00:00:18.530 ⇒ 00:00:19.600 Ashwini Sharma: Hey, Davey?
2 00:00:21.160 ⇒ 00:00:22.519 Demilade Agboola: Hi, Srini, how you doing?
3 00:00:22.930 ⇒ 00:00:24.150 Ashwini Sharma: I’m good, how are you?
4 00:00:24.450 ⇒ 00:00:25.609 Demilade Agboola: Pretty good, pretty good.
5 00:00:26.530 ⇒ 00:00:27.480 Ashwini Sharma: Hi, Michael.
6 00:00:30.460 ⇒ 00:00:32.040 Michael Thorson: Hey, how’s it going?
7 00:00:32.980 ⇒ 00:00:33.660 Ashwini Sharma: Good?
8 00:00:33.840 ⇒ 00:00:35.259 Demilade Agboola: Pretty good, pretty good.
9 00:00:37.340 ⇒ 00:00:38.069 Mary Burke: Hey guys!
10 00:00:38.320 ⇒ 00:00:39.020 Michael Thorson: Nice.
11 00:00:40.240 ⇒ 00:00:41.759 Demilade Agboola: Hi, how’s everyone doing?
12 00:00:43.370 ⇒ 00:00:44.050 Mary Burke: Good!
13 00:00:44.420 ⇒ 00:00:45.399 Michael Thorson: Pretty good.
14 00:00:45.400 ⇒ 00:00:47.109 Demilade Agboola: That’s good to hear, that’s good to hear.
15 00:00:48.030 ⇒ 00:00:53.089 Demilade Agboola: Are we… is JT gonna be joining us, or should we just get started?
16 00:00:53.730 ⇒ 00:00:55.059 Mary Burke: He will not be joining us.
17 00:00:55.060 ⇒ 00:00:56.910 Demilade Agboola: Oh, okay, alright, so we can just get started.
18 00:01:02.940 ⇒ 00:01:07.489 Demilade Agboola: Okay, so it has just, like, a… End of week review.
19 00:01:08.520 ⇒ 00:01:09.820 Demilade Agboola: Of this week.
20 00:01:10.510 ⇒ 00:01:16.369 Demilade Agboola: Again, like, just the general, like, what we’ve been aiming for.
21 00:01:17.050 ⇒ 00:01:25.559 Demilade Agboola: The Spins pipeline, the MMM Data Mart, and as well as the, just data audit.
22 00:01:26.430 ⇒ 00:01:28.049 Demilade Agboola: So this…
23 00:01:28.180 ⇒ 00:01:36.080 Demilade Agboola: Updates this week are around the Spins API, the NMM Mart, as well as our next steps and potential blockers as well.
24 00:01:37.380 ⇒ 00:01:47.090 Demilade Agboola: So high level, the Spins API update, we’ve been able to refactor the pipeline, so it now goes from API to S3 to Redshift.
25 00:01:47.470 ⇒ 00:01:52.340 Demilade Agboola: And that has allowed us to have less of, A data drop.
26 00:01:52.740 ⇒ 00:01:55.540 Demilade Agboola: And so now the disparity is…
27 00:01:55.750 ⇒ 00:02:00.870 Demilade Agboola: really close to what we expect to see out of the, Spain’s API.
28 00:02:01.290 ⇒ 00:02:09.199 Demilade Agboola: And also, like, in the warehouse, the dollar slash volume QA looks pretty good. Like I mentioned, it’s about…
29 00:02:09.380 ⇒ 00:02:12.570 Demilade Agboola: A range of about $200 for 52 weeks.
30 00:02:12.850 ⇒ 00:02:15.430 Demilade Agboola: In the test… in the tested slices so far.
31 00:02:15.750 ⇒ 00:02:21.840 Demilade Agboola: However, the TDP as well as ACV didn’t match, like, what we get through the platform.
32 00:02:22.540 ⇒ 00:02:26.269 Demilade Agboola: And so, we’ve reached out to Spins about that, and just how to…
33 00:02:26.870 ⇒ 00:02:30.429 Demilade Agboola: We’ll compute that and use that in our current models.
34 00:02:30.620 ⇒ 00:02:37.190 Demilade Agboola: So we’ve reached out to them, we’re currently waiting for feedback from them, and once we get that feedback, we’ll be able to proceed.
35 00:02:37.360 ⇒ 00:02:39.230 Demilade Agboola: with the spins data.
36 00:02:40.420 ⇒ 00:02:42.539 Demilade Agboola: Any questions on this update?
37 00:02:45.460 ⇒ 00:02:46.730 Mary Burke: No questions for me?
38 00:02:47.350 ⇒ 00:02:48.430 Michael Thorson: Aligned.
39 00:02:48.610 ⇒ 00:02:49.700 Michael Thorson: That’s the…
40 00:02:49.700 ⇒ 00:02:55.809 Uttam Kumaran: I’m pumped that we, like, got this S3. This is, like, a much better solution, like, overall. So I’m, like…
41 00:02:56.240 ⇒ 00:02:58.819 Uttam Kumaran: Really happy that we got to that point.
42 00:02:58.980 ⇒ 00:03:04.960 Uttam Kumaran: And… I will say, like, even in interacting with Spins for our other client.
43 00:03:05.180 ⇒ 00:03:13.519 Uttam Kumaran: it’s just slow. So, I think we’ve, like, done our best to just continue to push things. It seems like we’re working our way up the chain, so…
44 00:03:13.980 ⇒ 00:03:19.729 Mary Burke: Yeah, and if we can support at all, just, I don’t know, adding any sense of urgency to their team, let us know. Okay.
45 00:03:19.900 ⇒ 00:03:20.860 Uttam Kumaran: Okay.
46 00:03:21.760 ⇒ 00:03:24.049 Michael Thorson: Happy to poke more.
47 00:03:24.370 ⇒ 00:03:29.510 Michael Thorson: I think the next, like, we were all talking about this, just, like, the, kind of, like.
48 00:03:29.670 ⇒ 00:03:49.060 Michael Thorson: I think we could probably, based on, like, the pipes looking okay, like, actually start to pull the Magic Spoon, like, full history if we wanted to. And then the ACV and TDP calculations, like, we can kind of parallel path that conversation with spins while we’re, like, making sure all the Magic Spoon data came in.
49 00:03:50.210 ⇒ 00:03:56.630 Demilade Agboola: Okay, sounds good. We can make a note of that, and just, ensuring that we have the backfill for Magic Spoon data.
50 00:03:59.920 ⇒ 00:04:03.250 Demilade Agboola: On the MMM art,
51 00:04:03.610 ⇒ 00:04:10.190 Demilade Agboola: basically all spend is done. We’ve gotten… Qa from JT.
52 00:04:11.180 ⇒ 00:04:15.180 Demilade Agboola: And he’s also given us feedback based off of,
53 00:04:15.450 ⇒ 00:04:17.970 Demilade Agboola: Of feedback he’s received from clients.
54 00:04:18.149 ⇒ 00:04:23.369 Demilade Agboola: He’s also let us know that he wanted a daily march, so that was also done as well.
55 00:04:25.390 ⇒ 00:04:32.059 Demilade Agboola: And then we’ve been able to fix those QA disparity with the daily mat and the weekly math, so that has been fixed, so now…
56 00:04:32.290 ⇒ 00:04:38.420 Demilade Agboola: When I query it, everything matches to the exact, decimal, and to the exact send, so that’s pretty good.
57 00:04:38.970 ⇒ 00:04:46.000 Demilade Agboola: So yeah, basically the modeling Spins data as well has also been done, so that’s basically stuff around
58 00:04:46.120 ⇒ 00:04:56.029 Demilade Agboola: the, dollars and units. Again, there’s a slight thing I will have to push today, but that’s a fix that will be done today, and will be done, with…
59 00:04:56.180 ⇒ 00:04:59.659 Demilade Agboola: Having all the space there, so that’ll be the base table.
60 00:05:00.010 ⇒ 00:05:05.440 Demilade Agboola: And then all that will be left will just be ACV and TEDP based off of,
61 00:05:06.640 ⇒ 00:05:09.170 Demilade Agboola: the feedback from the SPEANS team.
62 00:05:09.490 ⇒ 00:05:14.450 Demilade Agboola: So basically, the status from that overall is just, like, we’re almost done, you know.
63 00:05:14.990 ⇒ 00:05:20.139 Demilade Agboola: And then, potentially, once we push the new model in, I would ask Michael to look at it.
64 00:05:20.290 ⇒ 00:05:25.940 Demilade Agboola: He feels like the numbers are much, like, the filters.
65 00:05:26.280 ⇒ 00:05:31.600 Demilade Agboola: Are what he wants to see, that’ll be good, and we’ll be done with that, and just basically be waiting for spins.
66 00:05:33.600 ⇒ 00:05:38.809 Demilade Agboola: Yeah, so I think the ask would just be, are there any other numbers based off of, like, what we have?
67 00:05:39.080 ⇒ 00:05:44.130 Demilade Agboola: just generally speaking, like, across spins and the different, like, CSVs that we might want to see.
68 00:05:44.310 ⇒ 00:05:50.150 Demilade Agboola: So that we know that, like, once we push the… once I send the PR in, we’re, like, basically done.
69 00:05:52.770 ⇒ 00:06:14.740 Michael Thorson: I think that’s, like, I’d love to take a, like, I’ll carve out some time next week to see what your base model is, and then I think the next steps in terms of that model is actually, like, there’s a lot of use cases that this will be… like, the raw data will be split into, so we have an internal meeting next week to discuss, like, how do we actually want to break this thing up, because it’s so many levels of aggregation.
70 00:06:14.740 ⇒ 00:06:24.030 Michael Thorson: So we should have some more direction in terms of, like, where we want to take the model next, probably next Wednesday or Thursday, so that’ll be…
71 00:06:24.030 ⇒ 00:06:31.070 Michael Thorson: Just, like, keep that in mind. Like, keep going with what you’re doing, and then we’ll follow up with some additional direction.
72 00:06:31.540 ⇒ 00:06:47.610 Demilade Agboola: Alright, sounds good then. So I think, potentially, I would need to put some time on your calendar for, like, Wednesday or Thursday morning, so we can just sync on that, have an idea of the next phase of this, but basically, I think this phase is… we’re about to just tie with the bull and be done with it.
73 00:06:51.300 ⇒ 00:06:56.059 Demilade Agboola: Okay, any questions or feedback beyond that? Are we all good on this?
74 00:06:57.100 ⇒ 00:07:18.899 Michael Thorson: I am curious, like, we’re kind of… I wanted to slow down and, like, walk before we run and focus on the Magic Spoon backfill, but obviously, like, the goal of this is actually to bring in, like, a lot more brands and, like, a higher volume of data. Do you have any concerns in terms of rate limits as we start that backfill process?
75 00:07:19.210 ⇒ 00:07:29.119 Michael Thorson: just wanted to, like, make sure we could get Magic Spoon in cleanly before we expand the search, because I think that’s where a lot of our issues came from. But, yeah, can you speak to that, maybe Yashmini or Utsam?
76 00:07:29.120 ⇒ 00:07:34.020 Ashwini Sharma: Yeah, I think even for Magic Spoon, we might run out of rate limits, so if we can…
77 00:07:34.140 ⇒ 00:07:45.479 Ashwini Sharma: you know, do it in smaller, chunks of data, I think that would be, better. Maybe we could pull one productive universe at a time?
78 00:07:45.800 ⇒ 00:07:48.640 Ashwini Sharma: Instead of pulling all 3 of them.
79 00:07:49.070 ⇒ 00:07:51.770 Ashwini Sharma: Right? What do you…
80 00:07:52.030 ⇒ 00:08:02.470 Uttam Kumaran: Shweeney, I’m probably on… probably on our side, we can work, I… I… maybe you can send me the API limit docs. I think I reviewed it maybe, like, a little bit ago.
81 00:08:03.800 ⇒ 00:08:15.859 Uttam Kumaran: And then, yeah, basically, I think we will just have to schedule our exports to adhere to that. Once we start to land stuff for just Magic Spoon, it’ll give us a lot more clarity on, like, the volume and how much time.
82 00:08:16.300 ⇒ 00:08:16.750 Michael Thorson: Hmm.
83 00:08:16.750 ⇒ 00:08:19.139 Uttam Kumaran: And then we basically, like, can do some…
84 00:08:19.330 ⇒ 00:08:26.810 Uttam Kumaran: envelope math to, like, scale from there, conservatively. And then, I think, Michael, just basically tried to give you an SLA on, like.
85 00:08:27.020 ⇒ 00:08:27.430 Michael Thorson: Yep.
86 00:08:27.430 ⇒ 00:08:31.230 Uttam Kumaran: okay, this pipeline is taking X amount of time to run.
87 00:08:31.390 ⇒ 00:08:38.400 Uttam Kumaran: give or take X, like, so we can reliably feel like we can get refreshed data within Y timeframe. It’s, like, sort of, like.
88 00:08:38.679 ⇒ 00:08:41.089 Uttam Kumaran: the message, I’m like, hopefully we can get to you.
89 00:08:41.740 ⇒ 00:08:47.730 Michael Thorson: I would… yeah, that would be awesome, like, next week, so we have something we can communicate very discreetly to, like, leadership and say.
90 00:08:47.730 ⇒ 00:08:55.990 Uttam Kumaran: Are they… do they have, like, goal… do they have goals or, like, minimum SLAs that you’re trying to meet, or sort of just based on whatever… however fast we could do it?
91 00:08:56.280 ⇒ 00:08:59.610 Michael Thorson: Honestly, like, there’s no…
92 00:09:00.150 ⇒ 00:09:16.119 Michael Thorson: like, it’s a monthly process that we, like, dive into, so it’s really, like, the time savings is from, like, our team, Heather is, like, manually extracting all this information, which takes her, you know, it’s like 4 days to process, so it’s, like, it’s a continuous improvement, rather than, like, a hard…
93 00:09:16.120 ⇒ 00:09:20.309 Michael Thorson: So it’s 4 days on close a month, like, right after close a month.
94 00:09:20.460 ⇒ 00:09:25.549 Michael Thorson: Four days right after… 4 days right after the data delivery, so I think.
95 00:09:25.550 ⇒ 00:09:26.430 Uttam Kumaran: Oh, okay.
96 00:09:26.920 ⇒ 00:09:29.749 Michael Thorson: Exactly. So the last delivery was, what, 1-28?
97 00:09:30.510 ⇒ 00:09:36.830 Michael Thorson: Is that right? So it’d be, like, the February release date is kind of our next opportunity to align with.
98 00:09:36.830 ⇒ 00:09:37.280 Demilade Agboola: Okay.
99 00:09:37.280 ⇒ 00:09:39.870 Uttam Kumaran: I see. Okay. Gotcha.
100 00:09:40.770 ⇒ 00:09:42.420 Uttam Kumaran: Okay, great.
101 00:09:42.580 ⇒ 00:09:54.469 Demilade Agboola: Alright, yeah, so we’ll turn, just, like, get some context about that. We’ll do some documentation, and just kind of just send that over, so you… we will loop you in on what can be done, and
102 00:09:54.660 ⇒ 00:09:59.789 Demilade Agboola: How we can realistically go about the rate limits so that we can get all the data that’s needed.
103 00:10:00.760 ⇒ 00:10:01.380 Michael Thorson: Cool.
104 00:10:01.590 ⇒ 00:10:02.260 Demilade Agboola: Great.
105 00:10:02.470 ⇒ 00:10:07.869 Michael Thorson: Yeah, excited to talk about that next week, and get building some interesting marts for our team. That’s awesome.
106 00:10:08.120 ⇒ 00:10:09.160 Demilade Agboola: Yeah, sounds great.
107 00:10:10.680 ⇒ 00:10:18.230 Demilade Agboola: Okay, yeah, so, like, the next steps slash blockers that we have, so, in terms of blockers.
108 00:10:18.500 ⇒ 00:10:24.560 Demilade Agboola: like we’ve mentioned, like, the TDP SEV thing is currently a blocker, we’re just waiting for the…
109 00:10:24.840 ⇒ 00:10:32.099 Demilade Agboola: Spins team to, respond to us, and, like, basically we’re just gonna have to keep following up. We’ll follow up with the Spins team.
110 00:10:32.450 ⇒ 00:10:36.920 Demilade Agboola: Through the email thread, and just kind of see…
111 00:10:37.270 ⇒ 00:10:44.550 Demilade Agboola: How they, you know, respond to us, and how we’re able to use that information to be able to push this bit forward and over the line.
112 00:10:44.810 ⇒ 00:10:51.939 Demilade Agboola: In terms of, like, next steps, we’re going to have meetings with the outgoing data vendor next week.
113 00:10:52.340 ⇒ 00:10:59.609 Demilade Agboola: Just basically to get business context on, like, what we need to do to take over, as well as just maintain things and keep things flowing.
114 00:11:02.020 ⇒ 00:11:09.499 Demilade Agboola: Also, in line with that, we will be documenting, like, dbt questions, as well as models that we might need to, like, look at and dissect.
115 00:11:09.870 ⇒ 00:11:18.000 Demilade Agboola: So that we can have, again, context for takeover, and while we have them still around, let’s get the knowledge transfer going.
116 00:11:18.280 ⇒ 00:11:21.569 Demilade Agboola: And use that to be able to,
117 00:11:22.330 ⇒ 00:11:26.250 Demilade Agboola: Stabilize the ship, so that when they go, things don’t, like, start rocking.
118 00:11:26.450 ⇒ 00:11:29.409 Demilade Agboola: And so that will be part of the next step.
119 00:11:29.520 ⇒ 00:11:48.560 Demilade Agboola: As well as, like, the final will just be, like, ASOW and renewal, so at this point, I know we’ve done that internally. It’s currently being reviewed, but that should be shared, today. And the idea is we will want to send that over, have the Magic Spoon team look over it, and then sign it and send it back to us, and we’ll be…
120 00:11:48.790 ⇒ 00:11:51.419 Demilade Agboola: Ready to go, basically.
121 00:11:51.990 ⇒ 00:12:00.010 Uttam Kumaran: Yeah, and Michael, maybe just some context. We spoke with Mary yesterday, I think one of the things is we heard about the partner transition, so the next two weeks, we want
122 00:12:00.360 ⇒ 00:12:01.570 Uttam Kumaran: Our focus to just…
123 00:12:01.780 ⇒ 00:12:11.909 Uttam Kumaran: kind of hit them with whatever questions we have about, anything related to Prefect and any of the core Shopify data models that they’ve been maintaining. In particular, we also heard and
124 00:12:12.180 ⇒ 00:12:23.059 Uttam Kumaran: snoop around in dbt to kind of see a lot of the failures, and kind of heard a little bit about how, like, alerting and, failure triaging, has been handled, and so…
125 00:12:23.460 ⇒ 00:12:35.219 Uttam Kumaran: Overall, like, in terms of, like, platform, stability, like, that’s our first thing that we typically go in and hit, like, making sure that all jobs are running, we have a clear path when jobs fail, and a clear, like.
126 00:12:35.280 ⇒ 00:12:51.240 Uttam Kumaran: Typically, we try to triage with, I mean, during business hours within, like, 2 hours, at which point we can basically say, hey, this is, like, gonna take, like, a long time to fix, or this is gonna take… I’m just gonna push a fix. So that’s how we typically manage DPT for, like, all of our clients.
127 00:12:51.350 ⇒ 00:13:09.039 Uttam Kumaran: So, ideally, when things fail, one, there’s not, like, fake alerts, so, like, you sort of don’t want to have, like, a cry wolf situation. So, we’ll go through and clear all… make sure all the tests and job failures that are happening are actually, like, worth investigating, and then we have a path to bring those into Slack.
128 00:13:09.440 ⇒ 00:13:11.249 Uttam Kumaran: So that we can see and triage.
129 00:13:11.350 ⇒ 00:13:20.999 Uttam Kumaran: So that’ll be, like, kind of the first thing, and then also from their team, we just want to get as much information on, like, things that were commonly failing, gotchas, like.
130 00:13:21.540 ⇒ 00:13:36.600 Uttam Kumaran: parts of the repo that, like, maybe they had a hard time maintaining. Just, like, anything, any context on that, so that we can start to help, like, build out a roadmap of, like, those fixes. I know there’s also a lot of, like, net new modeling coming down the pipeline, so that’ll just sort of be, I think, a week-to-week
131 00:13:36.860 ⇒ 00:13:43.429 Uttam Kumaran: discussion within us about, like, hey, there are these platform improvements or tech debt, versus there’s that new ask, how should we allocate? So…
132 00:13:46.870 ⇒ 00:13:55.520 Michael Thorson: Yeah, that makes sense, and like, just kind of a week-to-week discussion, I think, will be key to keeping the priorities, like, getting, like, balanced, I guess.
133 00:13:55.820 ⇒ 00:13:56.430 Uttam Kumaran: Cool.
134 00:13:56.560 ⇒ 00:14:01.149 Uttam Kumaran: And then even on, like, on, like, Prefect, for example, like, I think one of the things that…
135 00:14:01.700 ⇒ 00:14:09.860 Uttam Kumaran: outside of everything in your stack, I feel like that’s probably the one thing that we would say there are some better options.
136 00:14:09.940 ⇒ 00:14:29.889 Uttam Kumaran: But for us, I think we will kind of spend a little bit more time looking and seeing, like, okay, can we reliably maintain the system, or are there other, you know, basically same cost or less options for us to move orchestration to something? Like, commonly, we use Dagster, books have used Airflow before. Really what that allows us to do is to ship pipelines and observe them.
137 00:14:29.980 ⇒ 00:14:31.810 Uttam Kumaran: As they go, faster.
138 00:14:33.310 ⇒ 00:14:44.489 Uttam Kumaran: So, like, that’s something probably, like, in the year, we will, like, try to just, like, put… do a spike on it, and, like, kind of share for us to see, like, okay, if we want to recommend that, how long would that take, and…
139 00:14:44.590 ⇒ 00:14:49.770 Uttam Kumaran: what’s the… what’s the benefit? Apart from that, on the stack side, I feel like most of it is just gonna be…
140 00:14:50.090 ⇒ 00:14:56.599 Uttam Kumaran: making sure that, like, it’s gonna be a lot of just dbt clean up work. We haven’t heard anything about, like.
141 00:14:56.800 ⇒ 00:15:02.609 Uttam Kumaran: necessarily, like, Models are taking too long to run, or, like, things aren’t refreshing.
142 00:15:02.900 ⇒ 00:15:13.149 Uttam Kumaran: So, I feel like it’s probably… although we are gonna look at all the job times, it’s probably less about that. That’s something common where, okay, if we need to go into Redshift and, like, create
143 00:15:13.400 ⇒ 00:15:23.909 Uttam Kumaran: Query groups and things like that, but it seems like more… most of it is actually just, like, there are pieces of logic that are… that have been built that we just maybe need to, like, clean up or make more modular.
144 00:15:24.020 ⇒ 00:15:27.049 Uttam Kumaran: And, like, have really great documentation around, so…
145 00:15:29.340 ⇒ 00:15:33.160 Michael Thorson: Yeah, cool. Yeah, that makes… makes sense. It’s kind of in line with what we’ve been thinking.
146 00:15:33.420 ⇒ 00:15:34.030 Uttam Kumaran: Okay.
147 00:15:34.600 ⇒ 00:15:35.140 Michael Thorson: Yeah.
148 00:15:35.630 ⇒ 00:15:51.520 Michael Thorson: At least in terms of the modeling piece. Yeah, I feel like if Prefix had been working and that was, like, what was recommended, it’s like, I feel like I have a bias toward just kind of, like, leaning into the existing infrastructure, so if y’all can prove the worth, we can have that discussion.
149 00:15:51.720 ⇒ 00:15:57.290 Uttam Kumaran: No, there’s definitely a difference between, like, we’re not… I don’t think we care much about if it’s annoying.
150 00:15:57.540 ⇒ 00:16:01.969 Uttam Kumaran: But if there is, like, a worth, I think we’ll share, like, you know, and that could be, hey.
151 00:16:02.240 ⇒ 00:16:04.660 Uttam Kumaran: take… saves us X amount of time, or…
152 00:16:04.780 ⇒ 00:16:10.279 Uttam Kumaran: These things are brittle, like, we expect them to be failures, and the time to mitigate will be high.
153 00:16:10.420 ⇒ 00:16:13.689 Uttam Kumaran: So, we’ll put that together before we do anything there, so…
154 00:16:14.200 ⇒ 00:16:21.529 Michael Thorson: Yeah, definitely, like, in terms of priority, that’s, like, lowest on my mind, compared to, like, the modeling and just kind of, like, ways of working.
155 00:16:21.990 ⇒ 00:16:24.170 Michael Thorson: I’m getting through this transition.
156 00:16:26.770 ⇒ 00:16:27.460 Demilade Agboola: Okay.
157 00:16:28.040 ⇒ 00:16:35.499 Demilade Agboola: Yeah, so… we basically have, like, put down things around, like, that in the SOW.
158 00:16:35.630 ⇒ 00:16:42.009 Demilade Agboola: Also, like, contact points, because, like we said, you know, we will be able to reach out.
159 00:16:42.180 ⇒ 00:16:52.780 Demilade Agboola: Basically, every week, we’ll be able to, you know, do an end-of-week update, as well as, like, touchpoints within the week about things around modeling, and just priorities. So, like, if we need to, like.
160 00:16:53.430 ⇒ 00:17:00.159 Demilade Agboola: pre-strategize, or things… prices need to shift midweek, we can always touch base and be able to…
161 00:17:00.370 ⇒ 00:17:06.289 Demilade Agboola: realign those expectations of the week based off of the midweek touch point as well, so…
162 00:17:07.760 ⇒ 00:17:08.890 Mary Burke: Yeah, sounds great.
163 00:17:09.229 ⇒ 00:17:10.799 Demilade Agboola: Alright, Ben,
164 00:17:11.489 ⇒ 00:17:19.039 Demilade Agboola: Okay, yeah, so I think that’s it for us for the week in terms of updates. Do you have any, like, questions or feedback on the…
165 00:17:19.459 ⇒ 00:17:27.789 Demilade Agboola: interaction so far? Or, like, any things you’d like to, you know, just mention, so that we can keep top of mind for next week, or just going forward in general?
166 00:17:29.200 ⇒ 00:17:40.719 Mary Burke: No, I think you had it all there with the SOW, and then the, I know you mentioned just having a list of questions that you wanted to send to Orchard, if we could have that.
167 00:17:42.190 ⇒ 00:17:47.740 Mary Burke: earlier on Tuesday, too, so our team can just take a look, and we can provide that to them, just so we’re all prepared going into the call.
168 00:17:48.390 ⇒ 00:17:53.169 Demilade Agboola: Sounds good, yeah. Currently in progress, we’ll definitely get that across to you early next week.
169 00:17:54.180 ⇒ 00:18:00.099 Mary Burke: Okay, great. And then that call with them, too, is, Tuesday at 1PM Eastern, if that time works.
170 00:18:01.100 ⇒ 00:18:06.260 Demilade Agboola: Yes, it should… What do you mean?
171 00:18:06.440 ⇒ 00:18:08.059 Demilade Agboola: It works for me. Have you been…
172 00:18:08.060 ⇒ 00:18:08.750 Mary Burke: I agree.
173 00:18:09.180 ⇒ 00:18:11.570 Demilade Agboola: Yeah, just need to see the invite to our list.
174 00:18:12.010 ⇒ 00:18:17.280 Demilade Agboola: I found her to, like, reject all the calls, so… Yeah, okay.
175 00:18:17.590 ⇒ 00:18:25.400 Demilade Agboola: Alright then, sounds good, so I shall see you next week, Tuesday. I know it’s a long holiday, it’s a long weekend, so have…
176 00:18:25.550 ⇒ 00:18:27.230 Demilade Agboola: Have a great weekend, and see you next week.
177 00:18:28.480 ⇒ 00:18:29.740 Mary Burke: Thank you. Thanks, guys.
178 00:18:29.740 ⇒ 00:18:31.590 Michael Thorson: Thanks, Aaron. Thanks so much, everyone.
179 00:18:32.220 ⇒ 00:18:33.210 Michael Thorson: Bye.