Brainforge Knowledge

2026-03-11_lmnt_brainforge_retail_data_68e0069a

30 min read

LMNT <> Brainforge: retail data

Date: March 11, 2026 Source: Granola Meeting ID: 68e0069a-809b-44c2-aefa-75225f9b4d21 URL: https://notes.granola.ai/t/68e0069a-809b-44c2-aefa-75225f9b4d21

Participants:

Data Validation & Methodology Alignment

  • Revenue discrepancy identified between Brainforge and LMNT data
    • ~1% difference in retail POS numbers
    • Russell pulling daily data vs potential weekly aggregation mismatch
    • Need methodology walkthrough between Russell and Amber
  • Current Snowflake data includes:
    • Orders, customers, transactions tables
    • Inventory data from Emerson connection
    • Split by drink mix vs sparkling products
  • Russell validated dashboard utility for OKR automation
    • Currently spends 5 hours/month on manual data pulls
    • Wants automated population of retail metrics by product line

Russell’s Data Requirements & Tool Landscape

  • Forecasting tools in development:
    • Dan (Finance): Macro supply/demand forecasting across all channels
    • Russell: Retail-specific sales forecasting using Keen platform
    • Separate from production demand planning
  • Key operational analytics needs:
    1. Repeat offender stores (high velocity + frequent stockouts)
    2. Reorder pattern analysis and timing predictions
    3. Promotional analysis automation
    4. SPINS competitor data integration ($15K/year for API access)
  • Store-level operational metrics:
    • Presmin (units on shelf before reorder trigger)
    • EDI system lag times
    • Backfill timing by retailer

Inventory Dashboard Development

  • Current retail inventory view shows zeros - connection issue to resolve
  • Phil’s executive health scorecard delivered but limited operational value
  • Pivot to Omni-based inventory dashboard:
    • Store-level drill-down capability
    • Supply chain specific visualizations
    • Target retailer as initial focus
  • Allie (Supply Chain) identified as key stakeholder for inventory requirements

Next Steps

  • Amber: Schedule follow-up with Russell to align revenue calculation methodology
  • Amber: Add drink mix/sparkling split to existing dashboards before next meeting
  • Amber + Uttam: Design Omni inventory dashboard requirements this week
  • Russell: Document specific OKR metrics and ad-hoc analysis needs
  • Shivani: Share OKR template with team, create 4-person Slack channel for coordination

Transcript

Them: Hello. Hi. Good to see you. Just a quick note. Russell told me he only has 15 minutes for this call, so we’ll just see what we get through. He’s in. Hi. Hey, everybody. How you doing? Me: Hey. Hello. Them: How’s it going? Me: Good. Them: Have you actually met the Brainforge folks? I don’t think I have, actually. I think at one point we tried to do a discovery call, and then you weren’t able to join, so we ended up just connecting with Will briefly. But Phil has also. Phil has also been like a guide in terms of a dashboard view that he would like to see. So for context, Uttam is like one of the co founders of Brainforge, and Amber is an analyst kind of working on our element side on our LMN team. And we’ve had the Emerson connection, we’ve had the Emerson snowflake data for a while, but it’s just like, nobody held the bandwidth to start playing around with data or the skill set in SQL to, like, start, you know, modeling it out and things like that. And so Brainforge has come. In and, like, actually brought that snowflake data to our snowflake instance. And actually modeled it out in the. In the background and, like, tables in the background. So we have, like, orders tables and customer tables, and I don’t know, what else would you say? Transactions tables and things like that, right? Me: Yes. Them: We have some stuff about inventory. And so, like this. This file that Phil shared with you and Will was that Brainforge put together is just like a snapshot to see, like, ok, like, if you had access to this data regularly. Would it, like. Would it actually, like, help you? Right? Like, is it the right orientation and things like that? And Phil had his. His perspective, I think, from his circle days of, like, what he wanted to see. And I think when you looked at it, Russell, your take was like, okay, I can see that there’s a slight. Difference. And so you wanted to dive into the methodology a little bit more to understand, like, how are they defining a week? How are they defining days? Like, there. There might be some things at the edges. So what would be like, I know you only have 12 more minutes, so what would be the most useful to you? Yeah. So I think from my perspective, We were off by about a percent. So, I mean, I think we’re pulling everything from the same reports. I’d love to understand where you’re getting those reports and where you connected. And then if they’re weekly or by day, I think that’s probably where the disconnect is. I’m pulling the per day. One of us is pulling either or I think is where where the disconnect is. And then from a goal perspective. I did review that file, Shivani. I think all of us are kind of doing from a high level like all of our starting to implement some more data tools, portals, etc. And I’d love to understand what you guys are trying to pull out of this. What you’re going to report and use it for. And then what we’re doing. Because our production team is looking at a tool. I’m looking at a tool for forecasting and accounting reconciliation. And then you guys are helping pull all the snowflake data. So I think what Phil wants to see is a dashboard for the health score of the business or something along those lines. Right? Sales, marketing, you know, all the different buckets. Yeah, that’s like the executive level view, let’s say. Right. But then. You have your, like, tactical things that you need to like. I guess, Russell, at some point, because we don’t have much time today, if you could do some reflection and say these are the top questions I’m trying to answer with better data, like, you know, like a better data backbone. Like, I want to understand how to forecast X more effectively. I want to understand, like, how, you know, like, Will gave us an example. He was like, What if I wanted to know stores that had strong sales velocity, but then also high amounts of stock outs, so then I could have, like, structured conversations with them? To like, like or like conversations with them to say, how do we make sure that you always have product on hand? So it’s like things like that, like, if you can do some reflection, like, these are feather. These are the questions that I’m trying to answer. Like, it can be async, but I would love that from you when you have time. Yeah. Yeah, I can definitely make a note of that. It’ll inform what kind of dashboards we want to put together. Right. That will actually be used frequently. Like, there’s, there’s, there’s. The type of dashboard that is kind of like high level health scorecard or something that like, maybe an executive just wants to see, like, what’s the movement? What’s the trend? Like, how are things different than they were a month ago, a year ago, whatever. And then there’s like, there are the operators who are like, I want to start. I want to start doing this, like, specific intervention. And what I need is A, B and C. And this would be across all revenue channels, right? Ok, perfect. Because what I’m doing from my forecasting tool, which could expand other channels, but I’m using that specifically for retail. And so I just want to make sure we’re not stepping on our toes and doubling up work, you know, So I actually, when I hear you say forecasting on demand, I am thinking that there might be some doubling up happening. It’s that Dan in finance is. I don’t know if you guys are in lockstep on this, but he’s been interacting with a potential company that would help us on macro like supply demand forecasting, and that will be across our channels, not just, like, E commerce, demand planning, but across every channel. And so I’m hesitant. Like, when I hear the words, like, I’m looking for a forecasting tool, I’m like, ooh, okay, that. That might be like they’re already doing it. It might be like they’re already doing it. So take a beat kind of thing. So it’d be super helpful to know again, we don’t have that much time today, but super helpful at some point to know, like, okay, I have my okrs. These are the questions I’m trying to answer. These are the interventions I’m trying to, like, tackle. This is where I could use more data. This is, like, the type of thing that I’d want to, like, just understand the health of the business at large. And then we can start, like, forming dashboards and, like, figuring out. Okay, like, all the dependencies happening in the business and, like, connecting the dots. Right? I totally. Yeah. And. From my perspective for. Because, yeah, Dan is going to create a forecast, a production demand forecasting tool which is separate from sales forecasting because it would really go sales forecasting. And I partner with Dan on that. But then I’m implementing. Here’s the sales plan. He then bakes it into demand, and then that gets funneled into everything else. Production, accounting, etc. Yeah. And then from my perspective is I’m building this tool specific for retail. Right. So then I can go with Bess and reconcile what we think on the accounting side. So I don’t think we’re stepping too much. There’s going to be certain overlap, of course, but when you’re talking more in depth dashboards analysis. I love that because my dashboard is not going to be doing that. A lot of the stuff that we’re talking through is ad hoc. Right. Like, like you said, what’s the repeat offenders of stores? Me: Go. Them: The top or the bottom 10%. Me: We say operational. It’s like, I need to go execute this decision versus finding trends, insights or next. Them: Exactly. Yeah, exactly. And so that’s what I think this would be really helpful for. And then we’re using Keen. I don’t know if you guys heard of Kean, but Kean would then help me guide my top level for sales forecast. Right. So I think all of these tools are great. And again, from from what I would use exactly. Like what is the repeat offenders. The top selling stores will sell out first because they have the highest velocity and it’s hard to backfill those. And then there’s things called Presmin, which is how many units on shelf do you have to sell through until it triggers. The store to order. Then there’s a day or two lag time from the actual warehouse manager or store manager placing the order. Then it gets kicked back to the EDI system. So all of that is really helpful if we can figure out, like, the timings. Me: Trade. Go. Them: Of each different process, and then how long would it take to backfill those specific stores? Then you can get into a cadence of pulling a repeat offender list across all retailers. And you have these. Me: And predicting how long it’ll take to restock. So that’s something we’ve done in the past is not only helping operationally, but looking like these guys always place in POS on this cadence, and they always wait too long. And can we attack that? That’s more of the insights piece. Them: Exactly. Me: And then to your point, we’re going to tackle having everything in one view, but there still are going to be operational systems that run element, and that’s going to be business domain by business domain. There will be some subset of reporting in there. Of course, every tool these days has a dashboard. But what we’re hoping is for the business certified omnichannel view of all revenue that will be kind of powered by this data system. And then yes, every team is going to have super use case specific tools. We’re not going to build confido, were not building like an erp. Them: Right. Right. Right. No, and that makes a lot of sense. So what I can do is create, like, a list of the ad hoc stuff I do because. First off the bat, what I’m trying to do is. For okrs. I’m sure. I don’t know if you’ve shared kind of that platform. So I would love to have that just auto populate so I don’t have to five hours of my month. And just pull ad hoc stuff. So that would be number one is everything that I have to pull automate. So that would be like the automation steps. And then it’s the more granular in depth analysis steps for me from the sales side. Right. So again, missed opportunity, Ms. Revenue. I don’t know if we could go this but we can use SPINS data to reflect on trends. So like we have a variety pack. Liquid iv does not. Now, where are the top selling item which has triggered them to create a variety pack? Right. I don’t think it’ll be as successful as ours because they’ve. They own the majority of the market and people have already. They know their flavors that they like, so they’re not getting trial through the variety. We are. So just things like that helps a lot. And then where are we? Like, promotional analysis can help, but we’re doing that through a couple different ways to. But again, if there’s a way to set something up that automatically looks at this opportunity, this off shelf, this n cap, etc. Me: Yeah. So spins is going to be really helpful for anything competitor and we’re driving towards getting that data. So I think Shivani, that’s like a key use case I feel like we haven’t really heard about yet. Them: Yeah, I think, Russell, when you. When you put that in your notes, it’ll help us, like, build the case. I don’t think anybody’s opposed, but right now, with spins, we need to, like, pay extra to get API access, and there’s just, like. There’s like, a slight. We don’t. We can’t, like, pull that data into our warehouse right away? And so we’re just, like, trying to make sure that if we pay 15k or more a month, which is not a huge. Sorry, 15k more a year. 15k a month. 15k more a year. Like. Like, what does that actually get us? But one of the things that you’re talking about is, like. Hey, like I have to look at my okrs and say what the sales were for. Point of sales were for Target in the month of December. January, February. Right. And so this is coming from Snowflake. Right? This is like point of sales revenue. If this is something that you’re reporting out in your okrs, it might be something you can just start pulling. But I would want you to spend some time validating it. If you’re like, hey, like, and I. I can actually do this right now, right? If we say, let’s actually look at what it was retail. Yeah. So sorry. My. All the way down to the last. Yeah. Yeah. So, okay. Drink me mix. Pos so you have it split out by type? So it’s like a little bit. But if we said drink mix, point of sales and. Sparkling point of sales in November. Okay? Like, that’s 4.2 mil or something. So now let’s go back here. In november. A little bit, like, a little bit less is what they’re seeing, right? And so. So it’s like if we eventually want to get to a place where you can just grab this number and, like, maybe this is, like, deducting some refunds or something. I don’t know how this number is calculated entirely, but I think that that’s, like, a nice way to check it. And so we can go over and do the same thing for, let’s say, February drink mix sales. For? We’re still in target, right? Are you looking at sales or po. Oh, sorry. Point of pope. Buenos Aires. So point of sales and point of sales, 7.9. And then here we have. 6.5. Right. So I think. Oh, sorry. I’m looking at the target. Sorry. Yeah, that was the plan. Oh, my God. Okay, sorry. Just like doing this on the fly. Okay, six point 56 point five. Okay, it’s not the exact same number. But this is. This is like. Could this save you time? Yes. And if you want it split out, if we’re, like, already looking at this, we’re like, oh, he wants it split out by product. Type of sparkling and drink mix so it’s easier. Like, that’s a good adjustment that we can make. Here. So you can just, like, pull these numbers once you feel confident in them. Yeah, and that’s. That’s the thing. It’s like the way I have to splice things out, which is why it’s it, it. It’s not sustainable, ad hoc, but it’s, like, easier for me because I have to report from OKRs as separate by product line. But there’s sparkling in drink mix in Target. And so when I speak externally, I have to pull it a different. You know what I mean? Like, I have to. Because we have 16 ounce with drink mix, so it’s not as easy as, like, this number reflects what we’re talking with Target, and so that’s interesting. It’s just. It’s so complex, but, yeah, trying to get the most basic stuff done here and then the basic in depth spice store checks is kind of the next level of in depth analysis we can do, I guess. So I can definitely put that. Oh, I got to jump. But I can put a bunch of different stuff together. Yeah. Now you have a primer, kind of. But, like, again, like, I’m seeing, like, 3.5 million here, 3.9 million here. So then it, like, we’re starting to crack the things on. Like, where we want to follow up. And then if you have more time, Russell, like, you and Amber jamming on, like, okay, like, where are you pulling this 3.8 number from? How did you get there? How did she get there? And then you can kind of crosswalk it, so eventually we’re following the same methodology. Yeah. Yeah. And I think it’s going to be based on the timing, I think we’re pulling from the same source. And, like, I’m. I’m pulling by week, and then you’re pulling by day or vice versa. So however we can get through that, Amber, together. I have to jump, but I’LL. Work on that over the next week. And then if you guys are meeting over the next week as well, just loop me in, and then hopefully we can kind of go through that. Okay, perfect. Thank you, Russell. Cool, guys. Thanks. Good meeting you guys. Bye. See ya. Me: Thank you. Bye. Okay. I mean, I feel like we heard the same things as usual, but I think we hit it. Now we have the talking points. Stage on. Them: Okay. He hadn’t met you guys before, so there was also just. We had 15 minutes. It was kind of like. Okay, Sorry. Let me. Let me dig and swap my thoughts from the meeting. I get nervous when I hear how many people are trying to look for new tools. Me: This is what happens. Yeah, I agree. That actually may be something to flag, to fill on. What is the IT procurement process? Beyond just because. I don’t know. Yeah. I feel like people are fishing for their own tools. And even beyond what we’re doing. Them: Yeah. Me: What? This keen tool is very similar to some other stuff we heard about. So. Them: I had brought up came to you before. Me: Oh, right. It’s right. Okay. Them: Right. So Russell just, like, a customer of whatever Keen is providing. Me: Yeah. Okay? Them: But then I’m thinking, like, how is Kean getting the data? Is everyone want pipes from our data? Sorry, Amber. Like, a macro question that I’m thinking about is, like, with the atomic thing, it’s like if we’re like, we’re just in a rush to establish connections to atomic. Are we like, go ahead, build your own pipes. Or are we saying wait until we have what you need and we’ll let you know what that schedule looks like, and then you can start modeling based off of that. And that’s like. Me: I think prior to this group’s existence, there may not have been an answer. So maybe it is totally not urgent. We’re going to be displaying a lot of the data now here, so unless there’s operational, like. He talked about some operational things. Them: Yeah. Me: But even the things he said. I’m like, you will be able to show when people are making purchases and things like that. So it’s part of supply chain. Them: I think I just need him to, like, engage with it and start looking at it. And that was the goal. And so, like, Me: I don’t know. And I think he was like, oh, yeah, I can save, like, a bunch of time now. Them: But already, like, when. When I. Okay, so when we, like, what I just did live is something that I’m like, can we just institutionalize this? That, like, and if you guys need an updated version of this or to be on this, like, let me know. But can we actually just start doing checks this way without, like. If this is like 3.9 million. Me: Sure. But I just think if we find a difference, I still may not know how they got there. Them: How. Because they’re hard coding numbers here. Me: But ultimately, maybe Amber we can flag. We can use this. We can come to the table with like there is a difference not tell us no. Let’s qa. Them: And, like, you’ll just know that, like, every channel seems to want it split by drink mix and sparkling regardless. So, like, let’s just have that split for them like, as, like, I like it when we have the kind of like, collapsed rows of sparkling and stuff there. And then. And then we could, like, comment being like, okay, we’re coming up with, like, a 400k difference. Like, this is a point where we need to have, like, a methodology discussion. And he thinks it’s just the edges of, like, what he’s pulling from, But I actually then want to sit with him, be like, walk me through what you’re pulling. Me: Yeah. Yeah. Them: Right. Okay? Cool. So he wasn’t going to help us with the inventory piece, so. Me: Sorry. I messaged that to Amber, too. Them: The retail inventory person would be somebody from supply chain who oversees the retail part. And that is, I think, this person, Allie, who’s lovely. And so if. If I go now to this, what are your open questions here? Because I can kind of like just say, hey, can you review this? Me: Yeah. Amber, you want to go ahead? Them: Yeah. So I think our main question is one, we currently only have the pipeline in here. We have so much more data that we think will be helpful. We have it on a store level. But there’s a lot of stores, so. We put that in once we know. Hey, what do you need to make decisions? We have something planned that we want to share of. Here’s what it’s possible, here’s what we think will be helpful, so I think we can share that. But right now, this is a very basic understanding of. Okay, I agree. How much do we have? But that doesn’t help you answer problems. Me: So that’s why I think Shivani is like we were going to put together based on what we’re seeing. These are some good metrics. If Ali already has that and it would be redundant. And we could pull together a list and maybe put a little Doc together and have Allie comment on that. Them: Well, okay. Two things. One, I’m obviously distracted right now because I’m like, so why are there zeros? Here. So do we know? Why this section is yours. This is latest date. So I can go check. I don’t know. Let’s go to the weekly. Also zeros. So it’s like, already I’m like, okay, I don’t want to show this to anybody until I see it with numbers. That makes sense. So that’s one thing. Okay, then number two, I think. This comes into play with. What does done look like for a stakeholder? And if Phil is like, this is what I wanted, and you executed on it, and he’s happy with it, and it’s been Q8, which right now it hasn’t. Then maybe we move on. To other parts of the business. But I bet that you’re kind of like I feel. Aye, Shivani. Feel like this is okay, who’s actually gleaning anything from this? Who’s actually. Me: Yeah, really what Russell said is most of what it is, which is just like, understanding reorder patterns and making sure people have product. So this doesn’t really show that. Them: Yeah. Me: But again, you’re right that if we sign Allie up as a stakeholder, then we’re going to go. Work on stuff for her versus maybe. Yeah. So there’s a couple of ways to attack it. We propose like, hey, here’s actually some things that are helpful. And Phil is, like, cool. It shows something about stock out rates or whatever or. Them: I think let’s pursue that in Omni. Me: Yeah. Yeah, I agree. Them: Why don’t we do that? Me: Yeah. Okay, cool. Them: Right? It’s like fold. You’re thinking about what could be better. For some cool views in Omni. Me: Yeah. Them: And then that way it’s not like an Excel spreadsheet or whatever. It’s just like you’ve already done some thinking about, like, retail specific, retailer specific cuts that people might care about. Me: Inventory stuff. Yeah. Them: Inventory related. And then that will be like some of the demo stuff that Greg can work on. So you already have ideas that I don’t even know because I have not engaged with supply chain, but you know, supply chain from all your work without their businesses. So it’s like I would just rather than writing a memo and, like, getting people on board. I would just, like, make it in Omni and then. And then, like, walk me through it. And then we could say, oh, let’s walk Allie through it. Is this helpful to her? Me: Cool. Yeah. Okay? Cool. Them: Right once we don’t have zeros. Me: I have a. I have a feeling that. I have a feeling that unless it’s out of a tool, I feel like nobody is going to have this level of data anyways. Them: Yeah. Me: So I would be surprised. I’d be interested to see, like, as we didn’t do a discovery with her, so I’d be interested to see, but we’ll just put together, like. The hits around supply chain based on what we have. And then we’ll show, like, I think one thing that I’ll try. One thing we’re going to try to demonstrate is the different visualizations and how we show flow and, like, some things like that. So we could do that. Them: Yeah. That sounds great. Okay, cool. Because I. I get what you mean. That you’re like. This is like. Not that, like. Compelling to just, like, look at this, but I think he just wanted to see. My sense is that Phil just wanted to see that, like, yes, we have access to the inventory data, and it’s like, the connection is working. Which right now, like, is the connection working? Right? So it’s like, this is just like a gut check on, like, we have this data and, like, versus. Oh, okay, now know. Which stores I need to have a conversation with or whatever. Me: The connection is working and it can come into here. Yeah. Okay? Yeah. Me and Amber have been messaging, so I think Amber, this week we can just map out. We’ll just write our requirements for an omnidash that just highlights inventory. Them: Right. Me: And then I feel like, let’s just put some of that together. As part of the next few weeks. Them: That sounds great. That’ll be, like, part of the. What can we showcase with Omni for, like, a specific person versus the macro? And, like, I think that’s spot on. Me: Yeah. Yeah. Yeah, I think that’s a good way of thinking about it. Like, we’re going to have these, like, large, like, these types of cuts. Them: Y. Me: And then, like, there’s going to be things, like drill down into, like, one, like, one retailer. Them: Eah. Me: Or like 11 wholesale partner. Right. So I think picking this, like, just a view of target. And maybe doing a cut. That’s like, here’s the. Here’s an inventory report where you can literally pick, like, a store. I think that’s something like that could be pretty powerful, so. Them: Perfect. Cool. In terms of, like, next steps from Russell. Right. Amber, what do you think? Because he’s a little bit like. I think he’s. He. He often does this where he’s like, actually a retailer put a meeting on my calendar so I don’t find the. Even though I look went off his calendar, right. I think what happens? So now he had an element demo sampling sync that just like popped up. For. That’s why he had to hop today. Tomorrow. I see that he has some time. Like 4:00pm Eastern. So, Amber, if you want to just send it, like, do you feel like you need us there, Amber? Or you could just be like, cool, now that I’ve met you, let’s go through just the revenue and how you calculate it. Like, what. What support do you need? That part. Totally fine to take that on myself. And then later on, if he needs some brainstorming partnerships on, okay, what do I want inventory or what do I want on retail? Like that part. Maybe having you guys will be helpful. That sounds great. Me: Can we do a slack with the four of us and then Amber, Ali, we could jump in there and then get a schedule, and you could put me as. He could put me as optional. Them: Cool. That will be nice. All right. Me: Great. Them: Before you do that, Amber, before you meet with him, do you think you could split it out by drink? Bicks and sparkling, just like with the rose. Collapse rose underneath, just so that then he can, like, reference it that way. Cool. The OKR sheet that we pulled up in the meeting. Is that the same OKR sheet that you show me for wholesale? Yeah. So. Exactly. So let me just. I think. Let me. I’m just going to share it with you guys. Okay? That’ll be great. Me: Yeah, I think it’s in our shared. I felt like. Them: I mean, only, like, at one point. Snapshot. Yeah, I only have the wholesale snapshot for, like. Let me make a copy of it again and then put it into our. Into our folder. Like, where’s our folder? Okay? Bizops. Data and analytics. Brainforge. Okay? Just plopping it in. As, like an updated copy. And then I’ll send it to you guys. Okay? Perfect. And so, like, just to orient you, Amber, like, basically. We’ve got. E commerce retail, and you can see, like, okay. Retail. They’re reporting by point of sales. Sparkling point of sales. Then they go down and they report by Target. And they have these split out, then they have Walmart. These split out, right? And then they have, like, sparkling weeks of stalk, sparkling sales. So you can get a feel for, like, okay, if I could add these factors in and we’re doing this monthly. Like, if I could have a monthly thing that’s just, like, sparkling weeks of stock, what would that look like? And then it would eliminate him having to, like, find the data. Or whoever the owner of this metric is like, I’m like, who’s cool? Whatever. Yeah, I can start it with breaking the sales down and then some derived metrics. That’s just like weak of stock. I probably should ask him of how specifically he’s calculating that. Unless there’s formula there and. Then, like, just so you can see. You could see here that it’s like, this is kind of what I reference for my first layer of qa. I don’t know why these references are broken. Oh, because I guess I made the copy for you. I don’t know. But, like, this is what I reference. For the QA for wholesale also. I was like, okay. I start off and I’m like, does revenue make sense to me? And then I see that they split out by, like, drink a mix sparkling, and then they have whatever the international and the reseller, and that’s how they come up with their revenue and. Then that’s how, like, that’s like how my brain first goes to qa. It’s just like, does the revenue line up? Cool. Okay? Anything else. From me. Okay? Thank you, amber. Yeah, thank you. Okay, bye. Me: Thank you. Okay, talk to you soon. Bye. Yeah. Them: Do you have a meeting now? Me: I have something. Oh, it got moved. I swimming in 30? Them: Do you want to go through what you’ve been preparing on the Gantt, or do you need more time? Me: Yeah, I was gonna. We were just working on it now. If I could just get, like. Are you free for the next 30 minutes? Them: Hey, Amberi. I’m getting very. I’m starting to get sleepy. That’s why I’m kind of like, head. Me: Okay, okay, let me just check. Them: Like, I’m like, there’s a world where 3pm is like, I might be in. Me: All right. Okay, hold on. Let me just see, because some people were editing it. Them: Okay? Me: I was just surprised that you’re still, like, you didn’t go to, like, coffee shop or something. Them: Yeah. I’m surprised that my own self too. I’m like, I need to get. I go outside. Me: To stay awake. Okay. Can I just. Yeah, it’s in another sheet. Can I just, like, figure it out and call you right back? Them: Yeah, yeah, yeah. Okay, bye. Me: Okay? Okay?

Graph View