Meeting Title: Brainforge x Shinesty: Automation Opportunities Date: 2025-09-19 Meeting participants: Shelly, Uttam Kumaran
WEBVTT
1 00:01:23.130 ⇒ 00:01:24.209 Uttam Kumaran: Hey, Shelly.
2 00:01:29.820 ⇒ 00:01:32.420 Uttam Kumaran: I… I think you’re on mute.
3 00:01:34.860 ⇒ 00:01:35.950 Shelly: Hi, how are you?
4 00:01:35.950 ⇒ 00:01:38.180 Uttam Kumaran: Good, how are you?
5 00:01:38.180 ⇒ 00:01:47.489 Shelly: Good, good, good to see you. Sorry about that, I, you know, have too many windows open and can never find the thing that is yelling at me at the time that I need to find the thing that’s yelling at me.
6 00:01:47.660 ⇒ 00:01:56.029 Uttam Kumaran: No, no, no problem. I’m sorry, I was just a couple minutes late, we were just coming off another meeting. Very interesting client, where they’re a design firm, and we’re helping them automate
7 00:01:56.060 ⇒ 00:02:08.789 Uttam Kumaran: some of their processes, and it’s really cool, but like, as soon as we start talking about, like, what we can do, then everybody’s on the meeting, like, thinking about, like, what opportunities there are, so it’s been really great, but I appreciate the time.
8 00:02:09.070 ⇒ 00:02:13.200 Shelly: Yeah, that is a… I much prefer the meeting where everybody’s thinking about all the things.
9 00:02:13.200 ⇒ 00:02:14.420 Uttam Kumaran: Thank you versus the one.
10 00:02:14.420 ⇒ 00:02:17.850 Shelly: where they’re just, like, dead-eyed staring at me and not, like…
11 00:02:18.530 ⇒ 00:02:22.180 Shelly: And I’m like, I know you need help. I know you can be better.
12 00:02:22.180 ⇒ 00:02:30.829 Uttam Kumaran: I’m someone who, if I’m in a meeting and there’s no one talking, I’m like, someone’s gotta talk, and it’s not gonna be me. I literally say that out loud.
13 00:02:30.920 ⇒ 00:02:33.670 Shelly: Before I say up front, I said, I’m talking last.
14 00:02:34.180 ⇒ 00:02:41.030 Uttam Kumaran: Either we all sit in silence, because I have the tolerance for that, and someone will… it’ll get too awkward, and someone will break, so…
15 00:02:41.030 ⇒ 00:02:51.650 Shelly: Yeah, I… I love that theory of holding a meeting. I also will just… because I am… I always want to interject, I always have something to say.
16 00:02:51.650 ⇒ 00:03:01.610 Uttam Kumaran: Yeah, I’m trying to teach myself that, like, I want people… I don’t want people to get the answer, I want them to learn the decision-making, like, the principles behind the decision.
17 00:03:02.050 ⇒ 00:03:02.660 Shelly: Right?
18 00:03:02.850 ⇒ 00:03:04.460 Shelly: Totally.
19 00:03:04.460 ⇒ 00:03:18.990 Uttam Kumaran: I’m like, you come to the table with a proposed solution, and I’ll show you, like, what works, what doesn’t, versus… I, of course, know the answer for the most part, but I’m just trying to hold, like, see if you’re right.
20 00:03:19.610 ⇒ 00:03:21.329 Uttam Kumaran: Going forward, yeah.
21 00:03:21.330 ⇒ 00:03:33.429 Shelly: 100%. Every time we start a new implementation or a new process, I’m like, bring me your utopia. Tell me what this would look like if money, resources, and time were not in the.
22 00:03:33.430 ⇒ 00:03:43.029 Uttam Kumaran: Oh, that’s really, really great. I guess I… I usually tell people, because also, people will be like, oh, well, this thing is wrong, we don’t have this. I’m like, no, no, don’t put the constraints up front, like…
23 00:03:43.030 ⇒ 00:03:44.439 Shelly: Yeah, that doesn’t matter.
24 00:03:44.440 ⇒ 00:03:53.670 Uttam Kumaran: We will… we will… we’ll chisel at this, like, perfect thing, but you can’t… if you’re gonna put the roadblocks up front, then we’ll ne… you’ll… you’ll just think that that’s the way life is.
25 00:03:53.670 ⇒ 00:03:54.910 Shelly: Always.
26 00:03:54.910 ⇒ 00:04:07.469 Uttam Kumaran: like, remove those, and to always be like, this hurts, and maybe we can fix it, maybe we can’t, but yeah, some people put the artificial constraints in on things, and I’m the same. Actually, the way you described it is really great.
27 00:04:08.420 ⇒ 00:04:20.480 Shelly: Yeah, it changes the way that they, yeah, perceive the issue, and it gives me way better information, because you’re right, it’s often leading with the constraints, and never what the problem is.
28 00:04:20.750 ⇒ 00:04:21.160 Uttam Kumaran: Yeah.
29 00:04:21.160 ⇒ 00:04:24.059 Shelly: inside the problem, and I’m like, I don’t care about that.
30 00:04:24.880 ⇒ 00:04:27.530 Shelly: ride, or roll… railroad over, or…
31 00:04:27.530 ⇒ 00:04:28.089 Uttam Kumaran: Yes, yes.
32 00:04:28.090 ⇒ 00:04:30.650 Shelly: Ignore, look at me not paying attention to any of your
33 00:04:31.430 ⇒ 00:04:33.230 Shelly: It doesn’t matter to me at all, yeah.
34 00:04:33.730 ⇒ 00:04:42.360 Uttam Kumaran: Yeah. Well, also, I appreciate the time, and yeah, we got connected, through, Craig, Craig Folds. Yeah.
35 00:04:42.900 ⇒ 00:04:56.850 Uttam Kumaran: I know he’s… I think… I don’t know if he’s previously working with you guys, I’ve known Craig for a bit, but we’ve been connected for a while, and I started, you know, this business a few years ago, and over the last year, I’ve been doing a lot more, like, just…
36 00:04:56.850 ⇒ 00:05:07.290 Uttam Kumaran: AI workflows and building automations for folks. And so, our company, Brainforge, we started as a data analytics company. My background is in data engineering and leading data teams, so we do a lot of work.
37 00:05:07.290 ⇒ 00:05:14.880 Uttam Kumaran: A lot with e-commerce, a lot with B2B SaaS, so a lot of digital, and retail, and, like, omnichannel folks.
38 00:05:14.880 ⇒ 00:05:27.630 Uttam Kumaran: Again, people that are optimizing on marketing, on, you know, inventory, on fulfillment, so all across the business. And then, in my business itself, I was using a lot of AI and automation just to build a company.
39 00:05:27.630 ⇒ 00:05:36.330 Uttam Kumaran: And then we naturally found that a lot of our clients could use help in that area as well, so we started offering that as a service. But really, I like to look at us as
40 00:05:36.350 ⇒ 00:05:37.410 Uttam Kumaran: Sort of like…
41 00:05:37.550 ⇒ 00:05:46.109 Uttam Kumaran: now I think we’re getting more towards, like, kind of pure consulting, where we come and just hear the problem and say, okay, we… our shovels are, like, data, AI, and automation.
42 00:05:46.230 ⇒ 00:06:05.489 Uttam Kumaran: is there something in our arsenal that we can use to help alleviate, you know, the issue? Yeah. Whether it’s, like, connecting systems, whether it… like, commonly, we hear systems aren’t connected, or reports aren’t working, or we’re… this… some system doesn’t have an API, so we can’t get something out of there. Like, those are the things where we do a lot of fixing on the data side.
43 00:06:05.490 ⇒ 00:06:13.399 Uttam Kumaran: The automation side, it could be a little bit more broad. It’s… it’s work… theirs, I think, is a lot of working with companies and isolating
44 00:06:13.440 ⇒ 00:06:23.849 Uttam Kumaran: like, basically SOPs that you either have to now… you don’t have the right talent for, or you have to hire for, and is there any way we can help prevent that, or scale up your existing team?
45 00:06:23.850 ⇒ 00:06:35.669 Uttam Kumaran: And really, when we talk about an AI in my business, I talk about 20-40% efficiency. Like, it’s not something that I look at as, come in and fire everybody, like, we don’t do work like that.
46 00:06:35.670 ⇒ 00:06:36.020 Shelly: Yeah.
47 00:06:36.160 ⇒ 00:06:38.450 Uttam Kumaran: you know, for us, I think about…
48 00:06:38.580 ⇒ 00:06:46.839 Uttam Kumaran: If you think about the quadrant of work that is very, very painful, but also, like, very, very, like, important… That’s important.
49 00:06:47.270 ⇒ 00:06:47.789 Uttam Kumaran: Did that…
50 00:06:47.790 ⇒ 00:06:48.120 Shelly: Yeah.
51 00:06:48.120 ⇒ 00:06:52.270 Uttam Kumaran: not, like, high ROI, that’s… that’s the quadrant that we attack.
52 00:06:52.420 ⇒ 00:07:07.800 Uttam Kumaran: And we’re very sort of systematic, where we come in and we work with whoever does that every day, we map out their process, and then we build a solution for them, with them. Like, not… not, like, around them, or not, like, instead of them, you know?
53 00:07:07.800 ⇒ 00:07:08.890 Shelly: Sure, yeah.
54 00:07:08.890 ⇒ 00:07:21.710 Uttam Kumaran: Those people are all human beings, and they don’t want to be doing the very boring stuff, but I also know… we know a lot about what automation AI can seem like for a lot of folks, and so…
55 00:07:21.820 ⇒ 00:07:28.109 Uttam Kumaran: That’s a lot of our process. I mean, I have a deck, I’m happy to show you more about, like, what we do, but,
56 00:07:28.290 ⇒ 00:07:37.559 Uttam Kumaran: Would love to just, you know, even just have a casual conversation about if you’ve been thinking about any of this, or… Yeah, sure. …what opportunities could look like.
57 00:07:37.920 ⇒ 00:07:47.140 Shelly: Yeah, so very, very similar philosophy around AI in terms of how it can fit in, especially in the operations piece at Shinness C, is that
58 00:07:47.140 ⇒ 00:07:59.859 Shelly: I don’t want it to take someone’s job, and I don’t think it ever could. What I wanted to do is take the shit work out of the job so that we can use your brain to push our business forward, and not just, like, enter data.
59 00:07:59.860 ⇒ 00:08:07.069 Shelly: I don’t… like, we don’t need to be doing that. We’re better. We hire better people than that. That sounds really, like, snotty.
60 00:08:07.070 ⇒ 00:08:16.999 Uttam Kumaran: Sometimes you have to… you hire great people, and then the problem is they’re sitting and entering stuff into a UI, and you’re like, they’re so smart, like, I wish we could be doing strategy.
61 00:08:17.000 ⇒ 00:08:36.370 Shelly: I am paying good money for, you know, like, menial work that anyone can do. Yeah, no, so that… it is totally where I want to utilize AI as much as possible, so that we can get them off the grunt work, and then also leverage AI to help them…
62 00:08:37.450 ⇒ 00:08:46.510 Shelly: Navigate some of the more… super complex pieces of our business, like… Planning and forecasting in.
63 00:08:46.510 ⇒ 00:08:53.039 Uttam Kumaran: inventory, especially, is one where, like, as we’re becoming more omnichannel, as we’re trying to really push.
64 00:08:53.040 ⇒ 00:08:59.849 Shelly: our bigger pieces of the business, like Amazon and wholesale, where we’re looking at thousands of units constantly.
65 00:08:59.850 ⇒ 00:09:00.390 Uttam Kumaran: Right.
66 00:09:00.390 ⇒ 00:09:18.829 Shelly: But then having to, like, juggle that with e-com and subscription, and never running out in any one place, and having all the things available in enough places, and then, oh yeah, we’re doing stuff at airports now, too, and then, oh yeah, we’ve got all these sales shows to go to that we need to pull hundreds of samples for.
67 00:09:18.830 ⇒ 00:09:36.409 Shelly: So those are the two places right now that could be the most impactful, I think, in terms of, like, the operations piece. I know there’s a ton of stuff, like marketing and design and all of that, and customer service, I’m sure that we could find, there too.
68 00:09:36.540 ⇒ 00:09:42.440 Shelly: Oh, and accounting. Oh my god. Everything they do could be automated.
69 00:09:42.440 ⇒ 00:09:59.310 Uttam Kumaran: And what is sort of, like… like, what are the goals right now? Is it around revenue? Is it around diversifying revenue? Is it around, like, cost mitigation, profit? Like, where… if you could nail it down onto, like, a KPI, like, where do you see that?
70 00:09:59.310 ⇒ 00:10:10.959 Shelly: Sure, definitely around revenue, definitely around making sure that we have the best assortment and availability at all of our channels.
71 00:10:11.040 ⇒ 00:10:26.599 Shelly: to not leave any money on the table. Great. We have in front of our cus… whatever customer it is, whether it’s our subscription customer, or our wholesale customer, or Amazon, or our site, they are seeing what they want, whether they know it or not, you know what I mean?
72 00:10:26.990 ⇒ 00:10:27.750 Uttam Kumaran: Yeah.
73 00:10:27.750 ⇒ 00:10:36.330 Shelly: So yeah, the biggest pain points that we’re having right now…
74 00:10:36.400 ⇒ 00:10:53.429 Shelly: are… especially in wholesale, just because it is a very new channel for us, that we’re really trying to spin up quickly. So it’s not just the newness, but the, like, forced speed that we’re having to, like, make decisions and put up MVP products.
75 00:10:53.470 ⇒ 00:11:03.919 Shelly: and then kind of… kinda go back and fine-tune them if we can, or have the time, or think about it. But then also, we’re still moving forward, right? And, like, we still have to keep…
76 00:11:03.970 ⇒ 00:11:05.649 Shelly: Doing the next thing, and working.
77 00:11:05.650 ⇒ 00:11:06.010 Uttam Kumaran: Yeah.
78 00:11:06.010 ⇒ 00:11:11.690 Shelly: So we’re kind of falling into this trap where we’ve got a lot of, like, data.
79 00:11:12.120 ⇒ 00:11:21.550 Shelly: That we owe clean up to, like, some data debt, and some process debt, and it just, we haven’t been able to get to it.
80 00:11:21.820 ⇒ 00:11:24.809 Shelly: And it’s causing just some slowdowns, so…
81 00:11:25.740 ⇒ 00:11:40.979 Shelly: It would be really amazing if we had a solution for, like, scraping emails and finding orders inside of emails, and then just automatically creating them, getting them in NetSuite, and then sending them to someone to approve and send on to the next step.
82 00:11:41.000 ⇒ 00:11:48.100 Uttam Kumaran: Yeah. Same thing where we just have an internal process where sometimes, like, our marketing team wants to send out samples to influencers.
83 00:11:48.240 ⇒ 00:11:57.399 Shelly: all of those orders are right now manually inputted into a Monday board with what they… and an Excel workbook, essentially, with what they.
84 00:11:57.400 ⇒ 00:12:00.900 Uttam Kumaran: I will say, though, like, not to interrupt, this is how everybody is doing it, by the way.
85 00:12:00.900 ⇒ 00:12:01.350 Shelly: That’s true.
86 00:12:01.350 ⇒ 00:12:06.259 Uttam Kumaran: you’re not, like, too far ahead, you’re not too far behind, but what I will say is, like.
87 00:12:06.470 ⇒ 00:12:13.439 Uttam Kumaran: it’s… could be so much better. Yeah. You know, we… we’ve… and kind of come… couple things, maybe I’ll just, like…
88 00:12:13.590 ⇒ 00:12:17.180 Uttam Kumaran: I’ll highlight, like, and even just to talk about some of the…
89 00:12:18.350 ⇒ 00:12:36.910 Uttam Kumaran: things you mentioned. So, Pool Parts is one of our clients, and we did an entire sprint with them all around inventory management and shipping optimization. So, we negotiated their new contract with, with UPS, and basically got all of their zone information, got all of their shipping information, and went in…
90 00:12:36.910 ⇒ 00:12:41.700 Uttam Kumaran: renegotiated their contract, which they hadn’t touched in, like, probably 5-6 years, and they saved…
91 00:12:41.720 ⇒ 00:12:52.619 Uttam Kumaran: like, an insane amount of money, because they were paying a ton of fees, additional handling charges. It was a very season… it’s a cool part, so it’s very seasonal, very geographically concentrated business, so…
92 00:12:52.620 ⇒ 00:13:04.329 Uttam Kumaran: they were paying for things, like, all across the year, when they really just needed, like, peak season. So we were able to work with UPS on a lot of scalable things. Urban Stems, for example, is a perishable good.
93 00:13:04.330 ⇒ 00:13:13.970 Uttam Kumaran: So extremely… a different challenge, where they have a ton of DCs, you know, everywhere across the country.
94 00:13:14.050 ⇒ 00:13:28.620 Uttam Kumaran: And inventory cannot stay very long, and so they do most of their business on Mother’s Day and on Valentine’s Day. Again, very interesting, like, inventory forecasting data challenge.
95 00:13:28.930 ⇒ 00:13:34.740 Uttam Kumaran: And it’s something that, like, we’ve seen across the board, and again, there’s even, like, nuances to
96 00:13:34.740 ⇒ 00:13:50.509 Uttam Kumaran: for all these companies, like, many of them use the tools you mentioned, like NetSuite, a lot of… we’ve done a lot of work on Shopify, but it is these things where, for example, we’ve gone to clients where they’re entering in samples and things like that, it’s just, like, 100% discounted goods.
97 00:13:50.510 ⇒ 00:13:53.849 Uttam Kumaran: And so when we go to look at our discount, averages, we’re like.
98 00:13:53.970 ⇒ 00:14:03.289 Uttam Kumaran: where’s all these discounts coming from? Like, oh, you’re just entering in… you’re entering them as, like, we gave that money away, and so there’s, like, all these accounting challenges, right, about, like, oh, our
99 00:14:03.750 ⇒ 00:14:06.179 Uttam Kumaran: Rate is skewed, but it’s actually not that bad.
100 00:14:06.600 ⇒ 00:14:16.890 Uttam Kumaran: Similar things for refunds, like, okay, if you issue a refund, don’t create a 100% discounted product and ship it to them. Like, but again, the… some people just…
101 00:14:16.890 ⇒ 00:14:35.879 Uttam Kumaran: they… it’s not that they got it wrong, and they just… there wasn’t a guideline on how to do it, and they didn’t understand the downstream implication to a report, so they just were like, this is the easiest way to do so. You know, so we’ve… we’ve done a lot of, I think, what kind of, like, the things you’re describing, and then more on the stuff on the automation side, it’s actually, like.
102 00:14:35.950 ⇒ 00:14:55.519 Uttam Kumaran: not… doesn’t even… may or may not include AI, but it is, like, these systems talking to each other. Whether it is something has to enter Monday, and then something has to go to NetSuite, or it’s, like, we need to make, like, a little internal form that handles things and talks directly to NetSuite, or gives some people, like, someone submits something, there’s an approval process that’s in Slack.
103 00:14:55.520 ⇒ 00:14:59.039 Uttam Kumaran: Like, those are the kind of, like, the fun things that we’re working on, where
104 00:14:59.040 ⇒ 00:15:10.339 Uttam Kumaran: because we do… we develop a lot of our solutions with AI, and we’re able to move really quickly, we’re able to build these things that are bespoke to just your company, versus you having to go buy
105 00:15:10.340 ⇒ 00:15:22.610 Uttam Kumaran: something, or, like, having to get all your team to become really amazing with Zapier. Like, you can do a lot of stitching with Zapier, and I’m sure every company is doing that, but some of those have to graduate into something that’s a little bit more sophisticated, you know?
106 00:15:22.730 ⇒ 00:15:23.370 Shelly: Yeah.
107 00:15:24.670 ⇒ 00:15:39.140 Shelly: Yeah, totally, totally agree with all of that. And an additional benefit for us is anything that we can run and use and affect inside of Slack is going to be so much more affordable, because every NetSuite seat is.
108 00:15:39.140 ⇒ 00:15:39.810 Uttam Kumaran: Yes.
109 00:15:39.810 ⇒ 00:15:41.770 Shelly: A significant cost to us.
110 00:15:42.080 ⇒ 00:15:47.839 Shelly: And if you’re just… if you just have a seat so you can see some reporting, or enter in an order every once in a while.
111 00:15:47.840 ⇒ 00:15:50.140 Uttam Kumaran: Oh, that’s horrible.
112 00:15:50.760 ⇒ 00:16:01.799 Uttam Kumaran: So that’s a lot of… even in our company, we’re the same. We share logins for as much as we can, we’re… we’re in the same boat, because that’s just revenue, why would I give it to the software company? A software company…
113 00:16:01.870 ⇒ 00:16:21.710 Uttam Kumaran: you know, these guys come in, and I think for a long time, they’ve just, like, been like, oh, we’re gonna increase your seat-based pricing. Also, in software licenses, it’s… not everybody gets the value. Someone’s just a viewer, why are… you know? So, we… in my company itself, we run on Notion, and we run on Slack and Zoom, and everything has to get centralized, so everything comes back to Slack.
114 00:16:21.710 ⇒ 00:16:37.760 Uttam Kumaran: we do HubSpot for CRM, so we’ve been very mindful about the tools we use. Everything has an API, and that way everything, like, not everybody has to be in HubSpot, but somehow they have to be able… what their part touches needs to be able to get in and out of there, you know? And that’s a lot of what we’re building.
115 00:16:37.970 ⇒ 00:16:46.060 Shelly: Totally. I love that, because we do have a handful of instances, where people need information and they can’t self-serve, because.
116 00:16:46.060 ⇒ 00:16:46.389 Uttam Kumaran: They did.
117 00:16:46.390 ⇒ 00:16:57.119 Shelly: don’t have the access to it, and then it’s someone else, you know, stopping what they’re doing, assisting, you know, taking time away, and then it’s a slowdown for two people instead of just one, you know what I mean? Yeah.
118 00:16:57.120 ⇒ 00:16:57.460 Uttam Kumaran: Yeah.
119 00:16:57.460 ⇒ 00:17:03.119 Shelly: I’ve got lots of instances where even just, you know, something so simple as, like, lookups.
120 00:17:03.360 ⇒ 00:17:04.050 Uttam Kumaran: Yes.
121 00:17:04.050 ⇒ 00:17:04.999 Shelly: Based out of Slack.
122 00:17:05.000 ⇒ 00:17:07.049 Uttam Kumaran: Like, simple ID lookups and things like that.
123 00:17:07.260 ⇒ 00:17:08.280 Uttam Kumaran: Yeah. Yeah.
124 00:17:09.420 ⇒ 00:17:24.569 Uttam Kumaran: I guess, I guess, like, tell me, so tell me, what are you thinking? Like, is this something you guys are, like, actively investing in? Is there some particular pain point that you’re like, hey, if we could work together on figuring out how to get rid of it or mitigate it, there’d be some value? Or, like, yeah.
125 00:17:24.579 ⇒ 00:17:25.369 Shelly: Oh, yeah.
126 00:17:25.960 ⇒ 00:17:37.970 Shelly: Definitely. So, like I said, right now, our most, prominent pain point is with manual sales order entry inside a NetSuite for our wholesale business, because we’re getting in, you know.
127 00:17:37.970 ⇒ 00:17:40.760 Uttam Kumaran: You’re getting POs, like, manually over email, and then, okay.
128 00:17:41.040 ⇒ 00:17:53.670 Shelly: Correct. We do have a handful of reps who have joined us all in the 2000s and enter things on a cloud-based piece of software, but then we have our friends that are looking to fax us information.
129 00:17:56.750 ⇒ 00:18:00.440 Shelly: So I offered him an email address instead. Yeah.
130 00:18:00.690 ⇒ 00:18:13.180 Shelly: So, yeah, we do have a fair amount, and then, like, our large… we have some larger companies, too, that just… they, like, they make a big enough purchase that they can do whatever we want, and we’ll take it however they give it to us, so that we can get.
131 00:18:13.180 ⇒ 00:18:13.500 Uttam Kumaran: Yeah.
132 00:18:13.500 ⇒ 00:18:21.699 Shelly: Right? So a lot of times, those will just come in in a bulk order for, like, 60 stores all at once on one PDF that we have to, like.
133 00:18:21.700 ⇒ 00:18:22.310 Uttam Kumaran: Yes.
134 00:18:22.310 ⇒ 00:18:27.200 Shelly: and your eyes will go across, even with, you know, like, throwing it into ChatGPT and having it…
135 00:18:27.530 ⇒ 00:18:28.020 Uttam Kumaran: Yes!
136 00:18:28.020 ⇒ 00:18:31.240 Shelly: fudge around with it. It’s still gonna make mistakes, you still have to double-check all the.
137 00:18:31.240 ⇒ 00:18:31.730 Uttam Kumaran: guests.
138 00:18:31.730 ⇒ 00:18:35.739 Shelly: There’s still a lot of effort that goes into doing it.
139 00:18:36.200 ⇒ 00:18:37.939 Shelly: No matter how we do it right now.
140 00:18:37.940 ⇒ 00:18:45.639 Uttam Kumaran: And then what has been the solution so far, apart from hiring more people? Like, what… has anything worked so far?
141 00:18:45.840 ⇒ 00:18:46.650 Uttam Kumaran: Okay.
142 00:18:46.900 ⇒ 00:18:53.170 Shelly: That is our solution. Hiring more people, more vis… like, making sure that we have.
143 00:18:53.730 ⇒ 00:18:55.200 Uttam Kumaran: You might win a…
144 00:18:55.310 ⇒ 00:18:57.070 Shelly: on a Monday board, so.
145 00:18:57.070 ⇒ 00:18:57.680 Uttam Kumaran: Yes.
146 00:18:57.680 ⇒ 00:19:00.630 Shelly: Visibility into where things are going, so…
147 00:19:00.860 ⇒ 00:19:07.790 Shelly: From… from, like, my mid-manager level up to leadership, we can quickly look and see, like…
148 00:19:07.790 ⇒ 00:19:23.700 Shelly: what’s going on this week? Why has something been sitting around for two weeks? Where are the problems at? And making sure that if we do have issues, they’re an external issue, and not us just not getting to it, or seeing it, or missing it, but we’re, like, waiting on information, or…
149 00:19:23.700 ⇒ 00:19:24.900 Shelly: You know, whatever it is.
150 00:19:25.650 ⇒ 00:19:29.820 Shelly: But yeah, that is by far our biggest pain point right now.
151 00:19:29.820 ⇒ 00:19:32.829 Uttam Kumaran: And then on your team, is there, like, one person that’s, like.
152 00:19:33.110 ⇒ 00:19:48.870 Uttam Kumaran: the master at doing this, who, like, it would be right for us to just go basically watch and, like, sort of understand the process today, and then our process for a lot of these situations is we go in and we just observe, because even when you talk to people, it’s sort of what we started the conversation with, is
153 00:19:48.870 ⇒ 00:20:01.380 Uttam Kumaran: they describe… what they describe as may not be all the edge cases in, like, how this works, and so we basically go and we kind of, like, audit and figure out, like, what is the situation? Is there someone on the team that’s, like.
154 00:20:01.580 ⇒ 00:20:03.400 Uttam Kumaran: Yeah. For perks? Okay, alright.
155 00:20:03.400 ⇒ 00:20:06.939 Shelly: 100%. My, my girl Gracie.
156 00:20:07.100 ⇒ 00:20:20.790 Shelly: Has… is the… she manages most of the ops around wholesale. So she has entered a zillion of those orders, and she knows when we need to change something, or why we need to change something, or all of the little asterisks around it.
157 00:20:20.960 ⇒ 00:20:21.610 Uttam Kumaran: Okay.
158 00:20:21.830 ⇒ 00:20:26.990 Shelly: So yeah, when we get her in a room, she would absolutely be able to walk us through all of the exceptions.
159 00:20:27.330 ⇒ 00:20:42.900 Uttam Kumaran: Okay, I mean, so kind of like a bit about our process. So, for us, like, especially as we’re walking in and checking out systems, we usually just do, like, a 2-4 week audit, where we try to just deliver something that’s working, like a proof of concept of something. So usually that includes
160 00:20:43.230 ⇒ 00:20:50.339 Uttam Kumaran: coming in, meeting the people, and again, for any of our solutions, the people are number one. It’s not a…
161 00:20:50.340 ⇒ 00:21:05.220 Uttam Kumaran: hey, there’s a shiny tool here, go use it. It is a complete thing where I feel like we are the actual unlock, and so it’s something that’s molded around the people, so we have to meet all the cast and characters that are sort of involved with the process.
162 00:21:05.220 ⇒ 00:21:19.610 Uttam Kumaran: We really try to basically give you… we… in our… just by doing our process, we end up with really robust documentation about, like, what it is. So these are, like, flow diagrams, things, so we can understand what… what the systems look like, and then we basically try
163 00:21:19.610 ⇒ 00:21:36.689 Uttam Kumaran: in that 2-4 week process, deliver some portion of, like, an agreed-upon proof of concept as fast as we can work to show you that there’s… there’s something we can do. In that process, we also get a sense of, like, how much time it would take for us to continue to build and maintain. You also get a…
164 00:21:36.690 ⇒ 00:21:48.689 Uttam Kumaran: great chance to work with us, and then see, okay, once you see us kind of attack one problem, where else can you throw Brainforge, you know, at things, is sort of how clients have used us in the past.
165 00:21:48.860 ⇒ 00:21:52.199 Uttam Kumaran: So, if that’s something that, like, kind of seems…
166 00:21:52.390 ⇒ 00:22:10.699 Uttam Kumaran: you know, really align. Like, what I would do is I would take this conversation, sort of just quickly write up what we heard. We would have… definitely have some open questions, for sure, about current stack and things like that, and I could roughly give you a sense of, like, what it could look like for us to come in and just, like, tackle that for a few weeks.
167 00:22:10.910 ⇒ 00:22:12.340 Shelly: That sounds awesome!
168 00:22:12.340 ⇒ 00:22:15.169 Uttam Kumaran: Okay. I love that. That’d be a great way to get this started.
169 00:22:15.400 ⇒ 00:22:16.040 Uttam Kumaran: Okay.
170 00:22:17.010 ⇒ 00:22:18.230 Uttam Kumaran: Okay, cool.
171 00:22:18.430 ⇒ 00:22:22.949 Uttam Kumaran: I know we have 7 minutes left, how else can we be helpful, or what can I help with?
172 00:22:23.460 ⇒ 00:22:41.429 Shelly: I mean, I doubt… not much, honestly. Like, I looked through your deck before we chatted today, and this all seems pretty straightforward, and pretty much what I expected. I think, really, what I, like, I… I would love to just see it start working, so then we can start thinking about the other places in the business.
173 00:22:41.550 ⇒ 00:22:48.959 Shelly: Cool. We can really, leverage your guys’ help. I… I know it will immediately be accounting, but…
174 00:22:48.960 ⇒ 00:22:53.770 Uttam Kumaran: Okay, that’s great, we do a lot of financial data work as well, like, my background…
175 00:22:53.770 ⇒ 00:22:55.760 Shelly: Not surprised me.
176 00:22:55.760 ⇒ 00:23:05.940 Uttam Kumaran: My background is in a lot of financial data stuff earlier in my career, and I like it because that’s… those are the people that are in control a lot, but also.
177 00:23:06.200 ⇒ 00:23:19.260 Uttam Kumaran: again, in businesses like e-com, it’s extremely important to know the money coming in. Like, it’s not a very… it’s not, like, an extreme margin business, and there’s a lot of moving parts. Yeah. You know, so…
178 00:23:19.260 ⇒ 00:23:25.260 Shelly: And I just… there isn’t anything at Shinnessea that is not data-driven.
179 00:23:25.260 ⇒ 00:23:26.120 Uttam Kumaran: Great, great.
180 00:23:26.120 ⇒ 00:23:28.540 Shelly: Anything that we do that does not.
181 00:23:28.540 ⇒ 00:23:29.409 Uttam Kumaran: Oh, that’s really great.
182 00:23:29.410 ⇒ 00:23:31.480 Shelly: without numbers backing it up.
183 00:23:31.480 ⇒ 00:23:34.189 Uttam Kumaran: And what do you guys use for reporting right now?
184 00:23:34.340 ⇒ 00:23:43.040 Shelly: Looker, and, I think our DC is using, something something BI.
185 00:23:43.040 ⇒ 00:23:43.900 Uttam Kumaran: Power BI?
186 00:23:43.900 ⇒ 00:23:45.270 Shelly: Yeah, that’s it.
187 00:23:45.270 ⇒ 00:23:46.860 Uttam Kumaran: Okay, okay. Yeah.
188 00:23:47.880 ⇒ 00:23:53.519 Uttam Kumaran: Okay, cool. Alright, so then I owe you a couple things. I will get that as soon as I can. If there’s anything I can…
189 00:23:54.070 ⇒ 00:24:08.120 Uttam Kumaran: help with in the meantime, and again, we do a lot in data and AI, so even if you guys are thinking about tools, and I can help, like, give an opinion, we are very opinionated about what’s working, actually working, and, like, what’s not, like, a fake demo.
190 00:24:08.120 ⇒ 00:24:08.970 Shelly: Yeah, yeah.
191 00:24:08.970 ⇒ 00:24:12.620 Uttam Kumaran: If there’s anything I can be helpful with, please let me know, so…
192 00:24:12.640 ⇒ 00:24:16.789 Shelly: Awesome, that sounds great! I will absolutely use and abuse that opportunity.
193 00:24:16.790 ⇒ 00:24:19.100 Uttam Kumaran: Please, please, please.
194 00:24:19.100 ⇒ 00:24:22.670 Shelly: I love having someone more knowledgeable than me tell me if something’s good or bad.
195 00:24:22.670 ⇒ 00:24:25.719 Uttam Kumaran: Yeah, yeah. Okay, perfect. All right.
196 00:24:25.720 ⇒ 00:24:26.330 Shelly: Awesome!
197 00:24:26.330 ⇒ 00:24:27.220 Uttam Kumaran: Sally, I appreciate it.
198 00:24:27.750 ⇒ 00:24:29.449 Uttam Kumaran: Yeah, have a great weekend, hopefully.
199 00:24:29.450 ⇒ 00:24:30.589 Shelly: Same to you.
200 00:24:30.590 ⇒ 00:24:31.250 Uttam Kumaran: Okay.
201 00:24:31.590 ⇒ 00:24:32.130 Shelly: Bye-bye.