Meeting Title: Brainforge Final Interview Date: 2026-02-27 Meeting participants: Robert Tseng, Greg Stoutenburg, Advait Nandakumar Menon, Amber Lin, Kaela Gallagher
WEBVTT
1 00:00:09.320 ⇒ 00:00:10.250 Greg Stoutenburg: Blue.
2 00:00:10.430 ⇒ 00:00:16.139 Robert Tseng: Hey, sorry guys, I didn’t know that my meeting was being used for something else.
3 00:00:18.730 ⇒ 00:00:19.250 Advait Nandakumar Menon: Hello!
4 00:00:20.040 ⇒ 00:00:21.130 Greg Stoutenburg: Good to see you again.
5 00:00:21.130 ⇒ 00:00:23.810 Advait Nandakumar Menon: Hey, Greg, nice to meet you again. Hey, Robert.
6 00:00:23.810 ⇒ 00:00:25.680 Robert Tseng: Hi, is it Advait?
7 00:00:26.080 ⇒ 00:00:27.110 Advait Nandakumar Menon: Yeah, it’s Android.
8 00:00:27.110 ⇒ 00:00:29.440 Robert Tseng: Advait? Okay, great. Good to meet you.
9 00:00:29.910 ⇒ 00:00:31.200 Advait Nandakumar Menon: Nice to meet you as well.
10 00:00:32.850 ⇒ 00:00:36.519 Robert Tseng: Cool. And then we have Amber. Have you met Amber yet?
11 00:00:36.800 ⇒ 00:00:39.309 Advait Nandakumar Menon: Yeah, she was the one who took my first round, so…
12 00:00:39.310 ⇒ 00:00:56.919 Robert Tseng: First one. Okay, great. And then Kayla, I don’t know if you’ve met her yet, but she’s new here, too. She’s basically leading up recruiting for us, so I just asked her to kind of join this call. I know she wasn’t part of your process before, but, you know, she’ll definitely be orchestrating this in the background. Cool.
13 00:00:56.920 ⇒ 00:00:57.470 Advait Nandakumar Menon: Yep.
14 00:00:57.730 ⇒ 00:01:11.710 Robert Tseng: So, I mean, I chatted with Kayla before this call. I think what we’re doing on this call is we’re gonna first take a look at your take-home. We ask you to kind of put together an exercise. I want to give you a chance to present it.
15 00:01:11.710 ⇒ 00:01:23.140 Robert Tseng: Yeah, and then we’ll just kind of be, like, a panel on that side, just, like, kind of doing some Q&A, and then we’ll transition over to doing, just more, like, kind of broader questions. Does that sound okay?
16 00:01:23.140 ⇒ 00:01:24.529 Advait Nandakumar Menon: Yep, that sounds perfect.
17 00:01:24.840 ⇒ 00:01:36.389 Robert Tseng: Great, alright, let’s do it. I don’t think we have too much time, so I want to give you enough time to at least to go through it. Yeah. Don’t feel free, feel free, I think this section will probably take, like, 15 minutes, so I don’t need you to kind of… Okay.
18 00:01:36.390 ⇒ 00:01:47.600 Robert Tseng: walk through it, you know, spend, like, 5 to 7 minutes kind of giving us what you can, and then we’ll probably just do some Q&A for 15 minutes, and then I want to save more of the time for you to get to know Brainforge and everything, so…
19 00:01:47.600 ⇒ 00:01:52.609 Advait Nandakumar Menon: Okay, great. So, I will share my screen real quick.
20 00:01:53.320 ⇒ 00:01:55.789 Advait Nandakumar Menon: Let me know if you’re able to see it.
21 00:01:56.320 ⇒ 00:01:56.980 Robert Tseng: Yes.
22 00:01:58.940 ⇒ 00:01:59.590 Advait Nandakumar Menon: Okay.
23 00:02:00.010 ⇒ 00:02:18.129 Advait Nandakumar Menon: Right, so I’ll get right into it. Today, I’ll walk you through a quick market read of your new vertical’s delivery performance using the provided data, and then I’ll close with 3 practical recommendations across demand, supply, and fulfillment.
24 00:02:19.060 ⇒ 00:02:33.169 Advait Nandakumar Menon: So, really quick intro from my side. I’m Advait, and I’m a data analyst by background. I’ll keep this presentation tight and decision-oriented as to what’s happening, why it matters, and what I’ll do to improve the outcomes.
25 00:02:34.030 ⇒ 00:02:52.430 Advait Nandakumar Menon: So, here’s the flow for today. Firstly, just a quick context on the dataset and what I analyzed, then some key insights across the three-sided marketplace, that is, customers, dashes, and merchants. And finally, three recommendations with an execution plan and success metrics.
26 00:02:54.440 ⇒ 00:03:13.820 Advait Nandakumar Menon: So, to give you a quick overview, this is a Cincinnati-based data set, dating from September 15th to October 14th of 2022. There are 4 order providers represented, that is Dashmart plus 3 retail partners. So, at the high level, 3 things stood out to me.
27 00:03:14.030 ⇒ 00:03:28.460 Advait Nandakumar Menon: Firstly, demand is very concentrated in the evening. That is, about half of the total delays happen in the 5 to 10 PM window, with a peak around 8pm. So, that’s the time window where small improvements compound.
28 00:03:28.590 ⇒ 00:03:43.280 Advait Nandakumar Menon: Next, the customer experience baseline is pretty strong on defect-free deliveries, which is just over 92%, but there is still meaningful volume in late deliveries and cancellations, especially when the demand is at its peak.
29 00:03:43.560 ⇒ 00:03:59.519 Advait Nandakumar Menon: Lastly, the largest economic leakage is item availability, and it’s majorly driven by retail partners, rather than Dashmart. So, missing items create a measurable revenue hit, and substitutions cover some of it, but not all.
30 00:04:00.600 ⇒ 00:04:18.780 Advait Nandakumar Menon: So, I like to start with a simple baseline scorecard over here. It’s a quick KPI snapshot. We have around 13K total deliveries in this period, and the cancellation rate is about 1.6%, and late deliveries are about 4.8%.
31 00:04:18.930 ⇒ 00:04:37.940 Advait Nandakumar Menon: And the defect-free delivery rate is at 92%, which is really strong, but when you really translate the remaining 8% into customer experience events at this scale, it’s still a lot of impacted orders. So, on the commercial side, the requested revenue is about $300,000,
32 00:04:37.940 ⇒ 00:04:47.040 Advait Nandakumar Menon: And the adjusted revenue is $298K, which is nothing but the requested revenue, excluding revenue from the canceled orders.
33 00:04:47.050 ⇒ 00:04:58.500 Advait Nandakumar Menon: The average basket value is around $23, and there are about 2,300 active dashes, which matters when we talk about concentration and reliability.
34 00:04:59.630 ⇒ 00:05:12.819 Advait Nandakumar Menon: Now, I want to briefly touch on the market share, that is Dashmart versus retail partners. So, this split matters because the operating model and controllability are different.
35 00:05:12.830 ⇒ 00:05:30.370 Advait Nandakumar Menon: About 69% of deliveries are Dashmat, and 31% are retail partners. So, that’s important context for later, because there are some issues which are concentrated in the retail partner segment, and those likely will need a different operation framework than Dashmat.
36 00:05:31.580 ⇒ 00:05:51.300 Advait Nandakumar Menon: So, with that context, let’s look at when demand hits, because that timing drives both growth and operational strain. From the graph, we can clearly see that the demand rises to the day and peaks around 8pm, and 5 to 10 PM accounts for approximately 50% of the total orders.
37 00:05:51.300 ⇒ 00:06:08.079 Advait Nandakumar Menon: So, if we want growth that actually moves the needle, we should design, ideas around the peak window mainly. So, that could be higher conversion, bigger baskets, or better attachment, but the main idea is really to optimize where the volume already is.
38 00:06:09.430 ⇒ 00:06:12.830 Advait Nandakumar Menon: So now I will quantify the quality baseline.
39 00:06:12.960 ⇒ 00:06:30.489 Advait Nandakumar Menon: Here, I’m summarizing the overall experience quality. About 92% of deliveries are defect-free based on the combined delivery level defect indicators I used, and the remaining 8% includes late deliveries, cancellations, and customer-reported problems.
40 00:06:30.560 ⇒ 00:06:46.069 Advait Nandakumar Menon: This is a really good baseline, but it also tells us that the next step isn’t a total overhaul, it’s targeted improvements that should be implemented in high-leverage segments like peak window and concentrated dasher cohorts, which I will get into shortly.
41 00:06:48.300 ⇒ 00:06:57.229 Advait Nandakumar Menon: So now let’s talk about what the defects actually are. This graph, helps prioritize the shape of the defect problem.
42 00:06:57.230 ⇒ 00:07:08.819 Advait Nandakumar Menon: Late deliveries are the largest category by count in the sample, around 632 late deliveries, while cancellations and reported issues are around 200 each.
43 00:07:08.920 ⇒ 00:07:23.889 Advait Nandakumar Menon: So, for reliability, if we decrease lateness, we are likely to get the highest quality lift, and we should especially care about lateness during peak hours, since that’s when customers are most sensitive and the volume is the highest.
44 00:07:25.520 ⇒ 00:07:36.940 Advait Nandakumar Menon: So, to reduce the biggest defect, which is lateness, we should see where the volume sits across dashers. So, this is the supply-side pattern that really matters operationally.
45 00:07:36.940 ⇒ 00:07:45.630 Advait Nandakumar Menon: Even though there are over 2,300 active dashers, the delivery volume is concentrated to a core cohort of 155 dashers.
46 00:07:45.630 ⇒ 00:08:04.250 Advait Nandakumar Menon: who handle about 52% of the deliveries. So, that’s actionable because it means reliability and execution can be improved efficiently by focusing on a relatively small group, rather than trying to change behavior across the full long-tail SEC over here.
47 00:08:05.390 ⇒ 00:08:12.100 Advait Nandakumar Menon: Now, I’ll shift to the merchant side, because availability issues show a sharp store-type split.
48 00:08:12.130 ⇒ 00:08:22.889 Advait Nandakumar Menon: Missing items are heavily concentrated in retail partners. You can see it in the missing rate comparison, where Dashmart’s missing rate is around 0.2%.
49 00:08:22.890 ⇒ 00:08:35.409 Advait Nandakumar Menon: And retail partner missing rate is around 14%. So, while retail partners are only about 31% of the deliveries, they are where the availability issue is majorly happening.
50 00:08:35.500 ⇒ 00:08:52.339 Advait Nandakumar Menon: So, the practical implication is that the fastest road to impact isn’t a broad fix, but really, it’s a retail partner targeted availability program, and the reason I’m emphasizing this is because it tells us where to focus our time for the biggest one.
51 00:08:53.690 ⇒ 00:09:02.259 Advait Nandakumar Menon: Since we know that availability is a problem, the next question is, how big is it, and where is it coming from?
52 00:09:02.260 ⇒ 00:09:17.539 Advait Nandakumar Menon: So, the missing items here represent about 14.8K is recovered via substitution.
53 00:09:17.540 ⇒ 00:09:23.850 Advait Nandakumar Menon: But there’s still about $6.7K that’s not recovered because items weren’t substituted.
54 00:09:24.110 ⇒ 00:09:28.859 Advait Nandakumar Menon: So, the item category breakdown on the right helps to prioritize where to start.
55 00:09:28.980 ⇒ 00:09:44.810 Advait Nandakumar Menon: Missing revenue is spread across everyday essentials, especially household, frozen pantry, and drinks, which makes this very actionable. So, the immediate play here would be to not try to solve everything at once.
56 00:09:44.810 ⇒ 00:09:57.240 Advait Nandakumar Menon: Maybe start with a few high league categories, tighten the substitution options, and raise the substitution success rate, because that’s a direct lever to recover the lost revenue quickly.
57 00:09:58.540 ⇒ 00:10:06.169 Advait Nandakumar Menon: So, with all that context, here are 3 recommendations across demand, supply, and fulfillment.
58 00:10:06.350 ⇒ 00:10:11.259 Advait Nandakumar Menon: Recommendation 1 is on the demand side, and that is to win the peak window.
59 00:10:11.280 ⇒ 00:10:27.680 Advait Nandakumar Menon: This matters because half the demand sits in the 5 to 10 PM timeframe, and the baseline basket is about $23, with around 5 items per delivery. So, even a small lift in basket value during the peak window has an outsized revenue effect.
60 00:10:27.810 ⇒ 00:10:42.259 Advait Nandakumar Menon: So, I would act on this by running an effective peak window program focused on increasing basket size and conversion without over-discounting. So, the two levers for that I would focus on would be, first.
61 00:10:42.260 ⇒ 00:10:52.610 Advait Nandakumar Menon: Simple, product bundling, like dinner tonight, snack run, household refill, basically curated sets that reduce the decision efforts.
62 00:10:52.610 ⇒ 00:11:08.200 Advait Nandakumar Menon: And secondly, I would focus on time box discounts only during the tighter speed window, like 6 to 9 p.m, and tie that to adding items or hitting a minimum dollar value for the basket, and not just blanket discounts.
63 00:11:08.200 ⇒ 00:11:13.429 Advait Nandakumar Menon: So, to execute all of this practically, I would say within the next two weeks.
64 00:11:13.430 ⇒ 00:11:32.060 Advait Nandakumar Menon: I would pick the top peak cover items or categories and launch two to three curated bundles, plus a one-week time box promotion. And for the long term, I would refine by store type and introduce lightweight personalization based on reorder patterns.
65 00:11:32.310 ⇒ 00:11:33.839 Advait Nandakumar Menon: To measure success.
66 00:11:34.220 ⇒ 00:11:48.650 Advait Nandakumar Menon: I would focus on, KPIs like basket value, items per delivery, specifically during the 5 to 10 p.m. window, and make sure we don’t degrade the customer experience metrics during the peak.
67 00:11:48.910 ⇒ 00:12:04.740 Advait Nandakumar Menon: Obviously, there are risks involved, including growth efforts that can strain the operations and therefore increase lateness, so we really need to monitor the peak reliability parallelly to mitigate this, which ties directly to recommendation 2.
68 00:12:06.180 ⇒ 00:12:13.979 Advait Nandakumar Menon: And recommendation 2 is on the supply side, that is, protect peak reliability by focusing on the core Dasher cohort.
69 00:12:14.070 ⇒ 00:12:32.640 Advait Nandakumar Menon: So, this matters because lateness is the biggest defect in the defect bucket in the data, and supply is concentrated. That is, a relatively small group of dashers drive a majority of deliveries, so targeted interventions can move the overall metric pretty efficiently.
70 00:12:32.690 ⇒ 00:12:46.920 Advait Nandakumar Menon: I would act on this by focusing on three things. First is creating a peak reliability program for the core cohort, like priority support and incentives tied to on-time outcomes and not just the volume.
71 00:12:46.920 ⇒ 00:13:02.829 Advait Nandakumar Menon: And next, running a weekly late outlier review to focus on high-volume dashers with unusually high rate lates, then diagnose whether it’s a store-driven behavior or a time-driven behavior, or what it is that’s causing that late rate.
72 00:13:02.830 ⇒ 00:13:15.309 Advait Nandakumar Menon: And lastly, ensuring peak coverage is intentionally starved, meaning the incentives and positioning near stores should be strongest during the exact hours of peak demand.
73 00:13:15.310 ⇒ 00:13:30.779 Advait Nandakumar Menon: So, to execute all of this, practically, within the next two weeks, I would define the core cohort, define the outlier criteria, and pilot a one-week peak reliability incentive that rewards on-time delivery during the peak window.
74 00:13:30.800 ⇒ 00:13:38.730 Advait Nandakumar Menon: So, in the long term, I would simply operationalize a reliability dashboard and a weekly review cadence.
75 00:13:39.110 ⇒ 00:13:52.109 Advait Nandakumar Menon: To measure success, I would focus on KPIs like, the late rate overall, and the late rate during peak, and also include defect-free delivery rate as a quality parameter.
76 00:13:52.360 ⇒ 00:14:10.610 Advait Nandakumar Menon: Again, risks involved here includes incentives that can increase the cost. We can mitigate this by targeting only the code dashers and measuring the ROI, and if lateness is a factor, and it’s store-driven, that leads us directly into recommendation 3.
77 00:14:10.910 ⇒ 00:14:23.120 Advait Nandakumar Menon: And that’s the final recommendation, here that is on the merchant or fulfillment side, and that is to reduce retail partner missing rates and recover more revenue via substitution.
78 00:14:23.260 ⇒ 00:14:42.139 Advait Nandakumar Menon: So, this matters because, missing items are a clear revenue hit, which is about 6.7K unrecovered even after substitutions, and the missing item problem is majorly concentrated in retail partners.
79 00:14:42.140 ⇒ 00:15:01.880 Advait Nandakumar Menon: So, I would act on this, first by treating this as a store-level quality program. Since we have only 3 retail partners in the data set, the immediate move is to rank and focus on those three, compare their missing rates and missing revenue, and build a tight improvement loop with them.
80 00:15:02.200 ⇒ 00:15:17.120 Advait Nandakumar Menon: Next, improving the substitution experience, we can start off with a rule-based experience, example for the top missing categories, predefine acceptable substitutes, and nudge customers toward pre-approval wherever possible.
81 00:15:17.200 ⇒ 00:15:29.869 Advait Nandakumar Menon: And lastly, I would focus on starting with the highest lead categories first, which are household, frozen, and pantry, because that’s where you can recover revenue pretty quickly.
82 00:15:29.930 ⇒ 00:15:45.589 Advait Nandakumar Menon: So, to execute all of this practically, within the next two weeks for each of the three retail partners, I would identify the top missing categories, or SKUs, and deploy simple substitution rules and measure substitution lift week over week.
83 00:15:45.670 ⇒ 00:15:56.219 Advait Nandakumar Menon: In the long term, I would build an availability scorecard by, store and category, and operationalize monthly partner reviews.
84 00:15:56.410 ⇒ 00:16:06.759 Advait Nandakumar Menon: And to measure success, I would focus on KPIs like the retail partner missing rate, the substitution rate on missing items, and the undercover revenue.
85 00:16:07.300 ⇒ 00:16:26.509 Advait Nandakumar Menon: So, risks here include, aggressive substitution that may, probably increase wrong item perception among customers. So, we can mitigate this by constraining substitutes to the same category or brand, and also encouraging customer pre-approval.
86 00:16:27.350 ⇒ 00:16:46.660 Advait Nandakumar Menon: So, overall, we can see that the story is to optimize for peak demand, reduce lateness, and really just protect the peak reliability by focusing on the core supply cohort and fixed retail partner item availability, because that’s where the biggest leakage is.
87 00:16:46.660 ⇒ 00:16:53.350 Advait Nandakumar Menon: So, yeah, that’s the end of the presentation, and really thank you, I’m open to take any questions.
88 00:16:55.410 ⇒ 00:17:02.300 Robert Tseng: Cool. Thanks for the presentation, Advait. I guess, I’m just gonna jump back to… let’s go to Recommendation 2 first.
89 00:17:03.000 ⇒ 00:17:03.620 Advait Nandakumar Menon: Sure.
90 00:17:03.870 ⇒ 00:17:06.229 Robert Tseng: Yeah. So you go back to that slide, thanks.
91 00:17:06.420 ⇒ 00:17:20.660 Robert Tseng: Yeah, so in this situation, you know, you talked about this peak reliability scorecard. Yeah, I guess kind of walk me through how you would build this. You know, you’re the analyst on the team, you’re speaking with, you’re working with the ops team.
92 00:17:20.660 ⇒ 00:17:28.759 Robert Tseng: Yeah, what are some of the things that they should consider in building a scorecard, and how do you actually hold partners accountable to the scorecard?
93 00:17:29.450 ⇒ 00:17:33.030 Advait Nandakumar Menon: Okay, so in order to build,
94 00:17:33.290 ⇒ 00:17:45.999 Advait Nandakumar Menon: reliability dashboard, I would first focus on some of the KPIs I already mentioned, like, look at the defect rate, look at how long they’re taking to deliver, whether there’s an increase in the
95 00:17:46.000 ⇒ 00:17:53.579 Advait Nandakumar Menon: cancellations during the peak window by, employing the… some of the effective solutions that I mentioned.
96 00:17:53.580 ⇒ 00:18:00.600 Advait Nandakumar Menon: Really just, get the core KPIs I mentioned before, try to build,
97 00:18:01.500 ⇒ 00:18:10.050 Advait Nandakumar Menon: Like, do you expect me to provide an insight about the technical aspect as well, or just how, from a business standpoint, you want me.
98 00:18:10.050 ⇒ 00:18:26.500 Robert Tseng: Oh yeah, no need for the technical. I think, it’s all good. Yeah, it’s all on the business side. Yeah, let’s kind of make the scenario a bit clearer. So, let’s say, yeah, you build out this scorecard, there’s, like, a few factors, assuming that the team agrees to your KPIs, like, kind of defect-free rate, whatnot.
99 00:18:26.630 ⇒ 00:18:42.209 Robert Tseng: You get the data set, or you start to hold partners accountable, they have to… it’s self-reported, right? And then you start to see inconsistencies, where, maybe one partner is kind of showing, like, 100% defect-free rates, but your data is showing you that that’s not actually true.
100 00:18:42.210 ⇒ 00:18:47.460 Robert Tseng: How do you actually investigate, kind of, the discrepancy there, and then kind of go back to the partner?
101 00:18:48.170 ⇒ 00:19:03.440 Advait Nandakumar Menon: Okay, so, first things first, I would go back to the source of truth, that is the data, and compare with what the partner has reported. So, I would see how much of a difference they have reported, because
102 00:19:03.570 ⇒ 00:19:16.870 Advait Nandakumar Menon: In the end, this shouldn’t look like that we are blaming the partners, really. It’s really an… on the operational side, we’re trying to improve things so that, we are able to leverage the peak window here. So.
103 00:19:16.870 ⇒ 00:19:27.219 Advait Nandakumar Menon: I’ll try to see if the source of truth is saying something different, and the data is saying something different, and I would go back to the operations team and lay out the facts to them.
104 00:19:28.190 ⇒ 00:19:37.599 Advait Nandakumar Menon: I don’t think, as an analyst, I’ll be conversing directly with the Dasher or the partner, but I will give the facts to the operations team, and…
105 00:19:37.880 ⇒ 00:19:45.159 Advait Nandakumar Menon: Maybe they have to bump it from there and have an honest conversation with the partner to get an idea of what’s actually happening.
106 00:19:45.840 ⇒ 00:19:50.749 Robert Tseng: Okay, yeah, let me… quick response, and then I’ll do… go to another question. So, yeah, and, you know.
107 00:19:50.910 ⇒ 00:20:05.990 Robert Tseng: you know, in our… in our line of work, you would actually be talking to the partner. Likely the… you are the analyst, you’re like a word deploy analyst on a client. So, if you are able to… if you’re able to see some error here, and you have to…
108 00:20:06.030 ⇒ 00:20:21.130 Robert Tseng: I mean, yes, maybe there’s an OPS counterpart that will be able to take your info and go to the partner directly, but there will be situations where you would have to go to them directly. So, I think that’s just one… I wanted to call out that we have, like, a wide range of clients here, where some…
109 00:20:21.130 ⇒ 00:20:21.590 Advait Nandakumar Menon: Completely.
110 00:20:21.590 ⇒ 00:20:30.790 Robert Tseng: to be gatekeepers of… between us and other stakeholders. Others, you have to actually, you have to actually just go in and do it. So.
111 00:20:30.790 ⇒ 00:20:32.249 Advait Nandakumar Menon: Yeah, that’s completely fine, yeah.
112 00:20:32.250 ⇒ 00:20:34.419 Robert Tseng: Yeah. Let’s go back one more slide.
113 00:20:37.790 ⇒ 00:20:38.640 Advait Nandakumar Menon: Great.
114 00:20:38.640 ⇒ 00:20:49.969 Robert Tseng: Yeah, if you were to just pick one risk, and tell me, like, what do you think is the biggest risk, here? And, yeah, and then kind of elaborate more on your mitigation.
115 00:20:50.820 ⇒ 00:20:51.540 Advait Nandakumar Menon: Okay.
116 00:20:51.700 ⇒ 00:20:55.259 Advait Nandakumar Menon: So, the biggest risk here is, like.
117 00:20:55.390 ⇒ 00:21:03.749 Advait Nandakumar Menon: When acting within the peak window, we are trying to involve as much dashers and, like, basically looking
118 00:21:03.750 ⇒ 00:21:19.029 Advait Nandakumar Menon: for that growth prospect that we are chasing after. So, that could really, like, strain from the operations side, because there may be times where there are simply not enough dashes to accommodate the peak demand, and things like that, so…
119 00:21:19.040 ⇒ 00:21:28.460 Advait Nandakumar Menon: That’s, again, on the operations side. With the second recommendation, I, suggested, I would…
120 00:21:28.630 ⇒ 00:21:42.259 Advait Nandakumar Menon: monitor, like, how bad it’s getting, the lateness, or whatever that’s being impacted by this. And, like I mentioned before, this will be a piloting program, like, for one or two weeks that we’ll be running.
121 00:21:42.260 ⇒ 00:21:54.590 Advait Nandakumar Menon: And I’ll see if it’s getting worse or bad, and I will take it from there accordingly, and maybe come up with different alternatives to prevent the lateness from increasing.
122 00:21:55.330 ⇒ 00:21:57.500 Robert Tseng: Okay, great. No, appreciate you calling that out.
123 00:21:57.500 ⇒ 00:21:57.940 Advait Nandakumar Menon: question?
124 00:21:58.150 ⇒ 00:22:11.030 Robert Tseng: Yeah, it does. Great. So, I think, yeah, you identified where the bottleneck could be, which is on the supply side of the dashers, and then you also, you know, you’re like, okay, this is a contained experiment, one to two weeks, we can revert it back, so…
125 00:22:11.030 ⇒ 00:22:24.450 Advait Nandakumar Menon: The point here is that, like, whatever action I could implement, I would try to do it quickly and practically, and see if it’s getting better and iterate it from there, instead of waiting
126 00:22:24.450 ⇒ 00:22:36.119 Advait Nandakumar Menon: to see what really works for the long term, or delivering a complete solution, because I think it’s, important to be quick and practical, but also deliver a sizable solutions. So, yeah.
127 00:22:36.450 ⇒ 00:22:55.779 Robert Tseng: Yeah, last thought here, and we can move on from this exercise. Yeah, I mean, I appreciate that you have the now and later. I think we always have to give, like, a short term, because we’re consultants, for our clients. They expect the quick win, so definitely highlighting that, is important. The later is… it’s a…
128 00:22:56.080 ⇒ 00:23:08.920 Robert Tseng: it’s important to have that in our… in our belt as well, but usually it’s not like that. It’s not where the clients are focusing on. So, I think it’s good that you will… you kind of focus on something that’s more practical, yeah.
129 00:23:09.740 ⇒ 00:23:14.559 Advait Nandakumar Menon: Yeah, so on that point, if I may ask you a question, so you mentioned that
130 00:23:14.920 ⇒ 00:23:29.259 Advait Nandakumar Menon: About the later aspect, so, are you really building relationships with the client for the long term, or how do you, look at, the projects? Like, is it short-term, or, like, how do you go about that?
131 00:23:29.660 ⇒ 00:23:49.030 Robert Tseng: Yeah, it’s a big mix. I think our longest client has been with us for more than a year, coming up on a year and a half at this point. So, yeah, at that point, we are basically their data team. So, yeah, we get to do OKR planning with them, we set longer targets, we define work streams that go on for a long period of time.
132 00:23:49.080 ⇒ 00:24:07.840 Robert Tseng: I mean, Greg could probably speak to more of that, because he’s kind of revolving those… that work, but we also have clients that are more kind of short-term, where, you know, we’re in and out within… usually 3 months is, like, on the shorter end. So, yeah, we’re not really going to be doing that much strategy work with them. It’s very much just kind of a…
133 00:24:08.510 ⇒ 00:24:09.370 Robert Tseng: very…
134 00:24:09.550 ⇒ 00:24:24.549 Robert Tseng: tightly scoped project. We go in, do the stand-up of a… that’s very engineering-heavy, and then we like to give them a roadmap, kind of, coming out of it, but that’s… it’s not expected that we’re always having a long-term plan with them.
135 00:24:24.910 ⇒ 00:24:33.890 Advait Nandakumar Menon: Okay, no, that’s completely fine. I have worked on a few short-term projects myself in my recent consulting work, so yeah, that’s totally understandable.
136 00:24:34.540 ⇒ 00:24:52.379 Robert Tseng: Okay, cool. Well, I want to give the team some time to kind of ask questions outside of this exercise. I mean, if you guys have any, like, maybe we’ll just maybe do one question from Greg or Amber, if you want to ask anything about this exercise specifically, but I think we should do the remaining time on kind of just more Brainforge-specific.
137 00:24:52.380 ⇒ 00:24:54.759 Robert Tseng: Kind of questions and discussion.
138 00:24:55.470 ⇒ 00:24:56.120 Advait Nandakumar Menon: Okay.
139 00:24:56.120 ⇒ 00:25:19.720 Greg Stoutenburg: Yeah, yeah, I’ll ask, I’ll ask one about this exercise. So, just, like, just to simulate what sort of experience you might have working for Brainforge. So, now it’s, you know, it’s Friday afternoon, and, say I am the… I’m your contact, I’m your point of contact at the client, and I know that Tuesday afternoon, my boss is going to ask me what we’re going to do in the next two months, too.
140 00:25:19.720 ⇒ 00:25:24.069 Greg Stoutenburg: Increased revenue in this division by, you know, 10% over the next…
141 00:25:24.070 ⇒ 00:25:25.400 Greg Stoutenburg: 4 months, or something like that.
142 00:25:25.440 ⇒ 00:25:36.709 Greg Stoutenburg: And, I’m… I don’t have your analytics chops, and what I want from you is to tell me what to say in that meeting, so that I can convince my boss.
143 00:25:36.710 ⇒ 00:25:53.579 Greg Stoutenburg: that we’ve got a set of ideas that’s going to get us there. And so, so you have to be, you know, technical-ish without being too technical, have to say some things about priorities and deliverability in a short period of time, and I’m coming to you because I don’t know what to say, and I’m feeling nervous about that meeting coming up.
144 00:25:54.210 ⇒ 00:25:56.580 Advait Nandakumar Menon: Yeah. So…
145 00:25:56.690 ⇒ 00:26:05.919 Advait Nandakumar Menon: That aspect, from a technical point, I would see, like, how the data is, what’s the data telling. Again, I will do my quick validation and analysis.
146 00:26:05.970 ⇒ 00:26:16.330 Advait Nandakumar Menon: And I would see if the analysis yields that possibility of increasing the revenue within the next 2 months. So, I would…
147 00:26:16.330 ⇒ 00:26:33.970 Advait Nandakumar Menon: I would give this in business terms to my contact, because as you said, they don’t have the chops to be analytical or technical, so really just… I wouldn’t say dumbling it down is the right word, but give… like, explaining what’s happening behind the scenes with the data, and
148 00:26:33.970 ⇒ 00:26:41.950 Advait Nandakumar Menon: what can lead to better outcomes, and if it’s possible to hit that revenue that they are expecting in the next two months, I would…
149 00:26:42.240 ⇒ 00:26:46.939 Advait Nandakumar Menon: come up with some suggestions based on that, and I would take it from there.
150 00:26:49.040 ⇒ 00:26:50.340 Advait Nandakumar Menon: Does that sound good?
151 00:26:50.980 ⇒ 00:26:59.679 Greg Stoutenburg: Yeah, and in terms of, like, what to say to the boss specifically, like, here’s what we’re going to do. What should I say to my boss in that meeting?
152 00:27:00.200 ⇒ 00:27:06.679 Advait Nandakumar Menon: Okay, so I would, give you, like, specific metrics. Obviously, since
153 00:27:06.680 ⇒ 00:27:20.639 Advait Nandakumar Menon: when you’re talking to whoever and say, like, the boss and, like, letting them know if it’s possible or not, I would give key data points, because there should be supporting data to show that whether it’s possible or not.
154 00:27:20.640 ⇒ 00:27:25.970 Advait Nandakumar Menon: So I would give a quick report, or a visual, and…
155 00:27:26.470 ⇒ 00:27:33.800 Advait Nandakumar Menon: Basically, giving a few suggestions and explanation of the metrics, to the point of contact, so that they can…
156 00:27:33.800 ⇒ 00:27:49.020 Amber Lin: Sorry, can I quickly interrupt? Would you pretend that, you’re talking to the boss? Can you take, for example, this recommendation, or take some recommendations and do a 30-second,
157 00:27:49.660 ⇒ 00:27:57.639 Amber Lin: role play of, hey, this is… pretend that I am the person you’re speaking to. Can you tell me what you would say? Just…
158 00:27:57.640 ⇒ 00:27:58.060 Advait Nandakumar Menon: Okay.
159 00:27:58.060 ⇒ 00:28:02.440 Amber Lin: Tell me as if… I’m talking as the client.
160 00:28:03.400 ⇒ 00:28:21.429 Advait Nandakumar Menon: Okay, so, I would say, to you that, to make… hit that revenue, sweet spot over the next two months, we are, focusing on X factors, and these are the factors that is, driving the revenue right now, and
161 00:28:21.540 ⇒ 00:28:28.790 Advait Nandakumar Menon: I would, like, you have to look at it from, a perspective of…
162 00:28:29.370 ⇒ 00:28:33.309 Advait Nandakumar Menon: Whether it’s really possible to hit that revenue cap, and…
163 00:28:33.740 ⇒ 00:28:37.909 Advait Nandakumar Menon: I would lay out the facts to the boss and see how it goes.
164 00:28:46.430 ⇒ 00:28:49.950 Robert Tseng: Okay, we good to move on, Craig and Amber?
165 00:28:51.420 ⇒ 00:28:52.489 Robert Tseng: Okay, great.
166 00:28:52.640 ⇒ 00:28:58.739 Robert Tseng: Yeah, you can stop sharing your screen, we can kind of go back to just doing it more casual, get out of presentation mode.
167 00:29:00.430 ⇒ 00:29:12.879 Robert Tseng: Cool. Yeah, so I think, like, for the remaining time, yeah, why don’t give you some time to… we’ll probably ask a couple more, kind of, more behavioral questions, and then kind of give you the remaining time to ask questions of us.
168 00:29:13.350 ⇒ 00:29:15.990 Robert Tseng: So…
169 00:29:16.500 ⇒ 00:29:30.690 Robert Tseng: Yeah, I guess, like, I’ll start… I’ll throw one out there, and then I’ll let Amber and Greg kind of, jump in, see if they want to ask one as well. So, yeah, let’s say, like, we operate in environments where, kind of,
170 00:29:31.200 ⇒ 00:29:35.929 Robert Tseng: definitions aren’t… aren’t very clear. And, like, I think,
171 00:29:36.540 ⇒ 00:29:39.519 Robert Tseng: Yeah, both from a data perspective, obviously, like.
172 00:29:39.900 ⇒ 00:29:50.319 Robert Tseng: you know, if they had everything in order, they wouldn’t be working with us, so it’s typically a chaotic environment that we’re working with, and stakeholders think that different metrics mean different things, and so…
173 00:29:50.320 ⇒ 00:30:03.129 Robert Tseng: we’re… we’re expected to come in, help understand different perspectives, but be opinionated about how something should be defined. So, could you walk me through a situation where you kind of had to do that?
174 00:30:03.830 ⇒ 00:30:13.790 Advait Nandakumar Menon: Yeah, so I did go through something like that in my recent, work. So, I was, working with this, insurance tech SaaS client.
175 00:30:13.840 ⇒ 00:30:24.460 Advait Nandakumar Menon: Whose request was basically to whip up a dashboard and metrics to look at their sales pipeline health and forecast accuracy.
176 00:30:24.460 ⇒ 00:30:34.970 Advait Nandakumar Menon: So, this dashboard was being built for the RevOps team, but the sales reps that are using the dashboard as well were looking into the same view, so…
177 00:30:35.170 ⇒ 00:30:40.930 Advait Nandakumar Menon: With respect to the definitions of the metrics, there was a clear confusion
178 00:30:40.990 ⇒ 00:30:53.969 Advait Nandakumar Menon: with how the RevOps team saw it, and how the lower, reps saw it. So, like, there were inconsistencies in the raw definitions within themselves.
179 00:30:53.970 ⇒ 00:31:09.730 Advait Nandakumar Menon: So, what I did over there is, I had, one-on-one sessions with the stakeholder, like the RevOps team, and I also had, some sessions with the reps to understand what their definition or understanding of their data is, because we can’t really
180 00:31:09.800 ⇒ 00:31:22.319 Advait Nandakumar Menon: go off two different definitions and expect to come up magically with one metric that would work out for both of them. So, I had sessions with both of them, came to a common understanding, and…
181 00:31:22.600 ⇒ 00:31:33.310 Advait Nandakumar Menon: basically, built out the metrics and the definitions of business logic, modeled it into the semantic layer, and then took it to the dashboard. So, here I was.
182 00:31:33.310 ⇒ 00:31:51.530 Advait Nandakumar Menon: using Salesforce and Snowflake as a semantic layer, because it’s… it was all opportunity won or lost. It’s sales data, basically. So, from there, I took it to Tableau and, defined the KPIs and the visuals and the overall dashboard. So, that’s one specific instance where I did something like that.
183 00:31:52.100 ⇒ 00:31:53.120 Robert Tseng: Okay, thanks.
184 00:31:58.390 ⇒ 00:32:00.849 Greg Stoutenburg: I have a question. So…
185 00:32:01.100 ⇒ 00:32:06.789 Greg Stoutenburg: My question is that, so my experiences working at Brainforge has been that
186 00:32:06.810 ⇒ 00:32:13.029 Greg Stoutenburg: It might be Tuesday morning a client pings you with what they think is, like, the most important thing in the world.
187 00:32:13.040 ⇒ 00:32:26.030 Greg Stoutenburg: And it’s a surprise, it’s, it’s something that you hadn’t really talked about much before, maybe the general topic had come up, but they’re panicking, and probably their panicking is similar to what I was trying to do with the roleplay a moment ago, which is, like.
188 00:32:26.030 ⇒ 00:32:36.980 Greg Stoutenburg: I’m nervous, I have to talk to my boss right away, and I don’t really know what to say. So, you need to give me confidence, but it also has to be, like, a justified level of confidence. This question is focused more on, like.
189 00:32:37.200 ⇒ 00:32:55.099 Greg Stoutenburg: Can you think of a time where, or what could you say about a situation where a priority has just been introduced, and it is the top priority, and you need to be, you need to be fast and confident, even if you don’t know all of the answers?
190 00:32:55.100 ⇒ 00:32:55.460 Advait Nandakumar Menon: Okay.
191 00:32:55.460 ⇒ 00:33:01.640 Greg Stoutenburg: What’s been your experience about that sort of situation, or if not so much, then, you know, what do you think you would do in that kind of situation?
192 00:33:02.060 ⇒ 00:33:10.070 Advait Nandakumar Menon: Yeah, so I have been in that situation. So this was with another client. It was a manufacturing client, a medium-sized business.
193 00:33:10.070 ⇒ 00:33:32.019 Advait Nandakumar Menon: And I was directly working with the CFO and CEO there. So, their finance team, were really panicking because there was this set of Power BI dashboards that they had to manually refresh, like, basically, download the files from their ERP, drop it into SharePoint, and the Power BI had to be… dashboard had to be manually refreshed.
194 00:33:32.020 ⇒ 00:33:32.800 Advait Nandakumar Menon: So…
195 00:33:32.800 ⇒ 00:33:45.220 Advait Nandakumar Menon: Because of this all-manual process, they were facing delays for month-end reporting or board meetings, and there was this one time that they were really missing the data they needed, and
196 00:33:45.530 ⇒ 00:33:59.159 Advait Nandakumar Menon: For that, what I had to quickly do is, obviously, I can’t replatform the entire Power BI dashboard, or the semantic model, or whatever, so to quickly get a win, I used, Power Automate.
197 00:33:59.160 ⇒ 00:34:08.999 Advait Nandakumar Menon: To basically automate the entire process. Like, Power Automate can record your screen, like, dragging and dropping the files and all that, so there was a dedicated PC set up for this.
198 00:34:09.000 ⇒ 00:34:21.180 Advait Nandakumar Menon: So I implemented that Power BI… Power Automate, workflow, and that was a really quick, win, because it basically removed all the automation.
199 00:34:21.179 ⇒ 00:34:30.889 Advait Nandakumar Menon: I mean, all the manual work, that the finance team were doing, and basically resulting in a delayed reporting to the board meeting. So, yeah.
200 00:34:32.489 ⇒ 00:34:35.199 Greg Stoutenburg: Very good. Very good. How long did that take?
201 00:34:36.239 ⇒ 00:34:47.120 Advait Nandakumar Menon: I would say a couple of hours, because what they were really… I’m not sure why their dashboard was set up like that. It was taking the files from their
202 00:34:47.150 ⇒ 00:34:59.179 Advait Nandakumar Menon: I think one reason is because that ERP system is really old, and there’s no other way to get to it, and that’s also the reason why I couldn’t just fully change the platform of how it was working, so…
203 00:34:59.310 ⇒ 00:35:04.620 Advait Nandakumar Menon: I would say a couple of hours it took me to figure that out and, yeah, implement it.
204 00:35:05.120 ⇒ 00:35:06.189 Greg Stoutenburg: Cool, very good.
205 00:35:07.250 ⇒ 00:35:08.619 Greg Stoutenburg: Amber, do you want to take one?
206 00:35:10.350 ⇒ 00:35:17.819 Amber Lin: I don’t have specific questions. I also mostly asked for the behavioral questions in our first interview.
207 00:35:18.930 ⇒ 00:35:19.480 Robert Tseng: Cool.
208 00:35:19.820 ⇒ 00:35:21.500 Robert Tseng: Alright, well,
209 00:35:21.830 ⇒ 00:35:35.409 Robert Tseng: I mean, we can turn it over to you, Advait, just to let you ask some questions of the team. These are, you know, I think you already kind of met everybody at this point, but yeah, I wanted to share anything we can about Brainforce that is top of mind for you.
210 00:35:36.030 ⇒ 00:35:36.680 Advait Nandakumar Menon: Yep.
211 00:35:36.760 ⇒ 00:35:52.560 Advait Nandakumar Menon: So, I saw your post, Robert, about Omni’s AI agent, in which you said you have tested a bunch of AI analytics tools, and this was the first time you got the full answer without any follow-ups required.
212 00:35:52.560 ⇒ 00:36:04.880 Advait Nandakumar Menon: So, where do most tools fail in your experience? Like, is it misunderstanding the business context, bad metric definitions, data modeling gaps, or lack of traceability?
213 00:36:06.270 ⇒ 00:36:14.080 Robert Tseng: I think it… I mean, all of those things are… are true. I think it’s, it’s really bad… bad context, right, I think is the main thing.
214 00:36:14.090 ⇒ 00:36:32.019 Robert Tseng: You know, Omni’s a partner of ours, so we obviously got to hype them up, but, like, realistically, I think all of these agents are getting better. But what really makes them kind of have that magical moment for our clients, I think, is what… is what Brainforge does that’s really unique, is that we are a data engineering, kind of, centric,
215 00:36:32.310 ⇒ 00:36:48.619 Robert Tseng: firm, and so we help clients kind of get all of their data, well, well-structured, and kind of… it’s with complete documentation, so that when you plug in an AI tool on top of it, it actually will be able to pull the right answer, rather than just, you know.
216 00:36:49.010 ⇒ 00:36:57.679 Robert Tseng: using some generic definition that, like, an out-of-the-box LLM would. So, yeah, I think it really just highlights, kind of, the… the…
217 00:36:58.090 ⇒ 00:37:08.979 Robert Tseng: what we as Brainforge do that helps make Omni look good as well. So I think that’s why they like partnering with us. They’re not the only one that we use, and… but yeah, I think that’s kind of my response to that.
218 00:37:08.980 ⇒ 00:37:15.939 Advait Nandakumar Menon: Is it the best one among the AI agents talking to data? Like, would you say among the ones you use, that’s the best?
219 00:37:16.830 ⇒ 00:37:32.000 Robert Tseng: Yeah, well, I mean, compared to, like, Tableau AI, yeah, way better, and also, like, Gemini, yeah, I think out of any of the… these other, like, the… I mean, obviously, all the… all the legacy BI tools have been building out a similar product. We found Omni to be the best out of the ones that we’ve tested.
220 00:37:32.000 ⇒ 00:37:32.370 Advait Nandakumar Menon: Okay.
221 00:37:32.640 ⇒ 00:37:33.260 Robert Tseng: Yeah.
222 00:37:33.260 ⇒ 00:37:41.969 Advait Nandakumar Menon: Yeah, yeah, so I agree with you, like, the underlying data should be really proper, and it should be correct in order for the AI to work on top of it.
223 00:37:41.970 ⇒ 00:37:54.659 Advait Nandakumar Menon: It’s something similar I implemented for that manufacturing client, is users were really wanting to talk to their data, and using Power BI’s service, I implemented Power BI Copilot.
224 00:37:54.660 ⇒ 00:38:12.260 Advait Nandakumar Menon: where the users can just chat with the data and get simple questions answered in seconds. So, like you said, it’s really about the underlying data modeling and the business context and some of the logics that should be baked in for the AI agent to be able to do it. So, yeah.
225 00:38:12.260 ⇒ 00:38:15.490 Advait Nandakumar Menon: Now, that’s, really, insightful.
226 00:38:16.030 ⇒ 00:38:23.390 Advait Nandakumar Menon: I think the next question I have is… maybe, Greg, you can take this, but…
227 00:38:23.480 ⇒ 00:38:40.650 Advait Nandakumar Menon: I saw the post about the two-week BI migration, so congratulations on that, by the way. So, it sounded like the speed came from auditing first, defining metrics, and sequencing the work, really, so…
228 00:38:40.660 ⇒ 00:38:52.760 Advait Nandakumar Menon: Where could someone in this role, like, create the most leverage in that process? Is it the audit definition work, the build execution, the training handoff, or would you say all of it?
229 00:38:53.310 ⇒ 00:39:15.969 Greg Stoutenburg: Yeah, no, good question. So I… I mean, I think I can speak most to just my specific role in the project, and I think the things that made it fast were primarily, one, just having good data sources built already, so that when we connected BigQuery to Omni, all the… all the tables that we needed were there. And then it was a matter of, like, recreating things, in an ideal way.
230 00:39:15.970 ⇒ 00:39:19.230 Greg Stoutenburg: And then, the second, you know.
231 00:39:19.230 ⇒ 00:39:44.229 Greg Stoutenburg: I mean, the second, just being completely realistic, is knowing what your strengths are and what your co-workers’ strengths are, so that you can come up with a plan to delegate effectively and be working in parallel rather than sequentially. So, you know, I sort of led the project, but, probably 9 out of 10 dashboards that were moved over were not moved over by me, and then I did quality review, and the person who was moving
232 00:39:44.230 ⇒ 00:40:07.719 Greg Stoutenburg: over most of those dashboards, there were areas where he needed support as well, and we would coordinate and reach out to others to, you know, plug gaps in dbt models and things like that, while, again, so they’re working on that independently while he’s then moving over more dashboards as quickly as possible. I’m reviewing them, looking for, anything that’s off in terms of quality or accuracy. So it’s… it was having this, like.
233 00:40:07.770 ⇒ 00:40:27.549 Greg Stoutenburg: it was having good data in the first place, but then also just effectively defining roles and responsibilities so that you’re working together, rather than, you know, separately. And so, if I were to, you know, if I were to apply, like, a behavioral question to the presentation that you gave, or the example you gave about the AI tool that you built for Power BI.
234 00:40:28.470 ⇒ 00:40:30.960 Greg Stoutenburg: It would be around that. It would be around, like, you know.
235 00:40:30.960 ⇒ 00:40:31.300 Advait Nandakumar Menon: Okay.
236 00:40:31.300 ⇒ 00:40:47.450 Greg Stoutenburg: what’s your willingness or thoughts around how you approach when you know this is going to take me 10 times as long as it’s going to take Amber, so I need to find the right way to get Amber to do it, because she’s faster than I am on this, so that I can do something else, yeah. So, yeah, I would say it’s those things.
237 00:40:48.090 ⇒ 00:41:01.709 Advait Nandakumar Menon: Okay, yeah, that’s really helpful, because in my previous roles, there were times where I had to, wear all the hats to do all of it, like, since I’ve worked with lean teams and startups before, so…
238 00:41:01.710 ⇒ 00:41:11.039 Advait Nandakumar Menon: Yeah, no, it’s really great to have an environment where you can really collaborate with others and lean on the other’s strengths. So, yeah, that’s…
239 00:41:11.070 ⇒ 00:41:11.780 Greg Stoutenburg: Yeah.
240 00:41:11.860 ⇒ 00:41:13.470 Advait Nandakumar Menon: really insightful.
241 00:41:13.930 ⇒ 00:41:27.869 Advait Nandakumar Menon: I think, that’s pretty much is it… that’s it from me, but is there any area you would like me to clarify or go deeper on so you have full confidence in my fit?
242 00:41:29.690 ⇒ 00:41:41.299 Robert Tseng: Yeah, no, that’s a great question. Thanks for asking that. I think, I mean, I don’t necessarily need to go deeper, but what, you know, obviously we had a… 45 minutes is not a lot of time. I guess…
243 00:41:41.520 ⇒ 00:41:46.969 Robert Tseng: just a couple reactions that I had to, kind of, your presentation, just kind of going back to that.
244 00:41:47.050 ⇒ 00:42:04.199 Robert Tseng: I think your analysis is really thorough. I think, like, it’s clear that you know your way around data, the charts and everything are… look solid. I think you nailed all the takeaways that I wanted… I was hoping that you would get to. Okay. I picked this exercise… I designed this exercise specifically because I think it’s a good mix of…
245 00:42:04.310 ⇒ 00:42:16.360 Robert Tseng: ops analysis, product analysis, and then, like, some basic, like, revenue stuff. So, just wanted to see, like, which area you’re more familiar with. Seems like you have a good grasp of all three.
246 00:42:16.380 ⇒ 00:42:26.259 Robert Tseng: I will say that, like, you know, obviously this is a case exercise, and so in reality, you would be interrupted right from the start, so I would…
247 00:42:26.260 ⇒ 00:42:41.249 Robert Tseng: you would lead with the recommendation, and… Okay. Yeah, and then also, you know, you put a… I know you… it looks like you put a lot of effort into the slide, so I appreciate that. But yeah, you know, if you’re… if you’re presenting to our clients, they’re gonna read your slide and react to the first thing.
248 00:42:41.250 ⇒ 00:42:41.689 Advait Nandakumar Menon: Yeah, yeah, yeah.
249 00:42:41.690 ⇒ 00:42:51.560 Robert Tseng: So, yeah, I think that’s just something you’ll have to be prepared for if you were to kind of be in here. So yeah, I think, just…
250 00:42:51.650 ⇒ 00:43:05.549 Robert Tseng: one thing that I feel like I wanted to clarify about what that exercise was. And then the second, I kind of just doubling down on what I said, definitely focus on, like, that short, practical takeaway.
251 00:43:06.050 ⇒ 00:43:23.829 Robert Tseng: the next step, the quick win. That’s really what will keep the conversation going, because most of the work happens outside of the decks, you know, like, we’re not… we’re not, like, so far to peer consultants that we’re only just putting out decks. Like, we’re almost a million engineering and technical firms.
252 00:43:24.400 ⇒ 00:43:39.679 Robert Tseng: Most of the work is not gonna… is not gonna be about… about this. But this is, like, the time to highlight your work and really help establish your… your expertise and your thought partnership to… to your… to your stakeholder, so they know that, like, hey.
253 00:43:39.860 ⇒ 00:43:50.639 Robert Tseng: when I’m talking to Advait, and he’s representing, Brainforge, he gave me this nugget that, like, I never could have found on my own, and that’s, like, super valuable, and we want them to…
254 00:43:50.880 ⇒ 00:44:10.649 Robert Tseng: really, you know, call you out and say, I love working with Advait. And both Greg and Amber, like, clients love working with them. They both got shoutouts this week alone, so, it’s definitely a part of, like, like, what helps you to be successful here. But yeah, I think those are kind of the main, kind of.
255 00:44:10.840 ⇒ 00:44:13.600 Robert Tseng: feedback things that I would, I would want to give to you.
256 00:44:13.600 ⇒ 00:44:18.729 Advait Nandakumar Menon: Yeah. Just to clarify on your first part, like, I know that’s how,
257 00:44:18.880 ⇒ 00:44:29.840 Advait Nandakumar Menon: the deck or the presentation to clients usually goes, you start the recommendation. I want to clarify that since this was a technical exercise as part of the interview process, I want to really take you through the
258 00:44:29.840 ⇒ 00:44:44.379 Advait Nandakumar Menon: process of what I did, and, like, what led to that insight or recommendation, and what really, my thought process was. So, I completely understand that, because I’ve done, these kinds of things before with clients, like.
259 00:44:44.410 ⇒ 00:44:58.440 Advait Nandakumar Menon: giving them recommendations or presentations, and it’s, like, keep it short, and start with, what’s gonna work best for them, and take it from there. So, that’s completely fine, and coming to your second point, that’s…
260 00:44:58.680 ⇒ 00:45:08.489 Advait Nandakumar Menon: like, I think you’re trying to say that you basically have to wear a lot of hats, and that’s something I want to do and have been doing in my career so far, like.
261 00:45:08.530 ⇒ 00:45:20.080 Advait Nandakumar Menon: Whether it’s, talking to the client, or just engineering the data, or building dashboards, like the BI layer, the data engineering layer, so that’s something I’ve seen
262 00:45:20.080 ⇒ 00:45:38.020 Advait Nandakumar Menon: that… I believe that’s what’s going on at Brainforge as well, so that’s really something I want to take more ownership of, and get my teeth in, and really go deep into it. So, yeah, I appreciate you mentioning that. That’s something that really drew me to Brain Forge, so… yeah.
263 00:45:39.530 ⇒ 00:45:42.959 Robert Tseng: Great. Yeah, I mean, that’s all I got.
264 00:45:43.120 ⇒ 00:45:47.760 Robert Tseng: you know, if… Greg, Greg Amber, any… any last… any last thoughts?
265 00:45:49.000 ⇒ 00:45:52.940 Greg Stoutenburg: Cool, yeah, great talking with you, sounds good. You know, I’ve just been here for…
266 00:45:53.520 ⇒ 00:46:09.129 Greg Stoutenburg: feels longer, but only, like, 7 weeks, and, there’s plenty of opportunity to do lots of new things, and, you know, grow in directions that you want to grow, and work on cool stuff. So, that kind of breadth and depth is good, and, yeah, good talking with you.
267 00:46:09.380 ⇒ 00:46:17.619 Advait Nandakumar Menon: Yep, thanks, for talking with me, guys. It’s been really insightful, and, I look forward to the next steps.
268 00:46:18.010 ⇒ 00:46:20.030 Robert Tseng: Okay, great. Alright, thanks for your time, Advait.
269 00:46:20.030 ⇒ 00:46:21.590 Greg Stoutenburg: Excellent. Thank you.
270 00:46:21.910 ⇒ 00:46:22.940 Greg Stoutenburg: Thanks, bye.