Meeting Title: Uttam_Annie Date: 2025-03-12 Meeting participants: Annie Yu, Uttam Kumaran
WEBVTT
1 00:13:34.580 ⇒ 00:13:36.000 Uttam Kumaran: Hi, Annie! Can you hear me?
2 00:13:36.210 ⇒ 00:13:37.530 Annie Yu: Hello! Hi!
3 00:13:37.530 ⇒ 00:13:38.180 Uttam Kumaran: Hi.
4 00:13:38.180 ⇒ 00:13:42.598 Annie Yu: And I do wanna clarify how to best pronounce your name.
5 00:13:43.000 ⇒ 00:13:44.220 Uttam Kumaran: My name is Utam.
6 00:13:44.370 ⇒ 00:13:45.370 Annie Yu: Oh, Tom! Next.
7 00:13:45.370 ⇒ 00:14:02.569 Uttam Kumaran: Yes, really nice to meet you. Thank you. So sorry for the delay I was. I’m just on my way to a meeting. I’m I’m here in Austin, just going to a sales meeting. And we just had some new project manager start. So I was just on the phone with them checking in but you know, sometimes, you know, you’re in a meeting, and it’s like
8 00:14:02.920 ⇒ 00:14:07.340 Uttam Kumaran: I should leave. But it was actually very, very important to stay for another 10 min.
9 00:14:07.340 ⇒ 00:14:07.710 Annie Yu: I took.
10 00:14:07.710 ⇒ 00:14:08.170 Annie Yu: Oh, my God!
11 00:14:09.330 ⇒ 00:14:14.189 Uttam Kumaran: One of those I don’t know. I really, you know, we’re talking about goals for.
12 00:14:14.560 ⇒ 00:14:23.440 Uttam Kumaran: I’m 1 of our new project managers, and I don’t know. I really like talking to our team about like, okay, what? Where do you see yourself going? What do you want to do? And it just.
13 00:14:23.590 ⇒ 00:14:26.469 Annie Yu: I couldn’t leave. I was like, Okay, I don’t wanna like.
14 00:14:26.470 ⇒ 00:14:27.260 Uttam Kumaran: Cut this off
15 00:14:27.260 ⇒ 00:14:33.899 Uttam Kumaran: cause like it’s sort of disrespectful. But I want to respect your time, too. So I was just in a bind. So I really apologize.
16 00:14:34.270 ⇒ 00:14:44.740 Annie Yu: No, thank you so much for that, and I right now is near my like noon time, so I I don’t have any meeting until, like, I think one. So I do have time. So.
17 00:14:44.740 ⇒ 00:14:45.520 Annie Yu: okay.
18 00:14:46.130 ⇒ 00:14:56.117 Uttam Kumaran: Okay. Great. Well, it’s it’s really nice to to finally chat and and sorry for the delay. We’ve been very, very busy. But but you know no, no excuse.
19 00:14:56.620 ⇒ 00:15:16.059 Uttam Kumaran: and yeah, I’m glad Robert put us in touch. He shared a little bit about your background, and of course you know I’ll be more than happy to tell tell you about Brainforge. I think he may have explained a little bit about what we do, so I’m happy to skip a little bit of that, maybe just give you a little bit of my background, and then I certainly have
20 00:15:16.060 ⇒ 00:15:28.839 Uttam Kumaran: some questions. So again, my name is Yutam. I’m the CEO of Brainforge. Brainforge is the company I started about 2 years ago. I, previously my background is in computer engineering and
21 00:15:28.840 ⇒ 00:15:52.459 Uttam Kumaran: and in data engineering, I worked as a data engineer for a number of years and then led data teams and then finally led product at a data company startup. I left that company and then sort of was like, What do I do next? Did not really want to go to another startup, because I was pretty tired and then did not want to
22 00:15:52.570 ⇒ 00:16:11.569 Uttam Kumaran: go to another big company, because that seemed very boring. Instead, I decided that, hey, I think there’s a lot of companies that need the sort of stuff that that I could do both across the business side of deploying data, but also around the engineering side and wanted to see whether I can build, start to.
23 00:16:11.570 ⇒ 00:16:27.280 Uttam Kumaran: you know, build some, build a book of business, working directly for clients and building data teams to serve their needs. And so Robert probably mentioned, you know, a lot of what we do is stand up data, infrastructure, build analyses and build dashboards. But not only that, it’s also on answering questions.
24 00:16:27.300 ⇒ 00:16:31.750 Uttam Kumaran: And fundamentally, we’re there to help businesses save money or make more money.
25 00:16:31.780 ⇒ 00:16:38.969 Uttam Kumaran: And data is what we use to make that happen, so I don’t think it for us. We we no longer. We don’t think it necessarily stops at
26 00:16:39.459 ⇒ 00:16:57.379 Uttam Kumaran: the dashboard like we want to actually give businesses, insights that they can take to their teams and be like, let’s make this decision. A lot of data teams. They stop at the dashboard piece and like, Here you go figure it out. One thing that we’re finding out why our customers really like us is because we go one step further.
27 00:16:57.762 ⇒ 00:17:13.197 Uttam Kumaran: And so that’s kind of what we do. We also are starting to do AI services with clients. We’re building AI agents. And that’s been really, really fun. We also do a lot of AI stuff internally to help our data team and our and our operations team automate items.
28 00:17:14.270 ⇒ 00:17:37.280 Uttam Kumaran: but yeah, we’re we’re. We’re a team of like 15 people right now. And we’re we’re certainly looking to add folks on our analysis team. But I don’t you know, I don’t really like to to sort of pigeonhole folks like, really, we’re hiring just new folks on our data team. I think right now we do have a need for people that are expertise in analysis, building out, reporting, building out
29 00:17:37.648 ⇒ 00:17:53.491 Uttam Kumaran: things like dashboards. But frankly, I think for everybody in our data team. There’s this whole world of opportunity to learn more about data modeling. Learn more about working directly with executives, learn more about data, engineering. Learn more about AI
30 00:17:53.950 ⇒ 00:18:01.449 Uttam Kumaran: So really, we’re looking just to bring on, you know, more, you know, hungry people onto onto our team to to best serve our clients.
31 00:18:02.570 ⇒ 00:18:25.620 Annie Yu: Yeah, I really want to echo to that. I love to hear you say that. I know that I’m happy to share my experience in analytics and customer experience. And one question that I prepare for you was that if I do have interest to learn other like projects like you said, like data engineering modeling. Will I get like opportunities to
32 00:18:25.800 ⇒ 00:18:30.438 Annie Yu: get my hands dirty with that? So I I love to hear that really.
33 00:18:30.770 ⇒ 00:18:49.390 Uttam Kumaran: Yeah, that’s I mean, I I feel like I I think about what I if I were to work here what I would want to hear and like, I feel like many companies that worked out there like you only do, was one thing, and it’s like, of course you have a date. You have a job right? And you’re like, I signed up to do this. I’m gonna do this, but I want people to go learn everything
34 00:18:49.390 ⇒ 00:19:02.530 Uttam Kumaran: and find the best opportunity for you to deliver value for us today. It’s analysis. Tomorrow is on modeling the next day. It’s on AI. Then that’s it. But also, you know, like, if you can use AI to accelerate your job.
35 00:19:02.980 ⇒ 00:19:18.299 Uttam Kumaran: And that’s a great win for us. Right? So I wanna, I’ve tried to break down those barriers we do meet as a data team. We we want to meet more as an engineering team as a whole. But I think this is, we have a lot of opportunity for for that sort of stuff. Yeah, definitely.
36 00:19:18.920 ⇒ 00:19:21.464 Annie Yu: Awesome. I love that.
37 00:19:22.630 ⇒ 00:19:32.659 Annie Yu: yeah, I how how would we like to, I guess? Continue, proceed this conversation, because I do have questions, and I am happy to share more about my experiences and just.
38 00:19:32.660 ⇒ 00:19:34.070 Uttam Kumaran: Yeah, I guess, like.
39 00:19:34.070 ⇒ 00:19:35.180 Annie Yu: Yeah.
40 00:19:35.180 ⇒ 00:19:55.920 Uttam Kumaran: Yeah, I guess. My one question is, you could just talk to me about, you know, maybe just a few minutes about like the type of analysis and type of reporting work that you’ve done. You know specifically, if you could talk about the technologies, that types of questions that you’ve been able to answer, and sort of like your process, and working with stakeholders to answer those. I think
41 00:19:56.170 ⇒ 00:19:59.769 Uttam Kumaran: that that’s really the the number. One thing I would love to hear about.
42 00:20:00.120 ⇒ 00:20:14.160 Annie Yu: Yeah, yeah. Then I think I’ll do. Maybe like the most recent to earlier. So I do have. I feel like most of my roles during my career have
43 00:20:14.370 ⇒ 00:20:26.880 Annie Yu: a strong focus on consumer insights, and that involves both physical and digital products. So my most recent role at Microsoft as a contractor. My role has
44 00:20:27.340 ⇒ 00:20:30.280 Annie Yu: heavily focus on synthesizing.
45 00:20:31.023 ⇒ 00:20:55.840 Annie Yu: What’s that? Customer? Verbatim and sentiment metrics such as Mps and Csat across direct and partner channels and to drive action, ability in Mbr. And Voc. Newsletter as well as post launch reporting. And I also shared this with Robert. I like this team I love, how closely we can get to like customer verbatim, really
46 00:20:56.170 ⇒ 00:21:02.339 Annie Yu: telling what people are saying about us. But one thing that kind of lacked
47 00:21:02.740 ⇒ 00:21:20.320 Annie Yu: in my view, was, we don’t have access to business performance metrics which I think essentially for me. I really love both quantitative and qualitative side of things to make that storytelling complete, so that brought me back to, I guess my previous role in it.
48 00:21:20.320 ⇒ 00:21:43.040 Annie Yu: So I in there I work with more business performance metrics, and end to end analysis in the in the sense where I started every project with writing sequel from Snowflake or Databricks, and then building tableau visualizations for my analysis and crafting the storyline telling piece through my presentations.
49 00:21:43.100 ⇒ 00:22:10.449 Annie Yu: And here I was responsible for influencing my main stakeholders were the leaders from both merchandising and strategy teams. So here my biggest project, the longest project I worked on was building a measurement framework for sku rationalization to help the team to know where to start, maybe divest or invest more in certain sku.
50 00:22:10.650 ⇒ 00:22:16.740 Uttam Kumaran: So let’s let’s pause on that like, what? Where? What tool was that done in and like, what was your engagement on that? Like.
51 00:22:17.000 ⇒ 00:22:21.339 Uttam Kumaran: yeah, that’s basically the Askew rationalization overall.
52 00:22:21.340 ⇒ 00:22:33.530 Annie Yu: Yeah, yeah. So I I would say, most of my visualization or analysis tool, I use the most heavily. Here was tableau and really from.
53 00:22:33.920 ⇒ 00:22:42.759 Annie Yu: So we talked to our stakeholders, and they always know. So our sell through rate was never like great, and then always know that we had
54 00:22:43.160 ⇒ 00:22:48.010 Annie Yu: to remove some skus, but they did not know where to start.
55 00:22:48.140 ⇒ 00:22:51.713 Annie Yu: and there were like tons of sku. So
56 00:22:52.360 ⇒ 00:22:57.910 Annie Yu: I also like started here as in a summer intern. So my summer project was
57 00:22:59.015 ⇒ 00:23:06.039 Annie Yu: building a standardization to help group the color of our products. So
58 00:23:07.010 ⇒ 00:23:17.470 Annie Yu: it was more so like a manual work. So we dive in different, not just color families, but also the chroma as well as the color patterns on different.
59 00:23:17.470 ⇒ 00:23:18.130 Uttam Kumaran: Okay.
60 00:23:18.300 ⇒ 00:23:26.599 Annie Yu: So build on that. We say, built on that data dimension. So we have
61 00:23:26.940 ⇒ 00:23:39.814 Annie Yu: a dimension for each skew there already, and then coming back as a full time. I use that dimension to build the Pareto Pareto chart to help people to see. Okay,
62 00:23:40.960 ⇒ 00:23:54.449 Annie Yu: with. I know that typically we use like 80, 20 as the part of principle. But here we we use the ABC analysis. So like 8, 80%, 15% and 5% and.
63 00:23:54.450 ⇒ 00:23:55.599 Uttam Kumaran: Oh, nice. Okay.
64 00:23:55.600 ⇒ 00:24:13.880 Annie Yu: Yeah, to see. What’s the most productive versus unproductive. But there was also lots of nuances. So for Nike, we do have a lot of seasonal, more like limited, and they could have very low sales numbers, but very high sell through rate. So that was also another.
65 00:24:14.330 ⇒ 00:24:14.779 Uttam Kumaran: Okay.
66 00:24:14.780 ⇒ 00:24:17.380 Annie Yu: Manual discussion that we really had.
67 00:24:17.380 ⇒ 00:24:36.910 Uttam Kumaran: So like when so like, when when that sort of thing happens like, do you find that in the data? And then you’re like, what is this? Does someone tell you that like, how did that piece go? Because that’s this is like, this is a story of my life. Basically, it’s like, we found this thing. They’re like, Oh, yeah, yeah, we actually, we have this like, weird thing that happens like, how did you? How did that come up. And who did you talk to.
68 00:24:38.207 ⇒ 00:24:39.939 Annie Yu: You mean the.
69 00:24:40.830 ⇒ 00:24:43.160 Uttam Kumaran: The high sell through limited edition stuff.
70 00:24:43.160 ⇒ 00:24:46.139 Annie Yu: Yeah, yeah. Yeah, I I kind of
71 00:24:46.680 ⇒ 00:24:50.170 Annie Yu: I, I have like a merchant buddy that I call.
72 00:24:50.520 ⇒ 00:24:51.070 Uttam Kumaran: Nice.
73 00:24:51.070 ⇒ 00:25:12.030 Annie Yu: Really, we had a bi-weekly touch base where I just ask bunch of questions. And that’s also where I learned about the product lifecycle that our merchants use a lot. But that was something that was never stored in any data table. I think even till now it’s not in the data table. But they do have their.
74 00:25:12.030 ⇒ 00:25:12.740 Uttam Kumaran: Okay.
75 00:25:13.192 ⇒ 00:25:28.140 Annie Yu: Yeah, they do have their categorization in terms of this. This sku is in the seed stage. So they our team’s goal was also like developed a metrics according to each stage.
76 00:25:29.003 ⇒ 00:25:31.270 Annie Yu: That makes sense to to
77 00:25:31.610 ⇒ 00:25:48.530 Annie Yu: just to each stage, and then so incorporate that manually with our visualization. So that was a lot of I would say, not. Everything was automated already. So we more so was using our visualization.
78 00:25:48.530 ⇒ 00:25:49.390 Uttam Kumaran: Manual. Yeah.
79 00:25:49.390 ⇒ 00:25:53.820 Annie Yu: And then discussed with them in a bunch of different discussions.
80 00:25:54.050 ⇒ 00:26:04.100 Uttam Kumaran: And so is a lot of your work in terms of technically like, is it a lot of python? Is it more sequel like, where are you actually? Is it in notebooks like, where are you actually working.
81 00:26:05.113 ⇒ 00:26:21.879 Annie Yu: I would say in a professional setting. I’ve worked with more SQL. And tableau, but I am also getting my second master’s degree in data science, I’m doing online program actually with ut, Austin.
82 00:26:21.880 ⇒ 00:26:22.260 Uttam Kumaran: Nice.
83 00:26:22.260 ⇒ 00:26:27.929 Annie Yu: So so I do. I’m like trying to get more hands on experience with python as well.
84 00:26:28.400 ⇒ 00:26:40.220 Uttam Kumaran: Okay? Great? Well, yeah, we do. I feel like most of our stuff is all SQL, and then we do some python for yeah, for like some data, science things. But of course, you know, like most of the questions are like SQL, like.
85 00:26:40.410 ⇒ 00:26:53.039 Uttam Kumaran: you can answer like 90% of it. But like, as soon as you get more mature, we want to start running predictive analysis. We want to start doing forecast models across all of our clients, right? So because we work with clients who are like
86 00:26:53.330 ⇒ 00:27:05.410 Uttam Kumaran: 20 million plus in in annual revenue e-commerce. Right? So, very similarly, if they have skews, they have orders. They have order lines. They have different skew variance product, categorization, seasonality.
87 00:27:05.880 ⇒ 00:27:21.790 Uttam Kumaran: other concentration. So that makes a lot of sense. I mean, I I feel like this is your kind of your experience is sort of right up our alley. I mean to give you a sense of like who we’re looking for. We have 7 clients right now we will most likely sign another 2 or 3 people.
88 00:27:22.125 ⇒ 00:27:38.220 Uttam Kumaran: It was mainly me and 2 other people that were doing everything for a while. Now, there’s we’re sort of growing. We hired a couple more analytics engineers. They’re really tasked with creating all the data models. We also, you know are have brought on we have.
89 00:27:38.390 ⇒ 00:27:40.600 Uttam Kumaran: We have 3 analysts right now.
90 00:27:41.334 ⇒ 00:27:50.370 Uttam Kumaran: That are sort of working on everything, from tableau dashboards to predictive models, you know, just directly in a Jupyter notebook. As well as stuff in
91 00:27:50.673 ⇒ 00:28:19.890 Uttam Kumaran: we have a couple of other bi tools. So I think like, if if you’re if you’re sort of in the zone of like, yeah, whatever the tool is like, we can sort of make it happen. I think, really, we need someone to come in and one work directly with our project. Managers on. Okay, hey? The client, we need to 1st build a dashboard for this. But ultimately the what the project managers are always not going to be able to see is that the client wants like an answer right? And it’s up for us to know their business better than they do.
92 00:28:19.960 ⇒ 00:28:27.520 Uttam Kumaran: And this is what I really push our team is, the answer is not. The dashboard is ready, and they see a pie chart. It’s like.
93 00:28:27.700 ⇒ 00:28:39.119 Uttam Kumaran: Hey, you guys, you’re there’s this skew that you’re not putting money behind on the marketing side. That is great profitability and great sell through rate. You guys should start spending more dollars marketing this product.
94 00:28:39.460 ⇒ 00:28:42.459 Uttam Kumaran: That is something that we can go find out for them.
95 00:28:42.937 ⇒ 00:28:49.929 Uttam Kumaran: And so we really want folks to come in and see the full picture and see. And actually, like
96 00:28:50.430 ⇒ 00:29:07.480 Uttam Kumaran: the data is the shovel we use. But of course, the hole we’re trying to dig is making them more money and I’m always gonna push our team to to think that way. Because ultimately, if our client wins, we win, even if we have the best looking dashboard, we have all the insights there. If they don’t get it.
97 00:29:07.900 ⇒ 00:29:15.149 Uttam Kumaran: there’s we’re screwed, we’re loose. And so we’re trying to build a team of holistic data folks that
98 00:29:15.300 ⇒ 00:29:36.660 Uttam Kumaran: think about the businesses, you know, and can really act as a great partner for business. So I mean, I think if if this opportunity seems interesting, I’m I’m pretty open to seeing if you’d like to to sort of hop on and and join, and I’m happy to give you a little bit of context of how that works. But maybe I’ll pause there if you have any any questions, or anything else I can answer.
99 00:29:37.280 ⇒ 00:29:51.429 Annie Yu: I do have other questions. But I just want to say, I really love your vision in terms of getting the holistic data view. And I do understand that not everything is about dashboarding or so. I also work
100 00:29:51.959 ⇒ 00:30:12.910 Annie Yu: at Nike on another project. So they wanted to explore more opportunities for their y 2 k. Shoe line. And then so even though we do have some fancy visualizations, I also use, like the market research tool, really, just to see across social media and see how people style their y 2 k shoes with actually like more elegant.
101 00:30:13.370 ⇒ 00:30:14.250 Uttam Kumaran: Yes.
102 00:30:14.250 ⇒ 00:30:20.980 Annie Yu: Socks like laces and flower graphics. So that’s like, I think that’s also what makes it really fun, too. So I got.
103 00:30:20.980 ⇒ 00:30:26.249 Uttam Kumaran: Totally. I mean, we have a bunch of weird companies like that, too, like we have. Have you heard of Javi coffee?
104 00:30:26.830 ⇒ 00:30:27.600 Annie Yu: Hi!
105 00:30:27.600 ⇒ 00:30:33.139 Annie Yu: They’re big on Tiktok. They’re like a coffee concentrate product. Bobby, Coffee.
106 00:30:33.710 ⇒ 00:30:35.869 Uttam Kumaran: Yeah, JAVV, y.
107 00:30:36.500 ⇒ 00:30:50.439 Uttam Kumaran: But like they’re a coffee company, so they have flavors. They have concentrates. They’re doing cold brew, protein coffee, and I love coffee so like, I’ll be like, I go to Starbucks. And I’m like, Okay, like, let me think about like what what is important or I call my sister. I have a younger sister. I’m like.
108 00:30:50.610 ⇒ 00:31:03.509 Uttam Kumaran: like, Have you heard of these guys like, what do you think about their ads like, you know, you try to get you try to find other ways to round out the story. Right? Data is telling a story. So yeah, I mean, I I don’t know. I think we are.
109 00:31:03.780 ⇒ 00:31:24.189 Uttam Kumaran: I think we are getting better every day at getting more holistic as a data team. And ultimately, the reason why I feel like we are successful is companies. They hire us because they want business. They want data help. But they realize we are much more than that. Right? I feel like data and the C-suite. They’re the only people that work across the whole business. There’s not many other
110 00:31:24.330 ⇒ 00:31:37.100 Uttam Kumaran: teams in the company that see the entire thing, and so they rely on us a lot to be like, how do we define this like? How do we define revenue? And there’s no one else that can answer that except for us, you know. So I feel like we.
111 00:31:37.670 ⇒ 00:31:59.699 Uttam Kumaran: I I love what we do. Every day we work for some really really cool clients. We are going after bigger and tougher problems. I want us to work on the most complicated data analysis things that we could do like, I think we’re starting off just building basic profitability, sell through like we do a lot of work for e-commerce. But I want us to do predictive demand. Ltv.
112 00:31:59.700 ⇒ 00:32:20.040 Uttam Kumaran: how do we get? How do we boost retention? How do we optimize funnels like I want to do every single thing like the most cutting edge stuff that we can do in terms of e-commerce related analysis. We also do a lot of work for for B, 2 B software. So how do we look at product usage, churn retention, product, health.
113 00:32:20.588 ⇒ 00:32:44.071 Uttam Kumaran: customer 3, 60. We also do a lot of things with with a supply chain. So we’re doing inventory demand. Sales demand stuff with customer service data. So customer help. Call resolutions. Zendesk performance. We do everything for a lot of people. So it’s a blast like, I love it because we get to see so many problems.
114 00:32:44.920 ⇒ 00:32:59.199 Uttam Kumaran: and we just try to. We just try to solve those for folks, you know. So yeah, if it sounds exciting, I mean, I think that we do have a you know, we do have opportunity for someone with your background to to join and I’m happy to kind of explain sort of how that works. Next.
115 00:32:59.440 ⇒ 00:33:23.919 Annie Yu: Yes, yes, I would love that. And I also do. I just love learning about the dynamic nature of your team. And so, yeah, I do have like more specific questions around like, is there a typical timeframe for a project? And how does the team typically collaborate with the clients from kickoff to completion. But yeah, things of that nature. If if you still have time.
116 00:33:24.120 ⇒ 00:33:28.060 Uttam Kumaran: Yeah, I have to jump. But can we schedule some more time for this afternoon?
117 00:33:28.920 ⇒ 00:33:29.250 Annie Yu: Sure.
118 00:33:29.250 ⇒ 00:33:38.048 Uttam Kumaran: I can just hop back on. I would love to answer those questions, by the way, so I wanna make sure that you have all your questions answered. If you have more time this afternoon,
119 00:33:38.740 ⇒ 00:33:39.809 Annie Yu: That would be perfect.
120 00:33:39.810 ⇒ 00:33:51.140 Annie Yu: I believe. Okay, I believe I I will email you. I believe I will have some time, but I’m also aware of our time difference. So I’m gonna.
121 00:33:51.140 ⇒ 00:33:55.619 Uttam Kumaran: I mean, I’m basically gonna be free after 1 30, your time.
122 00:33:56.430 ⇒ 00:34:04.539 Annie Yu: 1 30. Okay, I I believe I do have a 2 to 2, 30, but I’m mostly free. After 2, 30.
123 00:34:04.680 ⇒ 00:34:14.280 Uttam Kumaran: Okay, that’s fine. If you wanna just like, I’ll just move the same meeting up to. So 2, 30, your time will be 4 30, my time right? So
124 00:34:14.520 ⇒ 00:34:20.099 Uttam Kumaran: I’ll just I’m just gonna move. I’ll just move this up and let’s catch up. Let’s catch up. Then. Sorry I was. I know.
125 00:34:20.100 ⇒ 00:34:20.790 Annie Yu: That sounds good.
126 00:34:20.790 ⇒ 00:34:34.069 Uttam Kumaran: And I’m and I’m running now. But I want to answer all of your questions. And then, if anything I said spurred more questions. Please just note it down, and I’m happy to spend as much time as needed. You know, when we catch up again.
127 00:34:34.400 ⇒ 00:34:39.289 Annie Yu: That sounds great, and this is exciting. I am excited, so I’ll talk to you soon.
128 00:34:39.290 ⇒ 00:34:40.350 Uttam Kumaran: I appreciate it.
129 00:34:40.350 ⇒ 00:34:40.850 Annie Yu: Okay.
130 00:34:40.850 ⇒ 00:34:42.239 Uttam Kumaran: Definitely. Thank you so much.