Meeting Title: Uttam <> Emily Date: 2024-09-19 Meeting participants: Emily Chan, Uttam Kumaran
WEBVTT
1 00:00:21.330 ⇒ 00:00:22.010 Emily Chan: Hello!
2 00:00:23.280 ⇒ 00:00:24.140 Emily Chan: Hello!
3 00:02:17.940 ⇒ 00:02:18.710 Uttam Kumaran: Hello!
4 00:02:20.770 ⇒ 00:02:21.890 Emily Chan: Hey, Tom!
5 00:02:21.890 ⇒ 00:02:23.149 Uttam Kumaran: Hey! How are you?
6 00:02:23.150 ⇒ 00:02:25.069 Emily Chan: I’m good. How are you doing.
7 00:02:25.070 ⇒ 00:02:27.439 Uttam Kumaran: Really nice to meet you. Thanks for taking the time.
8 00:02:27.440 ⇒ 00:02:30.900 Emily Chan: No, likewise really glad that we got to chat.
9 00:02:31.080 ⇒ 00:02:37.616 Uttam Kumaran: Yeah, I’m not sure how much ravine has told you, but we worked together briefly on
10 00:02:37.980 ⇒ 00:03:01.880 Uttam Kumaran: one of the contracts that he was kind of working for like kind of right as as I was starting this business. But it’s been it’s been kind of crazy since then. But happy to share a little bit about kind of like what we’re doing and about me as well. And then, you know, of course, always excited to meet more people and and see how what, what you’re up to and what you’re interested in. So
11 00:03:02.311 ⇒ 00:03:12.748 Uttam Kumaran: started Brainforge about a year ago. I’m based in Austin. Worked in New York before this for a number of different startups there.
12 00:03:13.270 ⇒ 00:03:37.488 Uttam Kumaran: And then I kind of moved here was leading product at a startup, decided to quit, was kind of deciding, like what I wanted to do in between some jobs. I’d done some freelance sort of consulting and contracting, so I knew there was money there. But of course, like you can probably only juggle like 2 of those, and kind of like before things get crazy.
13 00:03:38.160 ⇒ 00:04:04.399 Uttam Kumaran: So I was just kind of decided on starting like a firm around it. So the biggest thing for me that I felt like I had an edge on is just access to really amazing engineers that I worked with a lot of my friends after Covid like wanted to go travel or kind of wanted to do freelance. And I was like, Hey, I’m kind of starting to get work in. Do you want to come work for clients on behalf of Brainforge. And that’s kind of like where we’re at. So we have about
14 00:04:04.782 ⇒ 00:04:12.609 Uttam Kumaran: you know. Give or take like 5 or 10 people. We probably have, like 4 or 5 people that are full time, and then some more people that are on staff.
15 00:04:13.181 ⇒ 00:04:25.819 Uttam Kumaran: Most of the folks on the team are on the engineering side. So the side on the Ae side as well as on the analysis side, although that’s probably where we’re the weakest
16 00:04:26.258 ⇒ 00:04:34.739 Uttam Kumaran: most of our work is around like Snowflake 5 tran and like. Dbt, so a lot of like moving data to data warehouses.
17 00:04:35.289 ⇒ 00:04:50.129 Uttam Kumaran: helping people like measure their businesses, basically and then a lot of stuff on top of that. So continue to do a lot of data modeling. And that’s that’s a lot of what I did. I did was a data engineer, and then did a lot of analytics engineering before.
18 00:04:50.760 ⇒ 00:05:04.090 Uttam Kumaran: So that’s a little bit about about us, I mean, we have a kind of a variety of of clients we’ve worked for mostly private businesses. But sometimes they’re going from 0 to one like they’re just bringing on snowflake.
19 00:05:04.393 ⇒ 00:05:26.876 Uttam Kumaran: So we’re moving a lot of their data into Consolidated area. Cleaning a lot of it up, creating reporting marts. Sometimes they’re a little bit more sophisticated where they want to just bring on data science or bring on like a certain reporting method or work on a specific business domain. So a little bit, case by case, kind of like, who’s involved in what we do but for the most part,
20 00:05:27.580 ⇒ 00:05:34.237 Uttam Kumaran: we’re kind of it’s it’s mostly engineers and try to run the company pretty chill. Everything’s like on slack. And
21 00:05:34.890 ⇒ 00:05:51.590 Uttam Kumaran: yeah, just kind of the goal is to work with the best people and kind of work with some challenging problems for some good clients, hopefully, stable clients. And that’s for that reason try to avoid a lot of startups and stuff like that. So getting people that actually have data problems, and they know they have budget to solve them. So
22 00:05:51.620 ⇒ 00:05:53.779 Uttam Kumaran: yeah, that’s a little bit about about us.
23 00:05:54.440 ⇒ 00:06:00.755 Emily Chan: That’s amazing. I I just think it’s so amazing that like, you started this just last year. And it sounds like it’s growing
24 00:06:01.040 ⇒ 00:06:12.520 Emily Chan: Do you mind? Maybe just sharing a couple of examples of like the client? What kind of industries and what kind of work I know you talk about a little bit, just really want to like, dive in and understand what kind of work that you guys do.
25 00:06:13.020 ⇒ 00:06:17.009 Uttam Kumaran: Yeah. So you know, an example is like, we have a like A,
26 00:06:17.530 ⇒ 00:06:41.010 Uttam Kumaran: I think they’re probably making like 20 or 30 million these are. They’re an e-commerce company. Most of their staff is on the operations and executive side. They have no data team and they brought us in basically because they’re having issues, you know, wrangling all their sales data and building like daily Kpi reports. We’ve been working with them for over a year now. And we’ve done data all across shipping, marketing sales.
27 00:06:41.412 ⇒ 00:06:58.327 Uttam Kumaran: and customer service. This is data meaning we’re moving data from Zendesk or shopify or Amazon modeling it all using Dbt and snowflake. And then basically, if they needed a reporting layer as well. So we’re kind of managing the implementation of
28 00:06:59.060 ⇒ 00:07:00.249 Uttam Kumaran: a bi tool.
29 00:07:00.572 ⇒ 00:07:25.587 Uttam Kumaran: We have another client where we we came in, and they already had an analyst team, and they just were doing analysis. But they were hitting some roadblocks because they didn’t have data models. So they needed us to help kind of to develop those data models for them. Use 5 trainer, bring data into Snowflake build reporting marts based on the columns and business logic that they needed. So it it kind of depends on our scope.
30 00:07:25.900 ⇒ 00:07:50.379 Uttam Kumaran: what we do client by client again. My ability to go get clients is based on the team we have. So what we’re doing now isn’t what we were doing 3 months ago isn’t what we were doing a year ago. As I get, you know better people and people with more capabilities. I’m able to go into the industry and say, actually, we can do. XYZ. And you know, another thing that we started to do is we started using AI automation a lot internally
31 00:07:50.380 ⇒ 00:08:15.140 Uttam Kumaran: for stuff. And we have one person that’s full time that does AI work. And we actually just sold our 1st like AI contract related to scraping some stuff for some inventory checks and modeling some data. So it kind of depends. I would say, we’re evolving with my skill set and my confidence level is really on the on the data side, right? Like I’ve worked with Snowflake for a number of years. Done a ton of
32 00:08:15.140 ⇒ 00:08:22.769 Uttam Kumaran: snowflake implementations. Done a ton of modeling, but we’re also starting to do some stuff on the language model side as well.
33 00:08:25.670 ⇒ 00:08:29.049 Emily Chan: That AI stuff is really exciting. Can you talk more about that?
34 00:08:29.050 ⇒ 00:08:29.835 Uttam Kumaran: Yeah,
35 00:08:30.660 ⇒ 00:08:47.059 Uttam Kumaran: we. So initially, when I started the company, this was like July of last year, this is kind of like right around when, like Chat Gbt, like 3 came out and I started using it for a lot of stuff. And I realized that, you know, in data, there’s a problem of like, there’s a there’s.
36 00:08:47.190 ⇒ 00:09:15.330 Uttam Kumaran: there’s a lot of. There’s not a lot of great data. Folks who can work with the business directly. I feel like sometimes used to sitting in the background, and my experience has always been working with executives. So a lot of my, the people I met also are very good at that. So I knew that there was always a drought in data for really amazing people. I think now it’s starting to get a little bit more standardized and like the type of processes and tools. But AI is really like Wild West. Not only are there not a standard process. There’s like no talent.
37 00:09:15.604 ⇒ 00:09:33.319 Uttam Kumaran: I think a lot of this talent is like locked up into the biggest companies. So not only can you probably not get those people. There’s nobody that’s just available to hire right or to prototype. And so that’s why I knew that there was probably an opportunity for someone on the agency and consulting side to be able to say, Hey, we have worked on solutions.
38 00:09:33.674 ⇒ 00:09:46.330 Uttam Kumaran: The problem is, I don’t have any background doing that in professional setting like longer term. So I kind of decided like, Hey, the best way we can do is to start building stuff internally for us, right? Build automations. Build, for example.
39 00:09:46.550 ⇒ 00:10:09.899 Uttam Kumaran: this Zoom Meeting will get transcribed. I’ll get some action items that all happens automatically in the background. We have some other AI automation tools. That help us streamline the way we deliver for clients. So kind of realize that we should try to dog food this ourselves. And then we can figure out, okay, what’s the ideal stack? What are where ways? We can actually affect businesses. And then we could turn that around and and market that.
40 00:10:10.080 ⇒ 00:10:11.600 Uttam Kumaran: you know, client facing.
41 00:10:11.750 ⇒ 00:10:17.540 Uttam Kumaran: So it’s a lot of like, you know. Using openai assistance building agents.
42 00:10:18.068 ⇒ 00:10:21.049 Uttam Kumaran: building tools and things like that on the AI side.
43 00:10:22.030 ⇒ 00:10:24.611 Emily Chan: Yeah, that’s interesting. I saw that this
44 00:10:25.100 ⇒ 00:10:27.380 Emily Chan: video is being like transcribed. And
45 00:10:27.870 ⇒ 00:10:41.329 Emily Chan: I actually met this other company. They it’s an expert network. They basically are trying to sell these tools, too. So like they, they have a bunch of expert networks. They just conduct calls. And then they turn that around into like a library.
46 00:10:41.330 ⇒ 00:10:42.050 Uttam Kumaran: Oh, nice!
47 00:10:42.050 ⇒ 00:10:54.629 Emily Chan: Too, and then they can turn around and say, Hey, we not only can like hook you one on one with video calls, we also have, like a library of expert based testimonies. So I thought it was like an interesting product that they sell at both ways.
48 00:10:54.630 ⇒ 00:11:05.370 Uttam Kumaran: Yeah, no, that’s great. I mean for me, I never liked when another like when fireflies joins the call, or like, you know, that they have some of these apps that like join the call and record. I never liked that. I was like.
49 00:11:05.460 ⇒ 00:11:19.599 Uttam Kumaran: I don’t know. I’m using zoom, I’m zoom. The security is great. I don’t want this all of our stuff leaving somewhere, and Zoom has great Api. So I’m like, let’s call the Api. We’ll take up this transcript, and then I I for me. It’s like I’m in so many meetings
50 00:11:19.600 ⇒ 00:11:45.416 Uttam Kumaran: and all over the place, so I need that cause my my memory is not is not good for all this stuff. So if I have something that I need to do, I need to know. And so we, this this meeting gets transcribed. But we also added to notion, I get a slack message with like the To dos, and those are just not really quality of life improvements that we can do. And then also it allows us to go test 10 different tools. Right? We went and tested all the major vendors. We? Then we’re like, let’s try to do it ourselves.
51 00:11:45.948 ⇒ 00:11:49.920 Uttam Kumaran: And so it’s fun. It’s just doing engineering work, you know, and we’re we’re the client
52 00:11:50.338 ⇒ 00:11:54.739 Uttam Kumaran: and you know, of course, it comes out of our budget technically. But
53 00:11:55.160 ⇒ 00:12:05.280 Uttam Kumaran: I I want to know, like, if this stuff is actually working, and if it’s real. And then we have case studies now where it’s working for us. Right? I can share how this process is working. So we’re starting to do that around sales
54 00:12:05.290 ⇒ 00:12:20.639 Uttam Kumaran: and kind of like operations. And then, more recently, I’m actually having the team look into, how do we use chat? Gbt for code reviews? You know, to actually suggest code changes based on the scope of a project and do that all within github.
55 00:12:21.134 ⇒ 00:12:39.329 Uttam Kumaran: So you know, we’re gonna try to basically layer this on as in the company and help all of our engineers basically improve. And hopefully, it just gets us to do the things that actually that actually require our brain and then continue to deliver faster stuff for clients. So that’s how we’re thinking about it.
56 00:12:39.330 ⇒ 00:12:48.200 Emily Chan: That’s amazing. And you can like, productize that right? You cannot go around with like notion. We have this little add on, would you like to buy the code. It’s the same thing to Github.
57 00:12:48.520 ⇒ 00:13:15.210 Uttam Kumaran: And and I don’t know. We’re an engineering company, right? Like, I’m in the business of solving problems like art school. Our skill set is data, you know, that’s how we help companies. But they asked up is so new. And it’s actually compared to some other stuff in the data side. Not that technical and I was like, this is a perfect opportunity for us to try to use it ourselves. And then, you know, see if we can market in every company that we’ve worked with on the data side. They always ask me, like, you guys do AI stuff or like.
58 00:13:15.210 ⇒ 00:13:20.439 Uttam Kumaran: so it’s it’s like, yeah, that’s that’s kind of the vision there as well. So.
59 00:13:20.610 ⇒ 00:13:23.539 Emily Chan: Yeah, seems like you have a lot going on.
60 00:13:23.540 ⇒ 00:13:50.800 Uttam Kumaran: We have a lot going on. That is the truth. I I have. This is by far been the hardest thing I’ve ever done is starting this business. But I feel really thankful now not only that we’ve been in business for over a year. I think a lot of the relationships that I was able to build. You know, through working in data, has helped us get more clients and build partnerships. And I’m not like a I’m an engineer like I’m not like a I didn’t come from a business background.
61 00:13:50.800 ⇒ 00:14:01.539 Uttam Kumaran: But like I, I try to operate this really ethically, and I know there’s ways. I know what a consulting business is, but at the same time I think they always get a bad rap.
62 00:14:01.540 ⇒ 00:14:20.440 Uttam Kumaran: for like oh, we we hired this company. They came, and they didn’t do anything or did. They just threw random people on a call, and they have no no video. And like they’re outsourced like I. I knew the the obvious assumptions about this industry. So I knew that if you come in and you do things the right way. And you build long term relationships. There’s gonna be.
63 00:14:20.440 ⇒ 00:14:32.329 Uttam Kumaran: you know, an abundance of opportunity. And so for me, I’m in the business of people like the people on the team is actually the business I can like. I’m no longer working on a lot of these deals myself.
64 00:14:32.400 ⇒ 00:14:37.469 Uttam Kumaran: So for me, it’s the team that matters, and it’s the experience they have. And so
65 00:14:37.480 ⇒ 00:14:52.479 Uttam Kumaran: that is what I’m that’s actually what the clients are getting is, they may trust us. And then. But the delivery really really matters. And so that’s why we we tried to do a lot on Snowflake, because my background is in Snowflake, but we’ve had asked to do things across a bunch of tools.
66 00:14:52.800 ⇒ 00:15:11.779 Uttam Kumaran: But for me, it’s just like, Can I get the smartest data people? And of course we get the smartest people. Then we can charge more and the work gets more interesting. It’s not like low level stuff. So it’s all kind of like cascades there. But yeah, it’s a it’s been very. It’s been a tough. It’s been a very tough year. I don’t know. It’s ups and downs so.
67 00:15:13.050 ⇒ 00:15:17.189 Emily Chan: Yeah, I bet it’s hard starting a business. But you’re doing amazing. It seems like.
68 00:15:17.190 ⇒ 00:15:23.560 Uttam Kumaran: I appreciate it. I really really appreciate. But yeah, I mean, I would love to hear about, you know, sort of your background, but also like
69 00:15:23.600 ⇒ 00:15:35.982 Uttam Kumaran: kind of like what you’re thinking in life in terms of career and like why, this opportunity kind of I mean again I don’t. I don’t I? I don’t like to think about these things as like opportunity. I mean. However, I can be helpful. Please let me know.
70 00:15:36.290 ⇒ 00:15:41.129 Uttam Kumaran: But I know ravine mentioned that you’re interested in doing contracting and stuff. So yeah, just curious.
71 00:15:41.370 ⇒ 00:15:56.450 Emily Chan: Yeah. So Raven and I met when we were both at Spotify. I think when you met him he has already left. So I I have about 15 years of experience, mostly on like the analysis business analytics and product analytics.
72 00:15:56.700 ⇒ 00:16:10.649 Emily Chan: And then I am a full time. Mom. Now, my last role was at spotify. So when I started there, I was a data scientist. I work my way up, and when I left I was a manager leading a team of 10
73 00:16:10.760 ⇒ 00:16:17.670 Emily Chan: and as spotify. And basically, throughout my entire career, like, you know, my, my role has been like
74 00:16:18.730 ⇒ 00:16:34.099 Emily Chan: looking at different data sources, you know, like external data sources, internal data sources, and really putting that together to like, get a holistic assessment of the business and then use that to identify what the pain points are. And then just really taking that through and like
75 00:16:34.200 ⇒ 00:16:40.659 Emily Chan: owning that problem with, like the product or business partner, and then using data to help like the business get better.
76 00:16:40.740 ⇒ 00:16:44.370 Emily Chan: It’s probably more clear if I talk about an example. If there’s already.
77 00:16:44.370 ⇒ 00:16:44.930 Uttam Kumaran: Don’t need.
78 00:16:45.320 ⇒ 00:16:46.185 Emily Chan: So
79 00:16:47.110 ⇒ 00:17:00.679 Emily Chan: I was I led the data team that helped Spotify launch his services in India. So like, maybe like 5 6 years ago. Spotify wasn’t in India, and I was a team that, you know, brought Spotify to India.
80 00:17:00.760 ⇒ 00:17:12.440 Emily Chan: So like what my data team would do is that we would like, take in data from, you know, external marketing data marketing data, Facebook emails agency, you know, even just like good old Bill bots, right?
81 00:17:12.540 ⇒ 00:17:39.269 Emily Chan: And then we like try to like, map it with everything else like that’s step one of the customer journey. Then we try to piece everything together to create that customer journey right? So we have like marketing. Then we have internal data like, how is the user trying to sign up for spotify what happens when they get on the platform. Where do they go? What do they listen? And then really take it to the end to like, you know, monetization. Are they seeing ads, you know, are they subscribing
82 00:17:39.270 ⇒ 00:17:56.859 Emily Chan: so that we own that end to end? Data platform, and then, like, in the beginning, you know, the job is is just like, okay, let’s package it, summarize it and build dashboards for the right people. Right? So like, we have dashboards, we because India, the project was so big. We have dashboard that you know
83 00:17:56.920 ⇒ 00:18:24.159 Emily Chan: the marketing team use and try to like make better creative. They try to like optimize their budget all the way up to dashboard that the CEO will look at and be able to say, Okay, we have. We added 1 million users today in India. So like that is like a massive dashboard project. But then, what my team and I would do is we would also do like the data analysis work itself. So maybe we see that, okay, this campaign is performing better.
84 00:18:24.380 ⇒ 00:18:43.270 Emily Chan: Okay, let’s go talk to the marketing team. Can we move more budget there down to like, okay, this campaign is awesome. Can we then go away from the dashboard work, but also still do more data work? Can we try to work together and do a lookalike model? What kind of external data vendor should we take in? So like, the work is very like
85 00:18:43.310 ⇒ 00:18:55.958 Emily Chan: data inspired. We start with the data we pinpoint the problem. And now, can we like work with that business owner to continue to use data to help them make better decisions like another example was
86 00:18:56.430 ⇒ 00:19:06.239 Emily Chan: We noticed that customers were like having problems signing in, and then that we work with the product manager to figure out at which point of that process was to drop off.
87 00:19:06.240 ⇒ 00:19:29.289 Emily Chan: And then it became like this big project was like, Oh, okay, people in India, you know, a lot of times they want to sign in with their phone number. And as a Swedish company spotify didn’t know for that. And that became like, Okay, now, we need to scope the product size the market. And then it became like an A big, A B testing project. So like it was, it became a totally different project in the end. But, like, basically, my team.
88 00:19:29.290 ⇒ 00:19:39.340 Emily Chan: bring data into every part of making like spotify better for India. And so right now we have, I think we have, like maybe close to 60 million users.
89 00:19:39.340 ⇒ 00:19:41.210 Uttam Kumaran: I remember when you guys launched, yeah.
90 00:19:41.330 ⇒ 00:19:41.850 Emily Chan: Yeah.
91 00:19:41.850 ⇒ 00:20:03.530 Uttam Kumaran: And I was like, Oh, they’re gonna crush, because in India they listen to a ton of music even here there’s all all the Indians in the Us. There’s just a ton of music. But you’re right, is like their interaction with software is a little bit different. So, figuring out the monetization. But then, you guys now have the free ad supported. But you’re right. They all use Otp phone numbers to everything. So.
92 00:20:04.970 ⇒ 00:20:18.021 Emily Chan: So, yeah, I mean, I love that project. I think it’s a success like India is now 10% of spotify user base. And I think, like data really helped the team make better product for the entire like user journey.
93 00:20:18.550 ⇒ 00:20:21.730 Emily Chan: and I think I like working in that model. It’s just like.
94 00:20:22.060 ⇒ 00:20:44.050 Emily Chan: you know, a lot of business people they especially like if they’re not technical, they don’t see how data can help you. So I feel like dashboarding gives you a good in because they have that desire to understand. Like, okay, I launch this campaign. How am I doing so? They can tell their bosses, right? So that gives like data, people like us an in. And then you can like, I think, in the consulting world it translates to an upsell.
95 00:20:44.050 ⇒ 00:20:44.550 Uttam Kumaran: Yes.
96 00:20:44.550 ⇒ 00:20:48.800 Emily Chan: Say to like, Okay, now, you know, let’s do something else. Can we model it for you?
97 00:20:48.800 ⇒ 00:20:49.880 Uttam Kumaran: 100%.
98 00:20:50.165 ⇒ 00:20:55.304 Emily Chan: And I do feel like data fits into every part of the like business or product process. So
99 00:20:56.040 ⇒ 00:21:11.669 Emily Chan: yeah, that’s basically what I do. I did that at spotify. I did that for the India launch, and then I move on to like. I think we built something for Billy Irish at one time. And my last project was, I basically supported all of the decisions for the ads. So like.
100 00:21:11.860 ⇒ 00:21:20.370 Emily Chan: if you see lyrics, there’s an ad there. Now, all the podcasts now have ads. So if you get annoyed with the ads, that was my team trying to find more.
101 00:21:20.370 ⇒ 00:21:20.740 Uttam Kumaran: Let’s.
102 00:21:20.740 ⇒ 00:21:21.160 Emily Chan: Self, yeah.
103 00:21:21.160 ⇒ 00:21:24.921 Uttam Kumaran: Yeah, you need to skip it. Now skip that.
104 00:21:25.620 ⇒ 00:21:32.060 Emily Chan: Yeah. And then before spotify, I was at Facebook, I was on the sales analytics team.
105 00:21:32.160 ⇒ 00:21:42.550 Emily Chan: Facebook, you know, at 1 point had this problem was that they’re trying to like make growth, you know, basically make it into a hyper growth. And this was, Oh, my God! This was a while ago, like
106 00:21:43.040 ⇒ 00:21:44.220 Emily Chan: they.
107 00:21:44.250 ⇒ 00:22:08.780 Emily Chan: they didn’t have a lot of like sophisticated reporting tools. They obviously have a ton of data, right? So my team, basically just like tried to create dashboards again for every role. So then the sales managers know, like can track like quota, and like how much revenue is coming in for each rep for each team, for each vertical. So like really like creating tools for the sales managers to like better track the team.
108 00:22:09.110 ⇒ 00:22:14.300 Uttam Kumaran: Hmm, okay, great. And then I mean, you’re spot on in terms of like the engagement. So we
109 00:22:14.330 ⇒ 00:22:25.479 Uttam Kumaran: we have multiple things. And again, what we’re doing now is different than what we’re doing before. But there are times where we come in, and people rely on us as their data team for hire. Right? And so
110 00:22:25.480 ⇒ 00:22:45.449 Uttam Kumaran: the problem with that is like we may be working to and our the people we go after our C-level executives or executives like, that’s that’s how we try to get into the companies. Because I learned as a data person like, if you don’t have buy in, nothing happens politically. So I realize, like we have 2 ways. We either go after the engineers and try to get in or we go after the C levels.
111 00:22:45.450 ⇒ 00:23:13.090 Uttam Kumaran: And I feel lucky that, like again, I’ve worked with executives. I’ve done all the executive reporting I work. I was at my 1st job was that we work and then worked on the Ipo there. And so like, I’m just used to those like high pressure situations and how to like break this situation down for the executive. So I realize that, like, okay, we have an edge there. So we go after the high level executives understand what their problems are, and I boil it down to like. Do you want to make money? You want to save time, or you want to save money like.
112 00:23:13.090 ⇒ 00:23:29.079 Uttam Kumaran: what? What are the problems? And then for us, our tool is data. Right? So do you have visibility into this area, do you? The data is inaccurate, or you haven’t been able to measure. Those are the things that I try to figure out what the problem is, and then my goal is to help them make more decisions.
113 00:23:29.080 ⇒ 00:23:46.250 Uttam Kumaran: And then I want those decisions to be more accurate, very basic, right? Of course, there’s a whole host of things behind that. So when we come in, it’s really the dashboard and analysis stages when they feel like they can feel something tangible. But, as you know, there’s all this background. 80% of the work is like.
114 00:23:46.290 ⇒ 00:24:01.529 Uttam Kumaran: did the data land? Right? Do we detail? In fine, are the data models like, okay? Oh, we missed like a case when statement. Oh, like, there’s an error. Right? There’s all this pipeline stuff, and then we have the dashboards. But the the thing I also realize is that
115 00:24:01.590 ⇒ 00:24:13.729 Uttam Kumaran: the thing they really can hold onto is the dashboards and analysis. And you know, the the nice thing is, once you go through that whole process, you end up becoming probably the one of the few people in the company that knows how the whole thing works
116 00:24:13.730 ⇒ 00:24:32.439 Uttam Kumaran: right. Everybody in their own domain knows how marketing works, sales, works. The executive may know at a high level how everything works, but we quickly become the most knowledgeable people in the company, and, of course, what happens is, they then say, what should we do with the company? Right? And then that’s where it’s it’s like
117 00:24:32.500 ⇒ 00:24:40.169 Uttam Kumaran: what we’ve really struggled with, I will say is finding really amazing analysts, and I don’t think we’ve struggled to find
118 00:24:40.460 ⇒ 00:24:42.920 Uttam Kumaran: like analysts in general. Like, I think
119 00:24:43.170 ⇒ 00:24:53.280 Uttam Kumaran: I I’ve we’ve we’ve worked through with a couple. The thing that we really struggle with is finding analysts that can become an extension of the business right? And I know there’s always going to be a healthy
120 00:24:53.310 ⇒ 00:25:19.590 Uttam Kumaran: discourse between the engineers and the modeling side and the analysts. I’m like, I’m just gonna do this in excel to try to get an answer. Oh, you should go use this Kpi. But the analysts that we’ve worked with so far have always been like kind of like sitting in the back waiting like I need this analysis for me. When we go into companies, I want us to show that, like, Hey, we, we’re the most knowledgeable with your data. And we noticed this thing right? And how do you take it from?
121 00:25:19.590 ⇒ 00:25:42.209 Uttam Kumaran: We have the data it’s accurate to. Then we can see the trends to then understanding, like the contribution to the trend, and then, understanding what you should do right? And where are? How do we help the customer along that journey? Right? We, until the data is accurate and thorough. We can’t even put together a dashboard. But then, once you have a dashboard, you can then say, Okay, cool. This number went down, or this happened.
122 00:25:42.210 ⇒ 00:26:03.849 Uttam Kumaran: Oh, I didn’t know that. Okay, let’s look at the contributions. Okay, we noticed that gross, gross margin. These are the contributions. We did some promotion, or there’s a high refund rate. Okay? Then it’s the decision, right? And then what should they do? Okay, we need to cut this skew. We need to go hire somebody, but I don’t think it’s. I think the data team should be involved in all those things, because we know how all that works.
123 00:26:03.850 ⇒ 00:26:15.390 Uttam Kumaran: And the nice thing is what I found is that the businesses we work with they are very interested in us, helping them work that way, and I don’t feel like we are just like, come, come, hire us to go, do data, and then
124 00:26:15.410 ⇒ 00:26:24.099 Uttam Kumaran: we just hand you the table. It’s like we’re going to be intimately involved in everything that’s going on. And that’s where we really had a lot of success with companies
125 00:26:24.200 ⇒ 00:26:25.500 Uttam Kumaran: right? And so
126 00:26:25.570 ⇒ 00:26:45.180 Uttam Kumaran: the the folks that you know I will tell you about the folks that we bring on one are people that have like worked with a team, but also that want to work in like this multidisciplinary manner, because we may have. We have clients in the in real estate. We have clients in e-commerce, with client in like consumer packaged goods.
127 00:26:45.538 ⇒ 00:26:55.589 Uttam Kumaran: And the nice thing is we build a wealth of knowledge. But of course, sales data sales data. Like finance data, finance data, marketing data is like
128 00:26:55.630 ⇒ 00:27:01.210 Uttam Kumaran: ads campaign. You know, it’s it’s just the industries and the ways of working that change.
129 00:27:01.560 ⇒ 00:27:24.079 Uttam Kumaran: And so that’s for me. I’m always looking for like amazing people. And I I know you mentioned that. You know you’re you’re you’re now focused on being a mom. And I think working is somewhere like this, where it’s like, Hey, maybe we just need 20 h of your time. And I we pay really competitively. And it’s just like, how much time do you have? Do you want to do this deal like
130 00:27:24.080 ⇒ 00:27:31.760 Uttam Kumaran: that’s kind of the way I typically do things. And you know, that’s how I work with a lot of my friends and starting to even grow that which is.
131 00:27:31.760 ⇒ 00:27:37.749 Uttam Kumaran: hey, I have this client. They need 10 h of data engineering work like, it’s these technologies.
132 00:27:37.880 ⇒ 00:27:53.679 Uttam Kumaran: What do you think like? Plain and simple, right? Like, I think I think it’s like, once you’ve done this data stuff many times over, as long as we keep the stack pretty consistent and the types of things we go after. That’s the kind of way that that we try to do work, you know. So.
133 00:27:54.430 ⇒ 00:28:00.160 Emily Chan: Yeah, in terms of where I am. My kids are a little older now, which is why I’m like starting to.
134 00:28:00.160 ⇒ 00:28:00.790 Uttam Kumaran: And I think you.
135 00:28:00.790 ⇒ 00:28:25.670 Emily Chan: The workforce, which is, I think, you were asking like, why the interest in freelancing. I think the flexibility is just really attractive to me as a mom, and you mentioned the variety, too. So spot on. So my 1st job out of college was actually as a consultant. I work at Mckinsey a whole ton of different industries. And I like the variety, and also, like you say, like a lot of interaction with executives. I think that also is rare
136 00:28:25.670 ⇒ 00:28:39.639 Emily Chan: data. People that can talk to both like engineers. You can like really read the code and see what’s wrong versus like all the way to like. Okay, this is the data. This is how you should change your business. I think those those 2 skill sets is rare in the data world.
137 00:28:39.640 ⇒ 00:28:47.380 Uttam Kumaran: 100%. And that’s why I felt like I could start a business like this because I had the network of people that were like that.
138 00:28:47.390 ⇒ 00:29:07.199 Uttam Kumaran: because I know that there’s in data. I’ve worked a lot of people are not like that, right? And it kind of changes as you go. More business facing you go from data engineering to modeling to analysts. They get more business facing. But at the same time I think I’ve seen, you know folks like me and you other people like us who can bridge the gap, and it’s like
139 00:29:07.200 ⇒ 00:29:20.479 Uttam Kumaran: like it’s invaluable, and I don’t know I can’t do my! I can’t do the data work without that business context. And I also, I, exactly, we said, like, I, I really love working in all these industries. I spend my day talking to like
140 00:29:20.800 ⇒ 00:29:47.630 Uttam Kumaran: all types of business problems, like learning all sorts of stuff. And then, the second thing is, I still do data engineering work. So I’m able to still try to look to. How do we? How do we push better code? How do we add more testing? How do we improve observability, alerting like, it’s those same engineering problems that we work on. But yeah, for me, it’s I love working with all sorts of businesses. And now that now that we have brainforce, people pick up my phone and are interested in telling me about
141 00:29:47.630 ⇒ 00:29:51.470 Uttam Kumaran: their data. And it’s it’s it’s fun like that’s the real fun part, you know.
142 00:29:52.300 ⇒ 00:29:58.179 Emily Chan: Awesome you mentioned. You have, like some analysis, needs like, do you mind sharing an example of that.
143 00:29:58.560 ⇒ 00:30:16.520 Uttam Kumaran: Yeah, I think. You know, right? Right, we have some companies that we take them through the full cycle right where maybe the initial part of the engagement 1st 6 months is really just like getting data in finding all the other 3rd party providers that we need data from landing at all and cleaning up and modeling.
144 00:30:16.520 ⇒ 00:30:23.609 Uttam Kumaran: But after you kind of the data model reaches a good maturity, maybe you just need to add columns sometimes or change business definitions.
145 00:30:23.610 ⇒ 00:30:48.229 Uttam Kumaran: But some most of the problems become like an actually analyzing the data and making the executives make decisions right? And I’m not talking just dashboarding, although they may need some tools. And we have some tools that we use that are like more like Bi as code. So they’re a little bit easier to maintain. But they we may have to host meetings with them, which is like, Hey, they have a. They want to figure out how to expand the segment. Okay, what should we do? And they ask us.
146 00:30:48.460 ⇒ 00:31:12.919 Uttam Kumaran: right? And that’s where it’s like, okay, we looked at Xyz parts of the data. And then we made a recommendation. Another thing that we did for a client is we helped them negotiate shipping rates right? And this was, I love doing it, but it was totally outside of our purview. Is that like I help, they were like they were spending millions of dollars on shipping. They hadn’t looked at their Fedex contract in like years.
147 00:31:12.920 ⇒ 00:31:35.689 Uttam Kumaran: and they wanted us to help them take a look at that. And basically understand? Like, is there a way for us to take their compare that to what they’re getting from ups. And so we took their rate sheet from Fedex, wrote it all in sequel, are able to forecast. And then I took the forecast suspend, called the Ups person was like, this is what we’re going to do with Fedex
148 00:31:35.700 ⇒ 00:31:42.130 Uttam Kumaran: now like, let’s compare let’s shop. And then we were able to negotiate like that. We save them a half 1 million dollars.
149 00:31:42.574 ⇒ 00:31:45.489 Uttam Kumaran: And so those are things where it’s like.
150 00:31:45.700 ⇒ 00:31:58.130 Uttam Kumaran: could could I have easily been like, that’s out of our purview? Yes, but I mean, we we took it 90% of the way there. So I was like, yeah, you could put me on the phone with the ups. I don’t care like. It’s just numbers right. And so that’s the sort of stuff that
151 00:31:58.660 ⇒ 00:32:04.370 Uttam Kumaran: where I want wish that our analyst portion of our business goes one step beyond
152 00:32:04.739 ⇒ 00:32:25.200 Uttam Kumaran: and this is where I, even I, was having a conversation with somebody where I’m like, I think on the data engineering and modeling side, there is a floor, right? Meaning you have to be fairly technical. I think an analyst, there’s a whole. There could be anybody that says I’m an analyst. There’s a whole range. But I do think that the ceiling for analysts is very high meaning, like, really, really amazing. Talented analysts are almost like
153 00:32:25.340 ⇒ 00:32:28.250 Uttam Kumaran: for sales or relationship management, like.
154 00:32:28.340 ⇒ 00:32:34.070 Uttam Kumaran: there’s something else there. Right? And so I was like, maybe I have to look at like banking profiles or something, because
155 00:32:34.080 ⇒ 00:32:44.170 Uttam Kumaran: I actually don’t. I think on the analyst side, you don’t have to be that technical. Maybe you have to just know. Excel really? Well. But you’re really telling a story in a narrative and trying to tell people guide people
156 00:32:44.210 ⇒ 00:32:47.099 Uttam Kumaran: on the modeling side. It’s not. It’s pretty cut and dry. I’m like
157 00:32:47.160 ⇒ 00:32:52.849 Uttam Kumaran: this is the way we calculate this. Go calculate and make sure it’s running right. But I don’t know interested, like what you think about
158 00:32:53.300 ⇒ 00:33:02.889 Uttam Kumaran: like that analyst sort of portion. But I don’t know. I feel like we’ve we’ve struggled, and that’s not my network of people. But I’ve struggled to kind of build that as like a really great part of the business.
159 00:33:03.820 ⇒ 00:33:09.490 Emily Chan: How can I help you? So like, I am pretty full stack as a data scientist like I,
160 00:33:09.610 ⇒ 00:33:19.030 Emily Chan: I’m not the most sophisticated data engineer out there. My experience, you know, like I work in Google bigquery tableau.
161 00:33:19.110 ⇒ 00:33:27.020 Emily Chan: I’m happy to pick up new technologies. I mean, you know, in tech, there’s always new tools, right? But I think like what you’re trying to do, and the concepts don’t change.
162 00:33:27.020 ⇒ 00:33:27.550 Uttam Kumaran: Yes.
163 00:33:27.820 ⇒ 00:33:32.149 Emily Chan: But I really like, I think, as a data science leader in the last few years.
164 00:33:32.490 ⇒ 00:33:39.860 Emily Chan: that’s where, like, I spend most of my recent time as like the analysis part. And like taking that to decisions. Holder.
165 00:33:40.220 ⇒ 00:33:41.879 Emily Chan: yeah, how can I help you?
166 00:33:42.140 ⇒ 00:33:53.490 Uttam Kumaran: Yeah, I guess, like in the step. So I think we definitely have a need. I mean one, I think, just to know that you are open to work, I think helps me a lot. I I speak with.
167 00:33:53.740 ⇒ 00:34:08.280 Uttam Kumaran: We’re growing, and I and I get a lot of inbound on things I do. And really my ability to say yes or no is based on. If we have the people that I know can solve the problem, and over and not only solve the problem like, do we have the people that can over exceed expectations?
168 00:34:08.599 ⇒ 00:34:25.529 Uttam Kumaran: So that’s 1 thing. I think the second thing is just knowing, like kind of what areas you would prefer to work in. Right? Like I I mentioned, we have, you know, this need on the analysis side. But again, we a lot of the folks on the team are kind of like full stack data people they prefer to like. They’re like, Hey, I just want to do models, but if I ask them to like.
169 00:34:25.530 ⇒ 00:34:48.049 Uttam Kumaran: pull some numbers or do an analysis, they can do it right. So there’s there’s thing like about preference. And then there’s where what people can do. So I think that would be helpful to know is like, if you are you more interested in doing like kind of more on the analysis side. Are you interested? More on like the data science side? Or is it like, just keep me in mind on a project, and like.
170 00:34:48.590 ⇒ 00:34:55.980 Uttam Kumaran: I wish I can do. And I can just share. Hey, we’re we’re getting this client that’s coming in. Is this something that you’d be interested. But yeah, like, let me know what what’s interesting to you.
171 00:34:55.989 ⇒ 00:35:00.919 Emily Chan: Yeah, I would say, I’m definitely more interested on like the analysis side, like.
172 00:35:01.879 ⇒ 00:35:03.979 Emily Chan: if we go through like the
173 00:35:04.009 ⇒ 00:35:17.609 Emily Chan: floor, right? Like building like a data warehouse. Building pipelines. I can do that, too. But honestly, I think it’s gonna take me a bit of a ramp up to pick up the technologies, and I’m happy to do that. If you’re okay with that ramp
174 00:35:17.729 ⇒ 00:35:29.139 Emily Chan: and then to something down like, Okay, now the pipeline is ready. Can you build a dashboard? Can you, you know, hold some training sessions with the client? Make sure they know how to use the dashboards?
175 00:35:29.953 ⇒ 00:35:54.529 Emily Chan: Can you like, actually, you know, use a dashboard to do analysis, or just, you know, write some sequel, quote, or put some python to look at the analysis and figure out a recommendation for the clients. I think that is my sweet spot like that’s what I would love to do. So I say, like pretty full stack, happy to like help out where you need but I think you know my the best way to use me is towards the later part of that value chain.
176 00:35:54.530 ⇒ 00:36:18.580 Uttam Kumaran: Hey? Great! No, that’s like super helpful. And it’s convenient, because that’s exactly like where we need a lot of help. You know. And also in each of these areas, you know, I want not only just to bring people on is like, Hey, you come on and just do this job. It’s actually like building a culture of like excellence in the company like, how do we do great analysis? How do we do? Great modeling? And so that’s also what we’re starting to do is like.
177 00:36:18.580 ⇒ 00:36:33.290 Uttam Kumaran: you know, people come from various backgrounds. But we have a lot of internal tooling and things that we’re building to basically help us in the full life cycle. Right? We have internal tooling that we build around de stuff like around, you know, like setting up snowflake for the 1st time.
178 00:36:33.310 ⇒ 00:36:35.429 Uttam Kumaran: Boilerplate templates for stuff.
179 00:36:35.450 ⇒ 00:37:03.509 Uttam Kumaran: So we’re we’re working on a lot of things that allow us for the every single next client gets a better experience gets things delivered faster, higher quality. So yeah, I mean, I would love to just keep you in mind for stuff that come up. I mean, I I think we’re just now starting to kind of ramp up a lot of stuff on the sales side. So I’m like fingers crossed, but also kind of scared if things like, if we get a lot of inbound. But I guess that’s like a good problem to have
180 00:37:03.790 ⇒ 00:37:27.889 Uttam Kumaran: But I would love to just like if I see something that comes across that like looks like there’s a really great analysis component, you know. And again, I don’t. I don’t like. This is another thing is I don’t. I didn’t want the analysis that work at Brainforge to be like, change the color on a dashboard like for me that’s like that’s like, not not only like what I don’t want to do, I can’t. I? I just don’t like. When
181 00:37:27.890 ⇒ 00:37:44.017 Uttam Kumaran: people are reduced to just that. And so we’ve made some decisions, even on the tooling side, on picking some tools where the focus isn’t on like drag and drop this and like, spend 8 h cause you lost your looker dashboard like it’s actually on like being a human being and like thinking hard about problems.
182 00:37:44.615 ⇒ 00:38:02.909 Uttam Kumaran: But those are the also the clients that we go after. We’re not going after clients where the job is like dashboard, or it’s like line chart like that. We’re going after analysis where it’s like, actually like analyzing and making recommendations. So those are the things that I I look for like, I don’t want to take work where it’s like
183 00:38:03.673 ⇒ 00:38:19.810 Uttam Kumaran: you know the like, it’s reduced to less. It’s hard for us to over exceed expectations doing that. And it’s boring. It’s like not interesting at all. So why don’t I take that away? And I will like we. I have a kind of a list and I’ll just make sure you’re there. And when.
184 00:38:19.810 ⇒ 00:38:37.059 Uttam Kumaran: if something comes up hopefully in the next, you know, month or so that has an like analysis component even if it’s slightly technical, would love to share with you and discuss we are the types of engagements we have are kind of vary. Sometimes a client just needs
185 00:38:37.060 ⇒ 00:38:42.179 Uttam Kumaran: 10 to 20 h of work per week. Sometimes they need like a full team.
186 00:38:43.065 ⇒ 00:38:43.880 Uttam Kumaran: So
187 00:38:44.050 ⇒ 00:38:52.309 Uttam Kumaran: it this, it is like a higher touch sale like I. I go and I talk to people, and I learn like what they need. But as you mentioned once you’re in.
188 00:38:52.450 ⇒ 00:39:17.284 Uttam Kumaran: we just see the whole world open up in terms of the company like, and especially when we try to over exceed expectations. They’re then like, Oh, can you go look at data in another part of the business, you know. So I I find this to be so fun. We’re we’re starting with great case studies and manufacturing and shipping and a lot of industry interesting areas. We’re starting to do some stuff in medical so would love to keep your mind if that if that works. And
189 00:39:17.730 ⇒ 00:39:23.249 Uttam Kumaran: yeah, again, I, this has been a great conversation. So I appreciate you sharing everything, and with for the great questions as well.
190 00:39:23.250 ⇒ 00:39:35.260 Emily Chan: Yeah, thank you. Thanks for keeping me in mind. You mentioned, like, you don’t want to be the person that just changed color on the dashboard like we don’t want to be a dashboard monkey. And I think one way to like, get around that is just like
191 00:39:35.280 ⇒ 00:39:40.429 Emily Chan: you or I or your team just need to get in earlier that conversation right? It’s not about
192 00:39:40.640 ⇒ 00:39:47.990 Emily Chan: the question shouldn’t be, can you build me a dashboard? The question should be like, I have this problem, how do you solve it? And the dashboard is part of the solution, and.
193 00:39:47.990 ⇒ 00:39:48.690 Uttam Kumaran: I’m correct.
194 00:39:48.690 ⇒ 00:39:53.419 Emily Chan: Right? So like you shouldn’t just be responding to Rfp for a dashboard. So
195 00:39:53.630 ⇒ 00:39:54.170 Emily Chan: yeah.
196 00:39:54.170 ⇒ 00:39:57.680 Uttam Kumaran: And and you know the I even had this conversation this week is.
197 00:39:57.690 ⇒ 00:40:05.499 Uttam Kumaran: for we just started with a client and I, and we were starting like a little bit of requirements gathering. And I was like, we have a project manager. And I said.
198 00:40:05.550 ⇒ 00:40:29.550 Uttam Kumaran: I need. I want to create a running list of questions. And the question is not these aren’t questions like, Can you do this? These are questions like, why are customers acting this way or like, why has this not succeeded? And I want our tools. We’re going to go line by line and basically find out through dashboards, through point analysis, through data models how we can answer each of those. But I said, I don’t want this to be like.
199 00:40:29.550 ⇒ 00:40:36.339 Uttam Kumaran: I want the the questions have to guide the decisions on the data side, we we’re not just going to develop and
200 00:40:36.580 ⇒ 00:41:04.440 Uttam Kumaran: the data models, just because that’s what we do. They’re they’re gonna ladder up into one or many of these questions. Right? The ultimate goal is answering the questions. Ultimate goal is not pipelines. Pipeline is what we can do to affect it. And so that’s that’s the shovel that we bring. But the ultimate goal is answering these questions, and also to work with clients and to say, just like, tell me the questions that you have, and I can go back and find out how we answer. That’s the mutual layer, like language, right?
201 00:41:04.450 ⇒ 00:41:08.750 Uttam Kumaran: If I was so like, Hey, we have 5 kpis like, how would you like us to arrange this like?
202 00:41:09.070 ⇒ 00:41:15.340 Uttam Kumaran: I don’t know. It’s like, not I. I’m I’m no longer interested in doing that. And I know that in the long term
203 00:41:15.450 ⇒ 00:41:20.770 Uttam Kumaran: we can’t. We’re not going to build a sustainable business around that. And there’s a lot of other people that will go do that
204 00:41:21.113 ⇒ 00:41:24.639 Uttam Kumaran: and like for me, it’s like it’s a lot more higher touch.
205 00:41:24.690 ⇒ 00:41:27.760 Uttam Kumaran: you know, and I know that if we. Really.
206 00:41:28.090 ⇒ 00:41:32.590 Uttam Kumaran: I feel like it’s rare for other data coming to work for the other consultancies to have
207 00:41:32.790 ⇒ 00:41:45.729 Uttam Kumaran: that sort of mindset. Typically, they’re like, just throw someone at a problem. And okay, you get an engineer for 10 h. Great. See? Ya, like, I don’t know. It’s just not like how we function. So yeah, no, that. That’s exactly right.
208 00:41:46.260 ⇒ 00:41:51.529 Emily Chan: That’s why your business is growing your partner, not just like someone with a random shovel.
209 00:41:51.530 ⇒ 00:42:19.141 Uttam Kumaran: Yeah, I know. And and again, like these, the Ceos, like they text me and like they help, I help with any. And I’m like. Look again, we are the we’re the most knowledgeable about their business. We’ve spoken with all their 3rd party vendors that they use, so I just try to be an asset, and I know that it’ll come back around. You know whether in referrals, whether it will expand or or just like you work for a good client like it’s not a nightmare situation like that’s the that’s why I kind of like trying to avoid working for startups and stuff. Because
210 00:42:19.510 ⇒ 00:42:22.550 Uttam Kumaran: sometimes they’re just the product doesn’t work. So that’s not.
211 00:42:23.540 ⇒ 00:42:34.420 Uttam Kumaran: We can’t really do much about that and the clients that have turned from us are all because their business has been failing. Frankly, we haven’t lost a single client because
212 00:42:34.810 ⇒ 00:42:37.799 Uttam Kumaran: we like made a mistake, or like we ran out of stuff to do.
213 00:42:38.390 ⇒ 00:42:40.190 Emily Chan: Wow! Congrats!
214 00:42:40.190 ⇒ 00:42:42.460 Uttam Kumaran: Good feeling, but at the same time it’s like.
215 00:42:42.660 ⇒ 00:42:46.879 Uttam Kumaran: you gotta you gotta know how to pick the right ones. And so yeah.
216 00:42:49.070 ⇒ 00:42:50.780 Emily Chan: Oh, thank you for sharing.
217 00:42:50.780 ⇒ 00:43:10.065 Uttam Kumaran: Yeah, of course. Okay. Well, why don’t we? Stay in touch? And I mean again, I think I should probably have something you know to to share with you in the next month or so. So just keep us in mind. And then, yeah, if I can answer any questions, or you know, if interested in talking to other people in the company, or whatever I can do to help, please
218 00:43:10.470 ⇒ 00:43:11.990 Uttam Kumaran: let me know. Happy to.
219 00:43:11.990 ⇒ 00:43:21.690 Emily Chan: Yeah, I would actually be interested in like just learning more about your business. And the analysis need, like, maybe a case study or something. What would you suggest? I do.
220 00:43:21.690 ⇒ 00:43:24.130 Uttam Kumaran: Yeah, we have some stuff on the website, although.
221 00:43:24.260 ⇒ 00:43:54.149 Uttam Kumaran: And I’ve been saying this for like a week or so. But we’re changing some stuff on the website soon. The stuff on the website is a little bit like it’s gonna be a little bit like more businessy but I would love to maybe send you a couple of those. I mean, if you want AI, you can see that we’ll be publishing some more stuff in the next week or so, and then I think I mean, I think the easiest way is like conversations like this. I’m happy to, you know. Introduce you to anybody on our team where you can just chat. And you know everybody is.
222 00:43:54.190 ⇒ 00:44:07.120 Uttam Kumaran: Everybody is similar to me in one way or another, but all very relaxed. All technical data, people. So if if like, if this is a good way to just ask about their stories and stuff, then I’m totally open for that, too.
223 00:44:07.320 ⇒ 00:44:09.330 Emily Chan: Yeah, that would be great. I would love to.
224 00:44:09.330 ⇒ 00:44:15.450 Uttam Kumaran: Okay, okay, perfect. Alright. So I’ll maybe I’ll make some introductions. And then, yeah, again, I really appreciate the time.
225 00:44:15.770 ⇒ 00:44:18.460 Emily Chan: I really appreciate the time, too. Thank you so much.
226 00:44:18.460 ⇒ 00:44:19.350 Uttam Kumaran: Yeah. Thank you. Talk to you.
227 00:44:19.350 ⇒ 00:44:21.350 Emily Chan: All right. Keep in touch bye.