Meeting Title: Brainforge-Intro Date: 2024-10-04 Meeting participants: Casie Aviles, Uttam Kumaran
WEBVTT
1 00:06:34.990 ⇒ 00:06:35.660 Uttam Kumaran: Hello!
2 00:06:37.940 ⇒ 00:06:38.650 Casie Aviles: Hey! There!
3 00:06:39.180 ⇒ 00:06:40.299 Uttam Kumaran: Hey! How are you?
4 00:06:40.780 ⇒ 00:06:41.880 Casie Aviles: Yeah. Doing. Good.
5 00:06:42.570 ⇒ 00:06:43.119 Uttam Kumaran: Nice to see you.
6 00:06:43.120 ⇒ 00:06:45.010 Casie Aviles: Yeah. Nice to see you, too.
7 00:06:45.630 ⇒ 00:06:46.799 Uttam Kumaran: Yeah, how’s everything?
8 00:06:47.605 ⇒ 00:06:50.210 Casie Aviles: Yeah, I’m just chill chilling right now.
9 00:06:50.760 ⇒ 00:06:51.550 Uttam Kumaran: Nice.
10 00:06:52.021 ⇒ 00:06:55.810 Uttam Kumaran: Well, really nice to meet you. Yeah, we’ve been working with Miguel for
11 00:06:55.870 ⇒ 00:06:58.278 Uttam Kumaran: maybe like 2 months now, and he’s been
12 00:06:58.620 ⇒ 00:07:08.070 Uttam Kumaran: helping us a ton. He’s basically starting to lead a lot of the AI stuff we’re doing here. So I’m really, really happy. You know, he introduced us. I basically
13 00:07:08.130 ⇒ 00:07:11.420 Uttam Kumaran: told him, Hey, I wanna hire
14 00:07:11.990 ⇒ 00:07:20.149 Uttam Kumaran: people like you who with your background and do you have any smart friends? And you know he had a lot of really amazing things to say about you. So
15 00:07:20.508 ⇒ 00:07:26.189 Uttam Kumaran: I’m really appreciative and thank you again for for taking the time. I know it’s Friday night there. So.
16 00:07:26.580 ⇒ 00:07:33.319 Casie Aviles: Yeah, yeah, no, no worries. Yeah. He was also a really fun team lead when I used to when I worked with him. And I learned a lot from him also.
17 00:07:33.850 ⇒ 00:07:42.419 Uttam Kumaran: Nice. So yeah, again, my name is you, Tom? I guess today I really just have a little bit of a casual conversation and share a little bit about
18 00:07:42.788 ⇒ 00:08:02.480 Uttam Kumaran: what we do, Brainforge, and then learn a little bit from you about sort of what you do, what you’re what you’re interested in. And then, you know, hopefully, see if there’s a way that we can work together. But yeah, again, I I started Brainforge about a year ago, Brainforge started as a data analytics Consultancy
19 00:08:02.490 ⇒ 00:08:05.649 Uttam Kumaran: based here, I’m based in Austin, Texas, in the Us.
20 00:08:05.971 ⇒ 00:08:17.479 Uttam Kumaran: And I started a business. My background is in data and leading data teams doing data engineering at a bunch of startups in New York. And then I moved here to Austin a few years ago.
21 00:08:18.082 ⇒ 00:08:20.997 Uttam Kumaran: And then, yeah, I I
22 00:08:21.660 ⇒ 00:08:28.079 Uttam Kumaran: I was very interested in how to kind of build a consultancy and basically work with multiple clients and build a team around
23 00:08:28.090 ⇒ 00:08:30.720 Uttam Kumaran: client service, and so about
24 00:08:31.200 ⇒ 00:08:41.954 Uttam Kumaran: 6 or 7 months in I I hired my 1st like kind of full time hire, and I’ve been growing the team ever since, you know. At the same time I saw a lot of stuff really.
25 00:08:42.559 ⇒ 00:08:45.219 Uttam Kumaran: expand around generative AI.
26 00:08:45.660 ⇒ 00:08:45.960 Casie Aviles: Yeah, and.
27 00:08:45.960 ⇒ 00:08:52.529 Uttam Kumaran: I wanted to. I wanted to find way like, when I started the company, I literally wrote down automate 50% of the business
28 00:08:52.833 ⇒ 00:08:59.956 Uttam Kumaran: and so my goal is always to automate our own business. But I also knew that it was gonna be very hard for some clients to
29 00:09:00.700 ⇒ 00:09:10.649 Uttam Kumaran: to find folks like us. And you know, folks that are very skilled on the technical side, but also very skilled on the AI side
30 00:09:10.660 ⇒ 00:09:30.580 Uttam Kumaran: and are, have. You’ve been used to working with a lot of problems, right? And and you know, in in AI, it’s a lot different workflow than in engineering and engineering. You’re used to using github committing code working in a huge team. I think in AI, it’s still like there’s all these different tools. There’s like make. There’s relevance. There’s Zapier. There’s like.
31 00:09:30.600 ⇒ 00:09:32.919 Uttam Kumaran: there’s probably like 50 tools
32 00:09:32.960 ⇒ 00:09:38.650 Uttam Kumaran: depending on what you’re doing. If you’re doing voice text, the chat like.
33 00:09:39.050 ⇒ 00:09:43.389 Uttam Kumaran: you know. And so there’s so much to learn. And it’s changing every few months. And so.
34 00:09:43.440 ⇒ 00:09:52.010 Uttam Kumaran: you know, I wanted to really build up my expertise. So I did a lot of work, and I went after clients and we had some success. But I realized that we needed
35 00:09:52.500 ⇒ 00:09:58.980 Uttam Kumaran: We needed this industry to mature. And for us to think about, what are the key? Use cases that we want to go after. And so now we’re at the point where I’m starting to.
36 00:09:59.060 ⇒ 00:10:01.228 Uttam Kumaran: you know, kind of build a team around
37 00:10:01.660 ⇒ 00:10:07.719 Uttam Kumaran: around AI and this not only AI, it’s just automation in general, right? I think a lot of folks are still
38 00:10:07.770 ⇒ 00:10:16.950 Uttam Kumaran: not using things like Zapier and other and make in their business. And they’re really losing out. And so that’s really what we’re trying to build here. So
39 00:10:17.060 ⇒ 00:10:19.352 Uttam Kumaran: yeah, that’s a little bit about us.
40 00:10:20.050 ⇒ 00:10:26.489 Uttam Kumaran: sorry it’s morning here, so I’m I’m a little bit slow. But please ask me any, any question, any questions that you have?
41 00:10:26.815 ⇒ 00:10:31.869 Uttam Kumaran: I’m wide open. Anything about me or the business. And then, of course, I would love to hear.
42 00:10:31.960 ⇒ 00:10:39.119 Uttam Kumaran: I love to hear about. You know what you’ve done, but really more excited to hear about what you want to do, and what you’re passionate about.
43 00:10:39.927 ⇒ 00:10:43.942 Casie Aviles: Yeah, yeah, for sure. So I guess I’ll start with just sharing what I
44 00:10:44.250 ⇒ 00:10:45.349 Casie Aviles: do. So
45 00:10:45.390 ⇒ 00:10:55.740 Casie Aviles: yeah, I’ve been into AI so lately. Right? Like I, I agree that you know, the automation side is just, you know, it’s really helpful. And
46 00:10:56.100 ⇒ 00:11:01.860 Casie Aviles: like all these tools. I used to just approach things like, you know, like a classic engineering way, like.
47 00:11:02.010 ⇒ 00:11:07.269 Casie Aviles: you know, like Github and get caught up in all of that technical stuff. But yeah, and
48 00:11:07.656 ⇒ 00:11:13.789 Casie Aviles: when. But then I got working with, you know this zap zapier and stuff. And it, just you know, it
49 00:11:13.820 ⇒ 00:11:15.519 Casie Aviles: simplifies all of that.
50 00:11:15.660 ⇒ 00:11:23.040 Casie Aviles: And also with that, with AI at its core. It’s just, you know. It’s got become more accessible for people, right?
51 00:11:23.280 ⇒ 00:11:35.029 Casie Aviles: And yeah, I’m quite interested in seeing how we could, you know. Improve, you know, business operations and such to like, for example, right with the.
52 00:11:35.110 ⇒ 00:11:39.919 Casie Aviles: So you you guys deal mostly with data. Right? If I, if I understand correctly.
53 00:11:39.920 ⇒ 00:11:43.680 Uttam Kumaran: Yeah, I would say, most of the business is around data data, analytics.
54 00:11:44.520 ⇒ 00:11:49.970 Casie Aviles: Yeah, so yeah, I’m I’m interested in. You know how we could, you know, take basically take that data. And
55 00:11:50.688 ⇒ 00:11:53.559 Casie Aviles: you know, analyze, create some
56 00:11:54.154 ⇒ 00:12:06.740 Casie Aviles: insights for the businesses that way. So we’re using AI and yeah, before, maybe we use like, you know, data science stuff. But now with AI, it’s just going to be a lot easy, easier, and yeah, more accessible. And
57 00:12:06.910 ⇒ 00:12:09.529 Casie Aviles: yeah, I’m interested in how how we I could.
58 00:12:10.302 ⇒ 00:12:12.909 Casie Aviles: what do you call this how to
59 00:12:13.070 ⇒ 00:12:16.260 Casie Aviles: basically work with that? Right?
60 00:12:16.460 ⇒ 00:12:17.290 Casie Aviles: Yeah.
61 00:12:17.530 ⇒ 00:12:18.389 Casie Aviles: yeah, I know.
62 00:12:18.390 ⇒ 00:12:37.369 Uttam Kumaran: I. I just wrote a little bit of a blog post about how we’re trying to use AI, but basically what I I mean, I, even I, talked to Miguel when he 1st joined about this, which is like we’re trying to build AI internally across multiple, different sectors. So in in brain forge. We have operations. We have sales.
63 00:12:37.540 ⇒ 00:12:41.510 Uttam Kumaran: We have like project management. And then we have engineering.
64 00:12:41.550 ⇒ 00:12:45.029 Uttam Kumaran: So all of those things I’m like, how do we automate? Right?
65 00:12:45.460 ⇒ 00:12:53.062 Uttam Kumaran: And so. But I think you know, you have to almost start from like, what does a person in sales do? And you break down the tasks. And you kind of slowly automate the things.
66 00:12:53.310 ⇒ 00:13:06.409 Uttam Kumaran: So that’s what we’re doing. And on the engineering side, I would say, is where we it’s gonna be the hardest because sales and marketing like we’ve already done stuff where we’re we’re using clay. And we’re we’re automating things for marketing. And
67 00:13:06.888 ⇒ 00:13:15.490 Uttam Kumaran: we’re using Llms to write blog posts like we’re doing a lot of that. I think the part of it that we’re going to have to
68 00:13:17.120 ⇒ 00:13:22.460 Uttam Kumaran: figure out is how to automate like data analysis and data modeling with AI,
69 00:13:22.540 ⇒ 00:13:29.099 Uttam Kumaran: it’s gonna require a lot more of like, do we take a screenshot of the dashboard. Do you also pass it? Context
70 00:13:29.130 ⇒ 00:13:48.980 Uttam Kumaran: of the problem of the company? Do you then give it access to query? Right? Like all of these things? You kind of want to. We want to layer on. So that’s gonna be huge problem. But again for me, I’m trying to find ways that we can offer our solution for cheaper, and we can bring on more clients. And we can improve the quality of our of our work. And I think AI is gonna
71 00:13:49.430 ⇒ 00:13:52.940 Uttam Kumaran: like extremely benefit a ton of that. So.
72 00:13:54.310 ⇒ 00:13:55.949 Casie Aviles: Yeah, yeah, I agree. Like.
73 00:13:56.310 ⇒ 00:14:05.009 Casie Aviles: that’s also what I, I think I’ve been doing with with the past experience with Miguel. So yeah, I’ve we’ve worked with
74 00:14:05.060 ⇒ 00:14:06.950 Casie Aviles: lots of companies. And
75 00:14:07.170 ⇒ 00:14:10.815 Casie Aviles: yeah, it’s more about understanding, really the domain, right? Like,
76 00:14:11.440 ⇒ 00:14:16.470 Casie Aviles: understanding how we could use this technology. So it kind of comes second for
77 00:14:16.920 ⇒ 00:14:19.909 Casie Aviles: it. So it’s not really just mostly about that
78 00:14:20.010 ⇒ 00:14:28.813 Casie Aviles: technical. But it’s also about understanding how the business works. And that way we can find, like the simplest solution, right? And I like, how AI
79 00:14:29.250 ⇒ 00:14:30.982 Casie Aviles: basically helps us.
80 00:14:31.630 ⇒ 00:14:34.520 Casie Aviles: yeah, simplify the process of all of that.
81 00:14:35.640 ⇒ 00:14:40.069 Uttam Kumaran: So tell me about a project that you know you worked on recently or anytime that was like
82 00:14:40.300 ⇒ 00:14:43.929 Uttam Kumaran: very challenging, like anything super complicated that you did
83 00:14:44.302 ⇒ 00:14:46.719 Uttam Kumaran: that you know you want to share about.
84 00:14:48.387 ⇒ 00:14:49.282 Casie Aviles: Yeah, sure.
85 00:14:50.390 ⇒ 00:14:58.066 Casie Aviles: another one of the most one complicated problem that I’ve worked with was it’s also related to the tech right?
86 00:14:58.540 ⇒ 00:15:04.259 Casie Aviles: So we were exploring something that was not offered standard by the company. And
87 00:15:04.430 ⇒ 00:15:09.040 Casie Aviles: yeah, that took a lot of research that took a lot of figuring things out.
88 00:15:09.200 ⇒ 00:15:14.620 Casie Aviles: but sometimes I got bogged down by the like. I said the the technical stuff.
89 00:15:14.760 ⇒ 00:15:18.959 Casie Aviles: and I realize just understanding how the business works.
90 00:15:19.334 ⇒ 00:15:31.560 Casie Aviles: It’s just it. It you know it. You you find a way to just oh, I mean, you realize. Oh, I could have done this much simpler instead of, you know. Going down this rabbit hole of technical stuff. So
91 00:15:32.410 ⇒ 00:15:39.079 Casie Aviles: and yeah, I I we, we eventually found a way to like simplify the process and to
92 00:15:39.512 ⇒ 00:15:43.147 Casie Aviles: so it’s a basically, it’s a chatbot using a different kind of
93 00:15:43.780 ⇒ 00:15:46.409 Casie Aviles: tech if a different model. But
94 00:15:46.840 ⇒ 00:15:50.009 Casie Aviles: yeah, that’s how, basically, how I approached it.
95 00:15:51.530 ⇒ 00:15:56.050 Uttam Kumaran: Yeah. So and then, was that all that was all like, that’s all chat, bot stuff.
96 00:15:56.870 ⇒ 00:15:58.428 Casie Aviles: Yeah. And also we would.
97 00:15:58.790 ⇒ 00:16:08.719 Casie Aviles: basically, we would like, get leads right coming into the chat bot, and we want to connect that to the to their their crm, for example, right?
98 00:16:08.770 ⇒ 00:16:11.219 Casie Aviles: So, yeah, that’s what we did.
99 00:16:12.570 ⇒ 00:16:23.199 Uttam Kumaran: And then, what are some new, anything new technologies that you’re playing around with, that you’ve seen recently, or anything cool that you’re that you’re poking around with.
100 00:16:23.820 ⇒ 00:16:28.529 Casie Aviles: Oh, yeah, yeah, definitely. I I see. I saw that opening. I just recently
101 00:16:28.909 ⇒ 00:16:32.740 Casie Aviles: release their, I think real time. Api, so it’s about the voice.
102 00:16:32.800 ⇒ 00:16:37.880 Casie Aviles: Yeah. So I’m I’m playing around with that. And I I there’s just a lot of possibilities with that
103 00:16:37.960 ⇒ 00:16:47.469 Casie Aviles: like, with how we could. You know, automate like customer service and such. So yeah, I think it. Yeah, that’s what I’ve been playing with lately.
104 00:16:47.900 ⇒ 00:16:48.670 Uttam Kumaran: Awesome.
105 00:16:49.040 ⇒ 00:16:57.030 Uttam Kumaran: Well, yeah, I mean, look I I’ll kind of give you a sense of like where we’re at. So we’re currently starting to build a team around AI.
106 00:16:57.427 ⇒ 00:17:09.130 Uttam Kumaran: We are actively going after clients, around building AI solutions. Miguel is the only person on the team. But ideally, I’m trying to basically build a team around him.
107 00:17:09.140 ⇒ 00:17:13.180 Uttam Kumaran: cause you know, it’s it’s great. When he told me that he’s led these teams before. And
108 00:17:13.496 ⇒ 00:17:21.759 Uttam Kumaran: you know, again, you mentioned really amazing things about you. So I guess I wanted to ask, like, what is your current like work status? And
109 00:17:21.880 ⇒ 00:17:24.170 Uttam Kumaran: we’re I don’t know how much.
110 00:17:24.250 ⇒ 00:17:30.439 Uttam Kumaran: I think maybe the like. Later this month or next month we may have some opening for some
111 00:17:30.620 ⇒ 00:17:36.000 Uttam Kumaran: like part time work, but I guess. Like, let me know what you’re what kind of where you’re at. And
112 00:17:36.600 ⇒ 00:17:43.210 Uttam Kumaran: yeah, like, what you’re interested is once you’re what’s your interest in is in working, you know, at a place like Brain Forge.
113 00:17:44.360 ⇒ 00:17:48.680 Casie Aviles: Yeah. Yeah. So currently, I’m still with the company
114 00:17:48.840 ⇒ 00:17:50.810 Casie Aviles: that I work with with Miguel.
115 00:17:50.900 ⇒ 00:17:51.990 Casie Aviles: And
116 00:17:52.030 ⇒ 00:17:58.870 Casie Aviles: but yeah, I’m I’m willing to, you know. Also do part time. If you have the opening. And yeah, I’m just interested also with
117 00:17:59.100 ⇒ 00:18:03.199 Casie Aviles: using, you know, this latest technology. And yeah.
118 00:18:03.620 ⇒ 00:18:05.450 Casie Aviles: that’s it. Basically.
119 00:18:05.450 ⇒ 00:18:06.660 Uttam Kumaran: Cool. Okay.
120 00:18:06.820 ⇒ 00:18:13.120 Uttam Kumaran: yeah. And then and then, how kind of like, what were the how? How did you guys work together in teams like, what was the
121 00:18:13.380 ⇒ 00:18:18.569 Uttam Kumaran: general schedule like about these projects? And like, how did you guys collaborate on these things?
122 00:18:20.518 ⇒ 00:18:25.270 Casie Aviles: For the scheduling, like, we basically, we work around
123 00:18:25.790 ⇒ 00:18:28.839 Casie Aviles: 8 h in in one day. And we
124 00:18:29.473 ⇒ 00:18:37.319 Casie Aviles: we have calls like every every day. And then we also when we have, when we need work, we’re, it’s it’s a bit dynamic.
125 00:18:37.330 ⇒ 00:18:39.580 Casie Aviles: It’s not super structured. But
126 00:18:39.690 ⇒ 00:18:46.590 Casie Aviles: yeah, we’re dynamic. And then we we call when whenever we we huddle, we do huddles whenever we need to brainstorm on something, and
127 00:18:47.174 ⇒ 00:18:51.000 Casie Aviles: basically on how we want to tackle such a client in the project.
128 00:18:51.130 ⇒ 00:18:52.069 Casie Aviles: So yeah.
129 00:18:53.210 ⇒ 00:18:55.668 Uttam Kumaran: And then in terms of like
130 00:18:56.070 ⇒ 00:19:05.540 Uttam Kumaran: like, how is there a community for this sort of stuff there? Because I’m even trying to find people here like on how I learn more. But I’ve just been learning everything on Twitter
131 00:19:05.580 ⇒ 00:19:10.940 Uttam Kumaran: like, is there any other place that you’re going to learn these things or meet people that kind of in the space.
132 00:19:12.580 ⇒ 00:19:14.920 Casie Aviles: I you mean like with AI.
133 00:19:15.120 ⇒ 00:19:15.770 Uttam Kumaran: Yeah.
134 00:19:17.149 ⇒ 00:19:20.649 Casie Aviles: Well, I I don’t really hang around too much on social media. But
135 00:19:21.196 ⇒ 00:19:26.579 Casie Aviles: I guess Youtube is where I really learn a lot of the the stuff with AI,
136 00:19:26.640 ⇒ 00:19:30.119 Casie Aviles: and sometimes I also check out Linkedin. So yeah.
137 00:19:31.220 ⇒ 00:19:37.589 Uttam Kumaran: And then, but is there like, how did you guys, how did you 1st get into this sort of AI stuff like, what were you doing before.
138 00:19:37.830 ⇒ 00:19:40.379 Casie Aviles: Oh, okay, so well, I I start.
139 00:19:40.460 ⇒ 00:19:54.330 Casie Aviles: it’s started around when I was in college, right? So at the end of my college years I got into machine learning. And I thought, Okay, I thought that I might like this. And then eventually I got into an internship that
140 00:19:54.330 ⇒ 00:20:13.319 Casie Aviles: expose me to more use cases of AI like with computer vision. And you know, data science, all of that technical jargon. But yeah, and and that’s how I got into AI, and then I just you know, I would. Actively, I look for a job online. And that’s how I got into AI.
141 00:20:13.840 ⇒ 00:20:20.289 Uttam Kumaran: Nice. Okay? So you guys have, like a crew of people that you guys work with at this previous company or the company that you’re currently at.
142 00:20:20.740 ⇒ 00:20:21.600 Casie Aviles: Yeah, yeah, yeah.
143 00:20:21.960 ⇒ 00:20:24.530 Uttam Kumaran: Okay, cool. Do they have, like a huge AI division.
144 00:20:25.933 ⇒ 00:20:26.506 Casie Aviles: No,
145 00:20:27.740 ⇒ 00:20:35.391 Casie Aviles: it’s mostly just it’s not. We’re not a huge. We’re not huge yet, but we have like, we have devs that focus on
146 00:20:36.300 ⇒ 00:20:40.840 Casie Aviles: on the AI side. And then there’s also devs that focus on the Crm side.
147 00:20:41.150 ⇒ 00:20:50.559 Uttam Kumaran: Okay? And then also, how do you guys do testing for like the AI apps like, Do you guys use like Lang Smith or something, for like
148 00:20:50.570 ⇒ 00:20:52.950 Uttam Kumaran: running evals and stuff like that.
149 00:20:53.330 ⇒ 00:21:05.807 Casie Aviles: Oh, yeah, Langsmith, we don’t use Langsmith, though. But there is like an experimental still experimental automated tester that was set up. So basically, we would just give the
150 00:21:06.220 ⇒ 00:21:12.430 Casie Aviles: like, we have these ids, and we would plug it in. And then it’s like, kind of like, 2 ais talking to each other. And then
151 00:21:12.500 ⇒ 00:21:13.930 Casie Aviles: there’s like a
152 00:21:14.420 ⇒ 00:21:17.429 Casie Aviles: a rubric or an evaluation that
153 00:21:17.938 ⇒ 00:21:22.720 Casie Aviles: that displays. If the AI followed the system instructions, the prompt right?
154 00:21:23.370 ⇒ 00:21:24.140 Casie Aviles: Yeah.
155 00:21:24.510 ⇒ 00:21:26.180 Uttam Kumaran: Hmm, okay, okay, cool.
156 00:21:27.160 ⇒ 00:21:28.740 Uttam Kumaran: Okay. Nice.
157 00:21:28.760 ⇒ 00:21:35.510 Uttam Kumaran: Okay. Well, how about we? Maybe me, you and Miguel can hop on. I would love to actually just share with you some of the projects that we’re doing.
158 00:21:35.650 ⇒ 00:21:55.870 Uttam Kumaran: But you know, I I’m really excited. I think you know. Certainly we have some projects that we’re just now pitching, and that I would love to see if you can at least start by coming on in in a part time, and then maybe there’s an opportunity to get you full time like that could work out, too. Or again, I just wanna I just told them that
159 00:21:56.250 ⇒ 00:22:17.849 Uttam Kumaran: for me. I’m just like anyone on our team, I said. Introduce me to your smart, smartest friends, and then I’ll find a way that we can all work together. But you know for me and AI, it’s really great to be able to meet other folks. So how about we stay in touch? And I’ll tell Miguel and then maybe we can catch up. I’m going to be out next week, but maybe the week after 3 of us can catch up and
160 00:22:18.123 ⇒ 00:22:23.670 Uttam Kumaran: you know I would love for him to share a little bit about the stuff we’re working on and like where you could possibly fit in.
161 00:22:23.890 ⇒ 00:22:33.170 Uttam Kumaran: and then let’s just keep the conversation going. You have my email. So any other questions you have. Also, you can check us out on Linkedin or our website for more stuff about what we do.
162 00:22:33.618 ⇒ 00:22:41.639 Uttam Kumaran: But yeah, let’s just keep chatting. And I think I’m I’m pretty confident something will happen in the next like month, month and a half.
163 00:22:42.390 ⇒ 00:22:43.889 Casie Aviles: Yeah, of course, of course.
164 00:22:44.600 ⇒ 00:22:47.130 Uttam Kumaran: Okay. Cool any other questions for me.
165 00:22:48.487 ⇒ 00:22:53.572 Casie Aviles: For? Not not really at the moment. But yeah, I’m I’m just, you know, stoked about
166 00:22:54.040 ⇒ 00:22:55.929 Casie Aviles: possibly working with you guys.
167 00:22:56.310 ⇒ 00:23:02.289 Uttam Kumaran: Yeah, thanks. Yeah. I know, we’re working on a bunch of like amazing stuff like, we’re starting to do a lot of stuff on data
168 00:23:02.420 ⇒ 00:23:22.289 Uttam Kumaran: on sales. We’re starting to do some stuff in real estate. Ai automation. We just did some stuff in like e-commerce. So yeah, we’re doing a ton of stuff like. And I’m trying to use the most advanced models trying to go after the real like we’re doing some headless browser stuff like browser automation.
169 00:23:23.770 ⇒ 00:23:30.520 Uttam Kumaran: I think there’ll be some great use cases. We’re building like an internal data platform as well. So there’ll be some great things on the data side.
170 00:23:30.670 ⇒ 00:23:34.020 Uttam Kumaran: So yeah, there’s just a bunch of open opportunities. So.
171 00:23:35.170 ⇒ 00:23:36.920 Casie Aviles: Yeah, that sounds exciting.
172 00:23:37.430 ⇒ 00:23:42.969 Uttam Kumaran: Cool. Okay, well, thank you so much. I know it’s Friday night. So thank you for taking the time. I really really appreciate it.
173 00:23:43.380 ⇒ 00:23:46.610 Casie Aviles: Yeah, sure. Thank you also for taking the time. And, sir.
174 00:23:46.610 ⇒ 00:23:48.470 Uttam Kumaran: Yeah, no, problem. Okay, I’ll talk to you.
175 00:23:48.470 ⇒ 00:23:50.160 Casie Aviles: Alright, bye-bye.
176 00:23:50.340 ⇒ 00:23:51.289 Uttam Kumaran: Thank you. Bye.