Meeting Title: Google Calendar Meeting (not synced) Date: 2026-03-04 Meeting participants: Kyle Montgomery, Uttam Kumaran
WEBVTT
1 00:00:56.500 ⇒ 00:00:57.349 Uttam Kumaran: Hey, Kyle!
2 00:00:57.990 ⇒ 00:00:59.470 Kyle Montgomery: Heyo Tom, how are you?
3 00:00:59.470 ⇒ 00:01:00.990 Uttam Kumaran: Hey, good, how are you?
4 00:01:01.260 ⇒ 00:01:03.159 Kyle Montgomery: Doing well, one time to see.
5 00:01:03.160 ⇒ 00:01:05.329 Uttam Kumaran: Long time no see, how’s life?
6 00:01:05.330 ⇒ 00:01:06.780 Kyle Montgomery: It’s pretty good.
7 00:01:07.960 ⇒ 00:01:11.959 Kyle Montgomery: Is he, busy start to the year.
8 00:01:12.580 ⇒ 00:01:22.379 Uttam Kumaran: Yeah, it’s been good. It’s been a good start to the year. Yeah, it’s been a while. I feel like we’ve just been hustling, trying to grow the company, and it’s been going well, like, it’s,
9 00:01:22.560 ⇒ 00:01:35.329 Uttam Kumaran: it’s just been… it’s been the fastest, like, two… like, it’s been almost, like, two and a half years since I quit my last job. So, just continuing to push, and we’ve brought on some really, really amazing people, and…
10 00:01:35.480 ⇒ 00:01:47.839 Uttam Kumaran: you know, I mean, I’m kind of talking to you about the business you know well, but it’s tough. It’s a call-people business, and there’s certain physics that, like, AI or automation can’t really get past, but…
11 00:01:48.160 ⇒ 00:01:55.709 Uttam Kumaran: I feel like we’ve been really capital efficient, and really just tried to deliver for our clients super, super well, and it’s just sort of like…
12 00:01:55.840 ⇒ 00:02:08.810 Uttam Kumaran: been a game of, like, can we capitalize on the wins, and also just staying alive long enough to, like, get a call from… from you again, and, like, you know, building connections that we can somehow do business together over time, so…
13 00:02:09.380 ⇒ 00:02:24.400 Kyle Montgomery: Yeah, so what, what business model are you pursuing with Brainforge? Are you putting people on a bench, and then deploying them across multiple assignments, or are you really the sales engine, and then you’re pulling in people just as you need them?
14 00:02:24.760 ⇒ 00:02:39.140 Uttam Kumaran: Yeah, so it’s closer to the first model. I would say right now, we don’t have, like, a large bench. It’s everybody’s sort of working, and for the most part, people are… like, I think we’re basically, like, hiring a lot right now.
15 00:02:39.140 ⇒ 00:02:51.870 Uttam Kumaran: But we… originally, it was just, like, me sort of freelancing and then using that to sort of bootstrap the business. We had a team of, like, a lot of contractors originally, and then now we’ve moved towards, like, most people…
16 00:02:51.870 ⇒ 00:03:05.759 Uttam Kumaran: are, like, full-time or on a path towards that. So we will kind of go towards more of, like, yeah, bench-style model. I mean, we’re trying really hard to have people deployed at least across, like, two or three clients at a time.
17 00:03:05.760 ⇒ 00:03:16.719 Uttam Kumaran: And then we’re starting to get to the size clients where we can have some people that are dedicated, but these are all, like, own engagements where we are deploying our own people, so it’s,
18 00:03:16.810 ⇒ 00:03:33.139 Uttam Kumaran: But we kind of made the switch towards that. We just had a lot of favors from just people that we previously worked with, but of course, they thought of us second to whatever they were doing, or, like, they were just another gig, and so now we have people that are working, you know, full-time for us directly, which is great.
19 00:03:33.460 ⇒ 00:03:40.720 Kyle Montgomery: And are you the… sales team? How, like, how are you going out and driving new project opportunities?
20 00:03:40.720 ⇒ 00:03:52.459 Uttam Kumaran: Yeah, so, in the business, it’s me and my business partner. Both of us are the primary, like, AEs, like, primary account execs, so we’re both doing all direct sales, and then we started doing a lot more, like, partnerships.
21 00:03:52.560 ⇒ 00:04:02.389 Uttam Kumaran: And so we’re getting some deals through some of our vendor partners, some stuff through referrals, and then we’re still going out into the market and sourcing deals as well.
22 00:04:02.560 ⇒ 00:04:03.210 Kyle Montgomery: Yeah.
23 00:04:03.260 ⇒ 00:04:04.150 Uttam Kumaran: Yeah.
24 00:04:04.150 ⇒ 00:04:10.669 Kyle Montgomery: Exciting. Yeah. Now, do you ever run across Clarkston after the work you’ve done at AG1, and…
25 00:04:10.670 ⇒ 00:04:14.809 Uttam Kumaran: I haven’t run into them on a deal,
26 00:04:15.620 ⇒ 00:04:22.460 Uttam Kumaran: I feel like we, like, I think it would be amazing to do work with them, or to somehow, like, supplement
27 00:04:22.540 ⇒ 00:04:40.380 Uttam Kumaran: you know, I think a lot of the work we continue to do on the data side, and in particular, we’re doing a lot more on the AI side, is probably stuff that, you know, I’m guessing is just hard for them to spin up and get around, given how fast it’s moving. But having… I still keep up with Danny, like, we talk every, like, few months, you know, and she’s put me in touch with some great
28 00:04:40.380 ⇒ 00:04:54.109 Uttam Kumaran: data people as well. So searching for the opportunity to try to do more business with them, but it’s a good reminder for me to maybe call… I was working with Brandon from Clarkston, and I catch up with some of the engineers I worked with previously there, so…
29 00:04:54.110 ⇒ 00:05:06.920 Kyle Montgomery: Okay. Well, there… the topic of AI has come up in a couple of my conversations with them, and a couple of my partners there I know, were looking to make some advances in what they can
30 00:05:07.070 ⇒ 00:05:11.850 Kyle Montgomery: where they can even engage with their clients on AI, because they don’t have a great story.
31 00:05:12.250 ⇒ 00:05:17.490 Kyle Montgomery: to tell a pitch. So, there could be something really interesting there.
32 00:05:18.010 ⇒ 00:05:20.160 Kyle Montgomery: Did you ever work with Jenny McClain?
33 00:05:21.430 ⇒ 00:05:22.290 Uttam Kumaran: I…
34 00:05:22.290 ⇒ 00:05:22.670 Kyle Montgomery: I know.
35 00:05:22.670 ⇒ 00:05:26.710 Uttam Kumaran: Yeah, the name is familiar. I feel like I may have emailed with her…
36 00:05:26.710 ⇒ 00:05:33.990 Kyle Montgomery: She was the managing partner who would have been over AG1, but I don’t think she was very active at all.
37 00:05:34.120 ⇒ 00:05:41.730 Uttam Kumaran: Yeah, I think I maybe met with her once, or emailed exchange back and forth, really about, like, some of the work that we were doing. But I mean, for us, like, I think…
38 00:05:41.880 ⇒ 00:05:45.130 Uttam Kumaran: one… kind of how we got into the AI space is.
39 00:05:45.260 ⇒ 00:05:55.499 Uttam Kumaran: I use AI so heavily to run and grow this business, so we learned a lot about what it’s like to use AI in, like, professional service contexts.
40 00:05:55.590 ⇒ 00:06:03.730 Uttam Kumaran: But not only… not only in a service business, but, like, everywhere. So how… how does… how’s sales team affected? How’s the engineering team affected?
41 00:06:03.730 ⇒ 00:06:19.179 Uttam Kumaran: And we used a lot of that story to then basically say, like, okay, we could actually have some things we can go to market with. And a lot of what it’s really surrounding is, like, it’s all internal operations-related automation. So finding teams who… they have a lot of manual workflows.
42 00:06:19.180 ⇒ 00:06:35.050 Uttam Kumaran: transcribing information between systems, it’s actually a lot of the same data problem, but the output is more of, like, someone needs to chat over, like, a bunch of documents, or understand how to tackle a certain problem, and AI is the way they start, right?
43 00:06:35.140 ⇒ 00:06:53.349 Uttam Kumaran: I think a year ago, when we were pitching a lot of AI, people still hadn’t used, like, ChatGBT, you know, before, and so it was, like… I sort of describe it as, like, explaining, like, water is wet. I’m like, you got… you have to get… I don’t think you… I don’t think you’re, like… I think you need to try that first, because it’s hard for me to explain, like, what magic is.
44 00:06:53.970 ⇒ 00:07:12.499 Uttam Kumaran: And then now, I think we’re at the point where people have used ChatGPT, they’ve tried it for some work context, but then they’ve maybe also purchased, like, a tool, or they’ve tried to turn on Copilot, and they’ve kind of, like, realized that, oh, because all of our data isn’t connected, and there’s not, like, a context layer, they’re struggling.
45 00:07:12.610 ⇒ 00:07:18.789 Uttam Kumaran: And that is exactly, like, kind of like what we’re doing. So we’re building a lot of those context layers for companies.
46 00:07:18.850 ⇒ 00:07:33.110 Uttam Kumaran: Some of that built directly on Snowflake, some of that built more custom, and then also trying to build, sort of, the UIs on top of that, whether that’s a chat interface, whether that’s something more sophisticated to do specific work, like document writing.
47 00:07:33.110 ⇒ 00:07:44.770 Uttam Kumaran: Or, you know, so that’s sort of the way that we’re… we’re positioning ourselves, and we’ve not only done that in our company, we have a few case studies where we’ve done that for some clients now.
48 00:07:44.800 ⇒ 00:07:50.619 Uttam Kumaran: And we’re sort of using that as a story, which is, like, for the most part, if you try to implement AI,
49 00:07:50.780 ⇒ 00:08:03.020 Uttam Kumaran: the difficulty you’re gonna have is, like, your context is all over the place, it’s not, like, readily available, and it’s not… you can’t just, like, take a bunch of CSVs and throw it into ChatGPT, like, that’s not a scalable
50 00:08:03.220 ⇒ 00:08:10.549 Uttam Kumaran: like, offering for your team. And then the last piece of the enterprise is, like, all privacy, right? Like, your employees right now are…
51 00:08:10.660 ⇒ 00:08:24.970 Uttam Kumaran: are on their personal laptops, putting in company documents in there, like, you need to really prevent that and give them an offering internally that’s better than what they’re doing now, and so that’s sort of, like, how we’re positioning it and having conversations with folks.
52 00:08:25.170 ⇒ 00:08:31.089 Kyle Montgomery: Yeah, yeah, that makes sense. And… It’s interesting…
53 00:08:31.540 ⇒ 00:08:44.099 Kyle Montgomery: where you grew up and your emphasis yet today on, kind of, the data engineering aspect of it, and then how that’s playing into AI, because for this particular opportunity,
54 00:08:44.770 ⇒ 00:08:53.130 Kyle Montgomery: the… this guy who’s this Director of Digital Finance, he has a vision
55 00:08:53.460 ⇒ 00:09:08.779 Kyle Montgomery: for where they can really deploy Agentic AI to serve as a supplement and to help speed things along in the very, just, kind of day-to-day finance transactions.
56 00:09:11.630 ⇒ 00:09:19.849 Kyle Montgomery: and I… when he and I first met, and he was telling me his vision, and telling me about the help he was looking for,
57 00:09:20.140 ⇒ 00:09:26.630 Kyle Montgomery: I came at him with a guy who was very heavy on the AI side.
58 00:09:27.150 ⇒ 00:09:31.130 Kyle Montgomery: But light on, kind of, the plumbing or the data engineering.
59 00:09:31.400 ⇒ 00:09:39.110 Kyle Montgomery: Yeah. And I thought I had hit a home run. Like, this guy was fantastic, he had built some of the exact things that they’re talking about.
60 00:09:39.210 ⇒ 00:09:45.050 Kyle Montgomery: And he just swatted him down and was like, no, I really just need… I need, like, a pure data engineer.
61 00:09:45.550 ⇒ 00:09:51.840 Uttam Kumaran: Yeah, we need, like, the plumbers. Yeah, I mean, that’s… I actually feel that that is more of the hard work.
62 00:09:51.840 ⇒ 00:09:53.670 Kyle Montgomery: Which is funny, because…
63 00:09:53.670 ⇒ 00:09:57.420 Uttam Kumaran: I think we’ve been at… doing AI stuff for so long internally that
64 00:09:57.660 ⇒ 00:10:03.139 Uttam Kumaran: Picking the right model and the prompt engineering is, like, a lot easier, actually, than
65 00:10:03.290 ⇒ 00:10:10.019 Uttam Kumaran: getting all your data sources ingested into an area that AI can actually understand the semantic context.
66 00:10:10.240 ⇒ 00:10:15.510 Uttam Kumaran: And so, I… I mean, I wonder how he kind of arrived at that…
67 00:10:15.650 ⇒ 00:10:29.670 Uttam Kumaran: position where he was like, no more AI people, like, I need data people, but I tend to agree, and I feel like that’s what ended up being our competitive advantage, is we started from the data, versus some people start from the AI, and then they just throw it on top of, like.
68 00:10:29.870 ⇒ 00:10:32.569 Uttam Kumaran: Whatever’s going on, and they hit a wall really fast.
69 00:10:32.770 ⇒ 00:10:37.829 Uttam Kumaran: And if you’re just an AI person, you don’t have a toolkit to solve that. It’s not…
70 00:10:37.990 ⇒ 00:10:42.580 Uttam Kumaran: There’s, like, you can’t throw all your documents into one context, like.
71 00:10:42.800 ⇒ 00:11:02.149 Uttam Kumaran: it’s… it rhymes a lot with the work that we do with dbt, with, you know, with all of our business intelligence, right? Like, metric definitions, understanding the relationship between different data sources, and writing all that down so that your AI understands
72 00:11:02.260 ⇒ 00:11:06.450 Uttam Kumaran: how to write the SQL, to pull the right data, to provide it to the right person.
73 00:11:06.800 ⇒ 00:11:10.890 Uttam Kumaran: And so, yeah, that’s interesting that he sort of arrived at that conclusion.
74 00:11:11.170 ⇒ 00:11:18.239 Kyle Montgomery: Yeah, yeah, and again, the context is around finance.
75 00:11:18.720 ⇒ 00:11:28.410 Kyle Montgomery: So… I think I shared them in the email with you, things like reconciliation, journal entries. He ultimately wants to get to where they’re…
76 00:11:28.620 ⇒ 00:11:32.210 Kyle Montgomery: Ai is assisting with month-end close, even.
77 00:11:32.210 ⇒ 00:11:33.550 Uttam Kumaran: Yeah.
78 00:11:33.850 ⇒ 00:11:44.200 Kyle Montgomery: And they’re not starting from scratch. They’ve invested in, predictive financial statements, they’ve invested in machine learning.
79 00:11:44.310 ⇒ 00:11:53.279 Kyle Montgomery: to help with some of those more rote transactions. But he’s… it seems he’s kind of looking across this divide of, okay.
80 00:11:53.550 ⇒ 00:12:02.009 Kyle Montgomery: I see how the work we’ve done so far could serve as a foundation to get to a much more sophisticated AI solution.
81 00:12:02.880 ⇒ 00:12:09.899 Kyle Montgomery: But how do we get there? Like, what… and data engineering and the architecture required
82 00:12:10.090 ⇒ 00:12:16.800 Kyle Montgomery: is where he, where he’s stuck. There’s actually an excerpt from a note that I was…
83 00:12:16.980 ⇒ 00:12:20.310 Kyle Montgomery: as I’m thinking of that, I was gonna share with you,
84 00:12:21.110 ⇒ 00:12:24.320 Kyle Montgomery: Let’s see, where did I put it?
85 00:12:25.030 ⇒ 00:12:30.180 Kyle Montgomery: And this is a glo… so this… this… we’ve worked together before, so I’ll just say, this is J&J.
86 00:12:30.340 ⇒ 00:12:38.520 Kyle Montgomery: So this is a beast. Yeah. Now, what I like about the opportunity, though, and why I was so excited that this guy
87 00:12:38.760 ⇒ 00:12:41.430 Kyle Montgomery: Just talking to us about it.
88 00:12:41.540 ⇒ 00:12:49.400 Kyle Montgomery: is, oh, I’ve been shown by this. It… he’s taking something that,
89 00:12:50.710 ⇒ 00:12:56.260 Kyle Montgomery: could be really overwhelming when you think of it on the global scale of J&J.
90 00:12:56.530 ⇒ 00:12:57.359 Uttam Kumaran: Yes. Like…
91 00:12:57.360 ⇒ 00:13:06.110 Kyle Montgomery: some kind of enterprise AI platform that’s globally synchronized, and then all of the data, all the privacy and compliance.
92 00:13:06.110 ⇒ 00:13:06.540 Uttam Kumaran: Yeah.
93 00:13:06.540 ⇒ 00:13:11.059 Kyle Montgomery: issued, right? And I… He’s kind of, like…
94 00:13:11.450 ⇒ 00:13:17.609 Kyle Montgomery: Saying, okay, all that stuff’s happening, fine, but for my team, and for my purpose of
95 00:13:17.890 ⇒ 00:13:22.909 Kyle Montgomery: digital finance, I just want to attack this one thing.
96 00:13:23.100 ⇒ 00:13:23.690 Uttam Kumaran: Yeah.
97 00:13:23.910 ⇒ 00:13:26.349 Kyle Montgomery: And use the rest of this year
98 00:13:26.770 ⇒ 00:13:33.179 Kyle Montgomery: To essentially build the architecture and the data
99 00:13:34.010 ⇒ 00:13:39.040 Kyle Montgomery: structure, I guess, that, this data characteristics, to then…
100 00:13:39.270 ⇒ 00:13:48.799 Kyle Montgomery: Be able to pilot a couple of things to show worth, show value, to then plan a big project for, like, 2027.
101 00:13:49.030 ⇒ 00:13:55.919 Kyle Montgomery: Yeah. And so, really bright guy, he’s new to the organization, only been there for, I don’t know, 3 months.
102 00:13:56.140 ⇒ 00:14:11.989 Kyle Montgomery: got his feet under him to understand the work that had been done so far, and is now saying, hey, I’ve got a team of folks, but I’m missing these data engineers to really bridge that gap. And the note was, oh, this is… that’s right. So under a predictive
103 00:14:12.360 ⇒ 00:14:16.069 Kyle Montgomery: financial statements, they’re using Alteryx and Apex.
104 00:14:16.320 ⇒ 00:14:17.040 Uttam Kumaran: Okay.
105 00:14:17.040 ⇒ 00:14:19.050 Kyle Montgomery: And,
106 00:14:19.280 ⇒ 00:14:36.490 Kyle Montgomery: they’ve built this platform where they’ve got P&L cash flow, they can look at the top and the bottom lines, and they’ve got their balance sheet, but they’re missing the infrastructure to stitch all that together to give one full view, so to basically spit… so they’ve got pieces of financial statements.
107 00:14:37.050 ⇒ 00:14:43.259 Kyle Montgomery: But they want to be able to stitch it all together, and the… Sure. I don’t know if it’s data source issues, or if it’s…
108 00:14:43.260 ⇒ 00:14:44.469 Uttam Kumaran: Remodeling, you know.
109 00:14:44.470 ⇒ 00:14:52.209 Kyle Montgomery: modeling to get there, but that’s the kind of immediate, specific need. Okay. But then that…
110 00:14:52.460 ⇒ 00:15:00.650 Kyle Montgomery: He wants to parlay into getting rid of some of the more manual and time-consuming practices around,
111 00:15:00.650 ⇒ 00:15:05.479 Uttam Kumaran: Yeah, reconcili… all the month-end reconciliation, finding anomalies, audit, yeah.
112 00:15:05.480 ⇒ 00:15:19.890 Kyle Montgomery: Yep, and so that’s where he’s like, hey, if we can get this first part stabilized and in place to give us a holistic view, then we can graduate to looking at some of those potential opportunities for AI, leverage data science.
113 00:15:19.890 ⇒ 00:15:20.550 Uttam Kumaran: Yeah.
114 00:15:20.710 ⇒ 00:15:25.000 Kyle Montgomery: Start realizing the actual cost savings and time savings.
115 00:15:26.340 ⇒ 00:15:40.280 Kyle Montgomery: And so, like, they have, Power BI is in place, they have some web applications with some chat capabilities, but really they want to replace all these little one-off things.
116 00:15:40.280 ⇒ 00:15:45.079 Uttam Kumaran: They wanted something that they can own, that just does this one thing right first. Yeah.
117 00:15:45.080 ⇒ 00:15:47.320 Kyle Montgomery: Yeah, and so… Wait, is this around the vendors?
118 00:15:47.320 ⇒ 00:16:03.090 Uttam Kumaran: well, the vendors will be like, we have an AI thing, but it’s so broad, and it’s, like, a platform, versus, like, we have, like, a reconciliation task that takes 10 hours a month right now, we want to cut it to one, let’s just attack that, however fast we can get to that.
119 00:16:03.560 ⇒ 00:16:10.160 Kyle Montgomery: Yeah, and I… aside from this opportunity, Otong, I think… that this is…
120 00:16:10.550 ⇒ 00:16:20.870 Kyle Montgomery: a bigger opportunity in the realm of positioning AI services. Yeah. Even midsize, probably even more so, mid-sized companies
121 00:16:21.130 ⇒ 00:16:27.979 Kyle Montgomery: are feeling pressure, we gotta do something with AI, like, that’s being, you know, spewed at them from all directions.
122 00:16:27.980 ⇒ 00:16:28.600 Uttam Kumaran: Yeah.
123 00:16:29.150 ⇒ 00:16:35.160 Kyle Montgomery: And they don’t really know where to begin. Do we build some massive enterprise AI solution?
124 00:16:35.460 ⇒ 00:16:38.610 Kyle Montgomery: Or do we go and, like, tick off some…
125 00:16:38.660 ⇒ 00:16:45.940 Uttam Kumaran: just relatively… Yeah. I’ll say simple, not simple, but relatively straightforward tasks. Yeah. And I think that would be…
126 00:16:47.660 ⇒ 00:16:56.530 Kyle Montgomery: A great, like, way for us, even, or for you and what you’re doing, to package up messaging to companies, just to say, hey.
127 00:16:56.660 ⇒ 00:17:04.250 Kyle Montgomery: is AI overwhelming to you? Well, would you like to tackle just… Finance.
128 00:17:04.730 ⇒ 00:17:14.870 Uttam Kumaran: Yeah, and show the ROI, like, for the guy you’re talking about, right, he’s new to the company, and he’s like, well, we could embark on some two-year thing, I may not even make it.
129 00:17:15.099 ⇒ 00:17:15.569 Kyle Montgomery: That’s like shit.
130 00:17:15.569 ⇒ 00:17:26.029 Uttam Kumaran: You know, if we’re doing two, three years. So for us, we always are like, can we deliver some sort of something that you can demo to executive team in, like, 3, 6 months?
131 00:17:26.029 ⇒ 00:17:39.869 Uttam Kumaran: That’s really meaty, that shows the promise, that builds the hype for you internally. And that’s often how we think about supporting these stakeholders, because part of it is, like, someone in the… finding the person in the firm that has seen that this is the way to go is one thing.
132 00:17:39.929 ⇒ 00:17:45.279 Uttam Kumaran: And then it’s like… but they know that not only are they managing expectations and their team.
133 00:17:45.369 ⇒ 00:17:59.879 Uttam Kumaran: they also may not even have the talent internally to execute, or less about to execute, we’re finding that people just… even just wanted, like, the advisory, like, the middle layer. They’re like, hey, we have, like, 20, 30, 40 people, but, like.
134 00:18:00.299 ⇒ 00:18:18.909 Uttam Kumaran: my mess… I’m not… my message isn’t translating to them, like, we need this, like, translation. And so there’s some folks that we’re talking to, similar size at J&J in that arena, that they’re like, okay, well, we don’t need your team, but what we want is, like, advisory. We want… you guys to know what it’s like to translate
135 00:18:19.119 ⇒ 00:18:21.559 Uttam Kumaran: From, like, there’s this reconciliation problem.
136 00:18:21.859 ⇒ 00:18:32.429 Uttam Kumaran: I can tell you in the old world how we would have solved it. There’s a solution now in the new world, and you actually have… like, you can do this stuff on that exact same system that already exists.
137 00:18:32.489 ⇒ 00:18:41.929 Uttam Kumaran: It’s like, you just have to get… some of their team needs to get trained up to understand, like, how to… what the capabilities are, and there needs to be, like, a tight deliverable that we work backwards from.
138 00:18:42.139 ⇒ 00:18:43.759 Uttam Kumaran: Right? And so you’re not…
139 00:18:44.039 ⇒ 00:18:49.689 Uttam Kumaran: in AI right now, there’s, like, a shiny object syndrome problem, so then it’s like, you kind of remove that.
140 00:18:49.769 ⇒ 00:19:03.749 Uttam Kumaran: the noise is certainly up, and so how can we also give you, like, something that you can go to your management and show, like, we have a plan, we have a 6-month plan at achieving this, and there’s a clear ROI. It’s not like, let’s turn on a chatbot and, like.
141 00:19:03.749 ⇒ 00:19:14.469 Uttam Kumaran: there’s no measurement of how many people are using it. It’s like, in 6 months, we’re planning on delivering this, we expect it to augment this process, and if we do so, we’re expecting this much ROI, and like.
142 00:19:14.529 ⇒ 00:19:20.539 Uttam Kumaran: clean story, right? So part of it is even helping them understand how do you measure AI agents.
143 00:19:20.689 ⇒ 00:19:33.869 Uttam Kumaran: Right? Like, how do you show the ROI? And that’s actually… that’s also, again, because we come from the data world, we’ve always sort of pushed that. Like, you need to measure who’s using it, how much they’re using it, what are they using it for, are the outputs great?
144 00:19:33.869 ⇒ 00:19:44.039 Uttam Kumaran: Right? And that’s the dashboard that you can actually show. Like, there’s one thing about the shiny object of, like, I asked a question and it answered, but showing the adoption in organization.
145 00:19:44.089 ⇒ 00:19:46.889 Uttam Kumaran: That’s the data angle we’re always coming from, because
146 00:19:47.169 ⇒ 00:19:49.389 Uttam Kumaran: That’s just what we do anyways.
147 00:19:49.389 ⇒ 00:19:53.149 Kyle Montgomery: You know, so it was very easy, but then we found that that was actually what a lot of.
148 00:19:53.149 ⇒ 00:19:54.329 Uttam Kumaran: AI…
149 00:19:54.469 ⇒ 00:20:13.649 Uttam Kumaran: you know, AI motions were missing that, like, measurement piece, showing the measurement adoption and, like, really showing that, hey, every time this sort of role in your company is using this tool, they are accomplishing X task 10 times faster. It’s so easy to then drag out what the ROI is and make the pitch, you know?
150 00:20:14.990 ⇒ 00:20:16.580 Kyle Montgomery: Yeah, I think that…
151 00:20:17.270 ⇒ 00:20:30.960 Kyle Montgomery: that’s where this guy is, and he’s trying to think about it very pragmatically, to get… just get a win, get a story that shows that ROI, be able to measure it, and then use that as his platform to go big.
152 00:20:31.310 ⇒ 00:20:32.150 Kyle Montgomery: Yeah.
153 00:20:32.810 ⇒ 00:20:33.920 Kyle Montgomery: Right, so… Right.
154 00:20:34.510 ⇒ 00:20:36.160 Kyle Montgomery: So,
155 00:20:36.810 ⇒ 00:20:48.150 Kyle Montgomery: At the heart of it, he’s just looking for a couple of people, data engineers, a data engineer, and more of a infrastructure type.
156 00:20:48.410 ⇒ 00:20:50.599 Kyle Montgomery: Around, just around data infrastructure.
157 00:20:50.840 ⇒ 00:20:58.190 Kyle Montgomery: And… I’ve been reluctant to just spin up an open market search on that.
158 00:20:58.420 ⇒ 00:21:04.420 Kyle Montgomery: Among data engineers we’ve worked with, or that we’ve recruited, or maybe didn’t place.
159 00:21:04.870 ⇒ 00:21:12.169 Kyle Montgomery: Because… Well, I… I want… I want to come…
160 00:21:12.680 ⇒ 00:21:15.840 Kyle Montgomery: out swinging. I want to put quality in front of this guy.
161 00:21:15.950 ⇒ 00:21:24.829 Kyle Montgomery: And again, I either misunderstood what he was asking for, or when he saw what I put in front of him, he realized, no, no, no, that’s not what I need.
162 00:21:25.070 ⇒ 00:21:28.050 Kyle Montgomery: as more data engineer.
163 00:21:28.810 ⇒ 00:21:35.580 Kyle Montgomery: And so, that’s why I’m calling you. It’s like, well, you’re… you and the work that you’ve done, and what I’m watching you
164 00:21:35.800 ⇒ 00:21:37.779 Kyle Montgomery: on LinkedIn talk about.
165 00:21:38.430 ⇒ 00:21:47.910 Kyle Montgomery: Seems like the right blend, to be able to come at it and tell the story around data engineering and your roots in that, but then in the context of…
166 00:21:48.500 ⇒ 00:21:56.779 Kyle Montgomery: of AI. It could be the kind of thing where it’d be as simple as,
167 00:21:58.080 ⇒ 00:22:06.779 Kyle Montgomery: you and Brainforge just doing some… some really cool projects with them. The reality is…
168 00:22:07.340 ⇒ 00:22:11.709 Kyle Montgomery: They’re pretty hamstrung by who they can work with.
169 00:22:11.710 ⇒ 00:22:17.709 Uttam Kumaran: Yeah. Which is just then where I come in, is that we have the MSA, we’re in the system, we can make it happen, and so… Cool.
170 00:22:17.710 ⇒ 00:22:21.620 Kyle Montgomery: I’m not so proud to think that it has to be even…
171 00:22:21.930 ⇒ 00:22:24.579 Kyle Montgomery: I just want to put some good people in front of them.
172 00:22:24.580 ⇒ 00:22:29.349 Uttam Kumaran: No, no, no, totally, I’m with you, and I also, like, I think not only for this opportunity, I think…
173 00:22:29.930 ⇒ 00:22:43.799 Uttam Kumaran: given… I know your reputation in the market and who you guys work with, even to add the capabilities that you’ll see us hopefully do for J&J and otherwise to, like, some of your Rolodex that you’re able to market.
174 00:22:43.860 ⇒ 00:22:59.689 Uttam Kumaran: it’s a great way. I mean, again, we’re… we’re nobody. Like, we’re completely bootstrapping everything we have, but we… we rely on… partners is, like, the real… one of the main reasons we… we even made it to how far we are. So we do as much with partners
175 00:22:59.690 ⇒ 00:23:09.230 Uttam Kumaran: Whether it’s from the staffing side, on the vendor side, like software vendors, as well as, like, just to, you know, work on things where people own the relationship.
176 00:23:09.290 ⇒ 00:23:15.580 Uttam Kumaran: So, more than happy to work under those, you know, constraints, and, like, happy to see how we can
177 00:23:15.670 ⇒ 00:23:30.269 Uttam Kumaran: we can deliver this, and on our team, we have… again, a lot of folks have come from my background on my team, where we… we all learned… as data people, we learned all the AI stuff for the last two years, and we’re actively deploying, like, agentic data analyst-style
178 00:23:30.270 ⇒ 00:23:41.709 Uttam Kumaran: outcomes, right? So, we work with a lot of BI tools, but now we’re often recommending the tools that work best where there’s an AI component in it. Because a lot of our more progressive customers are, like.
179 00:23:41.940 ⇒ 00:23:49.510 Uttam Kumaran: I don’t want just Power BI again, like, what are the tools in the market that come with an AI component that can natural language query my data?
180 00:23:49.570 ⇒ 00:24:09.269 Uttam Kumaran: Snowflake, we’ve been doing a lot of work with Snowflake because of their new Cortex AI and the outcomes that are possible from that, which has allowed them to… allowed us to sell them better than Redshift or some of the older data warehouses. And so, across all our data stack, we are considering, like, where does AI fit in? And so.
181 00:24:09.330 ⇒ 00:24:27.500 Uttam Kumaran: it’s all merging into one story, which is great. I feel like it’s great, because previously it was like, there’s AI stuff, and there’s automation, and there’s this data stuff, and there wasn’t a concise story. Now it’s clear that the AI stuff has actually just pulled the demand for hardcore data plumbing work forward.
182 00:24:27.690 ⇒ 00:24:36.450 Uttam Kumaran: Which is great, because that’s all we do, but our… actually, I’m happy that the outcome is not just the nth dashboard, it’s actually able to power, like.
183 00:24:36.770 ⇒ 00:24:49.450 Uttam Kumaran: these larger systems, you know, and it’s like two birds, one stone. We still develop dashboards, still do analytics, but I’m hopeful that that work actually goes to power AI agents that could do more specific tasks.
184 00:24:49.590 ⇒ 00:24:52.750 Uttam Kumaran: Beyond just analytics, but it all requires the data work.
185 00:24:52.910 ⇒ 00:25:07.300 Uttam Kumaran: And then the data work also expands, you know? You need to ingest transcript data, you need to ingest logs, it’s not just structured queries, right? So that’s also a lot of things that we found interesting, is that you have to query data in different systems depending on
186 00:25:07.300 ⇒ 00:25:18.489 Uttam Kumaran: what type it is. Like, internally, we have our, like, meeting transcripts. We also have, Slack messages with clients, right? And we also have, like, SOPs and policies.
187 00:25:19.170 ⇒ 00:25:26.560 Uttam Kumaran: It’s a different flavor of data access for different types of data, and that’s the system… that’s, like, that is the engineering, like, that’s the architecture.
188 00:25:26.880 ⇒ 00:25:29.789 Kyle Montgomery: Yeah, yeah. Yeah. Well, let’s,
189 00:25:31.730 ⇒ 00:25:33.879 Kyle Montgomery: I think we’re kind of at this…
190 00:25:34.350 ⇒ 00:25:38.169 Kyle Montgomery: Bold word to say inflection point, because
191 00:25:38.340 ⇒ 00:25:49.609 Kyle Montgomery: whatever, it’s… for the last couple of years, the only thing that’s ever come across our desk that had anything to do with AI was really just, sort of, the preparatory work
192 00:25:49.920 ⇒ 00:25:51.060 Kyle Montgomery: and data.
193 00:25:51.660 ⇒ 00:25:52.530 Uttam Kumaran: Hmm.
194 00:25:52.530 ⇒ 00:25:55.080 Kyle Montgomery: A lot of companies are like, well, we gotta get this…
195 00:25:55.280 ⇒ 00:25:57.590 Kyle Montgomery: data governance in place, we’ve got to get our.
196 00:25:57.590 ⇒ 00:25:57.980 Uttam Kumaran: this state.
197 00:25:57.980 ⇒ 00:26:03.839 Kyle Montgomery: in place, so that we could eventually, maybe, someday, benefit from AI.
198 00:26:03.990 ⇒ 00:26:11.680 Kyle Montgomery: And it’s not really the sexy work that, when you’re watching the Super Bowl and they’re showing you all this crazy AI stuff.
199 00:26:12.060 ⇒ 00:26:12.430 Uttam Kumaran: Yeah.
200 00:26:12.430 ⇒ 00:26:17.459 Kyle Montgomery: So, to realize that at the enterprise level, you have to have all the plumbing done.
201 00:26:17.870 ⇒ 00:26:18.200 Uttam Kumaran: Yeah.
202 00:26:18.200 ⇒ 00:26:27.550 Kyle Montgomery: And now we’re… like, this example, this is a very tangible, like, okay, yes, we… yes, J&J still has some hurdles to overcome with data.
203 00:26:27.780 ⇒ 00:26:30.040 Kyle Montgomery: No doubt. That’s why they’re asking for help.
204 00:26:30.150 ⇒ 00:26:32.930 Kyle Montgomery: But, they’ve pinpointed one thing.
205 00:26:33.520 ⇒ 00:26:39.839 Kyle Montgomery: That they can actually deploy. So, that, to me, is like, okay, here it comes. Like, open the floodgates.
206 00:26:39.840 ⇒ 00:26:43.150 Uttam Kumaran: I think it’s gonna increase. I’m saying, like, the last two years.
207 00:26:43.320 ⇒ 00:26:54.609 Uttam Kumaran: I think in the last 6 months or 8 months, it’s definitely changed. And 2 years ago, we were pitching a lot of the same stuff, and it just wasn’t resounding. So I hope you’re right, like, I hope it’s, like.
208 00:26:54.840 ⇒ 00:26:59.669 Uttam Kumaran: These people who are being hired at these large firms to come in and do the transformation.
209 00:26:59.860 ⇒ 00:27:03.610 Uttam Kumaran: Are now bringing that, like, that understanding of, like.
210 00:27:03.840 ⇒ 00:27:07.739 Uttam Kumaran: yes, we need the data, but we need it for the AI, and I have a vision on both.
211 00:27:07.880 ⇒ 00:27:12.279 Uttam Kumaran: Versus just, like, I’m not exactly sure how the AI piece can affect us.
212 00:27:12.640 ⇒ 00:27:16.599 Uttam Kumaran: It’s always important to have, like, your data in the right place, but
213 00:27:16.970 ⇒ 00:27:25.090 Uttam Kumaran: Yeah, I feel the same way. I think it’s getting louder and louder. I think the consumer side is pushing that, though. I think consumer always drives…
214 00:27:25.370 ⇒ 00:27:41.230 Uttam Kumaran: like, you know, the business side, you know, first. I think this is the time where we’re seeing it really dramatically, that everybody who’s in business is using ChatGBT, and they’re like, oh, I wish it had my email, or I wish it had, like… right? And so, I’m hopeful that that’s actually what’s, like, dragging
215 00:27:41.360 ⇒ 00:27:46.859 Uttam Kumaran: this long, and so then, you’re right, and I describe it, like, the noise is up, the noise is really, really high. Yeah.
216 00:27:46.860 ⇒ 00:27:50.320 Kyle Montgomery: Yeah, it’s time to… great time to try to capitalize on it.
217 00:27:50.710 ⇒ 00:27:51.300 Uttam Kumaran: Yeah.
218 00:27:51.570 ⇒ 00:27:56.159 Kyle Montgomery: So, another piece just to mention, too, when we think about…
219 00:27:56.380 ⇒ 00:27:58.929 Kyle Montgomery: Trying to put something together is…
220 00:27:59.410 ⇒ 00:28:05.069 Kyle Montgomery: One of the pieces he came out swinging with was… And he was…
221 00:28:05.560 ⇒ 00:28:12.249 Kyle Montgomery: Trying not to throw his own team under the bus, but he was kind of throwing his own team under the bus, saying, like, hey, they…
222 00:28:12.430 ⇒ 00:28:22.059 Kyle Montgomery: they need direction. They… they’ve got so many things they could be doing that it’s hard to get them heads down doing the thing.
223 00:28:22.740 ⇒ 00:28:26.970 Kyle Montgomery: is, like, what I look for in a resource
224 00:28:27.130 ⇒ 00:28:35.240 Kyle Montgomery: Is that beyond the brilliance of data engineering, can rally his resources around
225 00:28:35.560 ⇒ 00:28:53.079 Kyle Montgomery: the steps they gotta take, right? And that’s more of a personality and leadership component, but as you and I are thinking about how to attack it, it’d be something to consider among your team, or however we want to put somebody forward.
226 00:28:53.390 ⇒ 00:29:00.949 Uttam Kumaran: Cool. Yeah, I hope that aligns a little bit, and again, it’s nice to hear that it’s what we’re seeing, is that a lot of the bigger firms, they have the staff.
227 00:29:01.130 ⇒ 00:29:08.770 Uttam Kumaran: it’s just that, yeah, they’re not, like, there’s a gap between the executive coming in and being like, we need AI, and then the person, like, well, I’m still…
228 00:29:08.940 ⇒ 00:29:11.829 Uttam Kumaran: building Python ETL, like, I don’t know.
229 00:29:12.250 ⇒ 00:29:23.790 Uttam Kumaran: You know, how are we speaking different languages? And they also still have, like, there’s still stuff to maintain, there’s still a job to do, but if the executives coming in and really, like, raising the noise on that, and there’s a gap.
230 00:29:23.860 ⇒ 00:29:26.380 Uttam Kumaran: And totally, I think, like.
231 00:29:26.410 ⇒ 00:29:39.379 Uttam Kumaran: for a lot of our clients, that’s how we’re fitting. Like, for a lot of our active clients, we’re coming in at the C-suite level, or directly under, and as, like, a partner, being like, who do you have? And we absorb some of their team members.
232 00:29:39.380 ⇒ 00:29:48.639 Uttam Kumaran: Or we’re… we’re bringing our own team, but it’s always, like, a partnership mode, where we’re not just throwing people at it and say, you guys tell us what to do. It’s like a work…
233 00:29:48.870 ⇒ 00:29:51.849 Uttam Kumaran: both joint gonna come up with a roadmap.
234 00:29:51.960 ⇒ 00:30:09.269 Uttam Kumaran: that we can achieve, that we think is the right thing, based on our experience, and that they want to fund, and that they know that internally they can drive the politics or whatever around, right? That’s their job to do, and so I think we’ve… I think we can hit that, and that’s actually good to know, sort of, like, in terms of who from our team could be
235 00:30:09.630 ⇒ 00:30:11.260 Uttam Kumaran: Could… could work here, so…
236 00:30:11.490 ⇒ 00:30:12.110 Kyle Montgomery: Yeah.
237 00:30:12.280 ⇒ 00:30:14.960 Kyle Montgomery: So, would it be helpful,
238 00:30:16.400 ⇒ 00:30:27.129 Kyle Montgomery: when he first came to us, we put together, based on what we thought we understood from him, a couple of job descriptions. One for more of the data scientist engineer role, and one for the
239 00:30:27.550 ⇒ 00:30:31.409 Kyle Montgomery: Or… Infrastructure role.
240 00:30:31.710 ⇒ 00:30:39.339 Kyle Montgomery: I could share those with you. I would only hold, kind of, with a grain of salt, because,
241 00:30:39.420 ⇒ 00:30:52.720 Kyle Montgomery: he… even though he blessed them, I don’t know that he really, like, studied them. Sure. But it at least gives you something to aim at, to then be able to think who on your team
242 00:30:52.960 ⇒ 00:31:08.540 Kyle Montgomery: would make sense. And if you don’t have anybody, that’s fine too. But I wanted to start with you, just knowing the caliber of the work you’ve been doing, to see if there may be somebody amongst your group that would do it. And this would be a pretty long…
243 00:31:08.680 ⇒ 00:31:12.559 Kyle Montgomery: So, the first contract would likely be for 9 months.
244 00:31:12.810 ⇒ 00:31:15.380 Uttam Kumaran: Okay. Basically, to take them through the end of the year.
245 00:31:15.640 ⇒ 00:31:20.560 Kyle Montgomery: Okay. And then… Presuming all goes well, and…
246 00:31:20.740 ⇒ 00:31:29.179 Kyle Montgomery: He does his magic with the internal scenario to get approval, then likely just grows into next year.
247 00:31:29.810 ⇒ 00:31:41.929 Uttam Kumaran: Yeah, so that’s why I’m almost thinking, like, maybe it’s helpful for me and somebody to come in, because I think he’s gonna find it tough to find a data engineer, an infrastructure engineer that can also lead
248 00:31:42.190 ⇒ 00:31:44.399 Uttam Kumaran: like, an engagement like this, because it’s…
249 00:31:45.020 ⇒ 00:31:50.380 Uttam Kumaran: It’s not only a new technolo… like, those two… doing just those two things are already, like, hard.
250 00:31:50.590 ⇒ 00:31:58.419 Uttam Kumaran: And then getting somebody who not only can lead the team and do the AI piece. So I’m wondering if he’s, like, if he maybe is interested in, like.
251 00:31:58.780 ⇒ 00:32:17.669 Uttam Kumaran: me for some time, or someone on my team that’s equivalent, and then also, like, having the engineers that on our team, again, like, it’s not that the folks aren’t, like, at my level of posture, or, like, the, the, you know, whatever it is, but it’s a different thing, like.
252 00:32:17.670 ⇒ 00:32:21.910 Kyle Montgomery: Working with someone like him and making sure he has the talking points, he has a roadmap.
253 00:32:21.990 ⇒ 00:32:32.849 Uttam Kumaran: it’s very different than, like, just making sure the day-to-day engineers are moving in the right direction. So, maybe it’s a little bit of a mix, and maybe he’d be open to something like that, because I think…
254 00:32:33.010 ⇒ 00:32:38.030 Uttam Kumaran: and we’re actively hiring, it’s gonna be hard. Like, it’s just hard to find that, right?
255 00:32:38.270 ⇒ 00:32:42.670 Kyle Montgomery: Well, I think an… I think an interesting play would be…
256 00:32:44.020 ⇒ 00:32:47.230 Kyle Montgomery: Yeah, if I could… if I put your profile in front of him.
257 00:32:47.380 ⇒ 00:33:00.589 Kyle Montgomery: And it’s more than just popping him a resume. Like, I want to sit and chat with him to say, okay, here’s why I’m bringing Utam and his organization to bear. My first-hand experience with him at AG1 and elsewhere.
258 00:33:00.740 ⇒ 00:33:15.240 Kyle Montgomery: first rate, highly reviewed from my partners at Clarkston as well, and just through his vision between the data plumbing, engineering, scientist side, and AI, can help you, Moran.
259 00:33:15.710 ⇒ 00:33:16.930 Kyle Montgomery: Bridge that gap.
260 00:33:17.330 ⇒ 00:33:17.700 Uttam Kumaran: Yeah.
261 00:33:17.700 ⇒ 00:33:21.210 Kyle Montgomery: And just get him to talk. And then, the two of you…
262 00:33:21.760 ⇒ 00:33:27.409 Kyle Montgomery: It could be that you walk out of that conversation and you yourself never bill him a dollar.
263 00:33:27.710 ⇒ 00:33:29.480 Kyle Montgomery: But you have the guy.
264 00:33:29.480 ⇒ 00:33:30.290 Uttam Kumaran: Yeah, yeah, yeah.
265 00:33:30.290 ⇒ 00:33:40.559 Kyle Montgomery: this billing. Or, it could be you’re in there in some oversight capacity, and helping set people in motion, and keep them on task, and then
266 00:33:40.940 ⇒ 00:33:55.150 Kyle Montgomery: conveying to Moran, like, vision, what’s next? How do we take what we’re doing here to the next? I don’t know. I’m not sure, based on how badly I whiffed the first time, I’m not sure that, what that conversation will go like.
267 00:33:55.150 ⇒ 00:33:55.820 Uttam Kumaran: Okay.
268 00:33:55.960 ⇒ 00:33:59.309 Kyle Montgomery: But my main thing is, I want him to talk to smart people.
269 00:33:59.500 ⇒ 00:34:03.259 Kyle Montgomery: Right? And not treat this as, just send me some resumes.
270 00:34:03.260 ⇒ 00:34:04.329 Uttam Kumaran: Send me whoever, sure.
271 00:34:04.330 ⇒ 00:34:10.550 Kyle Montgomery: I’m like, I keep trying to elevate above that to say, like, no, no, no, like, let’s… let’s have some conversations to help
272 00:34:10.670 ⇒ 00:34:13.449 Kyle Montgomery: Together, shape exactly what you need.
273 00:34:13.810 ⇒ 00:34:16.269 Uttam Kumaran: It’s also, he has a big opportunity, so…
274 00:34:16.620 ⇒ 00:34:23.349 Uttam Kumaran: is a big opportunity, and it sounds like tight timelines in an industry, in a landscape that’s shifting.
275 00:34:24.179 ⇒ 00:34:30.009 Uttam Kumaran: You can’t just… it’s… like, a lot of the data people you’re gonna find, and as we’re hiring a lot.
276 00:34:30.179 ⇒ 00:34:43.850 Uttam Kumaran: people, and they’re… they’re just using the… doing the normal data stuff, so we’re actually training people up. But I’m finding people, like, who I was in the past, just normal data people, teaching them the AI side, because nobody’s learned… you don’t, like… people are doing their normal job.
277 00:34:44.080 ⇒ 00:34:58.680 Uttam Kumaran: if they’re… unless they’re learning it on the side, they’re not learning at work. They’re… it’s just the same data stuff, right? It’s similar to all the people at J&J. It’s like, you can’t fault them for not, like, picking up the… it’s, like, not like that. It’s not, like, an extra… it’s not, like, an extra thing, and so…
278 00:34:58.890 ⇒ 00:35:15.730 Uttam Kumaran: unless we’re finding that the best people are people who are, like, on the side, they’re trying to do it, and their company is not letting them, I’m like, perfect. At my company, that side thing becomes the main thing. The main thing? Or I’m finding people with the aptitude that they can learn fast, and I’m like, just wait until you see this, you’ll grab it.
279 00:35:15.830 ⇒ 00:35:19.039 Kyle Montgomery: But we’re nurturing that, like, I am not finding…
280 00:35:19.040 ⇒ 00:35:32.289 Uttam Kumaran: data talent that has that. And so that also may be interesting to him in that, like, he may… he may be finding that same thing, and that, like, working with an organization that ours that is… that is actively promoting
281 00:35:32.610 ⇒ 00:35:35.379 Uttam Kumaran: Data people with, like, kind of that dual mindset.
282 00:35:35.730 ⇒ 00:35:36.440 Uttam Kumaran: like…
283 00:35:36.800 ⇒ 00:35:52.509 Uttam Kumaran: for a long-term partnership could be really helpful, you know, because that’s everybody on our team, from the analysts to the data modelers to data engineers, not only learning how to use Cursor and all the AI tools to do their work, but we’re actively, like, developing AI systems as well.
284 00:35:52.630 ⇒ 00:35:53.609 Kyle Montgomery: Yeah. So…
285 00:35:53.780 ⇒ 00:36:00.020 Uttam Kumaran: Yeah, I feel like, I would love to have a conversation with them and come out and tell you, sort of, like, maybe what the direction…
286 00:36:00.270 ⇒ 00:36:06.649 Uttam Kumaran: based on what I talked to him about, like, what direction he wants to head, and how he feels, you know? Okay. So happy to do that.
287 00:36:07.090 ⇒ 00:36:24.590 Kyle Montgomery: All right, what can I put in front of him? Do you keep an updated CV on yourself, or some profile? Doesn’t even have to be traditional resumes, but something I’d say, and here’s how… here’s how this guy presents himself, and use that to get him to accept the meeting?
288 00:36:24.750 ⇒ 00:36:36.729 Uttam Kumaran: Yeah, I can get you, you know, we can kind of do any or all of the above, like, I mean, one, you can send our website, we have some decks and materials. It’s really heavily branded Brainforge, so…
289 00:36:36.730 ⇒ 00:36:46.879 Uttam Kumaran: like, I think that’s… that’s something you may have to explain. Or I’m happy to send my resume and alongside a little blurb about, like, the company, the companies I’ve worked for.
290 00:36:47.020 ⇒ 00:36:58.569 Uttam Kumaran: you tell me, like, what’s… if you don’t want to overwhelm him with the Brain Forge stuff, then, like, let’s not do that, and just say, like, this is someone. We can say clearly, like, you know, we’re running… this firm exists, and we’re running it.
291 00:36:58.690 ⇒ 00:37:01.159 Uttam Kumaran: but I think it’s more emphasis on my background.
292 00:37:01.160 ⇒ 00:37:01.520 Kyle Montgomery: Okay.
293 00:37:01.520 ⇒ 00:37:05.830 Uttam Kumaran: and, like, what we learned. So I can put together, like, A little blurb on that.
294 00:37:05.990 ⇒ 00:37:08.270 Kyle Montgomery: Yeah, let’s do that, and .
295 00:37:10.280 ⇒ 00:37:26.639 Kyle Montgomery: and then use the platform. Part of why I’m eager to tell him about Brainforge is he realizes that there’s not a ton of these people out there. And so, in our first conversation, he said, hey, I’d rather, than just some
296 00:37:26.960 ⇒ 00:37:34.539 Kyle Montgomery: tried-and-true, age-old data engineer. I’d rather you pull somebody who is, like, Done a startup in this.
297 00:37:34.930 ⇒ 00:37:35.350 Uttam Kumaran: Yeah.
298 00:37:35.350 ⇒ 00:37:38.679 Kyle Montgomery: Oh, well, here’s a guy who started a business.
299 00:37:38.980 ⇒ 00:37:43.709 Kyle Montgomery: around data engineering and its parlay into AI.
300 00:37:43.910 ⇒ 00:37:45.969 Uttam Kumaran: Yeah. What better fit, so…
301 00:37:45.970 ⇒ 00:37:50.590 Kyle Montgomery: So I’d like that story to come out, but let’s start with you.
302 00:37:50.820 ⇒ 00:37:51.150 Uttam Kumaran: Sure.
303 00:37:51.150 ⇒ 00:37:58.900 Kyle Montgomery: And then… and then when you meet him, and in my conversation, I can say, well, here’s what he’s got going, and see if that turns into something.
304 00:37:59.340 ⇒ 00:38:01.759 Uttam Kumaran: Okay, so let me, like, what’s…
305 00:38:01.870 ⇒ 00:38:05.779 Uttam Kumaran: I can try to get you something this week. If you think it’s tighter, then I can try to work on it.
306 00:38:06.580 ⇒ 00:38:07.660 Uttam Kumaran: Today, tomorrow?
307 00:38:07.660 ⇒ 00:38:17.099 Kyle Montgomery: What is today? Wednesday. Wednesday? Yeah, if by Friday would be great. Cool. I could get something to him before the weekend, just to give him something to chew on.
308 00:38:17.440 ⇒ 00:38:26.829 Uttam Kumaran: Okay, okay, perfect. So I’ll just draft something up, with my team, and then email it over to you, and then we can edit it or whatever, and then go from there.
309 00:38:27.100 ⇒ 00:38:27.790 Kyle Montgomery: Alright.
310 00:38:28.140 ⇒ 00:38:29.320 Kyle Montgomery: That works.
311 00:38:29.460 ⇒ 00:38:30.579 Uttam Kumaran: Okay, and then let me know.
312 00:38:30.580 ⇒ 00:38:31.660 Kyle Montgomery: should think, like…
313 00:38:31.660 ⇒ 00:38:35.260 Uttam Kumaran: You should do other stuff, yeah, however I can be helpful, or… Absolutely.
314 00:38:35.260 ⇒ 00:38:39.190 Kyle Montgomery: really, just having the couple conversations I’ve had with him.
315 00:38:40.100 ⇒ 00:38:44.270 Kyle Montgomery: Just really inspired me to think, like, Okay,
316 00:38:44.670 ⇒ 00:38:49.470 Kyle Montgomery: it’s coming… what is our story? Like, how do we best pitch
317 00:38:49.720 ⇒ 00:38:53.609 Kyle Montgomery: people and solutions. And if… if my…
318 00:38:54.240 ⇒ 00:39:02.950 Kyle Montgomery: Prediction is right, there will be more like him, who have, like, an idea and a vision, or somebody told them, even, hey, you need to deploy…
319 00:39:03.150 ⇒ 00:39:05.169 Kyle Montgomery: some AI capabilities.
320 00:39:05.370 ⇒ 00:39:06.890 Kyle Montgomery: And they’re just left going…
321 00:39:06.890 ⇒ 00:39:08.209 Uttam Kumaran: They look out in the market, yeah.
322 00:39:08.210 ⇒ 00:39:11.879 Kyle Montgomery: Yeah, where do I start? That we somehow could package up.
323 00:39:12.340 ⇒ 00:39:14.670 Uttam Kumaran: I feel like you’re right in terms of there’s, like, a moment.
324 00:39:14.830 ⇒ 00:39:16.779 Uttam Kumaran: And so, even if it’s, like.
325 00:39:16.900 ⇒ 00:39:26.489 Uttam Kumaran: okay, we were right that it’s true, but maybe we were 6 months off. It’s still worth getting it all together, because this has not changed. Like, this has been the same problem.
326 00:39:26.670 ⇒ 00:39:44.359 Uttam Kumaran: I… I think, like, I’ve done… I think in the past 3-4 months, I’ve explained this more and more with this, like, concise story, so it’s worth getting that. We already are starting to draft a lot of materials that we can co-brand, or brand whatever, that, like, you guys can have access to, and, like, I think we should totally put a story together.
327 00:39:44.560 ⇒ 00:39:48.059 Uttam Kumaran: you know, would love to do that. In particular, I think.
328 00:39:48.390 ⇒ 00:40:00.650 Uttam Kumaran: for us, since we’re using it a lot as a professional service company, like, there’s also some ways for us to parlay that. Like, I don’t know how Clarkson is thinking about this even internally within their team.
329 00:40:00.650 ⇒ 00:40:04.339 Kyle Montgomery: I would like to sign up for your workshop on using whatever.
330 00:40:04.340 ⇒ 00:40:04.730 Uttam Kumaran: Okay.
331 00:40:04.730 ⇒ 00:40:07.590 Kyle Montgomery: Because you, as a professional service company.
332 00:40:07.850 ⇒ 00:40:13.920 Kyle Montgomery: I’m a professional services company. We do nothing but dabble in AI, but we have…
333 00:40:13.940 ⇒ 00:40:29.580 Kyle Montgomery: use cases for where it absolutely helps us, and then we have a bunch of, like, one-off, like, oh, I… I… ChatGPT helped me build a, what one of our team members was talking to a client about,
334 00:40:30.100 ⇒ 00:40:38.409 Kyle Montgomery: They’re going from… their first pilot of deploying SAP into their second pilot.
335 00:40:38.540 ⇒ 00:40:39.580 Kyle Montgomery: And…
336 00:40:39.710 ⇒ 00:40:54.469 Kyle Montgomery: she went to ChatGPT and pulled together, like, a white paper on the challenges you face when you’re transitioning from the first pilot to the second. And it gave her some really smart content that then I…
337 00:40:55.320 ⇒ 00:40:57.699 Kyle Montgomery: With some other help, human help.
338 00:40:57.840 ⇒ 00:41:08.700 Kyle Montgomery: Yeah. Tried to round it out a little bit. I was like, that’s great! Like, you’re able to then help the client something they don’t either have time to go look for, or wouldn’t even know what to ask.
339 00:41:08.700 ⇒ 00:41:18.000 Uttam Kumaran: So that’s exactly how we recommend, though. It’s all… it’s never, like, an end-to-end. It’s always, like, this rounding step happens, and you’ll see, like, in the webinar.
340 00:41:18.000 ⇒ 00:41:29.619 Uttam Kumaran: we talk a little bit about how we’re using it, and Luke, who will be presenting, is new on our, sort of, sales go-to-market team. He’s completely new to, like, data. It comes from, like, a brand world.
341 00:41:29.710 ⇒ 00:41:48.909 Uttam Kumaran: And he’s using it really heavily, using a lot of our internal systems, and has gone through their journey in the last, you know, two months on, like, wow, actually, this has augmented my workflow in selling, like, putting together quick demos, putting together talking points, preparing. But of course, this… we’re a people business, so there’s no replacing us.
342 00:41:48.910 ⇒ 00:42:02.439 Uttam Kumaran: It’s actually, like, we need to come to meetings more prepared, more excited, with more things to say, and that’s… that’s what it is. It’s, so some people are missing the boat completely when they’re like, oh, this is, like, replacing
343 00:42:02.450 ⇒ 00:42:06.170 Uttam Kumaran: In our world, it’s actually, like, we want to spend more time talking.
344 00:42:06.210 ⇒ 00:42:09.150 Uttam Kumaran: Less time in the Google Doc, or less time in email.
345 00:42:09.150 ⇒ 00:42:10.230 Kyle Montgomery: Yeah, yeah.
346 00:42:10.230 ⇒ 00:42:20.029 Uttam Kumaran: Like, that’s all it is, and so I think he’s gonna have some interesting things to share, and we’re planning a little bit of a series, so, like, I think it’ll be great. I’d love to have you sit in there.
347 00:42:20.030 ⇒ 00:42:24.200 Kyle Montgomery: I, I, I was saying, kind of, like,
348 00:42:24.430 ⇒ 00:42:27.760 Kyle Montgomery: metaphorically, your workshop. You actually have a workshop?
349 00:42:27.760 ⇒ 00:42:43.730 Uttam Kumaran: Actually, we are doing a webinar tomorrow, I think. I’m getting a dry run of it. I thought that’s what you were referencing, but we’re more than happy to share… I mean, you guys are friends, like, I’m more than happy to share how it’s working, but I think that’s gonna be a little bit more polished, and maybe you can just see that in…
350 00:42:43.730 ⇒ 00:42:44.460 Kyle Montgomery: Amazing.
351 00:42:44.460 ⇒ 00:42:56.960 Uttam Kumaran: And Luke would love to say hi, he’s great. And so… yeah, sorry, that’s… we actually have one tomorrow. It’s our first… he was like, maybe we should do… because we’re finding that showing is much better. Some of these things are really…
352 00:42:57.020 ⇒ 00:43:06.230 Uttam Kumaran: complicated, there’s a lot of jargon, and so he’s like, I’m just gonna start doing these webinars once a week, see who comes. So I can… I can send that over to you in an email, and…
353 00:43:06.230 ⇒ 00:43:06.879 Kyle Montgomery: If you would, I’ll…
354 00:43:06.880 ⇒ 00:43:08.260 Uttam Kumaran: We’d love your feedback, it’s our.
355 00:43:08.260 ⇒ 00:43:10.219 Kyle Montgomery: I’ll be one of your first attendees.
356 00:43:10.220 ⇒ 00:43:11.820 Uttam Kumaran: Yeah, yeah.
357 00:43:11.820 ⇒ 00:43:23.980 Kyle Montgomery: Okay, cool, well, yeah, if you wouldn’t mind, shoot that to me, and then, pull together whatever you have in the way of resume. I’ll send you the couple of job descriptions that.
358 00:43:23.980 ⇒ 00:43:24.500 Uttam Kumaran: Okay.
359 00:43:24.710 ⇒ 00:43:36.540 Kyle Montgomery: Just to… so you could see, kind of, what… what he was asking about. I’ll at least make sure, also, if you guys have put technologies and things there, we’ve checked some of those boxes. Okay. You know, so that way, it’s like, we’re not… we’re debating more of this.
360 00:43:36.640 ⇒ 00:43:40.500 Uttam Kumaran: The more complex pieces, you know, so it gives them some assurance.
361 00:43:40.500 ⇒ 00:43:42.479 Kyle Montgomery: Okay. Alright.
362 00:43:43.030 ⇒ 00:43:48.409 Uttam Kumaran: Okay. Thank you. Yeah, let’s not… let’s make sure not as much time goes by next time, so I will…
363 00:43:48.410 ⇒ 00:43:55.430 Kyle Montgomery: Absolutely. Well, hopefully this opportunity itself will keep us in close contact in the next, few to several days.
364 00:43:55.650 ⇒ 00:43:57.239 Uttam Kumaran: Definitely. It’s okay. Thank you, Kyle.
365 00:43:57.480 ⇒ 00:43:58.280 Kyle Montgomery: Thanks, Adam.
366 00:43:58.280 ⇒ 00:43:59.480 Uttam Kumaran: Okay, talk soon. Bye.
367 00:43:59.480 ⇒ 00:44:00.040 Kyle Montgomery: my…