Meeting Title: Uttam_Steven Date: 2025-03-18 Meeting participants: Uttam Kumaran, Steve
WEBVTT
1 00:00:42.440 ⇒ 00:00:43.379 steve: Hey udam!
2 00:00:43.550 ⇒ 00:00:44.830 Uttam Kumaran: Hey! How are you?
3 00:00:44.830 ⇒ 00:00:45.530 steve: That’s it.
4 00:00:46.650 ⇒ 00:00:48.040 Uttam Kumaran: Sorry the delay
5 00:00:49.580 ⇒ 00:00:50.739 steve: What can you hear me?
6 00:00:50.970 ⇒ 00:00:52.170 Uttam Kumaran: Yes, I can hear you
7 00:00:52.170 ⇒ 00:00:52.980 steve: Okay, awesome.
8 00:00:53.400 ⇒ 00:00:58.490 steve: I love your I love your setup. Is that a is that a real? You get wood planks on the wall
9 00:00:58.837 ⇒ 00:01:03.282 Uttam Kumaran: This is. Well, they’re not wood planks, they’re like, you know, the fake like
10 00:01:03.770 ⇒ 00:01:05.679 steve: Oh, yeah, yeah, just like the
11 00:01:05.680 ⇒ 00:01:08.462 Uttam Kumaran: But but it’s nice having an office. I
12 00:01:09.270 ⇒ 00:01:12.909 Uttam Kumaran: I never had the luxury of an office before I moved into this house, and
13 00:01:13.170 ⇒ 00:01:17.060 Uttam Kumaran: it’s like good, for, you know, like separation. It’s like good for that
14 00:01:17.360 ⇒ 00:01:19.230 steve: For sure. Do you? Do you have kids or
15 00:01:19.230 ⇒ 00:01:22.609 Uttam Kumaran: No, no, no kids, just a dog and a girlfriend.
16 00:01:22.980 ⇒ 00:01:32.648 steve: Well, yeah, they can be loud, too. My kids have to have separation, but I just got like glass French doors on my office, and when they’re home for spring, break and hear them scream, so
17 00:01:33.150 ⇒ 00:01:37.819 steve: you might you might hear some of that. So I promise they’re having fun. They’re not. They’re not getting tortured
18 00:01:38.169 ⇒ 00:01:40.619 Uttam Kumaran: No problem. Thanks for taking the time.
19 00:01:40.810 ⇒ 00:01:51.259 steve: Yeah, man. No, it’s a pleasure. It’s funny. Because, yeah, I moved down to Austin 2014 from Columbus, Ohio. One of my main goals was to network with a lot of the startups. But I’ve been in consulting for a long time.
20 00:01:51.780 ⇒ 00:01:53.100 Uttam Kumaran: Oh, great, yeah. I noticed that
21 00:01:53.330 ⇒ 00:02:08.930 steve: I haven’t done any any networking like I. I went to capital factory a couple of times. I got some friends that I knew from the startup scene back in Columbus, Ohio, that have now moved here, and none of us, I mean, you know nobody’s networking right now, and it’s weird. I don’t know if that’s just the Austin culture, because you gotta
22 00:02:09.225 ⇒ 00:02:14.549 steve: yeah. I took. I moved from New York and I think it’s a little bit of the Austin.
23 00:02:14.830 ⇒ 00:02:21.049 steve: Yeah, like, nobody wants to actually talk about work when you’re out in person, you know, it’s all everybody wants to be virtual. So
24 00:02:21.050 ⇒ 00:02:30.720 Uttam Kumaran: Exactly so. I don’t know. I I feel like part of it. Part of it is the Austin culture in New York. It was a lot of networking, but you really only meet people through work
25 00:02:30.720 ⇒ 00:02:31.310 steve: Yeah.
26 00:02:31.310 ⇒ 00:02:37.189 Uttam Kumaran: I feel like here, you know, we met, I met people sort of all sort of areas. And people don’t talk about work as much
27 00:02:37.190 ⇒ 00:02:37.570 steve: Yep.
28 00:02:37.570 ⇒ 00:02:59.279 Uttam Kumaran: For me like part of it was good, because in New York it was like everything was work, and I work for a bunch of startups in New York. So it’s actually a welcome change, partly. But as I’m growing the business I’m starting to, I want well, also, I want to start to do more business in town like that’s actually much more interesting to me than
29 00:02:59.630 ⇒ 00:03:22.799 Uttam Kumaran: having, you know, having relationships and doing inboard person stuff in town. I also have a lot of friends that go to capital factory, and they’re in sort of the the start. A lot of stuff is in Cpg or or Ecom like, not a lot in tech service or consulting like. Of course, there’s the big tech like oracle into it, those guys. But yeah, it’s interesting, like seeing all the crowd here, so
30 00:03:23.310 ⇒ 00:03:25.659 steve: Yeah, and there are. I mean, there’s there.
31 00:03:26.190 ⇒ 00:03:28.519 steve: you’re right. There’s not a lot of local
32 00:03:30.070 ⇒ 00:03:47.823 steve: like integration, or what you do, what you’re doing the data, and AI consulting there is. There doesn’t seem to be a ton of that. I mean, there’s the I’m I’m not sure if you’re familiar with spice works. I’m not what I don’t even know what capacity they operate in anymore. But spiceworks used to be a big, it consulting firm, but I think it was more like Staff Og, and then you had
33 00:03:48.410 ⇒ 00:03:53.389 Uttam Kumaran: Yeah. So I actually know, I know Scott Able, who used to run spiceworks
34 00:03:53.390 ⇒ 00:03:54.090 steve: Oh, really. Okay.
35 00:03:54.427 ⇒ 00:03:55.440 Uttam Kumaran: Yeah. I mean.
36 00:03:55.440 ⇒ 00:03:56.599 steve: How do you know him, or
37 00:03:56.600 ⇒ 00:04:09.809 Uttam Kumaran: I just got connected with him through another like Vc. In town, and and I’ve met him a couple of times, and then I’m I’m really close with one of his colleagues from spice, where it’s Scott Harmon who used to run motive
38 00:04:10.110 ⇒ 00:04:10.999 steve: Okay, yeah, yeah.
39 00:04:12.640 ⇒ 00:04:15.789 Uttam Kumaran: He’s like an advisor for us here at Brainforge. So
40 00:04:15.790 ⇒ 00:04:19.330 steve: That’s awesome, are you? Did you take Vc. Or some seed funding
41 00:04:19.339 ⇒ 00:04:23.858 Uttam Kumaran: No, no, it’s all it’s all. It’s all me.
42 00:04:24.329 ⇒ 00:04:41.739 Uttam Kumaran: yeah, I start. So I started the business in 2023. When I quit my, I quit my last job. I worked in startups as a data engineer for a while. I led data teams and then led up product org at a state startup called Prequel
43 00:04:41.979 ⇒ 00:04:55.129 Uttam Kumaran: they raised about 7 million dollars for a seed round. We built, you know, like a full product in about a year, kind of like burnt out of that for a number of reasons, and then left, and then was like deciding what to do it. I worked. Some did some contract work before.
44 00:04:56.509 ⇒ 00:05:23.399 Uttam Kumaran: But of course, like you can do this as like a solo contractor and like solo freelancer. But then there’s difference between that and like starting in, you know, an actual agency, or or but for me, my advantage is that I’m an engineer. So hiring and hiring the right people. For the right price is actually like one of our big competitive advantages, like, I’m able to find really great people around the world, us and elsewhere, but not just like going towards the lowest
45 00:05:23.569 ⇒ 00:05:28.149 steve: Dollar people who I know can become like senior level folks
46 00:05:28.810 ⇒ 00:05:44.319 Uttam Kumaran: And then being able to speak like to the seat like we sell to like a C-suite. But then I can also break down technically like what’s about to happen. And that’s been ease. That’s been. That’s been our advantage, and like going from 0 to anything you know.
47 00:05:44.540 ⇒ 00:05:49.939 steve: Makes sense. Yeah, I’ve seen. I’ve seen technical founders, you know, a lot of the partners that I’ve worked with.
48 00:05:50.190 ⇒ 00:05:55.460 steve: Well, the 1st one was blue granite. And that was we were 100% data. AI
49 00:05:55.460 ⇒ 00:05:55.840 Uttam Kumaran: Okay.
50 00:05:55.840 ⇒ 00:06:03.859 steve: Analytics, and the founder was technical. But he started in 98. I joined that group in 2012
51 00:06:04.170 ⇒ 00:06:04.890 Uttam Kumaran: Wow!
52 00:06:04.890 ⇒ 00:06:10.280 steve: He he had, you know, he had a decent understanding of
53 00:06:10.630 ⇒ 00:06:15.130 steve: like web app development. But I mean he, we’re talking days where it was still flash
54 00:06:15.130 ⇒ 00:06:15.580 Uttam Kumaran: Yeah.
55 00:06:15.580 ⇒ 00:06:24.199 steve: And stuff like that. And so they were doing mostly at MoD, up through 2,007. And you know then he everything that I’m
56 00:06:24.340 ⇒ 00:06:31.070 steve: familiar with from a data, or I’m sorry from a partner. Perspective has been a hundred percent through the channel of Microsoft relations.
57 00:06:31.070 ⇒ 00:06:31.450 Uttam Kumaran: Okay.
58 00:06:31.450 ⇒ 00:06:56.699 steve: So they were doing a lot of.net development, you know, as web apps and mobile apps back then, and they would go to the big conferences that Microsoft would kick off their fiscal years, or you know, different partner events. And they would hear, you know, hey, data and analytics is where it’s going to be. At the time it was enterprise data, warehousing with sequel on prem and analysis services, integration services, all that. And so he completely reorged in 2,007. He had about 40 people.
59 00:06:56.850 ⇒ 00:07:18.270 steve: And he said, Look, we’re going to go data warehousing. And Bi, because things on the.net side were to him they were getting commoditized. He wanted to be able to build higher rates, and so he was like, you’re either getting upskilled or you’re leaving. He lost half the staff totally restarted in 2,007. They were still probably only 4 to 5 million in revenue he was, he was pretty conservative with how he grew the business.
60 00:07:18.660 ⇒ 00:07:33.310 steve: and and maybe they were a little bit higher than that with 40. But they you know, he had about 20 people stick with him 2025 people, and they they became the like data. And AI partner for Microsoft on the well. It wasn’t AI at the time, data and analytics partner
61 00:07:33.310 ⇒ 00:07:33.820 Uttam Kumaran: Yeah.
62 00:07:34.103 ⇒ 00:07:57.090 steve: All doing sequel on prem stuff. And then he he just kept building that brand within Microsoft, and as Azure came about as power, Bi, you know, went from power pivot and excel to power. Bi. They were, they just stayed really hyper focused on being a hundred percent data and analytics. They hired some AI folks, but that was, you know, we had some people that came in that were purely AI, or, you know, really, machine learning data science
63 00:07:57.740 ⇒ 00:08:10.160 steve: in 2,016 and they were they were benched, I would say 90% of the time like we would get some some good projects. But most of the time they were just writing content for the inbound engine. And they were really, they’re really expensive content groups
64 00:08:10.160 ⇒ 00:08:11.355 Uttam Kumaran: Yes.
65 00:08:12.550 ⇒ 00:08:28.719 steve: And we and we were all us based. And then then I went to 3 Cloud, which 3 cloud was a bunch of Microsoft executives that left so very sales leaning. 2 founders. Jim. Oh, God, I’m blanking on. His name is Mike Rocco and and Jim.
66 00:08:28.820 ⇒ 00:08:58.599 steve: I can’t think of his last name, but they they were high up in the Sales Org at Microsoft and Smc. In the Smb. Group, and then they started 3 cloud. They they grew organically for a couple of years. They were doing a lot of infrastructure work. They were 100 focused on azure, but they did infra at MoD and data. And then they started acquiring companies. PE came in. They brought, I think it was like 100 million. And you know, yeah, they they went big, but these were former execs at Microsoft, and they had some inside
67 00:08:58.900 ⇒ 00:09:00.210 Uttam Kumaran: It’s in the book of business.
68 00:09:00.210 ⇒ 00:09:15.310 steve: That was, yeah. They they had some cheat codes. So Microsoft was bringing them a lot of business. I mean, a hundred percent of their business came from Microsoft. There was like no inbound going on. They started acquiring different shops, so they had a couple of app MoD shops that they acquired. Then they did an infra.
69 00:09:15.310 ⇒ 00:09:33.930 steve: And then they started going after the the data and AI or data and analytics groups. And they bought pragmatic works. They had they had a bunch of them that were well known in the Microsoft ecosystem. Now they’re at like a thousand people. I joined in 2020. I was probably like the 250th employee
70 00:09:34.000 ⇒ 00:09:41.050 steve: after all the acquisitions. When I left 2 years later, they were at 800 people already, so they were growing mainly through acquisition.
71 00:09:41.650 ⇒ 00:10:02.459 steve: And they were getting, you know. I I wouldn’t say 100, but they were getting a lot of Microsoft’s business like Microsoft is bringing them into every single deal, and they might. The way Microsoft works is they’ll bring 3 partners in. It’s kind of like a showcase where you’re trying to show that you got the expertise. Everybody bids on a project, and then you know, the the one who
72 00:10:03.097 ⇒ 00:10:09.020 steve: the the customer picks is who wins, obviously. And so 3 Cloud was winning a lot of business that way. But I just
73 00:10:09.020 ⇒ 00:10:12.570 Uttam Kumaran: Is it all? Is it all fortune, like 500, is was it a certain
74 00:10:12.570 ⇒ 00:10:23.179 steve: They they had they were so when I 1st joined I was, you know, strictly an ae, and I had half of my accounts were Smc. So I had about 100 accounts that were Smc. Smb.
75 00:10:23.280 ⇒ 00:10:32.369 steve: And then I had about 70 to 90 accounts that were Eou and and the way Microsoft enterprise. The way Microsoft works isn’t like size of the business twit like Twitter.
76 00:10:32.510 ⇒ 00:10:51.010 steve: Well, you know now, X. When they were as a customer they were always Smc. Which was all you know. Revenue wise. They’ve in valuation, they’re huge. So the way that it works is by seat, like, if you have their E 5 licenses, and if you have a bunch of sequel licenses depending on how much
77 00:10:52.495 ⇒ 00:10:52.860 Uttam Kumaran: Okay.
78 00:10:52.860 ⇒ 00:10:53.320 steve: Management.
79 00:10:53.653 ⇒ 00:10:54.320 steve: New sales.
80 00:10:54.640 ⇒ 00:11:09.639 steve: Yeah, exactly. Yeah. And so you know. But Eou, their enterprise groups generally are, are a lot are are the larger in revenue, too. I did have a couple that were like in the 50 million dollar revenue range that were in enterprise. But they were Mo mostly Isv, so
81 00:11:09.890 ⇒ 00:11:17.980 steve: they’re driving a lot of acr because their products are in Microsoft Azure’s marketplace. People can spin it up, and that helps Microsoft reps.
82 00:11:18.490 ⇒ 00:11:35.320 steve: or the Microsoft sales org retire numbers right? They’re all about acr, which is their azure consumer revenue. And so with that, I mean Isvs can be an Eou account like rackspace, all those because they’re driving a lot of revenue for Microsoft. And so
83 00:11:35.530 ⇒ 00:12:00.339 steve: 3 Cloud was focused on everything. I mean, the only thing they wouldn’t touch is like government accounts in some education. But outside of that their Smc, I mean, we had smb and then eou, and then you know that I got caught up in a layoff. They were trying to get acquired, so there was several rounds of layoffs. And then I started just floating around partners, and I found some smaller partners. And I was like, man.
84 00:12:00.590 ⇒ 00:12:20.199 steve: You know, it’s really hard now for a partner in a Microsoft ecosystem to get attention from Microsoft. Like, you gotta you gotta get some of the certifications. That’s important. But it’s not fully critical. The startup program is really good because it gives you a lot of azure credits, starts getting you some attention. But to really like break in the Microsoft ecosystem, you just gotta be bullish on
85 00:12:20.360 ⇒ 00:12:22.069 steve: going after those relationships. And so.
86 00:12:23.000 ⇒ 00:12:33.300 steve: you know, it’s just finding out who owns what accounts like. If you’ve got, you know, some customers that are probably Microsoft managed, where? What the ae is, who the A is, who the data
87 00:12:33.300 ⇒ 00:12:45.059 Uttam Kumaran: So can you talk to me about. So we we have a similar. So a lot of our business has come like my background is in snowflake snowflake. Of course you can do Snowflake on top of azure. You can do snowflake on top of on top of
88 00:12:45.618 ⇒ 00:12:47.250 Uttam Kumaran: on top of aws.
89 00:12:49.470 ⇒ 00:12:50.930 steve: Are you doing as well?
90 00:12:51.617 ⇒ 00:13:00.149 Uttam Kumaran: You can’t do. Gcp, I believe recently, basically, you can connect to blob, or you can connect S, 3,
91 00:13:00.280 ⇒ 00:13:01.540 Uttam Kumaran: and so
92 00:13:01.910 ⇒ 00:13:29.330 Uttam Kumaran: one of the problems we’ve had is, yeah, we’re like, way too small to like get attention from them. But of course, like I have some ae relationships. But for me, I’m like thinking about the strategy like, how do I turn those into like business coming our way? I know a lot like in data. I know a lot of the people that are vendors and in sort of modern data stack world. Some of the sales folks, the startups, or like the earlier companies. They’re definitely interested because they want people to do co marketing. They want people to
93 00:13:29.360 ⇒ 00:13:39.399 Uttam Kumaran: sell their business. And we we do. We do bring in the best tools and sort of do good job of explaining. Get getting people to use them. But, of course, for Microsoft Amazon Snowflake.
94 00:13:39.860 ⇒ 00:13:41.350 Uttam Kumaran: it’s tough, like
95 00:13:41.350 ⇒ 00:13:41.960 steve: Yeah.
96 00:13:41.960 ⇒ 00:13:45.869 Uttam Kumaran: But that’s the rising ship right now. So I’m like we’re trying to hitch the wagon
97 00:13:47.540 ⇒ 00:13:49.899 steve: The the problem with it. So we were
98 00:13:50.413 ⇒ 00:14:18.270 steve: blue granite when I was still there when databricks came into azure. We were really close to that. So we were doing a lot of hadoop projects on Prem, and they were just big and expensive, and and customers were few and far between. When databricks came in we knew it was about to explode. So we we really we were 100% Microsoft. When databricks landed, we went all in on databricks as well. So but 100 azure databricks like we weren’t deploying an aws. And I’m not sure if you’re familiar with
99 00:14:18.320 ⇒ 00:14:23.000 steve: with databricks relationship with Microsoft, but it’s, you know, call it a 1st party citizen. So
100 00:14:23.000 ⇒ 00:14:23.440 Uttam Kumaran: Yes.
101 00:14:23.440 ⇒ 00:14:31.419 steve: You go into azure, you know, it’s like spinning up the same thing. It’s 1 bill comes through, you know, data bricks. And in your azure subscription.
102 00:14:31.960 ⇒ 00:15:01.590 steve: and it’s really tightly integrated. And so for us, it still felt like a Microsoft service. It’s just a completely different org. And we built a partnership with them like I was doing webinars with the global Vp of retail Cpg at databricks when they were trying to launch some of their accelerators for stuff that they were putting on a github that would help you do customer lifetime, value or customer turn those types of things just little quick start templates that people would use to get running with azure databricks, notebooks quickly and
103 00:15:02.070 ⇒ 00:15:10.010 steve: we were doing webinars with them. It was great but me and trying to work with those reps, and I don’t. I’ve not tried to work with Snowflake, but I would imagine it’s similar when you’re
104 00:15:10.010 ⇒ 00:15:10.450 Uttam Kumaran: Pretty much
105 00:15:10.450 ⇒ 00:15:15.410 steve: Space, they’re 100% focused on selling their product. And so it’s a what have you done for me lately?
106 00:15:16.370 ⇒ 00:15:21.109 steve: Where Microsoft, the interesting thing about that partnership man is. It’s like they
107 00:15:21.270 ⇒ 00:15:32.800 steve: they don’t, I mean for for one, they’re hiring a lot of young people like out of school. They don’t know anything about what they’re selling like, you know. You’ll you’ll run into a data. Ssp, who has never even seen a data warehouse
108 00:15:32.800 ⇒ 00:15:41.780 Uttam Kumaran: You’re 100% right? I mean, a lot of my friends here in Austin do that. They’re like, Oh, I’m an enterprise seller for Microsoft. I’m like, what do you do like? I sell enterprise, I’m like, but what are you selling
109 00:15:41.780 ⇒ 00:15:42.169 steve: Yeah, yeah.
110 00:15:42.170 ⇒ 00:15:46.310 Uttam Kumaran: They’re like, they’re like, Oh, it’s like complicated. I’m like, you know, I’m like a data person like
111 00:15:46.310 ⇒ 00:15:46.889 steve: Yeah, but
112 00:15:46.890 ⇒ 00:15:48.609 Uttam Kumaran: Product? Are you selling
113 00:15:49.040 ⇒ 00:15:50.050 steve: Yeah, it’s not that good
114 00:15:50.050 ⇒ 00:15:54.809 Uttam Kumaran: Kind of ridiculous. I don’t know. I kind of find it ridiculous, but maybe it’s easy now
115 00:15:54.810 ⇒ 00:15:55.990 steve: So well the benefit
116 00:15:56.386 ⇒ 00:15:57.180 Uttam Kumaran: Don’t know
117 00:15:57.180 ⇒ 00:16:00.120 steve: Benefit of that for people like you who understand
118 00:16:00.390 ⇒ 00:16:12.619 steve: the technology and how to talk to all you know, all sides of the business. Right. You can go walk in. You can talk to it group who? They probably aren’t driving the decision. But they’re being told. Hey, we need to do this initiative.
119 00:16:12.740 ⇒ 00:16:38.219 steve: How are you gonna architect it? And then you can also run into you know the people in marketing or you know, Hr, or finance, that are the ones that have said, look, I need to see profitability this level. And that means I gotta get these 2 systems integrated together. And and you know, we need to model that data. That’s when you can come in and kind of speak to both sides. The the benefit of going through the Microsoft Channel is. You just need them to open the door, and
120 00:16:38.220 ⇒ 00:16:56.959 steve: if you can convey to them that dude, pass me the ball, I’m going to get you this deal like I’m going to drive acr for you. Here’s how we’re going to land it. Then they’re hands off, you know. They just they open the door depending on. You know, the the hard part with Smc and Smb is a lot of those guys don’t have relationships with those accounts. And Smc Rep.
121 00:16:57.070 ⇒ 00:17:05.919 steve: So the way that Microsoft’s organization is broken up, you get the Atu, which is their, you know, their account executives. They have account technology strategists that are in that group
122 00:17:05.960 ⇒ 00:17:27.784 steve: and it. And then an Ae has people from the stew or the stu, which is their solution technology unit. And that’s all their data Ssps, that’s their in for Ssps, and so the Ae is gonna drive everything around modern work, or M 365. You know, they’re responsible for getting the big renewals, and again, the way that they they categorize or
123 00:17:28.170 ⇒ 00:17:50.979 steve: cluster their accounts is about what type of licenses the customer has. And so you’ll have an Ae who has a lot of relationships. But he doesn’t know how to sell the solutions around. You know what if they’re going to try to drive some azure things. They don’t know how to sell those solutions, so they bring in the data. Ssp. Again. Some of them been in there for a while. They know how to sell it. They’ve been in the partner ecosystem. They’ve done the integration side. So they’re really good about
124 00:17:50.980 ⇒ 00:18:04.800 steve: teeing things up and then bringing you in. Some of them are just like, I don’t have enough time to call these accounts. If you give me a play that makes sense like your assessments are awesome, like those are. And I love the fact that you have them advertised on your website.
125 00:18:04.840 ⇒ 00:18:31.650 steve: You know, those are those are the types of things that you bring to Microsoft, and you just tell them some of the customer stories. Hey? We went into this customer. We led this assessment, we were able to show them that you know, they weren’t ready to do this use case. They didn’t have the data here. We were able to help bring the data into the data platform and then gave them a v 1 of this, you know, whatever AI solution you’ve developed or whatever. If you can tell that story to Microsoft, then they they start to understand. Hey, here’s how the evolution happens. I bring Brainforge in.
126 00:18:31.650 ⇒ 00:18:40.919 steve: They’re going to do this assessment 3 weeks later. They’ve got, you know, the assessment readout in front of the customer, and they’re pitching, hey? If you really want to achieve goal a
127 00:18:40.980 ⇒ 00:18:59.079 steve: here’s how you do it, then that that to them is, it helps. Put the puzzle pieces together, and and I think for the most part they’re willing to reach out to those customers and the problem with. And it’s not just you being a startup. It’s even 3 cloud. We could not go directly to customers and get their attention, and just wasn’t worth our time. Because.
128 00:18:59.080 ⇒ 00:19:12.530 steve: you know, Microsoft and any of the big brands even Snowflake those reps when they call somebody in in the data organization of a customer. People pick up the phone. They don’t always want to be talked to. They don’t always want to be sold to, but they at least will have the conversation
129 00:19:12.530 ⇒ 00:19:32.489 Uttam Kumaran: Like that’s like the fundamental layer of technology that runs the business or is like so integrated that. Yeah, I I totally hear you, I guess, like, what do you think about? How do you even find like? Let’s say we have a customer, and I mean, we have a perfect customer right now we know one of our clients is ABC home. And so
130 00:19:33.310 ⇒ 00:19:39.869 Uttam Kumaran: if I was to say, cool, these guys are, I mean, they’re they’re a Gcp place, but like, let’s say they’re they’re they’re Microsoft.
131 00:19:40.210 ⇒ 00:19:51.039 Uttam Kumaran: would I? Would I just go ask their head of it like, Who’s your guy there? Should I go directly to find someone in local here in Microsoft and be like, Hey, go find out who’s the rep for these guys
132 00:19:51.040 ⇒ 00:19:52.735 steve: That’s 1 way. So
133 00:19:53.620 ⇒ 00:20:15.330 steve: yes, that’s that. That could be your easiest path at the moment. If you get tighter ingrained if you start getting some of the certifications for the advanced specializations in Microsoft partner side. I don’t. You know. I don’t know if Snowflake would help you qualify for the advanced specialization and analytics. But you could like, if you, if you get a couple of people to pass the cert for fabric, which is the Dp 600
134 00:20:15.330 ⇒ 00:20:15.790 Uttam Kumaran: Yeah.
135 00:20:16.136 ⇒ 00:20:30.339 steve: And you and you do if I don’t know if you’re open to doing fabric. But if you do a fabric project on the Microsoft side. You can then apply to to get the advanced specialization for data and analytics.
136 00:20:30.460 ⇒ 00:20:36.850 steve: And and after that you get a little bit more reception from Microsoft when we reach out. So there’s
137 00:20:36.920 ⇒ 00:20:43.550 steve: a couple of different ways. It’s harder to get a partner development Manager, and that’s where things really get unlocked for you.
138 00:20:43.590 ⇒ 00:21:08.840 steve: If you can get to a Pdm. Or if you’re a managed partner, then they open up all kinds of doors and all the incentives that come down for Microsoft when they’re going to say, Hey, in this fiscal we’re focused on driving fabric or copilot. They usually bring a lot of funding into that, and if you have a Pdm. You start co-developing some offers, and then they push it out to the sales org. And so that’s the easiest way to do it, but they also give you access to their mouth. Their master account list, which has.
139 00:21:08.960 ⇒ 00:21:23.890 steve: you know, every seller. It’s got their minor work seller their security seller. And that’s really where you can map the accounts, and you can also go after. You know you’ve worked with ABC. So look at what are some peer, you know, companies that are in that space, and then go after those sellers and say, Hey.
140 00:21:24.020 ⇒ 00:21:39.429 steve: we did this with ABC. We can do it with this entire industry. But as you, as you saw, I sent you, you know, my, my email that I’m using is partner metrics.co, one of the things that I’m I’m building on the side is essentially a tool. 100 focus on Microsoft.
141 00:21:39.720 ⇒ 00:21:46.190 steve: And it’s it’s trying to give customers the end customer. Qualitative view of what partners are actually good.
142 00:21:46.380 ⇒ 00:22:10.850 steve: Like, you have technical background, I can tell. You know, I know you got some people in the Philippines. But like they’re 100% focused in the data space like that’s awesome. Microsoft needs more boutique partners that can actually get the job done. But they don’t know how to source it out of their 400,000 partners, and really they don’t have 400,000 active partners. When you look at the partner landscape. There’s maybe a thousand partners. That and half of those are probably Isv
143 00:22:10.850 ⇒ 00:22:40.100 Uttam Kumaran: Even in my world, like most of the companies in my world, are one or 2 people that just started a business, because it’s actually very hard to get past that point. And then there’s the big people who do everything. And there’s like we’re in this middle ground, where we move really fast with a couple of technologies. We do on data. We know really. Well, we’re probably one of the few people who have done production AI implementations in the past 6 months the technology is only been here for, like a, you know, like a year and a half
144 00:22:40.590 ⇒ 00:22:40.940 steve: Yeah.
145 00:22:41.250 ⇒ 00:22:46.930 Uttam Kumaran: And so for us. And again, you know, it’s like going cold directly to to accounts
146 00:22:47.060 ⇒ 00:22:50.240 Uttam Kumaran: is like couch. It’s like very tough
147 00:22:50.240 ⇒ 00:22:50.580 steve: Yeah.
148 00:22:50.875 ⇒ 00:23:11.554 Uttam Kumaran: And there’s everything we can do on Linkedin and stuff like that. But I want to go more through partners. We’re finding our vendor partners to be really great, and we don’t even sign referral deals. We’re just like, I want to implement the best thing. I don’t want to be conflicted. But let’s Co. Market. I’ll send you stuff. You send me stuff. There’s deals our our landscape and
149 00:23:12.140 ⇒ 00:23:13.990 Uttam Kumaran: leaning way. Probably there
150 00:23:14.290 ⇒ 00:23:41.219 steve: Yeah, and I can go over a little bit here. But I can help you with mapping some of the Microsoft ecosystem, because I have, you know, over the years just have built up my own kind of repository, of who owns what accounts it changes every fiscal right, I mean, reps. Some reps maintain their accounts fiscal over fiscal Microsoft. Fiscal ends in June June 30th is the end of their fiscal. So Fy Kickoff a lot of the accounts get shuffled around depending on where you’re at.
151 00:23:41.250 ⇒ 00:23:44.520 steve: But that isn’t a bad thing, because if you’re in an account
152 00:23:44.960 ⇒ 00:23:50.629 steve: and a rep moves well, the next rep that comes in has absolutely no clue. What’s going on? They do a very poor job
153 00:23:50.630 ⇒ 00:24:16.419 Uttam Kumaran: And our our account links for clients like again, we’ve only been in business, but we’ve had a we’ve our largest client like our our earliest client is from July of 23, and they’re still with us. We’ve had. We have clients from the last 8 months that are still with us. So because the data train it keeps going. There’s like, always new stuff and new service lines and and things like that. So we end up really knowledgeable about the landscape.
154 00:24:16.420 ⇒ 00:24:16.750 steve: Yeah.
155 00:24:16.750 ⇒ 00:24:29.949 Uttam Kumaran: And like again for me, I’m I’m looking now to sell AI on top of the data work that we do, but we also have insight into the fundamental. It stack because we source a lot of our data from postgres or or whatever
156 00:24:31.480 ⇒ 00:24:33.099 steve: Have you ever worked? Have you ever
157 00:24:33.100 ⇒ 00:24:34.400 Uttam Kumaran: Really good advisors.
158 00:24:34.400 ⇒ 00:24:41.290 steve: Yeah, for sure. And I would imagine you’re able to come in at lower rates than some of the big partners right
159 00:24:41.290 ⇒ 00:24:43.729 Uttam Kumaran: We are like we are. We are
160 00:24:44.070 ⇒ 00:24:49.746 Uttam Kumaran: dirt cheap compared to some like. But like I, that’s that’s what we have to do right now, you know.
161 00:24:50.030 ⇒ 00:24:52.509 steve: To a degree. I mean, I think that’s a you know. I wouldn’t get
162 00:24:52.510 ⇒ 00:24:56.100 Uttam Kumaran: We’re we’re growing it every time we’re growing it.
163 00:24:56.500 ⇒ 00:24:58.839 steve: What do you mind sharing what your rate card is?
164 00:24:58.840 ⇒ 00:25:06.269 Uttam Kumaran: Yeah, I mean, we charge so for the audit, we charge about 5 K, that takes a roughly around a week or 2 weeks, maybe like 10 h of work.
165 00:25:07.045 ⇒ 00:25:14.080 Uttam Kumaran: Our middle like implementation tier. Is around 15 KA month, and our higher tier is 25 KA month.
166 00:25:14.260 ⇒ 00:25:17.870 steve: And are you doing all fixed price like, Are you saying, Hey, yeah.
167 00:25:17.870 ⇒ 00:25:18.420 Uttam Kumaran: Yeah.
168 00:25:18.910 ⇒ 00:25:32.119 Uttam Kumaran: Moving everything to fixed price. That way again we have some, and then we’ll discount, based on the term length. And then that way, I can optimize for having trying to get a billable hourly above, like 200 basically
169 00:25:32.120 ⇒ 00:25:33.340 steve: Yeah, yeah,
170 00:25:34.690 ⇒ 00:25:37.620 steve: So I think the fixed price helps in some
171 00:25:37.720 ⇒ 00:25:47.459 steve: aspects. Right? I mean, you’re taking a lot of risk there, which in your. You know where you’re at is probably okay. You can absorb some, some hits, maybe but
172 00:25:47.490 ⇒ 00:26:10.479 steve: you know, as you, as you, as you get into bigger, more complex projects. It gets to be to the point where you can lose your ass. And so I mean, I think certainly, you know I respect that you you launched a business, and you’re out here fighting it out. That’s something I’ve always, you know, I’ve gotten comfortable in the consulting world, and so as I’m trying to build this like this side project to see if
173 00:26:10.480 ⇒ 00:26:14.279 Uttam Kumaran: It’s the hardest thing ever. It’s just the hardest thing ever. Yeah.
174 00:26:14.850 ⇒ 00:26:21.779 Uttam Kumaran: I mean, it’s a learn like I we did hourly for a while, but then there’s some clients where we can make a lot more margin going
175 00:26:21.780 ⇒ 00:26:22.310 steve: Oh, yeah.
176 00:26:22.620 ⇒ 00:26:28.210 Uttam Kumaran: Of course you have the other side of it, but we found enough talent at pretty good rates.
177 00:26:28.360 ⇒ 00:26:34.369 Uttam Kumaran: and I’m nurturing a lot of them up. So our margin is still staying healthy. But you’re right at the once we start getting to
178 00:26:34.540 ⇒ 00:26:37.989 Uttam Kumaran: bigger contracts. I’m not sure we’re gonna have to probably pivot again.
179 00:26:38.200 ⇒ 00:26:45.360 steve: Yeah, I mean. And and I I would say, you just feel it out in the engagement, you know, in, in or in the pre sales motion to figure out like.
180 00:26:45.380 ⇒ 00:27:00.829 steve: does this. If it fits into the box of things that you know you’ve de-risked through, you know, we’ve we’ve worked with this data source. We’ve built out these types of reports. Then, obviously, you know, you’re going into it with some good assumptions. But you know, I mean some customers, I mean, that’s
181 00:27:00.840 ⇒ 00:27:19.680 steve: customers suck dude like they. Some customers really do want to beat you up, you know, and so they don’t care that it’s fixed price. They’re going to get as much out of that fixed price as possible and and leave you with no margin. But I don’t think, you know, I mean, that’s not every customer. And again, if you’re working with the right partner inside the customer side, people that are trying to, I mean, you’re
182 00:27:19.990 ⇒ 00:27:21.229 Uttam Kumaran: Wanna win. Too. Yeah.
183 00:27:21.360 ⇒ 00:27:48.379 steve: You’re building their careers right? So somebody said, Hey, we can do this thing in AI, and they find you. And they’re like these guys are going to get it done. They bring you in there. You’re much better than their internal team, you know. They’re they’re going to trust you, and they’re not going to beat you up right. They want to treat you well, but some procurement teams at the bigger firms or bigger customers will beat you up and and take advantage of you, so I think you’ll you’ll know when you’re running into that. But regardless, I think your your approach is good. You’re getting your foot in the door with the assessment.
184 00:27:48.380 ⇒ 00:27:59.889 steve: You’re showing value quickly doing, you know, whatever it is. 40 K. Implementation. After after your assessment, I think you can grow that a lot. But also it depends on what you know.
185 00:27:59.980 ⇒ 00:28:16.145 steve: I I don’t know what your projects are, what they look like after you do the assessment. If you’re just doing like a generative AI use case that you’re implementing stuff like that, you know, it’s really finding those people that don’t have anything. Their data is a mess. They don’t. You know. They could do a lot cooler.
186 00:28:16.620 ⇒ 00:28:27.029 steve: AI implementations if they brought in, you know, disparate systems like those are the where you get the big win, because it’s it’s big lifts and shifts and and modernization plays. But
187 00:28:27.600 ⇒ 00:28:36.360 steve: so I think you know where you’re at you. You can get attention from Microsoft. I know a guy who he’s a genomics data scientist.
188 00:28:36.360 ⇒ 00:28:52.399 steve: and he gets a fair amount of business from Microsoft, and he’s just one dude like he’s that solo consulting firm. But he’s really good. He wrote a book through it’s like genomics and azure or something like that. And so he’s just kind of branded himself in a way that he’s being brought in. But to your point
189 00:28:52.400 ⇒ 00:29:08.000 steve: earlier, you know, that’s a solo guy who’s well known. He’s got a very niche area that very few people play in, and so he’s getting some of that business, but I think you can. Microsoft will definitely give you business, regardless of how big you are. If they trust you, you just need a couple of reps
190 00:29:08.120 ⇒ 00:29:11.480 steve: to. I mean, if you had a couple of reps that aligned to you, and they had, you know.
191 00:29:11.620 ⇒ 00:29:18.759 steve: 30 accounts each, and they were willing to open up that that book to you then I mean, you know, easily each rep could could be
192 00:29:18.920 ⇒ 00:29:22.190 steve: a million 2 million in revenue for you per year. Right? And so
193 00:29:22.190 ⇒ 00:29:46.649 Uttam Kumaran: Maybe it’s worth, because for me, maybe it’s because AI so early, maybe it’s worth looking for people in AI here in Austin, or or through some connection that because we’re implementing everything on azure stack and then we use some tools on top of that. But everything we’re sourcing the core models from azure and so I feel like. And then I mean, I can make noise on Linkedin and try to get some attention and then basically try to go after
194 00:29:46.650 ⇒ 00:29:49.139 steve: Do I would get. Do you have teams
195 00:29:49.860 ⇒ 00:29:52.499 Uttam Kumaran: No, we’re all on. We’re all on slack
196 00:29:52.500 ⇒ 00:29:53.600 steve: Okay, so.
197 00:29:53.856 ⇒ 00:29:55.909 Uttam Kumaran: Have teams like I have teams for some
198 00:29:55.910 ⇒ 00:30:04.450 steve: I I would I would set up a teams tenant, and I would, because with teams you can reach out directly to Microsoft. If if you’re federated, you can.
199 00:30:04.450 ⇒ 00:30:07.100 steve: I’ll just add you can type in an email address.
200 00:30:07.220 ⇒ 00:30:09.949 steve: and they use aliases. So you might know
201 00:30:09.950 ⇒ 00:30:11.210 Uttam Kumaran: Yeah, yeah, yeah, yeah.
202 00:30:11.210 ⇒ 00:30:32.089 steve: But they also, usually there’s there’s there’s 2 emails. They have an alias email. And they also have. If it’s John White, it’s John dot white at Microsoft most of the time, right? And so you can start looking for them that way. But you gotta know who they are again. You probably had to talk to the customer to figure out who their rep is, and honestly, I would send them. What I do. My approach is.
203 00:30:32.190 ⇒ 00:30:45.639 steve: if I’m starting a new territory. I find the Microsoft reps that I’m aligned to in my territory, and I hit them. If I’m not already connected to them. I hit them on Linkedin. I say, hey, I’m at partner X. You know. Here’s our I’d love to talk to you about our solution plays.
204 00:30:45.943 ⇒ 00:31:14.469 steve: Can’t wait to work with you. And then I follow up with them the next day and email and say, Hey, I sent you a connection on Linkedin would love to connect, and then I’ll send them a teams message like it’s just getting in front of them enough to where they’re like. All right, like. I’ll hear you out right? And and that that works well. But I mean, you still need to know who those reps are, and that’s where you know I can certainly help you with that. The the last piece is, you know, getting your plays in a way that shows how you’re driving acr, even if it’s with Snowflake.
205 00:31:14.470 ⇒ 00:31:21.187 steve: if you I don’t know if you’re familiar with how they track like how much revenue you’re driving, but you need to
206 00:31:21.480 ⇒ 00:31:23.129 Uttam Kumaran: Yeah, and stuff, like, I am, yeah.
207 00:31:23.130 ⇒ 00:31:29.031 steve: Okay, so yeah, you got, you gotta have your firm assigned to their dpor their
208 00:31:29.650 ⇒ 00:31:52.060 steve: What is it? Data, partner? No designated partner of record, and then, you know, they can go into the account to kind of see how much revenue you’re driving. But if you know how much revenue is coming out of your engagement, and any ancillaries that might be tied to that like I would just try to encapsulate that in like a 1 pager of, we worked with Client X, ABC, we did this solution. It’s you know, it’s driving this much acr. Here’s what’s on the roadmap
209 00:31:52.060 ⇒ 00:32:02.619 steve: like. Here’s how much more growth. We’re gonna see from that. And then just start getting that story in front of the right Microsoft people. And I think you’ll you’ll probably be, you know, you’ll open up those relationships. But I think
210 00:32:02.760 ⇒ 00:32:09.639 steve: you know, going to the customer asking who they work with could work. A lot of them may not know, because procurement owns that relationship like, if if
211 00:32:09.640 ⇒ 00:32:31.589 Uttam Kumaran: We end up getting closer to it. And and and again we we come in confident what we know. So you know, if you’re if you’re a problem solver, the problems sort of find you in the business. And the nice thing is we work from the top down. So we sell the CEO Cmo head of growth. And then we’re like fresh eyes on a data system that’s usually like, really messed up, you know, a lot of stuff up. And then
212 00:32:31.880 ⇒ 00:32:52.440 Uttam Kumaran: sort of new problems come our way. And we’re saying, we have to say, yes or no like, can you do this? This sort of analytics implementation. Or can you do this like operational database implementation? And that’s where we’re at with some of our customers as we get like past 6 months. They want us to take on more work. And there is. There’s there’s more scope. But I would love to to consider doing that through a partner.
213 00:32:52.887 ⇒ 00:32:57.440 Uttam Kumaran: I may not even have the talent right? And that’s a that’s a main problem. But, like
214 00:32:57.440 ⇒ 00:32:58.519 steve: What do you mean? You may have a tail
215 00:32:58.520 ⇒ 00:33:11.910 Uttam Kumaran: Meaning like, we’re focused mainly on. We have like analytics, engineers, we have, we have data engineers, and we have, like a few AI people. But I don’t have like pure, like people who I don’t have like database Admins like I don’t have some of the deeper
216 00:33:12.380 ⇒ 00:33:12.720 steve: Thank you.
217 00:33:12.720 ⇒ 00:33:13.100 Uttam Kumaran: Folks.
218 00:33:13.100 ⇒ 00:33:38.699 steve: What I would look for are data warehouse architects. Or, you know, modern data platform architects, people that you know can build the big, you know, star schemas, all that. And you guys probably have people building dimensional models, or, you know, building out data lakes, all that. But I would. Those are. The skill sets, I think, are really key and when you bring it up, Austin, I guess. Are you? Are you wanting to build an Austin team because you want to have that kind of
219 00:33:38.770 ⇒ 00:33:43.369 steve: local culture, or there’s not a lot of business on the Microsoft side in Austin. To be frank.
220 00:33:43.370 ⇒ 00:34:04.879 Uttam Kumaran: No, I want to build like I feel like it’s there’s a lot of people here who are well connected outside of Austin. Yeah, I don’t really. I don’t think the talent is here like I would love to build and like, contribute back to the economy here. But like but it’s not a lot of like all my engine, like everybody I know in engineering, is in Sfla or New York, and that’s where we hire a bunch
221 00:34:05.172 ⇒ 00:34:16.580 Uttam Kumaran: and then I, if I find people outside of the Us. Of course the price works great, and if they’re awesome, then yeah, where I go for Austin, though I feel like for sales definitely like I could consider
222 00:34:16.850 ⇒ 00:34:27.869 Uttam Kumaran: some more people here for sales or like I don’t know. I haven’t thought much about like what the play is for me here, I mean again, it would be nice to get out of this office and like, go see people in person
223 00:34:27.870 ⇒ 00:34:28.269 steve: Yeah, yeah.
224 00:34:28.270 ⇒ 00:34:30.749 Uttam Kumaran: That’s probably my real bias. You know.
225 00:34:31.510 ⇒ 00:34:56.879 steve: So Microsoft has Mtc. Offices in Dallas and Houston. Their office here is really small. Nobody ever goes there that. That’s the type of thing, though, again, if you start, I can help you with mapping out where the Microsoft reps are, and you know I mean, you probably spend more time networking if you’re gonna stay in Texas. Been more net networking out in Dallas and Houston. And you know, just going up taking people to lunch and stuff like that, who you are and what you’re trying to build
226 00:34:56.889 ⇒ 00:35:08.969 Uttam Kumaran: That’s like, we’re unique in that. We have. Like my business partner, he’s in New York. So we have a we have connection there for me in Austin there’s just not a lot of us which means, but for me, that screams. Okay, we can just be the one
227 00:35:09.099 ⇒ 00:35:13.789 Uttam Kumaran: like we could be 1 1 of a few, and just win whatever business is here
228 00:35:13.790 ⇒ 00:35:24.479 steve: Yeah. And I think I think there are a lot of business, and probably kind of where you’re playing now would be. I think you said the startups or the small, you know the smc or the smb space
229 00:35:24.480 ⇒ 00:35:37.779 Uttam Kumaran: First.st So a lot of our client base is like, so we have a lot of business that’s like, either really fast growing. Sas, so like, anywhere from like 5 million up. And then a lot of our e-commerce is 20 million annually or up.
230 00:35:37.780 ⇒ 00:35:43.550 Uttam Kumaran: Yeah, okay, we have a business like ABC, that’s like, you know, way bigger. But we’re like
231 00:35:43.550 ⇒ 00:35:44.960 steve: Very tech forward. Right
232 00:35:44.960 ⇒ 00:35:46.019 Uttam Kumaran: Huh! No, not very.
233 00:35:46.020 ⇒ 00:35:46.610 steve: They’re not technical.
234 00:35:46.830 ⇒ 00:35:47.819 Uttam Kumaran: Yeah, not. Very.
235 00:35:47.820 ⇒ 00:35:50.789 steve: Yeah, which is great, which is great. It so
236 00:35:51.080 ⇒ 00:36:05.069 steve: I think Austin’s a good market for that, right? But that’s a direct, you know. That’s a direct play like you’re gonna have to be building your business through referrals or through other partners. One of the other approaches I’ve seen. I don’t know how it’s played out, but I’ve been approached by a lot of people
237 00:36:05.070 ⇒ 00:36:25.879 steve: that one in particular that just wanted to lead AI workshops. So they had this framework around, how they do workshops that elicits use cases helps crystallize, use cases. And then you come out of that with a big scope. And so their their pitch to us was, Hey, we’re gonna we will be demand gen for you. Basically, you know, you sell this necessity.
238 00:36:26.080 ⇒ 00:36:41.159 steve: You sell this assessment. We go in and lead it, and then we we hand you over the the findings that essentially build your scope, for you know a big, massive project problem was, they charge like 150,000 or $250,000 a year.
239 00:36:41.370 ⇒ 00:36:51.069 steve: just to license their their workshop framework right and so, and honestly, they they weren’t doing anything special. It was a guy that had a British accent, so, of course, that sold well. But
240 00:36:51.070 ⇒ 00:36:51.803 Uttam Kumaran: That’s why
241 00:36:52.557 ⇒ 00:36:58.770 steve: They they. I think he was too heavy handed. I think you could go after partners that don’t have
242 00:36:59.040 ⇒ 00:37:03.040 steve: much on the AI side, because AI is driving all the deals in Microsoft right now.
243 00:37:03.720 ⇒ 00:37:10.619 Uttam Kumaran: That’s so. That’s why I wanted. That’s why. Because we started as a data business. But I started using AI to automate our own business
244 00:37:10.620 ⇒ 00:37:11.050 steve: Nice
245 00:37:11.050 ⇒ 00:37:34.120 Uttam Kumaran: And then I hired people just dedicated to doing that. And then I was like, Oh, my, gosh! There’s there’s nobody here who’s gonna be digging holes. There’s gonna be a lot of shovel manufacturers, and there’s gonna be a lot of problems. Nobody here is hitting holes or the people that are. It’s like the large consult. There’s no way they’re like on Twitter 24, 7, figuring out what’s latest like I am.
246 00:37:34.550 ⇒ 00:37:53.330 Uttam Kumaran: And I hire people who are just like that. And I’m like we’re doing things that you’re gonna read about are gonna happen soon. We’re doing them. Today. I’m doing even deeper stuff in my own business, that’s like, you know. And so I’m like, Oh, we’re on the edge, and that’ll help us. That’s gonna be the flashy thing we we’re riding that wave right now, I think, and
247 00:37:53.682 ⇒ 00:38:08.500 Uttam Kumaran: if we can hook onto some partners there, I mean again, my my job right now is shifting from implementation, which is what I’ve been spending a lot of time. We’ve hired a bunch of people in the last 2 months. We’re almost like 20 people now, and we’re
248 00:38:08.840 ⇒ 00:38:09.630 Uttam Kumaran: yeah
249 00:38:09.780 ⇒ 00:38:11.320 steve: What? What’s your what’s your revenue?
250 00:38:12.004 ⇒ 00:38:16.500 Uttam Kumaran: It’s gonna be we’re gonna hit probably a million or a million and a half this year.
251 00:38:17.895 ⇒ 00:38:23.440 steve: How? How are you building the and I don’t know if you’re only focused on Philippines. How? How are you getting the talent out of Philippines.
252 00:38:23.440 ⇒ 00:38:25.711 steve: No, we’re we’re every we’re everywhere.
253 00:38:26.355 ⇒ 00:38:28.480 Uttam Kumaran: Know, you’re everywhere in terms of. But in
254 00:38:28.480 ⇒ 00:38:36.220 Uttam Kumaran: Justin, Philippines. Yeah. So there’s like an online job board that I got sent from a friend. I hired one or 2 people there
255 00:38:36.330 ⇒ 00:38:40.299 Uttam Kumaran: and then. I basically those people, I said, tell me.
256 00:38:40.490 ⇒ 00:39:05.719 Uttam Kumaran: introduce me to any smart person, you know, and that’s sort of how it’s been going. And then, yeah, just for me, because interviewing for engineers for me is really easy, because I just know what I need, and they trust me a lot because I we speak the same language. So it’s easy, and then we also do all contract to hire meaning. I don’t. I’m not giving out a lot of offers until I see people actually execute work for us.
257 00:39:05.720 ⇒ 00:39:09.869 steve: That’s awesome, man. You’re taking very similar approach to East Wall.
258 00:39:11.360 ⇒ 00:39:20.960 steve: you know the the guy who started East will actually came from a Microsoft partner called 10th magnitude, that was acquired by cognizant, and they grew. They grew. I think there were.
259 00:39:21.950 ⇒ 00:39:47.330 steve: Oh, God! I don’t even know what the revenue was. I mean, it wasn’t quite. I think they were in the 50 million range when they were acquired, but they grew fast, but they were 100% focused on infrastructure. And they were in the wave of like 2016 to 2020, where a lot of people needed to move their infrastructure to azure, except it’s not happening anymore. I mean, it is like there’s still people that are either in aws will eventually move to azure. And again.
260 00:39:47.330 ⇒ 00:39:51.260 Uttam Kumaran: Starting cloud native like, you know, for sure.
261 00:39:51.260 ⇒ 00:40:06.060 steve: 100%. Yeah. And so that’s a harder play to go on the info side the data. So the the catch 22 with AI is a lot of times you’re landing. The size deals that you’re landing right? You’re not getting to a 200 to 300 to 500 k engagement
262 00:40:06.060 ⇒ 00:40:06.540 Uttam Kumaran: Yes.
263 00:40:06.540 ⇒ 00:40:12.340 steve: Strictly with AI. You have to go down the layers to get, you know, the data
264 00:40:12.340 ⇒ 00:40:12.819 Uttam Kumaran: The data.
265 00:40:12.880 ⇒ 00:40:26.550 steve: Integration work, and then even to step down to help with the infrastructure, work and charge for that. If you don’t want to do that. And I would not recommend hiring a bunch of infra people in any cloud. Because again, I think it’s it’s overhead that
266 00:40:27.360 ⇒ 00:40:35.059 steve: you’re just not gonna to your point. You know, people that have already started Cloud Native. They could be all screwed up, I mean, that’s another area where you could come in
267 00:40:35.500 ⇒ 00:40:44.090 steve: like we can help shore up your security. All that. But it’s also probably you want to stay 100 focused on. You want to be hyper focused on something. I think you’re taking the right approach.
268 00:40:44.805 ⇒ 00:41:05.890 steve: But you know, then it’s finding in for partners on the Microsoft whatever cloud side to kind of tee up projects for them to say, Look, we own this customer relationship, but we need help on the info side, can we, you know, either sub to you or bring you in. If you take a poke at the data work, like, you know, we’re done. And I think that you know that’s a good approach to just building some of those collaborative relationships in the Microsoft
269 00:41:05.890 ⇒ 00:41:06.910 Uttam Kumaran: We don’t want to be. Yeah.
270 00:41:06.910 ⇒ 00:41:09.410 steve: But I would say, Dude, I mean. So
271 00:41:09.810 ⇒ 00:41:23.968 steve: going back to the Snowflake relationship and the Databricks relationship to me, and I’ve not played in Snowflake. But Databricks is a very, very hard ecosystem play in as a as a partner, one they have. They’ve got their own Vc. Fund, which Microsoft kind of does.
272 00:41:24.270 ⇒ 00:41:30.479 Uttam Kumaran: I don’t think it’s the same thing you consider them. They both are very like. Now Databricks is caught up. They’re both very similar.
273 00:41:30.600 ⇒ 00:41:34.869 Uttam Kumaran: And yeah, they have their Vc fund. They pick the right people like things like that.
274 00:41:34.870 ⇒ 00:41:35.740 steve: Yep, and and
275 00:41:35.740 ⇒ 00:41:40.909 Uttam Kumaran: I’ve been implementing like I’ve been using Snowflake since 2,018. So
276 00:41:41.010 ⇒ 00:41:55.809 Uttam Kumaran: I just like, know a lot of the people that are in sales who now are like big sellers there who now sell for other companies. So like, I have some friend relationships into that business. But I I wanna make a if I make a phone call, I need to know, like what the play is. Basically
277 00:41:56.220 ⇒ 00:42:00.309 steve: Well. And and yeah, I mean you, you know.
278 00:42:00.830 ⇒ 00:42:13.599 steve: it’s act. It’s it’s it’s very fundamental, right? Like that. That is just the stuff that unfortunately, you gotta do a ton of it, and and the rep could help you with that right? But I would. I would say, you want to focus on. For now
279 00:42:13.860 ⇒ 00:42:34.780 steve: either one hyperscaler like aws or azure. And you want to focus on just building that partnership because you already have. You know, the snowflake world. You have contacts over there. But honestly, man, they’re just not going to bring you into much, because when they get into something it’s a long tail thing where you know, if they’re going to go sell Snowflake, it’s not already a snowflake customer.
280 00:42:35.090 ⇒ 00:42:49.519 steve: That’s where the opportunity is for you. If they’re already a snowflake customer. They probably don’t need you. They probably either A have a partner or B have built out their team enough to where they’re not. They’re they’re just not using partners right? And so, you know, Snowflake, they’re
281 00:42:49.530 ⇒ 00:43:11.339 steve: at least my experience with Databricks. The the reps would meet with you, but it’s like they only want to know what customers you’re in, and that’s it. And if you’re not in customers, they don’t really they’re they can’t get into the customer themselves without you or that Microsoft, so they can’t bring you into stuff, whereas Microsoft owns that relationship. And so Microsoft can bring you into that. But my! And and again, this is
282 00:43:11.420 ⇒ 00:43:12.440 steve: this is not
283 00:43:13.233 ⇒ 00:43:28.710 steve: anything. I’ve not built a business. I’ve just. I’ve just seen how consulting firms that I’ve worked for kind of built up. And everybody that I’ve worked for has been 100% Microsoft. And that’s kind of where I built my brand, too, is being 100%, Microsoft. But I would try to focus on like one hyper scaler, and just
284 00:43:28.710 ⇒ 00:43:29.160 Uttam Kumaran: So.
285 00:43:29.160 ⇒ 00:43:53.009 steve: It is grunt work, and you gotta go out there and build those relationships. All your networking should really be with Microsoft reps, and I would not focus on. There is a data Rep, who lives in in the Austin area. He has Smc accounts and SW. A. Or their southwest pod. I can introduce you to him at Brad Friel’s. But there’s not a lot of Microsoft reps here, and if they are, you know they’re they’re not.
286 00:43:53.010 ⇒ 00:44:08.530 steve: They’re not worth networking with. So it’s really like getting the account mapped. People that you’re already in would be step one. Just so you can find out who those reps are. Go to those reps and say, Here’s the good work we’re doing. I wanna you know, I want to bring you closer to what we’re doing with customer a
287 00:44:09.130 ⇒ 00:44:35.019 steve: sync every week, or can we set up a bi-weekly where I’ll just keep you apprised of the project and and where it’s going, and maybe you can help me get more relationships. All that like I would pitch it as a value. Add to them that you’re going to grow their acr, and this customer trust you, your trusted advisor, to this customer, then, you know, make a play at the rest of their account. Say, Hey, man, like we actually, we got some bench, or you know we we can. We can take on more projects like, if you have anybody that you’re not able to.
288 00:44:35.090 ⇒ 00:44:40.200 steve: You haven’t been able to get in front of that. That you think would be a good like AI candidate or something like that.
289 00:44:40.280 ⇒ 00:44:41.580 Uttam Kumaran: Yeah, put us with you. Come.
290 00:44:41.580 ⇒ 00:45:10.819 steve: Send them this play and literally write the email form. Say, Hey, I’m working with Brainforge. They’re, you know, very boutique, but expert on the AI side. You know they have this assessment. Just if we talked about this use case last time we went to lunch, that’s the Microsoft rep, and and just get Intel from them on the accounts, and then literally write emails for them and then have them fire them off. And but that’s the grunt work of getting into the Microsoft Channel, and I wouldn’t be afraid of the fact that you’re not. You know. They could go into Partner center and see.
291 00:45:10.960 ⇒ 00:45:37.850 steve: I don’t know if they can see it in partnership. They can see it in in their portal. How much acr you drive, how much acr you’re driving. Don’t worry about that shit like if they trust you, and they know that you’re doing good work and other customers. They’re going to bring you in, and you only need one champion, and then it’s much easier to start going after some of those certifications and and getting all the you know. The the lapel pins you need for Microsoft to start sending you a lot more business. But initially, it’s it’s literally just networking through the Microsoft Channel
292 00:45:38.960 ⇒ 00:45:55.760 steve: and and being in front of them as much as possible. What if I’ve got? And I would take like a small sub segment of their mouth, and just assign yourself a territory like, take everything in the total area and say, I’m going after all these reps and just focus on the data Ssps and the aes, because they’re they’re the only ones that are going to matter to you.
293 00:45:55.760 ⇒ 00:46:09.479 steve: and just make sure you’re in front of them every single month. And it could be anything from win wires like just sending them an email and saying, Hey, here’s the customer we worked with this month or here’s the customers we worked with this month, and and what we did, and and how it drove some success in azure.
294 00:46:09.480 ⇒ 00:46:32.639 steve: and, you know, come up with new plays like, take your assessment, put a spin on it somehow, or, you know, do a quick start around a certain technology like salesforce. Say, hey, you know, here’s what we’re. Here’s our quick play with salesforce. Or here’s how we’re enabling, you know, this platform, because then they can pull up their accounts. They can see who has salesforce, and if it’s a good one, pager, you know, to to show the customer
295 00:46:32.750 ⇒ 00:46:49.780 steve: how they get a win from salesforce, you know, for whatever you know, sales forecasting, whatever the use case might be. But just think creatively around that, and it’s really just getting collateral to Microsoft that they can send to customers. It has to be customer, friendly, collateral and then and they start sending that out. So
296 00:46:50.920 ⇒ 00:46:53.930 steve: yeah, I mean, look. And I’m talking
297 00:46:53.930 ⇒ 00:46:57.209 Uttam Kumaran: How can I be helpful? Tell me about what you’re up to and like. How can I help
298 00:46:58.055 ⇒ 00:47:18.750 steve: I mean this, right? So what I’m trying to build and I didn’t get too far into it. So partner, metrics is really a way for customers to get qualitative data around the Microsoft ecosystem. As I mentioned, there’s a lot of partners. East Wall is one of them where you know we’ve got strong infrastructure, some app MoD. But we really don’t have anybody in those data at all. And and we’re still selling fabric projects right?
299 00:47:19.495 ⇒ 00:47:41.360 steve: And the but the problem is, if you peel back the onion a little bit, you start to see that, you know we’re faking it till we make it, and that hasn’t been a problem for us. But it is a problem for a lot of customers. It hasn’t been a problem for any of our customers, because we’re working with people that are pretty low on the data maturity side. And so all we need to know is a little bit more than them. But it does become a problem when you’re trying to get into
300 00:47:41.410 ⇒ 00:47:55.239 steve: bigger and better accounts, right? Because they know really quickly. I’ve I’ve gone head to head with, you know, former employers like 3 Cloud, and deals with customers. And I’m as a rep carrying that data conversation because we don’t have anybody internally that can do it.
301 00:47:55.240 ⇒ 00:48:12.140 steve: But if you can, you know if light bulbs go off when you yourself, as an Sme or you get some of your other. You know, people that are good in front of customers that are still deeply technical get them in front of the customer in Microsoft light bulbs go off. They say, these guys are technicians. They can talk to customers. They know
302 00:48:12.140 ⇒ 00:48:17.160 Uttam Kumaran: We’re still selling like, I’m still selling everything in my business partner. We’re both still selling every single deal like
303 00:48:17.430 ⇒ 00:48:21.010 steve: I imagine you will be. You know, someone I mean. Again you find a good rep
304 00:48:21.010 ⇒ 00:48:21.350 Uttam Kumaran: Yeah.
305 00:48:21.350 ⇒ 00:48:48.210 steve: They can help you sell that. But I think you know the reps, and that so just to give you an idea of where the market is for for reps. You can find somebody that does, you know, that has come from a snowflake. They want to be part of Startup, and maybe you. You offer them a small bit of equity, you know, some somehow tied to whatever performance you need over the next couple of years. But you know a lot of the the reps in this space. I mean, they’re making. I mean, you know, the guys over at Snowflake and some of those others. I mean, they’re these guys, you know, they’re averaging
306 00:48:48.260 ⇒ 00:49:14.670 steve: anywhere from 2 50 to 350, and then the upper quartile. They’re making 700 a million right? And so to get those people in. It’s really tough, because the battle they have to fight like if they’re coming from a well known partner or a well known vendor like a snowflake. You know they have an uphill battle to get to that ote to get to their their target earnings, because, you know they got to help you sell. So it’s it’s a figuring out. Can you find
307 00:49:14.670 ⇒ 00:49:33.250 steve: the you? You just have to hire your chief revenue officer now, right. But you’re gonna call an ae, or maybe you call them your cro. I don’t know but you got to find somebody that you know is going to be invested in building the business, because usually even a new rep like anytime, I’ve started someplace, you know. My 1st year number at
308 00:49:33.430 ⇒ 00:49:35.320 steve: East Wall was 2 million
309 00:49:35.669 ⇒ 00:50:03.469 steve: my 1st year, and that’s really kind of baseline like. I haven’t had a job where your number as a rep isn’t too, you know less than 2 million. Right? But to get to that, I mean, you’re talking. You’re sweating bullets by, you know. Q. 2. Because it’s just really hard to build that book of business. It takes 6 months for anybody to ramp up, and quite honestly, probably closer to 9 months to 12, to to really get running. And so, you know, for that person they’re gonna have that ramp with you.
310 00:50:03.470 ⇒ 00:50:06.989 Uttam Kumaran: Opportunity for them is to run the whole thing like that’s what I feel right. I’m like
311 00:50:06.990 ⇒ 00:50:10.890 steve: Yeah, yeah, for sure. And there’s a lot of people that would want to do that right. And so
312 00:50:11.360 ⇒ 00:50:22.820 steve: I being one of them, I mean, I’m happy to talk with you more and kind of see what you’re looking for, and and if I can help there, what I’m what I’m trying to build is your. I wanted to say your name there. But I,
313 00:50:22.820 ⇒ 00:50:24.709 steve: who’s fine, is it? Udem
314 00:50:24.710 ⇒ 00:50:25.380 Uttam Kumaran: Some. Yeah.
315 00:50:25.380 ⇒ 00:50:34.980 steve: Good on. Thanks. So what I’m building on the side is that qualitative marketplace. So you can see kind of what partners are where and you know, if you’re talking to a part, usually.
316 00:50:34.980 ⇒ 00:50:59.389 steve: if Microsoft introduces partners, they introduce 2 to 3 at a time. Right? And so a customer interviews, those rep or interviews each partner 2 to 3 times they give them the same spiel on. Here’s the initiative. Here’s where we’re at with our data platform. Now, like, here’s what we need you to build. And then everybody pitches the project, and somebody wins so, but that that customer has a hard time differentiating.
317 00:50:59.710 ⇒ 00:51:03.170 steve: Everybody’s saying the same things right like, because we’re all pitching the same architecture
318 00:51:03.170 ⇒ 00:51:04.050 Uttam Kumaran: Yeah, yeah, yeah.
319 00:51:04.050 ⇒ 00:51:21.440 steve: We’re all pitching the same solution. And so I’m trying to give some qualitative edge kind of like your trust radar, or something like that for for the Microsoft ecosystem, but on the back end, how I’m making money, or how I intend to make money would be helping partners like you or helping other partners get further ingrained. And so it’s kind of like a
320 00:51:21.905 ⇒ 00:51:29.939 steve: you know. I’m I’m terming as a blueprint for the Microsoft Partner channel where I can help you start that effort around.
321 00:51:29.980 ⇒ 00:51:53.269 steve: I’ve got all the accounts mapped. I know where the Microsoft reps are. I have those a lot of those relationships. I just need to find partners that I trust that I’m willing to go to bat for to get in front of Microsoft. And it’s essentially taking the plays that work really well. All the other partners where we we look at your your current customers, we we kind of frame up those success stories in a way that makes sense for Microsoft. And we
322 00:51:53.650 ⇒ 00:52:18.070 steve: we just get in front of Microsoft as much as possible, and that’s the the that framework I mentioned. You know, Linkedin, to email to teams. It’s nothing, nothing revolutionary. But it’s the the tenacity, the discipline of just doing that. The rigor that you have to have to do that. Enough to where Microsoft starts opening the door for you. A lot of people just won’t go through that. And so that’s where again you don’t have the time to do it, you know. That’s that’s what
323 00:52:18.200 ⇒ 00:52:26.300 steve: I’m trying to help with, and it and it could be at any level. I mean just helping you get the accounts mapped. Certainly. Happy to work with you on that, and so loaded. What Crm are you using
324 00:52:26.470 ⇒ 00:52:31.580 Uttam Kumaran: We’re, we’re not, we. We just have. We have Hubspot. We’re not really using it to have it
325 00:52:31.860 ⇒ 00:52:32.530 steve: Yeah, I mean, it’s
326 00:52:32.530 ⇒ 00:52:39.119 Uttam Kumaran: Everything’s in notion right now, when we’ve pretty well mapped in notion. And that’s where, like a lot of our company docs are
327 00:52:39.860 ⇒ 00:52:41.830 steve: So one of the things that I’ve done in my
328 00:52:41.950 ⇒ 00:52:57.430 steve: own own roles is, you know, I look at again. I’m usually assigned a territory which in in Microsoft that could include Smc or Eou. But then you know that territory has X number of Aes X number of data reps X. Number of, you know, infra sellers.
329 00:52:57.430 ⇒ 00:53:14.950 steve: And then X number of you know, at MoD sellers. And I just I start systematically. Start reaching out to them. Let them know I’m there. Let them know what our plays are. Try to set up seats with them, you know you have to get in front of them to pitch what you’ve done in the past, and a pitch how you’re going to help them sell acr, but you only get, you know.
330 00:53:15.130 ⇒ 00:53:21.769 steve: had reps that take 2 to 3 years to open up. And that’s a lot of times, because they’re in accounts that already have partners. They don’t need a new partner
331 00:53:21.770 ⇒ 00:53:25.249 Uttam Kumaran: It takes a lot of time in market, you know.
332 00:53:25.450 ⇒ 00:53:32.439 steve: Yeah. And and I mean, I consider that partner sales right? I mean, it is marketing. But it’s partner sales where you you’re just trying to
333 00:53:32.540 ⇒ 00:54:00.029 steve: reframe your story and several different dimensions, so that it makes you applicable to, you know whoever has accounts right, so you might find somebody’s in financial services. But you haven’t. You haven’t done a financial services account or work with a financial services company, but still spinning your story. I mean, it’s all data work right? It doesn’t matter if you’re doing in healthcare, financial bringing systems together, you’re building out models. You’re building reports all the same. Damn thing. It’s just different vocabulary in the different verticals. And so I think you know what what
334 00:54:00.430 ⇒ 00:54:01.880 steve: you you I mean.
335 00:54:02.130 ⇒ 00:54:18.370 steve: I’m happy to, you know. Open up the Rolodex of the Microsoft side to you start mapping. Some of those accounts. Start doing some of this outreach with you. And and you know, I mean, I can just kind of either a coach you on. Here’s what works when I’m reaching out to Microsoft and trying to get meetings with them.
336 00:54:18.716 ⇒ 00:54:37.020 steve: You know which you you naturally do. Well, anyway, I mean, obviously, we’re having this conversation because the outreach you did in Linkedin. But those types of things, I mean. I think it’s just really focusing who you’re doing the outreach to and the stories you’re telling them. But if you’ve got 2 to 3 customer stories that you. You can tell to Microsoft
337 00:54:37.180 ⇒ 00:54:51.079 steve: and articulate how it helped land more azure revenue, that’s all you need. And so and and then also take your assessments. And you know, also, like, try to come up. I I didn’t see on your website. Do you have like.
338 00:54:51.700 ⇒ 00:54:56.452 steve: I love the fact? You worked at data nice. By the way, they they got me into data scraping
339 00:54:56.690 ⇒ 00:54:57.580 Uttam Kumaran: Oh, nice!
340 00:54:57.819 ⇒ 00:55:02.379 steve: I saw that I’m like, why isn’t everybody like this would be such an easy, fucking product to build
341 00:55:02.380 ⇒ 00:55:03.359 Uttam Kumaran: I mean
342 00:55:04.093 ⇒ 00:55:04.586 steve: So
343 00:55:05.560 ⇒ 00:55:21.419 Uttam Kumaran: How about this? I’m gonna I’m gonna connect. Let me connect with my business partner. He’s really the one that’s really doing a lot of our building out our sales flows. Maybe I’ll I’ll connect him and this group. And then, yeah, I mean, let’s see where this goes. I we definitely need a lot of. We’re definitely in the zone where
344 00:55:21.720 ⇒ 00:55:24.449 Uttam Kumaran: we’ve scraped our way from like
345 00:55:24.730 ⇒ 00:55:32.990 Uttam Kumaran: having a couple of projects. Now, we have like 10 projects. This next phase is like standardization and delegation like.
346 00:55:33.830 ⇒ 00:55:44.379 Uttam Kumaran: And the nice thing is, we’re both doing a still. A lot of founder led sales, which is really great for clients. But that’s where we need to spend. Majority of our time is on sales marketing.
347 00:55:44.730 ⇒ 00:55:51.819 Uttam Kumaran: I spend a lot of time on people, so I do like all the recruiting, and then working with our Pm’s to sort of make sure people are placed
348 00:55:51.970 ⇒ 00:55:58.310 steve: What? What’s your what’s your end goal, I mean, are you trying to build like a lifestyle business where you’re doing 20 million in revenue. And just kinda
349 00:55:58.990 ⇒ 00:55:59.870 steve: I think
350 00:55:59.870 ⇒ 00:56:02.159 Uttam Kumaran: We’ll try. I think we’ll try to sell the business.
351 00:56:02.757 ⇒ 00:56:18.770 Uttam Kumaran: I’m pretty confident, like, I know sort of the end targets like I know the margin profile I know in our space there’s there’s a lot of buyers, I know, also, like depending on how fast we can grow this like it can happen faster. So I think that’s where we’re both
352 00:56:19.020 ⇒ 00:56:24.820 Uttam Kumaran: sort of headed. I mean, I don’t know. I I never thought I’d be in consulting. This business is tough for a lot of
353 00:56:25.215 ⇒ 00:56:31.939 steve: Only having. I’m only saying this because I’ve been in it for a while, and I’ve seen
354 00:56:32.590 ⇒ 00:56:37.599 steve: founders that are doing it your way where you’re you’re you’re bootstrapping it right
355 00:56:37.600 ⇒ 00:57:00.480 Uttam Kumaran: It’s hard to cross the chasm, though, because money comes in and I have to reinvest all of it. I don’t have. I could go raise. I could go. But like it changes the incentives. I’ve only worked in DC back startups, and it’s horrible. So I have little interest in going and raising money especially, we’ve gotten this far. We’re a couple of big deals away from being able to scale easily, I think, to to the next big milestone revenue. Wise
356 00:57:00.480 ⇒ 00:57:03.650 steve: Are you tracking acquisitions like? Who would be your target?
357 00:57:03.770 ⇒ 00:57:06.940 steve: Acquire like? Do you have some of those in mind
358 00:57:07.259 ⇒ 00:57:29.969 Uttam Kumaran: Yeah, probably just as of like this month. I mean, I I feel like, still like the big for consulting. They’re buying up a lot of people we have to probably hit like 20 million annually to get to the to get to that range unless we’re growing really fast. But we’re doing the 3 big things right now is data, cyber security. And AI, and we’re doing 2 of those
359 00:57:30.490 ⇒ 00:57:38.629 Uttam Kumaran: partners that can do the 3rd one. And so I feel pretty good. I mean, there’s been big acquisitions like Ph, data, Brooklyn data.
360 00:57:38.860 ⇒ 00:57:42.010 Uttam Kumaran: big acquisitions in the modern data stack space.
361 00:57:42.290 ⇒ 00:57:44.250 Uttam Kumaran: So yeah, for sure.
362 00:57:44.250 ⇒ 00:57:52.030 Uttam Kumaran: depend on. If it’s like a people buying a platform, it’s gonna depend on our positioning. And that’s where, like, we’re sort of trying to land on like what
363 00:57:52.400 ⇒ 00:57:56.770 Uttam Kumaran: we should really focus on. But the AI stuff is so brand new, it’s hard to say
364 00:57:57.170 ⇒ 00:58:01.179 steve: Are you building any IP around like agent frameworks, or any of that
365 00:58:01.180 ⇒ 00:58:11.949 Uttam Kumaran: Yeah. So all we’re doing is building agents for folks, like for for ABC home, we’re building agents for their customer support reps. So while you’re on the call with them about your an issue at your house.
366 00:58:12.200 ⇒ 00:58:21.630 Uttam Kumaran: they currently have to go through like a hundred documents to answer that question. They put you on hold. They lose your call. We. We’re building them agents that sit right in Google, right in Google chat.
367 00:58:21.930 ⇒ 00:58:30.879 Uttam Kumaran: And you can talk. And we rag all of their like tons of documents. We clean all that up, and it’ll risk what would have taken, maybe anywhere from
368 00:58:31.230 ⇒ 00:58:36.430 Uttam Kumaran: like a minute to like a half hour to go get the answer you can get in like 2 seconds. Now.
369 00:58:36.680 ⇒ 00:58:37.330 steve: Yeah.
370 00:58:37.590 ⇒ 00:58:44.020 steve: And the the the thing where, when we’re in proposal with them right now is we’re trying to charge them on outcome based pricing
371 00:58:44.577 ⇒ 00:58:56.389 Uttam Kumaran: We charge them an implementation fee. Right now they’re they have a proposal where we’re going to charge, based on how many calls. We close within the 1st call, like 1st call, resolution.
372 00:58:56.390 ⇒ 00:58:57.030 steve: Yeah.
373 00:58:57.320 ⇒ 00:59:08.220 Uttam Kumaran: So, my, our thesis is that if you, if you don’t close in in the 1st call, usually like 25 to 50% of people won’t pick up on the second call, right? And how many of those people churn?
374 00:59:09.390 ⇒ 00:59:14.339 Uttam Kumaran: So we’re gonna charge some amount based on the A based on the acv of that customer
375 00:59:14.770 ⇒ 00:59:19.140 Uttam Kumaran: something for how many calls we? We get done on 1st call
376 00:59:19.613 ⇒ 00:59:30.280 Uttam Kumaran: and again, that’s where I think AI is interesting, because I I want to bet that we win if they win, and I’m not afraid to sort of be like, hey? I don’t want to charge you some sort of license fee
377 00:59:30.520 ⇒ 00:59:34.349 steve: I want to charge you on revenue gain. Yeah? And
378 00:59:34.560 ⇒ 00:59:38.470 Uttam Kumaran: I like. We’re pitching directly to Cfo like that proposals on their desk.
379 00:59:39.360 ⇒ 00:59:44.480 Uttam Kumaran: I think if that wins and it works, then we sort of have this like in perpetuity, almost
380 00:59:44.480 ⇒ 00:59:44.970 steve: Yeah, for sure.
381 00:59:44.970 ⇒ 00:59:56.669 Uttam Kumaran: When, if they’re doing that and their metrics suck right now? So you’d be so surprised like it’s incredibly painful to understand how their customer support reps are solving these problems.
382 00:59:56.670 ⇒ 00:59:58.070 Uttam Kumaran: Yeah, I mean.
383 00:59:58.611 ⇒ 01:00:03.220 steve: Yeah, I’m sure it would be surprising. But I mean, we see that everywhere, right? I mean
384 01:00:03.220 ⇒ 01:00:03.560 Uttam Kumaran: Yeah.
385 01:00:04.160 ⇒ 01:00:06.550 steve: And it’s a big opportunity. But
386 01:00:06.670 ⇒ 01:00:19.769 steve: you know the way that you’re going about it with the win win, or really, I mean taking a lot of the risk is is is good. I mean, it’s it’s gonna get you in the door. But you know again, you just gotta know you’re managing that risk, because
387 01:00:20.320 ⇒ 01:00:21.859 steve: if you get to a point where
388 01:00:22.090 ⇒ 01:00:28.860 steve: you take that hit, then you know you don’t have the PE money behind you right now to to absorb. You know, a a big lot
389 01:00:28.860 ⇒ 01:00:29.440 Uttam Kumaran: Oh, yeah.
390 01:00:29.440 ⇒ 01:00:38.739 steve: But so the the other thing last thing I’ll I’ll say is, there’s a lot of partners in the space that are doing acquisitions that are either peo now.
391 01:00:38.860 ⇒ 01:00:48.199 steve: like the PE roll ups in the Microsoft ecosystems. What’s what’s consolidating the market? It’s not the Big 4. And you know, being 20 million in revenue
392 01:00:48.360 ⇒ 01:00:54.730 steve: really anywhere from 10 to 20 million in revenue is where you’re seeing a lot of those acquisitions.
393 01:00:54.960 ⇒ 01:01:16.549 steve: Blue granite was 20 million when they were acquired. Pragmatic works, I think, was a little bit more than that. But I mean it’s the middle tier partners, people that are at like a thousand to 2,000 that are that are doing those roll ups. And that’s because again, it’s all PE that’s that’s driving that and so that to me.
394 01:01:16.690 ⇒ 01:01:21.050 steve: You know the big. I I would guess the Big 4. You have to have some
395 01:01:21.340 ⇒ 01:01:23.630 steve: pretty good IP around
396 01:01:23.700 ⇒ 01:01:49.179 steve: your agent frameworks, or something you built out that they would want to acquire. So it’s more of a technology acquisition. If you’re just doing 20 million revenue. And you got headcount, you know, expertise that’s probably not worth the M. And A. For them, because it’s it’s too expensive to buy you at that point when you’re only bringing, you know, 50, 60 people to the table like that. Those you know the acquisitions are either going to be IP, or it’s going to be for headcount.
397 01:01:49.180 ⇒ 01:02:08.210 steve: And the people that want the head count like 3 clouds. A good example. So 3 Cloud took on the PE they’re now at like 150 million in revenue, maybe a little bit more. I don’t know their exact numbers, but they they’re trying to exit for a billion right? Like they are trying to be. And they’re doing a lot of work with the Big 4
398 01:02:08.620 ⇒ 01:02:35.510 steve: and and basically giving them free work because they’re that’s who they’re trying to do the deal with, I think, unless you’re willing to build it to, you know 500 600 people, you you need to look at the 3 clouds. And and you know there’s a dozen of them in the Microsoft ecosystem, probably more in Aws side that are doing those types of acquisitions, and those are the ones that I think you have a better shot at, you know, if you’re 20 to, you know, 10 to 20 million in revenue getting a 2 to 3 x, you know. Exit
399 01:02:35.510 ⇒ 01:02:37.926 Uttam Kumaran: That’s my thesis right now.
400 01:02:40.730 ⇒ 01:02:47.720 Uttam Kumaran: yeah. But I mean, yeah, I I again. I don’t even spend. I don’t wake up and spend any time thinking about like I haven’t had a chance
401 01:02:47.720 ⇒ 01:02:48.130 steve: Offered.
402 01:02:48.130 ⇒ 01:02:48.900 Uttam Kumaran: So.
403 01:02:48.900 ⇒ 01:02:49.420 steve: Yeah, yeah.
404 01:02:49.420 ⇒ 01:02:58.269 Uttam Kumaran: Only in the past month. Right? We did a presentation with a banker who was part of this accelerator we’re in who who kind of gave us the numbers, and that was helpful to kind of hear that
405 01:02:58.810 ⇒ 01:03:16.770 steve: Yeah. And and the other thing I can do is with with the partner data I have, there’s 2 things, really, one is, if you want to go after the sell through partners like, you know, people that don’t have a big AI presence, or people that have data, engineering and data architecture, but they don’t have the AI
406 01:03:16.770 ⇒ 01:03:17.170 Uttam Kumaran: Yeah.
407 01:03:17.240 ⇒ 01:03:28.520 steve: Sell some of the assessments through them. Problem is, it becomes a bit competitive if they’re doing the data architecture because you’re not going to win that work afterwards. So you gotta you know, you’re just selling your thing to the customer.
408 01:03:28.984 ⇒ 01:03:57.150 steve: You’re you’re doing the assessment. But you don’t get where the big revenue is afterwards. So you could look at people that are just strictly at MoD or or infra, and need a data play and go after those to say, Hey, use our assessment. Get you in the door we can do. We’ll own all the data work, and we’ll give you all the app MoD work right like, if you know, they’re going to be building an agent framework that needs to integrate into a customer facing product. Then, you know. Let them own the app MoD there or the infrawork, and then you you become the data play. So that’s 1 outreach. I can help you with
409 01:03:57.150 ⇒ 01:04:11.669 steve: the other thing would be in the in the data space just mapping. I’m sorry in the azure space mapping where the acquisitions are cause. That’s 1 of the things I’m tracking is who’s acquiring who? And it’s all pr Newswires like, you know you can. You can get it. But like there’s
410 01:04:11.870 ⇒ 01:04:16.110 steve: 3 clouds acquired, just acquired another one called Data mine.
411 01:04:16.792 ⇒ 01:04:32.020 steve: or design. Mind. Sorry there’s there’s, you know, dozens of acquisitions going on throughout the year in the Microsoft ecosystem, and just seeing who’s acquiring, who gives you some intel on? How do you need to shape your go to market because one of the things that these, you know these firms are doing
412 01:04:32.090 ⇒ 01:04:48.829 steve: is again. They’re saying we’re 100, Microsoft. We’re 100 azure, or we’re, you know. Maybe they’ve got aws and azure. But they’re very specific around how they’re building that talent pool. And just like you’re doing now, you gotta find you’re selling snowflake because you get that experience, and other people in your firm have that experience. But it’s kind of like.
413 01:04:48.980 ⇒ 01:05:13.616 steve: you know. Skate to where the Puck’s going. Where where do people? Where are you seeing those acquisitions happen? And and what is that? You know? What’s the makeup of those organizations? And then you start building your Talent Pool from there. So you become attractive in that space. That’s that’s just the one thing that you know. Again, I can. I can give you at least some of that data around. Who’s acquiring? Who, if you don’t already have it? But what would I know? You got a lot going on, man. I’d love to continue to chat.
414 01:05:13.860 ⇒ 01:05:23.040 Uttam Kumaran: I appreciate it. No, this is really great. I appreciate all the insight I would love to let me connect you. Let me connect me, you and my business partner. He’s going to be in town actually.
415 01:05:23.220 ⇒ 01:05:26.940 Uttam Kumaran: mid April. We’re presenting at a conference here
416 01:05:27.370 ⇒ 01:05:28.060 steve: What conference.
417 01:05:28.060 ⇒ 01:05:40.149 Uttam Kumaran: Through our accelerator. It’s called vixel. If you go to vixel.com via x ul they’re a tech service accelerator. They’ve been tremendous. They’ve helped us out a time. They sold the company flux 7 to nt data.
418 01:05:40.660 ⇒ 01:05:44.989 Uttam Kumaran: Like around 2017, I think. And then they started this accelerator?
419 01:05:45.775 ⇒ 01:05:49.430 Uttam Kumaran: And yeah, we’re presenting at their sort of annual conference.
420 01:05:49.879 ⇒ 01:05:52.929 Uttam Kumaran: Happy to share that link. And if if you
421 01:05:52.930 ⇒ 01:05:57.088 steve: Yeah, man, I’d love to come. Are they up in Northwest Hill or no, that they must have?
422 01:05:57.320 ⇒ 01:06:00.999 Uttam Kumaran: Yeah, one of the partners is is up near, like a
423 01:06:01.380 ⇒ 01:06:07.740 Uttam Kumaran: yeah round rock cedar park area. But yeah, and then that that will have a ton of
424 01:06:08.050 ⇒ 01:06:11.860 Uttam Kumaran: people in data. I mean, every they do everything in tech service, consulting so
425 01:06:11.860 ⇒ 01:06:13.109 steve: This is awesome.
426 01:06:13.110 ⇒ 01:06:26.470 Uttam Kumaran: Really really good opportunity for you to come meet founders, and they, a lot of people, fly out. They graduate a lot of people out of this. Their cohorts? And they’re great people like been tremendous. Help us with our positioning and
427 01:06:26.700 ⇒ 01:06:32.589 steve: Yeah, I’d love. I’d love to come to that April event. Send me the information for that if I can. I go. Is that something that
428 01:06:32.590 ⇒ 01:06:38.219 Uttam Kumaran: Yeah, I think you just have to get a ticket. You’re totally fine to go. And then, yeah, I’ll put in a word. And whatever yeah.
429 01:06:38.640 ⇒ 01:06:56.980 steve: Well, whenever your partners in town would love to catch up for drinks or whatever. But and I’ll summarize kind of how I can help and send it over to you again. We can kind of look at it as like a trial basis. Love to just see, you know, if hey? I can add some value and help put you point you in the right direction. You know you could be a technical
430 01:06:56.980 ⇒ 01:07:11.770 Uttam Kumaran: So even this conversation has been really, really tremendous. I think you know, the problem is, it’s not what we should do. It’s like what we can do right like. It’s I would say this has been the hardest thing I’ve ever done in my entire life. Like
431 01:07:11.770 ⇒ 01:07:12.400 steve: Oh, for sure!
432 01:07:12.400 ⇒ 01:07:17.400 Uttam Kumaran: There’s nothing more difficult like it’s been a 2 year long.
433 01:07:17.720 ⇒ 01:07:46.380 Uttam Kumaran: Just wake up and go to bed like. So it’s it’s sort of it. Part of it is like, what can we do? What can we afford to do? But what do we have the bandwidth to think about? And of course it’s like, if I was to do 8 h of sales isolation, it would work really great. But we have people probably have everything. So the part of it is just. It is what it is. But we need for me. It’s the key thing is like, what is our core positioning, whether we sell other stuff. Once we get to a client, whatever. But
434 01:07:46.380 ⇒ 01:07:57.320 Uttam Kumaran: what is our core? Go to market positioning, and how do we do everything through referral or vendor or partner based sales, because the cold stuff is really really difficult. And the last piece is that
435 01:07:57.320 ⇒ 01:08:10.010 Uttam Kumaran: I really feel like we’re one of the few people who are doing AI in in sort of an agency sense. There’s a lot the big, the big companies are doing that. But they’re just pitching frameworks and Powerpoints.
436 01:08:10.050 ⇒ 01:08:18.679 Uttam Kumaran: There’s a lot of like one like individual freelancers doing AI stuff I really feel like that is gonna end up bigger than the data business. If I have to get
437 01:08:18.680 ⇒ 01:08:19.240 steve: Yes.
438 01:08:20.399 ⇒ 01:08:27.949 steve: Yeah, I mean, I it’s it’s interesting, because I think you know, obviously, the buzz is around that. And I think you’re you’re.
439 01:08:28.089 ⇒ 01:08:34.089 steve: You know your positioning is good, like, honestly like, that’s even at the bigger firms like you. You’ve got the assessments.
440 01:08:35.029 ⇒ 01:09:02.909 steve: you know. Those are the types of things that again. They get you in there. They let they let you sell something really small to a customer, so there’s barely anything out of pocket. Get you an get you the Msa. Which is the I mean as you get into the the Smc. Space. That’s the hardest thing man is just getting through the vendor procurement, and once you’re in, you know, they don’t want to go through that headache again. So as long as you keep doing work like you’ve already done, you know you’re you’ll continue to have those customers that you know. Obviously, it’s not a
441 01:09:03.369 ⇒ 01:09:09.309 steve: re renewing asset for them. But they, you know, they’re gonna if the projects are big enough, you can have a multi year
442 01:09:09.310 ⇒ 01:09:17.410 Uttam Kumaran: No, but that’s that’s the second phase problem is like, how do we grow this right? As soon as we’re in? I feel confident like that’s I feel pretty good
443 01:09:17.410 ⇒ 01:09:17.959 steve: Yeah, it’s
444 01:09:17.960 ⇒ 01:09:34.270 Uttam Kumaran: In the AI space. It’s moving too fast right where? I don’t know, unless you’re like me, who I have both sides of the equation where I have a business that can. Actually, I can point resources towards. And I’m also still learn. I’m reading everything.
445 01:09:34.270 ⇒ 01:09:51.269 Uttam Kumaran: It’s very, very hard, like I don’t know how you do this because it’s not like data. Data has been here for somewhat dimensional. All that, like the technology’s changed, sure. But like it’s so new. But the impact is actually so tremendous. Like, I, I’ve never seen a technology like
446 01:09:51.450 ⇒ 01:10:02.190 Uttam Kumaran: that’s had this much impact. And we’re actually so part of the IP thing that you mentioned is we’re we’re building a lot of internal AI systems, automate rainforge, like
447 01:10:02.370 ⇒ 01:10:24.219 Uttam Kumaran: every client we throw all of the code conversations, emails videos like for meetings all into AI. So all of our engineers and team get access to a bot that can answer any question about the client like we’re building internal systems like that which for me one, I want to lower our cost of goods. But second, that’s something that I don’t think there’s
448 01:10:24.460 ⇒ 01:10:29.420 Uttam Kumaran: I don’t know other consultancies that can do that right now.
449 01:10:29.890 ⇒ 01:10:44.169 steve: Yeah. And I know some of the bigger ones, you know, aren’t, because there’s a lot of risk with that right? You get well, and the risk is in internally with people kind of taking that themselves. Right? You give. When you when you have
450 01:10:44.370 ⇒ 01:10:58.709 steve: couple of 100 customers, you want to protect some of the sales. You don’t want everybody seeing what what work you’ve done, because just data leakage and stuff like that. But I think in your your world. Obviously, if it’s making you more efficient and you can get to 20 million in revenue with, you know.
451 01:10:59.190 ⇒ 01:11:12.040 steve: 1520 people. Because of that, then then great right? But the the one thing that’s really hard to do right now is what you mentioned around, you know, picking up the shovels and doing the work right. And AI, I think, will be a while if you’ve got a couple
452 01:11:12.080 ⇒ 01:11:36.190 steve: key vendors, you know. I don’t know what the data integrations would be again just using salesforce as a an example, because it’s well known. But like, if you had some agent framework around salesforce or another system that you built over and over again for a customer. I think that becomes fairly easy for the agents to do some of that work, but that I mean to me. I’ve not seen anybody execute that well enough to where an an enterprise customer is going to accept.
453 01:11:36.630 ⇒ 01:11:39.060 steve: You know AI agents doing the work for them
454 01:11:39.060 ⇒ 01:12:03.900 Uttam Kumaran: I’m not. I’m not interested in the AI even doing the engineering work like we will, we will win if the AI can just do a lot of stuff on the operations, sales and marketing side which we’re already doing like. I still think the hardest thing is going to be to have AI do the actual coding. It’s really hard. It’s just a tough problem. But like there’s so many other costs and not even cost, but just tax in terms of information transfer in this business.
455 01:12:04.300 ⇒ 01:12:22.350 Uttam Kumaran: Like, we have new people coming on every day, and they don’t need to onboard onto clients. And that process takes them 3 weeks to like, do. And they’re asking just the most basic questions. I’m like you should. We’re now have them all. Ask AI agents like, Who’s this customer? What have we done for them all? Those things
456 01:12:22.350 ⇒ 01:12:22.920 steve: That’s great!
457 01:12:22.920 ⇒ 01:12:35.830 Uttam Kumaran: And it’s all yeah. It’s all isolated just to those. And then, even for us, we have. We have agents that sort of do other operational things like transcribe meetings, send summary notes, the right people based on who’s in the meeting
458 01:12:36.070 ⇒ 01:12:43.110 Uttam Kumaran: like. And that’s why I’m like, I, I, my whole goal. Initially hiring AI people was to automate this business like I didn’t even really think twice about
459 01:12:43.395 ⇒ 01:12:43.660 steve: Yeah.
460 01:12:43.660 ⇒ 01:12:44.366 Uttam Kumaran: Selling it.
461 01:12:44.720 ⇒ 01:12:54.700 steve: No, I’m actually, really I I love that. And I’m aligned to it. Because that’s when I mentioned partner metrics. My play right now is to build that basically just the catalog of who the partners are.
462 01:12:54.700 ⇒ 01:12:55.340 Uttam Kumaran: Yeah.
463 01:12:55.340 ⇒ 01:13:10.950 steve: And give a qualitative like ratings and review thing from employees, from customers, from Microsoft even. But the whole play was just to start getting that audience and then basically sell them what you’re building internally, which is, you know, I’ve tried to automate
464 01:13:11.110 ⇒ 01:13:38.289 steve: through Llms like, have it, you know, summarize my emails. A lot of it’s manually, I’m just going in and saying, Hey, just had this conversation like, help me with this response. But I know if you can have that automation built in and something like teams, because all Microsoft partners use teams, and there’s other. I don’t know if you’ve heard of super glue, but check out super glue because they’re they’re doing very, something very similar for alliances, teams basically helping with a partner channel and all that messaging
465 01:13:38.940 ⇒ 01:13:48.540 steve: But what they’re doing is what what was what clicked for me with super glue is, their whole positioning is within slack, and it’s essentially
466 01:13:48.660 ⇒ 01:13:52.960 steve: get the sales reps, and the alliances what they need
467 01:13:53.030 ⇒ 01:14:13.180 steve: in the channel that they’re working with. Right? And so if you’re going to follow up with Microsoft, or if you’re going to follow up with, you know, customers, those messages that your sales reps need to be sending need to be in those spaces. So it’s like, you know, anybody can kind of build that chain that takes your emails or takes your notes and summarizes it. Now you got the text to shit to send. But how do you make that
468 01:14:13.180 ⇒ 01:14:26.189 steve: frictionless for the sales? Rep to say, Serve me up the message. I don’t even want to prompt it. Serve me up the message, and then, in super glue, I I watched the demo. I talked to the owner there. He just had people, you know, basically bubbling up the messages.
469 01:14:26.270 ⇒ 01:14:43.229 steve: And then basically, for the sales reps, they would see that they’d open up slack, and they would say, Hey, here’s the message. You haven’t touched this. Rep your your partner over here for a while. Here’s some messages you can send, because we’ve got these new offers. We got these new customer stories. So it was turning through that all all that customer story stuff you’re you’re feeding it.
470 01:14:43.530 ⇒ 01:14:49.870 steve: and then you just hit a button like, or you could. You could edit it so you could say, Hey, I want to clean that up and then now send it.
471 01:14:50.280 ⇒ 01:15:02.279 steve: But there was always that person in the middle which is good. I feel like it’s gotta come from the voice of the salesperson. They’re doing that all within slack. Nobody’s doing it in teams and and what you’re what you’re doing right now could be something that would be applicable to that.
472 01:15:02.823 ⇒ 01:15:08.900 steve: But that’s that was my goal, too, is is to try to build that automation. I’m I’m you know, really far behind on building
473 01:15:08.900 ⇒ 01:15:16.059 Uttam Kumaran: No, it’s not easy right now, like it’s in 2 years there’ll be applications that will do the basics. But that’s for me. That’s the opportunity is that
474 01:15:16.280 ⇒ 01:15:19.129 Uttam Kumaran: we’re writing code for all this stuff. And we’re
475 01:15:19.430 ⇒ 01:15:28.609 Uttam Kumaran: we’re doing fundamental stuff that’s really hard to do like you can get to the tragic Bt level. But how do you orchestrate like? For that’s why we’re also data company. So
476 01:15:28.730 ⇒ 01:15:42.290 Uttam Kumaran: all of our Zoom Meetings get transcribed and auto fed into drive and stored and and through, put through vector store and then get sent to slack like we have several agents in slack that you can message to create tickets to do all sorts of stuff.
477 01:15:42.923 ⇒ 01:15:46.579 Uttam Kumaran: And, like all of our slack messages, get sent all of our Github code like.
478 01:15:46.760 ⇒ 01:15:50.848 Uttam Kumaran: But it’s it’s hard. It’s like not it’s that’s a side project right now.
479 01:15:51.140 ⇒ 01:15:51.870 steve: Yeah, yeah.
480 01:15:51.870 ⇒ 01:15:57.910 Uttam Kumaran: And but like, that’s the IP, we I want to try to build to, to sort of lower, basically make us way more effective
481 01:15:58.100 ⇒ 01:16:02.600 Uttam Kumaran: with less people ideal. This business is hard because of the people like
482 01:16:03.060 ⇒ 01:16:08.929 steve: Honestly, man, you you know there’s not a lot of Microsoft partners that are on slack. But you could go.
483 01:16:09.080 ⇒ 01:16:15.099 steve: and I know you’re you. This is a collateral value you’re building, but you know, if you get it to the point where
484 01:16:15.510 ⇒ 01:16:28.970 steve: you can sell it to somebody else, even if you’re having to customize it like the partner. Ecosystems for Aws, for Gcp for azure, they’re massive. There’s so much revenue when you look at the partners. I mean it’s almost equal to.
485 01:16:30.240 ⇒ 01:16:53.120 steve: If if you aggregated all the services and Isv revenue going through their partner channels, it’s almost as big as the big platforms themselves, if not bigger. And I’ve only guesstimated it. I’m I’m probably way off on some of my guesstimates, but I mean it’s a massive market. And and so, you know, if you’ve got so my database of partners, just for Microsoft people that are just on the Microsoft side.
486 01:16:53.120 ⇒ 01:17:13.451 steve: you know, Microsoft advertises 400,000. That would be global. I don’t think that’s correct. That’s probably like 400,000 ever in the Microsoft ecosystem. But now, with you know some of the evolution in azure as well as you know the AI side. There’s really. I’ve got 10 or 11,000 that are us based. And then somewhere, like 35,000 that are
487 01:17:14.030 ⇒ 01:17:26.129 steve: that are worldwide. And you think about that. I mean, I don’t know. There’s maybe a few 1,000 of those that are between one and 5 people so small shops that probably aren’t going to be big customers for you. But there really are
488 01:17:26.180 ⇒ 01:17:50.730 steve: a ton in that 20 to 60 headcount. And then, even you know, there’s a good percentage that are above that, too. And when you look, those are good customers, because those are the people that are making money off these revenue. You know. They’re they’re making good cash flow because they’re selling services, or they’re an Isv that has traction, and you know something like what you’re building internally, you can turn around and sell to them. And that could be, you know, sizable
489 01:17:51.140 ⇒ 01:17:56.790 steve: solution. I mean it’s super glue. He told me his revenue. It was like
490 01:17:56.900 ⇒ 01:18:04.499 steve: I would say. I think it was like 14,000 a month. That was maybe 2 years ago now, and he’s been pretty active.
491 01:18:05.250 ⇒ 01:18:09.650 steve: But I mean, that’s I think it’s a huge customer base. And again, you know, if you can.
492 01:18:09.720 ⇒ 01:18:14.917 steve: if you can solidify that product. That’s something that I think obviously would be either
493 01:18:15.610 ⇒ 01:18:41.280 steve: cyclotron last jumping all over the place. But Cyclotron was a partner I was with for a very short time this year. Pretty good shot, but they were strictly modern work. And you know, biz apps, and I don’t that I’m not making a lot of off that from a services standpoint. So I left there pretty early. But they’re building other products for teams, migrations, and stuff like that. I’ve never seen a service partner do well with that, because it’s really hard to build both. It’s hard to build
494 01:18:41.280 ⇒ 01:19:06.819 Uttam Kumaran: No, it’s I wouldn’t. I wouldn’t like. Yeah, IA lot of people in the company. They asked me that. I’m like, it’s these building a product company is similarly hard to do. Both is like not want to really do that right now. I worked at a product company before, and it’s a different capital structure. It’s like a different play. But for me, it’s also like we could sell these as services. And like, we sell additional services
495 01:19:06.820 ⇒ 01:19:14.090 steve: And it’s a and it’s a and it’s a hybrid, because you can sub this out to where it’s working with your data. Your, you know you’ve got the.
496 01:19:14.390 ⇒ 01:19:25.999 steve: You’ve got the agent framework optimized for everything that you’re feeding it through slack. But the next customer may not use slack. They might use teams they might use, you know, Google, whatever. And so, but that’s the implementation work, right?
497 01:19:26.390 ⇒ 01:19:28.169 Uttam Kumaran: Still getting a deal. That’s
498 01:19:28.170 ⇒ 01:19:40.880 Uttam Kumaran: what we’re trying to do, basically is like what they’re going to be some unique technology piece that if I was to build a product, I would need 100 integrations instead, I’m going to price it on implementation. Probably 60 or 70% of that’s reusable.
499 01:19:40.880 ⇒ 01:19:41.929 steve: Yeah, right? And that’s
500 01:19:41.930 ⇒ 01:19:43.180 Uttam Kumaran: That that’s new.
501 01:19:43.180 ⇒ 01:20:09.479 steve: And that’s what I would start branding, you know, because what we did at Blue Granite when everybody was moving their data to azure, we. We just branded what our, we had, a metadata driven etl framework that used azure data, azure data factory and some other components in azure. And and it was really just a meta framework for us to, you know, start plugging into data sources and building the data pipelines from there. But it really did accelerate projects. I mean, it would take us
502 01:20:09.480 ⇒ 01:20:13.209 steve: 2 to 3 months to get data into azure when we didn’t have that after we’ve
503 01:20:13.640 ⇒ 01:20:37.719 steve: we took the time. And it just came through the client work we did to where we finally got it to the point where it was useful on every project, and we branded it as Data Lake Hydrator. We sold that to Microsoft in terms of like the branding of it, and and why it was a value to them. Hey? We use our data lake hydrator. It’s it’s only us. It’s proprietary. And it helps us get data in azure within 2 to 3 days of starting a project as opposed to
504 01:20:37.720 ⇒ 01:20:48.550 steve: 3 months. And so those types of things. And I don’t. You know, I think the agent frameworks the stuff that you’re building internally. That’s that’s something that, at least for the next couple of years we’ll have play
505 01:20:48.550 ⇒ 01:21:04.580 steve: if you brand it well, and you know, really show how it does speed things up, not just for you and improve your margins, but like for the customer, because that’s the biggest thing is shortening that time to value and de-risking it for them. And so I think if you can brand it in some way, you know, it doesn’t have to be
506 01:21:04.580 ⇒ 01:21:23.570 steve: like apple level branding, but it in a way that makes sense. Yeah. And then put that into your sales place, too. I mean, it should be something that the customers get is value as well, because if you’re you know, the the hardest part is building out these automations and and whatnot, and everybody’s trying to roll their own. But
507 01:21:23.600 ⇒ 01:21:28.739 steve: the the challenges you’re running into the benefit you have is you get to. You get to do this across.
508 01:21:28.860 ⇒ 01:21:39.260 steve: you know, potentially dozens and hundreds of customers, whereas an internal shop, they’re just doing it around their thing. And they’re gonna continue to hit. You know, roadblocks. And and you know, people are gonna get fired
509 01:21:39.260 ⇒ 01:21:47.770 Uttam Kumaran: I don’t mind picking off other vendors to then implement like, I’m only building this from scratch because there isn’t a solution right now.
510 01:21:47.770 ⇒ 01:22:14.999 steve: And that’s what everybody’s doing right. And so the scary part is obviously the to your point with how quickly things are moving. It’s like, I’ve we started working on stuff, or I’ve been pitching customers. When Openai came to azure. I was working with iheart media trying to get them across the line. I’ve been working on them for a couple of years. We finally had them agree verbally to an engagement with us, and it was to to build them a walled garden around Chat Gpt. They wanted to bring all their, you know, their media, their podcast everything into
511 01:22:15.455 ⇒ 01:22:30.539 steve: their you know their their agent what the the Llm. Model they were building, but they didn’t want people going out to chat Gpt. And that’s what most people did is they just built, you know their internal, you know. Chat gpt with, you know, some security around it, but
512 01:22:30.540 ⇒ 01:22:47.319 steve: that we were at the table to sign literally the day we show up to sign, Microsoft announced. Chat being what it was being chat enterprise, and it essentially did everything they wanted to do. So they’re like, oh, we’ll we’ll just use that. And so it’s like scary shit like that when you’re building. Something is like, when is open
513 01:22:47.320 ⇒ 01:22:56.399 Uttam Kumaran: That’s why for me, like, I don’t want to build software. I wouldn’t build software in that space because you get the rug pulled like all those companies you’re seeing.
514 01:22:56.800 ⇒ 01:23:21.300 Uttam Kumaran: they’re like 90% of them won’t exist in 2 years for me. I pick the best ones every month, and we swap it in and out, and we keep going. I don’t care. I just. I’m just want to win. And I I tell our customers, too. I have no allegiance to who we use. I use the best and what’s affordable. And today that’ll be someone. Next month. That’ll be someone else. We’ll switch, and we’ll use that it’s it’s easy now for us
515 01:23:21.300 ⇒ 01:23:23.450 steve: What do you? What are you using right now? Like, what’s your stack?
516 01:23:23.450 ⇒ 01:23:35.839 Uttam Kumaran: We’re using everything. Everything we use, either Gemini or Openai. Gemini is nice because of the context. Size is really nice. And then azure. We? We’re the starter program. So they gave us a ton of azure credit. So we’re building everything on azure
517 01:23:35.840 ⇒ 01:23:38.590 steve: Are you using? What what agent framework are you using, or what
518 01:23:38.820 ⇒ 01:23:44.350 Uttam Kumaran: We’re we’re building stuff off of any. Then it’s like this low code. Sort of agent builder.
519 01:23:44.500 ⇒ 01:23:50.150 Uttam Kumaran: It’s been working for us. Called N. 8 N. Dot I/O. It’s it’s been really nice, so
520 01:23:50.150 ⇒ 01:23:50.770 steve: Okay.
521 01:23:51.100 ⇒ 01:23:54.960 steve: Well, I I we’re, I think we’re half over our time, half hour
522 01:23:54.960 ⇒ 01:24:04.410 Uttam Kumaran: Yeah, no, this is really, really, this is really, really great. Thank you so much. I’ll yeah, if you wanna shoot me an email. And then I’ll circle some people from my side, and let’s keep talking for sure.
523 01:24:04.410 ⇒ 01:24:26.139 steve: Yeah, I’ll summarize kind of my thoughts here and send them over to you. But you know again would love to work with you just if nothing else, just to kind of continue to feel out. What you guys are doing. And as I mentioned my partner, metrics thing is something that you know has come together slowly, because I’m trying to do it in my spare time. But if there’s value I can add to you in terms of making connections and helping you with some of that.
524 01:24:26.190 ⇒ 01:24:35.760 steve: Just the brute force of getting in front of Microsoft, you know. That’s that’s the type of thing where I feel like I can add some value. And then also, I’ve just been in the space. I’ve seen a lot of founders struggle the same thing. You’re struggling with
525 01:24:35.760 ⇒ 01:24:36.100 Uttam Kumaran: Yeah.
526 01:24:36.420 ⇒ 01:24:39.260 steve: Every single one I talked to said, this is the hardest thing they’ve ever done.
527 01:24:40.470 ⇒ 01:25:02.490 steve: Services, business, but it’s the people aspect of it, man. It’s not the, you know, getting the deals and stuff like that. It’s just you’re gonna you’re gonna have people constantly leaving and coming. And you know I don’t. I don’t envy you, but I think you know the way that you’re you’re going about. It is good. And if you’re really just trying to get to a point where you can exit. I mean, I’m seeing a lot of acquisitions. I think you’re you’re on a good track. So that’s pretty exciting, man.
528 01:25:02.490 ⇒ 01:25:05.990 Uttam Kumaran: Appreciate it. No, thank you so much, and thank you so much for the time. I know we went over. So yeah.
529 01:25:05.990 ⇒ 01:25:06.879 steve: Oh, yeah, it’s not like.
530 01:25:06.880 ⇒ 01:25:09.219 Uttam Kumaran: And I’ll I’ll we’ll keep talking for sure.
531 01:25:09.430 ⇒ 01:25:11.200 steve: Alright, man, yeah, I’ll talk to you soon. Bye.
532 01:25:11.200 ⇒ 01:25:12.239 Uttam Kumaran: Thank you. Bye.