Meeting Title: Brainforge x PwC Candidate Interview Date: 2026-01-14 Meeting participants: Clarence Stone, Matthew Tracy
WEBVTT
1 00:00:07.670 ⇒ 00:00:08.830 Matthew Tracy: Hey, Clarence!
2 00:00:09.000 ⇒ 00:00:10.219 Clarence Stone: Hey, Matt!
3 00:00:10.220 ⇒ 00:00:11.470 Matthew Tracy: Hey, how are you?
4 00:00:11.470 ⇒ 00:00:13.650 Clarence Stone: Good, how about you? How’s everything going?
5 00:00:13.980 ⇒ 00:00:16.110 Matthew Tracy: Doing well, doing well, can’t complain.
6 00:00:16.280 ⇒ 00:00:18.500 Matthew Tracy: That’s awesome. Happy to connect with you.
7 00:00:18.740 ⇒ 00:00:32.080 Clarence Stone: Yeah, thanks so much for making the time, Matt. So, I guess we can just kick this off if you want to tell me a little bit about yourself. I know you’re from PwC right now, but I’d love to learn a little bit more about what you’re doing day-to-day.
8 00:00:32.470 ⇒ 00:00:47.209 Matthew Tracy: Sure, yeah, so I went to, just to give you a little background, went to UNC Chapel Hill, now doing Masters of Georgia Tech and Analytics, on the weekends. I joined PwC back in Jan of 2024, joined in the healthcare practice for the technology consulting side.
9 00:00:47.210 ⇒ 00:00:57.089 Matthew Tracy: decided I really want to kind of do more of my data science chop, so in about April, I messaged this one partner who got pulled into the CETA practice, which is the Cloud Engineering Data Analytics and AI practice.
10 00:00:57.090 ⇒ 00:01:09.309 Matthew Tracy: And then, from there, joined, this one project. We scaled that project up into, kind of this larger extension that’s been going on for now almost, like, 2 years, which is kind of crazy.
11 00:01:09.360 ⇒ 00:01:13.480 Matthew Tracy: And then, after that, in about January of,
12 00:01:14.720 ⇒ 00:01:31.930 Matthew Tracy: interest a little bit, they gave me the opportunity to kind of lead my own pod, so now that’s what I’ve been doing for the past year. So I’m, like, a dev, but also I lead the end-to-end development with ArgPod, and we do two different use cases every single quarter, partnering with our client, like, building out anything from… it could be automation, or it could be just, like, traditional machine learning work.
13 00:01:31.930 ⇒ 00:01:35.329 Matthew Tracy: And I can go into more depth on, like, specific ones in a little bit.
14 00:01:35.330 ⇒ 00:01:44.030 Matthew Tracy: And, been doing that, really been enjoying it, and, it’s been quite well. So, I can jump into some more specifics if you want in a second, but I’ll pause there.
15 00:01:44.380 ⇒ 00:02:01.450 Clarence Stone: Yeah, I mean, that was great, Nat. You know, I am less of a technical interviewer and more about learning more about who you are, how you like to think, and things that you’re interested in, so I think my follow-up for you is, what got you excited about studying analytics as a follow-up?
16 00:02:02.040 ⇒ 00:02:12.929 Matthew Tracy: Oh yeah, definitely. So, I feel like, for myself, I’ve always just been really interested in how computers work. I kind of grew up playing video games, I think most people did, like, played League of Legends when I was younger.
17 00:02:12.930 ⇒ 00:02:29.060 Matthew Tracy: And I’ve always been, like, curious about, games, computers, everything in between. So I’ve always been, like, a tech nerd, and I just wanted to kind of continue that education and development with it, and it’s getting paid for by PwC, so it’s… and it’s not that expensive at Georgia Tech doing it online, so it’s…
18 00:02:29.060 ⇒ 00:02:33.999 Matthew Tracy: seemed like a win-win for me, doing, like, just one class a semester, so I just wanted to continue doing that.
19 00:02:34.000 ⇒ 00:02:58.749 Matthew Tracy: more and more, and then obviously I’ve been doing that at work. I’ve been doing data science and AI for the past, like, two years, like, full-time, just completely, so it just seemed like a nice way to also just, like, level myself up a little bit more, and I think it kind of goes to, like, some of my… so just kind of… you kind of, like, touching on, like, learning a little bit more about me as well, too. So my core tenets are, like, continuous learning, so I do… I have, like, a Duolingo streak. I’ve been learning Spanish, and got to, like, Spanish fluency, and I have, like, a…
20 00:02:58.750 ⇒ 00:03:05.920 Matthew Tracy: 1,100, or maybe 1,200 day streak now in Duolingo, so I’ve been grinding that out, which is good.
21 00:03:05.930 ⇒ 00:03:07.900 Matthew Tracy: And yes, I think I’ll just pause there.
22 00:03:08.060 ⇒ 00:03:15.830 Clarence Stone: There’s a lot of us that are in Texas in the organization, so maybe you’ll be able to put that to practice if you ever visit us.
23 00:03:16.070 ⇒ 00:03:18.109 Clarence Stone: That’s really cool. Awesome.
24 00:03:18.110 ⇒ 00:03:19.880 Matthew Tracy: Oh, so cool, nice.
25 00:03:20.240 ⇒ 00:03:23.089 Clarence Stone: Yeah, so that, that, that makes sense.
26 00:03:23.290 ⇒ 00:03:31.369 Clarence Stone: So, like, hats off to you for going for continued learning. I really like the GTEC programs,
27 00:03:31.760 ⇒ 00:03:38.110 Matthew Tracy: they are incredibly affordable for the quality of education you get, so great pick there. Thanks, yeah.
28 00:03:38.550 ⇒ 00:03:48.129 Matthew Tracy: It was hard to, like, you know, fathom dropping, like, 120K or something like that on a one-year program, as opposed to, like, you know, 5K or something, and pretty much getting the same learnings.
29 00:03:48.770 ⇒ 00:03:57.559 Clarence Stone: Yeah, so I’m actually super curious about, like, how do you feel about the courses and what you’re learning? Are you, like, is this on…
30 00:03:57.730 ⇒ 00:04:10.029 Clarence Stone: Like, additive to the things that are happening in the industry right now? Or, like, are you seeing that, like, you know, the learning is a little bit behind compared to the changes that are happening?
31 00:04:10.030 ⇒ 00:04:33.069 Matthew Tracy: I think it depends on the course completely. So, this one course, for example, like, you can go into, like, some of these regression courses, and that’s obviously, like, not much is changing there. There’s some more interesting courses where people, they do have, like, you know, this is somewhat old now, but, they do have, like, RAG and knowledge-based AI learning. Obviously, they have, like, the classic reinforcement learning, deep learning, which doesn’t go anywhere.
32 00:04:33.070 ⇒ 00:04:37.509 Matthew Tracy: They’ve been, like, updating some of the other classes for, like, their machine learning for trading.
33 00:04:37.510 ⇒ 00:04:50.690 Matthew Tracy: their modeling and simulations classes are getting much better, which they’re trying to do stuff with, like, digital twins, which would be kind of cool, so I haven’t gone to that class yet, but I think that would be interesting. And they’re trying to have more, like, applied
34 00:04:50.690 ⇒ 00:05:06.459 Matthew Tracy: LLM applied natural language processing type classes, I feel like, as well, too, so they’re trying to, like, push towards it. It kind of depends on the track that you’re on to. There’s 3 different tracks. There’s the analytics track, there’s the business track, and then the computational data analytics track, and the… I’m in the C track, the third one.
35 00:05:06.460 ⇒ 00:05:10.889 Matthew Tracy: And that one has, like, more of the opportunities for some of those. But some are, like, the kind of…
36 00:05:10.890 ⇒ 00:05:18.660 Matthew Tracy: I would say… Basic interdisciplinary core courses are, like, very kind of static, and it’s not, like…
37 00:05:18.800 ⇒ 00:05:24.080 Matthew Tracy: some of the things I already learned during undergrad, but you kind of have to go through them anyways because they’re required.
38 00:05:25.010 ⇒ 00:05:28.600 Clarence Stone: Yep, that makes sense. So
39 00:05:29.140 ⇒ 00:05:37.139 Clarence Stone: Are there any projects that you’re working on, work or not work-wise, that are super interesting that you might want to share with me?
40 00:05:37.630 ⇒ 00:05:52.450 Matthew Tracy: Yeah, sure, I can just, like, pick one. So one that I’ve done at PWC is we did this risk adjustment project. So, we’re partnering with the nurses who are coders. So essentially what risk adjustment is, is we go through a bunch of different med… or the coders would go through a bunch of different medical charts.
41 00:05:52.450 ⇒ 00:06:17.370 Matthew Tracy: and try to identify the, dates of services, which are every single time you go into the office or into the doctor’s office, that’s a date of service, and then every single diagnosis that patient has under that date of service. So, you going in for the flu or, God forbid, something worse, those are all tagged to these things called, like, ICDs. And we built this, which are this classification of diseases. And we built this pipeline for them, which kind of automates that whole process
42 00:06:17.370 ⇒ 00:06:29.760 Matthew Tracy: them, so essentially we ingest thousands of medical charts that all come in, we OCR all the medical charts, and then after that, after they’re completely OCR’d, then we go through and we run it through, essentially, like, prioritization logic.
43 00:06:29.760 ⇒ 00:06:43.490 Matthew Tracy: Where we, kind of prioritize the charts based on, dates of services. So we pick the top 3 dates of services per medical chart, and some of these medical charts can be 1,000 pages long, and then all the medical charts are ranked against each other as well, too, so…
44 00:06:43.490 ⇒ 00:07:08.479 Matthew Tracy: So it’s kind of like a double ranking. We rank the medical charts within themselves to get the top 3 dates of services, and then we rank all the medical charts against themselves, so then it allows the nurses and the coders to easily go in there, pick the top charts to go through first, and then the top 3 dates of services, they can easily code, as opposed to, like, having to go through, like, 50 dates of services and, like, scrolling through the whole entire medical chart. So we did that with them, we partnered with them to do that, and we did, like, the whole end-to-end
45 00:07:08.480 ⇒ 00:07:24.470 Matthew Tracy: developments are starting with, like, business requirements gathering, you know, trying to see what the main pain points and problems were, and for them, it was just tedious and painful. They’re spending, like, hours scrolling through these charts when it can be automated in a way that can make their life a bit easier. The next part after that was data requirements gathering.
46 00:07:24.810 ⇒ 00:07:43.349 Matthew Tracy: And that, essentially, is we’re just making sure that we have data readiness. So, at the end of the day, if we can’t get the medical charts, if we couldn’t get the medical charts in a place where we could actually use them, then that’s not going to work. And then the third part of that would be the solutions architecture. For us, we were using AWS, so that was the hyperscaler that we use with this client, so…
47 00:07:43.350 ⇒ 00:07:56.620 Matthew Tracy: We used that, and then we built… and then it’s the build phase, so we built it all out with a Python, Lambdas, step functions, and AWS, and then it’s the last stage, or last… second to last stage, which is the, kind of…
48 00:07:56.630 ⇒ 00:08:18.829 Matthew Tracy: after we’ve done all of that, it’s kind of handing it over to the user and doing business user testing, so having them test out the whole application. For this instance, we built them a whole front-end application into it as well, too. So they would log into the application, test it out, they did that for a couple weeks, then they came back to us, gave us some feedback, we updated it for them, and then now it’s handed off to them so they can just use it. So essentially, what it does for them is, as opposed to.
49 00:08:18.830 ⇒ 00:08:35.939 Matthew Tracy: you know, going through a medical chart, which would take them, you know, one medical chart, depending on the size, can take them anywhere from hours to, you know, many, many minutes, dozens of minutes. Now it can be something just like that, where they just have their rankings, they just go into it, they easily find the medical charts they need, they pick the top 3 dates of services.
50 00:08:35.940 ⇒ 00:08:40.050 Matthew Tracy: And it’s much quicker. So for them, they’ve… I think it was, like, a…
51 00:08:40.049 ⇒ 00:08:55.849 Matthew Tracy: 2X increase in their revenue, so this, this solution alone, with our pilot, was, like, a million in recurring revenue added for the company, and then, on top of that was, like, $4 million now that’s annualized, because now we’re running it, like, fully for them, for their.
52 00:08:55.950 ⇒ 00:09:14.149 Matthew Tracy: current medical charts for one of their lines of businesses, and they want us to expand it to their commercial line of business as well, too, which is another subset of their medical charts, but we’re running it right now for them, too, doing… it’s gonna be about, I think, 68,000 medical charts in the next couple months, but that’s, like, a cool project I like.
53 00:09:14.650 ⇒ 00:09:33.230 Clarence Stone: Yeah, that’s super neat, and I love that you had a really strong understanding of the workflow on how you were going to do this delivery, and that makes me wonder, like, did you create this process, or, you know, is this a standard process that you’ve been using to do all your analytics delivery work?
54 00:09:33.420 ⇒ 00:09:46.530 Matthew Tracy: Yeah, so when we joined in January, or I guess it was April of 2024, we were kind of an AI pilot, so we were trying to develop a way to build, so we kind of… because we started with this client from the ground up, for, like, the first…
55 00:09:46.540 ⇒ 00:09:56.580 Matthew Tracy: one at PwC that was doing, like, actually hands-on keyboard AI pod work, starting in Jan… or April of 2024. So for us, we were trying to find a good delivery method.
56 00:09:56.580 ⇒ 00:10:21.319 Matthew Tracy: And we had a good director on the team that had done some stuff at AWS and other different places, and then that’s what it kind of settled to, like, that kind of biz requirements gathering, data requirements gathering, solutions architecture, build phase, biz or user… business user testing, and then finally the production handoff. And that worked out really well. There was some tweaking in between where we, like, changed some wordings around, and there was, like, some additional steps in there too, but
57 00:10:21.320 ⇒ 00:10:25.689 Matthew Tracy: It ended up, like, kind of, yeah, bubbling up to that, and that’s kind of what we’ve been doing since.
58 00:10:26.220 ⇒ 00:10:38.320 Clarence Stone: Cool! So I guess my next follow-up there is, like, did you end up in working in the medical sector in analytics, because you were interested in it, or, like.
59 00:10:38.320 ⇒ 00:10:38.910 Matthew Tracy: Yeah.
60 00:10:38.910 ⇒ 00:10:45.520 Clarence Stone: where you landed, wondering, you know, if you’d be interested in looking at data in other sectors as well.
61 00:10:45.520 ⇒ 00:11:03.809 Matthew Tracy: Yeah, definitely. I think I… it was kind of just, like, happenstance that I kind of fell into it, and for me, it was fun, too, because it’s, like, a bit of, like, tech and, healthcare. It’s, like, kind of is a bit of, like, that dual purpose, which is also fun, but yeah, I’m completely fine working in any other sector, as in, like, consulting, so, yeah, definitely.
62 00:11:03.980 ⇒ 00:11:07.389 Clarence Stone: Yeah, are there any sectors that are interesting to you specifically?
63 00:11:08.090 ⇒ 00:11:10.990 Matthew Tracy: I feel like for myself.
64 00:11:11.540 ⇒ 00:11:23.099 Matthew Tracy: I would like to get maybe to, like, some SaaS companies, like, something that has, like, a high user base, and that it could be, like, some type of automated process, I think that would be interesting.
65 00:11:23.180 ⇒ 00:11:37.699 Matthew Tracy: I think it would be interesting to do something in financial services, which I haven’t touched on before, but I’d studied business and data science at UNC, so, like, I have, like, a decent understanding of, like, the finance world, but not in, like, the professional sense, so I think that could be interesting as well, too.
66 00:11:37.700 ⇒ 00:11:55.079 Matthew Tracy: I think it would also be interesting to do maybe some data work as well, too. So, we obviously do data work, but not, like, a complete, like, maybe my cloud migration or something like that. That’d be interesting to try out one time. So, I think there’s, like, a couple different places, and I must… obviously, you don’t know what you don’t know, like, you’re…
67 00:11:55.120 ⇒ 00:11:58.129 Matthew Tracy: So, at the end of the day, I’d be just, like, curious to learn more, for sure.
68 00:11:58.280 ⇒ 00:12:00.220 Matthew Tracy: On any other industry?
69 00:12:00.710 ⇒ 00:12:18.129 Clarence Stone: Well, in that case, I have some good news for you. I mean, most of our clients are SaaS or e-com, and, the… like, most of my personal clients are more professional services, accounting, and finance, because I came to be as an MD, so I think we’re kind of… Yeah, I saw that.
70 00:12:18.130 ⇒ 00:12:20.149 Matthew Tracy: Like, yeah, nice.
71 00:12:20.460 ⇒ 00:12:22.190 Clarence Stone: Cool.
72 00:12:23.110 ⇒ 00:12:32.400 Clarence Stone: Do you use any AI, like, personally to build things? Like, is there any platforms or tools that you’re kind of into right now that you enjoy?
73 00:12:32.400 ⇒ 00:12:46.419 Matthew Tracy: Yeah, so personally, I use Cloud Code. I just think that one’s, like, my favorite right now. And then at work, we don’t have access to Cloud Code, so we have access to ChatGPT and Codex, so that’s the one that we can use.
74 00:12:46.720 ⇒ 00:13:02.930 Matthew Tracy: But those are kind of the main ones, so… and then I guess the ones that we’re using at work, because, like, Claude has an AWS partnership, we use Claude with a lot of our, automation work, too, so using, like, 4.5 Sonnet, or 4, or any of those through, like, AWS Bedrock.
75 00:13:03.130 ⇒ 00:13:10.049 Matthew Tracy: But for, like, any type of, like, personal work, like, any side projects or something, for myself, I personally use, yeah, Cloud Code.
76 00:13:10.560 ⇒ 00:13:13.270 Clarence Stone: Nice! That’s great to hear.
77 00:13:13.360 ⇒ 00:13:14.520 Clarence Stone: I…
78 00:13:14.520 ⇒ 00:13:38.269 Clarence Stone: I guess I’m sort of mandated to kind of highlight the huge benefit of this organization being that we don’t have any restrictions on AI usage. In fact, we encourage it. We have a, kind of an open-ended request system that allows you to just get subscriptions for different types of AI tools that you think might be helpful. Most of this organization is running on
79 00:13:38.270 ⇒ 00:13:55.719 Clarence Stone: a cursor, but there’s tons of people who also like using cloud code. So, I think it’s important to highlight because we’re a little different in that sense that, like, we’re putting AI first in how you implement and deliver versus saying, like, these are the tools you can and can’t use.
80 00:13:55.720 ⇒ 00:13:57.900 Matthew Tracy: Yeah, yeah, I like that. That’s good.
81 00:13:59.330 ⇒ 00:14:15.889 Clarence Stone: Yeah, well, man, I… those were all my questions for you. You know, this is also a great opportunity for you to learn more about us and what we’re doing, and, you know, any specific questions you might have of me as well. So I’ll give you the floor. You have any thoughts? Anything you want to chat about?
82 00:14:16.080 ⇒ 00:14:18.829 Matthew Tracy: Yeah, definitely. So, I guess some questions…
83 00:14:18.960 ⇒ 00:14:37.310 Matthew Tracy: So, I know Brainforge has been going around for a couple years, and I was kind of doing some research on your background, too. It seems like you started Trust Vicinity in April of 2025. Are you, like, kind of operating as, like, an LP, like a partner, or are you now completely under Brainforge? I was just kind of curious how that was… how that was working.
84 00:14:37.310 ⇒ 00:14:41.160 Clarence Stone: Yeah, so, so really interesting story here,
85 00:14:41.320 ⇒ 00:14:45.320 Clarence Stone: I, I started Vicinity because, at, like.
86 00:14:45.320 ⇒ 00:15:08.529 Clarence Stone: I was working in the wealth asset management space as the head of AI GTM at EY, and the technology restrictions actually prevented us from being able to implement a lot of tools and capabilities into our clients’ hands, because, you know, our clients are just not comfortable putting certain pieces of data on the cloud. It doesn’t really matter if we tell them they have a private instance or not.
87 00:15:08.530 ⇒ 00:15:11.539 Clarence Stone: So I created a…
88 00:15:11.610 ⇒ 00:15:24.540 Clarence Stone: easy-to-deploy, out-of-the-box, you know, platform that allows you to implement AI agents and different tools, that all run locally in a device that I ship over to clients. And… Awesome.
89 00:15:24.540 ⇒ 00:15:35.070 Clarence Stone: Yeah, it worked out really well. So last year, like, towards the end, the baseline technology licensing got acquired by a private equity firm here in San Antonio.
90 00:15:35.070 ⇒ 00:15:36.910 Matthew Tracy: Nice! Congrats, that’s awesome.
91 00:15:36.910 ⇒ 00:15:38.609 Clarence Stone: Yeah, so I guess I was, like.
92 00:15:38.610 ⇒ 00:16:02.040 Clarence Stone: left with not much to do, and for a while, Brainforge has been my go-to team to do, like, specific implementations. So, for example, if a client was looking for a certain type of agent or a certain type of feature or functionality, instead of having to build it on my platform, like, I send that work over to Brainforge. So, I’ve, you know, from
93 00:16:02.050 ⇒ 00:16:09.080 Clarence Stone: the beginning of last year, I’ve had a really close relationship with the leadership team at Brainforge. Nice.
94 00:16:09.230 ⇒ 00:16:26.309 Clarence Stone: So, now, we’re slowly integrating, you know, my entire book of business into Brainforge, and, yeah, I’ll somehow settle into some sort of, LP role, and running an entire book here, as we figure things out.
95 00:16:26.730 ⇒ 00:16:30.740 Matthew Tracy: Okay, nice, cool, yeah, that makes sense. That’s cool, okay, nice.
96 00:16:30.850 ⇒ 00:16:45.180 Matthew Tracy: Interesting, that’s super cool. Okay, and then I guess another one, too, is, since you kind of touched on it, like, book of business, what’s the pipeline looking like for you guys? Like, what’s… and I don’t know if you can touch on this or can’t, but…
97 00:16:45.180 ⇒ 00:16:48.740 Clarence Stone: So, yeah, feel free to ask anything.
98 00:16:48.740 ⇒ 00:17:00.260 Matthew Tracy: Okay, sweet. Okay, cool. So, like, what type of… I mean, I guess, what’s, like, the best client Brainforge has had, the deal size, is it recurring, and then how’s the pipeline looking?
99 00:17:00.410 ⇒ 00:17:17.239 Clarence Stone: Sure. So, let’s go top to bottom. One, this information that you’re about to ask me is always available for everyone at Brainforge. All meetings are recorded, and, there’s, chatbots that are attached to every single meeting recording that you can actually chat with to get information.
100 00:17:17.240 ⇒ 00:17:18.550 Matthew Tracy: Really? Okay.
101 00:17:18.550 ⇒ 00:17:30.580 Clarence Stone: So we don’t hold anything back, especially, like, you know, me and the two, founders have, you know, private meetings. We still record them, and we’re happy to let anyone listen to it.
102 00:17:31.470 ⇒ 00:17:32.200 Matthew Tracy: Whoa.
103 00:17:32.200 ⇒ 00:17:34.759 Clarence Stone: So, given that, like.
104 00:17:34.820 ⇒ 00:17:57.119 Clarence Stone: I’m not trying about answering those questions, right? So, last year, Brainforge ended with, $2 million in total, revenue. We’re currently on pace for about 160, 170 MMR. Okay. Trying to target about 4 mil at the close of this following year, but I think we’re going to exceed that massively.
105 00:17:57.200 ⇒ 00:18:16.939 Clarence Stone: So, what does the kind of, like, pipeline look like? Well, we’re having a lot of these calls with people like you, Matt, because, we are kind of already full for all the work coming in for Q1, and there’s still a bunch of deals that are still in the pipeline for us to figure out staffing on. So,
106 00:18:16.940 ⇒ 00:18:26.019 Clarence Stone: Yeah, we’re way ahead of target in that sense. What is, I guess, like, what were your other questions? Largest client? Is that what you’re saying?
107 00:18:26.280 ⇒ 00:18:30.210 Matthew Tracy: Yeah, best client that you guys… or you can kind of pick… pick whoever you want.
108 00:18:30.400 ⇒ 00:18:37.019 Clarence Stone: So, I think the two biggest accounts are probably going to be ABC, which is a home,
109 00:18:37.210 ⇒ 00:18:44.989 Clarence Stone: Like, pest control, electrical, plumbing, like, a full-service, like, home,
110 00:18:45.140 ⇒ 00:19:08.390 Clarence Stone: services company that’s in Texas. They’ve got 17 locations, and they do all of these different things, and somehow they haven’t been acquired by PE, and they’re fighting against it by, you know, investing heavily in technology. But, like, I think short of, like, having better management, better tools, like, this organization has more of a collection of, you know, these services companies than any other I’ve ever seen.
111 00:19:08.390 ⇒ 00:19:16.299 Clarence Stone: So they’re a massive account with, right now, 3 concurrent projects. We’re probably, you know, slating another
112 00:19:16.300 ⇒ 00:19:17.810 Clarence Stone: 3, probably.
113 00:19:18.070 ⇒ 00:19:25.040 Clarence Stone: And average value of each of those projects is, about 20 a month.
114 00:19:25.220 ⇒ 00:19:26.180 Clarence Stone: So, that’s.
115 00:19:26.180 ⇒ 00:19:26.730 Matthew Tracy: Okay.
116 00:19:26.730 ⇒ 00:19:48.430 Clarence Stone: And then we have Eden Pharmaceutical, which is another… it’s like a medical e-com. They’re selling things like GLP-1s and other healthcare products, and we’ve got tons of projects there, helping them with marketing strategy, positioning, and analytics on how they should target new users.
117 00:19:48.590 ⇒ 00:19:53.529 Clarence Stone: And that’s about the same size as ABC. We have…
118 00:19:54.520 ⇒ 00:20:12.959 Clarence Stone: some… I mean, other huge companies that we were doing little chunks of work at. Like, I don’t know if you’re familiar with, like, Magic Spoon or VitaCoco. We’ve got… where’s the other one? We’ve got Insomnia Cookies, just to name a few that’s, like, currently active projects.
119 00:20:13.810 ⇒ 00:20:20.550 Matthew Tracy: That’s awesome. That’s cool. I like… the transparency’s very, yeah, it’s kind of, like, sobering. That’s cool.
120 00:20:20.550 ⇒ 00:20:44.990 Clarence Stone: You know, I made this demand because of the environment I came from, right? There’s so much that I loved about being in Big Four. You know, the people I was with, the kind of work that we did, incredibly interesting, incredibly talented people, but there’s the structure of it that is really hard to overcome, especially in a new, you know, AI-first environment. So I kind of flipped everything on
121 00:20:45.450 ⇒ 00:20:56.760 Clarence Stone: on top of his head and said, like, for everything that was a challenge for Big Four, does it still need to be that way, and is there something we can do to make that whole cycle easier?
122 00:20:57.570 ⇒ 00:21:00.470 Matthew Tracy: That’s awesome. Yeah, I love that. I love that.
123 00:21:01.250 ⇒ 00:21:10.480 Matthew Tracy: Okay, that’s super cool. I’ve been hitting you rapid fire with questions. I have some more, probably, but I… I mean, if you want to ask me anything as well, too, I’ll pause for a second.
124 00:21:10.480 ⇒ 00:21:12.899 Clarence Stone: Yeah, keep going, keep going, I love these questions.
125 00:21:13.020 ⇒ 00:21:27.040 Matthew Tracy: Okay, that’s cool. So regarding the team structure, I was noticing that you guys… seems like you’re hiring in different places. Do you guys have, like, a goal? Like, I know you probably know, like, with Big Four, sometimes we operate around the sun.
126 00:21:27.040 ⇒ 00:21:43.979 Matthew Tracy: like, with me and my team, like, we have half our team is in India, half our team’s onshore, so we do our work during the day. We’re leading that, and then we hand it over to the team, and then we work around the sun. Do you guys have any, like, objectives to do, like, the same type of thing? I saw there’s some people from the Philippines, someone from Pakistan. Do you guys have, like, a specific
127 00:21:43.980 ⇒ 00:21:48.850 Matthew Tracy: Place you’re trying to hire from, or just, like, anywhere as long as, like, their work is good and high quality?
128 00:21:48.850 ⇒ 00:22:13.840 Clarence Stone: Yeah, so great question, Matt. Like, I think I am echoing the entire leadership team’s perspective when I say this, that we really don’t care where people are living. We want to hire the best talent. So, like, that’s why you might see people from different locations. Many times, it’s actually people who are living in the States, due to life circumstances.
129 00:22:13.840 ⇒ 00:22:19.899 Clarence Stone: ended up moving back, or, you know, deciding that they want to live in Austria for a year or two, and…
130 00:22:20.510 ⇒ 00:22:45.469 Clarence Stone: the way we operate, allows that to happen. We do have, you know, standard East Coast working hours, and really the perspective is as long as you make all your meetings, like, it doesn’t matter when you want to do your, you know, focus work. So, most people are able to hit, you know, the morning series of meetings, maybe a couple touch points in the afternoon, and then just figure out when they want to do their
131 00:22:45.470 ⇒ 00:22:48.480 Clarence Stone: Productivity work, you know, in between.
132 00:22:49.090 ⇒ 00:22:57.670 Matthew Tracy: Okay, that makes sense. Okay, that’s clear. And then one thing that I was curious about as well, too, is… and this might… like, I’m not really sure how the structure of…
133 00:22:57.790 ⇒ 00:23:07.180 Matthew Tracy: the role would be for me, but would there be… do you guys do profit sharing? Is there revenue sharing? Yeah, maybe. Do you guys have any thoughts on that?
134 00:23:07.180 ⇒ 00:23:32.149 Clarence Stone: Yeah, so let me explain that piece to you, too. And we’re flexible on this, because we’re definitely looking to hire a lot more people, but the way Brainforge has worked over the last 2 years is that they bring people on conditionally for about 3 months, and that allows, you know, people to adjust to the environment and decide if they actually want to work at Brainforge.
135 00:23:32.150 ⇒ 00:23:56.979 Clarence Stone: It gives them some time to decide, you know, where their best fit in the organization is. And then, you know, the full-time placement happens after that. In terms of comp structure, we are, like, angled heavily towards outcome-based rewards. So, what I mean by that is 5% profit sharing for new, projects that are sold to existing clients.
136 00:23:56.980 ⇒ 00:24:21.910 Clarence Stone: 5% for the first 6 months, and 10% of the first 6 months of revenue coming in from new client business. So, while, you know, some of the standard salaries might look lower than industry average, if you’re contributing to go-to-market, if you’re hitting your metrics, and hitting your success goals, and also winning some additional work, I’ve seen people, you know, take home in excess of $200K.
137 00:24:23.350 ⇒ 00:24:24.540 Matthew Tracy: Okay, that’s fair.
138 00:24:24.640 ⇒ 00:24:42.560 Matthew Tracy: And then, so regarding that, like, 5 and 10%, so 5% new projects, same client for the first 6 months, 10% for new projects, new clients for 6 months, and then, say, for example, it was, like, a team cell or a duo sell, would that, like, cut it to 2.5 and 5, or how would that work?
139 00:24:42.860 ⇒ 00:24:55.720 Clarence Stone: Yeah, so, the way we do pursuits is typically kind of driven entirely by me and the two co-founders, so, like, it becomes pretty easy for us to, to, to,
140 00:24:55.720 ⇒ 00:25:07.459 Clarence Stone: you know, say, hey, Matt, this is, you know, your deal, you’re helping us drive this. So, once, you know, that message is clearly given, you’re the one getting the bonus here.
141 00:25:08.590 ⇒ 00:25:10.350 Matthew Tracy: Makes sense, that’s clear.
142 00:25:11.800 ⇒ 00:25:26.939 Matthew Tracy: Yeah, I’d be curious, like, when we… if we keep going down the line with this, and it seems like you guys think I’m a good fit, I’d be curious to know more about, like, the floor, and then the ceiling. Like, you’re saying the ceiling’s, like, 200, I just want to know, like, how high it could go, and then…
143 00:25:26.940 ⇒ 00:25:27.509 Clarence Stone: Oh, so…
144 00:25:27.510 ⇒ 00:25:28.270 Matthew Tracy: That’d be nice.
145 00:25:28.270 ⇒ 00:25:51.980 Clarence Stone: salary limit, so I can… I can put that one to the side. But in terms of floor, I… I will pass it to Utam to kind of give you that full breakdown, but we do have, like, three, brackets that we would place you in, in terms of your experience level, and that would, you know, create your base salary. There’s going to be a 5% bonus that happens every 6 months if you hit all the KPIs for your
146 00:25:51.980 ⇒ 00:25:57.909 Clarence Stone: role, and then, all the other, you know, bonuses that come along with getting GTM work.
147 00:25:58.380 ⇒ 00:26:10.310 Matthew Tracy: Okay, cool. So it’s a 5% bonus every 6 months based on base salary, and that is… so essentially, it’s like, is it two 5% bonuses, or a 5% bonus split every 6 months?
148 00:26:10.620 ⇒ 00:26:13.910 Clarence Stone: So, two 5% bonuses, completely separate.
149 00:26:14.300 ⇒ 00:26:15.100 Matthew Tracy: Okay, cool.
150 00:26:16.500 ⇒ 00:26:18.690 Matthew Tracy: Yeah, alright, cool, that sounds awesome.
151 00:26:19.260 ⇒ 00:26:19.850 Matthew Tracy: Nice.
152 00:26:19.850 ⇒ 00:26:26.790 Clarence Stone: Yeah, so, like, long story short, this structure is designed, like, with
153 00:26:26.890 ⇒ 00:26:42.760 Clarence Stone: the, you know, the aspect of rewarding people’s contributions, right, more, and over-indexing on that versus, you know, giving a standard flat salary, that’s pretty high.
154 00:26:42.760 ⇒ 00:27:07.659 Clarence Stone: And the intention there is, like, we want people to get involved in helping solve, like, the market problems and help us win more work. We are also working on creating, like, this bounty board of, like, other items that are on our backlog that we just haven’t tackled, right? It could be an AI agent that helps, you know, create SOWs. By the way, we already have that, but, like, if that was a bounty, we’d put it on the board and say, hey, this is worth.
155 00:27:07.910 ⇒ 00:27:08.919 Matthew Tracy: I like that.
156 00:27:09.240 ⇒ 00:27:14.469 Clarence Stone: If you want to pick that up, you know, that’s a bonus you can also take home. So.
157 00:27:14.470 ⇒ 00:27:14.910 Matthew Tracy: Cool.
158 00:27:14.910 ⇒ 00:27:20.290 Clarence Stone: really about how much you want to show up, how much you want to contribute beyond the minimum, right?
159 00:27:20.290 ⇒ 00:27:21.279 Matthew Tracy: Yeah, that’s awesome.
160 00:27:21.280 ⇒ 00:27:27.220 Clarence Stone: up and, you know, do check the block work? You know, that’s why the base salary is kind of reflected in that way.
161 00:27:27.780 ⇒ 00:27:35.390 Matthew Tracy: Okay, that’s cool. Yeah, I like that. I like the upside. That’s fun. The bounty board, that’s funny. I like that.
162 00:27:35.390 ⇒ 00:27:44.480 Clarence Stone: Yeah, like, the whole point is, like, I want everyone to feel the immediate, you know, financial benefit to the contributions they make.
163 00:27:44.480 ⇒ 00:28:05.789 Clarence Stone: And, like, we want to work towards creating a meritocracy on everything that we do, instead of saying, hey, we’ll kind of look at how the entire firm did at the end of the year and just, you know, basically decide how much we want to give a bonus out for. That doesn’t seem directly correlated or connected to the work that you’re contributing, so it didn’t feel right to do it that way.
164 00:28:06.220 ⇒ 00:28:11.469 Matthew Tracy: Yeah, that’s so cool. I like this. This is interesting. This is really cool. That’s super neat.
165 00:28:11.890 ⇒ 00:28:17.540 Clarence Stone: Well, on that note, then, I’ll keep doubling down on the things that make this organization incredibly unique.
166 00:28:17.610 ⇒ 00:28:25.929 Clarence Stone: the one biggest change that I made compared to, like, organizational structures that you may see at Big Four is I’ve gotten rid of
167 00:28:25.930 ⇒ 00:28:45.000 Clarence Stone: the PMBA role entirely. Instead, we are providing tons of automations. For example, you know, that platform I was talking about that records meetings. It also goes through meeting notes and generates suggestions for linear tickets that go directly into your linear feed.
168 00:28:45.000 ⇒ 00:28:46.270 Clarence Stone: It also suggests…
169 00:28:46.270 ⇒ 00:28:46.670 Matthew Tracy: Nice.
170 00:28:46.670 ⇒ 00:28:56.079 Clarence Stone: do-list items that get assigned out to people who are on the call, so that, like, there’s not too much overhead for people doing PM and BA-type roles.
171 00:28:56.380 ⇒ 00:28:56.840 Matthew Tracy: Nice.
172 00:28:57.430 ⇒ 00:29:22.110 Clarence Stone: And on top of that, instead of, like, having a PM that may or may not know the topic or the technology, or what you’re doing, right? I split that product role into three different pieces. So there’s the client success owner, who’s going to make sure that the relationship on the account is well connected with the client. There’s the planner that’s going to make sure that, you know, everything is going to plan.
173 00:29:22.110 ⇒ 00:29:32.609 Clarence Stone: And, they’re tracking each of the milestones and, the weekly accomplishments. And then lastly, there’s a service leader or, like, an SME that knows that topic really well.
174 00:29:32.610 ⇒ 00:29:48.169 Clarence Stone: These are leadership roles that get added on top of, you know, what you would normally do, so if you’re an analyst and a CSO, you’re doing your day-to-day analyst work, but you’ll probably contribute 20% of your time making sure that you’re connecting with the client and maintaining quality client relationships.
175 00:29:48.570 ⇒ 00:29:49.870 Matthew Tracy: Yeah, it’s perfect.
176 00:29:49.870 ⇒ 00:29:54.470 Clarence Stone: Yeah, so people kind of kick in, right, as, as,
177 00:29:54.520 ⇒ 00:30:17.239 Clarence Stone: as contributors to a project to fill in this, like, gap that exists still. But, Matt, the goal, right, is we continue to automate more and more pieces of it, so that we will never have to have a PM or BA, and we just whittle out all that time that’s being eaten up by admin and organizational things.
178 00:30:17.240 ⇒ 00:30:19.369 Matthew Tracy: That’s awesome. Yeah, that’s great.
179 00:30:19.390 ⇒ 00:30:37.789 Matthew Tracy: And I like that there’s a freedom, too, to kind of, like, build different automation stuff, because as you know, like, big four-wise, like, that’s not something that’s, like, as easy to do. It’s like, you could have the best idea, but then at the end of the day, it’s just gonna kind of sit or not get completely utilized, or maybe they won’t even let you have the tool or give you the opportunity to build it.
180 00:30:38.110 ⇒ 00:30:50.759 Clarence Stone: Yeah, so Matt, I will even quadruple down on that and say, great news, we have an internal platform team. And the head of the internal platform team’s job is to actually take in your requirements.
181 00:30:50.760 ⇒ 00:31:15.080 Clarence Stone: create environments for you so that you can actually build it if you want to build it, or build it for you, right? And, you know, actually create these AI automations to help you, you know, find a better workflow. So, let’s just say that you join a new, project, and you realize, hey, there’s just, like, no way that we should be spending this much time on doing architectural diagrams, for example.
182 00:31:15.080 ⇒ 00:31:34.790 Clarence Stone: And you go, I have an idea on how we can, you know, get to an 80% automation of this for… that’ll work for most projects, right? You put those requirements together, you can, you know, send that to the platform team, have them build it, or, you know, you can decide you want to build it, and they’ll set up an environment for you to do it, and it’ll just end up on our platform website for everyone to use.
183 00:31:35.780 ⇒ 00:31:37.370 Matthew Tracy: That’s awesome. That’s cool.
184 00:31:37.790 ⇒ 00:31:44.170 Matthew Tracy: That’s super neat. And then what… so with that internal platform team, where… where would they build? Like, what environment would they be using?
185 00:31:44.460 ⇒ 00:31:44.810 Clarence Stone: So…
186 00:31:44.810 ⇒ 00:31:45.320 Matthew Tracy: But it…
187 00:31:45.320 ⇒ 00:31:54.220 Clarence Stone: There’s already an internal, like, an operations website. We have a platform that consolidates all the custom-built tools that we’ve already created.
188 00:31:54.220 ⇒ 00:31:54.720 Matthew Tracy: Okay.
189 00:31:54.720 ⇒ 00:32:10.739 Clarence Stone: There are tools like, you know, automated SOW generation to, helping you with your daily stand-up, so it’ll look at all the tickets that you closed on Linear the day before, and look at things that are still assigned to you, and say, like, hey, here’s your summary for what stand-up is.
190 00:32:10.900 ⇒ 00:32:14.290 Matthew Tracy: So, it covers the gamut.
191 00:32:14.290 ⇒ 00:32:21.579 Clarence Stone: all sorts of tools, you know, I’ll be very transparent that we haven’t, you know, actually figured out ways to
192 00:32:21.580 ⇒ 00:32:40.020 Clarence Stone: kind of connect all of them in a linear way. It’s really just like a bucket of tools right now. So, you know, that’s going to be our next year’s approach to kind of clean that up, operationalize it, improve the usability, right? Refine on all the tools that already exist, and continue to build new ones.
193 00:32:40.630 ⇒ 00:32:42.339 Matthew Tracy: That’s cool. I like that.
194 00:32:44.720 ⇒ 00:32:45.810 Matthew Tracy: Super neat.
195 00:32:47.680 ⇒ 00:32:50.420 Matthew Tracy: Yeah, I can just totally see that. That’s awesome.
196 00:32:50.980 ⇒ 00:32:53.150 Clarence Stone: Yeah. Anything else?
197 00:32:54.400 ⇒ 00:33:04.700 Matthew Tracy: I know we’re right at time. Yeah, I’ll keep it there, but this was great connecting with you. I really appreciate it. It was nice to learn a little bit more, and hopefully we can connect again. This is great.
198 00:33:04.700 ⇒ 00:33:24.260 Clarence Stone: Yeah, absolutely, Matt. So, if any questions come up between now and whenever we reach out to you again, feel free to send me or Utam or Rico, an email, and we’ll be happy to answer any of your questions. I’m gonna get back to Utam and let him know I had a great conversation with you, and we’ll figure out what next steps are if you’re still interested.
199 00:33:24.550 ⇒ 00:33:26.389 Matthew Tracy: Awesome, thanks so much, yeah, definitely.
200 00:33:26.620 ⇒ 00:33:29.029 Clarence Stone: Awesome. Thank you. Thanks, Matt!
201 00:33:29.030 ⇒ 00:33:30.700 Matthew Tracy: Thanks, have a great one. Bye-bye.
202 00:33:30.920 ⇒ 00:33:31.650 Clarence Stone: Bye!