Feb. 23, 2026
RIA Collective: Sam Sova | AI Coworkers Are Changing Advisory Firms
AI is everywhere in financial services, but most advisors are only scratching the surface. In this episode of RIA Collective, host Charlie Van Derven sits down with Sam Sova, Co-Founder of Subatomic AI, to explore how artificial intelligence is moving beyond basic tools and becoming a true collaborative partner inside advisory firms. Sam shares how RIAs can use AI to unify data across CRMs, custodians, portfolio platforms, email, and communication tools. The result is fewer manual tasks, more...
AI is everywhere in financial services, but most advisors are only scratching the surface.
In this episode of RIA Collective, host Charlie Van Derven sits down with Sam Sova, Co-Founder of Subatomic AI, to explore how artificial intelligence is moving beyond basic tools and becoming a true collaborative partner inside advisory firms.
Sam shares how RIAs can use AI to unify data across CRMs, custodians, portfolio platforms, email, and communication tools. The result is fewer manual tasks, more efficiency, and the ability to break through growth ceilings without adding headcount.
Together, Charlie and Sam discuss:
- Why AI is at a “1995 moment,” similar to the early internet
- How advisors are losing more than 40% of their time to tasks AI can now handle
- Why meeting preparation is one of the biggest hidden time drains in advisory firms
- How AI coworkers can automatically generate agendas, insights, and recommendations
- The future of personalized client journeys and proactive touchpoints
- How Subatomic approaches security, compliance, and transparency for RIAs
- Why AI is not about replacing people, but about doing more with the same team
Sam also explains how Subatomic helps firms save hundreds, and in some cases thousands, of hours per year. That time can be reinvested where it matters most: relationships, trust, and growth.
This episode is especially valuable for:
- Independent advisors and RIAs
- Firms hitting a growth ceiling
- Advisors curious about AI beyond note-taking tools
- Leaders planning for the next decade of advice
🎧 Watch now and subscribe to RIA Collective for conversations that help independent advisors grow, adapt, and lead the future of financial advice.
Connect with Sam Sova
🌐 Subatomic AI: https://subatomic.ai
Connect with Charlie Van Derven
🌐 Social Advisors: https://www.social-advisors.com
💼 LinkedIn: https://www.linkedin.com/in/charlievanderven/
Chapters
00:00 – Why this episode is different
03:00 – AI’s “1995 moment”
07:45 – Where advisors lose the most time
11:00 – AI-powered meeting preparation explained
15:30 – Unifying data across advisor technology stacks
19:00 – Security, compliance, and transparency
22:30 – The future of AI coworkers in RIAs
WEBVTT
00:00:16.399 --> 00:00:19.976
Thank you for joining me on another episode of RIA, a Collective.
00:00:19.976 --> 00:00:22.166
I'm your host, as always, Charlie van Derven.
00:00:22.446 --> 00:00:26.016
We're going in a little bit of a different direction with with this particular episode.
00:00:26.332 --> 00:00:41.781
Where those of you who have listened in the past, typically what we do is we're helping advisors transition into an independent space increase the trust in the industry, where I think that fiduciary role within an RIA certainly is impactful for some of the trust issues that financial services suffers.
00:00:42.182 --> 00:00:47.712
Today we'll go in a little different direction because I met a gentleman along the way named Sam Sova.
00:00:47.942 --> 00:00:48.841
Sam, welcome.
00:00:49.292 --> 00:00:49.597
Thanks for having
00:00:49.597 --> 00:00:49.716
me.
00:00:50.246 --> 00:00:51.266
Yeah, you got it, man.
00:00:51.266 --> 00:00:57.957
So Sam runs a a company called Subatomic ai, and from what I can tell, there's some tools out there in the industry.
00:00:57.957 --> 00:01:02.646
Of course, it's in its infancy but there's some AI tools in the industry that are doing some things.
00:01:02.646 --> 00:01:04.176
Sam, jump Zox.
00:01:04.176 --> 00:01:09.016
Those are, some of the call recording and CRM integrations that we see a lot of.
00:01:09.426 --> 00:01:14.887
You've taken it a little bit beyond, beyond what I'll call the tip of the iceberg with those tools.
00:01:15.227 --> 00:01:22.681
And, I don't know, from what I can tell, saving advisors hundreds of hours a year dozens of hours a month and meeting prep.
00:01:22.681 --> 00:01:25.201
That's an exciting use case scenario.
00:01:25.201 --> 00:01:34.111
And so I just wanna dive into that and, make our listeners aware of some of the front edge, front end tools that are out there, I should say, front edge tools that are out there because you guys are leading the way.
00:01:34.111 --> 00:01:35.132
Very much sam.
00:01:35.621 --> 00:01:36.312
Welcome, man.
00:01:36.312 --> 00:01:40.002
I do appreciate you spending some time with us and our listeners at RIA Collective.
00:01:40.481 --> 00:01:40.752
Yeah.
00:01:40.752 --> 00:01:41.831
Thanks again for having me, Charlie.
00:01:42.102 --> 00:01:42.882
Yeah, you got it.
00:01:42.882 --> 00:01:45.312
And we come from the same stomping grounds before we hit record.
00:01:45.312 --> 00:01:47.621
We were talking about cold, but Sam's in Wisconsin.
00:01:47.621 --> 00:01:52.871
I grew up in Wisconsin, so I don't think we talked about it, but maybe we got some Green Bay packer affiliation.
00:01:52.871 --> 00:01:54.132
Hopefully not Chicago bears,
00:01:54.181 --> 00:01:55.007
yes, absolutely.
00:01:55.331 --> 00:01:55.961
Packers
00:01:55.961 --> 00:01:56.352
all the way.
00:01:56.801 --> 00:01:57.551
Very good, man.
00:01:57.551 --> 00:01:58.002
Very good.
00:01:58.002 --> 00:02:02.141
I don't know if I told you on our call, Sam, I brought a packer player to third grade show and tell.
00:02:04.371 --> 00:02:05.752
I have a story for you too.
00:02:05.752 --> 00:02:09.641
I actually Ray Nitschke watched me and my friends in first grade play football.
00:02:10.032 --> 00:02:10.391
That's my story.
00:02:10.806 --> 00:02:11.016
Very cool.
00:02:12.246 --> 00:02:15.456
My first job outta a college, and we're gonna go quick through this 'cause I want to get into subatomic.
00:02:15.456 --> 00:02:23.197
My first job outta a college was at a Radisson Inn in Green Bay, which is where all the personalities in town would stay when they came for NFL games.
00:02:23.197 --> 00:02:27.187
And and got a chance to meet Ray Tke shortly before he passed away.
00:02:27.647 --> 00:02:32.687
And a whole bunch of Packer players and all the NFL people like John Madden and Pat Summerall that would come through town.
00:02:32.687 --> 00:02:33.287
And so anyway,
00:02:33.467 --> 00:02:33.887
love that.
00:02:33.887 --> 00:02:35.266
I was extremely starstruck.
00:02:35.266 --> 00:02:39.917
I think, I'd be more starstruck over that than than a Hollywood celebrity not Absolutely.
00:02:39.917 --> 00:02:41.057
It's just my bias in life.
00:02:41.236 --> 00:02:41.567
Awesome.
00:02:41.567 --> 00:02:41.807
I'm
00:02:41.807 --> 00:02:42.106
jealous.
00:02:42.106 --> 00:02:42.407
Yeah, man.
00:02:42.527 --> 00:02:43.051
Yeah, man, that's awesome.
00:02:43.557 --> 00:02:44.606
Let's jump in, man.
00:02:44.687 --> 00:02:45.236
Listen I I would.
00:02:45.516 --> 00:02:46.777
Say, what was it you?
00:02:46.777 --> 00:02:57.097
You can correct me if I'm wrong, but chat GPT launched November 22, I believe it was, might've been 21 if we've been using it that long, whatever it was by January, 2023.
00:02:57.097 --> 00:03:05.947
So a couple months later we were monitoring what that was all about and we started incorporating AI into social advisors, specifically content creation.
00:03:05.997 --> 00:03:07.917
Was the simplest way to jump into it.
00:03:08.397 --> 00:03:10.407
It changed the way I do business.
00:03:10.407 --> 00:03:12.747
It changed the way we structured our business.
00:03:12.847 --> 00:03:15.236
Here we are, three years beyond that.
00:03:15.236 --> 00:03:17.817
Sam, you've got your finger on the pulse more than I do.
00:03:18.486 --> 00:03:22.567
Dude, I'm stoked to hear about Subatomic and dive into some of the details.
00:03:22.567 --> 00:03:30.266
So how did you, first off, you I grew up in this industry, from doing websites way back in the day To where we are today.
00:03:30.627 --> 00:03:37.167
How did you choose financial services as where you wanted to stake your, stake your flag and jump in with an AI tool?
00:03:37.647 --> 00:03:38.217
Yeah, sure.
00:03:38.266 --> 00:03:41.537
So we started in 2023, and I have a co-founder, his name's Carl Simon.
00:03:41.537 --> 00:03:42.331
He is our CTO.
00:03:43.027 --> 00:03:45.067
And Carl was building complex projects.
00:03:45.067 --> 00:03:46.777
I was doing more of the consulting side.
00:03:47.127 --> 00:03:53.477
We come from the Fortune five hundreds doing the same thing, figuring out challenges, how do you solve 'em with technology?
00:03:53.477 --> 00:03:56.356
And that's really what we were doing at the early stages of ai.
00:03:56.926 --> 00:04:08.807
And what we found very quickly, you start talking to your network and your friends, and we found out that after talking to hundreds of leaders in this industry, there were common problems that we could really address with this long term.
00:04:09.391 --> 00:04:11.912
That were not just point and click solutions, right?
00:04:11.912 --> 00:04:11.972
Yeah.
00:04:12.002 --> 00:04:19.387
That were major pain points, not just for efficiency, but to really drive growth in an industry that is completely changing.
00:04:20.351 --> 00:04:21.221
So that's where we started.
00:04:22.341 --> 00:04:23.122
Very cool, man.
00:04:23.122 --> 00:04:27.921
I one of the things that keeps a lot of people out of financial services is compliance.
00:04:27.921 --> 00:04:30.351
And so I think we need to talk a little bit about that today.
00:04:30.351 --> 00:04:36.771
But before we dive in, what were some of those problems that you and you and Carl identified that you guys, were positioned to create solutions for?
00:04:37.101 --> 00:04:43.691
Yeah, at a macro level, when you think about technology, it was, Hey, we're experimenting with ai.
00:04:43.976 --> 00:04:46.526
But it's, it's just another tool to the tool set.
00:04:46.526 --> 00:04:48.866
So I have 15 tools I have to log into every day.
00:04:48.866 --> 00:04:50.846
Now I'm adding 16, 17, 18.
00:04:51.356 --> 00:04:56.781
The tools we have some AI capabilities, but they're just kinda wrappers on the current tool that we have.
00:04:57.596 --> 00:04:58.887
It's not moving the needle.
00:04:59.036 --> 00:05:02.866
They don't move they don't actually work into our workflows.
00:05:03.226 --> 00:05:13.516
And I just don't have this unified view of our data, which I feel, and this is comments we got, the people we talked to felt like that should be possible now with ai.
00:05:13.566 --> 00:05:15.336
So that's where we started.
00:05:15.336 --> 00:05:24.007
And then at a macro level, just 41%, it depends where you look, but the stat I have, 41% of advisor time is spent on tasks that.
00:05:24.322 --> 00:05:26.632
Quite frankly, we don't believe they have to do anymore.
00:05:26.931 --> 00:05:28.491
They need to be in front of clients.
00:05:28.822 --> 00:05:28.882
Yeah.
00:05:28.921 --> 00:05:32.191
And that is where the value is in this space is relationships.
00:05:32.401 --> 00:05:34.441
And that's what we've no questions over and over.
00:05:34.801 --> 00:05:44.981
So when we talk about growth, like what we're creating and what I'm gonna talk about further with you, Charlie, is really to break through that growth ceiling where we can do more with same, right?
00:05:44.981 --> 00:05:49.781
So we can grow without necessarily hiring incremental people to do backend work.
00:05:50.186 --> 00:05:54.656
Which I think is extremely interesting to all the RAs that we talked to.
00:05:55.166 --> 00:05:55.466
Yeah.
00:05:55.466 --> 00:05:58.016
I'm sure, man, and I we're gonna talk a little bit about meeting prep.
00:05:58.016 --> 00:05:58.106
Sure.
00:05:58.526 --> 00:06:05.016
I can only imagine, if you've got, 150 clients and pot, potentially that's 300 review meetings a year.
00:06:05.596 --> 00:06:08.326
One for just about every day when you pull the weekends out.
00:06:08.716 --> 00:06:08.776
Yeah.
00:06:08.856 --> 00:06:13.836
There's gotta be an hour of prep, whether it's assistant or the advisor themselves.
00:06:14.186 --> 00:06:18.236
Putting time into meeting prep, and I know we're gonna, we're gonna talk a little bit about that.
00:06:18.236 --> 00:06:29.796
And just, in, in that, in that scenario, of course they've gotta review everything, so maybe it's 15 minutes instead of an hour, but in that scenario, you're saving hundreds, 200, 300 hours a year.
00:06:30.246 --> 00:06:30.576
Yeah.
00:06:30.906 --> 00:06:31.206
Yeah.
00:06:31.206 --> 00:06:32.711
So let's you wanna dig into that?
00:06:32.711 --> 00:06:34.871
We can dig into meeting prep and some of the other things.
00:06:35.361 --> 00:06:35.691
Yeah.
00:06:35.691 --> 00:06:38.361
There, there's a couple things that I, I wanna make sure we talked about meeting prep.
00:06:38.361 --> 00:06:45.221
I also when we were getting acquainted you talked about the client or the prospect journey and, more touch points, but also more meaningful touch points, right?
00:06:45.221 --> 00:06:47.231
Based on who that person is versus.
00:06:47.586 --> 00:06:51.697
We do a lot of marketing automation, and in, in many cases, the message is the same.
00:06:52.126 --> 00:06:59.666
We segment people within that marketing automation, but if you fit in that segment, the messaging is the same for you as it would be the next person in that segment.
00:06:59.666 --> 00:07:02.576
So let's start with meeting prep and then we'll get into the more touch points.
00:07:03.236 --> 00:07:08.066
Yeah, and I can talk macro too, to actually, if you're okay with that.
00:07:08.066 --> 00:07:11.246
I'd like to start there on how we think of this and then roll in a meeting prop.
00:07:11.556 --> 00:07:11.736
Dude, it's
00:07:11.911 --> 00:07:12.661
Your time.
00:07:13.066 --> 00:07:19.637
So I, so we, just taking a step back, we think AI is almost at that 1995 moment.
00:07:19.666 --> 00:07:21.346
We talked about this a little bit, Charlie, right?
00:07:21.346 --> 00:07:21.406
Yeah.
00:07:21.436 --> 00:07:28.016
Like email come, comes up websites and like we when that happened.
00:07:28.841 --> 00:07:32.531
Nobody really had the context to what was going to come next.
00:07:32.831 --> 00:07:35.111
And what came next was dramatic, right?
00:07:35.111 --> 00:07:42.701
You had social media, you had the iPhone, Blackberry, you had different hardware and work changed forever after that, right?
00:07:42.701 --> 00:07:42.762
Yeah.
00:07:43.031 --> 00:07:44.501
I don't think anybody can argue that.
00:07:44.986 --> 00:07:46.576
Work and our lives change.
00:07:46.906 --> 00:07:49.066
We actually think the same thing with ai, right?
00:07:49.066 --> 00:07:55.526
So many people are thinking of this as just the next phase of technology of what they're currently doing, hence the wrappers.
00:07:55.526 --> 00:08:03.936
And some point and click solutions, like note takers, they're great, but that's not gonna dramatically change the growth trajectory of your IRA.
00:08:04.507 --> 00:08:08.766
So we think of this as like this is the pivotal moment that it's going to change.
00:08:08.766 --> 00:08:11.286
So what is the first thing you have to do, right?
00:08:11.466 --> 00:08:12.431
You have to unify our data.
00:08:13.801 --> 00:08:20.251
RAs in general have 15 different tools that they're accessing on a regular basis to get client information.
00:08:21.211 --> 00:08:23.911
So the way we look at it is you don't have to get rid of your tools.
00:08:24.151 --> 00:08:29.411
You already trust these tools a lot of them you need to use to make transactions and so on.
00:08:29.651 --> 00:08:36.927
So we orchestrate across all those tools, essentially create what those are familiar with of a data warehouse, data lake.
00:08:37.706 --> 00:08:42.297
Put a vector store on top of it, which makes it easy for AI to access.
00:08:42.567 --> 00:08:46.197
And when we do that, we have one view of our entire business.
00:08:46.346 --> 00:08:48.447
More importantly, one view of every customer.
00:08:48.986 --> 00:08:57.057
And now when you incorporate AI meeting prep you now have access to be able to do that in a matter of seconds versus ours.
00:08:57.246 --> 00:08:58.536
And that's how we look at it.
00:08:58.596 --> 00:08:59.886
We look at AI as.
00:09:00.562 --> 00:09:11.047
The fundamental thing you need to do is this orchestration and then the possibilities of what we do next to significantly grow the business is really endless of the possibilities we can do.
00:09:12.197 --> 00:09:12.797
That's pretty cool.
00:09:12.797 --> 00:09:15.496
So when we talk about tools, we're obviously talking about CRM, right?
00:09:15.797 --> 00:09:15.976
Yeah.
00:09:15.976 --> 00:09:18.736
Redtail Wealthbox and Salesforce derivatives.
00:09:18.736 --> 00:09:23.456
And then, for a lot of our clients they've got a client, CRM, and then they've got a prospect, CRM.
00:09:23.456 --> 00:09:25.226
Yeah, we are, we're a reseller.
00:09:25.226 --> 00:09:26.006
Go high level.
00:09:26.006 --> 00:09:30.096
But there's other, there's other good platforms out there portfolio tools, right?
00:09:30.096 --> 00:09:35.146
Orion and, black Diamond and these types of portfolio management tools.
00:09:35.486 --> 00:09:36.536
What else do you put in there?
00:09:37.031 --> 00:09:38.201
Custodial platforms.
00:09:38.381 --> 00:09:39.341
Custodial platforms,
00:09:39.641 --> 00:09:39.761
right.
00:09:39.822 --> 00:09:40.631
Email.
00:09:40.631 --> 00:09:40.641
Email.
00:09:40.871 --> 00:09:44.432
Most RAs have now dabbled with note takers, right?
00:09:44.761 --> 00:09:44.911
Yep.
00:09:44.971 --> 00:09:49.531
Client communications via email, RingCentral, if we're using that for text.
00:09:49.531 --> 00:09:49.542
Yeah.
00:09:49.542 --> 00:09:49.781
Yeah.
00:09:49.817 --> 00:09:50.026
Yeah.
00:09:50.026 --> 00:09:50.567
It goes on.
00:09:50.616 --> 00:09:56.256
The way that we've orchestrated this is like API connections are not good enough, right?
00:09:56.256 --> 00:09:57.456
Because if.
00:09:57.956 --> 00:10:03.086
Something on the endpoint changes or something with the host changes, like it breaks everything.
00:10:03.506 --> 00:10:08.906
So we, so I mentioned co-founder Carl, our CTO.
00:10:08.937 --> 00:10:14.036
His background is deep in data and integrations for the last 25 years or so.
00:10:14.336 --> 00:10:17.276
So like we built this platform knowing that.
00:10:17.906 --> 00:10:19.197
APIs weren't gonna cut it.
00:10:19.197 --> 00:10:33.506
So we have proprietary technology where we can use what we call AI coworkers to actually go in and get all this data in the platforms that APIs, F-S-F-T-P servers, et cetera, just don't get us to give that full view.
00:10:33.836 --> 00:10:38.516
And now we can pull it into one centralized area and orchestrate AI across that.
00:10:38.807 --> 00:10:41.236
That to us, that is the sweet spot where.
00:10:41.896 --> 00:10:44.266
Growth is inevitable for whoever does that.
00:10:44.687 --> 00:10:45.406
That's awesome, man.
00:10:45.766 --> 00:10:46.126
That's awesome.
00:10:46.126 --> 00:10:46.307
Yeah.
00:10:46.307 --> 00:10:47.476
So where do you wanna start?
00:10:47.476 --> 00:10:48.677
You wanna talk about meeting prep, you wanna
00:10:48.677 --> 00:10:49.451
talk about points?
00:10:49.451 --> 00:10:49.692
Sure.
00:10:49.692 --> 00:10:50.866
So like that example is great.
00:10:50.917 --> 00:10:58.057
We, in listening to the market, we know that meeting prep is just a complete time suck, but it's necessary.
00:10:58.386 --> 00:10:58.447
Yeah.
00:10:58.447 --> 00:11:02.677
So if you think about what I just explained of orchestrating all your data, so let's think about.
00:11:03.937 --> 00:11:05.346
Client, let's say Jane Smith.
00:11:05.376 --> 00:11:07.206
Jane Smith is in our CRM.
00:11:07.417 --> 00:11:11.706
We have emails, we have texts we have maybe some calls we've recorded.
00:11:11.706 --> 00:11:16.417
We have our custodial data and then all these other ancillary tools, Charlie, like you mentioned, right?
00:11:16.927 --> 00:11:23.697
So what we're able to do is take all that information and now create an agenda for a meeting.
00:11:23.697 --> 00:11:27.807
It can be anything that an organization wants from two pages.
00:11:28.197 --> 00:11:30.116
To 10 pages to visual.
00:11:30.376 --> 00:11:36.496
The output is now actually fairly easy with the AI platform that we built, right?
00:11:36.496 --> 00:11:39.376
Because we have AI building this, that can be done faster than ever.
00:11:39.797 --> 00:11:39.856
Yeah.
00:11:39.886 --> 00:11:41.716
We can do it in a dashboard, et cetera.
00:11:42.047 --> 00:11:48.047
But now what we can do is we can actually have one of our AI coworkers go in the calendars of all the advisors.
00:11:48.601 --> 00:11:56.011
And a week before the meeting, send all their agendas for them to review ahead of time and then collaborate with our AI team.
00:11:56.011 --> 00:12:00.091
We call it AI coworkers, to make any modifications right ahead of the meeting.
00:12:00.331 --> 00:12:04.711
Sync the data to be real time, and they walk into that meeting ready to go.
00:12:05.341 --> 00:12:06.841
And it's not just automation.
00:12:06.871 --> 00:12:12.601
What we're talking about is AI actually making recommendations of what you should talk about based on age.
00:12:12.961 --> 00:12:14.281
Maybe where they live.
00:12:14.281 --> 00:12:18.812
Maybe they're where they're at in life, if they have kids in college, things like that, right?
00:12:18.812 --> 00:12:24.991
So it's not just pure automation, it's actually having our AI coworkers think and make recommendations for us.
00:12:25.057 --> 00:12:26.586
It's pretty impressive.
00:12:27.036 --> 00:12:27.846
That's pretty awesome.
00:12:27.846 --> 00:12:37.461
And I'm sure, as with anything, when you've got human involvement there's even, even if a seasoned advisor or a seasoned assistant is pulling notes together, there's gonna be gaps left.
00:12:37.562 --> 00:12:37.891
Right?
00:12:37.891 --> 00:12:40.981
Based on their biases, based on things they're not even considering.
00:12:40.981 --> 00:12:44.461
Maybe they didn't look at the last three emails before they jumped into that meeting.
00:12:45.111 --> 00:12:46.312
That's pretty awesome, man.
00:12:46.317 --> 00:12:46.456
Yeah.
00:12:46.776 --> 00:12:46.836
Yeah.
00:12:47.047 --> 00:12:52.461
And let me even paint a kind of a bigger picture of how we see the future, and these are things we're even doing today.
00:12:53.331 --> 00:12:54.711
Memory, right?
00:12:54.711 --> 00:12:56.031
Cognitive workflows.
00:12:56.031 --> 00:12:59.451
Maybe some of you that are listening have read about this.
00:12:59.531 --> 00:13:03.371
But we built this into our platform where it learns over time, right?
00:13:03.371 --> 00:13:11.501
So as you give feedback, as an individual advisor or a at a firm level, we can build that feedback and memory into.
00:13:11.856 --> 00:13:14.947
How we now give outputs in the future, right?
00:13:14.947 --> 00:13:21.786
So we like to think of this again, I keep saying AI coworker, like if you're gonna hire somebody right?
00:13:21.906 --> 00:13:25.807
As you hire them, you're gonna train them, you're gonna give them access to your tools and data.
00:13:26.016 --> 00:13:30.576
We think about the exact same thing with how we build our AI coworkers on top of your data.
00:13:30.966 --> 00:13:31.057
Yeah.
00:13:31.086 --> 00:13:36.966
And if you hire somebody new, you're going to give them feedback along the way, right?
00:13:36.966 --> 00:13:38.136
You're gonna train 'em up.
00:13:38.496 --> 00:13:44.346
Same thing with our AI coworkers, but they incorporate it super fast and we'll make the changes right away.
00:13:44.447 --> 00:13:46.516
And never forget the feedback that you gave 'em.
00:13:47.552 --> 00:13:48.871
That's that's pretty awesome, man.
00:00:16.399 --> 00:00:19.976
00:00:19.976 --> 00:00:22.166
00:00:22.446 --> 00:00:26.016
00:00:26.332 --> 00:00:41.781
00:00:42.182 --> 00:00:47.712
00:00:47.942 --> 00:00:48.841
00:00:49.292 --> 00:00:49.597
00:00:49.597 --> 00:00:49.716
00:00:50.246 --> 00:00:51.266
00:00:51.266 --> 00:00:57.957
00:00:57.957 --> 00:01:02.646
00:01:02.646 --> 00:01:04.176
00:01:04.176 --> 00:01:09.016
00:01:09.426 --> 00:01:14.887
00:01:15.227 --> 00:01:22.681
00:01:22.681 --> 00:01:25.201
00:01:25.201 --> 00:01:34.111
00:01:34.111 --> 00:01:35.132
00:01:35.621 --> 00:01:36.312
00:01:36.312 --> 00:01:40.002
00:01:40.481 --> 00:01:40.752
00:01:40.752 --> 00:01:41.831
00:01:42.102 --> 00:01:42.882
00:01:42.882 --> 00:01:45.312
00:01:45.312 --> 00:01:47.621
00:01:47.621 --> 00:01:52.871
00:01:52.871 --> 00:01:54.132
00:01:54.181 --> 00:01:55.007
00:01:55.331 --> 00:01:55.961
00:01:55.961 --> 00:01:56.352
00:01:56.801 --> 00:01:57.551
00:01:57.551 --> 00:01:58.002
00:01:58.002 --> 00:02:02.141
00:02:04.371 --> 00:02:05.752
00:02:05.752 --> 00:02:09.641
00:02:10.032 --> 00:02:10.391
00:02:10.806 --> 00:02:11.016
00:02:12.246 --> 00:02:15.456
00:02:15.456 --> 00:02:23.197
00:02:23.197 --> 00:02:27.187
00:02:27.647 --> 00:02:32.687
00:02:32.687 --> 00:02:33.287
00:02:33.467 --> 00:02:33.887
00:02:33.887 --> 00:02:35.266
00:02:35.266 --> 00:02:39.917
00:02:39.917 --> 00:02:41.057
00:02:41.236 --> 00:02:41.567
00:02:41.567 --> 00:02:41.807
00:02:41.807 --> 00:02:42.106
00:02:42.106 --> 00:02:42.407
00:02:42.527 --> 00:02:43.051
00:02:43.557 --> 00:02:44.606
00:02:44.687 --> 00:02:45.236
00:02:45.516 --> 00:02:46.777
00:02:46.777 --> 00:02:57.097
00:02:57.097 --> 00:03:05.947
00:03:05.997 --> 00:03:07.917
00:03:08.397 --> 00:03:10.407
00:03:10.407 --> 00:03:12.747
00:03:12.847 --> 00:03:15.236
00:03:15.236 --> 00:03:17.817
00:03:18.486 --> 00:03:22.567
00:03:22.567 --> 00:03:30.266
00:03:30.627 --> 00:03:37.167
00:03:37.647 --> 00:03:38.217
00:03:38.266 --> 00:03:41.537
00:03:41.537 --> 00:03:42.331
00:03:43.027 --> 00:03:45.067
00:03:45.067 --> 00:03:46.777
00:03:47.127 --> 00:03:53.477
00:03:53.477 --> 00:03:56.356
00:03:56.926 --> 00:04:08.807
00:04:09.391 --> 00:04:11.912
00:04:11.912 --> 00:04:11.972
00:04:12.002 --> 00:04:19.387
00:04:20.351 --> 00:04:21.221
00:04:22.341 --> 00:04:23.122
00:04:23.122 --> 00:04:27.921
00:04:27.921 --> 00:04:30.351
00:04:30.351 --> 00:04:36.771
00:04:37.101 --> 00:04:43.691
00:04:43.976 --> 00:04:46.526
00:04:46.526 --> 00:04:48.866
00:04:48.866 --> 00:04:50.846
00:04:51.356 --> 00:04:56.781
00:04:57.596 --> 00:04:58.887
00:04:59.036 --> 00:05:02.866
00:05:03.226 --> 00:05:13.516
00:05:13.566 --> 00:05:15.336
00:05:15.336 --> 00:05:24.007
00:05:24.322 --> 00:05:26.632
00:05:26.931 --> 00:05:28.491
00:05:28.822 --> 00:05:28.882
00:05:28.921 --> 00:05:32.191
00:05:32.401 --> 00:05:34.441
00:05:34.801 --> 00:05:44.981
00:05:44.981 --> 00:05:49.781
00:05:50.186 --> 00:05:54.656
00:05:55.166 --> 00:05:55.466
00:05:55.466 --> 00:05:58.016
00:05:58.016 --> 00:05:58.106
00:05:58.526 --> 00:06:05.016
00:06:05.596 --> 00:06:08.326
00:06:08.716 --> 00:06:08.776
00:06:08.856 --> 00:06:13.836
00:06:14.186 --> 00:06:18.236
00:06:18.236 --> 00:06:29.796
00:06:30.246 --> 00:06:30.576
00:06:30.906 --> 00:06:31.206
00:06:31.206 --> 00:06:32.711
00:06:32.711 --> 00:06:34.871
00:06:35.361 --> 00:06:35.691
00:06:35.691 --> 00:06:38.361
00:06:38.361 --> 00:06:45.221
00:06:45.221 --> 00:06:47.231
00:06:47.586 --> 00:06:51.697
00:06:52.126 --> 00:06:59.666
00:06:59.666 --> 00:07:02.576
00:07:03.236 --> 00:07:08.066
00:07:08.066 --> 00:07:11.246
00:07:11.556 --> 00:07:11.736
00:07:11.911 --> 00:07:12.661
00:07:13.066 --> 00:07:19.637
00:07:19.666 --> 00:07:21.346
00:07:21.346 --> 00:07:21.406
00:07:21.436 --> 00:07:28.016
00:07:28.841 --> 00:07:32.531
00:07:32.831 --> 00:07:35.111
00:07:35.111 --> 00:07:42.701
00:07:42.701 --> 00:07:42.762
00:07:43.031 --> 00:07:44.501
00:07:44.986 --> 00:07:46.576
00:07:46.906 --> 00:07:49.066
00:07:49.066 --> 00:07:55.526
00:07:55.526 --> 00:08:03.936
00:08:04.507 --> 00:08:08.766
00:08:08.766 --> 00:08:11.286
00:08:11.466 --> 00:08:12.431
00:08:13.801 --> 00:08:20.251
00:08:21.211 --> 00:08:23.911
00:08:24.151 --> 00:08:29.411
00:08:29.651 --> 00:08:36.927
00:08:37.706 --> 00:08:42.297
00:08:42.567 --> 00:08:46.197
00:08:46.346 --> 00:08:48.447
00:08:48.986 --> 00:08:57.057
00:08:57.246 --> 00:08:58.536
00:08:58.596 --> 00:08:59.886
00:09:00.562 --> 00:09:11.047
00:09:12.197 --> 00:09:12.797
00:09:12.797 --> 00:09:15.496
00:09:15.797 --> 00:09:15.976
00:09:15.976 --> 00:09:18.736
00:09:18.736 --> 00:09:23.456
00:09:23.456 --> 00:09:25.226
00:09:25.226 --> 00:09:26.006
00:09:26.006 --> 00:09:30.096
00:09:30.096 --> 00:09:35.146
00:09:35.486 --> 00:09:36.536
00:09:37.031 --> 00:09:38.201
00:09:38.381 --> 00:09:39.341
00:09:39.641 --> 00:09:39.761
00:09:39.822 --> 00:09:40.631
00:09:40.631 --> 00:09:40.641
00:09:40.871 --> 00:09:44.432
00:09:44.761 --> 00:09:44.911
00:09:44.971 --> 00:09:49.531
00:09:49.531 --> 00:09:49.542
00:09:49.542 --> 00:09:49.781
00:09:49.817 --> 00:09:50.026
00:09:50.026 --> 00:09:50.567
00:09:50.616 --> 00:09:56.256
00:09:56.256 --> 00:09:57.456
00:09:57.956 --> 00:10:03.086
00:10:03.506 --> 00:10:08.906
00:10:08.937 --> 00:10:14.036
00:10:14.336 --> 00:10:17.276
00:10:17.906 --> 00:10:19.197
00:10:19.197 --> 00:10:33.506
00:10:33.836 --> 00:10:38.516
00:10:38.807 --> 00:10:41.236
00:10:41.896 --> 00:10:44.266
00:10:44.687 --> 00:10:45.406
00:10:45.766 --> 00:10:46.126
00:10:46.126 --> 00:10:46.307
00:10:46.307 --> 00:10:47.476
00:10:47.476 --> 00:10:48.677
00:10:48.677 --> 00:10:49.451
00:10:49.451 --> 00:10:49.692
00:10:49.692 --> 00:10:50.866
00:10:50.917 --> 00:10:58.057
00:10:58.386 --> 00:10:58.447
00:10:58.447 --> 00:11:02.677
00:11:03.937 --> 00:11:05.346
00:11:05.376 --> 00:11:07.206
00:11:07.417 --> 00:11:11.706
00:11:11.706 --> 00:11:16.417
00:11:16.927 --> 00:11:23.697
00:11:23.697 --> 00:11:27.807
00:11:28.197 --> 00:11:30.116
00:11:30.376 --> 00:11:36.496
00:11:36.496 --> 00:11:39.376
00:11:39.797 --> 00:11:39.856
00:11:39.886 --> 00:11:41.716
00:11:42.047 --> 00:11:48.047
00:11:48.601 --> 00:11:56.011
00:11:56.011 --> 00:12:00.091
00:12:00.331 --> 00:12:04.711
00:12:05.341 --> 00:12:06.841
00:12:06.871 --> 00:12:12.601
00:12:12.961 --> 00:12:14.281
00:12:14.281 --> 00:12:18.812
00:12:18.812 --> 00:12:24.991
00:12:25.057 --> 00:12:26.586
00:12:27.036 --> 00:12:27.846
00:12:27.846 --> 00:12:37.461
00:12:37.562 --> 00:12:37.891
00:12:37.891 --> 00:12:40.981
00:12:40.981 --> 00:12:44.461
00:12:45.111 --> 00:12:46.312
00:12:46.317 --> 00:12:46.456
00:12:46.776 --> 00:12:46.836
00:12:47.047 --> 00:12:52.461
00:12:53.331 --> 00:12:54.711
00:12:54.711 --> 00:12:56.031
00:12:56.031 --> 00:12:59.451
00:12:59.531 --> 00:13:03.371
00:13:03.371 --> 00:13:11.501
00:13:11.856 --> 00:13:14.947
00:13:14.947 --> 00:13:21.786
00:13:21.906 --> 00:13:25.807
00:13:26.016 --> 00:13:30.576
00:13:30.966 --> 00:13:31.057
00:13:31.086 --> 00:13:36.966
00:13:36.966 --> 00:13:38.136
00:13:38.496 --> 00:13:44.346
00:13:44.447 --> 00:13:46.516
00:13:47.552 --> 00:13:48.871