WEBVTT
00:00:00.100 --> 00:00:00.984
Hey, what is up?
00:00:00.984 --> 00:00:04.248
Welcome to this episode of the Wantrepreneur to Entrepreneur podcast.
00:00:04.248 --> 00:00:18.649
As always, I'm your host, brian Lofermento, and I am so very excited about today's episode and today's guest, because this is someone who has so much experience in a very complex world of winning government contracts.
00:00:18.649 --> 00:00:26.422
And what's even cooler is he's using technology to make it even more streamlined and more accessible for more people to do good work.
00:00:26.422 --> 00:00:28.266
Let me introduce you to today's guest.
00:00:28.266 --> 00:00:29.609
His name is Arthur Runeau.
00:00:29.609 --> 00:00:37.283
Arthur is the CEO of Turingon, which is an innovative AI-driven software company revolutionizing the government contracting space.
00:00:37.283 --> 00:00:47.061
With nearly two decades of experience in proposal and capture management, arthur has led successful bids totaling billions, with a B billions of dollars.
00:00:47.061 --> 00:00:51.237
He's a Navy veteran and he's a former cryptologic technician or linguist.
00:00:51.237 --> 00:00:55.633
He combines a strategic mindset with a passion for efficiency and innovation.
00:00:55.633 --> 00:01:07.993
At Turingon, arthur is dedicated to creating cutting edge solutions like Proposal Pilot, which is designed to simplify compliance and empower contractors to win more business in the competitive federal market.
00:01:07.993 --> 00:01:21.347
Now, what I really love about where Arthur is in the marketplace is that it doesn't get much more competitive than where he is, and so that's why, for all of us in hopefully slightly less competitive markets, we're going to get a chance to learn a lot from him here today.
00:01:21.347 --> 00:01:22.974
So I'm not going to say anything else.
00:01:22.974 --> 00:01:24.421
I'm excited about this one.
00:01:24.460 --> 00:01:26.926
Let's dive straight into my interview with Arthur Runeau.
00:01:26.926 --> 00:01:33.221
All right, arthur, I am so very excited that you're here with us today.
00:01:33.221 --> 00:01:34.043
First things first.
00:01:34.043 --> 00:01:35.027
Welcome to the show.
00:01:35.027 --> 00:01:37.421
Thank you Happy to be here.
00:01:37.421 --> 00:01:45.728
Heck, yes, I'm super excited to hear your backstory from you yourself, because obviously I've read your bios, I've gone through your work, work, but I want to hear the story of who's arthur?
00:01:45.728 --> 00:01:47.772
How the heck did you start doing all these cool things?
00:01:49.944 --> 00:01:57.344
well, basically, I spent, uh, ten and a half years in the navy, as you indicated, um as a linguist, uh, which is part of the intelligence community.
00:01:57.344 --> 00:02:06.051
Um came out of that after ten and a half years, um, and quickly realized that government contracting is where the money is at.
00:02:06.051 --> 00:02:13.784
I think I read a statistic once it's about one in four dollars in circulation is a federal, state or local government contract dollar.
00:02:13.784 --> 00:02:22.057
So I tried to start my first venture, a company called National Linguistics, after leaving the Navy, to provide linguists to the federal government.
00:02:22.057 --> 00:02:28.587
That didn't work out so well, but I learned a lot and joined another startup that did grow very quickly.
00:02:28.587 --> 00:02:40.792
We took it from zero to 60 million in revenue in four years providing linguists and doing intelligence work for the federal government, and subsequent to that time, I spent 20 years in the industry, both with my own ventures.
00:02:40.812 --> 00:02:53.174
I had a successful venture providing medical staff as well as law enforcement support to the federal government and working in proposal and capture roles for other organizations.
00:02:53.174 --> 00:02:55.062
So it's been quite a journey.
00:02:55.062 --> 00:03:30.111
With Turing on, I quickly realized that my profession, which largely I think of myself as a proposal professional, was becoming obsolescent, if not obsolete, with the advent of AI and I thought how better to be involved than to lead that charge, and what shape that takes, and how AI is utilized within the federal space to kind of reduce some of the headaches for proposal professionals and help companies win in a more efficient and less expensive manner.
00:03:30.111 --> 00:03:35.532
And that's how Turingon was born, together with my co-founders who are leading the charge on the technical side.
00:03:36.100 --> 00:03:51.346
Yeah, I really appreciate that overview, arthur, we are for sure going to dive deep into your proposal mind here in today's session, but before we get there, I'd love for you to paint the picture for people who have never navigated the world of government contracts and bidding for contracts on the whole.
00:03:51.346 --> 00:03:57.210
Paint the picture of what that landscape looks like, because I know it's super complex, but I'm excited for you to simplify it for us.
00:03:58.741 --> 00:04:01.953
Sure, proposals can be daunting, particularly on the surface.
00:04:01.953 --> 00:04:05.903
The government is by law required to publish proposals.
00:04:05.903 --> 00:04:11.180
Currently they put them out on a site called samgov and what you see if you go on there?
00:04:11.180 --> 00:04:20.274
They buy everything from rifles to tanks to rudimentary things like razors for the troops.
00:04:20.274 --> 00:04:24.803
So if you sell it, there's probably a chance that the government is trying to buy it.
00:04:25.625 --> 00:04:44.831
The issue is that when you look at a solicitation, if you're not experienced and not well versed in this field, you're going to see complex clauses in there, something called the Federal Acquisition Regulation that's omnipresent in all solicitations and they can kind of be hard to navigate.
00:04:44.831 --> 00:04:54.670
The way I was taught when I started in this business was to print them out and highlight every clause, go over everything and focus on compliance compliance compliance first and then try to make the bids compelling.
00:04:54.670 --> 00:05:11.144
But if you are successful, as I said previously, there's a lot of money to be made with the federal government as a client, and here in the Washington DC metro area where I am at pretty much every building you pass you could throw a stone and hit a government contractor.
00:05:11.144 --> 00:05:18.947
So what we endeavored was to try to make this more efficient for government contractors and, in turn, for the taxpayer.
00:05:19.750 --> 00:05:23.545
Yeah, Arthur, you brought up two big C words compliant and compelling.
00:05:23.545 --> 00:05:25.269
Let's go straight into that second one.
00:05:25.269 --> 00:05:27.473
What makes for a compelling proposal?
00:05:29.459 --> 00:05:30.802
Certainly a compelling proposal.
00:05:31.062 --> 00:05:32.846
I mean there's a variety of things that go into it.
00:05:33.346 --> 00:05:37.774
I always say that a proposal is a sales finalization document.
00:05:37.774 --> 00:05:45.613
You could have the best proposal in the world, but ultimately they need to have met someone from your organization first.
00:05:45.613 --> 00:06:11.264
They need to have shaken their hand and ideally you need to have helped shape that solicitation, by which I mean the government needs to have a good understanding of what it is that they need, and a good salesperson in any field not just in government contracting, I believe should really be a consultant to their customer to help them find a solution that fits within their budget and will best serve their needs.
00:06:11.264 --> 00:06:22.485
When I was active in proposals, I was endeavor to try to give the customer more than what I was taking out, by which I mean a greater value than the dollars that they were spending.
00:06:22.485 --> 00:06:39.117
So what really makes it compelling is to give them a good understanding that you know what their issue is, how you're solving their problem and, ultimately, how you're going to be efficient in delivering it and providing them as few headaches as possible along the way.
00:06:39.740 --> 00:06:42.971
Yeah, all important considerations, regardless of what our business is.
00:06:42.971 --> 00:06:44.427
That's what I've been really excited about.
00:06:44.427 --> 00:06:53.192
Our conversation today, Arthur, is obviously you work within the government contracting space, but for all of us, any service-based business that's sending proposals out these are important principles.
00:06:53.192 --> 00:06:55.990
You just said the greater value needs to be on that receiving end.
00:06:55.990 --> 00:07:07.605
Of course, we're not going to pay $20 for $10 in value, but we'd happily pay $20 for $30 in value, and so I really appreciate these principles that are steadfast across all industries.
00:07:07.605 --> 00:07:19.490
When we talk about AI stepping into the space and assisting us, obviously what you're doing with Turingon is not just quite simply asking ChatGPT hey, can you write a proposal for this potential job that I'm looking for?
00:07:19.490 --> 00:07:28.714
Talk to us about the intricacies of what's behind Turingon that's making it possible for people to succeed in this specific and very complex industry.
00:07:30.860 --> 00:07:32.185
Interestingly, you bring up ChatGPT.
00:07:32.185 --> 00:07:34.766
Of course, it's kind of the big daddy of it all.
00:07:34.766 --> 00:07:35.870
That made all this possible.
00:07:35.870 --> 00:07:42.012
However, I started playing with ChatGPT and I quickly realized its limitations when it came to government contracting.
00:07:42.012 --> 00:07:59.528
Limitations when it came to government contracting, Namely that, particularly when dealing with something called CUI controlled, unclassified information there are security safeguards that government contractors have to maintain surrounding that, so they're not able to just upload a document to chat GPT willy-nilly without worrying about where that winds up.
00:07:59.528 --> 00:08:05.180
Secondly, of course, anybody who's played with it realizes that there's limitations in terms of the output.
00:08:05.180 --> 00:08:07.362
You want it to provide five pages.
00:08:07.362 --> 00:08:08.262
It only provides one.
00:08:08.262 --> 00:08:18.225
You may not be getting the best information and, of course, there's oftentimes bleed over from other information or incorrect information that's provided.
00:08:20.086 --> 00:08:33.111
We set out to build a ground up solution, meaning that we designed a custom AI we are not a chat GPT wrapper that was specifically tailored for government contractors and that's done within what we call a closed container.
00:08:33.111 --> 00:08:40.315
Effectively, the model is only trained in terms of proposal responses on our customers data.
00:08:40.315 --> 00:09:01.251
So each customer is within a closed container with no possibility of false information arising from a different client and therefore that provides them the assurance that the output is going to be focused solely on their accomplishments as they seek to espouse their value to the federal government customer.
00:09:01.251 --> 00:09:19.183
And finally, we are hosted on something called GovCloud through Amazon Web Services, which is an accredited server, thus providing our customers with the assurance that their data is secure and they can upload information freely, without fear of it winding up in the wrong hands.
00:09:19.885 --> 00:09:25.828
Yeah, super interesting to hear about the tech side of it, arthur, although when you and I talk about technology, where my head goes is.
00:09:25.828 --> 00:09:30.788
I also know that you are a wizard when it comes to words, obviously as a linguist yourself.
00:09:30.788 --> 00:09:43.653
One acronym that we throw around a lot in the business world today and in the world in general is LLMs large language models and I think that people forget that underneath all of these complex AI models is the language element of it.
00:09:43.653 --> 00:09:46.422
Talk to us with that linguist hat still on.
00:09:46.422 --> 00:09:56.272
Talk to us about your perspective on where we are with regard to LLMs, what people's misconceptions might be about it and really how we can reshape it and get the most out of these.
00:09:58.160 --> 00:10:00.767
I mean, ultimately, LLMs are kind of in their infancy.
00:10:00.767 --> 00:10:26.687
I kind of think that the future may be in smaller language models, actually customized to each customer set, obviously driving down the cost, which is a significant barrier to entry right now in terms of training, the model cost running into the millions of dollars, as we saw recently with the controversy surrounding ChatGPT as well as DeepSeek coming out of China controversy surrounding chat, GPT as well as deep seek coming out of China.
00:10:26.687 --> 00:10:55.187
But I think as we move forward, this technology is going to become cheaper, certainly less energy intensive, as we develop better training technology for these models and I really think it has the possibility to make us more efficient and ultimately, I really think that this is going to revolutionize the world, not in just the way we do things, but ultimately in giving us back time through efficiency and hopefully making the world a more stress-free place.
00:10:55.849 --> 00:10:57.091
Yeah, I love that view on it.
00:10:57.091 --> 00:11:03.443
Arthur, Sign me up for a more stress-free place, especially when it's technology that can make our lives easier and better.
00:11:03.443 --> 00:11:18.909
One thing that you already brought up and I want to go back to it and go deeper is you talked about inputs and the importance of one thing that you're doing differently at Turingon is if I upload documents, I know that it's living within my own self-container, Whereas I think I'm not saying anything controversial here.
00:11:18.909 --> 00:11:24.330
But I'm pretty sure ChatGPT has made it clear that it continues to learn from all of us who are using it.
00:11:24.330 --> 00:11:27.414
Now GPT has made it clear that it continues to learn from all of us who are using it.
00:11:27.414 --> 00:11:27.515
Now.
00:11:27.535 --> 00:11:29.219
There's a difference between free plans and paid plans.
00:11:29.219 --> 00:11:32.458
People, if you want to go deeper into how ChatGPT is using your data, definitely go look it up, because it is in certain ways.
00:11:32.458 --> 00:11:49.197
But, Arthur, that's a key difference in consideration with Turing on is those self-containers that you've already introduced us to Talk to us about having the flexibility and the ability to have those document uploads and to really control those inputs so that we get better outputs?
00:11:49.197 --> 00:11:54.782
What are those inputs look like, especially within the the realm of writing proposals and submitting them.
00:11:54.782 --> 00:11:56.647
What are we inputting?
00:11:56.647 --> 00:12:01.115
What are your clients looking to give to the system for it to be more catered and personalized?
00:12:03.000 --> 00:12:03.662
Certainly Well.
00:12:03.721 --> 00:12:22.544
I mean, one of the big challenges in government contracting is maintaining kind of a library of both previous proposals and what's called past performance, which is basically just a written record of everything that you've done for previous clients that you may want to reuse and articulate in a future proposal what our system does.
00:12:23.066 --> 00:12:28.970
We built a tool, for example, that will integrate seamlessly with SharePoint If that's what the customer is using.
00:12:28.970 --> 00:12:30.256
We also have other tools available.
00:12:30.256 --> 00:12:43.863
Depending on their level of technology, it could really go in there and allows the ability to select or deselect certain documents to allow them to focus on where the AI is directed in terms of taking information out of it.
00:12:43.863 --> 00:12:53.701
So, for example, if a customer is targeting a Department of Defense customer, maybe they don't want the past performance references from the Department of Agriculture.
00:12:53.701 --> 00:12:55.105
That may not be as relevant.
00:12:55.105 --> 00:13:01.201
However, if they are doing a Department of Agriculture proposal, maybe they want to select stuff from Agriculture and the Department of the Interior.
00:13:01.201 --> 00:13:02.764
That may be more relevant.
00:13:02.764 --> 00:13:17.312
So continually updating the model, continually allowing it to learn and to train based on new data, and making that as seamless as possible, is a challenge within the industry and hopefully one that we feel we are doing a little bit better than some of our competitors.
00:13:17.940 --> 00:13:18.782
Yeah, I love that.
00:13:18.782 --> 00:13:19.804
Context is king.
00:13:19.804 --> 00:13:29.714
It's funny In business we have so many cliches Content is king, Cash is king, but context is most certainly king when it comes to the AI world, Arthur, I'm totally going to put you on the spot here.
00:13:29.714 --> 00:13:41.542
It's probably not an exact number that you can give us here on the air, but I'm sure a lot of people, when it comes to proposals in any domain, they realize that rejection is an essential part of the business of writing and submitting proposals.
00:13:41.542 --> 00:13:45.626
What sort of success rates have you seen within the government contract space?
00:13:45.626 --> 00:13:48.650
Or what are those targets that you look towards and you say you know what.
00:13:48.650 --> 00:13:53.015
This is what we want to aspire to, openly acknowledging that you're not going to land every contract.
00:13:54.820 --> 00:14:04.794
Traditionally in what was considered a good proposal shop within this industry, the aim was to win 33% of the bids that you submitted.
00:14:05.600 --> 00:14:10.513
Now this number is a little bit deceptive because, as I said before, I think a proposal is a sales finalization document.
00:14:10.513 --> 00:14:23.490
Oftentimes the sale is made prior to that proposal that solicitation ever.
00:14:23.490 --> 00:14:36.969
Comingpeople, or as we call them in the industry, capture people before going into this and really make sure that they've given no new objections to purchasing from them and kind of validating that and closing it.
00:14:36.969 --> 00:14:51.534
You know, we think with Turing on and proposal pilot we can increase the number of bids and that may be going after stuff that maybe was not as quite low-hanging fruit as what was traditionally gone after in the industry.
00:14:51.534 --> 00:15:07.407
But we could kind of lower that cost to bid, which right now runs anywhere from $50,000 to $5 million per proposal, as well as taking off to times 30 to 60 days of several people in an organization's time and that bid, that win rate may actually go down.
00:15:07.407 --> 00:15:17.131
But we think we can get the number of wins up by allowing them to bid some things that they may not have otherwise gone after, by making the proposal process more seamless and cheaper.
00:15:18.039 --> 00:15:18.881
Yeah, I love that.
00:15:18.881 --> 00:15:33.153
It's a few times already here in our conversation that you've brought up that the proposal is one component of the overall sales process and in the lifetime of that deal, arthur, walk us through what those beginning steps look like and is the proposal.
00:15:33.153 --> 00:15:41.011
Obviously it's not the end of that, because if you land that proposal then you have to track your outcomes and all of that Walk us through the lifetime of this deal cycle.
00:15:43.000 --> 00:15:46.932
Well, government contracting can be a little bit unusual for an outsider.
00:15:46.932 --> 00:15:51.171
First of all, competitors frequently work together in what's called teaming.
00:15:51.171 --> 00:15:55.711
There are certain laws surrounding government contracting.
00:15:55.711 --> 00:15:58.128
They have what's called set-aside contracts.
00:15:58.128 --> 00:16:05.071
For example, certain contracts must be set aside for small business, Some must be set aside for service-disabled, veteran-owned small businesses.
00:16:05.899 --> 00:16:23.765
So typically the life cycle begins with an opportunity identification, where a salesperson slash capture person identifies an opportunity, decides it might be a good fit for their organization and then they may go ahead and develop a team, find additional team members that may want to serve as subcontractors.
00:16:23.765 --> 00:16:24.509
Within that effort.
00:16:24.509 --> 00:17:03.823
They may have a missing component that the prime contractor is missing, where that kind of enhances their bid, after which ideally, they will go ahead and meet with the customer, kind of explain their value proposition, try to shape that RFP so that when the solicitation finally does release, a good portion of the proposal may already be written, because they really understand the customer's hot buttons, what the problem is that they're trying to solve, and they just kind of have to adjust it based on what came out of the final solicitation, go through and submit and then the hard part is which is waiting for a notice of award or non-award on something that you think you had a good chance at.
00:17:04.826 --> 00:17:16.132
Wow, arthur, hearing you effortlessly rattle that off makes me feel a little bit silly in asking you this question, because I think it's very evident why you have brought Turing on directly into the government contracting space.
00:17:16.132 --> 00:17:18.790
You're obviously incredibly well-versed when it comes to that space.
00:17:18.790 --> 00:17:34.352
But I do want to extrapolate out of there and I would imagine that all of your experience in this space makes you really good at proposals on an even more macro level is that you can look at proposals and identify their flaws, identify their holes, identify the things that they're not directly addressing.
00:17:34.352 --> 00:17:38.405
What are some of those principles, if we go outside of government contracting?
00:17:38.405 --> 00:17:46.508
That you think that service-based business owners, that you think that entrepreneurs in any industry, of any size, can really steal and apply to their own industries.
00:17:48.029 --> 00:18:03.977
Sure, I mean, I think ultimately when writing any kind of written doc sales document, ultimately it is really listening to the customer, listening and understanding what their requirements are, what their needs are, what their issues are.
00:18:03.977 --> 00:18:08.604
If you're doing a professional services contract and they've had issues with staffing, well, how are you gonna mitigate that?
00:18:08.604 --> 00:18:15.470
For example, one thing I always like to tell the operations staff is I want 100% recruitment.
00:18:15.470 --> 00:18:30.186
Whether or not we think we need it, whether or not we think we're gonna capture incumbents ahead of that proposal submission, let's go ahead and give them the assurance that this proposal or this contract will be 100% filled on day one of contract award.
00:18:30.186 --> 00:18:35.345
You know, and that kind of goes for everything Is there problems supply chain?
00:18:35.345 --> 00:18:39.012
Is it getting certain elements in a timely manner?
00:18:39.012 --> 00:18:51.576
And it's really thinking through that problem and kind of making the ultimate proposal or sales document a blueprint for how operations can execute on day one of that contract award.
00:18:51.576 --> 00:18:55.631
They should be able to take that and have a good roadmap for what that's going to look like.
00:18:56.400 --> 00:19:18.769
Yeah, arthur, hearing you say that it's something, it's a piece of advice that comes up so frequently here on this show and, I think, any entrepreneurial advice or business book or any educational resources, of course, listening to our customers, and when I hear you talk about these things, I would imagine that in your conversations which I love the fact that you tell us and emphasize a few times your proposal starts way before the pen ever hits the paper.
00:19:18.769 --> 00:19:21.662
You need to talk to the people that you're sending that proposal to.
00:19:21.662 --> 00:19:23.625
What sort of questions do you ask?
00:19:23.625 --> 00:19:27.153
How do you really get at the essence of what it is that they're looking for?
00:19:27.153 --> 00:19:28.970
Because, arthur, I'll transparently share with you.
00:19:28.970 --> 00:19:29.653
I think about that.
00:19:29.653 --> 00:19:35.625
Henry Ford quote all the time where he says if I asked my customers what they would have wanted, they would have said a faster horse.
00:19:35.625 --> 00:19:38.212
How do you get at the crux of what they're really looking for?
00:19:40.121 --> 00:19:47.974
I mean, I think really you need to have like an honest conversation and this needs to be a dialogue and really kind of getting to understand what their issue is.
00:19:47.974 --> 00:19:50.669
So, to use the Henry Ford analogy, what is the issue?
00:19:50.669 --> 00:19:53.108
Is it trying to get goods to market faster?
00:19:53.108 --> 00:20:05.196
Is it trying to move between A and B in a quicker manner and then trying to espouse what it is that you have to offer based on that conversation, that you have to offer based on that conversation.
00:20:05.196 --> 00:20:28.153
I think I said previously, you know it needs to be a dialogue and a good salesperson in any field should act as a consultant, and maybe they won't by this time, but I think people, sincerity comes through and people will remember when you did right by them and really advise them without bias, you know, in their endeavor to try to help them be successful.
00:20:28.153 --> 00:20:35.830
And maybe they're going to come back in a year, two years, five years, but I think eventually they will come back if you treat every customer in such a manner.
00:20:36.564 --> 00:20:37.660
Yeah, really well said.
00:20:37.660 --> 00:20:52.361
Obviously, Arthur, one thing that I so deeply respect about you and the way that you operate is that you've been clearly playing that long game, and you love playing the long game because you know that these things build up over time and good things always happen when you do, when you put good things into the world and when you do good work.
00:20:52.361 --> 00:20:57.862
I want to ask you obviously you've had a very accomplished business career, but you also had a very successful career up to that point.
00:20:57.862 --> 00:20:59.765
You have been in the navy.
00:20:59.765 --> 00:21:03.750
So I want to ask you what are some of those influences that shape the way that you do business?
00:21:03.750 --> 00:21:09.905
What are some of those ways that really made you say, hey, when I have my own company which, of course, now you've had that that you said.
00:21:09.905 --> 00:21:14.023
These are the principles, these are the habits that I want to inject into the way that we operate.
00:21:15.647 --> 00:21:16.210
I mean ultimately.
00:21:16.210 --> 00:21:18.903
I think the Navy shaped a lot of that, certainly in terms of leadership.
00:21:18.903 --> 00:21:26.674
You know, the core values of the Navy in terms of honor, commitment and courage are something that I've carried with me.
00:21:26.674 --> 00:21:37.055
I had a master chief when I was very junior as an enlisted person early in my career who you know told me Arthur, you're going to have good leaders and you're going to have bad leaders.
00:21:37.055 --> 00:21:41.330
You know, learn from both of them and figure out what kind of leader you want to be.
00:21:41.330 --> 00:21:45.984
And you know I did take that to heart and I learned from them.
00:21:46.964 --> 00:21:50.465
You know, and I think one of the things that I carried through from the Navy was taking care of your people.
00:21:50.465 --> 00:22:00.065
Surround yourself with good people, surround yourself with people who are smarter than you, particularly in their specific subject matter area, and then listen to them.
00:22:00.065 --> 00:22:03.626
A trusted lieutenant does you no good if you shoot them when they bring bad news.
00:22:03.626 --> 00:22:10.684
You know there was a new thing going around the internet Pete's evil overlord list.
00:22:10.684 --> 00:22:12.269
The mistake that every evil overlord makes is shooting the messenger.
00:22:12.269 --> 00:22:14.142
You know you never want to be that sort of leader.
00:22:14.142 --> 00:22:15.244
You want to listen to them.
00:22:15.244 --> 00:22:16.808
You hired them for a reason.
00:22:16.808 --> 00:22:22.269
Take it to heart and you know, ultimately, you know, take their advice.
00:22:22.269 --> 00:22:23.452
You're paying them for a reason.
00:22:28.880 --> 00:22:29.321
Yeah, really well said.
00:22:29.321 --> 00:22:33.078
I think that it sounds like simplistic advice, but we have to really put it into practice and it takes a long time for us to actually learn these lessons.
00:22:33.078 --> 00:22:37.681
Arthur, talk to us more about leadership, because I can't have you on here and not pick your brain in that regard.
00:22:37.681 --> 00:22:39.784
Obviously, listening to our people it's clear.
00:22:39.784 --> 00:22:44.270
Actually listening has come up several times in our conversation today, not just with regards to leadership.
00:22:44.270 --> 00:22:46.734
So I want to push you a little bit deeper into that.
00:22:46.734 --> 00:22:51.722
Those positive examples of leadership what have those look like for you and how has that shaped throughout your career?
00:22:51.722 --> 00:23:01.665
Because the one thing I'll put a big disclaimer in here is when listeners tune into business podcasts, it's easy to think this person is a finished product and in many ways, arthur, you've been wildly successful at the things you've done.
00:23:01.665 --> 00:23:04.834
But talk to us about that evolution of yourself as a leader.
00:23:07.078 --> 00:23:08.560
Oh, certainly you know.
00:23:08.621 --> 00:23:31.872
I think that when I look back at some of the leaders who have helped shape me and my career, you know the best ones took an interest in my career and they helped me out along the way and they provided me with training, expertise, anecdotes, whatever it was that was needed at a particular moment that they thought I could benefit from.
00:23:32.840 --> 00:23:47.294
You know, under the assumption that a rising tide lifts all ships, you know I've seen bad leaders that want to stand on someone's shoulder ultimately, and you know, think that makes them taller and kind of scrunch everyone beneath them down.
00:23:47.294 --> 00:24:06.602
The good leaders think that by building up everyone underneath them they will be better by extension, because part of their job is to train their subordinates for the next job to replace them, next job to replace them.
00:24:06.602 --> 00:24:26.542
You know, and I'd like everyone that works for me to have a good understanding of their next role, to have a good understanding of what it is that every role in the organization is, so that one day, whether it's with us or with someone else, that they can expand their career, have a positive, we can have a positive impact on them and ultimately I think that they reward us with loyalty in the organization as a result.
00:24:27.144 --> 00:24:31.583
Yeah, arthur, speaking of leadership, you are obviously you're one of us, you're a fellow entrepreneur.
00:24:31.583 --> 00:24:33.147
I constantly remind listeners of that.
00:24:33.147 --> 00:24:38.429
You're the CEO of an incredible tech company that is doing really cool things in an important sector.
00:24:38.429 --> 00:24:48.248
So, with that hat on, I always love asking other business owners and CEOs what's your time horizon look like when you sit down for your strategic time, for your executive time?
00:24:48.248 --> 00:24:50.682
What's the time horizon that you're looking at?
00:24:50.682 --> 00:24:51.965
I know for me personally.
00:24:51.965 --> 00:24:52.989
I'm a big fan of quarters.
00:24:52.989 --> 00:24:58.632
I think that quarters are super actionable, but I would imagine that in your space it's changing every single day.
00:24:58.632 --> 00:24:59.761
The technology is changing.
00:24:59.761 --> 00:25:02.207
Hardware, software, everything's constantly changing.
00:25:02.207 --> 00:25:04.592
So where's your head go when you sit down with executive time?
00:25:05.840 --> 00:25:07.285
I absolutely think about quarters.
00:25:07.285 --> 00:25:10.181
You know I think about it on a day to day basis.
00:25:10.181 --> 00:25:18.432
I sit down at the beginning of the week and look at what needs to be accomplished this week, particularly, you know, within the tech sector.
00:25:18.432 --> 00:25:21.084
What moves are we making, what moves aren't we making?
00:25:22.461 --> 00:25:26.863
I think tech startups are a little bit like a shark If you stop moving forward, you die.
00:25:26.863 --> 00:25:33.263
So in many senses, there's a risk more risk in doing nothing than in doing something and achieving bad results.
00:25:33.263 --> 00:25:39.808
But I also believe that you need to build an organization with some sort of exit strategy in mind.
00:25:39.808 --> 00:25:41.776
What does that look like?
00:25:41.776 --> 00:25:43.306
Are you building it to sell?
00:25:43.306 --> 00:25:44.638
Are you building it to go public?
00:25:44.638 --> 00:25:48.931
Are you building it to just have passive revenue one day and hire somebody else to manage it?
00:25:48.931 --> 00:26:18.060
So what that looks like, particularly within the changing tech sector, within the changing AI sector, is something that I contemplate a lot, because a lot of the moves we make now will affect our valuation four or five years from now, and you know, ultimately, what that means for the employees as well as the stakeholders and investors in the organization is something that I try to give consideration to in each move that we make.
00:26:18.682 --> 00:26:21.611
Yeah, gosh, it's so cool getting inside of your executive mind.
00:26:21.611 --> 00:26:24.950
Arthur, I so appreciate hearing the way you think about the future.
00:26:24.950 --> 00:26:29.619
Obviously, with regards to Turingon, you have Proposal Pilot, which we've talked extensively about today.
00:26:29.619 --> 00:26:36.705
When you look at the Turingon roadmap, are you looking to expand out as far as sectors go, as far as your clients?
00:26:36.705 --> 00:26:38.647
Are you looking out as far as features?
00:26:38.647 --> 00:26:41.064
What does that future roadmap look like for Turingon?
00:26:42.204 --> 00:26:56.594
Oh, we have several things on the horizon that I think are going to be exciting for the government contract market bringing out additional features such as teaming pilot to kind of facilitate that process of finding teaming partners for contracts.
00:26:56.594 --> 00:27:07.323
We are building a pricing tool, which is incredibly challenging because we are trying to source as much data as possible from historical bids to really understand where pricing should be.
00:27:07.323 --> 00:27:11.431
So automating that process is quite a difficult challenge from AI.
00:27:11.431 --> 00:27:15.432
But eventually we are looking to move into other sectors.
00:27:15.432 --> 00:27:18.766
Principle one that we've had a lot of interest with is universities.
00:27:18.766 --> 00:27:39.169
We are looking to facilitate the process of writing grants from an artificial intelligence perspective to allow grant seekers a lot of professors at universities, for example to really focus on doing that research rather than sitting down and going through the arduous process of, you know, spending hours and hours on paperwork in order to get funded.
00:27:39.169 --> 00:27:45.171
If that can be automated in an efficient and ethical format, I think that's an area where we could really contribute.
00:27:45.519 --> 00:27:48.068
And eventually we would like to go into the commercial sector.
00:27:48.068 --> 00:28:03.126
There are several areas that we've been approached by where commercial companies thought that they could benefit from our product, and we're looking at kind of putting out additional products for that, but I kind of want to take it one thing at a time.
00:28:03.126 --> 00:28:06.640
Putting out additional products for that, but I kind of want to take it one thing at a time.
00:28:06.640 --> 00:28:11.281
Focus on proposal pilot for the moment.
00:28:11.281 --> 00:28:14.771
Make sure my team stays focused so that we have the ability and the profitability in order to kind of pursue some of these additional endeavors.
00:28:15.579 --> 00:28:20.191
Yes, I love that Showcasing right here, right now, that executive mind in action.
00:28:20.191 --> 00:28:28.991
I love hearing about that future roadmap, big plans, but also strategic and intentional plans, understanding what matters now and, of course, that direction that you're going in.
00:28:28.991 --> 00:28:36.048
So, arthur, I so appreciate the way that your mind works and all these insights into the way your mind thinks and the work that you're doing.
00:28:36.048 --> 00:28:43.387
With that said, I really enjoy asking this question at the end of every interview, because it's super broad and I have no idea which direction you're going to take it in.
00:28:43.387 --> 00:28:51.619
But that is what's your one best piece of advice Knowing that we're being listened to by both entrepreneurs and entrepreneurs at all different stages of their own growth journeys.
