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Kevin Horek:Welcome back to the show. Today, we have Tom and Sean from I clerk. Guys, welcome to the show.
Tom Blair:Thank you. Hi, Kevin.
Kevin Horek:Yeah, I'm excited to have you on the show. We've been working together for a number of months now. Really love the technology and the product. But maybe before we dive into all that, let's get to know each one of you a little bit better. And, Tom, maybe you go first, and then I'll let Sean go.
Tom Blair:Sure. I've had a longer journey than Sean. My AI journey has been has spent decades. I in the mid eighties, I went to Eastman Kodak Company, and I was, exposed to AI at a really early age working on semantic image recognition using neural networks. Kodak also paid for my grad school where I did the same thing at school's work, which is working on these neural networks in order to build image database systems for Kodak's photo CD program.
Tom Blair:We ultimately built, Kodak Picture Exchange, which was a image database with search capability, pre Internet. Fantastic. Very large databases, that you couldn't access.
Sean Feehan:Nice.
Tom Blair:So I spent most of my twenties at Kodak, and in grad school. And then, once I got out, I went to work at Sybase. Sybase was an early relational database company. I was one of the founding team that started Sybase Japan, Sybase Australia, Sybase India, Sybase, China, and spent a number of years based in Tokyo and then later based in Hong Kong, building distributed database systems. I then left Asia and went, again with Sybase to Europe where I deployed their mobile their mobile and embedded computing small footprint databases, embedded databases, and different applications.
Tom Blair:And so we rolled that out over the course of a couple years. I ended up at Economy in Cambridge again, doing the, you know, pre transformer architectures. Even during the AI winter, you still had a lot of technologies out there, things like, Bayesian inference, probability theory that it could that could read any language. It could do semantic image recognition. It could classify text.
Tom Blair:A lot of the same character that you see now in with these transformer technologies, the ideas were there and the applications were there. So all of that really influenced my career and the product that we'll describe today, I Clerk. Later, in the February, the mid two thousands, I started, a number of different, startups. So I built a weather company, at Iteris, and we managed the nation's snow plows and, did agriculture applications using weather modeling, soil modeling, how water and nutrients move through the soil, and ultimately crop modeling so we could, optimize when to plant, when to harvest, when to fertilize, things like that. Out of that experience, we founded another company called Performance Livestock Analytics.
Tom Blair:Also used AI to aggregate multiple types of information, things like, what you're feeding them, what kind of nutrients, what kind of pharma or, medications are going into that feed, what's the weather, what's the cow's maintenance, ultimately optimizing the entire supply chain and, and care and feeding of those crops. That was purchased in February, '20 by Zoetis, the large animal health company. And that then led us into, where we are today with IT with, iCLERC and and what we'll talk to you about today. So
Kevin Horek:Sure. Sean, do you wanna give us a bit of background in yourself?
Sean Feehan:Yeah. For sure. So I don't know the AI pedigree that, Tom has. So I I lean on him for a lot of that stuff. But my, background is mostly in software development and specifically creating software applications, and all of the fads that go along with that.
Sean Feehan:So I graduated university in 02/2004, the bachelor of science in computing science, and then went to work for a consulting company for five ish years and did a lot of work for the Canadian and Alberta governments in terms of making small specialized applications for them. So I made about 12 or so applications in those five years.
Tom Blair:That's cool.
Sean Feehan:Built them all with whatever stack has to be, you know, whatever trendy stack there was Yeah. Yeah. At the time. So built a really, really broad base on not only how to build software, but also how to work with people, clients, end users to make that software, you know, usable and and really valuable. From there, I went and worked for the government for a few years, and got my MBA at the same time.
Sean Feehan:And that was really an experience in learning, process workflow and just having a longer vision than just getting something out to the client, but actually working on stability and longevity for applications. And then I jumped into the startup world. So I got pulled into a company called Drivewise, which is, located in Numbington, Alberta. And we built a trucking software application, which, ended up growing to about 200 or so people and sold about a year or I guess six months ago sold. And that was a really, really exciting journey going from I was one of the first, management employees there, and there's about 10 people, and I helped grow it to about a 60 people while I was there.
Sean Feehan:And so went through all of that growth and, just really loved the startup world. And, met a really interesting guy there, Leo, who introduced me to Tom, and, here we are.
Kevin Horek:No. That that's really cool. It's it's interesting. I'm always curious how people connect with their kind of co founders and because you hear from so many people, it's really hard to actually find a co founder, never mind somebody that you can work with and all the other fun stuff that that comes with. So I'm curious, how did you come up with the idea for iCLERC?
Kevin Horek:Let's talk about kind of the original and how it's kind of evolved to what it is today, and then we'll we'll dive a bit deeper.
Tom Blair:Yeah. Certainly, the the the critical aspect of starting a company is finding the right partners and teammates. And that's a long journey, and I'm thankful for John walking into my life because he's really transformed where the company started to where it is today. I had mentioned that we I had my prior two companies were in ag tech, one with weather modeling and the other with animal health applications. And as really an intellectual exercise during that time, we started looking at commodity price discovery.
Tom Blair:A lot of the our customers at that time were small farmers, so under 2,000 head of cattle or under, you know, couple hundred acres of cropland, and they don't have the benefits of the financial markets. They're unable to hedge at those quantities. And so they just basically get their lunch eaten in the commodities markets. And these are complex intersections of different commodities. So to price a cow, for instance, you have, you have live cattle, grain, feeder cattle, ethanol, fuel.
Tom Blair:It's really the intersection of five different commodities, and it's a difficult human problem to solve. If you look at the charts on that over time, humans don't do a very good job at that intersection of complex commodities. So the original intent was to build these autonomous agents that could understand each individual commodity and then interact between them. And, and so we, followed that intellectual exercise for a while without even having, customers identified or a specific problem was, like I said, more of an intellectual exercise. And so, over time, we spoke with literally hundreds and hundreds of customers cross sectors.
Tom Blair:The AI evolution, the current evolution phase, took place during all this time, right, the advent of LLMs and the current fervor around AI. And so people are very receptive to the conversations, but it's a different thing to actually find applications that can be solved with these types of tools. And so over the last thousand days or so, we have developed and talked to people and ultimately defined iClerk's value proposition where we, don't model different commodities rather than you you model things that are important for in in a business setting. So if it's a medical doctor, it could be all patient visits. If it's a teacher, it could be all student homework and tests.
Tom Blair:If it's, venture capital setting, it could be, generating specific reports each quarter. And so it's taken a while for the market to catch up with our ideas, the to for us to be able to have a nomenclature nomenclature to describe what we do for a living, which is automation of mundane and repetitive tasks, being able to reduce the toil, the work that nobody likes to do with these types of AI agents.
Kevin Horek:Sure. It's interesting that you you talk about just doing a ton of research and talking to people to basically validate an idea. I think so many people in the startup world think that you come up with an idea, you launch it, you get a million customers, you know, you get sold in six months to a year, but it's really rarely like that at all, if ever. It takes years of grinding and kind of iterating on a product to get to, you know, where you start getting your first few customers. But but I'm curious, how did you get your first few customers?
Kevin Horek:Because I think that's really the hard part.
Tom Blair:Really word-of-mouth. It's it's it's good. There there are some advantages to being old. And it's like and Matt, you know, and and I've worked all over the world. I had this experience with databases, which were are horizontal technologies.
Tom Blair:So you can work in anything from retail to telecommunications to transportation to weather, you know. And and this AI these AI agent technologies have the same characteristics.
Sean Feehan:Right.
Tom Blair:So, finding finding the low hanging fruit where there's a discernible value proposition. There's you know, the customer can actually see the automation. They can see the cost savings. It used to cost them a hundred thousand dollars a year to perform some sort of repetitive work, and now you can do it for 10,000 or $20,000. So, the the the the act of finding those solutions is hard because we're AI experts.
Tom Blair:We are not subject matter experts. We're not lawyers, then we're not professionals. We're not doctors. We're you know? And so it's a partnership, and you have to be able to listen.
Tom Blair:The the first our first customers were, literally through relationships that we had. I think Sean can explain some of them in our ed in our education sector, but they're, you know, friends or friends of friends or or two degrees of separation or three degrees of separation. Soon, we'll be meeting with Kevin Bacon.
Sean Feehan:Yeah. I would say a lot of our journey is learning how to speak about what our value proposition is across sectors and helping people understand, what value we're bringing to their to their organization without actually knowing how to solve their specific problem. We kind of ran at a startup backwards. We had a really good technology, a really good approach, but not an actual use case for that technology. And so to be able to communicate to somebody what we are able to do for them and reach out and then partner with them to to figure out what their problem is and how we can help solve it and identify problems that we'll do well at.
Sean Feehan:I think that's probably what took us the most time in terms of how we approach, the market. And so some of the, a lot of what we do, I think Tom said it earlier, is that we help take away those mundane, repetitive tasks, those things that you don't wanna do at work but have to do. Yeah. Those are the things that we attack and approach, with partnerships to help figure out exactly what people need, and then providing and and showing proof that we're actually doing it and, we're actually solving that problem. And then it frees them up from half of their workday, sometimes even three quarters of of a workday, to be able to focus on those, like, way more higher value solutions or or a higher value spent time.
Kevin Horek:No. That that makes a lot of sense. So can you give some examples or some advice around actually showing that value and getting them from just having a quick call to, you know, converting to a customer? Because I think that's the real hard part, right, is like, showing them that value, especially early on.
Tom Blair:One of the unique aspects of this these AI agent technologies is that they don't necessarily replace existing software systems. It's it's more of a human labor automation. And as such, it has a very low, footprint in an organization. You can you can implement these technologies and and achieve the benefits of these technologies without doing any physical integration or modifying your existing systems. It's really working with the client to understand what they do every day and then automating that process.
Tom Blair:It allows the customer then to focus on the higher level things, the analytics, rather than the groundwork of the toil.
Kevin Horek:Sure. Can you give us some maybe examples of tasks that we've automated going forward? Because I think that's where people will really grasp kind of how people can leverage these agents.
Tom Blair:Sean, you wanna
Sean Feehan:Yeah. Sure. Take a I'll take a run at that. So, like Tom said, we're kinda cross industry. We're we're taking away that grinding work.
Sean Feehan:So a really good example is our, venture capital LPR agent, our reporting agent. So essentially, for a venture capital fund, they invest in a whole bunch of companies. And quarterly, typically, the cadence is quarterly. They have to report out to all of their LPs, although the limited partners. Here's how the fund's doing.
Sean Feehan:Here's what the companies are doing. Here's the company's financials at a high level. All of those kind of things. Traditionally, that's a person sending 25, 30 emails depending on how many companies you're you're you're working with and are invested in the fund, possibly more. And then getting in all of the spreadsheets and information, all of the back and forths, and then taking that in for each company, producing a one page report.
Sean Feehan:So what we've done is, we've automated that full process. So all you have to do is sign a company up, and they can enter in a survey. And the survey can be one question or 50 questions depending on what you require for your fund, and then upload your financials. And that's really where the magic comes in and what iClark can do, because as a startup, I don't have time to produce financials in 20 different formats for my 20 different investors. That's just asking a lot for, somebody who's trying to get a business off the ground and spend money wisely.
Sean Feehan:And so what we do is we take all sorts of different formats from spreadsheets, PDFs, pictures of spreadsheets from iPhones. You know, we can even, even if it's a recorded meeting, we can take all of that information and turn it into a one page report on here's the state of this particular company, in the fund. And then from there, once we have all of the company information collated, we can then work with those fund managers to identify things that are going on across their fund.
Kevin Horek:Yeah. No. Well, and you're saving people tens of hours, if not hundreds of hours, just doing that stuff. Like, collecting all that stuff's a nightmare. Nobody wants to do that.
Kevin Horek:They wanna be, obviously, investing in new companies or finding new opportunities or etcetera. Right? Like, they wanna work on the fun stuff, not all the grunt work of chasing down documents and people and paperwork and etcetera, etcetera.
Sean Feehan:Absolutely. And for the for the example I gave, it's about a hundred and sixty hours every quarter, which is one month out of three where one eight one, analyst has to gather all of this data and produce these reports. Sure. That makes sense.
Kevin Horek:Do you wanna maybe give a couple other examples in other markets of how people are leveraging agents?
Tom Blair:Yeah. We can there's really at a high level, there's three general categories of agents that we found within these businesses. One is exactly what Sean just talked about with our our limited partner reporting agent where it's a very complex workflow. It contains numerical processing, math, and language processing. You you you're doing your financials and describing how the company's doing.
Tom Blair:You're doing that across a wide set of different companies, and it takes time to collect the information that it you have specific rules and ways of handling your financial data. So all of that is, complex set of tasks, and then it generates the reports and sends them to the LPs themselves. So that's a a workflow agent, one that emulates a complex human workflow where they're moving data from one system to another and compiling it and using their brain and reasoning and then outputting. The other two types of agents that we've seen are knowledge agents where you compile a specific set of information. One example that we've seen recently is for professional services organizations where over the decades, they've done hundreds or thousands of different professional services engagements, each of which has a a report at the end.
Tom Blair:And these reports are for different types of customers and sectors, but they all have some commonality. And so this company uses AI to build the knowledge base of all their prior art. And then when a new set of customer requirements come in, they can use that and interact and analyze and search across their entire content set using that knowledge base. So knowledge base is complex workflows. And then the third type of agent that we see commonly is integration agents where, a lot of these examples that we're that we've used to date where there's a human on one end and a human on the other.
Tom Blair:There are many applications where, it may come out of a machine and go to a machine. So an integration agent. Something like taking, we've got one example where they're a a manufacturing of lighting fixtures, and they have multiple sites in in country and then multiple partners in other countries. And so the monthly or quarterly act of going out and finding out what was sold, all their inventory, all their sales, all that information, and then go, from an ERP system, requirements planning system. And then they go and get all the customer information from a CRM or a customer relationship management system, and process and and analyze that information to do things like, inventory analysis and and and and prediction of inventory levels.
Tom Blair:That's something that, you know, it would take a human days per or or weeks per quarter, to move that data, process that data, and put that data back into another system. So that's really the third type of agent is an integration agent, and, with examples of those types of agents. Right.
Kevin Horek:And then you're doing that lot real or real time then too. Right? Like, it's not it's constantly happening. Like, somebody doesn't have to do it every week or every quarter or every month or whatever. Like, that's happening in real time.
Kevin Horek:So you're just saving them like, you're making it even way more efficient. Right?
Sean Feehan:Yeah. Exactly. So, our agents, have triggers, essentially. So that trigger could be a recorded meeting. We have a Right.
Sean Feehan:One of our agents goes to all of our internal meetings and provides us meeting notes and summaries, all of those kind of things. And it it's trigger is a recorded meeting. So, essentially a calendar invite, but we can we have agents that are set for time periods, agents that are set for document updates, agents that are set for different document uploads. And and so any of these processes can be automatically kicked off.
Tom Blair:The the real value is is not in just the automation. That's, you know, that's probably the most, obvious benefit to these types of systems. It saves time. You can measure how long it took the human to do, and now it doesn't. Right?
Tom Blair:And so but there's a number of other benefits to these technologies. I mentioned the way that it it basically acts as an index across all of your existing data. And that integration of different types of data from voice to meetings to text or if it's in a database or a CRM system or if it's a phone call. All of that capture and processing is very expensive for humans to do and very good. It's a good application for AIs.
Tom Blair:And so that it the in the ability to integrate into existing systems, allows you to, have a very low touch integration. Right? You don't need to change too much stuff. But the real value we provide is in the guardrails. Nobody trusts AI.
Tom Blair:If the people always want to be able to see where the decision came from or where, the data came from. And so that ability to emulate human reasoning requires guardrails things that can ensure that when data changes you can track back what was changed if you are looking at a number in a spreadsheet, you need to understand what was the source of that. So that grounding or that traceability is really important for the application of these types of AI systems.
Kevin Horek:Yeah. No. That that makes sense. Can you maybe talk about where you wanna take this, or where do you see these AI agents going? Because they're kind of in the headlines right now.
Sean Feehan:You want I could talk Yeah. Briefly about, yeah, our our road map internally, and then I think maybe pass it over to Tom to, sort of talk about generally what he sees happening. Our our main road map so right now, it's AI process automation is what we're really focused on. We're doing those grindy, painful processes. The really interesting thing about this is is as you're processing that data and as your agent gets more access to that data, you also have the ability to chat with your agent and ask questions about those processes.
Kevin Horek:Which is cool. Yeah.
Sean Feehan:It's not just the process that you're getting. You're you're also getting full interaction with that data. And so an agent essentially, has a horizon, the data horizon, how far it can see, what data it has access to, and then it can reach out, pull edit pull information from that data and do actions on it. Our our AI process automation is fairly linear and, you know, get the document, process the data, spit out the results, email them out, create a PDF, those kind of things. And we wanna do that so that, organizations don't have to, sit and type in, like, WICHEAT GPT every time an a document gets updated.
Sean Feehan:And so what we're what we're really looking for is to move away from a small c copilot where you're always driving to an autopilot where your agent can automate those processes without you there. You just sit there and train your agent. And then once your agent gets all of this data, you can now ask it questions. What do you know about this? What trends have you noticed here?
Sean Feehan:I you know, what is my so for a really good example of that is all of, like I said, all of our org our meetings get, recorded, by an iClark agent. So at the end of the week, I go and ask it, what was everybody up to this week? And it can go in and say, here's each person. Here's all of their updates. Here's what they were doing.
Sean Feehan:And all of that information is available. Where we're moving to now is being able to chat with an agent about a process, and then it will provide that process to, after, you know, interacting with that agent and it'll say, here's what I think the process should be step one, step two, step three, step four, step five. Once you're comfortable with that process, you then save it, assign it a trigger, and then your process is now automated. So you can do that without a developer at all in the loop. Yeah, that's cool.
Sean Feehan:Yeah. And iClick, that's where we're going. So I'll just, Tom, how about the whole industry?
Tom Blair:So, you know, I mentioned we've we've done hundreds or thousands of different phone conversations across sector and globally, and what and that's but that act of discovery has been has really formed where we're bringing our company. The the the the the first thing they teach you when you're starting a company is that you have to find a specific problem and focus. It's focus is the operative word. And that's almost counter to where I found success in my career. Early, it was with digital imaging, which is really a horizontal technology and databases, which is a really horizontal technology.
Tom Blair:And we see the same characteristics in AI. So, what we've built here is something that goes against that type of focus wisdom, and we built a cross sector, cross border platform because people use models for different problems. They use, you know, you you'll use OCR models or language models or math models or, you know, any combination of models based on cost or performance or jurisdiction where you're operating in the world. And so we built a solution that really matches that how businesses have evolved. Nobody is just on Microsoft or just on Yeah.
Tom Blair:You know, on Google or, you know, it's a heterogeneous environment. And we've built something that adapts to the requirements of those those companies. And I think that's what you'll see is for this to truly be adopted cross sector, cross border globally and and affect all workflows like like everyone's predicting, then you need these types of systems that can, bridge that gap between current manually operated SAS systems and these fully automated systems that everyone is envisioning for the future. So we're in this transitory phase where, you know, it it it takes what we have and converts it. And and so over the next couple years, five years, say, for instance, all workflows will benefit from these type of AI processes.
Sean Feehan:I think you said something really interesting there in the middle, Tom, just about how we're at iCleark, we don't ask people to adopt a whole new platform or learn a whole new platform or get rid of, you know, their current workflow. So we plug into that, and we allow businesses to just supercharge their current workflows as opposed to asking them for a huge process management and change management Right. Sort of exercise.
Tom Blair:And to the cost of the humans. Right? That's about what these things cost to bring in the market. So no integration cost. Usually, you can implement these things in a hundred man hours.
Tom Blair:So, you know, give or take a month of time, it doesn't affect your existing infrastructure. So there's very little training that's required for this or downtime required for integration. And, you know, it's it's of great benefit. It saves time immediately, and it's it's it, you know, it it it, shows you the great benefit that it brings, almost immediately.
Kevin Horek:The other thing that you mentioned that I thought was interesting that I wanna get dive a little bit deeper on is if you obviously, if you read all the startup books or whatever you feed online, it's you basically build a product and then for a market and try to get everybody into it as quick as possible. But the interesting thing about you you've done it kind of backwards. How has that worked for you? And give us some advice on kind of doing that. Because when you're probably getting advice building a company, people are like, you gotta stop doing it that way.
Tom Blair:Yeah. We're a team of hockey players. I I tell you that, and we forecheck a lot. So, gee, our mantra is shots on net. And at this stage of the game where the the industry has been searching for solutions for this technology, that's been where we've where we've been at for the last twenty four, thirty six months.
Tom Blair:Okay. There's really a lot of change that's going on right now. People are you you have to sell less. People are actually there's a lot lot of demand. This is demand generated rather than, you know, intent generated.
Tom Blair:And so that's the transition that's taken place. The vertical AI, which addresses AI specific for different sectors, is where we found found ourselves and and and, you know, and building these types of specific AI for specific solutions is where we finally found ourselves. Right? So I think you end up there. But but it you know, there's there's nothing against building to an idea rather than solving a problem.
Tom Blair:And and I've I've proven that throughout my career. So, it's not for everyone, but you fishy you know, it this type of technology calls for that type of breadth.
Sean Feehan:Anything to add to that? Yeah. Go ahead. Yeah. I was just gonna say it's definitely a longer road, and, have and longer costs more.
Sean Feehan:But if you have a good enough idea and a good enough approach, I think you'll eventually get there. And, one of the key things that we're doing is we are building a aging operating system, and that is all of our technology. But when we go out to companies, especially after we've found a really solid partner in a vertical, then we start to understand that vertical a little more, start to understand a little bit more of that vertical's need, and then apply that same agent over and over and over again in that vertical. And that's where we've been finding a lot of our traction and success is through really valuable first touch partners and then their networks and and word-of-mouth from there to be able to repeat that agent, and, address a very specific problem in that particular vertical.
Kevin Horek:It's also interesting because then you're not pigeonholing yourself into one vertical, right, where your startup becomes way more valuable because you're not just pigeonholed to one industry. And if something happens, like, I don't know, like Apple releases something in that vertical that's identical and gives it away for free, hypothetically, it doesn't wreck the whole company. Right? Like, you've you've seen that kind of happen. You're like future proofing the company.
Kevin Horek:Right? Do you agree or thoughts around that?
Sean Feehan:Yeah. I like to think of our company as, three small companies or 10 small companies in one giant trench coat. Okay. So all stacked on top of each other.
Tom Blair:Well, I think we are focused. We're just focused on AI, and that's why partners are so important is because all that subject matter expertise, whether you're a lawyer or a doctor or a teacher or an accountant, they don't expect us to know their business. Right? Right. And so that that interaction and that communication is essential.
Tom Blair:Right? Because, you know, what we're trying to do is the power of AI lies it's in its ability to make us more human. Right? And allowing us to focus on what matters. So if you can take out the toil of the actual LP reporting and focus on the analysis of your portfolio, right, that that that's beneficial to us.
Tom Blair:Right? It's not replacing your job. It's augmenting what's going on. Right? And and so I I really think that these types of systems will affect virtually every industry out there.
Tom Blair:There's a huge opportunity.
Kevin Horek:Yeah. Interesting. So you've both been through building multiple start ups. What advice do you give to people either before they're starting to actually make the leap? And what advice do you give to people that are, you know, kind of in the trenches building these things?
Tom Blair:I you have to swallow your pride and dial for dollars. I it's it's a hard thing to do, but I have called everyone I've met in the last thirty five years. I I just it's it's you have have to be obsessive, and and and persist. This startup we're working on right now, the original ideas I wrote down in in our early patents seven years ago. Uh-huh.
Tom Blair:Right? And, you know, we built for three years on a vision, and not everyone can afford to do that from a time or money perspective. It's not optimal. But, if you have a vision, the message is you have to persist and sometimes wait for the market to catch up to you. Right?
Kevin Horek:Yeah. That's good advice. Sean?
Sean Feehan:You know what? I'll I'll get some advice from the technology side that I've figured out. And, for anybody who's sort of leading the technology branch of these startup companies is really understand the business, understand the flex points in the business and where it is most likely to change, and design your application to allow that flexibility, because it things will change inevitably. And so if you've built something that is not flexible in the same places, you're gonna run into a whole lot of pain and problems in rebuilding. Whereas if you build an application with a bit of foresight, that can change, depending on how the company is built, it's much easier.
Kevin Horek:Oh, I % agree. It it it's a bit mind boggling how many companies don't do that from a tech side of things. But it's also I find like a lot of people can't see that, right, or like figure it out. And like, I guess a simple example is what I've seen recently was somebody somebody implemented just Google sign on and picked a technology that's all they could implement. And then another client came to them and said, oh, we need to sign in with Office three sixty.
Kevin Horek:And they had to rewrite the whole thing. And to me, I was like, well, that's a no brainer. But I've seen that happen time and time again. So how do you motivate or get your team to think about kind of that kind of crude example of it across the entire platform? Because sometimes it adds extra work, timelines, etcetera.
Sean Feehan:Yeah. That's that's a fine touch, and that's learning a lot of hard lessons to get there, to be perfectly honest. But I think the most important part is that your development team needs to be fully invested in the business. They need to understand what's going on. They need to understand what's gonna happen or at at at best how you can kind of how you're foreseeing the future and some of the possible changes.
Sean Feehan:And the more you can get your technology team to understand the business, the better your platform will be.
Kevin Horek:And and so how have you in the past worked with the entire team to understand the business? Because, like, is it meetings? Like, what is the what's some advice to actually make that happen?
Sean Feehan:Yeah. I think Tom's really good at that, to be honest. We do a weekly all hands meeting where Tom's just explaining here's some of the things we're thinking about, some of the things that go on our industry. Here's clients in who we're working with. And a lot of that just sort of sharing that information, and and exposing everyone in the organization to that information is really valuable.
Tom Blair:That's essential when you run a global distributed team where we're inherently dis distributed. I'm in Southern California, Sean's in Edmonton, and people are spread, you know, from Asia to Europe on our team even though we only have a dozen people. And so that communication and and the camaraderie and the community that you build is as essential as with any other business. And it's taken us a while to do that. You know, building a team is an evolution, especially as you're going through potentially brand changes or market changes and all the things that you, you know, the transitions that you go through as a startup.
Tom Blair:Right? And and so if you have a core set of people that believe in the overall vision, then, midstream changes don't really affect the team as much. It's that it's really important to have communication.
Kevin Horek:Makes sense. Advice for managing a remote team because a lot of the big companies are making everybody go back to an office, and a lot of people aren't happy about that.
Tom Blair:I I have a a strong philosophy about this. I, you know, I've worked in 38 countries, and and, I hire the type of of people that are independent. They're smart. There's not a lot of risk when you hire somebody that is a world traveler and educated and a specialist in what you're looking for. So it's right.
Tom Blair:I will see where we go as we grow. I've never had a fully, fully remote large company.
Sean Feehan:Okay.
Tom Blair:We'll we'll see where this goes. Right now, I'm I'm pretty confident that, our world headquarters will remain in our houses.
Kevin Horek:Fair enough. Sean, you were gonna add something?
Sean Feehan:Nope. I think talk good with that. Okay.
Kevin Horek:No. I think yeah. It's it's interesting. It like, I think the weird thing I've always found is somebody that's a creative person is it's like you want me to be creative nine to five, Monday to Friday in an office. And then if you put in a dress code where I have to be like, wear something I'm not comfortable in, it's like, okay, you're like shooting yourself in the foot in my opinion anyway.
Kevin Horek:And so it's just an interesting I never got it until, like, COVID hit, and then everybody seemed to get it or had to get it.
Tom Blair:But we take it a step farther. We try to hire, engineers in the North. So if you're in, like, Canada or Norway or, you know, Georgia or wherever, because it's dark most of the year and And cold. Got nothing else to do. So Yeah.
Kevin Horek:When you can't go outside, it's the you're just totally
Tom Blair:remote work. It's remote work from the tundra.
Kevin Horek:That's right. There you go. That's that's the word to live by there. Any any other kind of thoughts around kind of AI right now? There's a lot of hype around it.
Kevin Horek:There's a lot of, like, overpromise, underdeliver stuff. Like, what are your guys' thoughts on kind of where we're actually at with it? Because I think there's so much misinformation out there, and there's a lot of the misinformation is coming from the people actually building it, and it's driving me absolutely crazy.
Tom Blair:I have a rough time doing this. I got my degree in AI in the eighties. And, you know, what's happened since then was the Internet and 1,000,000 times computational improvement. And so Okay. That was even sitting there at Kodak and knowing about Moore's law and, you know, trying to solve the problem in real time, it's I I can't even imagine what thirty years from now will look like.
Sean Feehan:Makes a sense.
Tom Blair:And so we really do have a scope of five years ahead, you know, two years ahead, and even that gets very foggy. What we do know is that there's a huge market out there that needs to take advantage of these technologies and and but but yet are unable to do so because of capital constraints or human resource constraints. And that's where we come in.
Sean Feehan:No. That makes sense.
Kevin Horek:Sean, anything to add to that?
Sean Feehan:Yeah, I think part of what we've found success in is the show, don't tell methodology. So to actually demonstrate here's exactly how we'll help you solve your problems and get people, get people's feedback on it. Let them go through the process, and really understand not just the inputs and outputs, but how the whole process works together. And once we're able to show that's when you get belief. And, and so that's our entire approach with our partners is is to show them here's exactly how it works.
Sean Feehan:And and the more you can demonstrate, the more there is belief that it will actually do the things that are promised.
Kevin Horek:Yeah. No. I think that's really good. Do you wanna talk a bit about the business model? Because you have a different approach than, I think, traditional companies ever mind the startup take.
Tom Blair:Sean, do you wanna go over that?
Sean Feehan:Yeah. Like I said earlier, we're building technology wise, we're building a platform. But what we're doing is we're working with partners to solve actual tangible problems that they're dealing with. So we're kind of a weird hybrid consulting workflow and and platform automation. And then, I guess not really an LLM company because we don't build our own LLMs, but what we do is we call pick best of breed.
Sean Feehan:And we, allow companies not to have to become experts in AI or in which LLM is, de rigueur right now. And and so part of part of our methodology is to be that consulting partner to help them solve those reputable problems. And then part of it is to, essentially productize those agents that we build and then move into that individual market. And I think the really neat thing about this model is that, given enough the support constraints, we can go into five, ten, or 15 different verticals, and attack problems that may be smaller than, are able to support a regular, a one off company in that vertical. Yeah.
Sean Feehan:Go
Tom Blair:ahead. What we've really found effective, in our go to market strategy is to, alleviate people's concerns about accuracy, privacy, and security. And so the way we prove that to them is by running, a thirty day pilot where we work with them. You know, there's a human process and they're interacting with spreadsheets or PDFs or databases. Whatever that human process is, we work with them and effectively emulate that.
Tom Blair:So at the end of the thirty days, they can see what the human cranked output is, and they can see what the automated agent output is. And once they see that and see that it's secure, private, accurate, traceable, explainable, then it's much easier for them to sign up. So we do a thirty day free proof of concept. We prove that we're gonna do it, and then, that converts into a subscription agreement. You typically, it's for one tenth of the cost of what the human, is performing enough for.
Tom Blair:So if, you know, you have a a full time professional at a hundred thousand dollars a year, and they're performing, you know, x, y, and z set of work, and we can automate that. You can usually do that for 10 to $20 thousand or $2,000 a month.
Kevin Horek:Very cool. Well, we're kinda coming to the end of the show. So is there any other kind of advice or takeaways that you wanna leave the listener with, and then, let you guys get on with your day?
Tom Blair:Iclark dot ai. We're love to have conversations, and, we're learning along with everyone. So please contact us, and we would love to discover
Sean Feehan:together. Cool. Thanks, guys. K. Bye.
Sean Feehan:Thanks, Kevin. Awesome.
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