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- [00:00]
Jason
If you create an environment that is embedded into a company where it is people go to interface with AI. So think about like Chad GPT. If you give people a place that where that is where they go to ask the questions and that place is connected to everything the company's doing and it's connected to the company's knowledge and who they are and the team, then what you get is you get a cohesive place that everyone can get that same benefit. The people that are great at it, the people that aren't great at it, but what you ensure is that everyone that uses it is driving the company forward in the same direction. And that's what people think. How you're going to use AI in your company is like it'll be here one day and what I'm telling you is it's here today. And you can literally onboard an AI execution layer inside your business today and start getting value today.
- [00:51]
Chris
I've been really excited to do this episode on a business that my partner Jason and I just launched. Fosterai.com f o s t r a I.com today we are going to talk about the challenges in AI, the solution that we've come up with and what our company, Foster, is doing about it and where we are in the life cycle. We want to hear from you. So you can go to FosterAI.com and you can fill out an early request form to become a early customer. You can email me chrisosterai.com if you are interested in investing or you can email Jason jasonosterai.com f O-S-T r a I.com if you want to learn more about how to become a customer or get involved with the company or work for the company. We are actively hiring and have four critical roles. We have an AI specialist that we need to help lead AI innovation. We have a head of sales lead that we need to help build this pipeline that's already growing at a rapid rate. We need customer success in onboarding people. So if that is a background that you have and you want to be involved with us, we would love to hear you hear from you. Email Jason, because we already have a growing list of 30 plus organizations that range from five employees to 3,000 employees, not just in the US but globally. Already on our list and in the next few weeks we see that growing to over a hundred people. So we need people quickly. We need people that are passionate and that have a background in these areas. I can't wait for y' all to listen to this episode and hear what we've been working on. I think this is one of the coolest things we've done to date. What are the challenges that businesses face today when interacting with AI?
- [02:48]
Jason
There's a lot of them, but I think the biggest challenge is really broad. It's the noise of AI. If you go research today or just Google anything, Even go to ChatGPT, there are not just 10 different options that came out today, there's a hundred. And there's new things happening every day which make it very hard to understand as a business how to use AI. What is the best way? Where do I start? What are the tools that are going to work for me? Where do I deploy it in my business, how do I think about it long term, all those things, right? You could just keep going like that for an hour. There is no clear answer for a business today on how to utilize AI for their business to move their company forward in the direction they intend. A lot of people are looking at it as like, how do we get more efficient? How do we use a tool to speed up? How do we use a tool in a certain segment of our business? How do we start to automate a few of the tasks? There's a lot of these questions happening. A lot of people are doing things, A lot of people are using the tools. There's very little cohesion around how to actually do it as a business or how to implement it as a business. So a lot of companies are just frozen. And when I say a lot, it's 90% of the companies out there. And a lot of these studies have already been done over the past year is 80% of companies want AI, but almost no one of them are doing anything about it.
- [04:12]
Interviewer
Right.
- [04:12]
Jason
So 87% of companies that they survey say, yes, we would like to do AI, and none of them have a plan on how to do it. And so they're just trying the best they can. And so, you know, I say the challenge is trying to find the solution to make it easy for companies to. To utilize AI across their business. And for me, what that means is to create a place where a company has security insight and the ability to start using AI in a controlled way that is scalable across their organization and that they ensure drives their business in the direction they intend more efficiently and that it's measurable and trackable, and they can ensure that their data's protected and that their team is all using it the right way and that the intended outcome is somewhat controlled.
- [05:06]
Interviewer
Right.
- [05:07]
Jason
And so. Or actually very controlled. And so I think that's the, the challenge is that businesses are just frozen.
- [05:14]
Chris
Do you have an opinion on what some companies are currently doing that and we're, and mainly I think we should, the audience should know a lot of who we're talking about are, call it small to medium sized businesses.
- [05:27]
Jason
Yeah, mid market, but it's mid market, 50 person company to even a thousand person company.
- [05:33]
Chris
The ones that we've even talked to that were like are you using AI? And they're like well yeah, we use ChatGPT every day or we do this every day. Like is there a common thread amongst people that think they're using AI that you would make an argument is technically you're typing things into an AI but you're not really using it.
- [05:51]
Jason
Yeah, I think this is where I think the biggest issue is with that. When you ask that question, when you, when the person gives you the answer is that all the tools they currently, currently use, not all of them but many of them.
- [06:03]
Interviewer
Right.
- [06:03]
Jason
Slack, Salesforce, any pick them.
- [06:07]
Interviewer
Right.
- [06:08]
Jason
All of them are giving an AI option within that technology. And so the company starts to feel like oh well we don't really have to implement AI into our company because all of our tools are going to have AI. That is, I think that one of the biggest mistakes that people are making because they feel like they're using AI because one of those softwares have a place for them to ask better questions or get quicker insights. That is good, that is creates speed and efficiency within that thing you're using. It does not drive value the way AI is going to drive value in businesses like some companies are going to do. And those, some companies that do it internally in their business are going to excel 400% faster. That, that's actually a real stat, 320% faster than other companies because the embedded AI in the business is actually driving results for the business as opposed to one tool making a person more efficient in their job. And that's where we sort of will talk about the, the, the opportunity and the challenge that we're solving, which is the fragmentation of AI. Right. So all these things you currently use have an AI. What does that create? That actually creates more fragmentation of the data that you're trying to understand or that your company's trying to utilize to move forward quickly. You're going to have quicker access to that data, you're going to have AI telling you things and you're going to have really good insights in a silo. And when you get individual teams and people using individual solutions in silos, you can actually drive misalignment quicker, right? You can actually push people apart quicker. And our, our view on that, based on our last two years of using AI every single day inside of our company, is that if you, every person is going to eventually have to use a version of AI, even if it's just chat, GBT or a tool that they're currently using has an embedded AI, that person is going to find their own way to get more efficient at AI or use it themselves. So if you take a company that has, say 100 people, you're going to have 10 people in that company that are naturally just built to use AI in mentally, right? How they, how they operate, how they think, those people are going to become really good at AI and they're going to become really good at finding the tools that work for them. And that might not have anything to do with the goals of the company or the vision or the connection to the team. So what you're going to have is some people that outperform, appear to outperform, but may not be aligned with anything in the company. But the risk there is that the company has no view into how that person is being more efficient. And there's going to be risk there, right? And there's going to be challenges of how do you take advantage of the ability of that person who just happens to be really good at AI, and then the other risk factors. If you get bigger companies, you're going to have people that are able to take advantage of AI to make their world more efficient, which is essentially the ability to potentially take advantage of a company or to automate themselves. And some CEOs or companies will be like, that's great if they can make their job more efficient and automate themselves. But how do you know as a CEO, that's the problem is how do you know if somebody's automated some things in their role because they just happen to be really good at AI. So what we think is that if you create an environment that is embedded into a company where it is people go to interface with AI. So think about like ChatGPT, if you give people a place where that is where they go to ask the questions. And that place is connected to everything the company's doing, and it's connected to the company's knowledge and who they are and the team, then what you get is you get a cohesive place that everyone can get that same benefit. The people that are great at it, the people that aren't great at it. But what you ensure is that everyone that uses it is driving the company forward in the same direction. So what we're really saying is we believe that the benefit of AI within an organization is going to be how do you use it to create the alignment? So can you get all the people to become more efficient using AI to drive the company forward in a. In a direction that is intended.
- [10:18]
Interviewer
Right.
- [10:18]
Jason
To achieve the company's goals, which in turn would benefit all the people.
- [10:23]
Interviewer
Right.
- [10:23]
Jason
Because that's why they're there. And so we think that that alignment piece is what's going to be most valuable.
- [10:29]
Chris
Are there any additional challenges that you've observed besides what we've already talked about?
- [10:34]
Jason
Yeah, I mean, I'll just summarize real quick because I think it's a good, good way to just like wrap it up that that topic is there. There's no centralized AI execution strategy across organizations. There's no, there's no plan for companies to have a centralized AI execution strategy. Every business wants AI, but almost none of them have a plan to deploy it. Independent implementation is going to be a problem. Different teams implementing AI independently leading to siloed solutions is going to cause entropy inside organizations. And risk that's going to happen. It's going to be rogue AI use across organizations. There's no clear alignment. AI implementation rarely aligns with the company's businesses, their business vision, or the leadership. There's no one that we have found except us solving the problem of providing an AI solution inside an organization that aligns and maps to the company's vision and ensures that all the interactions with AI in that company drive towards that vision. And it's completely flexible. As the vision changes, as priorities change, as they fluctuate, as goals change, the AI is designed to keep moving towards the direction that is intended. That is decided by the leadership.
- [11:53]
Interviewer
Right.
- [11:53]
Jason
That is, that is not happening. And then the lack of government governance. So there, there is no plan right now of having oversight or, or control over how people use AI inside of an organization. And you think about all the risks inside big companies legally, there, there's got to be a place that you say, this is where we use AI, not you use ChatGPT, you use Claude, you use Grok, you use this. Everybody uses their own solution. I only use the AI inside Slack. I only use the AI inside this. Those things are going to become problems. Right? So we need a place where all that can connect in a unified layer inside the business. So we think that's the biggest challenge.
- [12:34]
Chris
How would you. How are we creating that alignment? How are we creating the thing that everybody logs into and then how does it. And you've used the word context a lot.
- [12:44]
Jason
Yeah.
- [12:45]
Chris
So how are we creating a digital twin of business? And again, maybe you'll be redundant. Why? Is that better?
- [12:52]
Jason
Yes. So I, I started with the problem of these, all these different tools and people being misaligned by using them and creating silos and data and silos and use of AI. Because you, if you want to use AI efficiently in a company, that AI, what we have discovered, and I think one of the biggest value components that we have utilized ourselves, is that if you start to use AI with a core knowledge base, which is context, which is what you just mentioned, if you start there, everything you do inside your company's AI from that point forward has that context. And it ensures that the information you're getting or the direction that you're going is only driving you in that direction. And so that context is really important in the way that we have decided to or discovered to do that the most efficiently is to make sure the AI understands who the company is, why it exists, who the people are, why are they there, what do they do, the roles, responsibilities, things like that, everything about the people, and what are the goals of the company. If you start there with this core knowledge base that you train the AI on initially, you can start adding data, adding more resources, adding more external tools to that AI, and all you get is improvement because the context is based, or the AI is based in the context of why that company exists, who they are and what they're doing. And that creates a whole new environment in order for people to go interact. With AI, what you get is a lot of really good information and insights that's grounded in truth. Where with general AI, what you have is relying on people to go create good questions, prompts.
- [14:35]
Interviewer
Right.
- [14:35]
Jason
And every person's going to prompt differently. And you don't know if that prompt is going to drive that person's outcome towards the company's goal or in the right direction, some people are going to be good at it, some people are going to be bad at it. And so when you use an external, general large language model, you can't ensure that that outcome maps to who that company is. But if you start, if you do that same question internally and you have that core knowledge, you can for sure guarantee that the answers are going to be rooted and driving in that direction. And we do that with a proprietary way that we've built our context engine, which is what Foster has, where, how we collect that information, how we structure that information and we use that information in our AI, which is Foster. And that context engine is very important because it creates that digital twin, right? So the first thing we do is we map that company to that knowledge. And when you start that way, everything you feed in there that you know to be true in your company. So think about like processes, processes in the company, financial data. If you want to add that, anything you want to add that becomes what we call a truth, a fact, right? That is hard coded into the AI's brain. That, that is something that you don't want the AI to ever hallucinate on. You want the AI to only be able to answer questions related to your company that you know to be true. And so that part is very important. And that's what a lot of companies are not getting. If they have teams using external large language models, you might have a good employee or a good even executive that's really good at prompting, but there is still going to be hallucinations that they're trying to solve or get past. In general, large language models. That has not been solved. And we're not really close to it being solved. We're getting better at it. But even still, you can't ever be sure that it's going to give you insights that have nothing to do with your business. Right. What we've done is we've solved that by building that into our company.
- [16:44]
Chris
How long would it take? That sounds like it would take a long time.
- [16:47]
Jason
Well, that's why we decided to launch Foster and because we solved this problem. So what we really solved was the ability to map data inside of a company into a structured way that is creating the environment immediately for them to interface with AI against their data and against who they are as a company. And so we've built an onboarding process that is automated using Foster. Foster does this on his own. And it literally takes minutes. And so with just going through a, a series of questions and dropping files in, you can start to build the organization digitally within. You know, in some cases we've onboarded clients. We've already onboarded many clients, and in some cases it's been 10 minutes because they already have, you know, their org chart and they're a list of their employees and their processes are just in a file, like documents, right. They just drop those in and Foster immediately maps them and Foster gets an understanding of who the company is. Another cool thing I'll just say is like the way that it really starts is a company that wants to use AI. An AI can learn more about your company. Than you can imagine today. And so the way that we've structured that is you by simply somebody typing in their website address when they're onboarding with Foster. Foster will go do all the research in less than 10 seconds and show you everything it knows about your company. And then that can become the beginning of like understanding who the company is. And then you just add the context from that point forward.
- [18:22]
Chris
Okay, if you're sitting here and you're like, that's awesome, I love this, but does my company need to already have a certain amount of stuff to be able to do this? Like, what are the requirements that a business would have today? That's not an AI that would quote, unquote, get them onto Foster.
- [18:41]
Jason
That's, that is why we see the biggest value component, because they don't really need to have almost anything. Every company. Now we want to work with companies that are, that are intentional about using AI. We don't, we're not trying to go transform companies that have no, we don't.
- [18:55]
Chris
Want to convince you that you need to.
- [18:56]
Jason
The people that are going to take advantage of Foster don't need to be convinced. They are looking for the solution and there are hundreds of thousands of companies out there looking for the solution. And so what they need to know is why their company exists. They need to know, they need to have the, the data around who their team is, just simply who the employees are. And they need to have a direction and vision of the company. If you have those three things, then within minutes you can be on AI asking questions that will be very impactful to your business. The next step is, is really up to the user. How much do you want to get Foster immediately to get that insight or to get feedback?
- [19:35]
Interviewer
Right?
- [19:35]
Jason
And that's where we have created the environment for them to connect any data, any outside service, any outside tool. So APIs, APIs, any connection that you can imagine. I mean, we already have 70 identified that are most common in the market. I mean, just think anything slack, email, calendar, outlook out, anything, right? So anything that people use on a daily basis, all that is easily connectable to Foster to, to fuel the context, the fuel that engine, right? So that every day that somebody asks a question, it has the context of those things, of what is happening in an individual's world, the company's world, what they're trying to accomplish. And so you, you get really good context by being able to connect anything you want. So the company doesn't need to have much. It's really where do they want to go with it? And how much do they want to add to it? And that's where we guide the company to do that. But the onboarding process is literally minutes, if they want it to be. But just say the first day you're going to be up and running with Foster, within a couple of weeks, you would probably have significant, significant amounts of data in there. If you chose to, to be able to really have insights into your company. Think KPIs, reports, dashboards, all those things can be connected. And then in the future, Foster, I mean, if they want to, Foster can even generate those for them because it's a robust operating platform that has that capability. And so they don't need much to get started. It's really. Are they looking for the solution to aggregate their data in one place and interface with AI? And then everything from that point forward will just fuel the company to be able to adopt AI quicker. So what we're really doing is we're creating an intelligence layer inside of every business that allows them to interact with AI against their own company's data instantly and scale AI across their organization in a controlled environment secure with. This is a big, big point. With the permissions around how they want their company to use it. So with general large language models, you don't want an employee dropping in a financial statement from a company that may be private.
- [21:49]
Interviewer
Right.
- [21:50]
Jason
That's what we've solved is that the permissions that live inside of an organization, who has access to what lives inside Foster, so that the person that's interacting with Foster in the company, they can ask all the questions they want, but they're not going to have access to data they're not supposed to have access to. So Foster's only going to give them the questions that are related or answers related to their world and give them all the solutions to their world. Not private information that, say, an accounting team would have to protect. Right. So all those permissions are built in.
- [22:23]
Chris
So to wrap it up, if you had to describe Foster in totality.
- [22:27]
Interviewer
Yeah.
- [22:28]
Chris
What is it?
- [22:28]
Jason
Okay. Foster is, is truly. It's the first AI execution intelligence layer that's actively monitoring people, goals, the organizational structure, and aligning with what the vision is of the company.
- [22:41]
Interviewer
Right.
- [22:41]
Jason
It's literally doing that on an ongoing, everyday basis. We create a digital twin of the company. So it digitizes all the company's DNA, right? Capturing the vision, the history, the strategy, everything. It uses a proprietary alignment assessment tool that we use where it understands the team, the team dynamics, the strengths and weaknesses of the people. And all this happens very Quickly think like Myers Briggs and Enneagrams and all those things, right? There's really good ways to understand people in the world we have. Foster is able to do that by just seeing the dynamics of a team and asking some very basic questions.
- [23:17]
Interviewer
Right.
- [23:18]
Jason
That, that gives Foster some really good insight. So we're mapping the company on, on one high level and a very deep level, right? So Foster has great context of the company and then we have an alignment engine, right? So it's continuously aligning that execution real time. That's our, our, our engine. Right, our context engine. It just happens every day. So every action, every time a goal changes, every time anything is updated that you want to have connected into Foster, Foster is going to have the context of what that means against what the company's trying to accomplish. So you have this constant fuel of context feeding your AI. So every time you ask a question, it's going to really know why you're asking it and what it means. And then it tracks execution across the key initiatives. So imagine it understands the goals and it's seeing what people are asking, it's seeing what people are saying. Because again, we didn't talk about this part, but if you feed in, say, meeting summaries, Foster understands what that meeting summary means because it has the context of that meeting, why that meeting exists in the company and who the people are in that meeting, who said what and what do they do in the company and why are they saying it. Foster knows that intuitively because it understands the everything about not only the company, but the team dynamics and the people themselves. So the context that gets out of those meetings is super valuable. And it can then compare that against the goals of not only that individual, but the company. So imagine it's tracking the execution across the initiatives of the company and giving feedback as to are you on track, off track, aligned, misaligned. Are people talking about things that are different from what we say? We're going to do that type of thing. So it's constantly tracking the execution and then we make it really easy to set it up. So all that, that I'm saying sounds complicated and sounds sort of like futuristic of being able to use AI to get all that information in your company and see it quickly and like we talked about briefly, but you can deploy AI in a company in just one day in this form. You can create this execution layer, this AI intelligence layer, in one day and compare that against like traditional enterprise solutions, even on the AI side. Today they will work for months or years to try to organize data Structure it in a safe way, take that data, go to, try to train it against a specific model, try to deploy that over time. That's what a lot of companies are doing. That takes potentially years. And that's what people think. How you're going to use AI in your company is like, it'll be here one day. And what I'm telling you is it's here today and you can literally onboard an AI execution layer inside your business today and start getting value today.
- [25:58]
Chris
So we've been working on this a while. Where are we in the life cycle of Foster right now?
- [26:04]
Jason
Yes, that's a great question because we've been working on this for a long time. And the process to getting to a point where you have a really valuable product is something that you want to make sure you've checked all the boxes, right? And so where we are in the life cycle of Foster is that we have built the tools completely to be able to do this instantly and deploy it into an organization and give value immediately. We have onboarded the first five customers or clients, organizations, and those range from companies that have 50 employees all the way up to 3,000 employees. So these are all companies that fit in that middle market that are all ready and willing and trying to adopt AI. So these are like ready, willing customers that we're onboarding. And we have gathered a list of 30 plus additional clients. We have a waiting list. At this point, we fully prepared Foster for deployment across large scale. So we now know that we can deploy across hundreds or thousands of organizations with no real limit to the number of employees they have. And we know we can do that at scale. So we're positioned now to start onboarding clients more rapidly. And so as we're preparing to do that, we're preparing to scale the team and grow the organization. And so now we feel it's the right time to go to market, raise capital, bring on the right partners. Because, you know, we've done this from the beginning with no outside capital. We've built our, our product completely ourselves. We've funded everything ourselves. And we've never really had the thought that we would even need partners or financial investment. But we do know that when you growing a product like this, that the, the partnerships and the strategic relationships that you form are really what make a product successful like this. And it connects you to the right expertise in this market to continue to scale the right way. And so we're not, we're not blind to that. And we know that we're going to need to scale across not just teams and grow teams, but we're going to, it's regions and locations. Right. So this is obviously a digital asset and digital product and so it's not a local thing. And so we already have companies that are onboarding globally and so we got to be prepared to scale globally. And so because we know that scale is coming, we're targeting some specific hiring that we're doing more immediately, which is customer success. Right. Onboarding, that is very important.
- [28:36]
Interviewer
Right.
- [28:36]
Jason
Even though it's very easy to onboard, we want people there to do hands on to help those companies scale quickly with AI. And so we're bringing that on. Obviously we need some, with that becomes some technical assistance behind that person that is doing the onboarding and then doing those integrations, right. Connecting those APIs, connecting those layers. And so we're going to have a, we have a really good mapped out plan for our team structure which is pretty standard in the industry. But we're going to scale those teams quickly so that we can ensure the rapid onboarding of all the clients that we currently have. And so where we currently stand is we anticipate having 30 to 50 fairly large organizations onboarded in the next three months. And during that same time we're rolling out new features and new additions and new things inside Foster that are currently being developed on the back end. And so we have our completed product today and we'll have new features coming very, very quickly thereafter and we'll have a continuous rollout of that where we're be able to offer additional solutions to companies using their AI.
- [29:40]
Chris
Are there any other additional critical roles right now?
- [29:42]
Jason
Yes. Obviously we want to continue the momentum with the sales side. Obviously between you and I, we have a lot of relationships and we've. The sales side has really been handled by us, but we do want to bring on really good experts in this, this market, in this industry to be able to basically go out there and talk about Foster in the right way and continue that momentum, which is what we're bringing on. So the sales side. And so along with the support of that, that implementation team and the technical people at the customer level, we are really looking for additional talent in the AI machine learning space that's really thinking longer term of where this goes so that we can stay ahead of the curve. We have a great team and great expertise on our team now and a lot of external resources, but we would like to bring another expert on that helps us scale rapidly. So we're looking for all of those positions and we plan to continue to do that as we scale.
- [30:38]
Chris
Okay. We're in the process of now raising our first round of outside capital. Jason and I have funded this and poured our lives into it for the last few years. We are looking for angels or top tier VCs to talk to. We are going to close this round on May 15th and if you're interested in hearing more about the company or potentially investing in this company, email me chrisosterai.com that's C H-R-I-S-O-S-T-R-A I.com I hope you've enjoyed this episode of the Fort Podcasts. Be sure to follow us on your favorite podcast platform or hop on over to YouTube to watch full video every episodes, if that's what you prefer. For more information, you can check out thefortpod.com.