C (2:37)

So let's start kind of at the beginning, right? And we won't, we won't go too much in it, but you know, first met you kind of speed read AI. So I guess kind of probably a question for Aaron and then probably followed up by Ben is that quickly kind of touch on what is speed read AI? Just high level. But what was kind of the catalyst of or what. What happened that prompted you to start full speed AI? Like what were you seeing out in the market where you're like, wow, there's a market need for this, we should go do this.

B (3:06)

So basically Ben is like gets very, very impassioned about like building different and new, new softwares. And he basically became fascinated with AI and said, I'm going to create something that completely fixes the entertainment industry and solves a problem, which is reading and analyzing scripts very quickly. Because, you know, if you're in an entertainment company, like a film studio or management company, the whole idea is how are you going to get through hundreds and hundreds of scripts? And the way it used to be is, you know, like people would have to read it and it would take hours and then you have to write these big reports so that people could kind of read a synopsis. Well, speed read AI, which Ben created was basically made this entire thing much easier. It can read a script in about 30 seconds. It can do a full analysis of it and it can completely, you can ask it questions. You can be like, can this script be made for under $2 million? And it could also, like if you had an edict at your company, like find horror movies that are under, under could be made for under $2 million. You could run 100 scripts to it and it'll say these two seem like the best ones. You should actually read by yourself, like actually with human eyes. And this got, you know, a lot of traction. People really like this. They said, this is making our lives easier. And we said, you know, we're on to something here. What if we could do this for all kinds of different industries? Like we could do it for, you know, any, any kind of industry. Could be education, could be entertainment, could be health care, could be legal, could, could be whatever. And we really started to talk to people and because the thing about AI is like, everyone knows it's here to stay and everyone knows it's going to change our lives. But really what I think excites people about it is that they are, it could help them. And we were talking with, you know, senior executives and they're like, I know AI can help us, I know it's powerful. We just don't know exactly what to do. Because there's all the time, right?

C (4:54)

There's like all the time.

B (4:55)

Exactly. You know, so there's like a thousand different softwares they can buy off the shelf, but they're like, this is a good software, but it doesn't really do exactly what our workflow needs or how do we integrate it? So we're like, we'll come in there, we'll do a full blown customized software solution for you that actually integrates into your exact workflow and so that you can use it and it's exactly catered to your needs. And that's the whole idea. And that's what people really like because they're like, oh, okay, great. So like I basically can click a button that's tell that does my job or not my job, but everything we need to do. And I can save six, seven hours a day and we can save tons and tons of money and this actually helps us more. And what we like about this is it's not trying to replace jobs. We're not like, all right, this is going to get rid of all the jobs if we just have software. The whole idea is by putting this out there, the people who were like slaving away on really menial tasks like all day, that can get done for them. And if so, if you have like really creative people in your offices and stuff, which a lot of people do, you can actually use their creative mind a lot better and do all kinds of new systems and new ideas and new development. And because the softwares that we're building are taking care of a lot of your workflow. And that seems to be very exciting for people. Like, they're really like, they're like, this is perfect. Come and build one for us. And that's what we've been doing at full speed. AI.

A (6:10)

Yeah. I feel like we were talking last night about how like anyone who's had a job has had moments where they're like, why am I doing this? Like, why? This isn't what you're paying me to do. There's always some aspect of the job that like, is just kind of a waste of brain power.

C (6:23)

Mundane.

A (6:24)

Mundane. Yeah. So I think those are areas where it can be really key. But yeah, I, I second everything Aaron said. And another thing is, as we Were talking to people, we get a lot of like, oh, this is great. Can it do this or this? You know, And I spent years and years as a freelance developer. My answer is always like, yeah, we can do it. Can do anything. You know, like, we can do, we can create what you're looking for. And so we started getting these conversations and even people would see the product and be like, I don't use scripts. But like, this makes me think of something we're doing is kind of similar. Like, you know, just all these custom requests where, like, you know, there's a lot of people who need full speed. AI is kind of more, a little more bespoke where we go in like, let's build it for your business instead of a, you know, one size fits all product. So that's been kind of fun. Like, that's, I think that's our favorite part is like figuring out not only what you need, but like, what is your workflow and how do you want it to work within that? Because it's, that's a question a lot of people don't actually consider because they're so used to being, having, you know, proprietary software forced down their throat that it's like, okay, now I have to do it this way. It's like, well, let's try to make it work so it works the way you work.

B (7:30)

You know, we've seen some really great software out there and it does amazing things. But like, if you're a company in Canada and this, the, you know, the software was built in Florida and, you know, it's like a one size fits all. When you start using it, you're like, all right, this is cool, but it's just not doing exactly what we need to do. They don't understand my Company X or whatever. They don't understand exactly what we need. And you can't exactly reach out to them and be like, hey, can you work with us on this? It's like, you know, like some kind of chat bot that's like, it's kind of in a box. Customer service. Well, with us, it's a very collaborative conversation. So when we build stuff for people, like we're talking to them, we're building, and then they say, oh, can it do this, can it do that? Or, or I thought I wanted it to do this. Now can you change this? So we're just constantly talking with them and if something's not working right in it, they just call us and we go, all right, let's see if we can tweak it to what you need now because things evolve. So it's just this constant handholding and getting the software where they need it to be. And we think that there's a need in the marketplace for kind of that we call it a translator between like all the AI stuff going on and then like a human actually programming all that AI stuff so that it works for the company. And that's what people have been excited about.

C (8:40)

That's what you're doing. So. Okay, makes it makes total sense. And I, and I agree is that there are, you know, a lot of different tools. And I mean, I think in AI is still very much. My opinion is in its infancy. Right. And you'll see consolidation of tools and all that. But like, I think every company right now is very much at A, hey, we want to adopt, but what's holding them back is we don't know what we want, or B, it doesn't necessarily work with what I want. Which is definitely something that I hear, you know, all of the time. Right. And I think for those listening is that we're not. When we say, you know, workflows replace humans, that's not the idea. The idea is the mundane task. And I'll give you a perfect one, not related to what you all are doing. But I like to talk. I'm not. I can write, but I'm not great at making slides. But I really hate to write sometimes. And so I do these episodes, right? They're great, you know, haven't prepped at all. And Ben and Aaron can verify other than like a couple of questions we talked about before. Like, I'm just spitballing at this point, but I was asked, hey, can we turn this into a blog? And I'm like, ooh, gross. Like, that means I'm going to have to listen to this episode again and type out notes. And that's what I was doing until I built a little radio app that uses an Nvidia model, can transcribe all of the audio and basically I put into a little rag. And then I write the blog post and prompt and the radio app kind of this rag chat bot that'll make me at least it gets me about 90% away there of a recap of this article. And that's the mundane task. It's the task that you have to do, but you don't want to do. And it's maybe not your favorite, right? So next question within, you know, full speed AI, when you're creating something custom, that really opens up for you to potentially do a lot of Things. But where, where are you focusing in terms of kind of the sweet spot of like those couple of different workflows, a couple of those different things where you feel like you have a good handle on the market. Like what are you focusing on? Is it, you know, extension of Speed Read AI? Is there other use cases that you're tackling right now? Because to say, hey, I do everything is not a sustainable model, you guys know that. But what are the couple of things you're focusing on?

A (10:52)

So like within the entertainment industry there are some cases where it's kind of, in some ways building on what we do with Speed Read, where it's focusing on a production pipeline, right? So the production pipeline is, you know, from idea to finished product. But if you change the script at any point during that, you change every part of the production pipeline basically. So kind of creating like a intelligent system that understands the connection between the script and you know, the even like the color gradients later on, you know, like understanding the context of it all in terms of the story and then automating various parts of that but using that as the basis. So we kind of have used it in some, in some cases as a launching off point because it's. The script is like a blueprint, right? And that's kind of like what we focus on building in general for whether it's this industry or others is like a blueprint. It's like where is that? Where's the, the nexus, like the main thinking machine here? What does it need to understand to accomplish what your task is in media? Maybe it is a production pipeline or maybe it's in post production. It' your, you know, your media library, that kind of thing. But just kind of building that layer of understanding where it's, where, where is it best to put this, this manager, so to speak, like where it has, where it can oversee all the information it needs and access everything.

B (12:12)

I would say I'm going to speak a little esoterically but what we've noticed is a lot of the companies that are coming to us, even if they're in completely different industries, have very similar needs. Like basically they saw Speed Read, they're like, whoa, that reads documents really fast and it can analyze a document. We have a lot of documents. We actually have decades of documents. Is there a way that you can kind of feed them into a system? Or instead of documents it's like we have three decades of interview material from our news station or something to that effect. So really the need is in like we have to deliver an output based on a lot of input that we have. I mean, again, I'm speaking a little abstractly, but also just generically. If you could somehow streamline that. Because right now we have to hire some guy to sit there and he's got to sift through, let's just say, footage for three hours to find one thing. Well, it's like, instead, whether that's a healthcare document or whether that's, you know, footage from an interview from 1999, it's like, we can build it so that you can find what you're looking for and put it out there. And it's like, so we're. We're seeing a lot of commonalities with what people are looking for. They usually have some arduous workflow to get to the end of their final product. And if they're like, can you just speed up this workflow? And what we built with Speed Read has a lot of proprietary stuff in it. So that we're like, we're building on top of the engines we already have. So as we continue to build and build, like, we learn more and more about, like, what makes a company more efficient. And they're sharing that with us. They're like, wow, this is, like, speeding me up. And then we're like, when we go out to other countries, companies, we're not like, hey, we'll build you anything. We're like, here's what we can build for you. We can completely, like, radically speed up your workflow as long as you have kind of an A to B to C structure. Like, we can shrink that. And I know that's, like, a little esoteric, but we're seeing that kind of commonality.

A (13:59)

And it is kind of shocking how similar requests are even. Even when they're different. If you get down to, like, in kind of a technical, like, sense, they're all really. You're creating an engine that understands something and uses logic, and we've built sort of logical workthroughs that we can tweak to do certain things better than others. But it. It still is. At the end of the day, it's like a good employee. You want someone who's a critical thinker, who checks their work and who, you know, has access to all the resources they need. You know, it's every. Every solution is different, but they are. There's a lot of surprising overlap.

B (14:27)

You know, just. This is actually like a. We should be honest. Like, this is where the Dell Pro Max and the Nvidia RTX Pro 6000, like, that's like, what makes this, like, a lot of this possible? Because we're running a lot of this locally. It makes it much faster and it's much more powerful. And it's like, it allows us to kind of work with all kinds of different industries. Like, I can let Ben talk to that a little bit, but it's like the machinery we're working with is a big part of why we can kind of continue to build on each. You know, on everything. Because a lot of these places, they also want everything. Not all of them, but some of them are like, look, a big part of this is like, it's got to be, you know, private. Like, yeah, it's got to be local. And like, that's, you know, that's Dell's thing. And it's like, okay, thank God we can have, you know, this machinery that we're working with because it just, it helps with that component. But, you know, Ben can speak to that pretty well.

C (15:15)

One thing, you know, and that's. And that's what I love. You know, when we first started working together before full speed was you. You were working in the cloud, doing all of your development. And I love to tell this story. I've told this story a bunch. But you were like, wow, like, that was really awesome working the cloud. So I got my first bill, and then you were like, to the point where you're like, hey, I have all these fantastic ideas, but I can't experiment anymore. Like, do. Is it worth me experimenting and spending the money for something that may fail? Which I. That's what I hate. And, like, not to knock the cloud or anything, but whether it is the data center, whether it is where you can't give the smart. And I'm not a smart person. I'm just the guy with a good voice on the podcast that's got the face for radio. But the people like Pennsylvania, who are the technical people that you. You give them this engine to iterate and then it costs them money. Or, like, you know, there. It's. There's a financial thing for a small business tied to it, or it's like, oh, man, you can't get access to the GPUs in the data center for a week. Ben, like, it just. That's. I just hate it because it really stifles creativity. So I'd love to hear kind of what you're doing with Speed Read kind of on the dell Pro Max T2 and the Nvidia RTX and how that kind of compares to what you were doing previously with the ada and then kind of from a deployment standpoint, are you seeing customers kind of come more local now or is that, are you still seeing them in the cloud? Are you migrating? I'd love to hear all about that.

A (16:37)

The killer of creativity is definitely API bills. It just, it crushes.

C (16:43)

I love that I'm stealing that. That is a great quote.

A (16:45)

Part of creativity is doing stuff that nobody asked for, like in your off hours, which is what I can do now with the Dell. I'm going to experiment with audio models tonight. Just kind of do something. And so I spend all night running things and then the next morning I wake up, maybe it's good, maybe it's bad, but then, you know, without fail, like a week down the line or a month down the line, I'm faced with a challenge where I'm like, oh, you know what? I learned something from that, like, random experiment I ran a week ago that I could actually apply here. That's just a small part of it. But like, that's really important because I would not be doing that if I didn't have like a powerful machine of my own. I would not be playing with like, you know, models that nobody asked for, you know, but in terms of. So with speed read, yeah, we were doing it all in the cloud and then it's suddenly it's like, well, I guess we're just going to have to use, you know, like foundation models for everything because it's so expensive to fine tune. It's so expensive to, you know, do all this stuff. So we were definitely able to start fine tuning our own models. Start, start experimenting, start running, you know, thousands of tests instead of hundreds of tests. Just because the main thing is it is money. It's expensive, the cloud is expensive. And if the resources aren't available or whatever, if you're like stuck in a queue somewhere, it's just like everything gets pushed back. So that was a huge turning point for us, was partnering with Dell and just kind of getting on. And in terms of the difference between the last machine I have and the new T2 tower with the, the new RTX Pro is it rather than having dual cards, you can kind of putting like the bigger models on like a single card that can accept so much is I'm not a huge hardware guy, right? So like me configuring things. Okay, we'll split it up between some of it's on this card, some of it's on this graphics card. It's nice to have one big beefy card that can handle like all the inference And I love that. The less I have to like configure things, the better for me. And in terms of what our clients are asking for, yeah, it's shocking how many, because like we were talking about off the shelf solutions, right. And how like, even if you have an off the shelf solution that does exactly what you want, chances are 99 times out of 100, it's in the cloud. There's no open source version of it. So if you're running like you need HIPAA compliance or things like that, it's, you've got to, you've got to build your own solution. And the way to do that is with Delpro Maxis, you know, just like having a powerful machine. And also it's more than AI. Like a lot of like we were talking production pipelines, like, well, you're doing a lot more than just running AI models. You need a machine that can do a lot of things. So for us it helps with fine tuning and building stuff. But for them, they're running like, like an animation house. They're running so much over and over all day long. You know, they need machines that can, can handle that. So from a deployment standpoint, it's not just privacy, it's, it all comes down to cost too. For the client and for us. We let people deploy to the cloud if they want, if they, you know, some people cloud burst or something, you know, just like, what if we run out of machine? We need a little bit in the cloud. But we always definitely recommend that if, if, if you're open to having like that infrastructure on premises, do it.

C (19:53)

Yeah, I mean, it makes total sense.

B (19:54)

It's like, you know, it's been such a fortuitous and outstanding partnership because, you know, we were doing a lot of this like on our own and just kind of going out there and finding clients and building in, know our, our homes and, and, and our offices. But like, you know, when we partnered with Dell and Nvidia, it's just like you guys are really trying to bring this whole AI thing to the next level, you know, and, and giving us like this great machinery that we can use and these, you know, everything with Nvidia, it's just been, it's taken everything to a new level and it also allows us to talk to our clients about what's possible not in the cloud and in a localized way. And it, it's just, they get also very excited because it's like, oh, okay, Dell and Nvidia are behind it. This, these are some really, you know, heavy hitting Companies and it's just fantastic. I think a great partnership and we love working with you guys and it was great to be at nab, the NAB conference in Las Vegas. It was fun. You guys know exactly what you're doing there. It was definitely the cool booth. You know, we meet all kinds of interesting people and get to have these kind of conversations all the time with people who want to listen to them, the select crowd.

C (20:53)

That's cool. I mean, yeah, no, it was a pleasure having you all in the booth. Yeah, I mean, the demo was kind of wrapped around them showing you speedri. I mean, we're going to have to talk about full speed next year at another event, maybe gtc. Ben, you kind of made an interesting point. I mean, we talked about kind of why Dell Pro Max with Nvidia RTX Pro GPUs are important and why, you know, the 6000 Blackwell having the 96 gigs versus the dual 48s, even though the VRAM is the total amount, having it all integrated into one car just allows you to own more models. It's faster and all that. But you kind of made kind of an interesting point about kind of cost, right. From kind of a cloud standpoint is, you know what, I kind of want to hone in on that. So you know what, tell me whenever you're talking to your clients, you know, who are looking at maybe an off the shelf tool, you know, an off the shelf like solution, right. Versus something building for you other than take out the obvious, right, Is that, you know, the off the shelf probably is going to do exactly what they want. It might be in the cloud, things like that. But what are some of the other differences there? Other cost differences? I mean, obviously you guys are building something exactly their needs, but what are some of the other differences?

A (22:02)

So we were talking about kind of the manager thinking brain of it all. And I kind of mentioned earlier, like, I want something that will check its work, right. And something that thinks ahead and reasons. I mean, this kind of gets into cost as well, but there's just more freedom to have it work harder in a sense when it doesn't necessarily. Assuming time is not of the essence, I always like to do multiple passes on things or have the AI do multiple passes, check your work, have a challenging agent that's like, is this the right way to do this? Is this the right thing to do? And if you're doing that on the cloud, yeah, it's more expensive and it's also. It's just not the most efficient thing to do. So that's one way is just having the, you know, the infrastructure in terms of. I also kind of touched on having. If you're doing something that's compute heavy other than AI, like it's nice to have it all within the same ecosystem, have it all on the same machine because otherwise you're like, oh hold on, let me go ping this API and then come back. And especially if there's huge amounts of data that the AI needs access to. Okay, now you're constrained by your bandwidth, especially if it's. If you're doing like 100 gigs of footage. Okay, now we're going to. If that's local, you have to wait for that to go up and then it analyzes and then comes back and you have to do that. Unless you're storing all of it in the cloud, you did it every time. And if you're storing it all in the cloud, then when you want to bring it back down, you know, it's just kind of a mess to me it's not. If you have it all locally, it's cleaner and it's faster. Does that make sense? I'm thinking specifically of like media libraries in this sense. But it kind of applies across the board, right?

C (23:29)

I mean and I get. And I, and I love the. I mean what I'll dub kind of the orchestration of what you're doing, right? It's not just building X, it's the orchestration of having the data thought out, reasoning and proven kind of use cases on top of whatever they're looking at beyond. Because it's very easy. Like use the example of my what I built, right? Very simple, nowhere near as you. I'm not a coder dev like anything but was able to build something that fixed my use case. But it's not reasoning, it's not saying hey, how well is this blog post going to resonate in the market and how much. But it could, but it doesn't because that's not what I need. But that's kind of what you're building and then you know, that kind of ties into. My next question is that. And I know and might be getting too specific and you guys can always tell me like I don't want you to share any company secrets or whatever but like give me one or two if you've got it. You don't have to say company name but a little bit more detail on like couple of things you've done and companies you've worked with with full speed AI and like the exact use case the problem and how you solved it. So, like, engineering company needed to do X. Here's what we did.

B (24:39)

Sure.

C (24:39)

Yeah.

B (24:40)

I mean, we can totally give you some kind of. Without getting into the company names, we can give you some kind of the example. And they are a little different. So one of them is a media company. They produce very large, you know, content that we all know and love. And basically, you know, they are looking at a system that it greatly increases, like their workflow because a big problem that they were having is changes. So they're like, if someone wants to make a change in this product, in this production workflow, that's a very costly and time consuming endeavor. So it's like, that's kind of the mission. And when we, what we do with these people is we have dream calls and basically it's a couple different sessions. They're about an hour each. And we just say like, you know, what is like the big pain points for you guys? And what if we could build like something amazing that would solve that pain point? So in that, in that case, what we're trying to build is we're like, okay. And I can't get too specific into the details without giving too much stuff away. But like, but conceptually, if they wanted to make a change, we allow it. And this is basically because we have Speed Read already built. We can kind of, we understand this thing. We can make it. So if you make a change in the script, you can look at the different ways in which the script is going to change. And before you go and produce anything, the script's going to give you different ideas. Let me give you kind of an example. If there's like, if there was a scene that was supposed to be in a fine dining restaurant, and then all of a sudden they're like, the director's like, you know what? I think this would be so much better in a casual pizza parlor. Well, that changes everything in production. They're like, oh, that's going to change the outfits, that's going to change what the characters look like. You know, there is some animation to the entire thing that's going to change the animation. This is going to be a complete headache. We're going to have all these different costs we're going to have to worry about. And then like kind of goes all the way towards the downline, the downstream, and. And it just changes everything. So we're like, okay, well, at the script level, we could, we could say, you could literally put in, hey, what if I change this to a pizza restaurant? And it would show you well, here's your estimated cost for doing that. Here's three ways in which you could put it at a pizza restaurant. Like, we could make it. So you could. Option A would be like, okay, yeah, it's a fine dining restaurant, but they serve pizza. See, that's like an easier solution, but a much more expensive solution. If they were animating this is like, okay, we'll turn it from, like, you know, the fine dining steakhouse to, like, a complete pizza parlor with, like, you know, dough.

C (27:04)

Yeah, the red Boo and Luigi pizza. Yeah, right.

B (27:08)

And. But it will estimate it for you. It'll say, if you did. If you did it this way, this is probably going to cost five times as much money. But. And here's some different dialogue that you could have in that scene. So it's kind of orchestrating it all when there's no cost to it, as opposed to producing it. Someone, a director coming in and saying, now I want to change it. And they're like, well, that's going to be a $50,000 change, is going to take us a week.

A (27:28)

And also it's going to change a scene eight pages later where something else happens that references, you know, that kind of thing where it's like, okay, so.

B (27:34)

On page, towards the ending, when the character's like, didn't we have a great time at that fine dining restaurant? It's like, well, that doesn't make any sense anymore.

C (27:41)

Makes no sense.

B (27:42)

So.

C (27:42)

Right.

B (27:43)

So the product that we're building for them, and this is just one component of what it does, it will then go to the end of the script. And this is a very generic example of an extreme example. They're like, wasn't that a great time at the pizza parlor? Or, you know, and they'll reference things. So it's like it's writing this script all through the script and making sure all the changes stay consistent. And that's where we're kind of leveraging the power of AI now, where I think it gets interesting is it might do the computerized version of it, but then a writer, you know, like, a really creative person could come in and write much better jokes. Like, you don't need to just completely lean on it. That's what I'm talking about. It's like, we're freeing up the creative people at the company. So instead of the writer going, oh, I just wrote all those great jokes for the fine dining restaurant. Now that's completely, like, doesn't apply. Now I have to go write all these jokes for the pizza restaurant. That's Just going to take hours and hours. It's just a waste of time. So this way it's like you just control the entire creative flow from beginning to end and we're kind of layering it on top. So it's like first we do the script thing and then you start to evolve and get better character animations and it can spit that out too. And that's one example.

A (28:49)

And it's all based on the kind of the orchestrator behind it. Another example is we kind of alluded to some of this earlier, but like a sprawling media library and they wanted to be able to generate social content from it quickly. You know, like, especially if like an event happened that was relevant to something in the library, they could. Basically what it does is it goes through the library, it goes through the, you know, obviously we already transcribed and did, you know, image recognition on all the footage and stuff like that. So it has all that information, but then it kind of puts together a package, you know, a rough edit and a basic script and it kind of put together these not automated, but basically little packages for social media. Like here's one for Instagram, here's one for LinkedIn. Leveraging this huge trove of content they have that they don't have. They can't afford to manually sift through it every time. So in. The social media arm has been kind of dormant in that particular company and this is kind of a way to help the people because the person running social media is doing 800 other things, you know, so it's like, it's different if you have like a dedicated person for that. But even if you do, it's like a tool. I'm really excited about that one to see that in action.

C (29:59)

Yeah, I won't name names, we'll keep it generic. But let's say political figure X is said something today which contradicted what he might have said 20 years ago. Here is said comment from that time. Like that, that general thing or you know, like stuff.

B (30:19)

Yeah, yeah, okay, that's an example. Yeah. And it's a way to do some of that stuff faster.

C (30:24)

That would be such a pain in the butt to search through all that. God, that would suck.

A (30:29)

I had a friend who used to work for a late night show and his job was, his title was TV watcher and he would watch her.

C (30:37)

That's great.

A (30:37)

He had to go into the office every day and he watched like 4,5 screens all day long and he, he didn't come home a happy man, you know, And I, we, we were roommates at the time was when I first moved to la and he always looked exhausted. I was like, he just watched TV all day. He's like, no, dude, this is awful.

B (30:51)

The politician example is a good one because we're used to seeing that on like some of the comedy, like satirical shows, but there's some that are a little bit more like they let me, let me best by example. So we have one company that is like looking to do like, they have a ton of sports content and sometimes someone, someone will come to them and say, you know, we want to do an entire, like it's going to play at a stadium. It's going to be a big news piece about this one celebrity athlete that meant so much to the city. Well, they have to put together this great four or five minute montage and they're like, oh, we got to go find that. And there was that clip from 99, that clip from 2005. And it's like, no, no, no. We're going to make this really easy so you can get this thing done pretty fast and therefore do it at scale and do a bunch more of them and show your clients what's possible. So that's, that's another example of like a use case that it's like they're really building all their product out for it.

A (31:41)

And in that example, then there's different quality footage too. Like the 991 versus the 2000. It's like, how do you make that all look good in an automated. There's a lot of little edge cases, little things that, you know, it gets tricky, but it's super fun.

C (31:53)

All right, so we're about 35 minutes. One more question and then we'll probably wrap it up. Is you mentioned, and I think the other thing that you all are doing different is when you have kind of an off the shelf tool, it's like, here's the off the shelf tool. But you mentioned when you were talking about when you talk to clients, you have dream sessions. Tell me a little bit about that. And what makes that different from what, you know, an off the shelf tool is because you're allowing them to speak their dreams.

B (32:19)

Well, Matt, I mean like, let's just use the kind of the adventure of dream seeking. So, okay, when you get an idea in your head of what, what you think could be possible, the current model is you go on Google and you shop for a product that might fulfill your dream. It's like, okay, if only we could do A, B and C. Let's go see what's out There, right? And then you'll find something that kind of looks like it. And. But there's like four different competitors and they all have different pricing plans. And, you know, you're just shopping. So what's more fun, I think, is like we call, we actually talk to the clients and we have these dream sessions. They're, you know, they go for about an hour and we'll talk for three hours. But you know, some people have to go. And the whole idea is what, like, tell us what is driving you crazy, what is taking forever, what is costing you so much money. And I always kind of like to say, if you could wave a wand and just like make everything different, what would that be? And it's very helpful to get people speaking in extremes. They're like, I wish I didn't have to even like, do this at all. And it's like, okay, at least we understand what you're talking about. And then we can go in and we can completely. Well, we go back to our lab, if you will, and we come up with something that will satisfy what they're looking for. They're like, we're like, okay, based on what you said, here is something that would completely at least meet 80% of what you're talking about. And it will just make your life so much easier. Your workday will be done faster, you can get home, you can go to your kids little league game. Like that's what people are looking for. Or like I was saying earlier, like, they, there's so much menial tasks that they're doing. They're like, if only I didn't have to do these 50 things, I could focus on moving the company forward. Creative stuff. And we're like, okay, cool, outsource that to us. We'll build you a proprietary software that actually like does what you want to do and it'll make just like everything better. And those dream sessions are a lot of fun. They're very collaborative. We, we usually get back to them. We're like, hey, we were working on this. What do you think? Like, do you, do you want this? And they're like, huh, good idea. Like, what if we could do this? And like, we keep going back and forth. It's. It's a good time.

A (34:17)

And we make, we make it kind of broad ranging, like, even. Because what's interesting is sometimes I'd be like, well, these things aren't related, but it'd be nice if we had this thing that helped us read our scanned documents. Something very basic. But then also. And then here's the big thing we want. But sometimes we'll find in those little requests. We're not consultants in this sense, but there's maybe a flaw in the workflow or something. We see a commonality between these small things. They're like, oh, this is a pain point. This is a pain point. It's like, well, actually it sounds like maybe the fix is here, you know, like we'd like to identify where in their workflow are the choke points and are those related? And sometimes we're surprised. So that's why we, there's kind of no holds barred. It's not like, yeah, tell us your main, your main issue, the main thing you're looking for, but also like, you can complain for like 30 minutes if you want because we'll listen and we might hear something important.

B (35:05)

And another thing that we really kind of pride ourselves on doing, which people have appreciated. It's like they tell us what they want, but they're still looking for us and our team to kind of give them ideas they weren't thinking about. And we go, you know, I know you do things like abc, but if we could kind of do it more like xyz, we could really speed this thing up. And that's really helpful for them. And I should mention too, sometimes companies like, how are we going to pay for this? Like, I don't even know if like the HR is going to, you know, allow a budget for this. We're like, look, we're going to, we can show you how. This would save you a ton of money. We will give you the pitch to your internal, like, if you will, upper management to kind of show you this is something definitely worth applying and people have appreciated that too.

C (35:42)

We're going to close it out, like between you both, I'm putting on a minute clock. Let's pretend that someone just came into the episode now and within one minute you've got to recap the most important takeaways for them about full speed AI. On my mark. 3, 2, 1, go.

A (36:02)

The great thing about now is that a lot of people are familiar with the new AI technologies available and they hear things and they see things that, oh, I, that looks cool, Maybe we can use that, maybe we can use that. But there's no solution that does exactly what you're looking for. So we're here to talk to you and listen to what you're looking for. Maybe there's something you heard about, maybe there's something that you don't think exists yet, but something that would help your business operate Faster and more efficiently and save money, obviously, and make money. That's the goal of every business. So what we do is we come in and we listen to your pain points and we, if you haven't identified them yet, we help you identify tools and technologies that we can build or customize or integrate into your workflow as you already have it. We don't want you to totally, you know, we don't want you to jump from, you know, like, I had a business once who went, who changed to QuickBooks and it took them like eight months because they were changing. We don't want you to change your entire workflow, whatever that is. We want to work within your workflow and we'll find a way to do that.

C (36:56)

There you go. 57 seconds.

B (36:58)

I would say the major takeaway is exactly what he said and I would strongly encourage anybody, any company going, we know we need to use AI, but we don't know how to do it and we don't know where to turn. We are the people you can turn to. We would love to have a dream call with you. It is a great time and it's like, like Ben was saying, we're just going to listen to you. There's no obligation to do anything. It's just a way for you to get your company moving into the AI direction. That is like a very efficient and fantastic way to do it. So you're going to just, you're going to enjoy it, we're going to find your pain points and I would encourage you go to our website. It's Go Fullspeed AI. Go Fullspeed AI. You can schedule a dream call on there and we just talk. And everyone we have talked to has enjoyed those sessions. Whether or not you decide to move forward with anything is totally up to you, but at least you will get some ideas about, okay, here's how we can start thinking about using AI and we would love to build that software for you. And, you know, thank you to Dell and thank you to Nvidia for making a lot of this possible because they've just been a fantastic partner and we've got some incredible equipment to help out with building your solution. So it's going to be fantastic.

C (38:04)

All right, so we're getting kind of up against it. So one more time, just so we have it documented for everyone listening this far into the episode. Aaron, Ben, can you give one the name of the website where they can find you for full speed AI to your socials, where they can find you and then any other pertinent information where if anyone's interested in having a dream session, they can locate you.

B (38:25)

Sure. If you want to schedule one of the. Schedule one of those dream sessions, please go to our website. We're at Go Fullspeed AI. Go Fullspeed AI. Our socials are full speed AI. You can find us on Instagram and you know, we're out there. We want to talk to you. It's a. It's a great time, I'm telling you.

C (38:43)

All right, well, Ben, Aaron, appreciate the time as always. Love the innovation, love how you're taking everything to the next level. So with that, it's been great. I love it. It's come full circle. I feel like, yeah, it's just a good episode. It's always fun catching up with y'. All. So with that, until next episode, keep your workflows running locally on Dell Pro Max and Nvidia RTX GPUs. And I'm Logan, and we'll see you on the next.

A (39:16)

Do what you want. This podcast was produced in partnership with Amaze Media Labs.