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Podcast - Category Visionaries: The Role of Data Fabric for Generative AI


Are you interested in learning more about the role data fabrics are having on data architecture discussions at F500 companies? Listen for yourself, by clicking the link above, or review the full transcript below.


Executive Summary:

Category Visionaries: Brett Stapper interviews Kaycee Lai, CEO and founder of Promethium, a data fabric platform. Lai shares his journey, starting with his role as an analyst at the Federal Reserve Bank of San Francisco in 1999. He discusses his early interest in technology, emphasizing the importance of technical skills in business. Lai admires Martha Stewart for her ability to identify market gaps and create a successful brand that resonates with her audience.


Lai talks about the influence of "The Count of Monte Cristo" on his life, highlighting its themes of resilience and determination. Lai then dives into the specifics of Promethium, explaining how it democratizes data analytics, making advanced tools accessible to smaller companies. He describes data fabric as a flexible, unified view of data, regardless of location or format, and its significance in AI and analytics.


Discussing market dynamics, Lai sees data fabric as an emerging category, reshaping how businesses approach data analytics. He attributes Promethium’s success to a focus on educating customers and being transparent about their product’s capabilities. Lai shares insights from his fundraising experiences, emphasizing the importance of aligning with investors who share the company’s vision.


Regarding Silicon Valley's unique ecosystem, Lai believes it offers unparalleled advantages, especially for first-time entrepreneurs. However, he notes the growing viability of other regions for seasoned entrepreneurs. If starting again, he would focus on solving specific problems for particular personas while staying true to the company's original vision.


Looking ahead, Lai envisions integrating Generative AI into Promethium, significantly impacting data analytics workflows and applications. He invites founders to connect with him through Promethium’s website or LinkedIn best next steps.


Video Transcript:

Welcome to Category Visionaries, the show dedicated to exploring exciting visions for the future from the founders who are on the front lines building it.


In each episode, we'll speak with a visionary founder who's building a new category or reimagining an existing one.


We'll learn about the problem they solve, how their technology works and unpack their vision for the future.


I'm your host, Brett Stapper, CEO of Frontlines Media.


Now let's dive right into today's episode.


Hey, everyone, and thanks for listening.


Today I'm speaking with Kaycee Lai, CEO and founder of Promethium, a data fabric platform that's raised $34.5 million in funding.


Kaycee, thanks for chatting with me today.


Hey, thanks for having me, Brett.


Pleasure to be here.


No problem.


So to kick things off, can we just start with a quick summary of who you are and a bit more about your background?


Yes.


So as you mentioned, I'm the founder and CEO of Promethium.


Started the company about five years ago because I just really wanted to make analytics a lot easier for everyone.


And it's something that's very passionate to me because I've been on every side when it comes to analytics.


I've been a consumer, a user of analytics.


I've been, you know, someone who built those products.


I've been someone who sold and marketed those products.


And so really have played a lot of different roles, which was very, very helpful when you're starting out a company.


And young as a 4-kid, so used to having to grind and bump elbows to make sure that you get your voice heard, which I guess is a great mentality to have when you're starting a new company from scratch.


One of the roles I want to ask about is the analyst role you were in at the Federal Reserve Bank of San Francisco.


So I don't think I've talked to anyone who's worked there.


That's very close to where I live.


So what was that experience like?


And I assume that was in 1999?


Oh man, it had taken me back.


Yes.


It was my first job and it was one of those things where I was confused why they called me.


But apparently if you take a lot of statistics classes, math classes in college, the Federal Reserve likes to talk to you.


So that was a great job for me right out of school.


You get to look at a lot of macroeconomic trends in terms of how that affects different industries, the economy and so forth.


But also as a young person, I think that's where you get exposure to how a lot of different policies actually kind of shape our banking industry and which has broader impact on the rest of society.


So it was a great opportunity for me to, you know, exercise my analytical skills and really kind of see how that played out from a much bigger impact perspective.


What were your thoughts back then, if you can recall, like how you viewed tech in general?


Because that was right when I guess things were booming, but they were about to go really bad, right?


So what were your views then?


Like, were you planning to go into tech?


Were you not wanting to go into tech?


What were your general thoughts?


It's actually a good question because I think, you know, I did everything I could to learn as many tech skills as I could at the time, because you're right.


I think at that point everything was tech, tech, tech.


And I felt, you know, being just a, you know, a math econ and psych guy that, you know, I didn't have that background.


And so I would create projects for me at work where I would have to learn different programming languages, use different tools, et cetera.


And so my view at that time was, and I still believe it today, the future of the business user or the knowledge worker is one that's going to have to expose themselves to a broad range of technical skills as a foundation.


And I think, you know, that's definitely holding true, right?


I think it's very rare to say, Hey, I'm going to do this job and I know nothing about tech.


I'm not going to use any tech tools at all.


And so I think that was definitely at that precipice where, you know, a lot of folks like me were kind of thinking like, Hey, we really need to make this as a foundation in order to make ourselves a little bit more marketable in the world and the job market out there.


And I'm glad that trend actually carried through.


And if you look at the future, you know, the current generation of kids coming out of school, I think they're all super tech savvy, right?


So I'm very happy that, you know, we were one of the OGs.


We kind of helped pioneer that wave and everyone has actually kind of taken that on.


And I think society is going to be better for it.


So pretty happy about that.


Nice.


I love that.


A few questions we'd like to ask.


And the goal here is really just to better understand what makes you tick as a founder.


First one, what founder do you admire the most and what do you admire about them?


This is probably a little unconventional, but I'm going to say Martha Stewart.


And it's because I think few people may not know this, but, you know, Martha Stewart actually started out as an investment bank, right?


As a very successful wall street executive.


And she saw an opportunity, right?


She saw an opening, a gap in a marketplace that wasn't being addressed.


And it's one thing to say, I'm going to start a company, just hire people, build products and everything.


But Martha Stewart is the center of her company, right?


And she became that persona that she was marketing and selling to.


And I think that's a big part of the success for a company and her brand is that, you know, her customers see themselves in her, right?


And so I think I have a lot of respect for someone who was willing to, you know, give up that lucrative career saying I'm going to start it from scratch, but then really being able to understand, right?


Their customer, their persona that they will go after and literally be that person on camera while running the business behind the scenes.


So kudos, Martha, lots of props.


Somewhere on the wall behind me, I have the cookbook that a friend got me and it's Snoop Dogg and Martha Stewart.


I didn't know that background though.


I didn't know she was an investment banker.


That I guess, you know, does connect the dots for me about the insider trading scandal that you had happen.


Like what is that?


I wasn't, that was where I was going, but you're right, you're right.


That makes more sense.


So she wasn't just like a chef who was, you know, doing some stock trading.


She came from that background.


That definitely makes a bit more sense.


I can neither confirm nor deny.


Now another question I'd like to ask about is books.


And yeah, as you saw on video, I love books.


I'm a big reader as well.


So how we like to frame this, and this comes from an author named Brian Holliday.


He calls them a quake book.


So a quake book is a book that like rocks you to your core.


It really influences how you think about the world and just how you approach life.


Do any quake books come to mind for you?


You know, I think lots of books kind of impact you at different points of your life, right?


Be it long-term or short-term and some have longer lasting impact and some don't.


There's one book that, you know, I've read a few times and it's like, it's a very different experience every time I read it.


And it's actually the Count of Monte Cristo, right?


It's a classic, a sucker for classics.


And I think what I appreciate, you know, about that book is really, it's really about life, right?


And all the ups and downs that you can have in life, right?


So the book resonates with me because it actually is a story about determination and grit, right?


In addition to, you know, the harsh lessons of life, right?


Around friendship, around betrayal relationships.


But I think one of the things that, you know, is definitely true, you know, and holds across, you know, the different generations is that life is always going to throw curveballs.


You can have ups and downs, you have things taken away from you but if you remain focused and determined and you have a goal, right?


Then that can actually, you know, continue and give you the energy and give you the focus.


And I think being an entrepreneur, I think there's, you know, we have our ups and downs every single day and there are days where, you know, you get, you know, questions, right?


You know, are we focusing on the right things?


Are we focused on solving the right problems?


I think that book is a great example of, you know, someone who has that focus and that clarity of vision, you know, and using that to kind of guide them through, you know, challenging and choppy waters.


And so I think because of that, you know, it's one of those books I come back to time after time.


How many times have you reread it?


Oh, probably about six times.


That's why I should maybe start asking people on the podcast, you know, what book have you reread the most?


Because I think that's, you know, everyone has probably like three or four books, right?


Where they just keep going back to them over and over again.


So I may start to switch it up and ask that because that could really- It's a good one because I think, yeah, as you change, as you get older, you see things differently, right?


So what was cool when you're young, where the different stages in life may not be that cool and what you didn't really think about, right, or didn't pay attention to all jumps out at you later on.


So I think that's a good call.


One thing I've found fun is, yeah, I go, I highlight books as I read them and then, yeah, I read them again and I try to use a different color.


And it's always interesting to me like looking back at like certain things, like how did I not highlight that before?


It's such a good takeaway.


It's such an important takeaway, but like at the time when I read it, you know, it had no relevance to me.


It didn't even get a highlighter, but all of a sudden I read it this time and like that jumps out.


So it's fun kind of going through those different phases.


And what I've been doing is trying to like write the date now of like, Oh, if it's, you know, a highlighter, it was done on this date.


And I think at some point I'll look back at that when I'm 80 or my kids will look back at it, fun to read.


Whoever picks it up, it's going to be like a codex rank, right?


There's a color codex and there's a timeline codex, et cetera.


Yeah, I love that.


Now let's switch gears and let's dive deeper into the company.


So how we like to start this off is let's talk about the problems.


How do you articulate the problem that you saw?


Yeah, I had trickled the problem that we solve in the sense that I think I don't need to convince anyone that you know, data is a strategic asset and that the companies need to leverage data and be data driven, right.


You have to solve a whole bunch of problems from experience to optimizing growth, right?


I think everyone understands that, but I think what people don't realize is it's still really hard to do that.


And unless you're one of the few elite large companies with a lot of money, a lot of people, you're still not really leveraging your data, right?


And you're not being able to use it.


And so you're kind of forever kind of kept below this bar of potential success.


And what we see Prometheum as is being able to be that equalizer, right?


Being able to say, Hey, what if we can give you the same tech that all these big companies are using, but package it up in a way that's so easy to consume that you don't need of experts in all these different areas, and then price it in a way where it's affordable and easy to actually pick up and just use.


So you can actually spend your time leveraging that data to solve the problems you need, as opposed to just trying to spend your time just to figure out how to buy and put it together.


And so that's really kind of where, you know, we feel a great, great passion in terms of our mission, if you will, is that we want to normalize and equalize the playing field so that any company who wants to be data-driven and leverage the data you already own, right?


That's the irony is like, you've got it, it's yours, it's your data.


The fact that you have to spend more time and more money to make it do something useful for you, it seems like a travesty, right?


So that's the problem that we're solving.


And then can you tell us what a data fabric platform is, or just what data fabric is for those who are listening in, who have no idea what that means?


Absolutely.


Data fabric, you know, the very, very simple definition is that it's a product or architectural framework that allows you to get a single unified, consistent view of your data, no matter where it is, because data can reside in different locations, different vendors, different formats.


So it doesn't matter.


Let's say one place you can see everything you have, and then being able to sit without having to move the data into yet another proprietary data store, right?


So if you think about a fabric, right, a fabric is flexible, right?


It's not, you don't, you know, you think of cloth, you think it's flexible.


You don't think of like concrete or steel, which is what a lot of data engineering architecture really is.


It's very rigid.


And so it's very brittle, right?


The fabric has that connotation, that imagery of flexibility and fluidity, which is really what it's all about.


It's really, Hey, if you could easily see what you have, connect to it and access all your data, regardless of where you're coming from, how would that change?


How do you leverage data and analytics?


So, you know, there's a lot of benefits to this.


One obviously is the simplicity, the cost savings, again, say the modern data stack, but two is as we enter the world of AI and generative AI, the data fabric is the best platform, best single platform where AI can actually now tap into that and leverage, govern and trust the data no matter where it is and have rapid, fast access so that you can actually get the answers and the outcomes that you want.


So that's how we kind of thought and think about the data fabric.


And when it comes to the market category, then is that market category data fabric?


It's an emerging category, which is what's very interesting because, you know, up until we started doing this, everyone looked at analytics as sort of a market where you had separate categories of separate products, right?


You're, you're in the metadata path, you're in the data path and you're going to be a catalog.


That's one category.


You're going to be a data ingestion vendor.


That's another category, right?


You're going to be a data warehouse, another category, your BI tool, that's another category.


And everyone just kind of, that was the landscape we played in.


And that was the landscape for customers to consume and buy as well.


And so that meant you had to go figure out how to put all these different products together, which is not easy.


It took a long time.


It was expensive.


So one of our first, you know, mission and goals was, well, we know people need capabilities across all these different categories.


You know, why not have a product where you can get this blending of capabilities for, you know, some of these key use cases so that it's not difficult and it's not time consuming.


Right.


And so that's what kind of set the trend in place.


And, you know, you started to see this started happening.


What Gartner starts talking about, like data conversions or, you know, analytics, conversion, governance, you know, we've started talking about the role of the citizen data scientists where they wanted a way to integrate governance with, you know, data integration, with data analytics.


And so it's been coming for a while.


And I think up until the data fabric, it wasn't really crystallized or it wasn't really demonstrated that it could be done.


And so now we're at a point where there is evidence that you can have a data fabric and it actually does give you these types of promise benefits.


And so it's pretty exciting.


Like Gartner recently just said that they believe by the end of 2025, 80% of all chief data analytics officers will have deployed a data fabric.


It's a big, big endorsement, right?


So we, I think you're going to see this morph into a real separate story very, very shortly.


How proactive are you in trying to, you know, shape that definition and define that definition of what a data fabric platform is?


So the data fabric is going to be a standalone category, I believe, very, very shortly because it is different enough and that you can't simply take an existing product, slap lipstick and marketing jargon on it and turn it into a data fabric.


That's been tried.


It doesn't work.


The definition of a modern data fabric is very, very clear in what it needs to do and what it needs to have, right?


You know, companies like talent have used the term data fabric a long time ago, over a decade ago, but that's no longer relevant for today's world where data is distributed across multiple locations, clouds and types.


It's not the single data warehouse environments and single data center environment that people have anymore.


So because of this, and we all know where the world's going, it's going more distributed every single day.


Because of that, the fabric is becoming much more relevant than ever before.


So Gartner has even said that they believe by the end of 2025, 80% A0 of all chief data analytics officers will have deployed a data fabric.


That's a bold, bold prediction, right?


And it's something that we're starting to see.


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Now back to today's episode.


Now you mentioned there the modern data stack.


Can you paint a picture for us?


What is like that dream modern data stack?


Like what would be the tools that would be in your toolkit?


Yeah, good question.


Well, I would say the modern data stack was the dream stack, like a few years ago, where we took best of breed, right?


And so you might have a best of breed data catalog, a best of breed data integration tool to do the ETL to the pipeline, some sort of data prep and data transformation tool to get the data in the state that you need.


Obviously a data warehouse or a data lake.


And then you might have no data visualization tool at the end.


So you can actually create a deliverable for the business.


So that's now a sort of the kind of idea behind the modern data stack is that you can pick the best of breed for a cloud data warehouse, cloud data lake type of architecture, right?


So the minute data was on prime into cloud, what we saw happening was a lot of the kind of fragmentation of the data management tools into multiple stacks, right?


As we just described.


So while that's sounding great, what we found in recent years is trying to piece that together is really expensive, really hard because they weren't designed for that.


And so you could easily buy four products or say a million dollars and spend seven to 10 million on integration fees, which is kind of silly, right?


If you think about it, but that is the reality of the modern data stack.


And it's the modern data stack is actually all built around this notion that you just have to keep moving data into your cloud data warehouse and data lakes.


So this new wave of data fabric architecture and data fabric thinking is really solving for the sins of the modern data stack, if you will, really making it much more easy, cost effective and faster to adopt.


Can you give us an idea of adoption and growth and just any numbers that you can share?


Yeah.


So I think we're on track this year to do about eight X over what we did last year.


Right.


And, you know, I think that's partly, I think one is, you know, 2022, things, you know, obviously there was a bit of a correction, but we're seeing budgets coming back right in 2023.


But it's also one of the things that's spurring our success and growth is, you know, when people figure out how the data fabric can actually directly impact their business, that's very easy to kind of really increase your adoption from there.


Right.


And that's one, that's always one of the Holy grails where you're building a product is, you know, are you just building a widget, right.


That, you know, everyone needs to have, you know, my buy two or three, but you know, it doesn't matter what you do.


Everyone just kind of needs a widget or are you going to have something that actually will significantly move the needle in terms of significantly transforming how business is done for your customers?


And I think when you can figure that out and figure out those use cases, I mean, that's when things get really exciting.


What do you attribute to that growth?


And maybe how we can think about this, you know, from a marketing perspective, what are you doing to really rise above the noise?


Obviously, there's a lot of vendors in the data space.


There's a lot of noise today.


What have you done to rise above all that noise?


Yeah, good question.


I think it is probably harder than ever from a marketing perspective these days to start a company because there is so much that people can do with SEO and SEM and spend, right?


And the bigger you are, the more resources you have to do that to kind of just steal mindshare, steal eyeballs, et cetera.


So I think one of the things that we've done and, you know, we continue to do well is rather than just marketing, right, making claims, we take an educational approach, right, in terms of, hey, let's teach you guys, you know, things that are not necessarily about Prometheum, but related, right, to analytics, related to data engineering, related to data analytics.


I think customers really appreciate that, that there's an honest place they can go to, to learn about things, number one.


And number two, I think it's to be transparent, right, to be real, right?


There's a lot of, yeah, there's a lot of, you know, products that they look great in a demo, they look great in a video, they look great in an ad, but the product actually doesn't look like that.


The product actually doesn't do that, or the product actually can't do that unless you spend six months preparing.


Where we really shine is we were actually very genuine, actually very transparent about like what you see is what you get, and what I'm showing you is happening live, real time, and I didn't spend six to nine months preparing, right?


It's actually happening now.


When customers can see that, I think that's better than any ad that you can buy or any marketing collaborator can do.


That's when they really realize, oh my gosh, right, this is actually real, and this is actually going to solve our problems.


And so that's been something that we've gotten very positive feedback consistently.


As I mentioned there in the intro, you've raised over $34 million to date.


What have you learned about fundraising throughout this journey?


It's hard.


No, I think fundraising, one of the things for me is people always say, hey, you've been in sales, this should be easy.


And maybe it is, I think for some, but I think for me, it's a little bit different because I think the way I've always sold was not so much on promises, but really selling on like taking a consultative sale, understanding kind of what the customer wants and helping them solve a business problem.


And what I found was that's a very slur approach that you can take to fundraising and be successful at it.


So if you start thinking about investors as customers in the sense that they have something that they want to buy, right, and they have a problem they want to solve, it's really, the old saying goes, you can't sell to someone who doesn't want to be sold to.


And so when it comes to investors, you know, the thesis matters, right?


You have to find investors who have a thesis and find one where the thesis actually aligns to your company's thesis.


And then, so I learned that, you know, that if you can do, spend a little bit more time doing that identification or qualification, if you will, right?


You shouldn't just talk to every investor.


I know I'm a little contrary.


People always say it's a numbers game, it's a numbers game.


I don't think so.


I think it's a quality game, right?


I think, yes, you need to talk to more than one, obviously, but I think if you can figure out and quickly narrow down, it's like sales prospecting qualification, right?


These are the set of investors that invest in this space.


These are set of investors who invest in this stage, these are investors, set of investors that have this type of outcome.


But this particular investor has this particular thesis, right?


And that thesis lies really well with your company's thesis.


I think then your chances of success are much, much higher, right?


Again, that thesis is effectively what they're trying to buy, what they're looking for.


And so, if you can kind of pair what they want to buy with what you're selling, it's a lot easier and you're not really selling at that point.


You're just simply explaining and presenting what your company is trying to do.


How important do you think it is to be based in Silicon Valley if you're building a tech company today?


Obviously, you're in the area, you didn't move to Austin, you didn't move to Miami.


Was that just because you had your eyes here and it was hard to move or was there like a business case for why you also stayed?


That's a good question.


If you asked me that question four years ago, I would say, yeah, it's really hard to move, right?


You have to be here.


But the fact that so many people have moved, that means a lot of roots have been laid down in those places now.


So, I'm not shy to say maybe my thesis needs to be challenged, right?


You got a lot of really smart and successful folks who have moved out the area, they've laid down roots, those areas that are going to give those areas a foundation.


Now, we'll see in time whether or not that proves out to be true because I think there's still a few things that the Bay Area has that's a huge advantage, right?


I think, yeah, the World Economic Forum did a study a while ago, like, here's why certain pockets of the world are great innovation centers and certain ones are not.


One of the things they identified is it's not just access to capital, it's actually the proximity of capital access to institutions of higher learning with research facilities.


So, the Bay Area has, you know, three, four, you know, multiple large universities with a great research program.


So, I think that in itself gives it a very unfair advantage.


And then secondly, is the access to capital.


And third is that network of people who have successfully started companies, exited companies, and so forth, have so much to teach you, right?


I think that is actually still a very, very big advantage than you have in the Bay Area.


I think if you're a first time entrepreneur, I do think you probably will have a higher chance of success if you start your company here.


I think if you're a multiple time entrepreneur and you figure it out and you've got a network, it probably matters less.


You can probably do it in other places.


Yeah, I moved here about a year and a half ago, and I've just been mind blown at like the density of amazing founders and amazing investors.


Most of these people are within like a five to 10 minute walk from where I live downtown.


And I've just never seen that in any other city where there's such a massive volume of just amazing people that are so close by.


And I've lived in other big cities and I've just never seen anything like that.


So, that's my view.


It's very, very hard to beat that.


And I think what you say too is very, very important.


If you're on your third startup or fourth startup, then maybe it doesn't matter.


But my advice, if someone's 20 years old, thinking about starting a company, they should move here.


I wish I had moved here when I was here during 20.


Absolutely.


I think you'd be surprised at how open and how willing people are here in terms of helping out.


Like, hey, I went through that.


Don't go through that.


Don't make the mistake I made.


Or like, oh, I know a great firm if you're looking for designs and help.


It's amazing what you can get from the network that's here.


So, yeah, I think there's still an advantage.


But I think, yeah, serial entrepreneurs, there's a reason why they're serial entrepreneurs.


They figured it out.


And so, for those folks, I think it may matter less, but there's still a lot of great advantages to building a company here for sure.


Yep.


Totally agree.


Now, let's imagine that you were starting the company again today from scratch.


What would be the number one piece of advice that you'd give to yourself?


Oh, wow.


Man, that's a long list, my friend.


There's a lot of things that make sense intuitively.


But when you're actually in the thick of things, sometimes you forget to seed the forage for the trees, if you will.


So, someone once told me, it was an investor that I was building great advice.


He said, what you're building and what you're selling versus how you position and market it may not be the same thing.


And in the early days of the company, they probably shouldn't be the same thing.


And it took me a long, long time to really understand that.


I wish I did much earlier.


And basically, there's a lot to impact there, right?


But there's a lot of wisdom there.


Number one, as a startup, you're going to be time, money, and people limited.


There's no way you can build everything you want to build, right?


And I know investors always talk about the focus and so forth.


I think the advice I would give is not just focus in building a smaller product.


It's actually focus in picking a specific problem for a specific persona, right?


That you want to focus on because that will give you enough.


It's wide enough of what you need to build, but then it's relevant enough for you to have something that you can start generating revenue on, right?


But it's important that when you're doing that, then you don't lose sight of why you did this in the first place.


So there must be a connection to the original vision, right?


And that's where the how you market it is different, right?


And then how you sell it, right?


That's where you learn a lot when you actually have a product and you're actually getting it into customer's hands and the customer actually wants to buy it.


Like I always tell my team that, you know, what we say we're going to build and that we actually get from the customer, varies greatly the minute the customer sees a demo.


Varies greatly the minute the customer actually tests the product.


Varies greatly the minute the customer decides to write a check, right?


At all those different points, you have different levels of understanding and both emotional time and capital investments from the customer and anything is important and not important.


And so knowing that it's going to be a sliding scale and knowing that there is no way at the beginning you could possibly foresee what that end is going to be, focus on what you need to build at that stage, you know, and just be flexible enough that, you know, you know that it's going to have to work, right?


And be just maniacal about getting as much feedback and data points and be open and honest with yourself, right?


And you get that so that, you know, when you do need to change, you need to make that change fast.


Final question for you, since we're almost up on time, let's zoom out three to five years into the future.


What's the big picture vision that you're building?


Yeah, I think the big picture vision that we're building is eventually what we're offering can sort of take on a very high degree of intelligence and automation, right?


And I think this aligns very, very well with Gen AI, right?


Gen AI is changing a lot of industries, a lot of how we do things, but I think it's still, it still hasn't really made its way into data analytics and the enterprise successfully yet, but I think that's the future, right?


I think integrating Gen AI is going to be a key part of the vision going forward.


And if you think about what that means, it means really being, you know, more than just data analytics focused, right?


I think this can now have a huge impact on everything from workflow to the tools that people consume, how they consume them, and even the roles of the people, right?


That are typically involved today in data analytics.


And so we're pretty excited about the speed and the types of this can solve.


With Gen AI, I think the challenge has been how do you actually, you know, incorporate that to solve the problem that you're looking at, you know, originally set out to solve and then some.


And so I think you can expect us to leverage the strong foundation of data analytics that we've built over the years and kind of go into not just how data is consumed, but just, you know, how these entire workflows and applications now can be consumed with generative AI.


So that's the part of that I think you can expect where it could be very, very exciting because I think that will be a exponential leap forward, right?


In terms of really leveling that playing field, as I talked about in the beginning, in terms of allowing, you know, the smaller companies to be able to use data as a force multiplier and as an equalizer against much bigger companies.


Amazing.


We love the vision.


We are up on time, so we will have to wrap here.


If there's any founders that are listening in and just want to follow along with your journey or potentially get in touch with you, where should they go?


Yeah, check out our website, www.prometheum.ai, or, you know, find me on LinkedIn.


It's a lonely job, and if I can be a little bit helpful to someone and make their journey a little bit smoother, I'm very happy to do it.


Amazing.


Kaycee, thank you so much for taking the time to chat, especially as we were joking there in the pre-interview, this is a Friday late afternoon, so appreciate you chatting at the end of the week and appreciate you taking the time to really just, you know, share what you're building and share some of the lessons that you've learned so far.


This has been a super fun conversation.


I know our audience is going to love it, and we really appreciate you taking the time.


No, I loved it.


Thanks for having me and really enjoyed it.


Take care, have a good one.


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