Amperity Reimagines Data and Developer Workflows with AI - Ep. 271

[ 00 min 10 sec ]

Noah Kravitz:

Hello and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. From Agentic AI to vibe coding, our guest has been at the forefront of showing how AI-powered systems can empower engineers while delivering measurable business value. Derek Slager is co-founder and chief technology officer of Amperity, a company that's redefining how enterprises use data to better understand and serve their customers. Derek's here to talk about his journey founding Amperity, the tools his team is building—like Chuck Data, an AI agent for data engineers—and his perspective on how AI is reshaping not just the enterprise landscape, but the developer experience itself. So let's get to it. Welcome, Derek, and thanks so much for joining the NVIDIA AI podcast.

[ 00 min 56 sec ]

Derek Slager:

Thanks, Noah. Great to be here.

[ 00 min 57 sec ]

Noah Kravitz:

Great to have you. So let's start at the beginning. Tell us a little bit about Amperity.

[ 01 min 01 sec ]

Derek Slager:

Yeah, so Amperity is an AI-powered customer data cloud. And we help brands unify, understand, and activate customer data at scale. Which is a lot of things. And you know, we're really particularly focused on data quality, right? Because we're big believers that better data equals better results, right? You're funneling that through agentic use cases or otherwise. And so we have a lot of great capabilities to help people all over a consumer business take advantage of that data. But it's all about getting the data right?

[ 01 min 35 sec ]

Noah Kravitz:

So what inspired you to cofound the company and how does your background, your own journey as an engineer, kind of shape the direction of Amperity?

[ 01 min 43 sec ]

Derek Slager:

Yeah, for sure. So we started in 2016, right? Like, which you know, I sometimes refer to as like the false start AI era.

[ 01 min 52 sec ]

Noah Kravitz:

Right, sure.

[ 01 min 52 sec ]

Derek Slager:

You know, there was a lot of, you know, a lot of excitement about kind of, you know, deep neural networks and a lot of other kind of innovation happening in the AI space. But certainly it was, you know nothing in comparison to the current AI wave. But nonetheless right, like that was kind of it was on a lot of people's minds. And what we observed in kind of researching Amperity was that almost every consumer brand had a project to unify all their customer data. And yet we couldn't find a single one. And we tried pretty hard. We couldn't find a single one that said, “Yeah, we solved it.” Right? Despite all that effort. And we just found that ridiculous. And so, you know what inspired us to start Amperity was really the opportunity to help all these people who were trying and failing to solve a problem. It was really important to their business they actually succeed. And doing that, and of course like the the the key point there was you know, all these people were smart. They were trying really hard to solve it, right? So clearly the existing tools were insufficient because if they were sufficient, people would be achieving more success. And so you know, the big idea of Amperity was really, how do we bring AI to this problem? And we felt like, again, this is through a 2016 lens, we felt like I was sort of a perfect fit because a lot of the reasons that people were struggling with this problem was that they were trying to make the data perfect, right? And, you know, AI kind of lives in that space where, you know, maybe it doesn't give you a perfect answer, but it gives a really high-quality answer. And when we applied it to that problem, it worked really well. And so, you know, we've kind of carried that forward, you know, into the modern AI era. And you know, that's allowed us to kind of, you know, bring acceleration to a whole bunch of other things which I'm sure we'll talk a bunch about today.

[ 03 min 33 sec ]

Noah Kravitz:

Maybe before we dive in a little deeper, can you give us kind of a high level overview of some of Amperity’s offerings, products, services, how you work with customers?

[ 03 min 42 sec ]

Derek Slager:

Yeah. So we have you know, kind of a core capability around helping users organizing unified data. So being a component of that and kind of our initial R&D innovation was around stitching. And that's about kind of finding all the Noah Kravitz's you know all over these different you know data sources, a typical Amperity customer will have 25, maybe 30 different you know data inputs that contain information about customers. And so you know, we're looking for all the all the Noah Kravitz needles across those various haystacks. So it's a, you know, it's a challenging at-scale problem. And so on the other side of that, then we can we can sort of utilize the outputs of stitch to create a really high-quality customer 360. And that allows people to kind of, you know, ask questions and understand their data better. And so we have a bunch of tools that allow people to, you know, kind of introspect and slice and dice and you know, many of those are, you know, powered, you know, in 2025 by modern conversational interfaces, which is really exciting because it empowers many different people from the organization. And then we have a lot of capabilities to take the data and take the kind of different segments of that data. And then get it out into the ecosystem, right? There's thousands of marketing tools and thousands of customer support tools and thousands of all these, you know, kind of tools that all are hungry for better data. So Amperity is really focused on getting that data right and then activating that ecosystem. And we have a number of capabilities, you know, from kind of campaigns with AB testing to journeys and and many other kind of, you know pieces of capability to sort of allow that to kind of integrate with the ecosystem.

[ 05 min 23 sec ]

Noah Kravitz:

Fantastic. One of the things one of the I don't know if you'd call it a tool per se, but one of the things that's getting a lot of attention currently in the world of AI, is AI agents, agentic systems. We've talked a lot about it on the podcast, talked about it from the NVIDIA perspective of building the blocks that allow folks like you and Amperity to build the tools to serve your customers. What does agentic AI look like from your point of view when you're out working in real-life business situations and developing applications? What does agentic AI mean right now?

[ 05 min 53 sec ]

Derek Slager:

Yeah.

[ 05 min 55 sec ]

Noah Kravitz:

Put you right on the spot, yeah.

[ 05 min 56 sec ]

Derek Slager:

How many definitions of agentic AI, and I'll admit, sometimes you know, I'm a little flexible with my definition you know

[ 06 min 03 sec ]

Noah Kravitz:

Sure, sure. I can rephrase and say, how do you approach it? What do you think about what's the lens?

[ 06 min 09 sec ]

Derek Slager:

Yeah, sure. So you know, I'll start with my own definition and I've heard many definitions that are very complex. You know, my own definition is very simple and I think pretty expansive to the definition of agentic, which is it's just a program where the LLM can impact control flow. And so you know, impacting control flow might be retrying. It might be calling a tool, it might be interacting with another agent. And so you know, it's pretty, it's pretty open-ended in terms of what could fit that definition of agentic. The reason I use that as the definition is because, you know, when the LLM is dictating control flow, that's very different from a traditional program, right? A traditional program that makes a call to an LLM is still a traditional program, and your eval metrics and other things look awfully familiar relative to some other things that you would do, but once you kind of, you know, in essence, let the algorithm take the wheel, things change a lot. And it changes kind of how you build systems, how you monitor systems, how you evaluate systems. And more importantly, it changes what those systems can do. You know for your business. And so you know, obviously we're working a lot with that technology, but also I have the opportunity to talk to a lot of customers who are, you know, working with that you know technology and their own organizations in various ways, oftentimes using you know, kind of Amperity data assets. But we see use cases, I think customer support is one of the biggest use cases. And I think you know, one of the interesting things is you know a lot of people think, “Yeah, of course, like you know agentic for customer support that makes sense as a cost-cutting exercise.” But that's terrible customer experience. I think what I've found talking to customers about it is, their customers like it. And so even when they're kind of fully eyes wide open, “Hey, I'm interacting with an agent.” There's just it actually yields a good customer experience. So, you know, I think that's what I'm seeing a lot. You know, analytics, introspection, kind of understanding the business. There's a lot of agentic use cases that I see there and many customers are experimenting with bringing agentic use cases to the end consumer. Not quite as many of those, but ultimately I think that's, you know that’s what we're going to start to see a lot more of as time goes on. I think, you know, I'm seeing a lot of those kind of up close and personal in prototype stage and a lot of them are a little bit stuck there at the moment as they kind of work through that kind of, you know, long tail of challenges. But I think over the next year, we'll see a big increase in that becoming a prominent component of customer experience for consumer brands.

[ 08 min 30 sec ]

Noah Kravitz:

Right. How do your clients—the users you work with—how do they feel about letting the LLM take the wheel? Is there, I'm sure there's a mix of things, but what's the sense you're getting and how are our companies adapting?

[ 08 min 43 sec ]

Derek Slager:

It’s a really interesting question. I would have given you a boring answer six months ago, you know? But, like, it's amazing how much it's changed in six months. And I'll start by saying like there's huge variance, right? And I don't always know, right? It just kind of depends probably the major variable there is kind of you know how much AI experimentation the leadership team has done. I think once they get it, it flows down really, really quickly. But, but yeah, I'll have conversations with people who are like, yeah, we want everything in our organization to work this way yesterday. How fast can we get there? And I work with other people who are like, “Yeah, we're still kicking the tires.” Right? And so, you know it's incredibly, you know, diverse the attitude. But boy, the trend line is clear, right? And in the last six months it's it's amazing how many people have gone from kicking the tires to all in. And I think after another six months passes, I think we're gonna see probably you know 80% of people are gonna be in the all in mode.

[ 09 min 42 sec ]

Noah Kravitz:

It feels like time has sped up. Over the past two years.

[ 09 min 45 sec ]

Derek Slager:

Really. Yeah. Like extremely, yeah.

[ 09 min 48 sec ]

Noah Kravitz:

Can you share a story of delivering an agentic-based, an agent-based experience application for a large client of Fortune 500 clients? Something like that and kind of tell us about how it went if there was a challenge or kind of a surprise to overcome and how the customer responded.

[ 10 min 08 sec ]

Derek Slager:

Yeah, absolutely. So you know, it's interesting. I'll maybe give an Amperity-centric story. Since you know those are the ones that I'm closest to. We started like a lot of companies are, you know LLM in the product journey with text to SQL. You know and that was kind of in a part of the product surface that was you know for people who already knew SQL and it was, you know, kind of a nice, augmented the experience. But then we challenged ourselves we said, “Hey, what if we built something that was for people who don't know SQL?” right? And people who want to ask questions of their data and better understand and make decisions about their data, but don't have that skill set. Like what could we build for them? And so, you know, we built a capability called Amp AI and to the point about a curve ball, I'm going to admit I was a little skeptical because what I thought was we're going to build the surface, people would try it a little bit and then be like, well, I'm not sure if the data is right, so I'm just going to go back to what I did before, which is, you know, ask one of these SQL people to give me the answer. And I suppose I could say I was pleasantly surprised because what we found is when we gave people Amp AI, there was a lot more energy for people to kind of really get it in introspecting the data than I had thought. Right? And it was surprising to me how much that kind of SQL interface between the user's question and the answer was a barrier. And so you know when we created the experience that was for them, I was amazed at how many people used it and not just used it a couple of times to try it. But like you know, we did a cohort analysis and we looked at usage over time and you know people who use the product, you know a little bit once they started kind of using the AI interface, went up to using it a lot and stayed there. And so it was really fascinating for me to see, you know, a whole bunch of people without a very technical skill set all of a sudden become more data informed, right? And all we had to do was put this, you know, little surface on top of the data. It was really, you know, kind of a great surprise and an exciting surprise and certainly is giving us the confidence to you know, lean more into these AI use cases.

[ 12 min 08 sec ]

Noah Kravitz:

Yeah. No, that's what you want to see, right? You get that adoption and it sticks. And yeah, along those lines of opening up more technical capabilities, possibilities for the less technical people. Let's talk about vibe coding maybe nine months, 12 months ago, whenever it was, I saw a video. And forgive me, I can't remember the name of the company, where somebody I think they were doing a mobile phone said to the phone, “Create an app that does XYZ,” and we watched as on the screen it spit out the code and ran the app. And it oh my goodness. Like, what's going to happen to the world? Now we fast forward and the other day I asked a coding tool like this to recreate one of the arcade games I grew up with. And, you know, as you alluded to before, that the results might not have been perfect, but it sure wrote a whole lot of code that I couldn't have written myself. This is amazing. It opens up possibilities and this is more from what I hear from people who are more versed in coding and security and things like that than myself. It also opens up some potential issues. Talk to us about vibe coding. What does it mean to you? What does it mean for you know, engineers, people who are actually steeped in what they're doing and how is this you know, changing the landscape now and going forward?

[ 13 min 26 sec ]

Derek Slager:

Yeah. So, you know, vibe coding, I love that as a name now, right?

[ 13 min 32 sec ]

Noah Kravitz:

And I didn't even explain what it meant. I just assumed at this point.

[ 13 min 35 sec ]

Derek Slager:

I know, yeah. But yeah, I think if they're listening to the NVIDIA AI podcast, they're up on things. So yeah, I think that makes sense. Yeah, I think vibe coding has, you know, impacted everybody and you can give the examples of kind of these, you know, increasingly impressive one-shot examples where you give a single prompt and something pretty amazing comes out the other side. That's maybe one category of vibe coding. And then I think like vibe coding for the you know professional software developer set looks very, very different. Right? And I think part of that is you know people are working with very large code bases and and you know just the properties of the system and what you get out are very different than the big one-shot thing where it's generating lots of code and and things need to integrate with an existing workflow. I think what's very interesting is vibe coding changes the workflow of programming, and I think this is something that, you know, I've really seen the people who in particular get tons of value out of vibe coding are not just doing what they've always done, but doing a little bit faster because they're involving in LLM. They've kind of rearchitected how they write code. And specifically, a lot of times they're taking advantage of asynchronicity, so you know some engineers will launch 10 different, you know, LLM-based processes at the same time and then you may go off and do something that frankly, if you were sitting there watching them, code would look exactly like what they've always done. And then, you know, at the end of that session, they're going to go back in, and check on the results of those agents and you know they might take three or four of those and just, “Ugh! Garbage, delete it!” Or just start from scratch, right? And then they might take a couple more of them, make a few refinements, you know? Check in on the code and there's a couple of just maybe just nailed it right and those will kind of go straight through following a quick review. But if you think about it like you know they've they've essentially you know, put kind of you know ten additional engineers on their desk. And engineers have some interesting properties, right? Sometimes they are really impressive, and sometimes they're astonishingly terrible. But they're, you know, they're effectively free, you know? They cost some token generation. And so I think you know this very different workflow that comes with vibe coding and obviously, that has a kind of profound impact on what a team can accomplish. I think the other interesting thing about vibe coding as we're increasingly starting to see people with different skill set profiles contribute directly to the product, right? So, you know, rewind a year, if you're a designer or you're a product manager, you're probably not impacting production code except by influence, right? But that's changing, right? You could say I think this should be this way, and you can go and try it and you know, we've built a lot of infrastructure here to enable these vibe coding use cases to be accessible to a lot of people. So you can try something and you know, it'll put it in an environment where you can test it so you don't have to spend, you know, two days setting up your local machine and then you can say, yeah, this is good and then have an engineer review the code and put it straight into production.

[ 16 min 32 sec ]

Noah Kravitz:

When it comes to things like quality assurance, security, other concerns around putting code into production, is the answer and I'm speaking from my own experience, kind of as a content creator and using AI systems to help in a very kind of similar way to what you're talking about. Just my words are content, not you know, instructions to a machine, is the answer as simple as having the right human in the loop to review the output? And like you said, you don't know how to incorporate this into the workflow. Launch 10 processes, review them, you know, discard, use, adapt. Is it that simple and sort of at a high level and what are some of the things kind of maybe more nitty gritty, that you think about when it comes to vibe coding, quality assurance, security, you know, and working with these large code bases?

[ 17 min 22 sec ]

Derek Slager:

Yeah. You know, it's interesting. I think there's a lot of discussion about this. The way I see it, it doesn't change much, right? Like and what I mean by that is you know human beings are typically the you know the weak link in a system, right? And I think building systems that are robust from an infrastructure perspective, from a scale perspective, from a security perspective requires you to be resilient to error and when designed intentionally you expect error. And so, you know, I do think that, you know, good system design looks the same. You know, when vibe coding is involved versus not. And arguably it's more important, right? Because you know the impact is larger. So yes, I think you still need accountability mechanisms, right? Like the, you know, at the end of the day, you know, we were very clear, you know, engineers are responsible for what comes out, and you know, just like rewind 10 years, somebody might have copied and pasted, you know, some code from Stack Overflow. They're accountable, you know, for that. Whether they use that as a shortcut or not. This is exactly the same in vibe coding, at least in terms of kind of creating the services that we have. Now there's security concerns, obviously that are new and different, you know and when we do you know penetration testing in 2025, you know we're looking at things like prompt injection and other things you know, in terms of the operational surface. But if we really kind of zoom in on the vibe coding era, it's like yeah, absolutely, humans are still accountable for sure.

[ 18 min 47 sec ]

Noah Kravitz:

I'm speaking with Derek Slager. Derek is co-founder and CTO at Amperity, a company that is doing, I think, it's safe to say a variety of things to help their customers, other companies’ users understand and make the most of their data. We've been talking about engineering the developer experience. I want to ask you about one of Amperity’s tools, Chuck Data. Tell us about Chuck Data.

[ 19 min 11 sec ]

Derek Slager:

Yeah. So Chuck Data is something that we launched and you know it's such an interesting extension of our vibe coding conversation because you know we were, you know, experimenting and understanding and learning the best ways to you know extract value out of the the various kind of interaction patterns around vibe coding. And then we asked ourselves what seems like sort of an obvious question like, why is this only for software engineers like, you know? And you know, we looked at some of the things that, you know, our customers or even some of our own teammates were doing related to customer data engineering, right? Where you know they're pushing around, you know, big, big mountains of SQL or, you know, trying to kind of manually build workflow coordination patterns or maintaining huge kind of DBT code bases is and we thought, “Whoa, there's an opportunity here.” And so the idea behind Chuck Date is, hey, what if we created a vibe coding tool for data engineers that are specifically focused on customer data use cases. And so you know, Chuck was kind of packaged up to be a very, very easy way for people to, you know, in essence have these kind of like, you know, one-shot experiences that you described earlier in sort of the class of software engineering sense. But for customer data engineering use cases.

[ 20 min 27 sec ]

Noah Kravitz:

Are there competitors? Are there other when you were building Chuck data, were there reference points and less asking about what those were, but what's different about Chuck data, what sets it apart.

[ 20 min 37 sec ]

Derek Slager:

Yeah, it's such an interesting question because the thought I had, you know, as we were kind of forming Chuck, and no doubt we took a lot of inspiration from Claude code, you know that's definitely kind of, you know, number one in the Amperity ranking as vibe coding tools. And so we took a little bit of inspiration from that. Though, Claude of course is aimed at more you know, traditional software engineering. But I expected, you know, there was a point in time where I was like, OK, let me go find what other people are doing. I'm sure other people have thought of this, and I couldn't find them, you know, perhaps they, you know, they did exist and hadn't made their way up the, you know, SEO and GEO rankings. But I literally couldn't find it unless you were trying that.

[ 21 min 18 sec ]

Noah Kravitz:

Which is always kind of an exciting and scary feeling, right?

[ 21 min 20 sec ]

Derek Slager:

That's exactly, yeah. Yeah. Yeah. I was like, Hooray. And then I was like.

[ 21 min 23 sec ]

Noah Kravitz:

Ohh what am I missing?

[ 21 min 25 sec ]

Derek Slager:

Are we too early? And so, yeah. No, we leaned in and I think you know Amparity we always really think about things starting from first principles right? And it's like, “Hey we can help people really accelerate some things that we know they're going to be challenged with.” And of course that's valuable. So let's lean into that and yeah, I'm sure the competitors will follow eventually. But yeah, I was a little bit surprised to have trouble finding

[ 21 min 50 sec ]

Noah Kravitz:

Where does the name come from?

[ 21 min 51 sec ]

Derek Slager:

The name it's inspired by you know, one of our very early engineers who, you know, kind of did a lot of work on, some of our, you know, initial R&D and so we kind of wanted to, you know have a little nostalgic throwback.

[ 22 min 04 sec ]

Noah Kravitz:

Yeah. Awesome. Very cool. So talking about vibe coding and AI-powered workflows and the experience of a developer and you know a new developer kind of learning the trade and getting their feet wet and an experienced developer as you said, kind of rethinking their workflows and how they do things. There's concern in some circles that you know again, I'll relate it back to my own experience, similar to how there's concern that students, adults, professionals, for that matter, are using AI to do their writing for them. Concerned around vibe, coding, AI-powered tools, kind of enabling programmers, if you will, to skip the process of really learning to understand how the code works, how to how to structure applications, how to do all those things. What's your take on that? Are there risks of over-reliance? Is it just kind of where the winds blowing and we'll adapt, you know, what's your take and what's Amperity doing to make sure that, you know, programming doesn't turn into just hitting tab repeatingly and then hitting ship?

[ 23 min 08 sec ]

Derek Slager:

Yeah, the concern sounds, familiar to me? You know, I've been around a little while and you know, my career started in the late 90s and you know, you know, a lot of the conversation in the late 90s was like, oh, you know, these kids today with their high level languages, right? They don't even, you know, they didn't even know how to. Do pointer math and then. That you know, and then it sort of moved on to all these kids with their IDE's, with their fancy auto complete and all these kids with their, you know, garbage collection language. Back in my day, you know, we used to manage memory ourselves etcetera, etcetera, etcetera. And so you know it sounds a little bit like the the, the grumbly old person rant. You know to me.

[ 23 min 32 sec ]

Noah Kravitz:

Right, right. 23:44 Totally.

[ 23 min 45 sec ]

Derek Slager:

That sure like Vibe coding has a bunch of properties that allow you to create a bunch of code quickly, and I think you know there's many prominent examples that are hard to find of people who didn't understand what was happening there and then really bad things happened, right? And so in some sense, back to maybe the accountability point from before, nothing really changes, right. You've got some tools. Allows you to to to work differently to work faster, but like you're ultimately accountable and and it's not optional, you know, to understand how that code works. It it it really isn't and I think. Then you know if we look over the horizon and imagine a step change in the model and you know kind of more, you know, agent to sort of verification and validation workflows. We'll get easier, we'll. Get faster, but at the end of the day, you know, I think I think society is built around the the notion that, you know, humans are gonna have accountability for, for the things they do. Right and and. You know, I I don't. I don't really think that changes and so I think it's a great new set of tools. I celebrate the great new set of tools. It allows us to you know build more faster for our customers. And I think that's amazing. And awesome.

[ 24 min 49 sec ]

Noah Kravitz:

So along those lines, then what will the engineers? What will the coders of tomorrow need? How how does the skill set change? How? How does the mindset, the approach to, you know, constructing something new, working from an existing code base, all those things? How does that change for the folks coming up now?

[ 25 min 05 sec ]

Derek Slager:

I think it's going to be really different. I think we're designing. A new way to build software and and and. I really mean that you know the workflow is different and I think that the skill sets that matter are also different. I think maybe the best engineers of 2015 won't be the best engineers of 2026, if that makes sense.

[ 25 min 25 sec ]

Noah Kravitz:

It does but. Why?

[ 25 min 27 sec ]

Derek Slager:

Because I think. You know, Once Upon a time, right, that developer who could master that algorithm, you know, or had this kind of like, you know, deep arcane knowledge of how a particular subsystem worked was, you know, specially valuable. But that skill set doesn't sort of automatically adapt to, you know, how do I go and and kind of build a whole network of different processes that are happening. Right. It's almost like. You know you're going from being, you know, an expert artisan to a general contractor. That's a different job, right? They're both important jobs, but it's a very different job. And being a, you know, great general contractor in a large complex, you know, problem space where you have lots and lots of subcontractors and you need to kind of orchestrate that all together. It's just different than. You know, kind of the core craft and. So and look. We're still learning as an industry what that skill set ultimately looks like, but I think you create a lot of opportunities for people.

[ 26 min 16 sec ]

Noah Kravitz:

Of course.

[ 26 min 19 sec ]

Derek Slager:

People you know who maybe wrote themselves out of the, you know, engineering game Once Upon a time. And I I think the the the skill sets are going to look different. So you know and I'm seeing some, I'm seeing some really evidence of of of that and I expect that will continue to evolve you know at impaired in the industry as a whole.

[ 26 min 35 sec ]

Noah Kravitz:

So how do we how does Amperity approach keeping engineers at the center of the process as these tools change, but as you said, you know the human accountability. And then sort of the it's not the other side really, but maybe at the beginning of that process the spark of innovation, the idea of humans innovating on our own working together using tools, yeah. How does Amperity approach you know, is there a philosophy around keeping the engineers at the forefront and not sort of having them sidestepped? By increasingly advanced automation.

[ 27 min 10 sec ]

Derek Slager:

Yeah, I think it's a fair question and and. And admittedly today. That's pretty easy, you know, because. You know, there's only very small components of the of the engineering life cycle and the product design life. So to your point that ideation and things like that, that could be at least in theory automated away. And so you know today, obviously, you know humans are still firmly planted in the in the in the feedback loop and they're driving driving that process entirely. You know, I think I can close my eyes and look over the horizon and imagine some pieces evolving. But I think it's. Still, you know, largely rounds to the same and, and I think you know it's sort of like, you know, if I look at sort of the backlog of ideas, you know that people have for how to make Amperity better, right? It's like there's thousands of good ideas. Yeah. You know, for how to make Amperity better. Right. And so, you know. In some sense, the. The challenge is always curation. Having better tools to to, to help. That are wonderful and I think you know, we all still are going to value sort of that, you know, kind of human touch and and and I think we'll we'll expect and appreciate that that human touch is going to be enhanced augmented and and made better by the inclusion of AI tools but but but I think we're a long ways out from you know kind of having having humans. Out of the loop, particularly in software engineering and customer data engineering use cases because you know the difference between, you know, right and accountable, I think is just sort of fundamental to the.

[ 28 min 35 sec ]

Noah Kravitz:

Well said. So shifting from the future and kind of coming back for a moment if you look across the work and parodies doing the work and parity is done with clients, specifically talking about agents and agentic systems. Any big wins, any really exciting little stories you can share or learning moments, if not a win? Something that, you know, kind of really stuck out that's really informing how you look ahead.

[ 28 min 59 sec ]

Derek Slager:

Yeah. I think the biggest wins. Really fit the category of. You know, enabling people to do things they didn't think they could do and and that might be, you know, from kind of the examples earlier where people can, you know, analyze data that weren't able to do it before or people who kind of always told themselves that we can't do really sophisticated segmentation because we're bottlenecked on creative resources. And so, you know, being able to empower. People you know to do things that they couldn't do before that ultimately are good for their customers and that they're motivated to do, I mean those are the things that are the big wins for me and get me excited. I think in terms of learnings, business context matters a ton. And I think that's one of the really kind of you know sort of an obvious thing. But but it's really important to design for that. You know it's like. I was recently talking to, you know, one of our one of our customers that sells cars, right and. And and if you're a customer that sells cars and you, you refer to a product as a Taco, you're probably talking about a Toyota Tacoma, right? Because that's a, you know, that's their short hand for Toyota Tacoma, right. And if you're in a quick serve restaurant and you say Taco, I mean something completely different, right? Just to to choose a silly example. But, you know, there's so many of those things and we see that. You know when you know, we watch people interact with the system, they're. There's a language of every company and the way they think about things, particularly when they're asking questions about customer data really infused with the language. And so, you know, a big learning is, you know, the faster we can kind of bootstrap the ellms with customer specific knowledge. It's, you know, it's a huge step change in kind of efficacy. Than the empowerment which kind of fuels more of those wins.

[ 30 min 37 sec ]

Noah Kravitz:

Yeah, yeah. So you've kind of touched on this, but I'm ask you to sort of to use your word kind of curate some of the things we've talked about and and you know some of the other things I'm sure you're seeing day-to-day. What gets you excited about what's coming next, specifically in the enterprise and and what are some of the things when you're working with say new clients or just talking to folks who are kind of on the forefront and new ADA? What are some of the things that not only excite you about what's coming, but that you think are really key for folks to keep in mind as they start exploring newer and and just you know, as we said before, everything's moving so fast, these increasingly advanced tools.

[ 31 min 14 sec ]

Derek Slager:

Yeah, I think what's exciting to me and and and what we're really seeing is I think people started out by saying how can AI make the thing I'm already doing go faster. Right. And and that's a great place. Start, but I think what's exciting is people are starting to rethink their businesses, right? Vibe coding causes you to rethink your developer workflow, you know? But but what? If you're planning a marketing campaign, you know well today in 2025, that probably still looks pretty similar, you know. But when we look ahead and particularly some of the things that you know we're working on with with our customers like. We can go from making tasks go faster to really helping to. To you know, sort of reinforce the strategy and how that comes to life. And I think you know, obviously you know anybody can go to their favorite LM and ask it some questions about marketing strategy and that's fine. But the thing that's so exciting is integrating that experience into a system and a platform that has the data that has the business context. That knows what's happening, can close the loop and you start to kind of like close your eyes and think about that. Wow. Like, that's really exciting at that point. You know, it's not just about making that task go faster. It's about making your business go faster. And I don't think that's hyperbole at all. Right. Like this right around the corner for us and. 32:16 Yeah, yeah.

[ 32 min 25 sec ]

Noah Kravitz:

No, no.

[ 32 min 28 sec ]

Derek Slager:

So, you know, I think we're extremely excited about some of the opportunities ahead and extremely excited that our customers are are are really kind of pushing us to be able to do that as quickly as possible.

[ 32 min 40 sec ]

Noah Kravitz:

So along those lines, best practices, words of advice for listeners out there for teams who. Want to harness agentic AI in particular, but want to be mindful of the things we've talked about innovating? Being accountable, being responsible.

[ 32 min 56 sec ]

Derek Slager:

Yeah, I would say the one piece of advice and I give this advice a lot is start now and and.

[ 33 min 03 sec ]

Noah Kravitz:

We've heard that one before.

[ 33 min 04 sec ]

Derek Slager:

Yeah, it's so important. It's so important because like it's early, right, we're still figuring out the patterns and the practices, you know, like as an industry, we're learning a lot about. Kind of how to, you know, put these incredible new technologies together in ways that that, that really, you know, move the needle and you know, right now you just have a choice, right? You can be. You can be a doer who's in that learning loop or you can be an observer and kind of you know, wait and see. And I think we talk a lot about this here. Like, you know, Speed's the only thing that matters. And so I don't think it's viable in in the current market to be outside that learning loop, right. And the good news is it's early, right? And so you're not too late, not at all. But it's getting to the point where pretty soon you will, you're late. And so I think, you know, I think we're certainly past the point. And again this is something that's changed in the last six months. We're past the. Point where people are like well. We'll see if this AI thing. Plays out or not like it's just it's it's overwhelmingly obvious. This where things are going. And so yeah, get off the sidelines. Get in there, try stuff. Learn. It's easier than ever, you know, to do that. There's more information out there. And of course, you know, AI feeds itself, right? I can also help people figure out where to start and how to get through. And so yeah, start now and go really fast. That's the path to success.

[ 34 min 23 sec ]

Noah Kravitz:

Fantastic. So to put a point on that for folks listening who are like, yeah, I'm ready to go. I want to start by learning more about. Amperity.

[ 34 min 31 sec ]

Derek Slager:

Yeah.

[ 34 min 31 sec ]

Noah Kravitz:

Best places for them to go. Website social media. Should they start?

[ 34 min 36 sec ]

Derek Slager:

Yeah, go to Amperity. Dot com I hope many of those people are thinking to themselves, wow, I'd love to work at Amperity. So, you know, hit that careers page. Awesome. You know, we're in a growth mode and and hiring, hiring for people, especially, certainly people who maybe have passion about how to bring AI to customers in ways that really move the needle on their across a variety of roles. So yeah, would love to see people. Visit that page and yeah, certainly we, you know, fairly open with information on our website about what we do and how we do it. And you know we've got a lot of work we've done over the over the years with published papers and patents and other things. So you know we love engaging with people who are kind of just interested in the. Base and you know to use a a phrase that gets used a lot around and Perry.

[ 35 min 17 sec ]

Noah Kravitz:

We love nerding out with people, awesome Derek Slager. This has been a great conversation, and I think just a lot of wisdom and a lot of, you know, really practical advice that really resonates on how quickly things are moving, how important it is to get started. And just. To. For all that we can do now, the possibilities, even in the very near term, are just, as you said. close your eyes and imagine. And it's it's really something.

[ 35 min 41 sec ]

Derek Slager:

Yeah, for sure. Well, I appreciate you having me, Noah. It's fun conversation.

[ 35 min 44 sec ]

Noah Kravitz:

Appreciate you having on let's do it again down the road.

[ 35 min 46 sec ]

Derek Slager:

Sounds great.