Hey channel insiders, welcome back to channel insider partner POV. I'm your host Katie Baboso and my guest today is Ryan Gross, head of data and applications at Kalin, an AWS services partner that is making migration to Amazon Web Services easier and faster than ever. Today we dig into how AI is both fueling change and uncovering technical debt and how Kalin is helping businesses of all sizes modernize to stay competitive. Welcome Brian. Thanks so much for being here. >> Hi Katie.
Thank you. I'm really excited to be here and share a little bit about the journey we've been on over the last year and a half or so at Kaitlin. >> Yeah. And speaking of journey, I know it's been a wild couple of weeks for you. You've had kickoff, you've had trips, so I'm hoping that they let you sleep a little bit before coming on with me today to talk to me about it. Are you feeling all right? You feeling ready? >> Feeling great. >> That's good news.
Well, like you said, I'm excited to talk about the journey that Kayn has been on. So, why don't we first kind of get to know Kayn, especially for those who haven't heard of you before. How do you explain what it is when you meet people and talk about not only what you do, but the company in which you work for? How do you describe it? And most importantly, who do you serve? Absolutely. So, we are the number one independent all-in AWS partner. So, that means we are working exclusively with Amazon Web Services as a cloud hosting provider.
And we've really gotten to that point because we've come up through the AI era as a firm that's really been taking advantage of first data and then AI over time to drive customer outcomes. And that includes everything across the AWS portfolio. So anything from migrating onto the cloud, modernizing your footprint or whether that's infrastructure, applications, data, and then building the product innovation that takes advantage of those platforms that you've built on top of the cloud.
And so we help our customers from startups, SMB customers all the way up to some very large enterprises and across most segments. But a lot of the innovative work that we've been doing has been in the independent software vendor space. So working with companies that are themselves building software products that serve a wide variety of industries >> in the ISV space. Yeah, that really does kind of span the gamut as far as what we could be talking about vertical wise, what we could be talking about size-wise.
It's always exciting to me to hear somebody that serves both SMB through mid-market to enterprise space because typically you have people who are only serving one of those segments at a time. So it's exciting to hear that. As far as AWS goes, talk to me a little bit about your journey with AWS. Was this something that it always was AWS from the beginning for Kalin or did you kind of find your way into that partnership as you went along? >> No, that was strategic from the beginning.
So, firm really started out in 2020 as a services firm with that uh bet that AWS was going to be the platform for innovation going into the future. And the partnership has been strong for a long time. So we were the rising star partner of the year in 2021, innovation partner of the year in 2022, application modernization 23, three different partner of the year awards last year which included both Genai solutions and migration which is really relevant to the journey we've taken this year where we've launched Kalin Accelerate which is a suite of migration and modernization services that are powered by generative AI.
Well, you said the magic words there with the Kalin accelerate. So, I'd love to dig into that. And first, I want to sort of set the tone for what we're seeing in the market. So, what is often standing in the way of successful cloud migration and database modernization for your customers in today's market? What challenges do they often face when it comes to this? The interesting thing here is, you know, tech debt has been building up over such a long period of time specifically because the payoff equation for that tech debt was upside down for most companies.
So, it's not just things that are standing in their way now. What's actually changed now is the ability with generative AI in the loop to pay down that debt faster. The the acceleration that we're hinting at in the brand is all around paying down that debt faster. therefore cheaper. And we'll kind of get into the better, faster, cheaper version of it in just a minute here. But the biggest things there are really three aspects to it. First is the amount of time from an opportunity cost that it would take to execute on those projects.
In many cases, you've been building up that technical debt over the course of 10, 15, 20 years. And the institutional knowledge even of what the size of the problem is doesn't exist in most companies. And so they're looking at a, you know, at least 18 months, but who knows, maybe it's three and a half years worth of work we'd have to do here. And in today's world, you can't really stop moving forward for more than 6 months. And so it simply is too impactful in terms of being able to continue moving the business forward.
Second piece of it is around the risk that goes with this. So you're making significant changes to the underlying infrastructure that is core to your business, whether that's the data center, the core application technology or the database layer. And you really can't take disruption, downtime, those types of things in most businesses, both from a reputational perspective, but then also just from a pure time is money, what anytime you're down perspective.
And then the final one here is really around skill sets gaps. So you may have a team that understands your current technology footprint. You're building out on top of that over years and years. Maybe that team's getting older and you know that part in itself is part of the risk and motivation here. And then you can hire a team that understands modern technology. But it's very rare that you'll find people that truly understand both and the mapping between them.
And so that skill set gap makes it really hard for companies to take this on themselves. And where they've tended to go look for external partners specifically around the skill set. The challenging part there was always the cost of working with those partners. Getting you know 35 people for a period of 2 years to go do a significant refactoring comes with a very high price tag. And so only those companies where it was truly existential to go fix the problems were the ones that went and did.
And as you pointed out with the customers that you serve, not all of them have the ability to kind of go and afford that talent, afford those new products and software and hardware pieces to be able to fix the problem that they've slowly dug themselves into over a number of years. So it is really about the ones who can afford to get it done the fastest and not everybody has that ability. So let's let's kind of dive into what that solution is. We hear the inspiration, right?
So talk to me about how Kalin said this is actually something that we can get in front of and we can try to help our customers. And I'd love to know from just the perspective of what you heard them telling you directly. You talk about tech debt, you talk about the inability to hire that talent. Was that all coming directly from your customers that kind of drove this creation of this latest solution or was it something you were just seeing across the marketplace generally? >> Yeah, I think a lot of it came from customers right there.
There were some key projects where we were going in, we were running the assessments for organizations who really did have that type of existential moment. So on, you know, on the database side, a company that was really looking at getting reclassified in terms of their billing and their costs of just paying for the database licenses were going to go up like eightfold >> because of that reclassification. And so they needed a plan for how they were going to get off of that database platform. and we worked with them on an initial plan and you know kind of using the manual approach.
This was back in like the probably late 2023 time frame and they were like well that's way too much money and way too much time. I don't think that that's the plan we want to go after. So then we were looking at that point we had been doing a number of generative AI projects building knowledge bases and building the applications that customers would use for whether like customer service chat bots and those things around the time. And it was like well you know a lot of the generative AI stuff like it can understand and write code too.
Let's take a look and see how we can have generative AI do this code translation problem for us. And so started out proof of concepts there. And as we started to see more and more that that was working. And as each new model release made it work even better, we more deliberately took a step back and said, "Okay, so what are the other things that we do as a company where this type of approach could be applied and then underneath of that there was kind of a fundamental principle that we put in place going back to the riskmanagement piece of this cuz you know we could build something that uses generative AI.
Everybody was well aware at the time and it's still very much the case that generative AI models have a tendency to hallucinate and come up with things that are just not correct but will look really nice to you know even the trained eye. So we built in the concept that for the Kalin accelerate versions of this we were going to use what we call test-driven automation. So we're going to be first using generative AI to say how can we validate that we are getting the same output in the new way that we are currently getting you know that our clients are currently getting in the old way.
And then by doing that we build a set of tests and if those tests pass we know we've actually completed whatever the task is whether it's translating database code or building the infrastructure as code to move off of a data center or rewriting entire feature functions from you know legacy application and you know the list goes on and on in terms of the other things you can look at in that manner. But by doing that, you know, we started with the, you know, better angle of better, faster, cheaper in that we know we're going to be able to at least maintain quality and, you know, do the migration work better than if you had brought in people to do it and they had to manually test each time.
They just don't have the, you know, the perseverance to really run that same test over and over and over again. And then that kind of naturally leads to it going faster because AI scales a lot better than people runs out horizontally can take on thousands and thousands of instances of the same problem at the same time. And then you know being a services firm that leads to it naturally being cheaper because well you're paying people by the hour in the alternative model here.
And so that's really the ethos that we've taken to solving these types of problems and we've continually expanded that uh you know the aperture of what all we look at over time. >> Can you walk me through how it works? First of all, start with me as a prospective customer going how do I identify that this is absolutely something that I would benefit from and then tell me all the way through how long it takes, what exactly needs to be done and just essentially how Kalin Accelerate is able to to do its job.
I do think in the very beginning, right, if you have tech debt that's weighing down your business in some form, then chances are you're the right type of candidate. And that that tech debt could be slowing down release cycles. It could be you're paying too much for licensing on VMware or on Oracle or SQL Server databases or for Windows operating systems or, you know, that list goes on. Or it could be that you are running into skills shortages. So you've got something legacy where it's harder to find the people that really understand how to maintain that and that's holding back your ability to really innovate in the business.
Any of those are are patterns we've seen and a lot of times it's actually an unlock of future agility. So you want to get onto the cloud, you want to do be in a more modern footprint for you know it was maybe a year ago, six months ago because you want to be able to build AI functionality on top of that. And now it's both that and you want to be able to use modern AI powered SDLC so software development life cycle because tools like cloud code or Q developer ko are making it so that if your application is built in a way that those tools can understand and work within your future innovation no matter what you're trying to do whether that's building AI features or just building new you know more screens or more APIs or whatever it is that your business does will go much faster right you know you're two three times faster probably.
So that's the this looks like something I want in my business version of it. Then the next step is we can come in and really start with a strategic engagement. So if you're really at the point where you just want to modernize, you don't really know the details of underneath of that what components of your overall you know infrastructure application data layers you want to focus on and you're looking for that overall business transformation. we can offer kind of what we call a cloud evolution strategy.
And that's something where we're coming in and really taking that holistic look across the business of how do you evolve from where you're at today to where you want to be to take advantage of all of the capabilities that um you know AI on the cloud provides to you today. Underneath of that, we'll then match you to the right areas where we can use our accelerators. Those really fit into that three tiers that I've talked about before. So at the infrastructure layer, we have Kalin accelerate for cloud migrations.
That's all around taking your current whether it's on prem or on you know another competing cloud and moving that on to AWS in a way that you're going to be set up to take full advantage of the cloud in the future. So that includes generating infrastructure as code so that you're not just left with you know the same data center that just happens to run in Northern Virginia or wherever you know whichever Amazon region you choose. You actually have something that takes advantage of the capabilities of the cloud.
You can run multiple environments of it. you can move it across regions from a disaster recovery perspective. And that's actually part of what we do during each of those migrations is put together a disaster recovery strategy so that once you're on the cloud, you know, uh, a lot of people have seen the news around the AWS outage that occurred recently in in those types of scenarios that you'll have the resiliency built in to take advantage of that.
Like I said before, they all have the testing built baked in as well. So we've got that testing capability that also plays into the DR strategy so that when you bring up the you know something went wrong, this one died, we brought up a whole new instance of our application, we run this suite of tests and we know it's not working. We can turn it on for live traffic. And then finally, because the AI saves our engineers so much time, we in addition to the DR strategy have baked in cost optimization or other modernization strategy alongside that migration so that you're really not just taking the first step, you're getting yourself set up for the journey onto the cloud.
So those migrate, you know, those modernizations could be database layer modernization where you're looking to move off of legacy proprietary databases with high licensing costs onto something like the Postgress platforms that AWS offers within Aurora or Redshift. They could be also at the application tier where you're looking to retire some of those legacy applications or maybe even consolidate. Maybe you acquired several companies and you need to consolidate four or five apps that all do the same thing into one go forward application for the future.
And so we're helping to set up that entire journey while also handling some of the change management of the new ways of working not only just in the cloud but kind of in a cloudnative way. >> Go into that a little bit more. What was what was the catalyst for that when you say some of the new ways of working? Just build on that a little bit more for me. >> Yeah, sure. So especially if you were on premise and not moving from like a you know Azure or GCP your security posture is going to oftentimes be perimeter based and you're focused on how do you keep people out of your network versus in the cloud you're looking at things in terms of a more of a zero trust model where in theory people could get into the cloud right AWS has access and then rogue actor could be doing something wrong there.
So you're taking that shared responsibility model and you're designing your infrastructure such that security is baked in between each of the application. That's just one example of the switch that needs to be made from a provisioning perspective. I mentioned infrastructure as code before. That's the ability to spin up the entire application portfolio. the networking, the firewalls, the connectivity, the security, all as code in multiple different whether it's regions around the world or you know having a dev test stage and production environment of something where you may have only had one or two in the past.
That means the operational aspect, you know, you're not going and plugging in a server and then logging into the server and then installing the application. You're spinning all of that up as part of a pipeline that's fully repeatable. Those learnings are a key part of the journey. But in addition, there's just the operational platforms that need to be put in place so that when people understand the new ways of working, they also have the information and the runbooks and other uh you know, aspects of that that they need to take advantage of the cloud.
The last piece of this is really around the kind of AI powered SDLC aspect that I talked about before. So many times people think, oh, cloud modernization, cloud native, that's all ops team. The ops team needs to learn the new ways to work. In today's world, the application development process looks dramatically different than it did a year ago as well. And so a lot of times what we're doing is how do you scale up AI powered development versus just, you know, give everybody GitHub copilot. you have a version of the world where you're looking at each team providing the right information out to each other team so that not just people but the AI coding agents that those people are actually using to do their job understand what your component provides and how to interact with it and then that allows the whole team to move faster again assuming that underlying layer of the core cloud infrastructure fully programmable and will be able to keep up with you.
So all in all, how long would this take? Obviously, every scenario is going to be different, but say the the very basic uh need to be able to use accelerate and say we just need to be able to have this work for us as quickly as possible. What metric do you typically give your customers when you say this is how quickly it will migrate your systems for you? Yeah, we talk about, you know, on average we're moving 50% faster on migr cloud migrations, the database modernization piece, somewhere in the order of 3x faster, so 300% faster in the code transformation portions of that. >> Wow. >> And then uh on the application side, you're looking at similar similar numbers in there, you know, multiple times faster than the legacy methods.
Now, that's obviously faster faster than what? And so what we actually do in each of these is we have a component where we come in and we scan whatever the input is. So if it's application modernization scan the full codebase including you know database and all the other dependencies using generative AI to produce the estimate of what it will actually look like for you and that's something that happens you know within the course of a week. Same thing on the database modernization.
We'll look at all the database code and the parts of the application that call into that database to be able to produce a very accurate estimate for the cloud migrations. We're going to look at the VM inventory and kind of the network configuration that you have and give you a very detailed estimate. So in many cases now we are building a kind of an outputbased or a fixed fee engagement structure for the execution because we're able to get to that level of fidelity in scanning and assessing the code.
And obviously we'll then work with you on the remainder of the components that need to go into a cut over. Right? Just translating everything and making it ready is only one piece of the puzzle. You need to be able to hit that cut over, maintain zero downtime, ensure that the teams are ready to go on the other side of that. And so that complete endto-end kind of white glove approach is what we're trying to estimate through the kind of assessment process.
Is there possibly a real world success story you can talk to me about uh with a customer? Whether you could name that customer or not, I'd love to hear how we can almost put a face to a name when we're thinking about Kalin Accelerate. >> I think one interesting one here is a company called Teamfront. So they are a holding firm over several smaller independent software vendors that serve niche industries. So they have Arbor Gold which is the first one we worked with on database modernization and Arborold was on legacy SQL server for their application back and wanted to move on to Postgress in order to reduce that licensing cost and unlock some more downstream innovation in terms of being able to run multiple environments on behalf of their customers.
So we worked with them to do the code translation all the way through cut over and we ended up saving about 90% of the time in that particular migration. Now some of that is you know upfront during the assessment we found that about 60 to 70% of their code was not actually being used by the application. So it was just sitting there and had you gone after and done all the work to translate it would have been a complete waste of your time. So then the remainder of you know of the process we accelerated the normal you know kind of 70ish% to hit that 90%.
Then based on the success of that project we've worked across many of their different they call them team codes but portfolio companies on a similar approach. So we just went live with a company called Forzap that has a similar application footprint kind of rinsed and repeat the same format. That's something we're seeing in other customer portfolios as well is you have several companies that maybe got brought together because they have a similar technology footprint, a similar set of problems and we're able to move across multiple of those.
Uh, another one on the cloud migration side is company called Nationwide Appraisal and they were simply trying to exit two remaining data centers. >> Not a massive footprint there but they needed to get out of these in you know a pretty short turnaround. been working with them on building, you know, the infrastructure as code, the testing and executing that. We've also been working along the way on that enablement piece. So, the cloud is relatively new to them.
So, ensuring that they understand how they're going to be working in the new way. And we're now, I think, just going to be starting in the next week a database modernization assessment, like I mentioned before, because we're freeing up some of our team's time from just going in and clicking the buttons and, you know, ensuring that the ports are open and so forth on servers or setting up the underlying migration tooling via the AI automation. They're able to spend some more time looking at that next tier to take the next stage in that journey.
There's other larger customers that we're working with too, some some in the Govve space. Can't really name the names of those, >> right? We've been looking at this as a kind of portfolio approach as well, taking a look at some very, you know, old both data center move and then the database modernization that goes in with that. So, one of those companies was just able to move out of a legacy data center over the course of I think it was something like 6 to9 months. >> Oh, wow. >> And as part of that also get off of Oracle.
So they're they're just about I think November 1st is their first date of being out of their Oracle contracts which is a big moment for that firm and so combining kind of the cloud migration and the database modernization into one cohesive program there. Several others there in the you know government technology space where we're moving across you know one application portfolio to the next and running these modernization efforts. I'm curious to know because you've mentioned a couple competitors to the AWS infrastructure such as Oracle.
Is there a pattern perhaps in which you see maybe the most switched from uh vendor? Is that something you might be able to point to? >> I think it's actually coming out reasonably even on the side between Microsoft SQL Server and Oracle. And then you you see a smattering of other like DB2 from IBM and >> Oh wow. Yeah. like really old stuff on the movement into the cloud side where people are looking for this acceleration. We have certainly seen quite a bit of VMware environments that companies are exiting although that's not universal.
I think we've used the same technique for just infrastructure as code running on Microsoft Azure or other you know digital ocean and some of the other uh providers of collocation services there. I do think the key thing on the application modernization side is you're seeing a lot of that really legacy tech stack. So like Deli or FORRAN and like some of the things that have really been sitting there a long long time that everybody thought was you know the untouchable set of services and those are the ones that you can now go in and because of that kind of 4x faster approach take that all the way to something where you're building microservices and leveraging serverless technology to achieve the same functional output.
I think that's been the most common there, although we've certainly seen lots of other various permutations of reasons people want to modernize the application layer. >> Now, something you mentioned earlier on was that Kalin is actually also able to provide those those disaster recovery services for instances when we see the inevitable happen, right? AWS did have an outage earlier this month at the time that we're recording this. These things do happen.
Can you talk a little bit more about what else Kalin brings to the table when building onto these solutions, your personal services that you tack on? Because it's not just being able to get you to AWS faster. Kalin is able to provide more services than that. So talk to me about those services, how else you're able to build on to it, what you bring to the table, and maybe even your favorite aspect of whether it be Kalin, Accelerate or generally another piece of your portfolio. >> The service portfolio is definitely all the way through.
So once you're on the cloud, a lot of Kalin accelerate is focused on getting onto the cloud and getting onto the cloud in the right posture. So uh you hit on disaster recovery as a part of that. There's kind of two flavors of how you look at disaster recovery strategy. First one is more I've just done a migration and I have my application infrastructure as it sits today and I need to make sure that that is resilient to you know a regional outage or the failure of some of the servers within a particular application workload and that's your more traditional disaster recovery approach.
We have just recently uh so um it's in the news Kalin acquired truck 10. A lot of the reason for doing that was to combine that service so that we have a migrate and run or modernize and run service offering with the managed services capabilities that the TRE 10 team is bringing into the fold. So that option allows us to both build the disaster recovery strategy and also be the ones that are on the hook for executing on that should something go wrong down the line. for modernization scenarios.
If you're moving on to a managed service, so you're adopting, you know, Aurora or the AWS relational database service, it becomes a little bit easier because they're responsible for a lot more of the uptime of those services. So if you were to go down on that database and one of the instances dies, well, it already has three and the failover is transparent to you. If you were to think about multi-reion, they have that baked in as a feature of the managed service.
So you simply can turn on the multi-reion replication for that database and ensure that you're up and running within you know the right RTO B recovery time objective. So basically how long you're down into another region and then things like as you move from the application modernization of something that's really legacy monolith running on one server because it's only been architected so that the database and the application are literally sitting on the same physical machine to something that's microservices oriented and runs on Lambda.
Well then downtime isn't really your problem at all. That's an AWS issue. And as long as you're able to point to multiple, you know, API gateways in two different regions, the rest of the service scaling and everything else will be handled for you. So that's just the DR side of things. I think the really interesting and the fun part for us is that we do a lot of platform development and then that platform development in service of product innovation.
So we do a lot of work uh you know we had won the generative AI solution partner of the year for some of the work we did building generative AI solutions for our customers. So a couple of really interesting areas there. We've been doing a significant amount of work in video understanding AI. So partnered with companies like 12 Labs and then Amazon and their Nova models having that multimodal capability to understand the details of what's happening in video.
Uh, you know, we've worked with providers of footage for the media and entertainment industry to make all of their footage searchable, whether that's, you know, looking for the right kind of animal or you're looking for the right kind of news archive or the right sports moment, making it so that you have all of the right tools to be able to discover that. We also have a lot more event driven ones where something's happening, you know, during a game alerts are going off because some particular thing happened and that's just based on the processing of the underlying video and audio content and not really anything rules driven.
It's about the AI and its understanding of that underlying capability. Now, in order to really do that, you have to be modernized and your core infrastructure up and running and taking advantage of cloudnative architecture. Otherwise, those components simply aren't going to fit with the rest of your business and your ability to serve that back to customers. So, in many cases, we were working on both the modernization of the core infrastructure and the new product innovation kind of with the same customer. >> I'm curious to know, maybe this is a silly question, but just based on the last 3 years, you even talked about the modernization that customers need right now because of what they're trying to do with AI.
I'd generally like to know for you how has artificial intelligence sort of impacted your customer base and in in turn impacted Kent over the last three fourish years because we've seen such an uptick in use of it and I'm sure many people now are just trying to recalibrate their entire business model to be able to work around it more which obviously takes a massive amount of of technical investment. What have you seen? Yeah. So it really breaks down into three categories from what we've seen kind of in today's world and and this is really mirrored Kalin's journey to what we're seeing at many of our customers, right?
We've gone through a lot of this transformation ourselves. So there's AI that you want to build into your products and services that often times is the first place people think about this when the you know the chat GPT moment happened. That's the first place people thought of is okay great. I'm going to use this. I'm going to improve. I'm going to put chat bots into my app and they're going to help customers access it better. We are working with customers um just rec released a case study with a company called Insightly owned by a company called Unbounce and they have a CRM product had great APIs maybe not the best UI in the past.
We rebuilt in from prototype into production in like 90 days and a gentic chat interface that allowed all of that CRM functionality. So I just go in and say, "Hey, I have an upcoming meeting with this customer. What are the five things I need to know?" and it'll give me by, you know, by scanning the CRM the insights I need or if I need to take some actions, you know, send emails to the last three customers. We talked about this with our new marketing.
Um, it'll take those actions on behalf of the customer as well. So, that aspect of how do you actually change the product and services in Kalin's sense, that's the Kalin accelerate >> launch there, right? We are now delivering those services using AI as a core part of that. Then there's AI for business capability development or business process improvement. And so that's really how do you take an agentic interface to the software and the things that you've already built to run your business.
Most companies these days have invested in a lot of SAS software. Most of that software has you know API capabilities with the advent of things like the model context protocol or you know more recently some of the things around clawed skills and chatbt equivalents of that you have the ability to have just your regular you know whatever AI you use on your day-to-day interact with your business capabilities whether those are your CRM or your customuilt applications or you know in some cases even your like ERP software in order to help just day-to-day workers do their jobs more efficiently, reduce the amount of time that they spend on manual tasks that are just wrote copying this to that or synthesizing this for the next step in the process.
So we work with a lot of c companies on that. In our case, that's, you know, we help from the very first time we're talking to a customer, transcribing that information, ensuring that the full context is available, matching that back to some of the the solutions that we know like we have a a best practice way to deliver all the way through to putting the right proposal together and so really streamlining the operations of the business and then you know moving forward from there into staffing the right teams and helping those teams to execute.
Final one there is AI for engineering or software development life cycle. We hit on that one a little bit earlier. So this is really just the advent of the number of tools kind of starting with GitHub copilot really starting the movement but then you know now is evolved to a plethora of different tools through cursor and now tools like claude code from anthropic and others uh you know kro that are taking a much wider array of the software development life cycle and accelerating it using AI.
The ability to scale that up is still something that requires some, you know, some special sauce, if you will. The right techniques built on top of these tools to enable teams of, you know, more than 15 or 20 people to still take advantage of AI acceleration. Really baking in a lot of what I talked about in KA accelerate of core principles like thinking about testing up front and then how do you layer that back into the way that you use those tools.
So those three pillars are really where we see generative AI strategy of 2025 going into 2026 evolving. I'm guessing those will stay relatively stable over time. Although you're seeing blurring especially on those last two are pretty similar. Right. Right. >> AI for accelerating typical business versus engineering um is more just in the level of technical sophistication of the people working with those tools. and you're seeing things that are developed for software starting to move into the core marketing and HR and everybody else's toolkit with cloud skills as an example. >> I'm also curious to know, we talked a lot about how in this case we're really trying to save those businesses time and money, get them out of that technical debt, get them out from under it.
Where would you suggest that they take that saved time and that saved money and and invest it back into? What what can they invest it into to perhaps maybe expand on their services that they gained from Kalin or maybe to avoid getting themselves underneath new technical debt in the future because technology is always accelerating forward. Where can they take that investment and put it back into? Yeah, I think the savings that you're, you know, provided via some of these earlier modernization steps, there's a progression, I guess is the right word for it.
As you move from something like a data center migration, now you're on the cloud. You have some of the, you know, baseline best practices in place. You set up some of those modernization cost optimization steps as a next phase. you probably, you know, in most cases in moving on to the cloud, AWS will offset the cost there against the future revenue that you're going to be driving through innovation on their platform. You then take a second phase where you're really starting to do more pure payown, right?
You're you're moving on to open source platforms. You're moving to a slightly newer architecture and then that rolls forward to money that's freed up for innovation. So during that whole time, that's where I mentioned that enablement, being able to spend the time while you're modernizing, helping to drive your team to develop the new processes you're going to be using to run, whether it's the business and some of the process improvement that you're going to be able to drive via the new software or it's really just the technology organization to start. many of our clients are there and then they first phase of innovation is really how do we start to use AI to tackle the process debt that's been built up over time in manual processes and from there you can really go forward to getting the innovation in your core product offering.
Now, that's for the more triedand-true, slowmoving, later adopting organizations that are really reaping some of the benefits of having waited and not spent $20 million to do what they can do for $2 or $3 million today in order to then get them back to that footprint where they can compete on innovation versus simply just being a, you know, larger player with the right distribution, the right market positioning that they've used to stay in leadership positions up until this point.
Some really good advice right there. Well, as you know, we're staring down the barrel of 2026 already. It is rapidly approaching. So, what does that mean for Kalin? What's next for you? And especially what's next for Kalin Accelerate as you continue to build out this this offering. >> The Kalin Accelerate portfolio is really going to continue to expand through 2026 to just about anything where you can drive that test-driven approach that I talked about before.
So, one of the key areas there is data platforms and analytics. A lot of the data you have in your applications today gets used for building reporting, building downstream analysis. And that data with AI on top of it is really where people are trying to go. That like insights that you can derive from understanding what's actually happening. But that requires the data to be structured a little differently than what your day-to-day application that's serving your customers or your business people looks like.
So that's one of the next areas we're definitely going to focus on is how do we do the data transformation work in an automated way and that would include not just you know your traditional like data is sitting in a database in tables rows and columns but also a lot more of your you know semi-structured or even thinking about things you're pulling out from documents or videos like I was talking about before as part of that overall equation. So if you know what answers you're getting to XYZ questions today, we can ensure that you're getting the same answers to those questions tomorrow, but under a platform that will allow you to ask a much wider array of questions much more efficiently with better quality in terms of trustworthiness of the answers.
Other areas where that makes sense is many more forms of the application modernization equation. So there's a lot of different decisions that people make. Um AWS has the seven Rs of migration. So anything from the pure lift and shift migration that we tackle right now with Kalin accelerate for cloud migrations through the complete rewrite of the application for uh cloudnative principles that we have with the accelerate for application modernization right now.
But underneath of that there's many different ways you may want to slice and dice your technology footprint. And so we're going to continue to expand to have direct acceleration for more of those over time. And that'll allow customers the right flexibility to fit exactly what they need for their strategic direction while still getting those same kind of, you know, multiple X benefits in terms of their speed to get there. Then more broadly, I do think that the evolution of the tools that are supporting software development today are going to get to the point where they kind of stack.
So you see right now you you started out with like individual person typing with a chatbot then baked into the IDE and they're you know again still mainly at the individual then with some more of the recent CLI tools you're getting acceleration across more of the entire SDLC but still generally within the scope of one developer. We see the patterns now where that's scaling up to teams of people all tackling shared problems and each focused on their own little aspect of it.
But you can see AI then starting to take on more of those tasks of oversight and you know one person equals the whole team now. And so then as you scale that up then the actual team members focused on wider and wider apertures of what could get built. And one theory under this, and this is more Ryan's theory, not necessarily a full endorsed Kalin theory, is that more and more companies will be willing to build their own software rather than go purchase software so that they're really focused on those aspects that are tailored towards their business and how they want to differentiate.
So I I I do think that acceleration for the broader application development portfolio is also one of the core things and that is something we are focused on at Kalin is how do you scale up those development practices going into the next year. It'll be a wild ride. It's one of those like time horizons are so short now. If you look back just 9 10 months ago at where we were at the beginning of this year to where we are now and the level of acceleration happening across so many different areas in parallel.
You project that forward a little bit. It is going to be a very very fun year next year and we are really excited for I mean that's one of the fun things about Kalin is because of the pace of growth, because of the ethos of the firm and because of the pace of technology. It's a place that you come to embrace that change that you know nothing is going to be the same 6 months from now as it is today and we're still building the foundations at each layer so that we can keep taking that next leg of the journey up but doing it in a way that we are keeping our eyes on the horizon and getting out ahead of the next couple of steps.
So, what you're saying is we need to have another conversation at the end of Q2 of 2026. >> Yeah. And it'll be really interesting to look back at this one and be like, "Oh, wow." Yeah. Like that was a whole different era. >> Look how far we've come. Yeah. Well, Brian, it's been such a treat to talk to you and get to know Kalin and more about Kalin Accelerate. But if I'm a provider and I'm listening to this and saying, "This is definitely something I'd like to learn more about." Where can I go to reach out to maybe you or reach out to your team at Kalin and learn more?
Yeah, you can find me on LinkedIn. Just, you know, Ryan Gross and Kaylin, I'm sure, will find that uh the company. Just go to kalin.com. You can find, you know, a form there to submit. You can also email us at sales@calin.com if you have a specific need that you want to discuss. But, uh, you can also always find us if you're at any AWS summit or conference. You can look for whether it's a Kalin booth or a few kalens to be walking around there for a live conversation. >> Kalens, I like that a lot.
Well, thank you very much. I appreciate your time today, Ryan. And best of luck into next year. >> Thank you. >> Thanks so much to Ryan for joining me today. And thank you for watching or listening. You can check out every episode of Channel Insider Partner POV on channelinsider.com or watch us on YouTube at youtube.com/ channelinsider_news and trends. You can also listen to us as a podcast wherever you get your podcasts from. Don't forget to like, subscribe, and follow wherever possible so you never miss an episode.
Come connect with us on social media. We're on LinkedIn and X at Channel Insider and me, Katie Bavoso. Once again, I'm your host, Katie Bavoso, and I'll see you next time.
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