How to Catch Up With Competitors Shipping Faster With AI

If you're running a software product business doing $500K to $10 million in revenue per year, I want you to do some quick math with me. Think about the last 90 days. How many features did your team plan to ship that they didn't ship? Or maybe they shipped late, or maybe they shipped it and things broke in regress. How many experiments did someone on your team pitch? A new onboarding flow, a pricing page test, an upsell or cross-sell trigger, and that got killed because the codebase just can't support it right now. Too much tech debt. It's frail and fragile. How many bugs showed up in your production that burned a week of engineering time, a chunk of customer trust, and a little bit more of your team's morale? Now try to put a dollar sign on that. If you're at $2 million annually in revenue with a 15% monthly churn and your codebase is the reason you can't ship the retention experiments that would cut that churn in half, that is literally $150,000 a year walking out the door. Not in theory. That's actually lost revenue. You can calculate even back of the napkin math. And that's just churn. That's just retention. That's your retention story. That's not even talking about upsells or a conversion story, right? The things that you could test if you had confidence your codebase wouldn't regress with every new experiment you put out the door. You're leaving money on the table, simply put. And it's because your engineering team is probably spending more than half their time on maintenance and firefighting and fire drills instead of growth. Here's the pattern that we've seen every time. You maybe have a live web or mobile app, real users, real revenue. You've got product-market fit, but the codebase is years old, 3, 5, maybe 8 years old. Multiple developers or agencies have touched it. Some of them have left. They've taken the tribal knowledge with them. There are entire modules that nobody on your team fully understands. Maybe you have too many entities in the codebase than even your app needs, and your team calls those parts of the codebase the scary parts that you don't really wanna touch. Shipping anything meaningful probably takes 2 to 3 times longer than it should. And then when you do ship, your engineering team probably has to spend a lot of time firefighting regressions after production. And maybe you don't even have a QA process and your QA is just deploying and praying, which is not a strategy, by the way. Or maybe you track daily active users and monthly active users and maybe some events, but then maybe when your investors or your board asks which customers are most likely to upgrade or respond to a cross-sell or upsell, what journey get some there, you don't have an answer. You just have guesses. And then let's say you're thinking about putting AI in the product or improving your AI or LLM-based product with better personalization or some model that helps you understand lifetime value and minimizing your cost of acquiring customers. And then your engineers look at the code base and they quietly think, I don't know, maybe we can't do that cool stuff with how legacy and how much duct tape there is holding this thing together. So what actually happens? What actually happens, and I've seen it, is you ship fewer experiments. You're making money, but your growth plateaus. Your competitors who started 2 years after you are now shipping faster than you because they don't have your baggage. And so you avoid touching the most critical flows, the ones that affect your conversion story, your acquisition story, your onboarding, your payments, the core loop of the product, because the risk of breaking something is just too high. So those parts of the product that matter most never get better simply because you can't trust that investing more into the tech stack at this point, or the codebase, or the software product at this point, won't just regress more and break things and lose even more customer trust. Maybe you can't even trust your own data because you have so many different data sources and you're trying to be data-driven because you know that's what industry standard is and that's what probably all your competitors are doing, but every roadmap decision is just kind of a bet based on intuition and gut feel. Instead of real evidence. And these bets can get quite expensive. And you don't know who exactly your customer is. You know that a certain 20% of them might drive 80% of the revenue or most of the revenue, but you don't know the causal structures or the why that led to their how of getting here and spending with you. And you don't know how to convert more of the bottom 80% of customers into more behaviors of your top 20% of customers. Meanwhile, maybe your investors or your board, or maybe you and your co-founders are talking about how are we going to get AI-native architecture? How are we going to build a data moat that compounds our enterprise value, maybe for a future exit, or at least giving us the ability to go into a raise and have leverage because our enterprise value is so high and we've captured a virtuous data moat and cycle. Around an economically valuable problem. I've been in the software industry for a long time. I was Chief Product Architect at Dayforce. We did about $1.8 billion in revenue and we were a public company when I was there. I used to lead product architecture at Workday, helped them grow from $2 to $4.5 billion during my time there with patented product analytics work, obsessing over how do we actually increase LTV of customers while reducing CAC. And now I'm running my own startup. And I've seen this pattern at companies doing $500,000 to companies doing $500 million. The math is the same. If your codebase is slowing you down, it is slowing the amount of growth experiments and the amount of learnings, and therefore the throughput and efficacy of your feedback loop back to your team to acquire customers and to capitalize on them with your product or service. And that's compressing your LTV. Let's be honest, it's capping your ship velocity. You cannot ship value into market and capture and capitalize on that value unless you have a high throughput of quality experiments. And if that's capped by your codebase being legacy, full of tech debt, full of duct tape, then every single growth bet, every single lever you pull or experiment you run or hypotheses you have is only going to get more expensive. So here's what we've built. We have an AI-native software modernization sprint. It's 4 to 6 weeks depending on the code base, done for you. And it's an engagement where my team comes in, we stabilize your app, we understand your ideal customer profile, the jobs to be done that they have, and we modernize the parts that matter most. We instrument everything with modern analytics so your product can finally see the customer journeys that drive LTV. This is not a 12, 24-month rewrite. That's a thing of the past. It's not an audit that gives you a PDF and says, "Good luck, go hope," 'cause that's not a real strategy. And this is not a consulting engagement where someone tells you what to do and then leaves. That's not what we do. We own the outcome, we'll do the work in 4 to 6 weeks, fixed scope, fixed fee. Depending on the damage that we see, it's starting at $50,000. And here's specifically what you get. Number 1, a stabilized, faster application. We fix the critical bugs and the regressions that are burning your team's time. We address the performance bottlenecks that are hurting your user experience, and we clean up the legacy areas that everybody's too afraid to touch. And if you don't have best practices in your deployments and your pipeline, we'll get you those foundations in place as well, using AI-native development patterns that let us move through complex codebases that are brownfield, not just greenfield, 5 to 10 to 20x times faster than a traditional refactor. By itself, if this saves your team 15 hours a week of firefighting at a blended engineering cost of $100 an hour, that's $78,000 a year in just recovered engineering capacity just from the stabilization phase. Not even to mention how much more velocity you get for growth experiments. The second thing you get: modern development rails. We've implemented CI/CD pipelines like the back of our hands with AI-native development workflows. We'll do the same for you. We'll set up a proper staging environment with representative test data if you don't already have that, and we'll design a QA architecture with end-to-end testing patterns around your core user journeys and workflows that your team can actually follow. Beyond our time with you. So regressions stop making it to production because you got to stop the bleeding. If you're currently shipping once a week because your deploys are scary and we get you to daily deploys with confidence, well, then you've just 5x'd your experiment velocity. Just, just think about how that might affect your growth rate over the next 12 months. Third thing you get, number 3, product analytics that actually answer the questions you have about your customers. We'll instrument your product with clear events and user journeys so that you can finally answer Who are your best customers? What do they do that's different than the worst customers? Why do they stay? How do I get the other customers to ascend to where they are? How do they move through the product? Where are the drop-off points I didn't even know existed? We'll help you answer those questions and more. Most software companies at your stage are making million-dollar roadmap decisions based on data. You're making them on gut feel. After this, you're going to be data-driven as well. And you'll form hypotheses and experiments based on evidence to validate and compound context towards an AI-native world. That's not a nice-to-have, that is a must-have to compete in this landscape. And that's the difference between a roadmap to compounds with a codebase that compounds and a roadmap and a codebase that just wander off into legacy tech debt land again. The last thing we'll give you, number 4, is a product growth roadmap. At the end of the sprint, you don't just get cleaner code, You get a prioritized roadmap with our suggestions of where we see the biggest unlocks and level-ups for your business that are likely to move the needle most, grounded in evidence. Your team's gonna walk away with complete documentation, a full knowledge transfer, and a clear path forward. Not a mystery, but a real plan. Now, let me tell you about one of the customers that came to us a little while ago. They had a codebase that was years old. Multiple developers had touched it, key people had left, and there were core modules nobody fully understood. Their database probably had 27 entities too many. Their data model was too complex. Any new feature meant days of QA and then testing and crossed fingers at launch. They had experiments they knew would work, but they just couldn't ship them because the code fought them at every turn. We went in, we learned about their customers, their ideal customer profiles, the difference between their top 20% and their bottom 80%. We interviewed their users, we built persona cards of them, we really understood the functional side, and we modernized the codebase in parallel. And the ugliest parts that were band-aided and duct-taped together, we set up with proper analytics and development rails and completely modernized the UI and UX of the application. Within 2 months, they unlocked more than $2,500 $2,500 in daily average revenue. Annualized, that's projected to be over $900,000 on a $50,000 engagement. This isn't because we built some magic feature, it's because the app could finally support the experiments they've been sitting on for months. That's an 18x projected return, and it keeps compounding now because every future experiment ships faster too. That's compounded leverage. So here's how the engagement would actually work. Phase 1 is we have a discovery call, 30 to 60 minutes depending on your use case, how gnarly it is. You show us your GitHub repo, your architecture, the application, the current state. We'll identify from this the hottest areas, the P0 and P1 issues, the performance bottlenecks, the UI/UX issues, the breaking customer trust kind of issues. And the things that are costing you the most right now. We'll assess whether or not we're the right team to make this happen for you, and you get a plan for what we recommend based on what we see on how to get the most leverage out of any investment into your codebase. Specific deliverables, concrete plan, not a vague SOW, something tactical. And by the end of the call, we get a clear go/no-go for you. If you're not the right fit, we tell you. We only want to go and do good work that's transformative and makes our customers money. So if your code base isn't actually a bottleneck, we're gonna tell you that too and be honest with you. That's just a core value of ours. And we'll schedule this call within, we will schedule this call within 72 hours of you reaching out. Phase 2, if we both align on, hey, this is a fit both ways, we're gonna execute. Expect weekly check-ins so you always know what's shipped, what's next, what blockers there are. No surprises, no scope creep, no more, we found more problems than it'll take 6 more months and this much more money. None of that. We keep it simple. Phase 3 is you get the modernized codebase, you get the documentation, the knowledge transfer, and the growth roadmap we promise. You're no longer dependent on us, and you will be faster because of us. You'll get like a supercharge to your batteries. So a few things that, that people ask now that they understand kind of phases is, what if our codebase is, is really messy? We say, good, that's actually where the leverage is. We specialize in complex messy legacy code. The worse it is, the more room there is for improvement. We've worked with codebases that had 5 to 10 years of accumulated decisions from developers who are long gone. That is absolutely our sweet spot. We know what we're doing there. Another question we get: we already have engineers, why can't they do this? Especially with AI, can AI do this? They probably can, but then they haven't, and there's probably a reason. They're probably busy fighting your fires, and they're probably also busy shipping features. Asking them to do a focused modernize sprint is like asking your pilot to also fix the engine while also flying the plane while also serving the customers. We're the best in the game at AI native development, and we've worked with massive multi-billion dollar companies running AI workshops for executives and developers in the hundreds and thousands. Every single day we are trawling Twitter and GitHub and YouTube for the latest knowledge the signal in the noise for advantages in our AI-native workflows. In working with us, your engineers will level up, will level up their development workflows too. We come in really focused. We do the work that your team knows needs to happen but cannot prioritize, and we unlock your leverage. Then we hand everything back with documentation so that they can move faster on a better foundation for growth. Another question we get is, we don't have a CTO or deep technical leader. How's this gonna work? That's exactly who we built this for. Founder-led teams where perhaps you have more marketing, product growth, or sales expertise or capabilities at the founder level, but you don't necessarily want to quarterback a risky refactor without an experienced technical partner. That's us. And we have a great track record for doing so. So here's the decision. It's up to you. You can keep doing what you're doing, keep finding your codebase, keep shipping slower than your competitors, especially in the age of AI, and keep guessing at which customers drive LTV and what might minimize their CAC. Keep telling your board or your investors or your founding team, we'll get to the modernization eventually. Or you could fix all of that in 4 to 6 weeks and spend the rest of the year growing on a foundation that actually supports growth. There's not gonna be any sort of long-term commitment, no vague strategy chats. I'm not into wasting time. In our discovery call, we'll look at your repo and your app and tell you plainly, are you a fit for what we do? What would we tackle first? And how quickly you would feel difference in shipping speed and stability. If it's a fit, we could pick a start date. If it's not, you walk away with a clearer view and strategy and tactical advice for your bottlenecks in your legacy app for free. Either way, your codebase stops being a mystery and starts becoming the asset you know it should have been all along. Book the call. Let's look at your code. And what are some of the draft names? Book the call. Let's look at your code.