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Chapter 1: What is the main focus of Berk Yilmaz's work at Noah Labs?
Welcome to Corozant Technologies, home of the Digital Executive Podcast. Do you work in emerging tech, working on something innovative, maybe an entrepreneur? Apply to be a guest at www.corozant.com forward slash brand. Welcome to the Digital Executive. Today's guest is Birk Ilmaz.
Birk Ilmaz is the co-founder and chief technology officer of NOAA Labs, where he's building Sentinel, an AI-native IDE designed for secure, regulated, air-gapped, and mission-critical software environments. His work sits at the intersection of agentic software engineering, legacy code modernization, AI systems, formal verification, compliance tooling, and aerospace-grade software reliability.
At NOAA Labs, Birk leads product and engineering architecture for a secure development platform that includes AI-assisted migration pipelines, deterministic audit trails, agent runtimes, compliance automation, formal verification integrations, and cross-platform desktop deployment. Well, good afternoon, Bert.
Chapter 2: How does Berk Yilmaz's background influence his approach to software development?
Welcome to the show. Thank you. Glad to be here. Brian? Absolutely, my friend. I appreciate it. Really do. I know you are in the San Jose, California area. I'm in Kansas City. Appreciate you up in a couple of time zones and several calendars to get here today.
Chapter 3: What challenges do legacy code modernization projects face?
That's just so special to me when people take the time out of their day to do this. So thank you. And Burke, if you could, let's jump into your first question. Your path runs from dual undergraduate degrees at New Jersey Institute of Technology and Istanbul Technology University through electrical engineering at Columbia and into NASA supported space research before co-founding NOAA Labs.
What through line connects those experiences and led you to be building Sentinel?
Of course. And first of all, thank you. I'm glad to be here, Brian. And, you know, looking back, the thread was actually pretty simple. I kept up ending in places where software failure wasn't an option.
Chapter 4: How do AI-assisted migration pipelines enhance legacy code modernization?
And the tooling actually never matched the stakes. For example, at NGIT and Istanbul Technical University, I was splitting time between electrical engineering and computer sciences, which sounds a little scattering, but it gave me something that is important. I learned to think about the software the way hardware engineers do. You don't ship circuit board and hope it actually works.
You verify it, you prove it. And that mind stack actually stuck with me through my whole university life. And Columbia actually depended that. And then NASA research really finalized this pushback. And when you're working on systems that might end up in space where you can just push the hotfixes, you learn the fast that the gap between that it compiles and it's actually correct.
Chapter 5: What role does compliance play in mission-critical software development?
It's just enormous. And most developer tools just don't care about this gap. And that's, that's what led to Sentinel. At NOAA Labs, we're building the development environment for engineers who work on software where the consequences of getting wrong is actually real.
Defense systems, critical infrastructures, EuroSpace, the kind of code where you need a provable audit trial and just not a green check mark in CI. So the through the line is I kept seeing high stakes software build with low stake tools and Sentinel is the tool that I wished existed at every step along the way.
That's amazing. And I appreciate that. The back story here, essentially, and, you know, looking at your resume, it looks like a lot.
Chapter 6: How does energy efficiency factor into AI development for sensitive environments?
You had to juggle both electrical engineering, computer science and school. But that actually brought everything together for you, which I think is awesome. And it has definitely gave you that line or that path to what you're building today at NOAA Labs. So I really, really appreciate that. Berth, NOAA Labs' legacy code modernization is central to NOAA Labs' pitch to defense and federal teams.
What makes modernization decades old, regulated code bases so difficult, and how do AI-assisted migration pipelines change that equation?
Great question. So let me paint the picture. There's an enormous amount of critical software out there. It's in defense, aerospace, in the financial tech sector that was written in C, in COBOL, in Fortran decades ago.
Chapter 7: What is the significance of deterministic audit trails in software verification?
And it actually works. These decades of operational use have shaken out the bugs that actually matters. But the engineers who wrote it in many cases retired and engineers inheriting the code often don't know the source language well enough to verify that rewrite is the correct thing. So that's the first problem. A generational knowledge gap on both sides of the migration.
But the harder problem is, for these customers, a migration isn't finished when the new code compiles. It's finished when an external reviewer can be pursued that the new code is equivalent. That means that a paper trial, every prompt, every model response, every input, every proof certificate, if you can produce that trial, the migration doesn't count legally and no matter how good the code is.
So what Sentinel does is we pair an AI translation engine with formal verifications and a deterministic audit bundle.
Chapter 8: How will AI-native environments transform regulated software engineering in the future?
We break the code base into translation units and translate each one. To verify it through the differential testing, we literally put the old program and the new program against the same inputs and compare the outputs. When very applicable, we dispatch formal obligations to check mathematical equivalence.
And every large language model call is cached deterministically, so any run can be replayed byte by byte. So the AI changes the speed. We know that. But the verification and the audit trial change whether the results are actually usable for dysregulated customers. And that's the equation actually we're solving at Sentinel.
That's awesome. Appreciate that. And just so you know, I started my career as a developer, but looking back, I had a lot of exposure to Fortran, Cobalt, C, mostly C. But, you know, that stuff's been around since the 60s, 70s, 80s. And it's hard to believe that's still being used, especially in the critical areas of like Department of Defense. But I like what you're all doing.
You're obviously translating those old code bases where it can be verified byte by byte to make sure that the testing and the translation and then the testing that you're doing is 100 percent accurate, which is so important in these critical areas. So, again, thank you.
And Bert, you've written about moving away from the assumption that AI must process everything all the time, pointing toward sparsity and energy efficiency for edge and space applications. How does that philosophy influence your approach to building developer tooling?
Yeah, this is something I think about a lot, and it directly shapes how we architect the Sentinel. The dominant approach was, in AI tooling right now, the biggest available model sends everything all the time. And that works fine if you're in the cloud, and latency is nice to have.
It completely falls apart when your customer is operating an SICP or an air gap facility where the model runs on a local hardware and every watt matters now. So our approach is you don't use AI where we don't need to. Our compliance checking, for example, is entirely deterministic and no large language model is involved. We map the findings to control frameworks using a rule engine.
not a language model, because a regulator doesn't want to hear your compliance assessments was probably right, right? It needs to be the same answer every time. So for this, we have to get a deterministic approach. On the model side, you're designing for right sizing, using smaller, faster models for orchestration and routing, and larger model only when the task actually demands it.
The goal is that the system gets more efficient as it matures and not less. The more you use it, more of the pipeline hits the cache, the fewer expensive models calls you need. And efficiency isn't a performance optimization for us. It's a deployment requirement. If it doesn't run on custom hardware behind the locked door, it doesn't ship, basically.
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