Case study

From 25 weeks to 5:
How AI transforms legacy airline software with confidence and speed

In mission-critical environments, the biggest risk isn't moving too fast - it's moving too linearly.

Starting point

A global airline software provider faced the classic enterprise paradox: their most mission-critical system had become their greatest vulnerability. For years, their Oracle PL/SQL-based monolith had been the reliable backbone supporting millions of airline operations worldwide. But success had created a trap. The very stability that made the system trusted had made it rigid—unable to adapt to today's demands for real-time responsiveness and cloud-scale growth.The real problem wasn't technical debt. It was strategic paralysis—the fear of taking action when modernization seemed too time-consuming and risky.

What if your developers focused on strategy while AI handled the heavy lifting?

Most consultancies would have proposed a traditional "lift and shift" migration—essentially recreating the same constraints in a new environment. Instead, we identified the precise leverage point: the critical business logic trapped inside the database layer. Our proprietary G.Tx platform became the catalyst for non-linear transformation.

Approach

The initial focus was twofold:

Developing a transformation strategy

We deployed AI-powered analysis to map the optimal modernization path, identifying strategic intervention points rather than traditional risk mitigation. This assessment aligned transformation priorities with business impact, creating a data-driven foundation for decision-making across technical and executive stakeholders.

Launching the first modernization project

We strategically targeted the core business logic trapped within the PL/SQL database layer, using G.Tx platform to decouple mission-critical procedures and transform them into Java-based microservices. This preserved decades of business logic while establishing cloud-native scalability.

We didn't just modernize code.  The breakthrough was in the execution speed.

Outcome

We didn't just modernize code. The breakthrough was in the execution speed. AI-accelerated code generation compressed traditional development cycles while maintaining the precision required for mission-critical operations.

5 weeks of work instead of 6 months

AI-accelerated transformation that compressed typical timelines by 5x.

70% automated code generation

Human expertise focused on strategic decisions, not mechanical translation.

33% of code lines remained unchanged from initial AI generation

Strategic discoveries that transformed not just code, but organizational capability.

The real breakthrough wasn't in the numbers. We transformed clients' relationships with change itself. What started as a modernization project became a template for continuous transformation - proving that mission-critical doesn't have to mean change-resistant.

AI reconstructed lost institutional memory

GT.x didn't just translate code—it reconstructed decision-making patterns and business rules that would have taken months of archaeology to rediscover.

Initial success unlocked scalable transformation methodology

The first batch of transformed code established proven procedures that accelerated each subsequent phase across the entire legacy system.

Exponential ROI trajectory

AI-generated microservices delivered cleaner architecture that enabled faster response to business changes, transforming software from constraint into enabler.