Minutes, Not Months: How Wynxx Accelerates Legacy Modernization in Financial Services

In financial services, legacy systems are both an asset and a burden. Built over decades to meet growing demands, these applications underpin business-critical processes but their complexity can make them hard to understand, maintain and evolve.

For one leading global bank, this challenge had become acute. Senior engineers were spending up to four weeks manually creating documentation for a single legacy application: thousands of lines of code analyzed line by line. Modernization projects slowed, compliance reviews became time-intensive and valuable expertise risked being lost as experienced staff moved on.

The bank needed a faster, more reliable way to understand its systems – one that improved accuracy and efficiency without compromising on trust or security.

GFT Technologies introduced Wynxx, the industry’s most flexible generative AI platform for enterprise software delivery.

Wynxx accelerates enterprise software transformation by making generative AI operationally viable across the Software Development Life Cycle (SDLC). Responsible AI by design, model-agnostic, and measured by business outcomes. It integrates seamlessly into enterprise tools and infrastructure, enabling teams to focus on high-value work while accelerating delivery and improving quality.

To prove its value in a controlled, measurable way, the team adopted a three-sprint approach. This agile framework allowed them to set a clear baseline, test Wynxx on real applications and evaluate performance and cost objectively. Each sprint built on the insights of the previous one to demonstrate scalability and accuracy at enterprise level:

• Sprint 0 – Preparation: Defined metrics and prepared the environment
• Sprint 1 – Execution: Tested Wynxx on a “lighthouse” legacy application.
• Sprint 2 – Evaluation: Measured quality, efficiency, and business impact

The lighthouse application selected was a 20-year-old Java system: complex, with multiple upstream and downstream connections.

Using Google Cloud Vertex AI and Gemini 1.5 Pro, Wynxx ingested the source code directly from the bank’s repository and generated structured outputs aligned with the bank’s standards:

• Architecture and data-flow diagrams
• Component-level explanations
• Natural-language functional summaries

What once took weeks was now completed in minutes. The generated documentation was iteratively reviewed and refined through prompt engineering and expert feedback until it met and, in several areas, exceeded the quality benchmarks defined at the start of the project.

After validating the initial use case, the team expanded the experiment to ten additional legacy applications, demonstrating Wynxx’s scalability and consistency.

The efficiencies achieved went far beyond time saved. By removing repetitive documentation work, engineers could focus on development and innovation. Job satisfaction rose, compliance teams reduced audit-prep effort by up to 80 %, and technical knowledge became transparent and shareable across teams.

Following the pilot, Wynxx became a blueprint for enterprise-wide modernization. Its model-agnostic foundation preserves choice and control, allowing integration with existing enterprise toolchains to support:

• Code Quality Analysis: Identifying vulnerabilities and improvement opportunities.

• Automated Test Generation: Accelerating regression and functional testing.

• Architecture Recommendations: Guiding modernization pathways.

• Predictive Maintenance: Detecting anomalies before failures occur.

Wynxx has since evolved from a documentation accelerator into an AI co-pilot for continuous software engineering improvement, accelerating delivery, reducing cost, and ensuring consistency across the SDLC.

For financial institutions operating under tight regulatory and competitive pressure, Wynxx demonstrates that generative AI can deliver measurable, trustworthy results today.

By automating and improving time-intensive development tasks, Wynxx helps organizations understand, maintain, and modernize their systems faster – transforming legacy complexity into a foundation for innovation.