Learn how vFunction helped a leading global consumer goods manufacturer modernize its architecture and future-proof a mission-critical app quickly and cost-effectively.
Overview
A leading, privately held global consumer goods manufacturer faced a challenge all too common in enterprises. A 20-year-old application built to orchestrate the company’s entire consumer goods production process had become mission critical. It powered everything from production to logistics, but built on outdated technologies, had grown hugely complex—lacking scalability, visibility, and engineering velocity. Realizing it must modernize the architecture of this core technology, thecompany considered a range of strategies that included off-the-shelf and DIY options, but realized it needed an experienced partner with a solid (and automated) modernization solution that uses AI. Enter: vFunction.
The app equivalent of a ‘Model T’
Developed by contractors and tightly coupled across domains, the app had gone from savior to ponderous problem. The contractors had built the app more than 20 years before on now-outdated technologies like Java 6 and Java Data Objects (JDO). It had more than 615,000 lines of code and more than 5,000 classes. Over the years, the architecture had become deeply entangled (spaghetti code, to be precise) and poorly documented. None of the current engineers had been with the company when the contractors built the app, which now had limited scalability and velocity, and was increasingly difficult to modularize.
The company’s engineers felt a deep investment in improving the situation, but architectural modernization required more than commitment. It required visibility into architectural complexity, a strategy for reducing technical debt, and a scalable path forward. Over a period of years, the team explored commercial off-the-shelf replacements, internal refactoring, and rewrites with system integrator partners.
One rebuild proposal came with an $18 million price tag while other options stalled under the weight of outdated code and unclear architecture. The company wanted to move toward a cloud-based infrastructure. Of huge significance, this wasn’t a faulty but minor legacy component of a larger system, this was a core application of the business which meant getting it wrong was not an option.
For vFunction, this is not an isolated problem. Companies are sitting on a growing backlog of complex, monolithic applications that are difficult, risky, and expensive to refactor. For many organizations, these systems have become roadblocks to innovation and business growth. Yet most modernization efforts focus on code upgrades, security vulnerabilities or lift-and-shift migrations without addressing the root of the problem: the application’s architecture.
Static traditional documentation that quickly becomes outdated or service catalogues and APMs that don’t show true architectural flows create a lack of architectural visibility. Without architectural insight it’s hard to know where to start, what to prioritize, or even how to measure progress. Additionally, teams often find themselves blocked by challenges such as technical debt, resiliency, and/or downtime issues. As a result, modernizing legacy systems can be manual, expensive, and high risk.
An iterative pathway from outdated to future-proof
The turning point for the company came when Amazon Web Services (AWS) introduced them to vFunction. In the first session, vFunction’s runtime-based architectural analysis validated what the company had long suspected: circular dependencies, outdated libraries, and undefined boundaries were holding them back. Data-driven visibility then led to a clear, prioritized strategy for reducing complexity and modularizing the app. Working with vFunction, the internal team targeted a high-value backend business domain focused on shipping and bills of lading. They removed dead code, untangled dependencies, and upgraded the service from Java 6 to Java 21 with a modern Spring Boot framework to replace the legacy JDO-based data layer. That early success became a blueprint for the rest of the system.
Again, this is routine for vFunction. While other tools generate code for greenfield projects, vFunction’s architectural modernization cuts through the complexity of brownfield applications. It combines static and dynamic analysis with data science to uncover architectural technical debt, provide relevant context to code assistants for automated refactoring, and breaks monoliths into scalable, cloud-ready services for faster service transformation. vFunction connects its architectural modernization engine to modern developer environments, enabling teams to query architectural issues, generate GenAI prompts, and trigger remediation directly from the command line. vFunction bridges the gap between architects and developers, making architectural transformation fast, actionable, and fully embedded in a company’s software development life cycle.
Underscoring these advances is the partnership between vFunction and AWS. As organizations move to AWS, they face the dual challenge of refactoring legacy systems and managing growing architectural complexity. vFunction complements AWS by identifying what to modernize, where to refactor to accelerate cloud migration, and how to enforce architectural integrity over time to reduce technical debt. Through this partnership, AWS also helps expand vFunction’s global reach, connecting the solution with more customers and partners that are navigating legacy modernization and cloud infrastructure modernization.
“We can deliver unmatched solutions that drive faster, more reliable architectural modernization, reduce costs, and enable enterprises to achieve greater agility and long-term business success.”
Moti Rafalin, Co-Founder and CEO, vFunction
By modernizing and continuously optimizing applications, vFunction with AWS helps companies fully leverage AWS services such as Amazon Elastic Container Service, AWS Lambda, and Amazon Elastic Kubernetes Service (EKS). And as cloud-native development scales, vFunction provides the visibility and control needed to manage growing architectural complexity via continuous modernization for more resilient and scalable applications without breaking the budget.
vFunction: Setting the stage for transformation at scale
With the first services modernized, the roadmap for the consumer goods manufacturer is clear. The DevOps team will continue extracting and modernizing services iteratively to keep the business running while transforming the architecture from the inside out. The company is also exploring Amazon Q Developer to accelerate progress even further.
By combining vFunction’s precise, architectural prompts with Amazon Q Developer remediation capabilities, the team is laying the groundwork for a self-healing architecture to enable faster modernizations while maintaining expert oversight and control.
It is the kind of foundational work that sets the stage for long-term agility and resilience leading to transformation at scale, not just to fix but to future-proof.
