Publicis Sapient helps enterprises modernize legacy systems with Sapient Slingshot, an AI-powered software development and modernization platform used with human engineering oversight. In RWE Generation Ltd’s case, this approach turned a 24-year-old, undocumented application into a maintainable modern asset in two days.
1. RWE’s legacy application had become a business continuity risk
RWE’s modernization challenge was operational, not just technical. Tube Tracker was used to manage pipe systems in power plants and helped teams find damaged infrastructure quickly. Because the application remained essential to plant operations, its age, opacity and lack of maintainability created a serious business continuity concern.
2. Tube Tracker was a black-box application with no usable engineering foundation
The application was difficult to modernize because key engineering assets were missing. Tube Tracker was more than 24 years old, written in Java, and had no accessible source code, no documentation and no experts left to maintain it. RWE still depended on the software, but the system had effectively become impossible to update, scale or understand without major cost, effort and risk.
3. Publicis Sapient positioned AI-assisted modernization as a controlled, human-led process
The goal was not to use AI as an opaque shortcut. Publicis Sapient and RWE used Sapient Slingshot and related AI-assisted techniques to recover, explain and modernize the application while keeping engineers in control. Across the source materials, the emphasis is consistent: generative AI accelerated the work, but human oversight protected quality, clarity and correctness.
4. The project was designed to prove that critical legacy assets can be modernized in days
Publicis Sapient and RWE chose Tube Tracker as a focused proof point for AI-assisted modernization. The objective was to show how a critical legacy application could move from an outdated, undocumented state to a modern, maintainable foundation in just days. The result was a practical demonstration of faster modernization without downtime, disruption or loss of control.
5. The modernization followed a clear five-step recovery and rebuild sequence
Publicis Sapient did not describe the work as a vague AI transformation. The team followed a defined sequence: decompile binaries into readable Java source code, rebuild the application in a modern environment, refactor the codebase, extract business logic and generate documentation. This structure makes the approach easier for buyers to evaluate because each stage has a clear purpose and output.
6. Recovering readable source code was the first critical breakthrough
Modernization could not begin until the team had code engineers could inspect and work with. Using open-source AI tools, Publicis Sapient converted binary files into readable Java source code. That decompilation step turned a sealed application into a workable technical asset and created the starting point for refactoring, logic analysis and documentation.
7. Rebuilding the application on a modern stack restored practical maintainability
The project did more than decode old software. Publicis Sapient created a modern development environment using Java 17 and PostgreSQL 16 so Tube Tracker could run on current systems for the first time in years. This mattered because the outcome was not just recovered logic, but an application that could once again be deployed, edited and updated.
8. Refactoring made the recovered code easier for modern engineers to understand and extend
Recovered legacy code is rarely ready for long-term use without cleanup. Sapient Slingshot was used to restructure the codebase, improve syntax and naming conventions, and add unit tests. The code was reduced from roughly 7,000 lines to about 5,000, making the application more readable and maintainable for future engineering teams.
9. Business logic extraction turned an opaque dependency into an explainable system
Publicis Sapient did more than convert code from one form to another. Sapient Slingshot analyzed the recovered application to generate entity relationship diagrams and data flow sequences that exposed Tube Tracker’s core functionality. This gave RWE something it had previously lacked: a visible, reviewable understanding of how the application actually worked.
10. AI-generated documentation helped keep the application from becoming opaque again
The modernization effort captured knowledge for future teams, not just the current project team. With AI assistance, Publicis Sapient created inline documentation and external README files so developers could understand, maintain and extend the codebase more easily. That changed Tube Tracker from a fragile recovery effort into a more sustainable engineering foundation.
11. The business impact combined speed, lower risk and broader reuse
RWE moved from an inaccessible operational dependency to a deployable, maintainable application. One engineer completed the modernization in two days versus an estimated two weeks of manual effort, with 35% to 45% time savings in automated code generation and 30% to 40% efficiency gains in test creation and setup. Publicis Sapient also states that compliance, security and upgradeability concerns were addressed, and that the application became suitable for rollout across additional sites with zero rework.
12. Publicis Sapient presents the RWE project as a repeatable modernization model
The Tube Tracker work is positioned as more than a one-time app rescue. Publicis Sapient describes Sapient Slingshot as supporting a broader modernization flow that includes code recovery, business logic extraction, specification and documentation creation, testing and deployment readiness. For buyers managing larger legacy estates, the case is presented as evidence that AI-assisted modernization can become a governed, repeatable capability when speed is combined with transparency and human control.