Case study

    AI‑first approach turns a complex legacy application into a clear modernization blueprint

    Modernizing long-running business-critical systems is rarely a question of whether it should be done, but how to do it without years of uncertainty and disruption. When the true scope of a legacy system is unclear, modernization programs can become slow, expensive, and difficult to plan. We have adopted an AI-first approach, and this helped create a clear modernization blueprint in just a matter of weeks.

    Service

    Data and AI

    Industry

    Manufacturing Industry

    A global industrial manufacturer relied on a legacy WebForms application to configure product variants and generate technical documentation for customers. Over time, the system became increasingly complex, dependent on outdated libraries, and constrained by infrastructure that limited development. The question was not whether modern architecture using .NET and Angular was feasible - it was. The concern was the scale of work that traditional modernization would require, and the need to understand in concrete terms what that effort would look like.

    We conducted a brief, structured proof of concept (PoC) using an AI-first, spec-driven workflow. This approach combined human expertise with coordinated AI agents to analyze the legacy codebase, extract functional intent, and build a working prototype. The PoC validated our AI‑First Delivery approach, produced comprehensive documentation, and delivered a fully working implementation - not just example snippets but a substantial, runnable codebase - demonstrating how an AI‑assisted build can support a broader modernization program.

    A decade‑old application with hidden functional complexity

    The customer relied on an application built more than a decade ago. It handled the core logic for how products were configured and evaluated, and it generated the technical documentation needed in daily operations. Over time the system had become difficult to work with: documentation was sparse, several libraries were long past their lifecycle, and the application still depended on a Windows‑based server for hosting. The team knew the system could be modernized but understood that doing so would require significant effort to untangle how the application behaved, how it had evolved, and which parts were no longer fully understood.

    They wanted clarity on three questions:

    • What functionality exists in the legacy application?

    • How would the system need to be modernized to eliminate the legacy library dependencies?

    • How, and to what extent, can AI‑assisted delivery speed up development while maintaining high quality?

    The customer also wanted evidence of how AI‑driven development performs in practice, so they could better assess where it could be applied in future work.

    Combining human expertise with coordinated AI agents

    We followed a structured workflow that combines specification driven development with human-guided AI agents. The process began by analyzing the legacy source code, UI behavior, and configuration logic. AI agents generated architecture summaries, rule interpretations, and API proposals, which human engineers then validated and refined to ensure accuracy.

    The team then generated backend, frontend, test suites, and infrastructure templates through orchestrated agent workflows. Multiple UI variants were created to help the customer compare design options and to clarify if their existing component library - built on an older version of Angular - would need to be modernized before it could be used in a new environment.

    The entire effort followed a clear sequence: legacy analysis, specification, generation, review, correction, and validation.

    From analysis to a working modern application

    The PoC delivered a modernized version of the customer‑facing application. It featured a clean .NET backend with extensive test coverage, multiple Angular‑based frontend variants, and documentation detailing architecture, data flow, and integration points.

    It also showed that the customer’s existing Angular component library - built on an older version - would need to be updated before it could be used in a modernized UI. In addition, the PoC delivered a runnable application deployed in our cloud environment and packaged so the customer can continue with their own release process, with the backend already ready for production use.

    Key deliverables included:

    • Legacy system analysis

    • Architecture and API specifications

    • Modern .NET backend with comprehensive tests

    • Multiple Angular UI options

    • Infrastructure templates and helper scripts

    • Documentation that explains decisions, patterns, and test results

    Clear modernization path with reduced uncertainty

    The PoC helped the customer reduce uncertainty and understand their modernization path

    • It clarified how the legacy system actually works, including where outdated dependencies truly affect modernization and where they do not.

    • It provided clarity on what a full rewrite would entail in practice, helping the customer better understand the scope and effort involved.

    • It showed that AI-assisted development can speed up delivery while maintaining high quality.

    • It delivered a production‑ready backend that the customer can take forward through their standard review and release processes.

    • It gave the customer’s engineering team visibility into how AI-first workflows operate in practice.

    What began as proof of concept became a practical modernization reference. Instead of relying on assumptions, the customer now has a working implementation that demonstrates how the system can be rebuilt using modern technologies and AI-assisted delivery. This allows the organization to plan future modernization steps with far greater clarity and confidence.

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