Using AI to migrate with speed and security
Overview
Industry
Location
Software and IT services
USA
Technology Used
.NET 10
ASP.NET Core 10
Azure Pipelines
Central Package Management
EF Core 10
Mapperly
NUnit
Playwright
SonarQube
SQLite
WolverineFx
Show more
Project overview
An automotive digital platform provider with a large production application portfolio and wanted to modernize core systems without disrupting delivery. Our client needed a faster way to understand upgrade complexity, effort and risk before committing to broader modernization work. This meant upgrading from .NET 8 to .NET 10, taking into account legacy package dependencies, CI/CD constraints and internal package compatibility risks. To speed up work, we used Software Mind’s custom-built platform that uses AI-enabled SDLC, human-in-the-loop controls and reusable agents to safely transform legacy systems into modern, extensible architectures in record time.
Client background
An automotive digital platform provider with a large production application portfolio that wanted to modernize core systems without disrupting delivery. They needed a faster way to understand upgrade complexity, effort and risk before committing to broader modernization work. This meant upgrading from .NET 8 to .NET 10, taking into account legacy package dependencies, CI/CD constraints and internal package compatibility risks.
Goals
Our client needed a fast and smooth upgrade from .NET 8 to .NET 10. To deliver the speed and security required, we combined a small team of engineers with our AI modernization tool. Beyond the migration, we wanted to convert an uncertain upgrade into a scoped, prioritized migration plan. This involved using AI-driven analysis to identify which critical files, packages and pipeline areas required attention, with the aims of reducing migration ambiguity and migration costs. Our goal was to give our client a concrete path to move from assessment into delivery without restarting discovery, while establishing a repeatable approach that can be applied to additional repositories and future modernization work.
Results
Higher speed and lower risks
Providing faster analysis and scoping that identified breaking changes and package conflicts pre-execution
90% coverage
Increasing unit coverage from 60% to 90%
Measurable cost reduction
Cutting down set up costs by 67-83% for future engagements through AI-driven analytics
9 in 40
Delivering 9 projects in 40 hours over a 5-day span
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