AI-Assisted Legacy Modernization

Legacy applications carry compounding costs: scarce talent that understands aging codebases, undocumented business logic embedded in code, mounting technical debt, and rigid architectures that slow every new initiative. Traditional modernization programs are expensive, slow, and high-risk because they depend on manual code analysis and institutional knowledge that may no longer exist within the organization. 

Claude Code and Anthropic’s AI agents change the economics of modernization. They can analyze codebases at scale, extract business rules from undocumented logic, generate test cases from current behavior, and accelerate refactoring and migration planning – cutting migration effort by roughly 60% compared to traditional approaches.  

Our engagement follows four phrases, each designed to deliver standalone value while building toward a fully modernized, independently sustained codebase.  

1. Analyze (1 – 2 weeks) 

An AI-powered assessment that maps your existing codebase, its dependencies, and its embedded business logic to build the context Claude Code needs to migrate it well. Manual dependency analysis cannot scale, which is why most monolith-decomposition attempts stall; AI agents generate dependency graphs, documentation, and test coverage in days rather than weeks, speeding up codebase review by up to 50%.  

Key outputs: Dependency graph and graphical visualization of the codebase. Legacy codebase documentation by module. Requirements and design pattern specification. Generated test suite covering unit, end-to-end, and security testing.  

2. Plan (1 week) 

A two-agent workflow that automates the heavy lifting — drafting the target architecture and a detailed, dependency-ordered task backlog — while your team reviews, validates, and adjusts the migration plan and feature implementation before any code is migrated, replacing manual design sessions and running up to 40% faster.  

Key outputs: Technical architecture and implementation plan. Dependency-ordered task backlog. Supporting specification and schema documentation. 

3. Migrate (Variable) 

A multi-agent workflow – developer, reviewer, and QA agents – that codes, reviews, and tests each module until it is production-ready with human sign-off at every gate. Comparable Claude Code-assisted migrations have moved 10,000 lines of code in four days and cut overall migration delivery time by up to 80%.  

Key outputs: Migrated, peer-reviewed code modules. Regression test results and functional-equivalence confidence report. Migration completion log per service or module. 

4. Sustain & Scale (1 week) 

An enablement phase that transfers knowledge, practices, and a new engineering operating model to your teams so they can sustain and extend AI-driven modernization independently, without regressing to manual practices once the engagement ends,  

Key outputs: Claude Code workshop and hands-on labs. AI-generated code review checklist. Target operating model and adoption roadmap for future migrations.  

Proven Business Impact 

When AI is applied strategically to modernization, organizations see measurable results:  

• 10,000 lines of code migrated from Scala to Java in 4 days in comparable Claude Code-assisted engagements 
• Up to 50% faster codebase analysis compared to manual review 
• 60% reduction in total migration effort versus manual approaches 
• 85-93% faster test authoring with autonomous test generation 
• 60 days to under 10 days: the drop-in Mean Time to Remediate (MTTR) for vulnerabilities with AI-assisted patching 

Sia brings together deep expertise in AI, software engineering, and organizational transformation. We do not simply recommend a target state. We work alongside your teams to assess what you have, define where you need to go, and build the plan to get there, with AI accelerating every step. Our approach generates the evidence and documentation your leadership needs to make informed modernization decisions.