Our 5-Phase Approach to Legacy Migration
Phase 1: Dual-Lens Analysis
Every migration begins with understanding. Our dual-lens approach combines two complementary analysis methods. First, automated code analysis parses the entire codebase — identifying patterns, dependencies, technical debt, and architectural decisions embedded in the legacy system. Second, AI-driven browser crawling navigates the running application, mapping every screen, workflow, and interaction pattern. Together, these lenses produce a comprehensive inventory that captures not just what the code does, but how users actually experience it. This phase typically completes in 2-5 days, compared to the 4-8 weeks a traditional discovery process requires.
Phase 2: Agent Orchestration
With a complete system map in hand, AI agent teams decompose the migration into optimised task sequences. Each task is annotated with dependency relationships, complexity scores, and risk assessments. The orchestration engine identifies which components can be migrated in parallel and which require sequential handling due to shared state or tight coupling. This phase also generates the target architecture, selecting modern frameworks and patterns that align with your team's capabilities and business objectives.
Phase 3: Parallel Execution
Execution is where AI-powered modernisation truly differentiates itself. Rather than a single team working through a backlog, we spin up 3-5 parallel Kubernetes sandboxes, each handling a distinct migration workstream. AI agents supervise each sandbox, writing code, running tests, and flagging issues in real time. Human engineers review critical decisions and architectural boundaries, but the bulk of the translation work — the repetitive, error-prone mechanical conversion — is handled by AI at machine speed.
Phases 4 & 5: Quality Assurance and Iterative Refinement
Automated code review via CodeRabbit catches style inconsistencies, security issues, and performance anti-patterns across every pull request. Visual regression testing compares the modernised application against the legacy original, ensuring pixel-level fidelity where required. The final phase introduces continuous improvement loops: edge cases discovered during QA feed back into the migration pipeline, and performance optimisations are applied before delivery. This iterative approach means defects are caught and resolved during the migration, not after launch.
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