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by Airy

Why We Built CodeSplit AI

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Every engineering team we have worked on has faced the same problem: a monolith that has outgrown its architecture. The business needs to move faster, but the codebase has become a bottleneck. Teams spend quarters, sometimes years, on mechanical modernization work instead of building what matters.

The Problem We Lived

We spent years modernizing legacy systems. We saw the same pattern everywhere:

  • A monolith accumulates years of business logic
  • The team decides it is time to extract services
  • Engineers spend months mapping dependencies, extracting code, rewiring integrations, and writing tests
  • The work is mechanical, repetitive, and error-prone
  • Meanwhile, feature development stalls

This is not an engineering problem that requires creativity. It is a mechanical transformation that should be automated.

What CodeSplit AI Does

CodeSplit AI delivers end-to-end code modernization as software. You connect your repository, and the platform:

  1. Analyzes domain boundaries. AI identifies bounded contexts and maps dependency relationships.
  2. Extracts microservices. Creates new service repositories with clean, tested code.
  3. Refactors the monolith. Removes extracted domains and wires in API calls.
  4. Delivers PRs. Two pull requests (monolith and microservice) ready for code review.

The result: modernization in days instead of months.

Starting With Extraction, Building a Platform

Microservice extraction is step one. The CodeSplit AI platform is designed for every stage of code modernization:

  • Live today: Domain boundary analysis, microservice extraction, integration testing
  • Coming next: Framework migration (Java 8 to 17, Angular to React)
  • On the roadmap: CI/CD generation, cloud deployment automation

Our goal is to make code modernization a solved problem. Automated, fast, and accessible to every engineering team.

Try It

We are accepting early access requests at codesplit.ai. Connect your repository and see what automated modernization looks like.