How Claude Code Helped Rewrite a Complex IT Platform in Just Two Months

How Claude Code Helped Rewrite a Complex IT Platform in Just Two Months

For four years, the team behind Codenrock, a platform for running IT competitions, tried and failed to carry out a large-scale refactoring and update its architecture. Five attempts, four failures: technical debt seemed to have become an insurmountable obstacle. But the fifth attempt turned into a real triumph.

A compact team of just two developers, one tester and a UI designer completed the ambitious project in only two months. The key factor behind the breakthrough was an advanced version of Claude Code, an AI-based tool. The result exceeded all expectations: about 80% of the code was generated by AI under close human supervision.

The experience shows how modern large language models (LLMs) can radically cut the cost of eliminating technical debt and let small teams take on tasks that once required enormous resources and a great deal of time.

Context: Accumulating technical debt and looking for a way out

Codenrock's story is a classic example of how technical debt piles up like interest on a loan, gradually slowing a product's development and making changes harder to introduce. For four years the team wrestled with an outdated architecture, trying to find the best path to modernize it. Each new attempt started with enthusiasm but ran into unforeseen difficulties, forced a rethink of the chosen technology stack, or simply got bogged down in the sheer volume of work ahead.

These failures not only consumed valuable time and resources but also sapped the team's morale. Realizing that putting the problem off further would not solve it, the team decided to look for fundamentally new approaches. That was when advanced AI tools appeared on the horizon, promising to revolutionize the development process.

A closer look: Claude Code as a catalyst for change

The deciding factor in the fifth attempt was bringing Claude Code, running on the top-tier subscription, into the team's workflow. It let developers use AI to generate code, write tests, refactor existing sections and even suggest architectural solutions. It is important to understand that the phrase "80% of the code is written by AI" does not mean the process was fully automated.

The AI acted as a highly skilled assistant that handled routine and the most labor-intensive tasks, freeing people for the more complex, creative and strategic aspects of development. Developers oversaw every step, reviewed the generated code, made necessary corrections and took the final decisions. The UI designer and the tester also worked actively with AI, getting help creating mockups, writing test scenarios and automating checks.

This synergistic approach sped the process up several times over.

Consequences: Rethinking development processes

Codenrock's experience has far-reaching implications for the whole IT industry. It shows that modern LLM tools are not just a passing fad but powerful allies in the fight against technical debt and resource constraints. Small, highly effective teams armed with such tools can now take on tasks that once seemed within reach only of large corporations with multimillion-dollar budgets. That opens new opportunities for startups and small companies, letting them bring competitive products to market faster and cut development time. The approach also boosts developer productivity, lowers the likelihood of errors caused by the human factor, and lets teams focus on innovation rather than routine work.

Conclusion: The future of development with AI

Codenrock's story is vivid evidence of how artificial intelligence is changing the landscape of software development. Four years of failure gave way to rapid success thanks to the integration of Claude Code. The case is an inspiring example for many teams facing similar problems. It shows that with the right approach and competent use of AI tools, even the hardest refactoring and architecture modernization tasks can be solved efficiently and on a tight schedule. The future of development is undoubtedly tied to the further growth and integration of AI, which will make it possible to build better products at lower cost and respond faster to changing market needs.