AI-generated System Stabilization

Brief description

A stabilization and architecture modernization project for a rapidly evolving AI-generated MVP with inconsistent structure, fragile AI workflows, security risks, and growing maintainability problems.

Role

I worked as a senior full-stack engineer focused on turning a fast-moving AI-generated codebase into a maintainable product architecture. My role covered system design, codebase restructuring, AI integration design, backend architecture, security review, and technical documentation.

Full description

I was engaged to stabilize a rapidly evolving product that had been built with heavy use of AI-generated code. The system was moving quickly, but the architecture had become inconsistent: responsibilities were mixed across layers, AI logic was coupled to product logic, backend access patterns were fragile, and the codebase was difficult to evolve safely.

My main focus was to introduce clear architectural boundaries without slowing down MVP development. I restructured the application into more explicit layers for data access, AI integration, and business logic. This reduced coupling to infrastructure details, made the code easier to reason about, and created a foundation for future decomposition of major product areas.

A major part of the work was improving the reliability of AI-driven workflows. I designed a structured LLM integration layer with strict JSON contracts for AI outputs, validation around generated data, and clearer orchestration boundaries. This helped make AI responses less fragile and easier to process downstream.

I also designed a queue-based architecture with background workers to move longer-running operations out of request-response flows. The system could control concurrency, process jobs more reliably, and provide real-time progress updates to the frontend using server-sent events.

During the stabilization process, I identified and mitigated important security risks in backend access patterns, especially around authorization and user data handling. I redesigned parts of the backend to make access control more explicit and safer.

Alongside implementation, I produced architectural documentation and formalized system design decisions so that the project could continue evolving with a shared technical understanding rather than depending on scattered AI-generated implementation details.

This project was not only about refactoring code. It was about turning an AI-generated MVP into an engineering foundation that could support real product development, future features, and long-term maintainability.

Tech stack