Modernizing a Complex Codebase with AI‑Assisted Development
Case Study
Case Study
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Our client is a Scandinavian media and digital service holding with extensive operations across Europe and leader in their industry.
They have more than 1000 employees across Europe and constantly growing revenue.
The purpose of this project aligned with the client's overall strategy to adopt AI-based engineering methods, to improve their time-to-market, and to enable their platform to quickly and continuously evolve in a rapidly changing digital marketplace.
The main goal of the project was to increase productivity by introducing workflows that have AI-based support into the software development process. Prior to this project, all feature development and debugging was performed manually. Consequently, this caused slower delivery cycles and very limited automation.
Debugging takes longer than expected to interpret large and constantly changing codebases before new features are developed and/or validated.
Feature-limited system and slow development process with significant time spent understanding the existing codebase before implementing new features.
Ensuring that the AI model was implemented in accordance with existing implementation patterns and structural guidelines.
Risk of AI model "hallucinations" introducing subtle architectural regressions or ignoring existing system constraints.
Initial learning curve and friction while defining and fine-tuning custom AI behavior rules to match the development patterns.
Risk of developers accepting AI suggestions without rigorous manual code validation.
Bringing all of Cursor’s existing AI models together to reduce context switch and improve developer workflow.
Established the use of maximum coding standards to ensure consistent behavior and controlled outputs.
Maintained the architectural integrity of code; avoiding divergence from domain logic.
Defined expected AI outputs for validation, code reviews and testing to ensure quality and correctness.
AI was continuously integrated into daily development work rather than following a fixed timeline or formal milestones.
The team follows a flexible workflow based on Kanban to support continuous delivery and fast iteration.
Clearer and more restrictive control mechanisms and validation for the use of AI-generated output.
Improved overall development performance, reduced errors, and easier verification of existing functionality in a large and constantly evolving codebase.
Faster feature delivery to end users and deeper understanding of the entire system.
The model flexibility allows the client to expand capabilities when needed while maintaining predictable and sustainable operational costs.
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