Moving a design system into a large codebase is usually a **slow, difficult process**. Updating components, coordinating tests, and managing knowledge across teams can take **thousands of hours**. PushBased worked with a major gaming platform to speed up this process by building a **custom AI orchestration system**.

## 1\. The Challenge

### The Problem

The platform **hit a major roadblock**: their old UI components were spread across more than 10 different projects and sub-brands. This led to **inconsistent visuals and a heavy maintenance burden**.

### The Scale of the Issue

Audits showed over **6,800 design system legacy code implementations across 28 components** that all needed to be fixed by hand.

### The Resource Gap

Internal teams **estimated the migration would take over 7,200 man-days**. This would have effectively **stopped all new feature work** for the next two to three years.

### The Complexity

Trying to **coordinate updates** across a fixed release schedule while **integrating with GitLab and Jira** meant that manual work often led to **mistakes and team burnout**.

* * *

## 2\. Methodology - The AI Orchestration Layer

Instead of manual refactoring, the team **moved to an AI-driven workflow** that used a **constant feedback loop**.

### Codebase Discovery & Scope Analysis 

Our team **scanned the entire codebase** **with** [**Code-Pushup**](https://code-pushup.dev/) to find every violation and set a baseline for the project.

### AI-Powered Strategy & Estimation 

The system created **multiple migration plans at once**. This cut the initial 7,200-day estimate by about 95% through **automated planning**.

### Workflow Orchestration & Automation 

Our team built a workflow that used AI to **sort tickets and generate tests**. This was run by **four specific engines**:

*   **Investigation Engine:** Looked at code patterns and how different parts of the app depended on each other.
    
*   **Planning & Migration Engine:** Grouped tasks together and refactored the code.
    
*   **Testing Engine:** Automatically created tests to make sure the changes didn't break anything.
    
*   **Retrieval & Integration Engine:** Handled the connections to external tools and project data.
    

### Continuous Learning & Context Improvement 

The team **reviewed migration tickets and bugs regularly**. These **findings were fed back** into the AI’s data files to make the system **more accurate as the project went on**.

* * *

## 3\. Technical Architecture

The architecture focuses on a **centralized AI context system** that manages the workflow between the codebase and the developer tools.

Architecture of the AI context system and orchestration layer.

* * *

## 4\. Key Results

**Area**

**Manual Baseline**

**AI-Augmented Result**

**% Improvement**

**Effort (Man-Days)**

**7,200+** days

~360 days (**95%** Reduction)

**1770%**

**Migration Speed**

**Multi-year** roadmap

**6 Months**

**~500%**

**Scalability**

**Manual** Workflows

Process designed for reuse across **all product pillars**

**100%**

**Legacy Code**

Manual **error prone** process

Merging of legacy patterns **blocked**

**100%**

### Cutting Manual Work 

The AI-driven process **handled nearly 95% of the manual labor**. This let the **engineers stay focused on the high-level architecture** instead of tedious code swaps.

### Future-proofing the System

The process was built so it could be used again for other parts of the business, making it **easy to scale as the company grows**.

## 5\. Conclusion & The Future

In **six months**, this project turned a massive **manual task into a repeatable, automated process**. By using AI for ticket management and testing, the move was **faster and much cheaper than expected**.

The resulting system is **now a permanent asset**. It ensures that future design updates can be handled in small, manageable phases across any team, **turning a difficult migration into a routine task**.

**In collaboration with Stephen Jayna, VP Engineering, Entain**

* * *

### Check out our new Workshop and learn how to build your own context ecosystem! 👇

Intermediate workshop

### AI-Assisted Development Foundations

Master the principles behind AI code generation and build a context ecosystem that works with any AI coding assistant.

[Learn more](/workshop/ai-assisted-development-foundations)