# Modernizing Wealth Planning Platforms with AI-Driven Engineering

## Industry
Asset and Wealth Management

## Location
Global

## Our Contributions
- Platform Modernization  
- Cloud Native Engineering  
- AI-Led Development

## Technologies
- GitHub Copilot  
- Cursor AI  
- Terraform  
- Helm  
- Playwright

Coforge transformed a decade-old legacy COTS-based wealth planning platform into a cloud-native, API-first, AI-powered solution designed for scalability, resilience, and extensibility. By leveraging AI-assisted engineering tools across the software development lifecycle, the program accelerated development velocity, improved code quality, strengthened governance, and reduced total cost of ownership.

The modernized platform introduced advanced capabilities such as risk tolerance assessment, reusable planning templates, model portfolio integration, seamless onboarding workflows, and unified data connectivity across custodial, CRM, and market data systems, enabling a superior advisor and client experience.

## Transformation Timeline

### Phase 1
Legacy platform assessment and domain decomposition

### Phase 2
Microservices architecture and API design

### Phase 3
AI-assisted development and DevOps automation

### Phase 4
Integration, testing, and platform rollout

#### The Challenge
The client’s legacy wealth planning platform was built on a tightly coupled COTS system that lacked flexibility, scalability, and integration capabilities. This rigid architecture limited innovation and made it difficult to adopt modern APIs, data pipelines, and AI services.

Development cycles were slow and resource-intensive, with engineers spending significant time on boilerplate code, test scaffolding, and integration development. Release cycles were long, and defects were often identified late in the lifecycle.

Data fragmentation across financial planning, onboarding, CRM, custodial, and portfolio systems created inconsistencies and inefficiencies. Limited automation in testing and DevOps further impacted release quality, while the lack of observability made performance monitoring and root cause analysis reactive and time-consuming.

## Our Approach

### Cloud Native Platform Re-Architecture
Rebuilt the legacy system into domain-aligned microservices supported by a real-time planning engine, enabling scalability and seamless integration.

### AI-Accelerated Development
Leveraged GitHub Copilot to generate boilerplate code, test cases, pipelines, and infrastructure templates, improving development speed and consistency.

### AI-Driven Refactoring and Optimization
Used Cursor AI to automate cross-module refactoring, dependency optimization, and performance tuning across services.

### Automated DevOps and Infrastructure as Code
Implemented AI-generated Terraform, Helm charts, and GitHub Actions pipelines to standardize deployments and improve release efficiency.

### AI-Driven Quality Engineering
Enabled automated creation of unit, integration, and contract tests, along with synthetic datasets using Playwright-based test generation.

## Partner / Technology Ecosystem
- GitHub Copilot  
- Cursor AI  
- Terraform, Helm, GitHub Actions  
- Playwright Testing Framework

## Impact to Date

+30%  
Faster Development Cycles  
-20%  
Reduction in Defects  
+60%  
Increase in Test Coverage  
Improved  
System Observability and Reliability

## Business Impact
- Accelerated time-to-market through AI-assisted development and automation  
- Improved code quality and governance with AI-driven validation and refactoring  
- Enhanced platform reliability with proactive monitoring and observability  
- Reduced total cost of ownership through optimized engineering workflows