Website Replatforming with AI: A Three-Flow Approach

Replatforming a website traditionally involves complex manual processes, extensive documentation, and countless hours of content migration. With the emergence of AI-powered tools and Model Context Protocol (MCP) servers, we can now automate much of this process while maintaining quality and consistency.

The Challenge of Traditional Replatforming

Website replatforming typically involves:

• Manual UI design recreation from existing websites
• Content auditing and restructuring
• Complex content modeling decisions
• Time-consuming content migration
• Risk of losing content or design fidelity

These processes are not only time-consuming but also prone to human error and inconsistencies.

The AI-Powered Solution: Three Parallel Flows

Our approach breaks down the replatforming process into three distinct, automated flows that can run in parallel, significantly reducing project timelines while improving accuracy.

Flow 1: UI Migration - From Website to Design System

Pipeline: Old Website → Figma → New Application

This flow focuses on preserving and modernizing the visual design language of the existing website.

Process Overview

Step 1: HTML to Design Conversion Using Figma's HTML to Design plugin, we capture the existing website's visual components and layout structures. This automated process converts live web pages into editable Figma designs while preserving:

• Layout structures and spacing
• Typography hierarchy
• Color schemes and brand elements
• Component relationships

Step 2: Design System Development The captured designs are refined in Figma to create a modern, consistent design system that:

• Maintains brand continuity from the original site
• Follows current design best practices
• Establishes reusable component patterns
• Provides clear design tokens and guidelines

Step 3: Code Generation Using Figma MCP Server integration, the refined designs are converted into production-ready code for the new application, ensuring pixel-perfect implementation.

Status: ✅ Clear and Tested

This flow has proven reliable and effective, providing excellent results with minimal manual intervention.

Flow 2: Content Modeling - Structure Before Content

Pipeline: JSON Files + Prompting → Confluence Documentation → Contentful

This flow establishes the content architecture before migrating actual content.

Process Overview

Step 1: Content Structure Analysis Starting with JSON representations of content and strategic prompting about desired content modeling approaches, we define:

• Content types and their relationships
• Field structures and validation rules
• Content hierarchy and taxonomy
• Editorial workflow requirements

Step 2: Documentation Generation Using Confluence MCP Server, we automatically generate comprehensive documentation that includes:

• Content type specifications
• Field definitions and purposes
• Content relationships and dependencies
• Editorial guidelines and best practices

Step 3: CMS Configuration The documented content model is then implemented in Contentful using the Contentful MCP Server, creating:

• Content types with proper field configurations
• Validation rules and constraints
• Asset management structures
• Publishing workflow configurations

Status: 🔄 In Development

This flow shows promise and should work based on the underlying technology, but requires further testing and refinement.

Flow 3: Content Migration - Bulk Content Transfer

Pipeline: Old Website → JSON Files → Contentful

This flow handles the actual content migration from the existing website to the new CMS.

Process Overview

Step 1: Content Extraction Using Firecrawl MCP Server, we systematically extract content from the existing website:

• Page content and metadata
• Images and media assets
• Navigation structures
• SEO data and URL mappings

Step 2: Content Processing The extracted content is processed and structured into JSON formats that match the content model established in Flow 2.

Step 3: CMS Population Using Contentful MCP Server, the processed content is bulk-imported into the new CMS:

• Content entries with proper relationships
• Media assets with optimization
• URL redirects and SEO preservation
• Content status and publishing states

Status: ✅ Clear and Tested

This flow has been successfully tested and provides reliable content migration capabilities.

Key Insights and Best Practices

The Power of Intermediate Steps

One of the most valuable lessons learned is the importance of intermediate outputs in each flow. These staging points provide:

Review Opportunities: Each intermediate step allows for quality control and validation before proceeding to the next stage.

Manipulation Flexibility: Content and designs can be refined, edited, or restructured at intermediate stages without starting the entire process over.

Error Prevention: Issues can be caught and corrected early in the process, preventing compound errors downstream.

Stakeholder Involvement: Intermediate outputs provide tangible deliverables for stakeholder review and approval.

Parallel Processing Benefits

Running these three flows in parallel offers significant advantages:

• Reduced Timeline: Overall project duration is dramatically shortened
• Resource Optimization: Different team members can work on different flows simultaneously
• Risk Mitigation: If one flow encounters issues, others can continue
• Quality Improvement: Each flow can be optimized independently

Technical Architecture

The success of this approach relies heavily on MCP (Model Context Protocol) servers that provide:

• Standardized Interfaces: Consistent APIs across different tools and platforms
• AI Integration: Natural language processing capabilities for content analysis
• Automation Capabilities: Bulk operations and workflow automation
• Quality Assurance: Built-in validation and error checking

Future Enhancements

As AI capabilities continue to evolve, we anticipate improvements in:

• Content Quality Analysis: Better detection of content gaps and quality issues
• Design Consistency: More sophisticated design system generation
• SEO Preservation: Enhanced URL mapping and redirect management
• Performance Optimization: Automated optimization of migrated content and assets

Conclusion

AI-powered website replatforming represents a significant evolution in how we approach large-scale website migrations. By breaking the process into three parallel, automated flows, we can achieve:

• Faster project delivery
• Higher consistency and quality
• Reduced manual effort and errors
• Better stakeholder visibility and control

While some flows are still being refined, the overall approach shows tremendous promise for transforming what has traditionally been a complex, time-consuming process into a streamlined, efficient workflow.

The key to success lies in embracing the intermediate steps as valuable checkpoints rather than obstacles, allowing for human oversight and refinement while leveraging AI for the heavy lifting.