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Anonymized Case StudyDigital Publishing

Major Digital Publishing Enterprise

Led AI-driven content migration of legacy data and content management systems to AWS and Contentful cloud stack. Designed program-level data strategy leveraging Knowledge Graph and AWS technologies. Drove AI SDLC modernization by onboarding MCP servers as a foundation for engineering and test.

4B+
Documents
300K+
Users
-15%
Cycle Time

Challenge

A major digital publishing company had 4B+ documents trapped across legacy CMS platforms built over 20 years. Content was siloed, search was broken, and editorial teams spent more time finding content than creating it. The migration to a modern cloud stack was deemed 'impossible' by previous vendors.

Approach

Designed a phased migration program: 1. Built a Knowledge Graph to map all content relationships, metadata, and dependencies before moving anything. 2. Created AI-powered content classification pipeline to auto-tag and categorize documents during migration. 3. Deployed MCP servers to enable AI-assisted engineering — engineers used Cursor + MCP to write migration scripts 3x faster. 4. Migrated to AWS + Contentful in parallel workstreams across a multinational team.

Outcome

Complete migration of 4B+ documents to modern cloud stack. 300K+ users transitioned with zero downtime. Publishing cycle time reduced by 15% through ML-powered content classification. The Knowledge Graph became the foundation for a new semantic search product. AI SDLC tools (MCP + Cursor) are now standard across all engineering teams.

Architecture

Legacy CMS Systems (Source)
Knowledge Graph (Content Mapping)
AI Classification Pipeline (ML)
AWS Cloud Infrastructure
Contentful (Target CMS)
MCP Servers (AI Engineering)
Quality Assurance & Rollback

Interactive Demo

Technology Stack

AWSContentfulKnowledge GraphMCPCursorAI SDLC

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