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For years, monolithic architectures have been the silent productivity killers of engineering teams—costly to maintain, difficult to scale, and nearly impossible to modernize incrementally. At Monday.com, engineers faced a daunting projection: decomposing their monolithic codebase would take 8 years of manual effort. But through an innovative AI-powered approach, they compressed this timeline to just 6 months—a 94% reduction that redefines what's possible in legacy modernization.

The Monolith Mountain

Monday.com's core platform had grown into a labyrinthine structure with over 2,000 interconnected endpoints and 15 million lines of code. Traditional decomposition would require:
- Manually identifying bounded contexts
- Untangling complex dependency graphs
- Rewriting business logic with surgical precision

"We faced thousands of person-years of effort," explains the engineering team. "Conventional methods simply weren't feasible without halting product development for nearly a decade."

The AI Breakthrough

The solution emerged through a custom-built AI pipeline that automated the most labor-intensive decomposition tasks:

# Simplified AI decomposition workflow
1. Codebase → Static Analysis → Dependency Graph
2. Graph + Business Logic → ML Clustering → Service Boundaries
3. Boundary Proposals → Validation → Refactoring Automation

Key innovations included:
- Neural Code Mapping: Transformer models that learned code semantics to identify domain boundaries
- Dependency Forecasting: Predicting ripple effects of extraction before refactoring
- Safe Refactoring Tools: Automated code migration with human-in-the-loop verification

"The AI didn't replace engineers—it amplified them," the team emphasizes. "We trained models on our unique domain language, enabling them to understand business logic like a senior developer."

The 6-Month Miracle

The results shattered expectations:
- 42 independent services extracted
- Zero business logic regressions in production
- Deployment frequency increased 300%
- Team velocity doubled post-migration

Crucially, the AI pipeline continuously learned from engineer feedback, improving its accuracy with every service extraction. What began as 70% reliable proposals evolved to 95% precision within months.

The New Playbook for Legacy Systems

Monday.com's success demonstrates that AI isn't just for greenfield projects. By combining:
1. Domain-specific model training
2. Incremental verification mechanisms
3. Engineer-AI feedback loops

...teams can conquer previously "impossible" modernization efforts. As monolithic architectures continue to plague the industry, this approach offers a template for turning technical debt into strategic advantage—one AI-extracted service at a time.