At a Glance
A major U.S. government defense contractor operated a mission-critical logistics application that had been in continuous development for 25 years. Built on .NET Framework with a Windows-only architecture, the application had grown into a massive monolith — millions of lines of code, tightly coupled modules, and deep dependencies on Windows-specific services.
Using CHAI, CloudHedge decomposed the monolith into 64+ independent microservices, containerized them for Kubernetes, and delivered a fully modernized, cloud-native architecture in 6 months — saving an estimated 25,000+ human hours compared to a manual rewrite.
The Situation
The application was the backbone of the contractor's logistics operations, managing supply chain workflows, inventory tracking, and compliance reporting for government contracts. It served thousands of users daily and processed millions of transactions annually.
Over its 25-year lifetime, the application had been extended, patched, and modified by dozens of development teams. What started as a well-structured .NET application had become a monolith with:
- Millions of lines of C# code across hundreds of projects and assemblies
- Tightly coupled modules that shared databases, in-memory state, and Windows services
- Windows-specific dependencies including COM objects, Windows Registry access, and MSMQ
- No containerization path: The application required a full Windows Server environment to run
- Multi-hour deployments with manual steps and frequent rollbacks
The contractor's modernization mandate required moving to a Kubernetes-based, cloud-native architecture that could deploy on government-approved cloud infrastructure. The mandate also required the application to support Linux containers to reduce licensing costs and improve security posture.
The Real Problem
The modernization challenge was not just technical — it was structural. No single engineer or team understood the full application. Architectural documentation was years out of date. Module boundaries had eroded over time as teams took shortcuts to meet delivery deadlines.
The contractor evaluated three approaches:
- Manual rewrite: Estimated at 3-5 years with a team of 40+ engineers, costing tens of millions of dollars with high delivery risk
- Strangler fig pattern: Estimated at 2-3 years, requiring sustained parallel development of old and new systems
- AI-assisted decomposition with CHAI: Estimated at 6 months for full analysis and initial microservice delivery
The contractor chose CHAI because it offered the fastest path to production results with the lowest risk. Unlike a manual rewrite, CHAI's approach preserved existing business logic and domain knowledge rather than attempting to recreate it from scratch.
The most expensive part of a legacy rewrite is not the coding — it is the re-discovery of 25 years of business rules, edge cases, and institutional knowledge embedded in the codebase.
How CHAI Changed the Equation
Deep Analysis with CHAI DART
CHAI DART performed a comprehensive analysis of the entire codebase. This was not a surface-level scan — DART analyzed the application at the code level, building a complete dependency graph of every class, method, database call, message queue interaction, and external service integration.
Key findings from DART's analysis:
- 87 logical modules identified within the monolith, many with circular dependencies
- 14 database schemas with over 800 tables, including 200+ shared across modules
- 23 Windows-specific dependencies requiring replacement or abstraction
- 340+ configuration parameters hardcoded across the codebase
DART generated a decomposition plan that identified 64 viable microservice boundaries based on domain-driven design principles, data ownership patterns, and actual runtime communication patterns. Each proposed microservice came with a dependency map, an estimated effort score, and a recommended implementation sequence.
Decomposition and Deployment with CHAI Flow
CHAI Flow executed the decomposition plan in phases. For each microservice, Flow:
- Extracted the code: Identified all classes, interfaces, and resources belonging to the service and separated them from the monolith
- Resolved dependencies: Replaced direct in-memory calls with API contracts, converted shared database access to dedicated schemas, and replaced Windows-specific services with cross-platform alternatives
- Containerized: Generated optimized Dockerfiles targeting Linux containers, replacing Windows-only dependencies where possible
- Generated Kubernetes manifests: Created deployment specs, service definitions, ConfigMaps, and Helm charts for each microservice
- Built CI/CD pipelines: Generated pipeline configurations for automated testing and deployment
Each phase was validated through automated testing. CHAI Flow generated integration tests that verified the decomposed services maintained behavioral parity with the original monolith.
The Outcome
The results transformed the contractor's development and operations capabilities:
- 64+ microservices delivered: Each independently deployable, scalable, and maintainable on Kubernetes
- 25,000+ hours saved: Compared to the estimated timeline for a manual rewrite
- 75% faster release cycles: Deployment time reduced from hours to minutes with independent service deployments
- Linux container support: Eliminated Windows Server licensing requirements for most services, reducing infrastructure costs by 40%
- 6-month delivery: From project kickoff to first production microservices running on Kubernetes
- Zero business logic loss: All existing functionality preserved through automated code extraction and behavioral testing
The contractor is now able to update individual services without risking the stability of the entire application. Teams can work on separate microservices in parallel, releasing features independently and scaling services based on actual demand rather than provisioning for peak load across the entire monolith.
What was once a 25-year-old legacy liability is now a modern, cloud-native asset — built to evolve for the next 25 years.