Industry
Client Overview
A global insurance carrier with a 50-year history was facing an existential crisis. Their core policy administration and claims processing systems were running on an IBM mainframe, written in over 5 million lines of COBOL. The handful of developers who understood the system were nearing retirement, maintenance costs were astronomical, and the business was unable to launch new, data-driven insurance products.
Client Testimonial
"The idea of rewriting 5 million lines of COBOL felt impossible. The cost and timeline were terrifying. CIS came to us with a radical proposal: AI-driven code translation. We were skeptical, but their proof-of-concept was undeniable. They translated a critical claims module to Java in weeks, not years. The full project was delivered in 18 months, at a fraction of the cost of a manual rewrite. It saved our business." - Chief Information Officer, Fortune 500 Insurance Carrier
Problem
The company was facing a "talent cliff" with the impending retirement of its COBOL programmers. They were paying millions in mainframe licensing fees, and the system's batch-processing nature meant they couldn't get the real-time data needed for modern underwriting and dynamic pricing models. A manual rewrite was quoted at 5-7 years and over $100 million, a non-starter for the board.
Key Challenges
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Massive Code Volume: The sheer scale of the codebase made a manual approach impractical.
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Extreme Complexity: Decades of patches and undocumented business rules were woven into the COBOL code.
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Talent Scarcity: Finding engineers who could both understand COBOL and write modern Java was nearly impossible.
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Maintaining Business Logic: The translated code had to be 100% functionally equivalent to the original to ensure claims were processed correctly.
Our Solution
CIS proposed a groundbreaking AI-Powered Rebuilding strategy, leveraging generative AI to automate the code translation process.
AI-Powered Code Analysis: We used specialized tools to scan the entire COBOL codebase, creating a detailed map of all programs, copybooks, and data structures.
Automated Code Translation: We utilized a proprietary, AI-powered platform to automatically translate the COBOL code into modern, object-oriented Java. The AI handled the painstaking line-by-line conversion and initial refactoring.
Human-in-the-Loop Validation: Our senior Java and COBOL experts supervised the AI, reviewing the generated code for accuracy, optimizing it for performance, and ensuring all business logic was perfectly preserved.
Cloud-Native Deployment: The newly translated Java application was deployed as a set of services on a managed container platform (Azure Kubernetes Service), connected to a modernized Azure SQL database.
Implementation & Execution
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AI / ML Rapid-Prototype Pod
We started with a small, focused POD to run a proof-of-concept on a non-critical module, proving the viability of the approach.
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Iterative Translation
The codebase was broken down into logical business domains (e.g., Underwriting, Claims, Billing), and modules were translated, tested, and deployed iteratively.
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Parallel Testing
We built a sophisticated testing harness that ran transactions through both the old COBOL system and the new Java system, comparing the results to ensure 100% functional parity.
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Data Offloading
Data was progressively migrated from the mainframe's VSAM files to the new Azure SQL database.
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Mainframe Decommissioning
Once all functionality was migrated and validated, the mainframe was officially decommissioned, leading to massive cost savings.
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Team Upskilling
We worked with the client to train their existing Java developers on the new system, ensuring a smooth handover.
Positive Outcome
The project was completed in just 18 months, a feat impossible with traditional methods.
1. 70% Cost Reduction
The project was completed for under $30 million, a 70% savings compared to the manual rewrite estimate.
2. Bridged the Talent Gap
The entire system now runs on modern Java, accessible to a wide pool of engineering talent.
3. Real-Time Data Access
The move to a cloud database and real-time services enabled the launch of new data-driven insurance products, creating new revenue streams.
4. $15M+ Annual Savings
Decommissioning the mainframe resulted in over $15 million in annual savings on licensing, hardware, and maintenance costs.
Why Choose Us
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Innovative AI Leadership
We are at the forefront of using AI to solve real-world modernization problems.
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Dual-Technology Expertise
Our rare combination of mainframe/COBOL and modern cloud/Java experts was essential.
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Risk-Reducing POC
Our proof-of-concept approach de-risked a highly innovative and complex project.
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Rigorous Automated Testing
Our parallel testing framework guaranteed the preservation of critical business logic.
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Focus on Business Value
The project was framed around cost savings and enabling new business capabilities.
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Predictable, Phased Approach
The iterative process provided continuous value and transparency.
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Cost-Effective Global Delivery
Our delivery model made this ambitious project financially viable.
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Full Knowledge Transfer
We ensured the client's team was fully equipped to manage the new system.
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A True Partnership
We worked as one team with the client to tackle a seemingly impossible challenge.
Conclusion
This case study demonstrates CIS's unique ability to combine deep legacy knowledge with cutting-edge AI innovation. We didn't just modernize a system; we provided a lifeline to a business trapped by its technology, proving that even the most daunting legacy challenges can be overcome with the right partner and the right strategy.