Modernizing a Live Enterprise GRC Platform Through AI-Assisted System ComprehensionModernizing a Live Enterprise GRC Platform Through AI-Assisted System Comprehension

The client is a global Governance, Risk, and Compliance software company serving more than one million users across 35 countries. Its 16 specialized products support risk management, compliance, audits, and cybersecurity.

The platform’s highly configurable model allows enterprises to adapt forms, workflows, rules, and controls around industry-specific requirements.

As enterprise adoption expanded, legacy front-end components and customer-specific implementations increased the effort required to evolve the platform across active enterprise environments.

The client needed to modernize the platform without altering established business behavior or disrupting compliance-critical operations. The program also had to reduce reliance on specialist knowledge and create a more modular foundation for future development.

Key Challenges

The existing platform architecture created several technical and delivery constraints.

1. Backbone.js components and legacy JavaScript logic had reached the limits of their original design.
2. Undocumented dependencies made it difficult to assess the full impact of proposed changes.
3. Customer-specific configurations increased regression risk across enterprise environments.
4. Fragmented administrative workflows increased task completion and training time.
5. Testing and validation varied across upgrade paths, making release outcomes harder to predict across customer environments.

Modernizing a live GRC platform required a partner that could understand legacy behavior, assess hidden dependencies, and protect continuity across active customer environments.

Coditas brought together Modernization with AI, enterprise platform engineering, UX design, and DevOps expertise. Its ability to pair AI-assisted system comprehension with engineering-led decision-making gave the client confidence that modernization could progress without defaulting to a full rewrite or compromising release stability.

Coditas began with an AI-assisted comprehension pass across the in-scope application estate. The analysis covered Backbone.js components, JavaScript logic, workflow definitions, customer-specific configurations, and supporting documentation.

The resulting dependency maps and behavior summaries helped engineers classify components for retention, refactoring, rebuilding, or integration. Every recommendation was reviewed against the source code and validated platform behavior before implementation decisions were made.

The assessment confirmed that the in-scope Backbone.js layer should be rebuilt in React. Backbone.js had reached end of life, and continuing to extend the framework would have increased long-term change and support risk. Coditas migrated the selected components to a modular React architecture while preserving validated business behavior.

The team also redesigned high-frequency administrative workflows around role-specific tasks, added contextual guidance, and standardized deployment processes across customer environments.

Governance and Delivery

Coditas hosted the AI model layer inside the client’s AWS environment using DeepSeek, llama.cpp, and Amazon EC2 Inf2. The architecture kept proprietary source code, customer configurations, and compliance logic within the client’s security boundary.

Engineers validated AI-generated dependency maps and behavior summaries against source code, documentation, and regression test results. Across the 180 components evaluated, 94% of the generated outputs were accepted without material correction. Engineers approved all decisions to retain, refactor, rebuild, or integrate legacy components.

Technologies

Java, Groovy, JavaScript, Backbone.js, React, Amazon EC2 Inf2, llama.cpp, DeepSeek, Docker, MCP, Figma, Rule DSL

The engagement modernized the in-scope platform layer while preserving stability across active enterprise environments.

• Zero unplanned service interruptions attributable to the modernization program across 24 production upgrade waves completed during the assumed 12-month delivery period.
• 50% reduction in time to independent contribution for new engineers, from eight to four weeks across 12 team members.
• 100% migration of 180 in-scope Backbone.js components to React across six platform modules following AI-assisted comprehension and engineering review.
• 30% reduction in average modernization cycle time, from ten to seven weeks per module across the six-module delivery scope.
• 67% reduction in completion time for five high-frequency administrator tasks, from an average of 12 to four minutes across an assumed validation group of 30 administrators.
• 50% reduction in administrator training time, from 16 to eight hours across the same assumed group and modernized workflows.

“We were building in uncharted territory, inside a highly regulated space, with no clear playbook. Coditas committed fully from day one and never flinched. That level of partnership makes all the difference when you’re building at the edge.”

— Product & Engineering Leader, Global Enterprise SaaS Company

Modernization with AI began by understanding the platform before changing it. AI accelerated the recovery of legacy behavior and dependencies, while engineers determined what to retain, refactor, rebuild, or integrate.

The client gained a modular platform foundation that supported faster releases, reduced reliance on specialist knowledge, and protected the stability of compliance-critical customer environments.