Standardizing Enterprise GRC Customization Through AI-Augmented EngineeringStandardizing Enterprise GRC Customization Through AI-Augmented Engineering

The client is a global Governance, Risk, and Compliance software company whose platform serves 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, customers expected faster customization and quicker responses to evolving compliance requirements. The platform’s configurable model remained a core strength, but the effort required to deliver customer-specific changes was placing greater pressure on engineering capacity and review cycles.

The client needed a more repeatable engineering model that could accelerate customization, improve review consistency, and preserve governance and release control. Faster delivery was also important to protecting enterprise relationships, as implementation speed and platform adaptability increasingly influenced retention.

Key Challenges

The existing customization model created several recurring engineering constraints across customer environments.

1. High-frequency customization depended on direct code edits across forms, workflows, and business rules.
2. Approved rule patterns and implementation knowledge were difficult to reuse consistently across customer environments.
3. JavaScript, Groovy, and Rule DSL logic required specialist interpretation, increasing review effort and limiting team autonomy.
4. Manual setup and fragmented tooling slowed troubleshooting and made customer-specific changes harder to prepare.
5. Customer-specific changes moved through manually coordinated validation and release steps, increasing lead time and delivery variability.

Introducing AI into a compliance-critical engineering environment required a partner that could protect proprietary code, work within established release controls, and keep engineers accountable for consequential changes.

Coditas brought together AI-Augmented Engineering, enterprise platform expertise, and governance-led delivery. Its ability to combine controlled AI adoption with structured validation and human oversight gave the client confidence that customization could move faster without weakening engineering discipline or release stability.

Coditas built AI-assisted engineering workflows for rule standardization, form customization, workflow changes, and troubleshooting.

The Rule Standardization Agent converted recurring requirements into structured change packages containing the proposed rule logic, affected dependencies, validation results, and supporting context. Specialized sub-agents handled rule interpretation, configuration retrieval, dependency checks, and change preparation before engineers reviewed the combined output.

A Model Context Protocol, or MCP, access layer provided the agents with approved Rule DSL libraries, platform configuration metadata, issue records, source-control context, and the validation resources required for each task.

Governance and Delivery

Because the agents accessed proprietary source code, Rule DSL logic, and customer-specific configurations, Coditas self-hosted the model layer inside the client’s AWS environment using DeepSeek, llama.cpp, and Amazon EC2 Inf2. The architecture kept proprietary code, customer configurations, and compliance logic within the client’s security boundary.

The MCP layer exposed only the approved resources required for each task. Proposed changes remained in isolated branches and validation environments until engineering approval.

Coditas evaluated outputs against 250 previously approved configurations. The evaluation covered Rule DSL validity, agreement with engineer-approved rule intent, dependency completeness, and the level of rework required before approval. Engineers reviewed every critical configuration change before it could progress through the release process.

Technologies

Java, Groovy, JavaScript, Amazon EC2 Inf2, llama.cpp, DeepSeek, Docker, MCP, Rule DSL

• The engagement reduced repeated engineering work and created a more structured approach to high-frequency GRC customization.
• 25% reduction in rule standardization cycle time, from eight to six engineering hours per request across 120 changes evaluated during the pilot.
• 40% reduction in engineering effort for customization, from five to three hours of preparation and first-pass review across 80 form and rule changes evaluated during the same pilot.
• 92% agreement with engineer-approved rule intent across a benchmark set of 250 previously approved configurations.
• 89% first-pass engineer acceptance rate across the same benchmark set, reducing material rework before proposed configurations could progress.

“Coditas approached the problem with an AI-first mindset, not just traditional application engineering. Their thinking aligned closely with how we build relevant, outcome-focused AI use cases using emerging technologies for our clients.”

— Head of AI Offerings, Customer Outcomes & Forward Engineering, Global GRC Platform Company

Mature enterprise platforms rarely need AI in isolation. They need AI applied to the repetitive engineering work that slows customization and increases review effort.

For the client, AI-Augmented Engineering created a repeatable delivery pattern that helped teams respond faster to enterprise requirements while engineers retained authority over compliance-sensitive rule logic and release approval.