Secure AI for Legacy Code in Regulated Environments

The challenge

Noah Labs was advancing a system to transform legacy codebases—languages such as COBOL and C—toward modern targets like Java and Rust. The setting was unforgiving: security-sensitive (including U.S. government–adjacent constraints), fully air-gapped operation, brittle legacy assets, and pressure to avoid both unsafe automation and open-ended manual rewrites.

• Heavy reliance on scarce, expensive legacy engineering talent.
• Long modernization timelines and high risk of defects in critical systems.
• Most AI approaches assume APIs and connectivity that this environment cannot allow.

What CVision identified

The gap was not merely “code conversion.” Under audit, the blockers looked like:

• No structured transformation system robust enough for legacy → modern translation.
• No credible path to run AI inside a secure, offline perimeter.
• Limited execution bandwidth to move from research concept to working tooling.
• A missing bridge between R&D ideas and deployable systems.

That is where many deep-tech efforts stall: strong science, thin execution layer.

What we built

CVision operated as an execution and R&D extension for the team—working toward a system that behaves as:

• A code transformation pipeline with characteristics of an AI-assisted compiler / dev environment.
• Purpose-built legacy-to-modern language migration, not a generic wrapper around a remote model.

Key characteristics:

• Air-gapped architecture so workloads can run fully offline to meet regulatory and security requirements.
• AI-powered transformation logic to assist translating legacy syntax and structures into modern equivalents—within controlled boundaries.
• Controlled execution environment so transformations can be tested, validated, and iterated safely.
• Modular design so transformation logic can evolve without rebuilding the entire stack.

Why this is not a typical AI build

Many AI product patterns assume APIs, cloud access, and permissive networks. This system was aimed at:

• Zero external dependency in deployment—suitable for isolated environments.
• High-security contexts where failure modes must be contained and reviewable.
• Engineering usability for teams who ship systems, not slideware.

It is not a thin automation layer on top of someone else's hosted model. It is system design around constraints most AI vendors never touch.