CASE STUDIES
CASE 01 / DEFENSE
Embedded real-time software modernization.
Customer/program type: Major defense contractor, mission-critical. Source → target: C + Assembly → Modern C + Assembly. Scale: approximately 9k LOC. Timeline: 2 months. Full functional equivalence validated end-to-end with a complete chain of evidence.
Primary outcome: Full functional equivalence validated end-to-end.
CASE 02 / FEDERAL
Self-modifying Assembly to Java.
Customer/program type: Federal agency. Source → target: Self-modifying Assembly → Java. Scale: Not disclosed. Timeline: 2 months. Delivered a maintainable, auditable Java codebase for agency ownership.
Primary outcome: Maintainable, auditable Java codebase delivered.
CASE 03 / U.S. AIR FORCE
Legacy UI modernization at scale.
Customer/program type: U.S. Air Force. Source → target: AngularJS → Angular 17. Scale: 108 kLOC and 361 components. Timeline: 6 months. Delivered with 99.9% accuracy and zero regression.
Primary outcome: 60%+ savings against a $2M budget.
CASE 04 / FEDERAL
Rule engine modernization, fully documented.
Customer/program type: Federal program. Source → target: Ruby on Rails + Drools → Java Spring Boot. Scale: Not disclosed. Timeline: 3 months. Delivered a fully documented modernization against an 18-month baseline.
Primary outcome: 67% cost savings against the original baseline.
CASE 05 / GOVERNMENT SI
Two million lines of COBOL to Java.
Customer/program type: Government systems integrator. Source → target: 2M LOC COBOL across 150 processes → optimized Java. Scale: 2M LOC and 150 processes. Timeline: 18 months. Delivered the full program against a 4-year baseline.
Primary outcome: $2.5M saved against a $3.5M estimate.
CASE 06 / ENTERPRISE
Legacy COBOL to idiomatic Java.
Customer/program type: Major fintech provider. Source → target: Legacy COBOL → idiomatic Java. Scale: Not disclosed. Timeline: Effective coverage measured in hours rather than months. Delivered a stronger coverage profile for modernization evaluation.
Primary outcome: 85% effective coverage in hours versus 40% after months.
Rajiv Gidadhubli
Chief AI Transformation Officer, Alpha Omega
WHERE EACH APPROACH FITS
LLM code generators
Accelerate code generation and exploration when a team can review and govern the output.
Legacy syntax translators
Preserve familiar syntax when a like-for-like conversion is the right starting point.
SI modernization factories
Scale delivery capacity with repeatable teams, methods, and program operations.
Static analysis suites
Understand and measure code structure before a modernization or governance decision.
Holonic CodeIntent®
Create a deterministic, source-derived baseline with evidence for modernization and ongoing change.
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