Thousands of undocumented business rules, governing calculations, eligibility, and validation were embedded in over 500,000 lines of COBOL code. Manual extraction was cost-prohibitive and prone to human error.
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Large-scale, regulated institutions face a "paradox of scale" where the reliability of legacy systems acts as both a foundation and a primary obstacle to innovation. A major public-sector institution initiated a "Transformation Programme Discovery" to modernize one of the largest digital estates in the region. The mandate was to reconcile a "Human-plus-Machine" approach to establish a definitive ground of truth across a federated estate, ensuring that tactical discovery feeds a strategic, scalable asset.
Thousands of undocumented business rules, governing calculations, eligibility, and validation were embedded in over 500,000 lines of COBOL code. Manual extraction was cost-prohibitive and prone to human error.
Insufficient data management input during discovery phases traditionally leads to a high probability of delays, cost overruns, and reduced strategic ambition in complex environments.
Mainframe architectures built on COBOL and JCL created a reactive IT posture, making it difficult to support agile economic aims or resilient administration.
The institution requires a bespoke solution to connect a mandated toolchain (BPMN 2.0, ABACUS, and AXON) to a complex, federated estate without creating new data silos.
In regulated environments, any modernization must guarantee 100% functional equivalence to avoid breaking critical financial or administrative calculations.
We deployed specialized AI agents using Natural Language Processing (NLP) to scan 500,000+ lines of code. The system identifies rule-dense areas and translates legacy logic into plain-language business rules, decision tables, and flow diagrams.
A five-phase iterative framework was implemented where automation handles 70-80% of the foundational work, while human "Data Translators" provide the 20-30% of critical context and policy validation.
A conceptual interface allows users to click from a business process in ABACUS directly to the granular data lineage in AXON.
To manage risk, the AI operates in a bounded context. We extract all implicit dependencies (JCL/shared definitions) before processing, ensuring every AI-generated specification is traceable back to its COBOL source.
We moved beyond one-time exercises to create persistent capability. Tactical teams execute discovery, while a Strategic Advisory Team codifies standards into "Insight Cards" for knowledge of retention and reuse.
Accelerated Modernization Cycles:
AI models mapped 1 million lines of COBOL in under 48 hours, achieving a 90% increase in requirements reuse and 50% faster project timelines.
High Precision Logic Extraction:
Achieved an 87% precision rate in extracting core business logic and a 93% accuracy rate in code conversion during benchmarking.
Significant ROI & Cost Savings:
Modernization efforts typically resulted in a 30-60% reduction in compliance costs and a 40% reduction in audit findings.
Dynamic Impact Evaluation:
Programme Managers can now assess the "blast radius" of retiring a legacy system in days rather than months by querying the linked ABACUS-AXON model.
Proven Scale at 500,000+ Lines:
Utilizing the "Automation-First" approach, we delivered a functional target system a year earlier than expected with an 82% automation rate for over half a million lines of legacy code.
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By combining AI-driven automation with human oversight, this approach transformed half a million lines of legacy COBOL into a transparent, agile, and risk-mitigated foundation for large-scale digital modernization.
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