Case Study | Fintech
De-Risking Legacy Modernisation with AI-Driven Engineering
Establishing a risk-controlled foundation for legacy modernisation through AI-assisted analysis, structured methodology, and validated execution patterns.
Overview
A leading B2B financial operations platform faced the challenge of modernising a sprawling legacy codebase where business logic was buried in stored procedures and traditional migration approaches carried unacceptable risk. 10Pearls applied AI-augmented analysis, test-driven validation, and an incremental delivery model to establish a defensible foundation for the modernisation journey.
AI-augmented modernisation path
validated for incremental delivery
Initial production deployments
with phased rollout underway
Challenge
The client’s invoice generation system relied on 40–50 stored procedures spanning more than 20,000 lines of SQL logic and calling into a broader code tree of nearly 25,000 lines. The environment also involved more than 100 backend tables, creating a highly interconnected system where business rules were difficult to isolate, document, or safely migrate.
With limited technical documentation, no reliable end-to-end automated tests, and incomplete manual test cases, the team lacked a dependable baseline for validating system behavior during modernisation. Early GenAI experiments also revealed hallucination risks when models attempted to interpret large, interconnected code paths without sufficient grounding data.
Solution
10Pearls helped the client de-risk modernisation of one of the most complex areas of their platform while preserving business continuity. Instead of pursuing a disruptive Big Bang rewrite or a slow manual rebuild, the team defined an incremental Strangler Fig migration strategy that allowed legacy and modernised capabilities to coexist, giving the client a safer path to modernise critical invoice generation functionality without interrupting ongoing operations.
The engagement combined deep code and database analysis, AI-assisted business rule extraction, human validation, and automated test planning to create a more reliable foundation for modernisation. We also evaluated CrewAI and other AI tools to determine where AI could accelerate requirement generation and migration readiness, where guardrails were needed to reduce risk, and what inference costs could look like at scale. We then provided the client with a practical, staged modernisation roadmap that included clearer risk controls, stronger validation, and measurable progress toward a modern microservices architecture.
Customer
Multinational financial technology
Services
Tech stack
CrewAI
SQL
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