Modernising FinTech Infrastructure through AI-Assisted Engineering
The engineering team knew the legacy system needed to go. It was slowing every new feature, accumulating compliance risk, and holding the business back from cloud-native scale. But manually migrating millions of lines of code was estimated at two years minimum. AI changed that calculus entirely.
A fintech company running critical financial services on a legacy Java monolith that had not been meaningfully refactored in over a decade.
A decade-old Java monolith was blocking product velocity and compliance readiness — full modernization estimated at two-plus years of manual effort.
Quick Summary
We used AI-assisted engineering to migrate a decade-old Java monolith at a fintech company to microservices, generating tests first to create a regression baseline and enforcing compliance checks in CI/CD. The project took 45% less engineering effort than the manual estimate, kept 100% test coverage on critical paths and delivered 3× feature velocity after go-live.
Challenges Identified
10+ year old Java codebase — deeply coupled, minimal documentation, and written by engineers who had since left the business.
Compliance requirements demanded zero regressions — financial services rules meant every migrated function had to be provably equivalent to its predecessor.
Feature development was paused during migration — the business could not afford an 18-month freeze on new capabilities.
No existing test suite — the legacy code had minimal coverage, making automated equivalence checking impossible without first generating tests.
Sakha Solution
AI-assisted code analysis — GPT-4 used to parse legacy Java, identify module boundaries, and generate migration candidates with dependency maps.
Automated test generation — AI wrote test suites for legacy functions before migration, establishing a regression baseline for equivalence validation.
Incremental microservices extraction — modules migrated one at a time, with the legacy monolith continuing to serve unaffected services in parallel.
CI/CD pipeline with automated compliance checks — OWASP scans, coverage gates, and equivalence tests ran on every migration PR.
Technical Highlights
Impact
Measurable outcomes delivered to our client.
45% less engineering effort versus manual migration estimates — AI acceleration compressed a multi-year project into a fraction of the timeline.
100% test coverage on all critical paths — the migration generated more test coverage than the legacy system ever had.
3× feature velocity post-migration — microservices architecture allowed parallel development across teams immediately after go-live.
Full OWASP compliance achieved on all migrated services — security debt cleared as a side-effect of the migration process.
"AI did in weeks what our team estimated would take two years. The codebase we have now is something we are proud of."