PraxentDecodeSM case study
Modernizing payment reconciliation at a top-5 global processor
How Decode, Praxent’s reverse engineering agent system, modernized a critical, end-of-life Delphi reconciliation system processing $2B per day.
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100x
cost reduction vs. traditional modernization
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$2B+
daily transactions reconciled
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48x
faster than manual legacy analysis
A global fintech leader ran its payments reconciliation on a legacy platform their team could no longer build against.
The platform, written in Delphi, managed exception ticketing, executed funds movement, and carried seven-year data retention obligations. A single drive failure or bad reboot created a real probability the system would never come back up, and manual reconciliation at that volume was not an option.
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The Delphi compiler license had lapsed
The compiled language can no longer be licensed. Rebuilding the system was not an option without the tools to compile it.
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End of life operating system
The OS running the platform was no longer receiving security patches or vendor support.
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Discontinued hardware
The physical machine in the data center could not be replaced. A drive failure with no path to recovery.
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Black box system
14,000 lines of code nobody knew about. Every engineer who built the system had left. No documentation existed outside the source code itself.
PraxentDecode℠ ran an agentic reverse-engineering process against the full codebase.
Every file fingerprinted and scored on a six-factor complexity model.
Calculations, validations, and SQL intent recovered line by line. 143 SME questions raised for workshopping.
Findings organized across seven business domains with traced requirements and user stories.
Blueprint of every class, form, API surface, and module.
Full dependency graph with circular-dependency and coupling-risk reports.
Every record, class, and database schema mapped with code-to-SQL traceability.
SME answers reconciled, producing implementation-ready requirements with full traceability.
Cross-checked against source code, database structures, and documented system behavior.
PraxentDecode℠ often surfaces architectural boundaries the original team did not know existed. In this engagement what the organization treated as one system was actually three applications running as one: downloads, daily reconciliation, and bank reconciliation.
Every phase of Decode has a Praxent forward-deployed fintech engineer in the loop. They calibrate scope, interpret findings against payments patterns, decide which of the SME questions need human input, and translate the recovered logic into a build plan compliance can sign off on. That loop produces specs you can act on.
Paul Tidwell
Head of AI, Praxent
Paul Tidwell
Head of AI, Praxent
A reverse-engineering agent reads the source code, builds an internal model of how the system behaves, raises the questions only humans can answer, and folds those answers back into traceable specs.
EXAMPLE BUSINESS RULE EXTRACTED
The bank statement balance is calculated as the sum of all line item amounts for the statement period, but only for top-level items where the parent line item is null or 0. Detail and child line items are excluded to prevent double-counting.
Rules like this live inside stored procedures and calculation methods, often hard-coded hundreds of lines deep. They are the kind of detail a rebuild misses unless the original logic is recovered first.
Legacy modernizations break in the third month of production when a business rule no one remembered surfaces incorrectly. Decode pulls those rules out of the source first. SMEs validate them before the build starts. Parity comes faster, with less risk.
Paul Tidwell
Head of AI, Praxent
Paul Tidwell
Head of AI, Praxent
THE OUTPUT
SECURITY FINDINGS
stored in source, visible to any developer with repository access.
with inputs reaching SQL execution without parameterization.
of the type common in older compiled languages.
still active in the date logic, hardcoded assumptions from 26 years ago.
in critical workflows, including compliance logic embedded inside exception handlers.
Modern security vulnerabilities, exploited by AI-powered attack tools, are making 20-year-old codebases even more exposed. The risk is getting higher. And the barriers are getting lower.
Paul Tidwell
Head of AI, Praxent
Paul Tidwell
Head of AI, Praxent
THE RESULTS
Decode℠ produced a complete map of what the platform does, traced to source line by line, and a clear path forward.
Prioritized modernization roadmap
MVP / Post-MVP across seven domains.
636 sprint-ready development tasks
loaded into GitHub with labels, priorities, and sprint assignments.
Risk inventory
across security, compliance, and architectural coupling.
Parity-testing strategy
anchored on Praxent design principles.
Actionable modernization
A previously stalled modernization effort, unblocked.
SYSTEM PARITY GUARANTEE
Two validation passes get Decode from 95% to 100% parity.
Independent scoring
Pre-implementation
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1Praxent generates the analysis
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2Independent model reads recovered requirements
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3Locates each requirement in source, scores coverage
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4Validation pass runs 2–3 different ways for the same engagement
Parallel test harness
Implementation phase
Tested against synthetic, real, and historical data. Differences become test failures.
Before LLMs, this kind of recovery relied on people, and the error rate was significantly higher than 5%. The structured, validated, agentic approach is faster and more accurate than what a human team typically produced.
We're in an era where you can fully understand and rebuild parts of your system that are slowing you down. When you can do that for a fraction of the cost, you fundamentally reevaluate what legacy means and how it is affecting your velocity as a business.
Paul Tidwell
Head of AI, Praxent
Paul Tidwell
Head of AI, Praxent
Traditional Manual Discovery Approach
Your financial platform is a black box.
PraxentDecode℠ is an AI-native modernization approach powered by Claude. It runs against Delphi, COBOL, BASIC, Visual Basic, and C/C++. Anywhere institutional knowledge is gone, Praxent reads the legacy codebase and produces a complete picture of what's actually in it before any modernization work begins.
Talk to us about decoding it →