Enterprise · AI & Modernization
AI-Powered Legacy Modernization Platform
We built an AI-assisted platform that reads decades-old Perl codebases, maps their hidden business logic into living dependency graphs, and accelerates migration to modern Python services — all running on secure, on-premise AI.
Client: Global Enterprise Software Organization·Enterprise Software / AI
70%
less manual analysis effort
3x
faster migration to Python
50K+
legacy files mapped
100%
on-premise, secure AI
The Challenge
Decades of business logic, locked inside legacy code
The client's most critical systems ran on tens of thousands of aging Perl files — code written over 15+ years by engineers who had long since moved on. The business rules that kept the company running were buried in undocumented scripts, with no map of what depended on what.
Every modernization attempt stalled at the same wall: no team could confidently say what a change would break. Estimating a migration meant weeks of manual code archaeology, and the risk of quietly breaking a revenue-critical workflow made leadership hesitant to touch it at all.
Untangling undocumented dependencies across a sprawling legacy codebase.
Objectives
What the platform had to deliver
Automatically understand legacy code without human archaeology
Visualize the true dependency map of the entire system
Accelerate migration from Perl to modern Python services
Keep every line of code and AI inference inside the enterprise network
Reduce modernization risk with automated validation
Our Solution
An AI engineering platform that reads code the way a senior architect would
We designed a platform that parses source code into Abstract Syntax Trees, then builds a Neo4j dependency graph that captures every relationship between files, functions, and data flows. On top of that graph, we layered AI agents that explain what code does, surface hidden business logic, and draft migration paths to Python.
Everything is exposed through a clean React engineering dashboard — so architects can explore the system visually, ask the AI questions about any component, and plan migrations with confidence instead of guesswork.
The engineering dashboard: dependency intelligence meets AI code understanding.
How It Works
From raw legacy code to migration-ready intelligence
What We Engineered
Our contribution to the platform
Python backend services and parsing pipelines
REST APIs powering the engineering dashboard
Neo4j graph modelling for dependency intelligence
React dashboards for visual system exploration
AI agent workflows for code understanding & migration
Automated validation using Gherkin-based specs
Secure integration with locally-deployed LLMs
See the whole system at a glance
Interactive dependency graphs turn an opaque legacy monolith into a system architects can actually navigate.
Business Impact
Modernization that finally moved forward
70%
reduction in code analysis effort
3x
faster Perl-to-Python migration
50K+
files mapped into a live graph
0
code or data leaving the network
Capabilities
What each capability unlocked
Instant visual understanding of complex systems
Faster comprehension of undocumented logic
Dramatically reduced modernization effort
Operational visibility for engineering teams
Enterprise compliance with zero data exposure
Technology
Built with
Oarlock Labs turned a decade of undocumented legacy code into a system our architects could finally see, understand, and modernize with confidence.
Engineering Leadership · Global Enterprise Software Organization
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