Situation
A client in the legal and records management sector relied on a mission-critical legacy application to manage sensitive case data and documents. Over the years, the system grew to encompass hundreds of interdependent SQL tables and multiple thousands of SQL stored procedures.
This sprawl of undocumented logic, bridge tables, and foreign key dependencies created significant technical debt. Manual migration was slow, risky, and error-prone, relying on trial-and-error insertions, spreadsheets, and tribal knowledge. The client needed to modernize their environment into a Python microservices–based architecture while ensuring data integrity, performance, and scalability.
Task
The client's objectives were clear:
- Migrate thousands of SQL stored procedures into modern, maintainable Python microservices.
- Transform legacy schemas and ETL logic into reusable, dependency-aware modules.
- Generate documentation and test scaffolding for previously undocumented business logic.
- Accelerate delivery speed and reduce overall migration costs.
- Create a repeatable modernization model for future initiatives.
Actions — Codemorphology's MORPH Process in Action
Map
Clustered hundreds of tables into logical business domains ("data islands"), simplifying orchestration and enabling parallel migration. Extracted undocumented business rules from stored procedures, mapping them into a modern design for Python microservices.
Orchestrate
Inferred foreign key chains and rebuilt bridge table logic to generate dependency-aware, type-safe ETL scripts that reduced trial-and-error rework.
Reconstruct
Converted SQL stored procedures into Python-based microservices with API scaffolding, delivering maintainable, testable, and cloud-ready components.
Produce
Provisioned Python services, APIs, SQL services on AWS, and Lambda-based microservices, enabling scalable, cloud-native modernization.
Harvest
Delivered generated unit tests and documentation frameworks while helping the client adopt and operate the AI Agents themselves, ensuring long-term ownership and reduced reliance on external teams.
Results
- 70% Reduction in Manual Effort for SP-to-microservice conversions — from 2–3 days down to 2–3 hours.
- 2–3x Faster Migration Cycles across schema, ETL, and SP modernization.
- Improved Accuracy & Integrity with automated dependency detection and referentially correct test data.
- Cloud-Ready Architecture with modern APIs and microservices provisioned directly into AWS.
- Empowered Client Teams through structured handoff of AI Agents and supporting assets.
Summary
By applying its MORPH process and deploying specialized AI Agents, Codemorphology.ai helped a legal and records management client transform a sprawling, undocumented legacy system into a modern, testable, Python microservices–based architecture. The result: faster delivery, reduced risk, and a sustainable path forward for modernization at scale.