A major North American food manufacturer needed to migrate a business-critical warehouse management system from Oracle PL/SQL to Python. Rather than translating the code mechanically, Coherent built an AI-native pipeline that used agents to analyze the legacy logic, generate idiomatic Python and equivalence tests, and validate translated units against the system’s original Oracle procedural language and behavior before advancing.
During the process, Coherent’s engineers governed every logic gate to guarantee accuracy. As a result, they were able to deliver the migration framework, a shared technical foundation, and the first migrated screen in roughly three weeks, with the remaining 112 screens committed to a fixed price.
Client: A major North American food manufacturer
Industry: Food manufacturing/warehouse operations
Challenge: The client couldn’t maintain its older business-critical warehouse management system or deploy it to the cloud. The Oracle PL/SQL-built system needed to be migrated to Python, but with 112 screens and approximately 3,100 routines, a manual rewrite was too slow, too risky, and dependent on a shrinking pool of PL/SQL specialists.
Solution: Coherent created an AI-native migration pipeline in which AI agents handled the initial rewrite and engineers governed security gates and validated work. The framework recovered business rules from legacy code, generated idiomatic Python, automated equivalence tests, and escalated ambiguous logic before it advancing to the next stage.
Outcome: Coherent built a pipeline and a shared technical foundation and translated the system’s first screen. The screen contained a total of 68 Oracle packages, 2,001 routines, and approximately 1,865 equivalence tests) in roughly three weeks. The remaining 112 screens were fully traced and committed to fixed-price delivery.
Coherent’s role: Embedded migration engineering team; designed and iterated the agentic pipeline; built the equivalence-test harness; produced the evidence-based estimate and wave-by-wave delivery plan.
The client is a major North American food and dairy company. Their warehouse management system had grown over many years into a large Oracle PL/SQL codebase: hundreds of packages, thousands of routines, and a web of stored settings that encoded how the business operated.
The system worked, but it was also a liability. Every change was funneled through a dwindling pool of PL/SQL specialists. The platform could not be containerized or cloud-deployed, and Oracle’s licensing costs kept rising. Also, the business logic that mattered most (rules governing inventory, appointments, production, and shipping across high-throughput warehouses) was implicit, undocumented, and buried in stored procedures that were difficult to access.