Streamlining legacy system migration with an AI-driven pipeline

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.

At a glance

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.

Client overview

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.

The challenge

Legacy system migration is usually framed as a code translation problem, but that's not what it is. With the scale of the client’s system, a manual rewrite was still possible, even if translation moved slowly. The bigger issue was the system’s codebase; it was dense with NLS-dependent formatting and default row limits, the kind of detail that never appears in documentation but always pops up in production.

In this project, migration was a knowledge-extraction problem. Coherent’s engineers needed to recover what the system actually did, and then prove the new code accomplished the same thing, test by test.

Our approach

Rather than translate the code mechanically, the team built a three-stage migration pipeline:

• Stage 1: An analytics agent read each PL/SQL unit alongside all available sources (business documents, test scenarios, frontend call patterns), extracted the business rules, mapped data types, and identified edge cases, producing a structured migration spec that engineers could review before any code was generated.

• Stage 2: A transform agent generated idiomatic Python from the approved spec, in a consistent router-service-repository-model layout, along with an equivalence test suite. A validated agent ran the suite, checked for linting and type issues, compared behavior against Oracle, and auto-corrected failures up to three times before escalating to an engineer. The auto-fix loop was intentionally bounded to prevent the system from self-correcting indefinitely without human input.

• Stage 3: Four AI agents reviewed every unit through specialist lenses: a Solution Architect lens for architecture and decomposition, a Senior Python lens for idiomatic quality, a Senior PL/SQL lens for missed or implicit Oracle behavior, and a QA lens for untested paths. Human engineers resolved all findings.

The pipeline used AI to generate role-tailored handoff bundles — translating technical specs into documents accessible to project managers and domain experts, providing crucial business context.

The team proved the pipeline’s effectiveness on a real screen from the legacy system: On-Hand Inventory. In roughly three weeks, the team delivered that screen and the entire shared technical foundation it sits on: 68 Oracle packages and 2,001 routines ported to native Python, covered by approximately 1,865 automated equivalence tests. While AI generated roughly 80% of the code, the model deferred the most complex write paths for engineers to hand-finish.

The solution

A strict definition of “done” governed every unit: a screen counts as complete only when every endpoint is fully native Python (no temporary pass-through wrappers, no stubs) with all business rules ported, equivalence-tested, and passing quality and security gates. Those gates run automatically:

• Decimal arithmetic for every numeric type (prevents floating-point errors in inventory and financial calculations).

• UTC timestamps enforced at all API boundaries (prevents Oracle/Python date-semantic mismatches from surfacing in production).

• OAuth2 scope validation on every endpoint (prevents unauthorized access to migrated APIs).

• Strict type-checking via mypy --strict (catches type errors at commit time, not in production).

• Every query uses safe, parameterized SQL — no injection risk.

On the client’s side, a toggle architecture provided an additional production safety layer: the old Oracle implementation and the new Python implementation can be switched in either direction at any time. If a migrated screen behaves unexpectedly, the team can revert without a deployment event while the issue is resolved.

Human engineers held authority over every decision affecting correctness or architecture. The AI review lenses produced structured findings; engineers resolved them. Ambiguous business logic was never resolved by the model alone: it was flagged, packaged for the appropriate reviewer, and locked only after explicit sign-off.

The impact

For the client, the engagement resolved four problems at once. The manual migration pace (multiple months per screen) is no longer a constraint. AI-generated output with proper context and acceptance criteria reliably reaches senior-level engineering quality, without the variation that comes with individual contributor differences. The fixed-price structure transfers cost risk to Coherent. And when the migration is complete, the client’s dependency on scarce PL/SQL specialists ends: the resulting Python codebase is maintainable, testable, and portable to whatever infrastructure decisions come next.

The shared technical foundation is the primary driver of the economics. Because the shared code is built and counted once rather than duplicated across every screen, the remaining 112 screens are delivered at a fixed price by a team of three engineers over roughly six months — work that would take substantially longer if each screen were rebuilt in isolation.

The remaining 112 screens are sequenced into nine domain waves, grouped by functional area so each wave builds on shared logic and context from the last, reducing risk as the program progresses rather than front-loading it.

Scope became a measured fact rather than an estimate, 93% of API calls traced and classified before migration began. That evidence is what made a fixed-price commitment credible: if the remaining work runs longer than estimated, Coherent absorbs the overrun.

The most demanding single function required 8 hours and 22 minutes of human judgment to produce 31 lines of Python. Review, equivalence validation, and security sign-off accounted for roughly three-quarters of total human effort. Generation is fast and cheap; the value is in the governance that surrounds it.

What’s next

This engagement is a demonstration of AI-native legacy modernization in practice — a framework for migrating business-critical systems at scale. The pipeline, the agent architecture, and the equivalence-validation harness are not specific to PL/SQL-to-Python; only the translation patterns change with the stack. Coherent is successfully adapting this framework and pipeline to other migration projects, refining and making the process more reliable with each engagement.

This engagement is also a concrete example of Coherent's Digital Value Creation (DVC) and Continuous Delivery Loop (CDL) frameworks in practice, where delivery discipline produces outcomes you can actually measure.