Weeks to one week
estimated refactor timeline in this case
The largest time savings came from research, explanation, and method mapping.
Government engineering case study
Legacy service refactor time dropped from weeks to about one week.
The developer narrowed the problem. AI mapped the path. Engineering judgment validated the change.
A government engineering team used AI-assisted development to refactor old SOAP-based SharePoint service calls to a newer client model. The useful shift was not handing the whole problem to AI. It was using AI to accelerate explanation, library understanding, and method mapping inside a developer-owned workflow.
HALLBERGAI
Legacy refactor workflow
Legacy service
WCF service
SOAP calls
manual mappings
limited tests
Mapped path
explain logic
map methods
test alternatives
validate scenarios
The old job was research. The new job was validation.
Hours
to understand the newer client model
AI helped the developer see what the newer client model offered without days of documentation review.
Alternatives
surfaced when first approaches failed
AI mapped legacy calls to newer equivalents and suggested alternatives from the same library.
Manual
validation still owned by the engineer
Testing affected scenarios remained the quality gate in the legacy environment.
Full write-up
See how AI helped with the hard middle of legacy refactoring.
The public page summarizes the story. The full write-up explains the environment, constraints, task, workflow, directional results, patterns, what did not work well, and what it means for team adoption.
It is anonymized for public use and written for leaders evaluating how AI can help engineering teams modernize legacy systems without removing human validation from the process.
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Problem
Legacy refactoring was hard because the work lived between old behavior and new libraries.
The team needed to replace old service calls with a newer integration pattern inside a tightly coupled application. That meant understanding old code, reading newer library behavior, mapping methods, testing alternatives, and manually validating affected scenarios.
Step 1
Narrow the legacy problem to specific files, methods, and failure paths before prompting.
Step 2
Use AI to explain the existing service behavior and data flow before changing code.
Step 3
Map old SOAP-based calls to newer client-model equivalents and compare alternatives.
Step 4
Test the suggested changes manually and feed failures back into the next iteration.
Value
The value was compressing research and mapping, not skipping review.
In this case, a refactor that could have required multiple weeks of manual documentation review, method comparison, and trial-and-error was completed in approximately one week with AI assistance.
Validation did not go away. In a legacy environment with limited automated tests, the developer still had to run the application, test affected scenarios, and own correctness.
Repeatable process
What the team could keep using
- A narrow-before-prompt workflow for legacy code.
- Explain-first behavior before refactoring unfamiliar systems.
- AI-assisted library and method mapping with alternatives.
- Cross-validation and manual testing before confidence rises.
Offer
8-Week AI Adoption Pilot Implementation
The goal of the engagement is to help one team turn an AI pilot into a repeatable workflow they can keep using. For legacy modernization, that means pairing AI acceleration with clear human validation.
Find the first repeatable workflowPhase 1
Identify one modernization workflow with enough context to validate.
Phase 2
Capture the old behavior, target library, constraints, and review expectations.
Phase 3
Run the workflow in real work while the engineer keeps ownership of quality.
Phase 4
Package the repeatable pattern so another developer can reuse it.
Related case studies
Use this with the testing and GitHub-first workflow examples.
Want to modernize one legacy workflow without turning AI into a loose experiment?
Start with one constrained refactor, a clear validation path, and a process another engineer can repeat.