AI-Charged: How We Cut Migration Planning Time by 70%

The product is a next-generation transportation management system that brings together data from warehouses, cars, and customer systems into a single control tower. But the true victory came from using AI-assisted analysis to speed up discovery and planning so that engineers could do their jobs more securely and quickly.

This project became the first large-scale implementation of Devox Software’s proprietary AI Solution Accelerator™ framework in a real production modernization environment.

The client faced a familiar modernization challenge: a highly interconnected legacy environment with limited documentation, hidden business logic, and operational workflows too critical to interrupt. Initial manual analysis estimated that it would take more than 10 weeks just for dependency discovery and migration planning across multiple disconnected systems.

Instead of relying solely on manual architecture audits, Devox introduced an AI-assisted modernization workflow capable of:

• automatically identifying dependencies
• mapping integration chains
• predicting migration risks
• and simulating migration waves before execution

The result was a measurable 70% reduction in modernization planning timelines while preserving operational stability and deployment confidence.

The team included:

• A Solution Architect,
• .NET engineers,
• A DevOps expert,
• A QA specialist.

This way, every role was enhanced with AI-powered acceleration.

Unlike standard enterprise modernization projects, logistics and automotive operations introduced additional operational constraints:

However, our focus was on delivery timelines, since the first estimate for only manual finding and planning across three disconnected systems exceeded 10 weeks. The client couldn’t spend so much time. That’s why we sought ways to accelerate the process.

We utilized Azure DevOps with Terraform, containerized .NET 8 services, Angular for the admin interfaces, PostgreSQL as the target data store, and TensorFlow combined with Azure Cognitive Services to power automated code clustering, provide data lineage hints, and enable risk scoring within the AI Solution Accelerator™.

A major differentiator of the project was the use of ML-assisted dependency analysis and migration intelligence. For this purpose, Devox used TensorFlow-based classification and clustering models trained on historical modernization projects and internal migration datasets.

The TensorFlow models were trained on anonymized architectural metadata gathered from internal modernization engagements. This way, the AI models analyzed the following:

• repository structures
• database schemas
• dependency graphs
• operational logs
• service interaction frequency

Then, through the framework, the team generated dependency aging analysis, service coupling scores, migration risk rankings, and modernization recommendations. Additionally, Azure Cognitive Services assisted in extracting undocumented business rules and identifying operational terminology hidden across codebases.

Human Validation Layer

While AI significantly accelerated discovery and migration planning, human validation workflows and enterprise review checkpoints fully governed all architectural decisions.

To ensure operational safety and migration accuracy, Devox Software combined ML-assisted analysis with architect-led oversight. The governance process included:

• architect’s review of all AI-generated dependency mappings
• recommendations’ confidence scoring
• manual validation of high-risk service boundaries
• installed governance checkpoints before each migration wave and SLA-sensitive modules

Every AI-generated recommendation was assigned a confidence threshold. If not aligned, recommendations were removed from the roadmap and put into manual architectural analysis.

We set up the job as a pipeline based on evidence, reinforced by AI-driven migration planning to any degree:

• Deterministic Discovery. We searched repositories and SQL workloads to figure out call graphs, shared schemas, batch windows, and other hotspots that weren’t evident. The result was a live system map with levels of confidence and gaps marked for interviews. 
• Set Boundaries. We created a small, versioned API known as the “contract of record” for managing orders, carriers, and tracking. This technique allowed teams to change things behind the scenes.
• Wave Planning Simulations. The Accelerator grouped the code according to its usage and interoperability. It also suggested candidates for migration and ran “what-if” waves against traffic, batch windows, and SLA limits. We selected waves that would provide the greatest reduction of risk during each sprint.
• Checked Design. Every wave had clear rollback points, database diff checks, and contract testing. Feature flags and blue-green delivery made a difference between deployment and release. These features meant that modifications could be dark-launched, watched, and then switched on.

CI/CD included contract tests, regression packs, and performance probes. If you failed a gate, the wave never moved forward. Success automatically generated new maps and dashboards, facilitating coordination among planning, engineering, and QA teams.

• Migration planning time: Reduced by 70% in total as compared to non-AI-powered migration.
• Discovery acceleration: 45–55%.
• Module identification acceleration: 40–50%.
• Migration planning acceleration: 50–60%.
• Operational risk exposure is minimized through AI-assisted simulations.

As a result, the client was fully satisfied with the course and results of the planning phase and proceeded with the migration itself. In particular, AI-assisted analysis, instead of relying on manual discovery, reduced the planned modernization time to 3 weeks instead of 10.