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Zero-Downtime Cloud Migration with AI-Assisted Rollback Strategy

A U.S. EdTech firm teamed up with Devox to rebuild its learning platform through an AI-driven, zero-downtime cloud migration that brought order, speed, and scale.

Zero-Downtime Cloud Migration with AI-Assisted Rollback Strategy

About the client

A U.S. EdTech company with more than half a million active users. For years, its platform for online learning and exams expanded across frameworks and databases.

Background:

The story begins in Austin, Texas, at a conference where some of the industry’s top minds were championing Applied AI in Software Engineering, and one of the speakers was Devox Software, known for its deep expertise in legacy modernization.

In the crowd was Daniel R., VP of Engineering at a mid-sized EdTech firm with a long-running learning platform that had history, but little structure for what came next. When Devox spoke about using AI-assisted analysis and zero-downtime migration to bring legacy systems back to life, Daniel perked up. He reached out to see if a more disciplined, risk-aware modernization could work. What began as a quick coffee at the conference turned into a real partnership.

Project Team

Composition:

  • 1 Project Manager
  • 1 Cloud Architect (AWS/GCP hybrid)
  • 2 Backend Engineers (Node.js, Python)
  • 2 Platform Engineers (Kubernetes, Istio, Argo Rollouts)
  • 1 Data Engineer (CDC / AWS DMS)
  • 1 AIOps Specialist (Datadog / Dynatrace / Kayenta)
  • 1 QA Automation Engineer

Challenges

We Faced:

  • Layered Legacy Code. Years of quick extensions created hidden ties between modules and services. Understanding how each part interacted became the first mission before any migration.
  • Opaque Dependencies. Lack of consistent documentation limited visibility. Every new feature involved guesswork and digging through outdated scripts.
  • Continuous Load. Thousands of live exam sessions run daily. The migration had to progress in parallel without affecting real users.
  • Unrecorded Logic. Core business rules existed only in code. Without structured documentation, engineers risked losing valuable operational knowledge.

Tech

Stack:

  • Infrastructure: AWS (EKS, RDS, CodeDeploy Blue/Green), GCP (Cloud SQL, Dataflow, Monitoring)
  • App Layer: Node.js, Python, Kubernetes, Argo Rollouts, Istio
  • Data Layer: PostgreSQL, AWS DMS, Debezium (CDC), Kafka Connect
  • AI / Observability: Datadog Watchdog, Dynatrace Davis, Kayenta (Automated Canary Analysis)
  • Feature Management: LaunchDarkly (feature flags, kill-switches)
  • CI/CD: GitHub Actions, Terraform, Helm

Solution:

Daniel often said the hardest part wasn’t the migration itself — it was realizing that the system he had helped grow for years had outgrown him. “We had dozens of interconnected applications built over time on different stacks. Eventually, it became clear that we were no longer driving our IT — it was driving us.

That realization marked the true beginning of the project. What followed was a structured, zero-downtime migration — a process where every dependency was mapped, every risk anticipated, and every change documented through AI-assisted tooling. We delivered a secure, cloud-hosted SaaS platform with the following core modules.

  • AI-Powered Discovery. Before the first deployment, AI assistants analyzed the full codebase and database structure. They built a live dependency graph linking modules, data flows, and historical commits.
  • Automated Documentation. Each code change automatically produced Markdown-based documentation with rollback steps, version history, and updated diagrams. This provided engineers with transparent knowledge of how each service behaved through migration.
  • Zero-Downtime Migration. We used a blue-green deployment model combined with canary releases via Argo Rollouts and Istio to ensure uninterrupted service. Kayenta’s automated canary analysis validated every rollout phase against live metrics.
  • AI-Assisted Rollback. AIOps tools monitored latency, user behavior, and anomaly signals. When deviations appeared, the system automatically reverted to the previous stable state using prebuilt rollback scripts.
  • Data Synchronization. AWS DMS and Debezium continuously replicate data between on-prem and cloud databases. The expand/contract pattern enabled old and new schemas to evolve in parallel.

Results:

BUSINESS OUTCOMES

  • Platform uptime was maintained through the entire migration window.
  • Migration cycle shortened by 40% through AI-based dependency analysis.
  • Documentation coverage increased from 30% to 95%, all fully auto-generated.
  • Mean time to recovery decreased to under five minutes through AI rollback triggers.
  • Development teams gained clearer system visibility and a faster, more consistent release cadence.

TECHNICAL OUTCOMES

  • Code dependencies are visualized and version-tracked automatically.
  • Fifteen major modules were refactored with built-in rollback and audit logic.
  • Continuous canary validation integrated into CI/CD pipelines.
  • 60% reduction in post-migration support incidents.
  • Introduced a reusable Devox “Explainable Migration” framework for complex legacy transitions.

Sum Up:

Every migration is as much about people as it is about code. Daniel’s team had carried their platform through years of change — a system that taught millions, yet struggled to evolve itself. Together with Devox, they found a way to move forward without losing the essence of what they had built.

AI became the silent translator between generations of code — bridging what past engineers built with what modern infrastructure understands. As the migration unfolded, documentation grew naturally, dependencies surfaced, and the system started to reveal its own logic. The platform finally made sense of its own history — clear, self-aware, and ready for whatever comes next.

If your platform has grown faster than its architecture, Devox can help it find balance again. Our engineers build modernization paths that keep systems alive while preparing them for the next decade. Let’s design a migration plan that understands your code as well as you do.

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