Enabling Zero-Downtime Modernization for a Customer-Facing Application

An AI-accelerated modernization of a high-traffic e-commerce platform, enabling zero-downtime migration to Azure microservices.

Enabling Zero-Downtime Modernization for a Customer-Facing Application

About the client

A fast-growing e-commerce company based in New York, known for live shopping events, real-time recommendations, and one-click checkouts. In just six years, they scaled to over 1 million users across the US and Canada, processing close to $800M in annual transactions — with 40% year-over-year growth.

About

the Client:

A fast-growing e-commerce company based in New York, known for live shopping events, real-time recommendations, and one-click checkouts. In just six years, they scaled to over 1 million users across the US and Canada, processing close to $800M in annual transactions — with 40% year-over-year growth.

Mark L., Chief Product Officer, carried direct responsibility for revenue and conversion. His focus was to keep the shopping experience seamless while enabling new features to meet rising demand. But the cracks in the technology became impossible to ignore. The 500,000-line .NET monolith slowed delivery, and every flash sale carried the risk of latency or downtime. For Mark, that meant lost transactions, reduced conversion rates, and a growing threat to customer trust.

Mark first learned about Devox Software through peers in his network who had faced similar modernization challenges in fintech and retail, and their recommendation led him to start the conversation.

 

Background:

The client’s core platform had grown quickly — but unevenly.

Originally built on a 500,000-line .NET monolith, it combined C# backend logic, SQL databases, and a patchwork of third-party APIs. Over five years and multiple handovers, architecture drifted. Complexity crept in. Change got slower. Risk got higher.

As user numbers grew 30-40% annually, the system struggled to keep up. Flash sales triggered slowdowns. Release windows narrowed. Any update carried the threat of downtime.

In 2025, the leadership team committed to a shift: move to a cloud-native setup on Azure, introduce microservices, and adopt serverless components to support future growth. The budget was capped at $1.2M — a significant investment for a mid-sized team. Nearly half was earmarked for keeping the business online throughout. 

As one exec put it: “We weren’t afraid of the rebuild. We were afraid of the interruptions it might cause.”

Project

Team Composition:

  • Modernization Architect
  • 2 × Senior Software Engineers (Refactoring & Microservices)
  • DevOps Specialist (Azure, IaC, CI/CD)
  • AI Ops Lead

Challenges:

This was a customer-facing platform under pressure — fast growth, real revenue, zero room for delays.

Every deployment touched critical flows: checkout, inventory, and payment. Even minor updates required caution. A slip could ripple across systems and erode trust in seconds.

Three conditions shaped the challenge:

  1. Compounded technical debt. Years of layered development left behind fragile logic and silent dependencies. Each module relied on assumptions buried deep in code. The system resisted change because no single team held the full mental map.
  2. High-stakes infrastructure. The architecture operated as one block — shared runtime, shared risk. Any release affected the whole platform. Compliance standards like PCI DSS demanded tight control over transitions and data handling.
  3. Capacity at its limit. The internal team carried core delivery, support, and roadmap execution. Time for structured refactoring didn’t exist. Shifting engineers to rewrite legacy systems meant slowing down product momentum.

Unaddressed, these constraints capped the company’s ability to scale, introduce features, and meet rising demand.

Tech

Stack:

NET Framework, .NET 8, C#, SQL Server, Azure Functions, Azure App Services, Cosmos DB, REST/gRPC APIs, Terraform, GitHub Actions, SonarQube, AI coding agents, E2E testing pipelines.

Solution:

The team applied a structured, AI-accelerated modernization flow — one slice at a time, without disrupting users.

  • Semantic backlog extraction. AI parsed recordings and transcripts from stakeholder sessions into a clear, prioritized backlog. Ambiguities were resolved, and requirements grounded in real user flows. High-impact areas, such as checkout, live events, and inventory sync,  were moved to the front.
  • Legacy code mapping. Each module was analyzed for responsibility, risk, and coupling. The team surfaced entangled logic, dead paths, and hidden side effects. This enabled clean isolation and safe modularization.
  • AI-guided refactoring. Modern IDEs powered by embedded agents accelerated the rewrite. Agents proposed structural refactorings while developers focused on intent. This preserved logic, increased clarity, and reduced manual overhead — all under production load.
  • Automated testing. Each refactored component was backed by generated tests — unit, integration, and E2E. Visual diffs and headless validations confirmed behavior remained intact. This closed the confidence gap between change and release.
  • DevOps acceleration. Infrastructure was codified using Terraform. CI/CD pipelines handled provisioning, deployment, and regression checks. Blue-green deployments enabled controlled rollout across staging and prod — fully traceable, fully observable.
  • Framework modernization. Legacy .NET dependencies were audited, upgraded, and verified against modern APIs. Components transitioned to .NET 8 through mapped, test-covered migration paths, with zero flow interruption.
  • Compliance-First Governance. Sensitive data stayed shielded. AI outputs were monitored, and access was logged. Every transition aligned with PCI DSS and internal compliance policies — by design, not afterthought.

Each step delivered production-grade increments. No rollbacks. No freezes. Just forward movement, one tested slice at a time.

Results:

BUSINESS OUTCOMES

  • 30% budget efficiency gained. Final spend landed at $840K vs. the $1.2M projection, driven by automation, modular execution, and fewer production incidents.
  • Faster time to revenue. The full modernization was completed 3 months ahead of schedule, accelerating roadmap delivery and new feature launches.
  • +20% user growth supported. Improved scalability enabled higher concurrent traffic and sustained platform performance during campaigns.
  • Reduced churn, higher conversion. Checkout speed increased, errors dropped, and session stability improved.
  • Stronger market position. The team regained velocity, shipping weekly without fear of regressions or downtime.

TECHNICAL OUTCOMES

  • 99.99% uptime maintained. All migrations completed live, with no service interruptions or rollbacks.
  • 2× peak load capacity. System scaled horizontally under Azure with lower response times under stress.
  • 40% latency reduction. Key flows like add-to-cart and payment processed faster under load.
  • 60% less manual refactoring time. AI-assisted IDE workflows accelerated rewrite efforts while preserving business logic.
  • Full PCI DSS alignment preserved. Every component transitioned under audit-ready governance, without requiring external exceptions.
  • 50% lower infrastructure overhead. Azure-native architecture reduced on-prem dependency and resource spend.

Sum Up:

This modernization proved one principle: continuity matters as much as change. The platform scaled under pressure, released faster, and aligned compliance with every cycle. Revenue stayed live and teams delivered without pause. For Mark and his colleagues, the real difference was that peak sales no longer felt like a risk, and the focus shifted back to building new products. Impressed by the results, the company invited Devox to explore further collaboration on scaling data-driven personalization and future cloud initiatives.

For companies that run customer-facing systems, this approach turns modernization from a risk into an advantage. Devox Software applies the same zero-downtime model across finance, retail, and SaaS. Explore how our AI-accelerated modernization can secure growth without disruption — start with a confidential technical audit.

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