Case study: Data & AI
AI-Driven Development: Modernizing a Manufacturing ERP Inventory Management System
Case Study at a Glance
Client
A leading CPG manufacturing company with $6.4 billion in annual sales for the business unit driving the modernization.
The Problem
A highly customized Windows-based ERP had complex system dependencies, manual development processes, and legacy plugins that slowed modernization and increased integration complexity.
The Solution
Vertex applied an AI-first SDLC across requirements, UI prototyping, development, testing, deployment, and operations to modernize the legacy inventory management system.
The Result
Delivered the project in 8 months instead of an estimated 14 months, reduced software defects by over 90%, and reduced MTTR from 4.5 hours to 1.2 hours.
OVERVIEW
Vertex helped modernize a highly customized, Windows-based manufacturing ERP inventory management system using an AI-first development approach.
The modernization transformed legacy desktop workflows into modern web-based applications while maintaining critical integrations with SAP, Enovia, Coupa, and Spider.
AI was applied across the SDLC to accelerate requirements analysis, functional prototyping, engineering, testing, deployment, and production monitoring.
OBJECTIVES
- Modernize the legacy Windows-based ERP architecture
- Transform inventory workflows into modern web applications
- Preserve critical SAP and Enovia integration points
- Accelerate requirements, development, and testing
- Reduce integration errors and downstream rework
- Improve delivery speed, quality, and system stability
- Establish an AI-first development lifecycle
DELIVERABLE
An AI-enabled modern inventory management platform built through an AI-first SDLC, featuring:
- Modern web-based inventory workflows
- AI-generated requirements and functional specifications
- Responsive React UI prototypes
- Modern APIs and enterprise integration architecture
- Multi-agent AI-assisted development
- Autonomous testing and self-healing automation
- Automated cloud deployment and production monitoring
KEY FEATURES
AI-Driven Requirements & Design
- AI extraction of requirements from legacy screens and system documentation
- Automated conversion into structured user stories
- AI-generated OpenAPI / Swagger specifications
Modern Inventory Experience
- Responsive React inventory dashboard
- Modernized stock adjustment and transfer workflows
- API-driven inventory services
- Integration with SAP and Enovia
Multi-Agent Engineering
- AI-assisted business logic development
- Automated security validation
- AI-generated documentation and unit tests
- Context-aware development using Cursor and GitHub Copilot Enterprise
Autonomous Testing
- Synthetic inventory data generation
- Automated browser testing
- Edge-case and high-concurrency testing
- Self-healing regression automation
Automated Deployment & Operations
- Terraform and Kubernetes-based deployment
- Azure DevOps integration
- Production observability
- AI-assisted identification of API and infrastructure issues
BUSINESS RESULTS
- 8 months | Project delivery vs. estimated 14 months
- 45% faster | Discovery phase
- 35% reduction | Engineering cycles
- 60% lower | Regression overhead
- 90%+ reduction | Software defects
- 5 hrs → 1.2 hrs | Mean Time to Repair
The AI-first approach accelerated delivery while improving software quality, testing efficiency, and operational stability.
TECHNOLOGIES
- Frontend: React
- Backend: Node.js / Java
- AI-Assisted Development: Cursor, GitHub Copilot Enterprise
- API: OpenAPI / Swagger
- Enterprise Integrations: SAP, Enovia, Coupa, Spider
- Testing: Synthetic Data, Automated Browser Testing, Self-Healing Automation
- DevOps: Azure DevOps, Terraform, Kubernetes / Helm
- Operations: AIOps, Production Observability
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