AI-Powered Workflow Framework for Global eCat Teams

Overview

A global electronic catalogue platform needed a faster and more scalable way to build workflow-driven applications across teams in Asia, Europe, and North America.

Previously, every new workflow or builder required teams to develop dedicated UI implementations from scratch. As the number of workflows increased, development became increasingly resource-intensive, difficult to maintain, and heavily dependent on centralized engineering support.

To solve this, we designed and developed the eCat Common Workflow Framework - a unified workflow platform built with React and TypeScript that enabled regional teams to create and manage workflows through configuration rather than custom UI development.

As adoption grew, another challenge emerged: developers still needed to understand complex documentation, workflow rules, and framework conventions before they could successfully create configurations.

To further simplify the experience, we introduced an AI-powered workflow generation system that automated workflow creation directly from design inputs such as Figma files and screenshots.

The Challenge

The original workflow development process created several operational bottlenecks:

• Every workflow required dedicated frontend development

• Teams repeatedly rebuilt similar UI patterns across regions

• Maintaining multiple workflow builders increased engineering overhead

• Regional developers had to learn complex framework configurations

• Documentation-heavy onboarding slowed delivery cycles

• Developers frequently depended on core platform teams for implementation guidance

The organization needed a scalable system that could standardize workflow creation while reducing engineering dependency across global teams.

The Solution

We built the eCat Common Workflow Framework - a centralized, configuration-driven workflow platform designed to standardize workflow creation across all regions.

The framework provided:

• A unified workflow UI architecture

• Strongly typed configuration-driven workflow generation

• Shared workflow components and services

• Reusable templates and execution patterns

• Real-time workflow execution management

• Encapsulated backend integrations across eCat applications

Instead of building workflow UIs from scratch, developers could now create workflows using structured configuration objects, dramatically reducing implementation effort and maintenance complexity.

AI Workflow Generation System

As the framework scaled across teams, we identified another opportunity for optimization.

Even with configuration-driven development, developers still needed to:

• Understand extensive framework documentation

• Learn workflow rules and configuration structures

• Manually map UI layouts into framework templates

• Coordinate with platform teams for advanced workflow behavior

To solve this, we developed an AI-powered workflow generation system built around intelligent agents and rule-driven automation.

Developers and designers could now:

• Upload a Figma design or workflow screenshot

• Automatically generate workflow templates

• Create UI structures aligned with framework standards

• Generate rule configurations dynamically

• Validate workflow behavior with guided interactions

• Apply rule properties directly to workflow components

The AI system significantly reduced onboarding complexity while accelerating workflow delivery across regional engineering teams.

Technologies Used

• React

• TypeScript

• AI Agents

• Configuration-Driven Architecture

• Rule-Based Workflow Engine

• REST APIs

• Shared Component Libraries

Impact

The Common Workflow Framework fundamentally transformed how workflow applications were built across the organization.

Previously, creating and integrating a new workflow builder into the main application required multiple development cycles and often stretched across long sprint timelines. Every workflow involved dedicated frontend implementation, manual configuration, integration work, testing, and ongoing support from core engineering teams.

With the AI-powered common workflow platform:

• Workflow delivery timelines were reduced from 5 to 6 sprint cycles to approximately 3-4 days, including testing and monitoring

• Teams shifted from repetitive UI development to configuration-driven workflow generation

• Designers and non-framework specialists could create workflows using AI-assisted generation from Figma designs and screenshots

• Regional teams became significantly less dependent on centralized platform engineers

• Workflow implementation became more standardized, scalable, and easier to maintain globally

• Engineering teams could focus more on business logic and product innovation rather than rebuilding workflow infrastructure repeatedly

What once required dedicated frontend engineering effort could now be generated, configured, and deployed significantly faster through AI-assisted workflow automation.

What This Enabled

By combining configuration-driven engineering with AI-assisted workflow generation, the platform evolved from a traditional workflow builder into a scalable internal development ecosystem.

Regional teams across Asia, Europe, and North America could now create sophisticated workflows faster, with fewer engineering dependencies, while maintaining consistency, scalability, and long-term maintainability across the entire eCat platform.