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Development of a Visual AI Model Composer with Configurable Pipeline Tools
  1. case
  2. Development of a Visual AI Model Composer with Configurable Pipeline Tools

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Development of a Visual AI Model Composer with Configurable Pipeline Tools

neoteric.eu
Information technology
Artificial Intelligence
Software Development

Challenges in AI Model Development

The client required a visual tool to compose AI models without traditional programming, while struggling to find developers with expertise in diagramming frameworks like Rappid (JointJS+). Existing development processes were time-consuming and lacked configurability for future extensions.

About the Client

Silicon Valley-based AI and cognitive computing company developing applications that enable the 'internet of everything' to emulate human brain functionality through big data, AI, and NLP technologies.

Project Goals

  • Create a visual pipeline composer application for AI model development
  • Reduce AI model creation time by 40-50% through drag-and-drop components
  • Implement a configurable system for easy addition of new task types
  • Deliver a user-friendly tool accessible to non-technical users

Core System Functionalities

  • Drag-and-drop interface for visual component assembly
  • REST API integration for dynamic configuration loading
  • Customizable task libraries for pipeline composition
  • Browser-based visual designer with Angular framework

Technology Stack

Angular
Rappid (JointJS+)

System Integrations

  • Backend configuration management system
  • REST API endpoints for task definitions

Non-Functional Requirements

  • Cross-browser compatibility
  • Scalable architecture for task extensions
  • High-performance rendering of complex pipelines
  • Secure data handling practices

Expected Business Impact

The solution is projected to reduce AI model development time by 40-50%, significantly lowering development costs while providing a future-proof platform through its configurable architecture. The visual composer will enable faster iteration cycles and democratize AI development across technical and non-technical teams.

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