AI Visualizer
An interactive reference architecture factory for exploring Generative AI model types.
Overview
Building Generative AI solutions means navigating a maze of terminology, components, and reference architectures. There’s no single “right way” to assemble an AI stack—various technologies can be combined in different ways to produce similar results. The AI Visualizer is an interactive SVG-based tool that makes these architectures explorable and understandable. The diagrams are linked to a database of relevant technologies, allowing users to navigate through AI workflows with interactive popups that provide insights into key steps and industry players.
Key Goals:
Develop an interactive walkthrough of AI reference architecture patterns.
Provide an engaging, visual way to explore different Generative AI models.
Make AI workflows more transparent by linking architecture steps to real-world technologies.
Complexity: Medium
Components
SVG Visualization Factory
A metadata-driven framework that integrates SVG-generated visualizations with a structured database—enabling real-time, interactive exploration of AI architectures.
SOARL Summary
While researching IBM reference architecture diagrams, I found them too simplistic, often skipping critical AI workflow steps.
At the same time, I had been building a database of AI products as a reference for my own work.
I had no prior experience with SVGs or how they could solve this type of problem.
Co-developed with ChatGPT**, I asked how to approach this challenge.
Learned to create reference architecture diagrams in Lucid, export them as SVGs, and enhance them using Inkscape for better labeling.
{“Built a metadata-driven factory module, integrating”=>[“A series of database tables defining available model types.”, “Links** from architecture labels to equivalent AI technologies.”, “An interactive HTML page with JavaScript-powered navigation & popups.”]}
A scalable framework where new elements can be added with minimal effort.
This was a perfect example of leveraging structured intelligence—most key components (database, metadata menus) already existed, meaning the only missing pieces were SVG knowledge and an interactive front end.
The entire project was completed in ~12 hours.
Situation:
Obstacle:
Action:
Result:
Learning:
Key Learnings
- AI architectures aren’t static—visualizing them in an interactive, metadata-driven way improves understanding and adaptability. - SVGs + metadata = powerful interactive tools—this combination enables dynamic AI model exploration. - Having structured data in place accelerates development—with a well-defined database, the missing piece was figuring out the visualization mechanism.
Demos
Final Thoughts
AI reference architectures shouldn’t be abstract PDFs—they should be interactive, navigable, and connected to real-world technologies. This metadata-driven SVG visualizer turns static models into living, explorable AI workflows—making the alphabet soup of AI architectures actually make sense. 🚀