Website
Tools
Highlights
Reduced production time by more than 90%
Produced complete 1,500-word articles in approximately 30 minutes
Built category-agnostic architecture that scales beyond baseball
Created structured JSON and CSV outputs for CMS publishing
Automated SEO metadata generation
Designed a repeatable editorial workflow for hundreds of future articles
Overview
VintageBaseball.club began as an exploration of how AI could support structured editorial production without sacrificing quality or consistency.
Rather than building another AI content site, I designed a publishing system where voice, structure, workflows, and data models were defined before any content was generated.
The result is a category-agnostic platform that combines long-form storytelling, structured commerce, and AI-assisted production into a scalable publishing system.

The Challenge
Most AI-generated content struggles with consistency, structure, and editorial quality.
Most affiliate sites optimize for products instead of storytelling.
The challenge was designing a system that could:
Produce consistent long-form editorial content
Maintain a controlled voice across hundreds of articles
Connect historical narratives with product recommendations
Reduce production effort without sacrificing quality
Support structured publishing inside a CMS
Scale beyond baseball into additional product categories
My Role
I designed the product architecture, AI workflow, editorial system, and publishing infrastructure.
Responsibilities included:
AI workflow architecture
Voice system design
Information architecture
Content modeling
CMS architecture
Notion production system
Framer implementation
Prompt engineering
Structured output design
SEO automation
Approach
I approached the project the same way I approach design systems.
Instead of asking an LLM to write better content, I first designed the system the model would operate inside.
Voice System
I created structured JSON files defining:
Tone and pacing
Sentence structure
Narrative constraints
Reference influences
Editorial boundaries
The objective wasn’t imitation.
It was creating explicit rules that produced predictable, repeatable outputs.
Content Architecture
Before generating a single article, I designed a repeatable editorial structure that separated narrative storytelling from historical research while defining where products, metadata, and supporting content belonged.
Publishing Infrastructure
I built a production pipeline spanning Notion and Framer that managed:
Research
Draft generation
Product mapping
Metadata
SEO
CMS publishing
Structured exports
Everything was modeled so the workflow could operate consistently at scale.
AI-Assisted Workflow
LLMs became one component inside the system rather than the system itself.
The workflow handled research synthesis, structured draft generation, metadata creation, and CMS-ready outputs, while editorial direction, quality control, and final decisions remained human.






Results
Reduced article production time by more than 90%
Produced complete 1,500-word articles in approximately 30 minutes
Built a reusable voice system using structured JSON rules
Created a category-agnostic publishing architecture that scales beyond baseball
Automated metadata generation and structured CMS publishing
Established a repeatable editorial workflow supporting hundreds of future articles
Key Takeaways
This project reinforced my approach to AI product design.
The value wasn’t generated by the language model itself.
It came from designing the system around it.
Voice, interaction rules, structured data, workflow architecture, and production pipelines determined the quality of the outcome.
AI accelerated execution, but the product depended on well-defined constraints, information architecture, and human judgment.
VintageBaseball.club demonstrates how I approach AI-enabled products: build the system first, define the rules, then let automation handle the repeatable work while people remain responsible for direction, quality, and decision-making.
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