Problem
AI-generated content is difficult to trust at scale when research, editorial decisions, rendering, and publishing are handled as one opaque process.
Case Study
AI publishing system
I conceived, designed, and led the development of Daily World Holidays, an automated content product that turns global holiday data into curated daily pages and evergreen WordPress content through deterministic software and AI-assisted editorial generation.
Result: a production-ready publishing pipeline that researches, validates, renders, and publishes new content daily with minimal manual intervention.
Work
Product strategy, system architecture, AI workflow direction, WordPress development, UX, content modeling, automation, testing, and QA.
Outcome
A live automated product demonstrating that rapid AI-assisted development can still produce reliable, maintainable production software.
What the product needed
- Global holiday data needed to become a curated discovery product rather than a raw directory of API records.
- AI needed to improve research and editorial quality without controlling data matching, page visibility, URLs, or publishing decisions.
- The unattended workflow needed to produce consistent pages, recover from failures, and prevent incomplete or incorrectly linked content from reaching the public site.
Key Decisions
Defined the product promise.
Positioned the site as a curated daily discovery product rather than an exhaustive directory, giving the experience a clearer purpose and keeping the operating model manageable.
Controlled the project scope.
Prioritized the Today experience and permanent content library while deferring calendars, category archives, newsletters, image automation, and other features that did not support the MVP.
Separated AI from product decisions.
Established that deterministic code would control matching, validation, visibility, URLs, rendering, and publishing, while AI would be limited to research synthesis and editorial writing.
Designed a recoverable workflow.
Separated preparation, editorial generation, and finalization so each stage could consume saved artifacts, be inspected independently, and resume without unnecessarily repeating earlier work.
Created the content architecture.
Defined structured holiday entities, daily editorial artifacts, country entities, canonical URLs, shared data relationships, and deterministic renderers for each public page type.
Directed the AI-assisted build.
Broke the work into bounded tasks, supplied context to the appropriate model, reviewed implementation reports, identified weak assumptions, and kept development aligned with the current product goal.
Designed and refined the UX.
Shaped the today, holiday, country, and index pages through repeated visual review, simplifying weak sections and improving hierarchy, responsive behavior, content density, and system consistency.
Set the quality threshold.
Required stage-level tests, integration safeguards, explicit failure states, source controls, publishability checks, and protections against predictable broken links before treating the system as production-ready.
Result
3 weeks
from concept to reliable production system.
- Decisive product leadership kept the build focused and moving quickly.
- Deterministic safeguards turned AI speed into dependable output.
- Product, UX, engineering, and QA were directed as one system.
What this shows
This project shows how I lead complex web production across product definition, UX, content systems, engineering, AI orchestration, QA, and launch operations. My primary contribution was making the decisions that kept the product focused, the architecture maintainable, and the automated output safe to publish.
Want to talk?
If you need someone who can improve a website without losing sight of users, revenue, content, code, or production reality, I’m probably worth a conversation.