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Case Study

Turning a Static Index into a Maintainable AI-Assisted Publishing Workflow

A publishing system that keeps notes, sources, and draft output aligned without forcing a CMS.

Client
Independent product build
Role
Information architecture, frontend engineering, workflow design
Duration
3 weeks
Published
2026-05-01
Next.js
TypeScript
MDX
OpenAI API
Vercel

Context

Where the work started

The content surface started as a flat list of notes and references that was hard to search, update, and reuse.

Problem

What needed to change

Publishing required too much manual assembly, so writing and reference material drifted apart.

Constraints

What shaped the solution

  • Keep the setup local-first
  • Avoid a CMS before the editorial model stabilizes
  • Make AI output reviewable before publishing

Process

How I moved through it

  1. Mapped content primitives and writing states.
  2. Defined a narrow schema for topics, sources, and draft status.
  3. Added AI-assisted drafting with review points.
  4. Kept output compatible with local MDX files.

Solution

What shipped

Built a structured index and publishing workflow with clean content boundaries, making edits predictable and reviewable.

Result / Impact

What changed

Writing and reference material could move through one simple surface instead of being split across ad-hoc files.

Less time reshaping content; more time refining the actual ideas.

Reflection

What I learned

  • Small schemas stay editable longer than broad CMS models.
  • Workflow clarity matters more than fancy automation.

Related Project

Kizuna Index

A living index that turns scattered references into a maintainable publishing surface.

View project

Services Involved

Content Systems & Local Editors
Personal / Brand Websites
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