Why I’m Building Practical AI Utilities for Content
I’ve spent the last 15 years building customer-focused engineering solutions, mostly around content systems and structured data. A big part of my work has been solving real-world problems—messy inputs, inconsistent formats, and workflows that don’t scale as cleanly as we expect.
Recently, with the rise of AI—especially LLMs—I started experimenting with how these tools can actually help in day-to-day content workflows. Not in a “replace everything” way, but in a more practical sense: small utilities that reduce friction, automate repetitive tasks, and improve content quality.
What I’ve noticed is this: most AI demos look impressive, but they often fall apart when you try to use them in real systems. They lack structure, consistency, and reliability.
This blog is my attempt to explore that gap.
I’ll be building and sharing small, focused AI utilities—things like:
Converting unstructured content into structured formats (JSON)
Lightweight RAG systems for internal documentation
Content linting and validation tools
Simple pipelines that combine AI with traditional engineering
Everything here will be:
Practical (not just demos)
Open source (where possible)
Built using low-cost or free tools
Designed to work with any content system, not tied to a specific platform
I’ll also share what doesn’t work—failures, edge cases, and trade-offs—because that’s where most of the learning happens.
If you’re working with content systems, APIs, or structured data pipelines, this space might be useful.
Let’s see where these experiments go.
