Data & AI Systems Engineer
These projects begin as tools I build for my own use: to learn, to test ideas, and to solve real problems in a practical way. When they become useful, I publish them so others can use them too. What you see here is not a collection of demos or portfolio exercises, but a public view of an active engineering practice.
Scenario-based certification study across providers and exams
Built because I needed a structured study system I could actually use — not flashcards, but scenario-driven practice at exam depth.
A study site featuring 1,300+ sample questions covering 26 certification exams across four providers — including the recently added CCA-F Claude Certified Architect, Foundations.
Updated March 2026
Labor market analysis with twenty lessons learned and architecture discussions
Built to understand how government job data is organized and what patterns emerge when you process it through a real pipeline.
An analysis of government job classification data supported by extraction and processing pipelines, warehouse build steps, and release pipeline output. The live site includes common practices, lessons learned, and technology explanations.
Updated March 2026
AI-powered search over 4,000+ publicly released DOJ documents
Built to see if RAG and local AI infrastructure could make a large public document release genuinely searchable — not just downloadable, but answerable.
A Retrieval-Augmented Generation system that collects, processes, and indexes 4,049 PDF documents from the DOJ's Epstein disclosure releases. Combines vector-based semantic search with local LLM inference to enable natural-language queries across the full corpus.
Updated March 2026
Exploring the gap between AI adoption and trust
Built to make a compelling data point impossible to ignore — three-quarters of us use AI, but two-thirds don't trust it.
A focused presentation of the paradox that roughly three-quarters of people use AI while only about one-third trust it — designed to highlight that gap in a visually engaging and easy-to-understand format.
Updated February 2026
Storyline structures with deep theory and the ability to save your work
Built to test whether agentic generation could produce constrained creative output — dialogue within defined story structures and guardrails.
A tool that began as a story arc evaluator and now supports exploration of archetypal beats, worldbuilding refinement, and agentic generation of dialogue between characters and narrator. Uses a bring-your-own API key model.
Updated March 2026
More projects are in progress. This is a living collection — each project grows as I learn, and new ones get added as new questions come up.
Claude is my primary coding platform. I use AI daily as part of a structured engineering process — design, prototyping, coding, testing, refinement, and content generation.
Several of these projects use agent-based patterns — coordinated AI processes that work within defined structures and guardrails rather than open-ended generation.
Data flows through clear stages: extraction, processing, transformation, presentation. Each stage is designed to be understandable and independently inspectable.
I run local AI infrastructure alongside hosted services — including local models supported by my own hardware — giving me flexibility across the stack.
Every project here follows the same pattern: start with real data, build a visible pipeline, and produce something usable at the other end.
I don't hide complexity behind abstractions — I make it understandable. Pipelines, processes, and logic are exposed so you can see how things work, not just what they produce. The goal is always clarity: accessible without being dumbed down.
These aren't demo projects or isolated experiments. They're working systems I use myself, built with the same engineering discipline I'd apply to production work. The fact that they're also useful to other people is the point — that's how I know they actually work.
I'm John Boen — a senior data and AI systems engineer with 30 years of experience in database engineering, data engineering, analytics, and production data systems.
Over the past two-plus years, I've focused heavily on applied AI — turning a long foundation in data pipelines, system architecture, and reliability engineering into practical AI systems work. That means agentic workflows, AI-assisted development, local and hosted model integration, tool-connected systems, and data-driven applications.
My engineering philosophy is straightforward: build structured systems with visible pipelines that produce usable outputs. Use AI as a daily tool, not an occasional experiment. Make the hard parts accessible without hiding them.
These projects are where that philosophy meets real questions I wanted to answer.