Five messy briefs. No magic wand.
Most learning projects do not fail because someone forgot how to make content. They fail because a fuzzy request became a fixed deliverable too early. This portfolio shows how I slow down that first decision—then make the work move faster.
Evidence before format · Tradeoffs before promises
Choose your problem
These are fictional situations built from familiar project tensions. Each one gives you a decision to make, a tool to try, and enough evidence to disagree with me.
Portfolio note: Client scenarios and illustrative results are fictional. The frameworks, tools, code, analysis, and design are original. See the full methodology.
Completion looked healthy. Employers still did not trust the credential, and instructors kept answering the same basic questions. A course redesign would have treated the symptom.
A course team wanted speed from AI without letting it make technical, instructional, or consequential decisions. “Keep a human in the loop” was not specific enough.
Every stakeholder had a favorite. The organization needed credentials, mobile access, analytics, privacy, integrations, and a realistic administrative burden—not a prettier sales demo.
A global manager program had six months, six countries, four languages, and a long wish list. “Estimate the project” was really a request to expose the consequences of every scope choice.
The promise was already made. The SMEs were unavailable, source content was missing, accessibility was undefined, and nobody owned the AI review. Starting development would only hide the risk for a few weeks.
How I work
Technology and format come later. First I want to know what is failing, what we can prove, and what the delivery team can support.
A request for training is a starting point, not a diagnosis. I look for the work, evidence, friction, and consequences behind it.
The useful question is not “What do stakeholders prefer?” It is “What would prove this direction right—or wrong?”
The architecture has to survive the actual deadline, technology, source content, review capacity, and people available to build it.
Assumptions, controls, and tradeoffs become explicit so the delivery team is not left to rediscover them under pressure.
Keep digging
Use these sections to inspect the working files, understand what is fictional, and see how the portfolio was made.
Ten practical files for the decisions that tend to stall: discovery, AI controls, technology, scope, and delivery risk.
Find the file for your problem →Judge the work honestlyWhat I invented, what I built, where AI helped, and what this portfolio can—and cannot—prove.
See what is real and what is not →Open, do not just downloadPreview the exact guides, maps, scorecards, and risk tools used in the cases—or take the whole set.
Browse downloads →Where I am useful
I am most useful when the request crosses content, technology, process, politics, and delivery—and the team needs someone to make the tradeoffs visible before they become expensive.
