Rapid Prototyping to Learn & Prove Value
Why Prototyping Matters
Most service systems are designed as if they will work as intended once implemented.
In real environments, behavior does not follow design assumptions. Attention is fragmented, decisions are social, timing matters, and small changes in access or structure can significantly shift what people do.
Irminsul is a way of testing ideas before they are fully implemented, to understand how they behave under real constraints.
What I Do
I translate early service concepts into structured experiments that can be tested in real environments.
The goal is to understand what works, what breaks, and what changes behavior in practice.
Outcomes
Across environments, the work produces:
- Testable concepts for real-world conditions
- Experimental variations of interaction and access models
- Observations of how behavior changes under different service conditions
- Early evidence for what should or should not be scaled
- Direction for next-stage service design decisions
Examples
Video Game Strategy App
The Challenge
My kids and their friends play Genshin Impact, a large-scale fantasy action RPG with a global player base and deep systems built around characters, equipment, and team composition.
Existing community tools try to solve this problem, but they fall short in two ways:
- They require expertise to interpret
- Lorem
Neither approach answers the real question players are asking: Given what I have, what should I do?
Methodologies
- Design Thinking
- Ethnographic Field Research
- Direct & Indirect Observation
- Opportunity Gap Surveys
- Anchored Max-Dif Surveys
- Intercept Interviews
- In-Depth Interviews
- Jobs to Be Done
- Journey Mapping
- Behavioral Segmentation
- Co-Creation Workshops
- Insights Development
- Research Synthesis
My Approach
I started by interviewing my kids to understand what they were trying to achieve in the game and where the experience was breaking down for them. I observed how they played and how their approaches differed, which helped surface differences in how much structure they wanted versus how much decision support they needed.
I compared these needs against existing tools in the ecosystem and mapped available data sources that could support better recommendations. From this, I developed a strategy for generating high-quality, context-aware recommendations based on publicly available information.
I then built a prototype mobile application that generates conflict-free team recommendations with plain-language explanations. The system compares a player’s actual account inventory to community discussions about character builds, equipment pairings, and team variations across different scenarios.
What this work demonstrates
This project shows how I use AI as a practical design and development tool. It demonstrates how I approach complex, data-rich environments by translating real user needs into functional systems, and moving from problem definition to working prototype without losing focus on the intended user experience.
Status
Live and in closed testing. Early feedback validated the core concept: users prefer proactive recommendations over searching through fragmented online resources.
Active development continues, including refinement of recommendation logic and exploration of AI-assisted content strategy, with the goal of a public release in 2026.
Understanding Contexts & Behaviors in Physical Spaces
Designing services and environments that respond to real human needs
Strategies & Experiences for Human Problems
Designing services and environments that respond to real human needs
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