Design Systems & AI-Native Product Architecture
I design reusable product systems where interaction behavior, component architecture, implementation, governance, and increasingly AI-generated experience need to remain coherent.
My design-systems work spans more than a decade of financial products, enterprise platforms, reusable interaction architecture, cross-product standards, front-end implementation, and team enablement. My current AI work extends the same systems discipline into rules, constraints, evidence, review, and repeatable automated behavior.
A design system is more than a component library.
Reusable UI matters, but the deeper system is the agreement between interaction behavior, business rules, responsive structure, accessibility, implementation constraints, product semantics, and the people responsible for changing the system over time.
I have repeatedly worked where visible interface consistency was only one part of the problem. The harder work was making reusable decisions concrete enough that designers and engineers could apply them across products, workflows, user roles, and changing technical environments.
The system succeeds when product behavior stays coherent through implementation and change.
I use system models, working prototypes, reusable patterns, component behavior, implementation guidance, evaluation, and team enablement to reduce repeated interpretation. The goal is not standardization for its own sake. It is to make important product decisions reusable without making the system brittle.
At Softwise, I owned UX across five business-critical financial products spanning loan processing, point of sale, title lending, customer service, collections, and lending administration.
I designed and scaled reusable interaction patterns, component structures, responsive behavior, business rules, and complex workflow logic while working directly in the Angular production environment. Research, component behavior, front-end structure, and implementation constraints were treated as one product-system problem rather than separate design and development phases.
Management credited the approach with a 35% reduction in development time. I also trained 20–30 colleagues in HTML/CSS to strengthen technical fluency and reduce friction between design intent and implementation.
At TEKsystems, I directed interaction architecture for multi-role enterprise systems serving approximately 76,000 users. The work included a reusable pattern library and responsive HTML/CSS/AngularJS prototypes carried into implementation and QA during modernization from JSF toward AngularJS.
At Novell, I worked across GroupWise, Filr, Vibe, iPrint, Messenger, and ZENworks. I contributed to cross-product UI standards and shared interface assets, including a library spanning more than 1,500 icons, while also designing administration and dashboard experiences for large enterprise user populations.
Across both environments, the recurring problem was how to make product-level decisions reusable across teams and surfaces without losing the meaning of the workflow they supported.
I work close enough to implementation that system decisions can be discussed in the medium where they will operate. My background includes Angular/AngularJS, HTML/CSS, JavaScript/TypeScript, responsive interfaces, CSS architecture, browser-based tools, and working-code prototypes.
At Booz Allen Hamilton, I designed and prototyped directly in Angular on an Army geospatial application. At Softwise, I worked inside Angular production. Earlier roles included implementation-focused pattern libraries, CSS architecture, and prototypes carried through engineering and QA.
That fluency lets me reduce ambiguity before implementation decisions become expensive: component behavior, states, responsive rules, technical constraints, and intended interaction can be discussed early enough to survive implementation.
Reusable systems need evaluation as well as definition. I use heuristic review, workflow and usability evaluation, real user and stakeholder questions, working prototypes, and implementation feedback to find where the system is creating ambiguity or forcing teams to reinterpret the same decision.
I look for evidence that the system is reducing repeated interpretation and improving delivery: development friction at Softwise, patterns carried through implementation and QA at TEKsystems, cross-product standards at Novell, and team training intended to make good system decisions less dependent on a single practitioner.
My current work with custom GPT and AI-agent systems starts with the operating contract: what the system is for, what evidence it may use, what it may change, what must remain stable, and where a person needs to review or override the result. Corpus and knowledge structure, retrieval behavior, instruction hierarchy, constraints, scoring, and output calibration support that contract rather than replacing it.
I test the system against real questions and edge cases to expose drift, conflicts, weak signals, retrieval failures, false positives and negatives, and context limits. Those failures become clearer rules, review paths, and boundaries for automated behavior.
The model below is a non-proprietary reconstruction of how I think about a reusable system when both people and automated tools may participate in producing experience.
Design contract
What problem the pattern solves, what meaning must remain stable, which states are required, and where variation is acceptable.
Implementation contract
Component behavior, state logic, responsive rules, accessibility considerations, technical constraints, and what engineering needs to implement consistently.
Automation contract
What an AI system may generate or modify, what evidence or context it must use, which constraints cannot be overridden, and when review is required.
This is a reconstructed working model, not a former-employer artifact. It is intended to show the decision structure without exposing proprietary product designs.
Automated generation creates the same kind of systems problem as any other source of variation: teams need to know which decisions are flexible, which are governed, and what happens when the system cannot satisfy both.
I design human review, escalation, evidence visibility, fallback behavior, and operational boundaries so uncertainty is handled explicitly rather than hidden inside an automated output.
The design question is not simply “Can the system generate this?” It is “What must remain true before this output is safe, coherent, and useful enough to act on?”
A design system succeeds when other people can make good decisions without its original author standing beside them. Patterns therefore need enough semantics, examples, implementation guidance, and contribution boundaries that teams can tell when variation is legitimate and when it breaks the system’s intent.
Training and enablement have been a recurring part of my work. At Softwise, I trained 20–30 colleagues in front-end practices. Across other roles, I have trained developers and cross-functional teams in web standards, Design Thinking, user interviewing, UX methods, assessment approaches, and implementation concepts.
My current AI work follows the same pattern: turn an individual discovery into explicit rules, evaluation methods, and repeatable structures that a broader team can understand, challenge, and evolve.