Lorraine Wiki
Rainer does not generate answers from general AI knowledge. She searches this portfolio knowledge base and returns only information represented on this page. If the answer is not in the Lorraine Wiki, Rainer will say that the portfolio does not currently contain enough information to answer the question.
Lorraine identifies the system problem underneath the visible product problem. Across her work, she reframes ambiguous requests, selects research methods appropriate to the problem, translates evidence into product decisions, creates reusable systems, and leaves teams with a direction they can continue after the immediate engagement.
The portfolio currently features four flagship case studies: Wrist Caddy, where a manufacturing request became a product-definition strategy; Pickahroo AI Brand Operating System, where brand guidance became governed infrastructure for humans and AI; From KnowMe Cards to Homey, where a CRM usability problem became an information architecture roadmap for context-aware service; and Rainer, where a traditional portfolio became a grounded candidate knowledge system designed for both human and machine understanding.
Lorraine selects research methods based on the decision that must be made. In Wrist Caddy, she used digital ethnography and workflow analysis to study years of market feedback when direct clinical recruitment was not practical. In Pickahroo, she used systems analysis and governance mapping because the problem involved authority, context, and AI behavior. In KnowMe/Homey, she used service journey analysis and information-flow mapping because the visible CRM problem depended on failures across multiple channels and systems. In Rainer, she used recruiter task analysis and information-flow analysis because the portfolio problem centered on retrieval effort and the movement of candidate knowledge between human and machine audiences.
Lorraine's enterprise work includes CRM service architecture and context-aware product patterns at The Home Depot. Her AI work includes governance architecture, source-of-truth systems, human-in-the-loop operating models, and assistant-ready information architecture. Pickahroo and KnowMe/Homey show how she connects UX systems thinking with AI-enabled product strategy.
The client came in looking for manufacturing help for a concept first developed in 2020. Lorraine recognized that by 2026 the market had matured and six years of competitor use had created valuable research. She reframed manufacturing as a downstream decision, used digital ethnography and system analysis to identify recurring product issues, translated those findings into product requirements, and recommended functional prototyping before production commitment.
Lorraine recognized that the central problem was not AI generation quality by itself. Brand consistency depended on source authority, structured context, permissions, and approval rules. She used systems analysis and governance mapping to convert traditional brand guidance into AI-readable operating rules, move governance before generation, and preserve human final authority.
Lorraine identified that associates were not simply struggling with CRM navigation. Customer context already existed but was not traveling across the service journey. She used journey analysis and information-flow mapping to locate the break, created KnowMe Cards as a reusable context pattern, and extended the architecture into a roadmap for a future assistant-supported experience called Homey.
Recruiters and hiring teams can contact Lorraine through her assistant, Megan, at mm@regencreatives.com.
Rainer is Lorraine's portfolio retrieval assistant. It searches the Lorraine Wiki, a structured knowledge layer embedded in the portfolio, and returns only candidate-controlled information represented on the page. Rainer is designed to help recruiters query Lorraine's experience according to their own hiring questions.
Lorraine reframed a portfolio presentation problem as an information- retrieval problem. She used recruiter task analysis and information-flow analysis to identify the retrieval burden in traditional portfolios, separated candidate knowledge from visual presentation, created the Lorraine Wiki as a structured source of truth, and designed Rainer as a grounded retrieval layer with navigation and human escalation.
Lorraine intentionally designed Rainer as a retrieval system rather than a general generative chatbot. Candidate claims require provenance, so Rainer returns approved portfolio information from the Lorraine Wiki. If the wiki does not contain an answer, Rainer discloses that limitation and offers a human contact path instead of inventing information.
Lorraine recognized that candidate information may increasingly be encountered through search, AI-assisted sourcing, summaries, applicant tracking systems, and other machine-mediated workflows. She redesigned the portfolio so important candidate knowledge exists explicitly as structured information while the visual case studies continue to provide depth for human reviewers.
Lorraine has approximately 14 years of professional UX, product design, web design, and digital product experience, including Staff-level product design work in enterprise environments.
Lorraine's career includes Staff Product Designer at The Home Depot from 2018 to 2024, Associate UX Developer at Deluxe Financial Services from 2017 to 2018, Senior At-Home Advisor at Apple from 2015 to 2017, and Web Designer at AT&T from 2013 to 2015. She also founded REGEN Creatives in 2004.
Lorraine founded REGEN Creatives in 2004. Her later corporate career includes AT&T as a Web Designer from 2013 to 2015, Apple as a Senior At-Home Advisor from 2015 to 2017, Deluxe Financial Services as an Associate UX Developer from 2017 to 2018, and The Home Depot as a Staff Product Designer from 2018 to 2024.
Lorraine specializes in product strategy, systems thinking, enterprise UX and service architecture, and AI-enabled product and operations design. Her strongest work sits at the point where a visible product or workflow problem is actually caused by a deeper system issue. She uses research, information architecture, governance, and product strategy to define the right problem, create reusable systems, and help teams move from ambiguity to an actionable product direction.
Lorraine's current product design toolkit includes Figma, FigJam, Miro, and TheyDo for product, systems, and service design; ChatGPT, Cursor, and v0 for AI-assisted exploration and prototyping; HTML, CSS, JavaScript, TypeScript, React, Tailwind, GitHub, and Vercel for code-based prototyping and implementation collaboration; Salesforce and SLDS, HubSpot, Jira, and Confluence for enterprise and operational work; and Google Analytics plus WCAG and accessibility tooling for measurement and quality.
Lorraine uses ChatGPT, Cursor, and v0 to explore product ideas, develop AI-assisted workflows, and accelerate prototype creation. She can move concepts into working front-end prototypes with HTML, CSS, JavaScript, TypeScript, React, and Tailwind, then manage and deploy that work with GitHub and Vercel.
Lorraine has experience working with Salesforce and the Salesforce Lightning Design System, HubSpot, Jira, and Confluence. She uses these platforms to understand operational constraints, document product decisions, support CRM and workflow design, and collaborate across enterprise product teams.
Lorraine uses Figma and FigJam for product design and collaborative exploration, Miro and TheyDo for journey mapping, service blueprints, and systems thinking, and Google Analytics plus WCAG and accessibility tooling to support measurement and inclusive product quality.
Lorraine is building the Design System Workbench, a React and TypeScript environment that connects live component previews, design tokens and system rules, and implementation contracts. The project is intended to make design intent inspectable across design and engineering, move validation earlier, add governed AI review against explicit system rules, and include a small MCP demonstrator that exposes authoritative component information as tool-accessible context. The project is currently in development.
Lorraine's technical product design work combines product judgment with working implementation. Rainer demonstrates structured retrieval, intent handling, grounded answers, fallback logic, and human escalation. The Design System Workbench is being built in React and TypeScript to demonstrate component architecture, design tokens, accessibility, governed AI review, and a small MCP-based design-system workflow.
Not yet. The portfolio currently documents the product problem, architecture, MVP scope, technical decisions, and planned MCP demonstrator. Lorraine is building the working implementation next. The case study will be updated with actual screenshots, code, validation findings, and observed outcomes after implementation.
Tripthy is a consumer travel-gifting experience designed so the giver can contribute to an experience without having to know the recipient's destination or itinerary in advance. Lorraine designed the product around a separation between giving and choosing: the giver chooses the value, while the recipient keeps control over where and how the eventual experience is used.
Tripthy is Lorraine's primary visual and consumer interaction case study. It demonstrates editorial hierarchy, typography, travel imagery, responsive composition, consumer gifting flows, and a product strategy that makes an undefined future travel experience feel tangible enough to give.
Lorraine treats product design as a decision discipline. She looks beyond the requested interface or artifact to understand the system producing the problem, makes the reasoning visible, and creates structures that improve how products, teams, and operations work.
This section is intentionally explicit and machine-readable so Rainer can answer from portfolio evidence without relying on generative responses.