Overview
I’m working on AI-connected workflow design for Validity Engage, a B2B SaaS product that helps marketing teams review email campaigns before launch, identify quality and implementation issues, and decide how to resolve them more consistently.
The work spans product strategy, UX architecture, interaction design, visual design, and systems thinking. Because the product is public but some details remain internal, this case omits product screens, customer-specific context, implementation details, and internal workflow mechanics.
Challenge
Email QA involves many small but consequential decisions. Teams need to understand where an issue appears, why it matters, what evidence supports it, who should act on it, and how similar issues should be handled in the future.
The design challenge is not just to make the interface cleaner. It is to make a dense review process easier to understand, evaluate, and act on without hiding the judgment required to make good decisions.
Design Focus
My work has focused on issue triage, email preview patterns, supporting evidence, remediation guidance, reusable interaction patterns, and workflow structures that can scale across the product.
A typical flow might involve a user finding an issue in an email, inspecting the related content, reviewing guidance, and deciding how to resolve, defer, escalate, or handle similar issues more consistently over time.
I’ve worked hands-on with product and engineering to turn loosely formed requirements into clearer flows, stronger patterns, and a more coherent product experience.
AI-Assisted Design Workflow
AI is part of my design process, not a replacement for design judgment.
For this work, I used Claude, Claude in the terminal, Figma MCP, and GitHub repository context to move more fluidly between requirements, design patterns, prototype logic, and implementation constraints.
That helped me explore alternate interaction models, generate flow variations, evaluate edge cases, and stress-test patterns around issue review, remediation, supporting evidence, and the relationship between email preview and issue detail.
AI expands the search space quickly. Senior design judgment still determines what is useful, coherent, and appropriate for the product.
Impact
The work helped shift the conversation from individual screens toward the underlying product model: how issues are identified, how evidence is presented, how decisions are made, and how automation can support users without obscuring control.
It also helped create a clearer design direction, stronger alignment across product and engineering, and a more reusable foundation for future workflow design.
For me, this is the kind of design work that matters most in complex systems: translating advanced technology and dense product behavior into experiences that feel understandable, actionable, and well-crafted.