Improving critical flows at scale while balancing fun + trust.
Research
Interviewed patients and providers to identify routine pain points.
Mapped high-frequency, high-friction tasks suitable for conversational design.
Concept Exploration
Sketched AI omnipresence across apps: chat interface, voice integration, proactive nudges.
Defined task types AI should handle: scheduling, reminders, aligner tracking, FAQs, patient-doctor communication.
Design Iterations
Wireframed conversational flows.
Explored system personalities → empathetic, approachable, trustworthy.
Prototyped “moment-based” interactions: e.g., “I lost an aligner” → AI suggests next steps + contacts provider.
A conceptual framework for Align’s AI assistant, including:
Cross-App Integration: consistent assistant across patient & doctor platforms.
Task Logic: prioritized high-impact tasks for AI automation.
Design Language: approachable tone, natural interactions, omnipresent but unobtrusive UI.
Created a vision blueprint for how Align could integrate AI into its ecosystem.
Fed directly into long-term product roadmaps and design system considerations.
Positioned Align as forward-thinking in digital healthcare, aligning with industry AI trends.
Unlike feature design, this project demanded comfort with ambiguity. It taught me how to balance feasibility with vision, and how to communicate future-looking ideas clearly enough for product leaders to see their potential. It also sharpened my ability to design not just interfaces, but ecosystems and interactions at scale.