Align Technology (creator of Invisalign) had a need for a flexible system that could improve AI consistency without prescribing the same interface for every product.
I created an AI experience guide that helps product leaders, designers, product managers, and developers design more consistent AI experiences across Align’s product ecosystem.
As teams began exploring AI across different products and platforms, foundational experience decisions were being made independently.
There was no shared guidance for questions such as which AI icons or badges to use, how thinking and streaming should behave, how generated information should be presented, or what language should communicate AI behavior.
This created a need for a flexible system that could improve consistency without prescribing the same interface for every product.
A system spanning visuals, behavior, patterns, and language
I reviewed AI experiences being explored across Align’s platforms and identified the decisions that would benefit from shared conventions.
I organized the guidance into four connected layers:
Components — Icons, badges, and reusable interface elements
Interaction states — Thinking, streaming, loading, completion, and recovery
AI patterns — Conversation, search, recommendations, and explainers
Language — Shared terminology and reusable AI interaction copy
Together, these layers give teams a common starting point while leaving room for each product to adapt the guidance to its users and context.
01
Components
Icons, badges, and reusable interface elements that make AI recognizable.
02
Interaction states
Thinking, streaming, completion, and recovery across the AI response lifecycle.
03
AI patterns
Reusable models for conversation, search, recommendations, and explainers.
04
Language
Shared terminology and reusable copy for clear, consistent AI communication.
I designed and built the guide using Claude Code, transforming the framework into an interactive resource rather than a static collection of documentation.
The guide brings examples, usage guidance, interaction behavior, and language recommendations into one place. Building it in code also allowed me to evaluate patterns in context and explore a closer connection between design guidance and implementation.
I also began translating the AI language library into a reusable skill for tools such as Claude Code and Replit. The goal is to help teams apply pre-approved AI language within the tools they already use, reducing the gap between documentation and execution.
The AI experience guide established an initial shared framework for how Align identifies AI, communicates system behavior, and applies common AI patterns across products.
It gives cross-functional teams a clearer starting point for evaluating new concepts, makes inconsistencies easier to identify, and reduces the need to redefine foundational decisions for every experience.
The work also creates a foundation that can evolve beyond documentation toward guidance embedded directly within design and development workflows.