Problem
Users could generate many AI design options but struggled to decide what to refine, compare, or commit to.
Impact
Users had options but no way to evaluate, compare, or act on any of them.
Executive Summary
AI generation tools optimized for output volume. This project focused on what happens after generation when users have options but no way to evaluate them.
Discovery
Foyr's value proposition was fast AI generation. The problem was that generation speed had outpaced the product's ability to help users decide what to do with what they had generated.
Key Product Decisions
Decision 1
Progressive decision steps vs open-ended generation
Why
Users could generate many AI outputs but struggled to determine what to refine or commit to.
Decision
Structure the experience around sequential decision steps that guide users from exploration to commitment.
Trade-off
Removed the freedom to explore openly in exchange for a more directed experience.
Decision 2
User control vs full AI automation
Why
Users needed confidence before committing to design decisions.
Decision
Allow users to refine, override, or narrow AI suggestions at each stage.
Trade-off
Slightly slower interactions in exchange for stronger decision confidence.
Design Direction
Refreshing Existing Spaces
Users explore alternative layouts, styles, and decor for rooms they already live in, using AI suggestions to rethink aesthetics and spatial arrangements while preserving the existing structure.
Users had an existing reference point, their own room. Starting from something known reduced the blank-canvas anxiety that open-ended generation was causing.
Customizing Spaces with Furniture
Users can add, replace, or remove furniture elements within a room, allowing them to explore different furniture options and configurations as part of their design.
Some users didn't need a full redesign; they needed to change one thing. A focused furniture path lets them act on a specific intent without reopening decisions already made.
Iteration Based on Early Use
Direct Access to Furniture Customization
Users looking to make small, targeted furniture changes felt slowed down by broader redesign steps. Introducing a focused entry path allowed quicker updates without pushing users through unnecessary decisions. This reduced unnecessary decision branching and helped users make progress without reopening earlier choices.
Outcomes
Behavior Change
Sequential structure was designed to reduce the need to revisit earlier choices.
Guided steps were designed to reduce aimless regeneration by giving each stage a clear exit condition.
Workflow Gains
Separating layout and furniture paths reduced decision branching for focused updates.
User Confidence
Preview and comparison steps were designed to let users confirm direction before committing, reducing uncertainty at the point of decision.
Reflections
AI generation alone does not create value.
Structure only works when users can see their earlier choices reflected in the outcome.
How I would approach this differently today
What I got wrong
I invested in high-fidelity design before validating the core interaction model with real users.
What I learned
Testing flows interactively reveals what static screens cannot, how users actually move through decisions, not how we assume they will.
What I would do differently
I would prototype the decision flow in Lovable first and test it before moving to high-fidelity, with fewer assumptions and more evidence earlier.
Try the prototype yourself
As part of evolving how I work, I also ran this project through an AI-augmented UX audit framework I have been building, to re-examine where the deeper friction lived. You can see that output here.