Interio

Interio is an AR room planning app with advanced scanning for personalized floor plans. It helps users to be aware that every aspect of one’s space is meticulously considered and optimized to meet their unique room needs. With Interio, people can have peace of mind knowing that their space is perfectly tailored to them.

My Role
UX & UI Design
Visual Identity

Promo Video

With
Justin Soza Soto

Timeline
4-Month

The Problem

A significant knowledge gap among many individuals exist when it comes to interior design and optimizing the functionally of their living or working spaces.

Does not know where to start and what to change.

Numerous unorganized tips out in the world / Endless web searching

Does not exactly know about my surrounding/environment

Interio Home Page

Sophisticated array of filter options, allowing individuals to tailor their preferences based on specific improvement areas

Real Time Analyzing

Motion Detector Camera that provides real-time motion feedbacks on alternative suggestions for improving the functionality and aesthetics of the space, beyond furniture alteration.

Optimized
Floor Plans

Ensures every aspect of the space is meticulously considered and optimized to meet unique needs by providing bespoke approach of room arrangements.

Insipiration Boards

Explore other rooms to discover new ideas for future events. Collaborate with like-minded individuals and create unforgettable experiences together.

Design Opportunity 💡

How can we raise awareness of how their space works and provide suggestions for improvement seamlessly?

Undstanding User Needs…

7 initial user interviews in age from their 10s to 20s

Keyword Insights:

  • Quality

  • Theme

  • Efficiency

  • Furniture

  • Lack of space

  • Floor plan

  • Divide

  • Mood board

Key thought Insights:

  • Your greatest value that your want your space to offer you?

  • What purposes do each area actually serve?

  • When do you wish your space could be better?

Understanding Targets 🔎

Two rounds of surveys on various age groups, ranging from their 10s to 30s, to determine their perception of a particular space for a specific occasion.

Provided:

We learned:

Difficulty verbalizing choices
Vague statements
Differing scale interpretations

For our updated second user research, ranging from 10s to 50s, we pivoted:


create + explain -> premade + explain

People care about:

Window-Bed light relationship
Door-Bed movement relationship
Entrance-Furniture Flow relationship

Affinity Diagram Insights:

Highly aware of space
Detailed explanations
Keen on design choices

Possible Improvements…

Direct onboarding process yet less-complicated steps.

Option with character filter categories to establish and develop diverse relationship.

Enough professional feedback suggestions and friendly connections for users.

Solution ✍️

An AR scanning app that allows people to have their space, perfectly tailored to them by suggesting optimal floor plans of their rooms with elevating user engagement options.

Core Values ⚡️

People know the aspects that work for them, but struggle with executing them in real-life scenarios.

Therefore, we decided to: raise spatial awareness & suggest improvements

Rapid, Straightforward, yet Handy Configurations

Pre-made soulful characters with diverse personalities & roles

Stronger User and Listener Connection

Specific customizable traits like religion, race, relationship, etc.

Enhanced Accessibility and Improved Interaction Capabilities

Video & audio recognition with instant summarized key Points

Visual Identity 💭

Reflection 📍

For this study research project, it was a valuable opportunity in creating a user-centric product that meets the needs of a diverse group of people in both long and short term. It helped me to learn the significance of conducting generative research on both a micro and macro level, which helped me to better comprehend the users' needs, desires, and priorities. If I had more time, I would have loved to take a deeper step in understanding the integration between real-time feedback and in-app further suggest features. Overall, I look forward to applying these learnings in future projects and continuing to improve my research methods.