Spatia

proposals from Spatia Gemini powered solution

Inspiration

The idea for Spatia came from a simple but universal frustration: designing or reorganizing a room is mentally exhausting. People often know something feels off, but they do not know where to start. Measuring space, imagining layouts, comparing furniture, checking budgets, and making sure everything fits together becomes overwhelming. As a result, many people postpone improving their space or settle for something that never truly feels right. Spatia was inspired by the question:

What if your space could understand your needs and rearrange itself without any effort from you?

What It Does

Spatia is an AI-powered personal space designer that removes the hassle from organizing and improving your room.

It:

Scans your space using camera input (currently images only, with LiDAR and video support coming next)
Understands room layout, dimensions, and existing furniture placement
Detects clutter, lighting balance, and spatial flow
Reorganizes the space based on your selected mood or purpose (styling)
Suggests new furniture that fits your room, style, and budget, or intelligently rearranges the furniture you already own
Performs online research using APIs from furniture providers, compares prices, and recommends the best options within your budget
Provides visual previews of the proposed new layout

Instead of manually measuring, searching, and guessing, users receive a complete, ready-to-use design plan tailored to their space and constraints.

How We Built It

Spatia was designed as a modular, intelligent system that combines spatial understanding, AI reasoning, and human-centered design.

Room Capture

LiDAR and camera-based scanning
Depth estimation and spatial reconstruction

Scene Understanding

Object and furniture recognition
Zone detection such as work, rest, or storage
Identification of clutter and unused space

AI Reasoning Layer

Mood-based layout generation
Ergonomic and lighting-aware logic
Budget and space constraint handling

Recommendation Engine

Furniture suggestions matched to room size
Price comparison and availability
Style consistency scoring

Visualization

Before and after layouts
Clear spatial zoning
Easy-to-understand visual feedback

Challenges We Ran Into

Translating human comfort into measurable design rules
Avoiding overwhelming users with too many options
Handling scale distortion from phone cameras
Making furniture suggestions realistic and affordable
Ensuring recommendations felt helpful rather than prescriptive

Accomplishments That We’re Proud Of

Turning messy real rooms into structured, livable layouts
Helping people to live in spaces that support mental health and emotional balance
Bridging users and furniture providers seamlessly
Removing decision fatigue from room design
Creating layouts that respect both aesthetics and budget
Building an experience that feels intuitive and human
Producing results that users can act on immediately