GlowAI — AI Interior Design Assistant
INTRODUCTION
GlowAI is a mobile app that turns a photo of your room, your measurements, your inspiration and your budget into practical, personalized design ideas you can actually act on.
Think of it as the bridge between "I saved 200 pretty rooms on Pinterest" and "here's exactly what to buy for my room, and it fits my €800."
This was the final MVP project of the Postgraduate in Digital Experience Design (PG-DXD, FBAUL). We built it end-to-end with Lean methodology — from spotting a real need to a working interactive prototype — and pitched it in 10 minutes at DxD Open 2026.
The Problem
The pitch opens with a moment most people recognize. You've just moved into a new home. You finally have a space that's yours — and also a hundred decisions. Where do I start? What style do I even like? Will this fit? Can I afford it?
So you do what everyone does: browse inspiration, compare products, save images. But at the end of it, you're still the one who has to make every call.
We asked ourselves: is this just us? It wasn't.
We Listened Before We Assumed
Instead of starting with interviews, we mined real conversations people were already having in public — Reddit communities like r/InteriorDecorating, house-flipping threads, Portuguese renovation forums and news articles. This is called social listening: reading unprompted, honest discussions rather than asking people questions they might answer politely.
- "I'm going to be living on my own for the first time."
- "I really need help figuring out how to fit everything into such a small studio."
- "I want to turn my room into a warm, cozy, earthy safe haven."
- "I want my husband to see that this could be such a cute little space."
We expected product questions — which sofa? which colour? What we actually heard were spatial and emotional questions of a completely different kind: What should I do with this space? Where does the furniture go? How do I make this feel cozy? Can I do it on my budget?
THREE PATTERNS EMERGED
Can't Visualize Possibilities
People genuinely can't picture what their room could become. Inspiration never maps onto their four real walls.
They Think in Feelings
People say "cozy," "earthy," "bold" — not "mid-century modern." The product has to speak emotion, not design jargon.
Confidence Before Spending
The fear of an expensive mistake freezes every decision. People need proof it will work before the money leaves.
THE CORE INSIGHT
The problem isn't inspiration — it's turning inspiration into decisions.
A Market That Mirrors the Pain
Research into the Portuguese renovation market reinforced the opportunity: widespread distrust of contractors (who "disappear and reopen under new company names"), fear of renovations that just hide defects, renovation regret, and real financial anxiety.
Interestingly, investors and house-flippers had a mirrored version of the same need — for them, cheap cosmetic updates deliver the best return, and every decision is about managing risk, not maximizing profit. Same core anxiety, different clothes.
- 54% of homeowners renovated in 2025 (Houzz & Home Study 2025).
- 27% struggled to find the right products.
- 26% struggled to stay on budget.
- 13% struggled to define their style.
- GlowAI targets exactly those three friction points.
Goals & Process
The Goal
Ship a working MVP that proves one core idea: constraint-aware AI room design — AI that designs around your real budget, real furniture and real space, not a generic fantasy room. It had to solve a validated problem, be technically buildable, and have a way to make money from day one.
My Role
UX Research, Interaction Design and Prototyping, working alongside Gonçalo Almeida (CX Intern, Brisa Group) and João Rodrigues (UX Design Intern, Siemens AG). I owned the research synthesis, the end-to-end flow and the design-to-code handoff.
How We Worked
- Discovery & Ideation: A Double Diamond process — explore widely, then narrow down, twice — on a shared FigJam board, with a raw brainstorm wall, dot-voting to pick directions and "How Might We" clusters, kept on track with weekly standups.
- Social Listening: Real quotes became "insight cards" (English headline → original Portuguese quote → source), organized into affinity maps across five research boards. A standout theme from UK property research: value is less about "what adds value" and more about "what removes friction and buyer objections."
- Turning Needs Into Features: Using Jobs-to-Be-Done — framing people by the "job" they hire a product to do rather than by demographics — we built a table of 10 jobs mapped to features: "visualize possibilities I can't imagine," "arrange furniture practically," "translate a feeling into a design," "feel confident in my decisions."
- Inputs vs. Outputs: We matched what the user gives the app (room photo, dimensions, inspiration, budget, existing furniture, desired vibe) against what they want back (design concepts, layouts, style suggestions, budget alternatives, explanations).
- Interaction Design: Scenarios became wireframes, benchmarked against competitor apps (including an IKEA analysis), prioritized on an effort/impact matrix and iterated toward the final flow.
PERSONAS & SCENARIOS
Mariana — The Emotional Nester
30, pregnant, decorating a nursery on a tight €600 budget. She types "cozy vintage, warm and soft," protects an antique crib she can't replace, and the app finds an €85 Marketplace alternative to a €220 chair. A before/after view finally convinces her sceptical partner.
Bruno — The Rational Investor
42, a real estate investor renovating a Porto duplex remotely from Lisbon. He extracts the "design DNA" of a top-performing rental, keeps the structurally fine kitchen cabinets, sets a strict €4,200 materials ceiling, and uses ROI ranking to skip low-return work.
Lucas — The Kitchen Refresh
The everyday scenario we chose to build as the full prototype: modernize an outdated kitchen for €800. Anchoring everything to one real scenario made every later decision easier to reason about.
THE PRODUCT — THREE PILLARS
Design Around Reality
Recommendations that fit your budget, your style and the furniture you already own — not a generic fantasy room.
Compare Options
Explore different directions and product alternatives side by side, so a choice feels like a choice rather than a gamble.
Move with Confidence
Turn ideas into a clear plan and a real shopping list — something you can actually buy and act on.
How It Works, in 4 Steps
- ① Scan Room: capture your room and add inspiration.
- ② Add Requirements: style, budget, existing furniture.
- ③ Customize Design: review and adjust the proposed design.
- ④ Review & Shop: final design plus shopping list.
The Full Flow — Lucas's Kitchen, €800
- Scan: Welcome → "What are we designing today?" → map the kitchen with a guided 3D scan.
- Keep what matters: snap the solid-wood table to preserve existing furniture instead of designing it away.
- Describe the feeling: "Describe what you love" → "modern and bright" → the app translates the feeling into a design, then you pick a reference image.
- Set the constraint: budget locked at €800 → "Building your kitchen design…"
- Budget optimiser: the rendered concept appears, with the money spread deliberately across seating and lighting.
- Tradeoff explorer: when the render proposes a €300 pendant lamp, it swaps in a near-identical €80 option.
- Review & shop: final products list — €779 of €800 used, €21 to spare → save to Folders → before/after slider → buy through affiliate links, delivered to your door.
Design System & Prototyping
Brand Foundations
Everything started with the logo — colours, shape and overall feel — before a single screen was designed. The logo's orange became the primary colour, its opposite (blue) the secondary, with a neutral off-white palette plus green for success and red for errors.
Components & Tokens
We built components in order — buttons, navigation bar, inputs, switches, labels, tags, progress bars — each with all its states (empty, active, error), then turned it all into design tokens for colour, spacing, sizing, radius and typography. For anything touching native phone behaviour we referenced Apple's official design assets directly.
Wireframing — An AI-Assisted Twist
For the first wireframe we tried something different: we wrote a plain-text description of what we wanted, used Claude to turn it into a prompt, and fed it — along with our own design system and components — into Claude/Figma Design. The result used our UI kit instead of generic placeholders.
Building on that, we used Figma Make to generate a high-resolution mockup, then layered in real copy, real images and a concrete usage scenario — turning a screen layout into an actual story.
Final Design
Scan the Room
The flow opens by grounding the AI in reality. A guided 3D scan maps the actual kitchen — its dimensions, its light, its awkward corners — so every later suggestion is anchored to a real space rather than a stock render.
Keep What You Already Own
Users snap the pieces they want to protect — a solid-wood table, an antique crib — and the engine designs around them. This single interaction is what separates a fantasy render from a plan someone can actually execute.
Describe What You Love
Research showed people speak in feelings, not design jargon. So the input is a plain sentence — "modern and bright" — which the app translates into a design direction, then confirms with a reference image the user picks.
The Budget Optimiser
Budget is treated as a first-class design constraint, not a filter applied at the end. The optimiser spreads €800 deliberately across seating and lighting, making the tradeoffs visible while there is still room to change them.
The Tradeoff Explorer
When the render proposes a €300 pendant lamp, the explorer surfaces a near-identical €80 alternative side by side. The final list lands at €779 of €800 — under budget, with the reasoning shown rather than hidden.
Before & After
The closing moment of the flow, and the most persuasive one. A slider between the room as it is and the room as it could be is what turns a private idea into something a user can show a partner — the screen that does the convincing.
From Figma to Real Code
Annotating for Handoff
We annotated the entire project so both developers and an AI could understand it — how each button and element should behave, plus technical notes on corner measurements, what's visible, fixed vs. scrolling elements, and how each card and tag links back to its underlying logic. Every asset was sorted before handoff.
Design-to-Code, Shipped
With the file fully annotated, we connected Figma to Claude Code through an MCP server. Links and images downloaded and organized themselves into folders, and we built the real screens in HTML, CSS and JavaScript — ending with a working, published prototype that ran on a phone.
Keeping the AI Workflow Tight
- A status file tracking what was and wasn't done, so no work was duplicated or lost between sessions.
- A feedback file logging every correction and preference, so nothing had to be repeated twice.
- The result: once the design system, components and annotations were solid, most screens were finished in just one or two prompts.
How It Makes Money
Click-Through Revenue
Affiliate revenue when users tap through to retailers like IKEA or Amazon from their shopping list — the shopping list is both the user's payoff and the business model.
Consumer Insights
Aggregate, anonymized activity reveals real design preferences, budgets and shopping behaviour — data that barely exists anywhere else at this granularity.
Where It Goes Next
Usability Testing
Test and optimize the flow with real users, validating that the constraint-first sequence holds up outside the pitch scenario.
Design Explanation Layer
Telling users why a suggestion works — "This rug balances the dark furniture and makes the room feel larger." The explanation is what converts a render into a decision.
Renovation ROI Ranking
Scoring work by return — kitchen refresh 8.7/10 vs. full bathroom remodel 4.2/10 — plus step-by-step improvement plans. This is where the investor persona becomes a real audience.
Over-Improvement Detector
Warning users against putting a luxury kitchen in a mid-market neighbourhood — protecting people from spending they won't recover.
KEY INSIGHTS
The real differentiator in AI design tools isn't image generation — it's constraint handling. People don't lack inspiration; they lack a bridge between inspiration and their real room, furniture and budget. Designing the whole flow around inputs — scan, keep, mood, reference, budget — before any AI output is what makes results feel achievable instead of aspirational. The order of the questions is the product.
What I Learned
Social Listening Validates Fast and Cheap
Real quotes from real frustrated people grounded our thinking, revealed that users ask spatial and emotional questions rather than product ones, and killed weak feature ideas early — long before we'd invested in building them.
One Engine, Two Very Different People
Emotional nesters like Mariana and rational investors like Bruno ultimately need the same thing: confidence that spending limited money on a space will be worth it. The "why this suggestion" explanation matters as much as the render itself — because the real product isn't the picture. It's trust.