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Case Study. Furniture. AI visualization.

Showroom turns furniture imagination into a product experience.

I built the app that uses Google Gemini image generation to create furniture visualization for real rooms.

Product build, AI visualizationShowroom site
AIVisualization
MVPBuild
RoomsUse case
MVP buildProduct designAI image generationFurniture visualizationUser flow

Why it mattered

Furniture ecommerce has a basic problem: people do not know how a piece will look in their space.

That uncertainty slows the sale. It also creates returns, hesitation, and endless back-and-forth with sales teams.

The situation

Showroom was built to make furniture visualization faster and easier.

The product uses AI image generation so customers can see furniture concepts inside room environments before they buy.

The conflict

AI can hallucinate. Furniture brands cannot afford random output. A visualizer has to keep the room believable and the product useful.

The challenge was not just generating a pretty image. It was building a product flow that helps someone make a decision.

What I did

I built the app, handled product design, and connected the AI visualization workflow.

The focus was on a simple buyer experience: upload or select a room, generate a useful visualization, and make the next buying step easier.

Resolution

Showroom became a usable visualization product rather than a demo.

The lesson: AI visuals are strongest when they remove buying friction, not when they exist as a gimmick.

What this proves

  • AI visualization should help a buyer decide.
  • A useful product flow matters more than raw image generation.
  • The best AI tools reduce uncertainty in the sales process.

Quick answers

What did Carlos build for Showroom?

Carlos built the app and AI visualization flow for furniture in real room contexts.

What technology does the product use?

The user brief references Google Gemini image generation for furniture visualization.

Why is this a case study?

It shows product thinking applied to a commerce problem: reducing buying uncertainty.

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What I am building now

For the past years, a lot of my time was tied to long contracts with CGMA and Domestika. Around that work, I kept building my own products. That matters because every product forces the same work in a tighter loop: product decisions, AI workflows, onboarding, pricing, conversion, retention, and the small details that make people come back. This case study shows the client work. The projects show what I learn when I have to ship the whole system myself.

See the projects