
Generative Room-Concept Application for an Online Interior-Design Marketplace
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Overview
What we built
An online interior-design marketplace with 14 regional design hubs had designers hand-assembling every room concept board from scratch, and clients often rejected the first one anyway. We built a generative application that turns a brief, room photos and a style quiz into three ready-to-refine room concepts.
In plain terms: every concept board started as a blank canvas. A designer hunted for products across catalogue spreadsheets and image folders for hours before a single concept was ready to show a client, and clients frequently rejected that first concept outright, sending the designer back to hunt again. Lead volume kept growing across the 14 regional hubs, but the number of paid projects each designer could actually carry plateaued, because so much of their time went into assembly rather than design judgement.
We built a generative design application that turns a client brief, room photos and a style quiz into three rendered room concepts, composing catalogue furniture into photorealistic scenes through a fine-tuned diffusion model with product-preserving inpainting. Designers refine rather than start from blank, swapping items from live inventory with prices and shoppable links attached automatically. Time to first concept board fell from an average of 6 hours to under 45 minutes, and concept boards produced per designer per week rose from 9 to 21 across the 14 hubs.
The Problem
Hours per concept board
Every room concept began the same way: a designer opening catalogue spreadsheets and image folders and hunting, item by item, for furniture and decor that fit a client's brief. That hunt took hours before a single concept board was ready to present, and it was the same hours spent again for every brief, regardless of how similar it was to work the designer had already done.
The first attempt often was not the last. Clients frequently rejected the first concept outright, which meant the hours already spent assembling it produced nothing a client wanted, and the designer went back to the same slow hunt for a second attempt. Across 14 regional hubs, that pattern repeated constantly, with no way to shortcut it.
Growth exposed the ceiling. Lead volume kept growing, but the number of paid projects each designer could carry plateaued, because assembly time, not design judgement, was the limiting factor. Hiring more designers meant hiring more hours of manual catalogue hunting, not more of the creative work clients were actually paying for.
Hand-assembled concept boards
Designers hunted for products across catalogue spreadsheets and image folders for every single concept board, with no shortcut for briefs similar to past work.
Hours per first concept
Assembling one concept board took hours of manual product hunting before a designer had anything ready to show a client.
First concepts often rejected
Clients frequently rejected the first concept outright, so hours of assembly work regularly produced nothing the client actually wanted to move forward with.
Plateaued designer capacity
The number of paid projects each designer could carry plateaued even as lead volume kept growing across the 14 regional hubs.
What it was costing them
Every hour a designer spent hunting through spreadsheets and image folders was an hour not spent on the design judgement clients were paying for, and every rejected first concept meant that time produced nothing usable. With lead volume growing across 14 regional hubs but designer capacity plateaued, the marketplace was turning away exactly the growth it was trying to capture.
The Solution
Generated shoppable room concepts
We built a generative design application that starts from the client's own brief. A client's description, their room photos and a style quiz feed a fine-tuned diffusion model that composes catalogue furniture into photorealistic scenes, using product-preserving inpainting so the furniture placed into a scene stays recognisably the real, purchasable item rather than a generic render.
Every brief produces three rendered room concepts rather than one, so a designer has options to present instead of a single attempt that might be rejected outright. Designers refine the generated concepts rather than starting from a blank spreadsheet, swapping items from live inventory with prices and shoppable links attached automatically, so a refined concept is already a shoppable one.
We kept the designer firmly in charge of the outcome. The model proposes scenes built from real catalogue products, but a designer decides what stays, what gets swapped and what the client ultimately sees, so the judgement that made the marketplace's concepts good in the first place stayed exactly where it was, just no longer buried under hours of manual hunting.
Key decisions
Generate three concepts per brief
Each brief produces three rendered room concepts rather than a single attempt, giving clients options and reducing the odds that the first look gets rejected outright.
Product-preserving inpainting
The diffusion model was fine-tuned with product-preserving inpainting, so furniture placed into a scene stays recognisably the actual catalogue item rather than a generic look-alike.
Designers refine, not assemble
Designers start from a generated concept and refine it rather than hunting through spreadsheets and image folders, keeping their time on judgement rather than assembly.
Attach live inventory automatically
Swapped items pull prices and shoppable links from live inventory automatically, so a refined concept board is shoppable the moment a designer finishes it.
Feed the model the whole brief
The client brief, room photos and style quiz all feed the same generation step, so concepts start already shaped by what the client actually asked for.
Measurable Impact
What changed after launch
Speed changed first. Time to first concept board fell from an average of 6 hours to under 45 minutes, and concept boards produced per designer per week rose from 9 to 21 across the 14 hubs, freeing designers to take on more paid projects without the plateau they had hit before.
Client response improved alongside speed. First-concept client approval improved from 38% to 61% with three generated options per brief instead of one, and product click-through from concept boards to purchase rose 34% quarter on quarter, showing shoppable, designer-refined concepts converted browsing into buying more often than the old hand-built boards did.
Time to first concept
An average of 6 hours per concept board
Under 45 minutes to a first concept
Designer throughput
9 concept boards per designer per week
21 concept boards across the 14 hubs
Client approval
38% approved the first concept
61% approval with three options per brief
Purchase click-through
Click-through baseline before generated concepts
Up 34% quarter on quarter
Headline results
Time to first concept board cut from an average of 6 hours to under 45 minutes
Concept boards produced per designer per week rose from 9 to 21 across the 14 hubs
First-concept client approval improved from 38% to 61% with three generated options per brief
Product click-through from concept boards to purchase up 34% quarter on quarter
Tech & Tools Used
What powered the build
Every tool below earned its place in this engagement. Here is the part each one played.
Python (FastAPI)
Serves the generation API that takes a client brief, room photos and style quiz and returns three rendered room concepts back to the design tool.
PyTorch
Runs the fine-tuned diffusion model's training and inference, learning the product-preserving inpainting that keeps catalogue furniture recognisable inside a generated scene.
Stable Diffusion (fine-tuned)
Generates the photorealistic room scenes, composing catalogue furniture into each concept and producing the three options a designer refines for every brief.
ControlNet
Guides the diffusion process using the client's room photos, so generated concepts respect the actual room layout rather than inventing an unrelated space.
Next.js
Delivers the designer-facing application where concepts are reviewed, refined and swapped against live inventory before a client sees them.
Node.js
Runs the backend that joins generated concepts to live inventory, attaching prices and shoppable links automatically as a designer swaps items in.
PostgreSQL
Stores briefs, generated concepts, designer edits and the inventory links attached to each item, tracking every concept board from generation through to client review.
Redis
Caches in-progress generation jobs and frequently referenced inventory lookups, keeping the refinement workflow responsive as designers swap items across the 14 hubs.
AWS S3 + CloudFront
S3 stores room photos and rendered concept images while CloudFront serves them quickly to designers and clients reviewing boards across regions.
Docker
Packages the generation and inference services so the fine-tuned diffusion model runs consistently from development through to production.
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