Contributor Content
Jon Stojan
10:57 am, PT, September 3, 2026
While much of the AI industry competes to build general-purpose assistants, a different kind of company is betting on the opposite: narrow consumer products that use generative AI to address one specific problem. Deniz Güney, founder of the app studio Rocket Digital, has built his business on that idea. Over the past several years, he has shipped a portfolio of consumer AI apps spanning image generation, video creation and computer vision, each one a test of what current models can do for ordinary users.
The lesson he draws from that work: for consumers, the value of generative AI can lie not in the technology itself but in simplifying tasks that once required professional software or expert skills, sometimes reducing them to a photo and a few taps. The clearest example in his portfolio is DecorAI – AI Interior Design, an AI interior design platform that turns a photograph of a room into photorealistic redesign concepts. It runs on iOS, Android and the web.
From general image generation to AI room design
Early diffusion models could already produce beautiful interiors, but only imaginary ones. Ask for a Scandinavian living room and you got a convincing render of a room that does not exist. For interior design that misses the point. Homeowners are not looking for inspiration in the abstract. They want to see their own living room, with its awkward corner, low ceiling and north-facing window, looking different.
Getting there requires a different technical approach. DecorAI is built around the goal of structural consistency: the system aims to preserve recognizable elements of a room’s architecture, including walls, windows, doors, ceiling lines and perspective, while changing furniture, materials, colors and overall design direction. Users upload a photo of an existing space and generate redesigns across dozens of interior styles, from minimalist and Japandi to industrial and mid-century modern, each anchored to the geometry of the original image. That anchoring separates AI room design from ordinary image generation: the output must work as a preview of a real space.
This is where specialized applications diverge from general-purpose generation. A general-purpose image model optimizes for aesthetic quality across a broad range of prompts. A vertical product like DecorAI can instead optimize for a narrower set of constraints: photorealism under real lighting conditions, plausible furniture scale, realistic shadows and materials, and above all the sense that the output aims to provide a recognizable preview of how the input space might look rather than a loosely related image.
Understanding intent, not just prompts
The second problem is that mainstream consumers do not write prompts. “Make it cozier” is not an instruction a diffusion model can use. DecorAI handles this by translating high-level intent into structured generation tasks behind the scenes, giving the user simple choices such as style, room type and mood instead of a text box.
That intent layer also makes room for features beyond one-shot restyling. Users can furnish empty rooms, which brings virtual staging, a technique real-estate professionals have paid for per photo for years, to anyone with a phone. The reverse works too: the app can remove existing furniture from a photo, clearing a cluttered space before exploring new directions. Users can also import furniture from photos, taking a sofa spotted on Pinterest or a retailer’s site and placing it in their own room to check the fit before buying.
On top of generation, DecorAI runs AI room analysis on uploaded spaces and produces improvement suggestions covering layout, lighting, color accents and finishing touches. The same photo-in, render-out workflow extends outdoors to exterior and garden redesigns, stretching the product into full AI home design rather than interiors alone. Taken together, the feature set moves DecorAI beyond basic image generation toward a broader design-assistance tool.
The hard problems: photorealism and trust
For a product like this, generation quality is the whole game. A redesign that warps a window frame or floats a chair a few inches off the floor breaks the illusion immediately, and with it the user’s willingness to base a real renovation decision on the output. Güney’s team treats consistency failures as the central quality metric: does the render respect the physics and geometry of the
That bar rises as the underlying models improve, which is exactly why the application layer matters. Model capabilities are becoming commoditized. The durable work sits in evaluation, guardrails, intent translation and the product design that makes advanced generative AI usable for someone who has never heard of a diffusion model. DecorAI’s users may be less concerned with which model produced their kitchen concept than with whether the result is useful for discussing ideas with a contractor.
A template for vertical consumer AI
DecorAI’s trajectory fits a broader pattern in how generative AI is reaching mainstream users. The first wave was horizontal: chatbots and open-ended image tools that demonstrated raw capability but left the application up to the user. The second wave is vertical, with focused products that embed AI inside a specific job, whether that is designing a room, planning a garden or staging a listing.
AI interior design may be a useful proving ground for this thesis. The market is sizable, and many people face similar challenges when trying to visualize a space. Traditional options can require added time or expense: hiring a designer may not fit every budget, while learning CAD software can involve a steep learning curve. For some users, AI tools offer an additional way to explore and visualize options before making decisions.
Distribution follows the same accessibility logic: the official DecorAI app is available on iOS and Android, with a web version alongside, in more than 20 languages including Spanish, Japanese, Korean, French and German. The workflow is designed not to require professional tooling or specialized design knowledge. The entire interaction is: photograph, select, compare.
Güney expects the category to compound from here, with tighter connections between generated designs and purchasable products, better dimensional accuracy, and AI home design recommendations that account for budget and constraints rather than aesthetics alone. Each model improvement may help narrow the gap between visualizing a change and executing it.
For the AI industry, products like DecorAI illustrate one way consumer value may emerge. Frontier-model releases dominate headlines, but the work of packaging generative AI into tools that people can use when considering real decisions about their homes and money is one way the technology can move beyond demonstrations toward practical applications.
VentureBeat newsroom and editorial staff were not involved in the creation of this content.
