AI is coming to the design industry. In fact, it’s already there. Over the past few years, a range of players, from small startups to large institutions, have started deploying artificial intelligence as part of their day-to-day operations. Modsy and Havenly use AI to help their e-designers work faster. Skipp uses AI to design kitchens. And now an Israeli startup, Renovai, is using an AI-powered design engine to help retailers sell home goods online.
“People search online for over 30 hours before making a [purchasing] decision”, says the co-founder Alon Gilady. “The first problem is that they have too many options. The second is the lack of trust from customers who match items and try to design. This is a major obstacle, and that’s why we created Renovai.
Gilady founded the company in 2019 with other entrepreneurs Alon Chelben and Avner Priel after running a rendering studio that made 3D models for architects and designers. “From that experience, we thought it was possible to create technology that mimics the way interior designers think,” he says.
A B2B company, Renovai sells its technology as a plug-and-play solution that retailers can integrate into their own sites. It offers a suite of products, ranging from a recommendation engine (Liked that minimalist coffee table? Would you like that Moroccan pouf!) to more sophisticated tools, including a module that automatically populates a rendered room with purchasable products . This all relies on a combination of computer vision and machine learning which, when combined, can achieve a rudimentary version of interior design.
AI Design School
Renovai starts by taking images of products from retailers and uses an algorithm to scan them, identify them and label them with features. Let’s say, for example, that the object is a beige armchair. The business system not only perceives that the object is an armchair, but also analyzes its silhouette, its color, its fabric pattern, the shape of its legs, etc. According to Gilady, Renovai’s system identifies dozens of features for each SKU.
This is, in some ways, the easy part. The real challenge is to teach a computer to understand that this The beige armchair chair looks great next to it that table. Or, even more complicated, that that beige armchair only looks good next to that table if you go for matchy-matchy traditionalism. If you’re more into a bohemian vibe, then no, it doesn’t look good at all.
Courtesy of Renovai
To teach its algorithmic taste, Gilady’s team gathered thousands of images of interiors and fed them, relying on human designers to train the system to recognize styles. (“I always liken it to taking our AI to design school,” says Gilady.) Breaking down interior design into a purely computational process has led to some interesting observations, for example, that “styles” are more slippery than we think. .
“At first, we thought we would train the system to understand whether a sofa is ‘classic’, ‘modern’ or ‘contemporary’. What we’ve found is that the difference between ‘modern’ sofas is huge,” says Gilady. “The size, shapes, colors can all be very different from each other, but interior designers will still consider many types of sofas to be modern.” There was also confusion between the styles – taken in isolation, a “Scandinavian” sofa is not that different from a “modern” sofa, and it is difficult for an algorithm to tell the difference.
Gilady’s team tried a different approach. Rather than determining if a sofa was “modern”, they scanned hundreds of thousands of sofas and let the computer generate its own categories (they are numbered, from 1 to just under 100). “These archetypes represent everyone sofas,” says Gilady. “When we receive a new sofa from a retailer, the algorithm is able to understand if that sofa belongs to archetype 1, 2, 3 or 10. Then, once the user tries to create a room design complete, the system does not look for a “modern sofa”, it looks for archetypes that go together.
The third and final piece of the puzzle is to combine its identification and design features with customer data. Through quizzes and analysis of shopping behavior (what customers have browsed or added to their cart), Renovai builds a profile of those for whom it is designed. This translates, in theory, into personalized product recommendations to the customer’s taste.
Software can’t do a favor and get the best contractor in town for a project, or spend hours on the phone tracking down a missing sconce. But at a rudimentary level — finding out what people like and showing them homewares that match their tastes — Renovai uses AI to replicate a basic design skill.
put into practice
That’s the theory. Then there is the real world application. Renovai has sold its tools to a range of companies, from European brands like Made.com to Australian e-comm giant Temple & Webster to US subscription furniture platform Fernish. To test its design chops, I spent an afternoon playing around with its software on various sites.
On the Fernish site, I took a simple design quiz and tried to answer the questions as closely as possible to my liking (for reference, I asked for a “natural, vintage, cozy, artistic living room and minimal”), then clicked on inspiring images that appealed to me. The result was a mood board of products that included a mid-century gray sofa, abstract art, and a Moroccan rug – exactly the items I have in my real living room right now.
Courtesy of Renovai
I tried the same quiz again, this time deliberately choosing options that were do not me (“chic, glamorous, formal, elegant, sophisticated”) and clicking inspiring images, I doesn’t to like. Interestingly, although the resulting selection included a glamorous mirror and plush chair in a wacky print, it wasn’t nearly as drastically different – another gray sofa, another abstract print – from my first experience there. was waiting. Whether it was a win or a loss for Renovai is unclear. Had the algorithm failed to react to my false taste, or had it seen through the trick, looked at my site history, and shown me what I really wanted?
While there were occasional idiosyncrasies (an e-commerce site led me to a depressingly digitally rendered room), I signed off with the impression that the software essentially does what Renovai claims. The algorithm doesn’t generate Architectural Digest cover-ready rooms, but it does seem to understand at a basic level that a sisal rug, cream-colored sofa, rattan accents, and blue-striped pillows and white go together – and this one Home business the editor is predisposed to gray sofas.
Renovai’s website boasts some impressive stats, including that brands using its AI tools can expect a 30% increase in revenue on average, as well as double-digit increases in average product value. orders and conversion rate. Michael Barlow, CEO of Fernish, explains that the introduction of the tool has coincided with an increase in conversions from the brand. “It drives tremendous engagement – and if you engage with us, you’re much more likely to convert and go through the ordering process than if you don’t.”
Companies pay anywhere from $20,000 to nearly $100,000 a year for Renovai, depending on the volume of designs generated by its service, Gilady says. Currently, the company is working to expand its reach into the US market and is looking to add fashion into the mix.
Should Renovai be considered a threat to real designers in the flesh? At this point, basically no, with one caveat. Although the company’s promotional literature sometimes includes spurious lines about “removing the need to hire a professional designer,” what it offers is more like rudimentary electronics design experience. If your business is all about providing customers with a mood board and a few shopping links, this technology could hypothetically be dangerous. However, Renovai by design is limited to individual retailers. So unless your customer wants help buying everything from West Elm, e-designers who can browse the web for options will have the upper hand.
There’s even less of a threat to full-service designers. As sophisticated as Renovai’s algorithm is, the basic result – a mood board or a buyable render – doesn’t compare to working with a designer in real life. It’s not designed for.
Gilady says that in previous incarnations, the Renovai team experimented with a feature that would have allowed buyers to upload their floor plans and design their own spaces. The challenge there, he said, was not technical but psychological. “It’s less than one percent of shoppers who buy online [who want that level of engagement],” he says. “We had the ability to draw the living room, but we think most buyers don’t want to have that. Basically, they want to have a user-friendly and easy-to-use shopping experience, and that’s it.
Homepage photo: Courtesy of Renovai