# Zelig Technology > Generated by All in One SEO Pro v5.0.2, this is an llms-full.txt file, used by LLMs to index the site. An AI commerce platform for fashion retailers that turns any catalog into a live styling experience, building complete shoppable looks in real time. ## Posts ### [What 20 Retail Leaders Talked About Over Dinner in New York](https://zelig.com/nyc-dinner-retail-ai-priorities/) **Published:** October 1, 2026 **Author:** zeligadmin **Content:** On 16 September we put around twenty senior retail leaders around one table in New York. No panel, no deck, no pitch. Just dinner and a question: what are you actually working on right now? Five topics came up without prompting, and they came up in nearly every conversation. Taken together they are a fair read on retail AI priorities going into 2027. What follows is what the room said, aggregated and unattributed. The interesting part is not the list. It is what connects it. ## 1. Item discovery The framing was consistent: how do we get shoppers to see more of the catalog, and see the right parts of it? Three signals came up as the basis for relevance. What she is looking at right now. What she has shown interest in before. And the one that got the most attention: what she already owns or has bought. That third signal is the one most retailers are sitting on and not using. Purchase history is treated as a record of completed transactions rather than as an input to the next recommendation. A shopper who bought a blazer in March is a different shopper from one who did not, and most sites do not behave that way. ## 2. Inspiration The second theme was about bringing merchandising to life. Every retailer in the room employs people with real styling expertise. That expertise shows up in campaign imagery, lookbooks and editorial. It mostly does not show up on the product page, which is where the shopper actually decides. The question several people asked in different words: how do we help her understand style, build a look, and feel confident she will look good in it? That is a merchandising problem being solved with photography, and photography does not scale across a catalog of tens of thousands of items. ![A shopper holds a cream blazer and a black piece against the wardrobe laid out in front of her, deciding what works together](https://zelig.com/wp-content/uploads/2026/09/nyc-dinner-choosing-from-the-wardrobe.webp)## 3. The digital closet This one had the widest range of ambition of any topic, and the most energy behind it. At the modest end, it is simply making the closet a more useful part of the web experience rather than a saved-items list nobody returns to. At the ambitious end, people described something closer to the wardrobe in Clueless: a closet that knows what you own and can build outfits from it. What was striking is that nobody treated this as a novelty. Several leaders described it as a feature they expect to become a significant part of their site’s value over the next few years. It is worth reading alongside what [Shoppable Closet](/zelig-shoppable-closet-revolve-app/) does today. ## 4. Optimization, under real financial pressure The fourth topic was a catch-all, and it was the one with the most urgency behind it. Everyone is focused on conversion rate and units per transaction. That is not new. What has changed is why. Acquisition costs are higher, operating costs are higher, and the consumer is stretched. The room described genuine pressure to produce financial results, not engagement metrics. That pressure changes which tools survive procurement. A feature that lifts engagement without touching conversion or basket size is a harder sell this year than it was two years ago. We wrote about the conversion side of that in [how to increase fashion ecommerce conversion rate](/increase-fashion-ecommerce-conversion-rate/). ## 5. Fit and visualization are two different problems The fifth topic arrived as one subject and should be treated as two. The first is **fit confirmation**. Will this item fit my body, given my shape and size? That is a measurement question, and the goal is confidence that what arrives will fit. The second is **visualization**, or what we call see on me. How does this look on someone like me, and does it suit me? That is a style question, and the goal is confidence that she will like how she looks. They overlap naturally, which is why they get discussed as one thing. But they answer different doubts, they are solved with different technology, and a shopper can be sure of one and unsure of the other. Conflating them makes it harder to tell what a given product actually does, and harder for a retailer to work out which problem they are buying a solution to. Our piece on [styling, not sizing](/styling-not-sizing-cutting-fashion-returns/) covers why the style half is the one the industry has left alone. ## What connects these retail AI priorities Read the list again and something becomes obvious. In most organizations, those five topics are five different purchases. A discovery vendor. A recommendation engine for inspiration. A closet feature built in house or bolted on. A conversion optimization tool. A fit or try-on provider. Five contracts, five integrations, five sets of data that do not talk to each other, and five separate cases to make to finance. But the room was not describing five problems. It was describing one, from five angles. Every topic reduces to the same question: how do we help her decide, with confidence, using what we already have in the catalog and what we already know about her? That is a single job. It is solved better by one layer that sees the whole session than by five tools that each see a fragment of it. The fragmentation is not a technology problem. It is an artifact of how the category was sold. ## What we took away Two things stayed with us after the room cleared. The first is that the digital closet has moved from interesting to expected. A year ago it was a differentiator worth discussing. Now it is on roadmaps, and the leaders describing it were talking about scale and site value, not experimentation. The second is that the financial pressure is real and it is sharpening how retailers evaluate. Nobody at that table was shopping for something impressive. They were looking for the shortest path between a shopper’s hesitation and a completed, kept order. *Thank you to everyone who joined us in New York. If you would like to see what this looks like on your own catalog, [book a demo](/demo/), or read the [Revolve results](/results/).* [Book a demo](/demo/) **Categories:** AI Commerce --- ### [Shoppable Closet at Fashion Week: What Actually Shipped](https://zelig.com/shoppable-closet-what-shipped/) **Published:** September 29, 2026 **Author:** zeligadmin **Content:** Fashion week produces a lot of announcements. Most of them are intentions with a date attached. This is a short account of what actually went live during the September window, written plainly enough that you can check it. Two things shipped, and between them they put a working digital closet in front of shoppers. Both are in production with REVOLVE now, not in pilot, and both were reported by [Forbes](https://www.forbes.com/sites/angelachan/2026/09/09/retailers-are-spending-millions-on-ai-can-they-prove-the-roi/) on 9 September. The full announcement is on our [press release](/zelig-shoppable-closet-revolve-app/). ![New York Fashion Week logo](https://zelig.com/wp-content/uploads/2026/09/NYFashionWeekLogo.jpg.avif)New York Fashion Week, September 2026## 1. Shoppable Closet went live Shoppable Closet is Zelig’s digital closet. It joins three things that normally sit in separate places: a customer’s purchase history from the prior two years, the items she has saved or hearted, and the pieces she has already stored inside Build a Look. The practical effect is that she no longer starts from an empty page. She can pair a jacket bought today with a dress bought six months ago, because the system knows she owns it. Grace Hong, Chief Product Officer at REVOLVE, described it to Forbes as “a deeper integration for that closet feature,” embedded directly into REVOLVE’s site rather than bolted alongside it. She also put the ambition plainly: “This is what personalization is really about: one-to-one.” ![A shopper lays out pieces she already owns alongside a new top, assembling a look from her own wardrobe](https://zelig.com/wp-content/uploads/2026/09/shoppable-closet-assembling-from-wardrobe.webp)## 2. Build a Look expanded into the REVOLVE mobile app The second launch is distribution rather than a new capability. Build a Look, the real-time mix-and-match experience, now runs inside REVOLVE’s native mobile app as well as on web. That order was deliberate. REVOLVE tested on web first because deployment and iteration cycles are quicker there, scaling from an initial 9,000 SKUs to more than 59,000 before extending to the app. The app expansion followed the web result, not the other way round. It matters because mobile is where most fashion sessions happen and where the smallest screen makes browsing a grid least useful. A styling surface earns more on a phone than it does on a desktop. ![A shopper holds a cream tweed jacket against the black slip dress she already owns to see whether the two work together](https://zelig.com/wp-content/uploads/2026/09/shoppable-closet-pairing-new-with-owned.webp)## What a digital closet changes for the shopper The shopper-facing change is narrow and specific, which is what makes it worth writing down. Before, her wardrobe and the catalog were two separate worlds. What she owned lived in her order history, which is a receipt, not a tool. What she might buy lived on the site with no knowledge of the first. A digital closet closes that gap. It lets her style forward from what she already has rather than starting cold, which is closer to how getting dressed actually works. She is rarely buying an outfit. She is usually buying the piece that completes one. ## What it changes for the merchant This is the part that gets less attention and is probably the bigger story. Traditional systems record outcomes. Point of sale tells you what sold. Return logs tell you what came back. Neither explains why an item failed. Did she dislike the skirt, was the price wrong, was the fit wrong, or could she simply not find the top and the shoe that made it work? A digital closet captures the attempt, not just the result. It shows what shoppers are trying to build, which items they combine most often, and where a combination does not come together. That is intent data rather than outcome data, and it is a category of signal most retailers do not currently hold. For a merchant, that is directly useful. It informs assortment gaps, it shows which pieces do the most work across looks, and it does it from customer behaviour rather than instinct. We covered the commercial side of that in [how to increase fashion ecommerce conversion rate](/increase-fashion-ecommerce-conversion-rate/). ## What this is not Worth drawing a line, because these get conflated constantly. Shoppable Closet is not fit technology. It does not tell a shopper whether a garment will fit her body. That is a measurement problem, and it is a real one, but it is a different problem solved with different methods. What a digital closet addresses is the style question: does this work, with what I own, for where I am going? A shopper can be completely confident about her size and still not buy, because she cannot picture the outfit. That second doubt is the one this closes. Our piece on [styling, not sizing](/styling-not-sizing-cutting-fashion-returns/) covers why that half of the problem has been left alone for so long. ## Why it shipped when it did One detail is worth noting for anyone evaluating this kind of technology. Zelig spent five years building its own recommendation and vision models rather than assembling third-party APIs. The payoff is release speed: new features ship in under six weeks, with minimal draw on the retailer’s own engineering team. Shoppable Closet and the app expansion landing together is a consequence of that, not a coincidence of scheduling. For a retailer, the question that follows is not whether a vendor can build the feature. It is how long the vendor will take to build the next one, and how much of your engineering capacity that will cost you. ## The short version Shoppable Closet is live with REVOLVE, joining two years of purchase history with saved and hearted items and the pieces already in Build a Look. Build a Look now runs in the REVOLVE native app as well as on web, after scaling past 59,000 SKUs on web first. No pilot, no phased announcement. Both are in production and shoppers are using them. *See the numbers behind the deployment on our [results page](/results/), or [book a demo](/demo/) on your own catalog.* [Book a demo](/demo/) **Categories:** AI Commerce --- ### [How AI Styling Lifts Conversion](https://zelig.com/increase-fashion-ecommerce-conversion-rate/) **Published:** September 16, 2026 **Author:** zeligadmin **Content:** Most fashion sites convert between 1% and 3% of visits. To increase fashion ecommerce conversion rate past that ceiling, teams reach for the usual levers: faster pages, sharper copy, a shorter checkout, a new button color. Those help a little. They do not touch the reason most shoppers leave. The reason is not friction. It is doubt. A shopper cannot picture the product in her life, so she does not buy. AI styling removes that doubt before checkout, and the conversion math changes. This guide explains why fashion conversion stays low, how AI styling lifts it, and what the numbers look like in production. ## Why fashion ecommerce conversion rates stay low Fashion is a confidence purchase. A shopper is not just asking “do I want this?” She is asking “will it fit, will it match what I own, is it right for the occasion, will I actually wear it?” A static product page answers none of that. It shows a garment on a model who is not her, in a setting that is not her life. So she hesitates. And hesitation shows up in two places: sessions that never convert, and carts that never close. Baymard puts the average online cart abandonment rate at [70.22% across 50 studies](https://baymard.com/lists/cart-abandonment-rate). In fashion, where the confidence gap is widest, the drop-off runs higher still. The traffic arrives. The decision does not. ![A shopper pauses at her laptop beside an open wardrobe, weighing whether an item suits her](https://zelig.com/wp-content/uploads/2026/09/how-ai-styling-lifts-conversion-pause.webp)## Why the usual conversion fixes stop working Most conversion programs treat conversion as a friction problem. Remove steps, load faster, test the layout. That logic is real, but it has a ceiling, because it optimizes the path to a decision the shopper has not made yet. You can perfect the checkout and still lose the sale on the product page, where the shopper is trying to imagine the item in a complete outfit and cannot. Better photography and tighter copy do not close that gap. They make a static page slightly better at being static. To move the number, you have to change what the page does. Our piece on [why your product detail page works against you](/your-pdp-is-working-against-you/) goes deeper on this. ## How AI styling increases conversion AI styling changes the product page from a display into a decision. The shopper picks one item, and the platform builds a complete look around it from the live catalog, on a model, in real time. She swaps pieces, adjusts for the occasion, and sees the outfit come together before she buys. Two things happen. First, she engages. Building a look is active, and active shoppers convert far better than passive ones. Second, she gains confidence. She is no longer guessing how the piece works. She has seen it. That is the shift that turns a browse into a buy. For the wider view, see our guide to [AI styling for fashion retailers](/ai-styling-for-fashion-retailers/), and the pillar on [AI commerce for fashion](/ai-commerce-for-fashion/). ## The proof: 9% item conversion against a 2% baseline The clearest evidence comes from production, not a lab. In Zelig’s live deployment with Revolve, shoppers who used Build a Look reached item conversion of 9% against a roughly 2% industry baseline. The styling layer surfaced adjacent purchases that search and filters miss, so more of the outfit converted, not just the one piece the shopper started with. That is not a rounding-error lift. It is a different order of magnitude, on a live catalog of more than 54,000 SKUs. You can see the full case in our [results](/results/). ## What moves when you add AI styling Conversion is the headline, but it does not move alone. When shoppers build looks, three numbers shift together. - **Conversion rate rises** because engaged, confident shoppers buy. - **Average order value rises** because they buy the look, not the single piece. - **Returns fall** because they saw the complete outfit before they bought. We cover that in [styling, not sizing](/styling-not-sizing-cutting-fashion-returns/). In the Revolve deployment, session time rose more than 300%, average order value on styled sessions rose 1.5x, and returns fell 10%. Those outcomes come from the same behavior: a shopper who builds and sees the look before she buys. ![A shopper browses a fashion catalog on a laptop in front of her closet](https://zelig.com/wp-content/uploads/2026/09/how-ai-styling-lifts-conversion-browsing.webp)## How to increase fashion ecommerce conversion rate Here is the practical version. To increase fashion ecommerce conversion rate with styling, focus on the product page and the moment of decision. - **Style the product, do not just show it.** Put an interactive look-builder on the product page so shoppers can assemble a complete outfit from the live catalog. - **Answer “how do I wear this?” in the same view.** The strongest cross-sell is a complete look, not a “you may also like” row that sends her away. Read why in our guide to the [outfit recommendation engine](/outfit-recommendation-engine/). - **Keep the experience on-site and on-brand.** The styling should live inside your site, in your voice, with your catalog, so the confidence you build stays with you. - **Protect page speed.** A conversion tool that slows the page costs more than it earns. The integration should be a lightweight, asynchronous snippet. ## How to measure the lift Measure AI styling against the metrics it actually moves, not top-line traffic. - **Conversion rate for styled sessions** versus non-styled sessions. - **Item conversion** against your current baseline. - **Average order value** on styled versus non-styled orders. - **Return rate** on styled SKUs. Run it as a clean A/B test, hold the rest of the funnel steady, and the styling effect shows up clearly. ## Frequently asked questions **What is a good fashion ecommerce conversion rate?** Most fashion sites land between 1% and 3%. The goal is not to chase an industry average but to lift your own baseline, which is where a styling layer helps most.**How fast can AI styling affect conversion?** Because it deploys as a snippet on the existing stack, it can ship on the timeline of a feature release, and the conversion effect shows up in the first styled sessions. **Does it work for a large catalog?** Yes. Zelig runs across catalogs of more than 54,000 SKUs and can scale well beyond that. *Want to see what AI styling does to your conversion math? Read the [Revolve results](/results/), or [book a demo](/demo/) on your own catalog.* [Book a demo](/demo/) **Categories:** AI Commerce --- ### [Forbes: Retailers Are Spending Millions On AI. Can They Prove The ROI?](https://zelig.com/zelig-shoppable-closet-revolve-app/) **Published:** September 10, 2026 **Author:** zeligadmin **Content:** Press release · September 9, 2026 Zelig today announced the launch of Shoppable Closet. The new experience turns a shopper’s own wardrobe into a place she can style from. Zelig also expanded Build A Look, its real-time mix-and-match feature, into REVOLVE’s native mobile app. Lulus goes live with Zelig at the same time. Reported by Forbes on 9 September 2026 in [Retailers Are Spending Millions On AI. Can They Prove The ROI?](https://www.forbes.com/sites/angelachan/2026/09/09/retailers-are-spending-millions-on-ai-can-they-prove-the-roi/) by Angela Chan-Danisi. ## Shoppable Closet Shoppable Closet brings together a customer’s purchase history from the prior two years with the items she has saved, hearted, or already stored in Build A Look. Instead of starting from an empty page on every visit, she can pair a jacket bought today with a dress bought six months ago. For retailers, it also produces a signal that older systems miss. Point of sale data records what sold. Return logs record what came back. Neither explains why. Shoppable Closet shows what shoppers are trying to build. It shows which items they pair most often, and where a pairing fails. That is intent, not just outcome. ![Zelig Build A Look and Shoppable Closet running in REVOLVE's native mobile app](https://zelig.com/wp-content/uploads/2026/09/2-zelig-revolve-app-shoppable-closet-517x1024.png)Build A Look and Shoppable Closet running inside REVOLVE’s native mobile app.Zelig## Build A Look expands into the REVOLVE app REVOLVE first deployed Build A Look on web. It scaled the feature from 9,000 SKUs to more than 59,000. Testing ran against the same standards REVOLVE applies to every feature on its platform. Test periods were reset as changes went in, so the results stayed clean. Forbes reported that among customers who interacted with the feature, the rollout significantly increased conversion rate, lifted average order value by a strong double-digit percentage, and produced a meaningful reduction in returns. ![Grace Hong, Chief Product Officer at REVOLVE](https://zelig.com/wp-content/uploads/2026/09/3-revolve-grace-hong-cpo-221x300.jpg)> “I was pleasantly surprised because both return rate and conversion rate were pointing in the right direction together.” **Grace Hong, Chief Product Officer, REVOLVE**Speaking to Forbes. Photograph: Revolve Hong described the closet integration as “what personalization is really about: one-to-one.” ## Lulus launches Zelig Lulus is launching Zelig to reduce discovery friction across its catalog. Rather than viewing static products one at a time, Lulus shoppers can combine pieces and see the full outfit rendered live on a model. ![Mark Vos, President and Chief Information Officer at Lulus](https://zelig.com/wp-content/uploads/2026/09/4-lulus-mark-vos-president-cio-300x300.jpg)> “We firmly believe that our customers are actually the chief merchants.” **Mark Vos, President and Chief Information Officer, Lulus**Speaking to Forbes. Photograph: Lulus Lulus has worked with AI systems for more than five years. It built its own machine learning models to place inventory across its warehouse network. ## Built in house, deployed fast Zelig spent five years building its own recommendation and vision models. It did not assemble third party APIs. That foundation supports real-time, stylist-level fit and on-body simulation. New features ship in under six weeks, and they ask very little of a retailer’s own engineering team. ![Sandy Sholl, Founder and CEO of Zelig](https://zelig.com/wp-content/uploads/2026/09/5-zelig-sandy-sholl-founder-ceo-201x300.jpg)## About Zelig Zelig is an AI commerce platform for fashion retailers. It styles a retailer’s live catalog in real time. Shoppers mix and match thousands of items to build complete looks on a model before they buy. Zelig is in production with REVOLVE and Lulus. It was founded by fashion industry veteran Sandy Sholl. The platform is built to raise conversion and order value while cutting returns. Learn more at [zelig.com](/platform/). [Book a demo](/demo/)See the numbers in detail on the [results page](/results/), or read the [Lulus announcement](/lulus-ai-styling-zelig/). **Categories:** Press Releases --- ### [Lulus Brings AI Styling to Its Catalog with Zelig](https://zelig.com/lulus-ai-styling-zelig/) **Published:** September 8, 2026 **Author:** zeligadmin **Content:** ![Zelig and Lulus announcement lockup](https://zelig.com/wp-content/uploads/2026/09/zelig-lulus-announcement.webp) [Lulus](https://www.lulus.com/) has always described itself as “your favorite boutique”, the kind of place where you feel understood and can find exactly what you are looking for. Delivering that kind of personal experience online, at scale, is a much harder challenge. That’s why we’re excited to welcome Lulus to [Zelig](/platform/). With Zelig, Lulus is turning its catalog into a more interactive styling experience. A shopper can start with one item she loves, and within seconds explore a complete look built around it using Lulus’ own assortment and merchandising direction. ![The Build a Look button on a Lulus product page](https://zelig.com/wp-content/uploads/2026/09/lulus-build-a-look-product-page.webp) ![Build a Look on Lulus styling a black off-the-shoulder midi dress, with matching shoes and bags alongside](https://zelig.com/wp-content/uploads/2026/09/lulus-build-a-look-black-midi-dress.webp) She can change her shoes. Try another layer. Explore different combinations. Share the look with friends. And shop the complete outfit with more confidence. ![Build a Look on Lulus swapping in a champagne clutch to finish the look](https://zelig.com/wp-content/uploads/2026/09/lulus-build-a-look-clutch-swap.webp) ![Build a Look on Lulus styling a cream top with black shorts for a Classy occasion](https://zelig.com/wp-content/uploads/2026/09/lulus-build-a-look-classy-outfit.webp) ![A gold strapless mini dress styled with a clutch and heels in Build a Look on Lulus](https://zelig.com/wp-content/uploads/2026/09/lulus-build-a-look-gold-mini-dress.webp) It brings an important part of the boutique experience online: not simply showing a customer more products, but helping her understand how those products can come together in a complete look. Lulus has long believed that getting dressed should be fun and enjoyable, not intimidating, now within Zelig every shopper has an AI stylist guiding and supporting their outfit creation. Their investment in this experience is another step toward delivering on that promise for every shopper, at digital scale. We’re proud to be part of it. Welcome to the [Zelig](/results/) family, Lulus. **Categories:** Press Releases --- ### [Styling, Not Sizing - Cutting Fashion Returns](https://zelig.com/styling-not-sizing-cutting-fashion-returns/) **Published:** August 3, 2026 **Author:** zeligadmin **Content:** The fashion return problem has a familiar villain: sizing. Ask a retail team how to reduce fashion returns and the instinct is to check the size chart, the fit model, the sizing tool. That makes sense. Fit is measurable and fixable, and the industry has built a whole category of technology around it. But sizing is only part of the story, and focusing there alone leaves the bigger problem unsolved. To reduce fashion returns in a way that actually moves the number, you have to solve the returns that have nothing to do with fit. This piece is about [AI styling for fashion retailers](/platform/), and about the half of the problem the industry has left alone. The National Retail Federation’s 2025 Retail Returns Landscape report projects total retail returns of [$849.9 billion](https://nrf.com/research/2025-retail-returns-landscape), with online returns running at 19.3% of sales across all categories. In fashion and apparel, that climbs to 25% to 40%, and some specialty retailers report rates above 50%. McKinsey puts the share of fashion returns tied to [fit and style at about 70%](https://www.mckinsey.com/industries/retail/our-insights/returning-to-order-improving-returns-management-for-apparel-companies). Fit and style. Not just fit. That distinction is the whole point. The industry has poured money into the fit side: 3D body measurement, size recommendation engines, virtual fitting rooms. Those tools solve a real problem. But style-mismatch returns, the shopper who kept the size but sent the item back because it did not work with anything she owns, or did not suit the occasion, are mostly unaddressed. That gap holds a large share of return volume, and almost no one owns it. ## The problem sizing tools don’t solve ![Seeing a complete styled outfit before buying, the confidence that helps reduce fashion returns](https://zelig.com/wp-content/uploads/2026/08/StylingNotSizing1-300x225.png)Sizing tools answer one narrow question: will this garment fit this body? That is solvable with measurements, size charts, fit history, and construction data. Style mismatch is a different problem. The shopper knew it would fit. She returned it because, in the context of her real wardrobe, her life, and the occasion, it did not work. The item was right on its own. It was wrong as part of the whole picture. That is a confidence problem, not a measurement problem. And it is built into how eCommerce shows product. The standard product page shows a garment on a model or a white background. It shows the item. It does not show what the item becomes in a full outfit. It does not show how the jacket pairs with the trousers, how the dress reads at the event, or how the new piece fits the wardrobe she already owns. She fills those gaps with imagination, and imagination is unreliable. So she buys, the item arrives, the picture in her head was wrong, and it goes back. We cover the product-page side of this in [why your product detail page works against you](/your-pdp-is-working-against-you/). ## What the return rate actually costs The $849.9 billion figure is industry-wide, and its scale can feel abstract. The operating reality is concrete. Take a retailer doing $500 million in online revenue at a 30% return rate. That is $150 million in merchandise coming back every year. Behind each return sits inbound shipping, inspection, processing, restocking or liquidation, and the lost margin on a sale that cost money to win. Industry estimates put the cost of processing a single fashion return at $20 to $30, before the margin hit. At scale, returns are one of the biggest line items in fashion eCommerce, and they get a fraction of the attention that acquisition or conversion do. The industry spends billions driving traffic in, and comparatively little solving the problem that erodes what the funnel produces. Style-mismatch returns are especially costly because they are largely preventable. A wrong sleeve length may be a real sizing failure. A shopper who returns an item because she could not figure out how to wear it is an experience failure, one a better pre-purchase experience could have solved. ## How AI styling helps reduce fashion returns ![A shopper viewing a full look rather than a single item, which helps reduce fashion returns](https://zelig.com/wp-content/uploads/2026/08/StylingNotSizing2-300x225.png)Solving style-mismatch returns means giving shoppers what they lack before they buy: a credible, visual answer to whether the item works as part of a complete look. AI styling does that. It turns the product page into an interactive styling space. The shopper selects an item and starts building a full outfit, pulling matching pieces from the live catalog and seeing them assembled on a model in real time, before she buys. The mechanism is direct. When a shopper has built and visualized a complete look before buying, she buys with confidence that goes past “does this fit?” to “does this work?” That shift closes the gap between expectation and reality, which is exactly where style-mismatch returns live. The effect shows up in production: in Zelig’s live deployment with Revolve, across more than 54,000 SKUs, returns fell 10% while conversion and average order value rose. You can see the full case in our [results](/results/). ## Bracketing and the uncertainty tax There is another angle to reduce fashion returns that styling reaches directly. A large share of return volume comes from bracketing: buying several items meaning to keep one and return the rest. NRF data shows nearly two-thirds of consumers do this. Bracketing is a rational response to uncertainty. If you cannot tell which version works, you buy them all and decide at home. AI styling cuts the uncertainty that drives bracketing by giving her the information to decide before she buys. It is the same confidence mechanism working on a different behavior, and it comes from real styling logic, not a recommendation carousel. We explain that difference in our guide to the [outfit recommendation engine](/outfit-recommendation-engine/). ## Why this is the right moment to solve it The NRF’s 2025 numbers show a return problem that is not fixing itself. Online return rates have stayed high despite years of work on logistics, policy, and measurement. The retailers who cut returns meaningfully are the ones who fixed purchase confidence at the source, before the item ships. There is also new pressure from an unexpected direction. As AI tools spread across how people shop, in retailer sites, AI agents, and chat interfaces, shoppers’ patience for uncertainty is falling. They now expect a complete, visual, personal picture of how a product will look and work before they commit. Retailers that cannot meet that will watch returns keep rising, as decisions get made with less information than before. The returns problem has a better answer than the one the industry keeps applying. Sizing matters. But the shopper who knows her size and still returns the item because she could not picture how it worked is the one the industry has left alone. She is also the one AI styling is built for. ## Frequently asked questions **What is the biggest cause of fashion returns?** Fit and style together account for about 70% of fashion returns, per McKinsey. Sizing tools address the fit half. Style mismatch, when a shopper keeps the size but returns the item because it did not work with her wardrobe or the occasion, is largely unsolved. **How does AI styling reduce fashion returns?** It lets a shopper build and see a complete outfit before buying, so she buys with confidence that the item works, not just that it fits. That closes the expectation gap where style-mismatch returns come from. **Does styling help with bracketing?** Yes. Bracketing is driven by uncertainty. When a shopper can see which pieces work before she buys, she has less reason to order several versions and return the rest. *See how Zelig helps reduce fashion returns by building confidence before the buy. Read the [Revolve results](/results/), or [book a demo](/demo/).* **Categories:** AI Commerce --- ### [Your PDP is Working Against You](https://zelig.com/your-pdp-is-working-against-you/) **Published:** August 3, 2026 **Author:** zeligadmin **Content:** Here is a moment every fashion eCommerce team has lived through. You pour resources into a product page: better photography, tighter copy, social proof, user content. Product detail page conversion still does not move. You test the button color. You add a size guide. You shave 200 milliseconds off load time. Nothing. The problem is not execution. It is architecture. The modern product detail page was built to answer questions. Somewhere along the way it became something else: a decision loop that pulls buyers away from the product they came to buy. For eCommerce leaders, it is one of the most expensive structural problems hiding in plain sight. This piece is about [AI styling for fashion retailers](/platform/), and it explains why the page fights you. ## The page built to educate has become a page built to distract Think about what a typical fashion product page hands a shopper who arrived ready to buy. An image gallery. A color and size selector. A price. An add-to-cart button. Then: related items. “You may also like.” “Complete the look.” “Customers also viewed.” “Recently viewed.” A link back to the category. A style filter still sitting in the nav. Maybe an editorial module. A user-content carousel at the bottom. Every one of these was added with good intentions: help the shopper discover more, reduce bounce, lift average order value. Together, they do something counterproductive. They teach a buyer to leave. The behavioral research backs this up. The Iyengar and Lepper jam study found that when shoppers saw 24 varieties, only 3% bought. When the display dropped to 6 varieties, 30% bought. Fewer choices produced ten times the sales. A meta-analysis of nearly 100 studies on choice overload confirmed the pattern: too many options reduce satisfaction, raise regret, and lower the chance of buying at all. On a product page, a shopper drawn to a specific item should not have to make decisions. She should be guided toward commitment. Instead, the standard layout restarts the consideration process, pulling her back into browse mode at the exact moment she was ready to buy. ## The path teams assume vs. the one actually happening Most product-page decisions assume a straight line: a shopper finds a product, lands on the page, gets the information she needs, and buys. The page is built for that shopper. That is not how fashion shopping works online. The average fashion shopper views 32 pages in a single session before placing an order. That number is striking. Not because it shows indecision, but because it shows the absence of a clear path. The layout is built to be explored, not to close. So shoppers explore, cycle through alternatives, pile up options, and struggle to commit to any of them. ![A product detail page surrounded by exits that cost product detail page conversion](https://zelig.com/wp-content/uploads/2026/08/YourPDPIsWorkingAgainstYou1-300x200.png)Here is how it actually unfolds. 1. **Discovery.** She finds a product through search, social, email, or an editorial feature. She arrives with some interest. Not strong intent, but interest. 2. **Evaluation.** She looks at the images, checks the price, reads the description. This is the trust and inspiration window, where the page either deepens her interest or does not. 3. **Drift.** The “you may also like” row appears. One item catches her eye. She clicks. Now she is on a different product page, starting from scratch. 4. **The loop.** This repeats. Four, six, ten products. Each new page restarts consideration. Each click refreshes her options. The longer it runs, the less likely any single purchase becomes. 5. **Abandonment.** Decision fatigue sets in. Research shows measurable fatigue after comparing more than seven to nine options. A 2024 study of 1.6 million users found that 64% of the drop in conversions came from users simply not clicking on anything. Paralysis, not preference. This is the loop the standard layout creates. It is not user error. It is a structural feature working exactly as designed, just not in the direction that drives revenue. ## The two hurdles a product page must clear When a shopper lands with real interest, she has two questions. Leave them unanswered and she goes back to browsing. **Do I trust this product?** Does it look right? Will the quality match the price? Will it fit? Is the brand credible? Can I return it easily? **Can I see myself in this?** Is it right for my life? How would I wear it? What would I wear it with? Does it work for the occasion I am imagining? These are not informational questions. They are emotional and aspirational. A fabric table cannot answer them. They need context, vision, and styling. The standard page mostly ignores the second one. It shows the product alone: a model on white, a flat lay, maybe a lifestyle shot. It does not show her what a world with this product in it looks like. Without that picture, the decision stays abstract, so she leaves or starts clicking through alternatives, hoping the right combination finally clicks. That is the browse loop, and the page keeps it running. ## What “more discovery” actually costs The urge to load a product page with cross-sells comes from a fair goal: lift AOV, keep shoppers on-site, reduce bounce. The data tells a more complicated story. ![A shoppable styled look replacing the browse loop on a product detail page](https://zelig.com/wp-content/uploads/2026/08/YourPDPIsWorkingAgainstYou2-300x200.png)Fashion carries one of the highest cart abandonment rates in retail. Baymard puts the average online cart abandonment rate at [70.22% across 50 studies](https://baymard.com/lists/cart-abandonment-rate), and fashion runs higher still. Conversion is thin at the other end too: IRP Commerce, measuring live trading data, puts the fashion conversion rate at [1.74%](https://www.irpcommerce.com/en/gb/ecommercemarketdata.aspx?Market=3). Meanwhile, the average product page spends its most valuable space cycling shoppers back into consideration instead of moving them toward a decision. “More discovery equals more purchase” only holds if the shopper does not already have a product she likes. If she arrived at a specific page with real interest, every distraction is a conversion risk, not an opportunity. The irony is that the best cross-sell is not an exit to a new product. It is a reason to buy more of the current one. Showing her how the jacket pairs with the right trouser and shoe does not pull her away from the sale. It builds toward a bigger one. ## What the numbers say about product detail page conversion The clearest evidence comes from production. In Zelig’s live deployment with Revolve, across a catalog of more than 54,000 SKUs, shoppers who used Build a Look to assemble complete outfits on the page converted at 3x the control group. Session time rose more than 300%. Average order value on styled sessions rose 1.5x. Returns fell 10%. You can see the full case in our [results](/results/). Those numbers move together because the fix addresses product detail page conversion at its root. Outfit context clears the second hurdle. It gives her the aspirational picture she could not build from the isolated product view, and it moves the question from “should I buy this?” to “how many of these pieces do I need?” We break the conversion mechanism down further in our [results](/results/). ## The architecture problem is also a metrics problem Part of why the layout stays broken is that the metrics reward the wrong things. High page views look like engagement. Clicks on related products look like interest. Long sessions look like depth. But if none of it turns into sales, these numbers measure the cost of the browse loop, not the value of the experience. The KPIs that actually measure product detail page conversion are narrower and harder to game: - Add-to-cart rate from that specific product - Conversion rate from product-page visit to purchase - Multi-item order rate (AOV growth through complementary pieces, not cross-sell wandering) - Return rate (a signal of whether the context set the right expectation) Measure the page against these and the picture changes. The page that drives the most downstream clicks is often not the page that drives the most revenue. And the page that is easiest to navigate away from is, by definition, the one least likely to close. ## The way forward: build the look, then close The fix is not to strip pages down to bare product info. It is to replace distraction with direction. The emerging model gives the product page one job: build conviction, not present options. - **Show the product in complete looks.** Not adjacent products. Full outfits, styled and contextual, that answer “how would I wear this?” in the same view. This keeps her attention on the product she came for while expanding the natural basket. - **Make the look shoppable, not linkable.** A “you may also like” row links out to other pages and resets consideration. An interactive outfit builder lets her swap pieces within a look and deepens it. For the logic underneath, see our guide to the [outfit recommendation engine](/outfit-recommendation-engine/). - **Protect the trust and inspiration window.** The space above the fold should do one job: build desire for this product. Everything else is secondary. - **Use personalization to reduce options, not multiply them.** The strongest use of personalization is showing the right thing, not more things. A shopper who arrived from a summer-occasion editorial should see this product styled for summer occasions, not the same generic grid as everyone else. - **Measure conversion over engagement.** Traffic to a page that does not convert is expensive traffic. Success is not whether the page holds attention. It is whether it produces sales. ## What this means for eCommerce leadership The product page is the most expensive real estate in your funnel. Every dollar of traffic, every influencer partnership, every email, every ad eventually lands on one. If that page is built to send buyers back into consideration instead of forward into a purchase, you are paying to fuel a loop. The good news: this is solvable, and the brands that solve it first gain a real advantage. Interactive, look-building experiences that replace the browse loop with a buying path do more than lift conversion. They increase basket size, reduce returns because she reaches checkout with better context, and build the kind of product relationship that drives repeat purchase. Because a confident shopper returns less, the same fix also cuts returns, which we cover in [styling, not sizing](/styling-not-sizing-cutting-fashion-returns/). The standard page was not designed to fail. It was designed in an era when “more information” was the answer to every conversion problem. The modern buyer’s problem is not a lack of information. It is a lack of clarity. The page that wins gives her exactly what she needs to say yes: a clear picture of the product, a clear picture of herself in it, and a clear path to checkout. That is not a redesign of the product page. It is a rethink of what the page is for. ## Frequently asked questions **Why is product detail page conversion so low in fashion?** Because a static page answers whether the product exists, not whether it suits the shopper. It leaves the “how would I wear this?” question open, so she leaves or cycles through alternatives instead of buying. **Do product recommendations help or hurt conversion?** On a page where the shopper already has a product she likes, a row of other products is an exit that restarts consideration. A styled look that expands the current item helps far more than a list that links away. **What is the single highest-impact change to a product page?** Answer the shopper’s second question on the page: show the product in a complete, shoppable look so she can picture herself in it before she decides. *Zelig builds interactive look-building experiences for fashion eCommerce, designed to close the trust and inspiration gap and turn browsers into buyers. Read the [Revolve results](/results/), or [book a demo](/demo/).* **Categories:** AI Commerce --- ### [What Is AI Commerce? (for Fashion)](https://zelig.com/ai-commerce-for-fashion/) **Published:** September 3, 2026 **Author:** zeligadmin **Content:** AI commerce for fashion is the shift from a storefront that waits for a search to one that understands what a shopper means and styles a response. It reads intent, builds a complete look from your live catalog, and helps the shopper decide. This guide explains what AI commerce is, why fashion feels it first, how it works, and what it does to conversion, average order value, and returns. The change is already underway. Traffic from AI sources to U.S. retail sites grew 393% year over year in early 2026. A year earlier, shoppers arriving from AI tools converted 38% worse than normal web traffic. By March 2026, that same group converted 42% better and stayed 48% longer. That is not a trend. It is a new category, and fashion is where it lands hardest. ## What is AI commerce? AI commerce is a shopping interface built on intent, not on catalog structure. Traditional eCommerce assumes the shopper already knows what she wants. It gives her a search bar, a set of filters, and a product page built to close a sale. AI commerce assumes the opposite. It starts with a context and works toward a decision. Think about how people actually describe what they want. “Something for my sister’s outdoor wedding in June.” “Polished but not overdressed for a board meeting.” Those are not search terms. They carry occasion, body, taste, and mood. AI commerce reads them and answers with styled looks pulled from the retailer’s live inventory. The catalog does not go away. It gets a brain. ## AI commerce vs. traditional eCommerce The difference is easiest to see side by side. - **Traditional eCommerce presents a catalog. AI commerce presents a conversation.** One asks the shopper to adapt to a grid. The other adapts to the shopper. - **Search returns results. AI commerce returns looks.** A search engine hands back a ranked list. AI commerce hands back a finished outfit. - **Filters narrow options. AI commerce narrows the confidence gap.** It moves the shopper from browse to decide, which is where sales are won. This is not a better search bar. It is a different job. The problem is the setup, not the individual tools. ## Why fashion needs AI commerce Every retail category will feel AI commerce. Fashion feels it first, because fashion is not a SKU problem. It is a taste, confidence, and context problem. A laptop has clear specs. RAM, screen size, battery. Filters work. A blazer does not. Does it suit my body? Will it match what I own? Is it right for the event? A static page cannot answer those questions. It can show the product. It cannot style the outfit or picture the look on you. That gap has a price. Online fashion returns run 25% to 40%, among the highest in retail, and “wrong size” or “didn’t look right” cause most of them. The confidence a shopper used to get in a fitting room never made it online. AI commerce closes that gap on the screen, for every shopper, not only the ones with a personal stylist. We cover the returns side in detail in [styling, not sizing](/styling-not-sizing-cutting-fashion-returns/). ## How AI commerce works: the four layers AI commerce works across four layers. In fashion, each one helps close the sale. 1. **Understand.** It reads context, not just a query. Occasion, intent, body, taste, and budget, often from a sentence or two. 2. **Style.** It builds complete looks from the live catalog, the way a skilled stylist would, at the scale of thousands of shoppers and tens of thousands of SKUs. 3. **Visualize.** It shows the shopper what “yes” looks like before she adds to bag. This is the moment that closes the confidence gap. 4. **Convert.** It guides her to the product page, the cart, or the next best item. The sale is earned, not forced. At Zelig, this is Build a Look. A shopper picks one item, and the platform assembles the rest of the outfit in real time, using the retailer’s catalog and merchandising rules. Two engines sit underneath it. The Conversion engine surfaces adjacent purchases that search and filters miss. The Discovery engine turns browsing into a styled experience organized by occasion, mood, and style. ## What AI commerce is not Three quick corrections, because the term gets stretched. **It is not a chatbot.** Most chatbots answer FAQs and deflect support tickets. AI commerce is a shopping interface, not a service one. **It is not a better search bar.** Semantic search still works the old way: you query, the catalog returns results. AI commerce flips that. You describe a context, and it builds a response. **It is not a legacy recommendation engine.** A behavioral engine shows more of what is popular or co-viewed. AI commerce reads style and occasion and builds a look. For the full contrast, read our guide to the [outfit recommendation engine](/outfit-recommendation-engine/). ## ![Woman At Desk Looking At Photos Of Outfits](https://zelig.com/wp-content/uploads/2026/09/WhatIsAiCommerceForFashion1-300x200.jpg)What AI commerce does to the numbers The case for AI commerce is not theoretical. It shows up in the metrics retailers care about. Zelig’s live deployment with Revolve styles more than 54,000 SKUs across the catalog. Shoppers who engaged the AI interface converted at 3x the rate of the control group. Session time rose more than 300%. Average order value on styled sessions rose 1.5x. Returns fell 10%. Those numbers move together for one reason. Engaged shoppers build looks and buy the outfit, not the single piece, which lifts conversion and basket size. And a shopper who has seen the complete look before buying returns less, because she was right more often. We break down the conversion side in our [results](/results/). The prize at the category level is large. McKinsey estimates generative AI could add [$400 billion to $660 billion a year in value](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier) across retail and consumer goods. The retailers who capture it will not be the ones who added a chatbot. They will be the ones who rebuilt the interface. ## How to add AI commerce to your stack The common worry is integration cost. A new shopper-facing experience that needs a platform swap is hard to prioritize. AI commerce avoids that by working as a layer on top of the existing stack. Zelig’s front-end is a lightweight, asynchronous snippet that does not slow the page. It connects to the existing catalog and inventory feed, routes through the existing cart and checkout, and works inside existing analytics and testing. No replatform. No re-architecture. It ships on the timeline of a feature release, not a migration. For more on where a static page loses the sale, see [why your product detail page works against you](/your-pdp-is-working-against-you/). ## The compounding advantage Here is the part that matters most over time. A static platform improves only when you rebuild it. An AI commerce interface improves on its own. Every styled session is feedback. Every purchase or return is a signal. Each one makes the next look better. That builds a moat. More catalog data means better styling. Better styling means better outcomes. Better outcomes mean better data. A rival cannot copy that by licensing the same tools. The retailers who start now build a lead that grows every month. ## AI commerce for fashion, in one line eCommerce let fashion brands sell to anyone, anywhere. AI commerce for fashion lets them style anyone, anywhere, reading each shopper’s context in real time. The catalog becomes a wardrobe someone put thought into. The brands that build it will own the next decade of retail. ## Frequently asked questions **Is AI commerce the same as virtual try-on?** No. Try-on shows one product on a body. AI commerce builds and styles a complete look across the catalog, then helps the shopper buy it. **Does AI commerce replace my eCommerce platform?** No. It sits on top of your existing stack as a styling layer and routes through your current cart and checkout. **How is AI commerce measured?** Track conversion for AI-engaged shoppers, average order value on styled sessions, and return rates on styled SKUs, separately from general traffic. **Which retailers is it for?** Any fashion retailer with a catalog deep enough that shoppers need help deciding: luxury, contemporary, and multi-brand marketplaces. *Want to see AI commerce for fashion on your own catalog? [Book a demo.](/demo/)* **Categories:** AI Commerce --- ### [Outfit Recommendation Engine](https://zelig.com/outfit-recommendation-engine/) **Published:** August 3, 2026 **Author:** zeligadmin **Content:** Recommendation engines have been part of eCommerce for more than two decades. Amazon’s “customers who bought this also bought” launched in 2001. It set the template for the whole industry. Today the global recommendation engine market is worth more than $7 billion and growing. Nearly every fashion retailer of scale runs some form of product recommendations. And yet most shoppers can sum up the experience fast. Items they already own. Versions of what they just looked at. Products that are related on paper but wrong for their style. The same ten pieces in the carousel every session. The fashion recommendation problem is not a data problem. Retailers have more behavior data than ever. It is a signal problem. What these engines measure does not match what fashion shoppers need. ## What recommendation engines were built to do Most recommendation systems were built for commodities. These are goods where price, availability, and past use drive the choice. Say you bought a coffee brand and then a coffee maker. Collaborative filtering spots that pattern and repeats it for other users. It works because coffee and coffee makers go together for almost everyone. Fashion is different in nearly every way that matters. A cream blazer and wide-leg trousers do not have a fixed link. The link is about style. It depends on proportion, color, occasion, season, taste, and the rest of her wardrobe. Two shoppers can share the same age, price point, and history, and still have opposite taste. These engines assume similar behavior means similar taste. In fashion, that breaks. The result is the experience shoppers know. Recommendations that feel generic. Related on a chart, useless for styling. More of the same, with no help on what goes with what. ## The three failure modes Legacy engines fail fashion in three ways. **The [cold start problem](https://en.wikipedia.org/wiki/Cold_start_(recommender_systems).** These systems need history to work. A new arrival has no data yet. It cannot be recommended on co-purchase or co-view patterns, because those patterns do not exist. In fashion, catalogs turn fast and newness sells. So the items that need exposure most are the ones the system can surface least. **The long tail problem.** Fashion catalogs are deep. A retailer with 50,000 SKUs makes most of its data around a few bestsellers. The rest sits in the long tail with too little data to recommend. So the system pushes what is already popular. That repeats popularity and hides most of the range. It is expensive too. Those long-tail products carry margin and inventory cost, and they never get styled into a look. **The context collapse problem.** Behavior engines cannot read context, the why behind a session. A shopper bought a cocktail dress last month. Now she is browsing weekend casual. The system treats her as the same person with the same intent. The occasion and the gap she is filling stay invisible. It reads only what she did, not what she is trying to do. Each one ends the same way. Recommendations aimed at a demographic, not a person. ![Woman in a black evening gown interacting with a floating holographic display of outfits and accessories in a luxe boutique.](https://zelig.com/wp-content/uploads/2026/08/OutfitRec1.png) ## The signals fashion actually requires An engine that works for fashion needs a different signal set. Not just what shoppers did. Also what they are doing, what they want, and how products relate as a look. Those signals sit across four areas. **Browse and styling behavior.** The choices a shopper makes while building a look are far richer than a browse click. She picks a jacket, drops it for another, then pairs it with a specific trouser and a boot, not a sneaker. That sequence shows real taste. It is intent at a detail that page views and purchase history cannot reach. **Product type and combination logic.** Fashion recommendation needs to know how categories relate. Tops relate to bottoms in set ways. Outerwear layers over an outfit. Footwear is chosen against the shape of the whole look. An engine that knows the catalog at this level can build combinations that follow real styling logic, not chance. **Occasion intent.** The occasion is the strongest context signal, and the one old systems capture worst. A work meeting, a weekend trip, a wedding, a casual night. Each shapes formality, color, shape, footwear, and accessories. An engine that reads occasion can recommend with a precision generic engines cannot match. **Purchase in context.** A purchase made after a full look is assembled means more than one made off a listing page. It is a confirmed style choice inside an outfit. Over time, it builds a profile of how a shopper actually dresses, not just what she once clicked. ![Woman in a black evening gown uses a holographic virtual wardrobe, with outfits and accessories displayed in glowing panels in a luxury boutique.](https://zelig.com/wp-content/uploads/2026/08/OutfitRec2.png) ## What this data foundation makes possible Together, these signals enable a class of recommendations old engines cannot deliver. **Real-time personalization.** The system reads live styling choices, not just history. So it adjusts as the session runs. A shopper who starts casual and moves to something dressier is shifting. A live engine follows the shift. This is personalization at the session level, not the segment level. **Complete-look intelligence.** The engine knows how products relate as a look. So it surfaces items that finish an outfit, not items that are simply popular. Not “shoppers who bought this jacket also viewed this jacket.” Instead: “this jacket, in this proportion and color, is finished by these pieces.” **Long-tail activation.** It recommends on product type and styling logic, not popularity. So it can surface long-tail products that fit the look being built. A new arrival can appear the day it lands, matched to looks that fit its style. Old engines would need months to earn that exposure. **Fewer filter bubbles.** Old engines show more of what a shopper has already seen. A styling-aware engine can introduce new categories and products, because the pick is driven by outfit logic, not by her past. ## The data compounding effect The advantage grows with scale. Every look built on the platform makes data. Which combinations worked. Which occasions drove which buys. Which product types paired well. As that adds up across the shopper base, the engine improves. The styling logic sharpens. The occasion signals get sharper. Even a new shopper’s first session is better, because the model starts from a richer base. This is the moat behavior engines cannot copy. Behavior data tells you what happened. Styling data tells you why, and what it means next. ## The recommendation engine fashion has been waiting for The fashion recommendation problem has been misread as a scale problem. The idea was that more data, more compute, or better filtering would fix quality. But more data from the wrong signals does not fix the core mismatch. The category needs a different engine. One that reads the look, not just the product. One that reads occasion, not just history. One that recommends on how items work together in real life, not just how they were co-viewed in a log. That is what an outfit recommendation engine does. It is what [Zelig’s AI Commerce Suite](/platform/) is built to deliver: a data foundation that captures the signals that matter, real-time personalization, and styling intelligence that turns the catalog from a product list into a living wardrobe. *See the outfit recommendation engine on a live catalog, or read the [production results](/results/). [Explore the experience.](/demo/)* **Categories:** AI Commerce --- ## Pages ### [Home](https://zelig.com/) **Published:** July 6, 2026 **Author:** zeligadmin **Content:** # Your catalog, styled in real time. Zelig is an AI commerce platform for fashion that turns any catalog into a personal styling experience. Built for the way today’s shopper actually buys. [Try It](#try) [Try It For Yourself](#) ## She’s building an outfit. You’re selling one item at a time. Zelig builds the rest of the look around any piece she taps, live on your catalog, so the whole outfit lands in the bag. ![Zelig Discovery Engine](data:image/svg+xml;charset=utf-8,%3Csvg%20xmlns%3D'http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg'%20viewBox%3D'0%200%201000%20800'%2F%3E) ### Discovery engine Browse stops being a list. It becomes a styled experience. Shoppers explore by occasion, mood, and style, with the model updating in real time. Session time goes up because shoppers want to stay. ![Conversion Engine](data:image/svg+xml;charset=utf-8,%3Csvg%20xmlns%3D'http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg'%20viewBox%3D'0%200%201000%20800'%2F%3E) ### Conversion engine A shopper picks one item. Zelig builds the look around it in seconds. Then another. Then another. The styling layer surfaces adjacent purchases that search and filters miss, lifting item conversion to multiples of the industry baseline. ## Proof at $3B GMV scale. In a production deployment with a $3B-revenue fashion retailer, shoppers engaging with [Build a Look](/platform/) spent 3x longer on site, converted at 3x the rate of the control group, drove 1.5x average order value, and reduced returns by double digits. That is what an AI commerce platform for fashion does on a live catalog, measured against an industry conversion baseline of [around 1.7%](https://www.irpcommerce.com/en/gb/ecommercemarketdata.aspx?Market=3). [Read the full case study →](https://zelig.com/results/) ![A shopper reviewing styled outfit options on an illuminated display](data:image/svg+xml;charset=utf-8,%3Csvg%20xmlns%3D'http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg'%20viewBox%3D'0%200%201200%20800'%2F%3E) > “At REVOLVE, we’re always looking to innovate at the intersection of fashion and technology. Zelig is the perfect partner for us – they don’t just bring cutting-edge tech, but a deep understanding of fashion and how customers want to experience it.” — Grace Hong, Chief Product Officer, REVOLVE ![Zelig styling widget. Baby blue top cream bottoms.](data:image/svg+xml;charset=utf-8,%3Csvg%20xmlns%3D'http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg'%20viewBox%3D'0%200%202560%202048'%2F%3E) ## The AI commerce platform for fashion, on your catalog. Pick three categories that matter most. We’ll show you Build a Look running on your real SKUs and walk you through the conversion math in 20 minutes. [Book a demo](https://zelig.com/demo/) --- ### [Press](https://zelig.com/press/) **Published:** July 6, 2026 **Author:** zeligadmin **Content:** # Press For Zelig press and media inquiries, contact . ## Recent Zelig press coverage [September 9, 2026 / Forbes“Retailers Are Spending Millions On AI. Can They Prove The ROI?”Read →](https://www.forbes.com/sites/angelachan/2026/09/09/retailers-are-spending-millions-on-ai-can-they-prove-the-roi/)[September 8, 2026 / Zelig announcement“Lulus Brings AI Styling to Its Catalog with Zelig.”Read →](/lulus-ai-styling-zelig/)[October 14, 2025 / The Drum“How Revolve is utilizing AI styling to fix fashion’s billion-dollar returns problem.”Read →](https://www.thedrum.com/news/how-revolve-utilizing-ai-styling-fix-fashion-s-billion-dollar-returns-problem)[October 9, 2025 / Forbes“Sandy Sholl’s Next Act: How Zelig Is Teaching AI The Rules Of Fashion.”Read →](https://www.forbes.com/sites/angelachan/2025/10/09/sandy-sholls-next-act-how-zelig-is-teaching-ai-the-rules-of-fashion/)[October 9, 2025 / WWD“Revolve Prompts Easy Outfitting Through Zelig Technology.”Read →](https://wwd.com/business-news/technology/revolve-enables-easy-outfitting-through-zelig-technology-1238288061/)[October 9, 2025 / Retail Technology Innovation Hub“Revolve Group taps Zelig AI powered technology for launch of Build a Look fashion experience.”Read →](https://retailtechinnovationhub.com/home/2025/10/9/revolve-group-taps-zelig-ai-powered-technology-for-launch-of-build-a-look-fashion-experience)[October 8, 2025 / Business Wire“Zelig Debuts AI-Powered Build a Look Fashion Experience on REVOLVE.”Read →](https://www.businesswire.com/news/home/20251008971478/en/Zelig-Debuts-AI-Powered-Build-a-Look-Fashion-Experience-on-REVOLVE)[November 7, 2023 / Business Wire“AI-Powered Virtual Try-On and Styling Company Zelig Secures $15 Million in Series A Funding, Led by Hilco Global.”Read →](https://www.businesswire.com/news/home/20231105736950/en/AI-Powered-Virtual-Try-On-and-Styling-Company-Zelig-Secures-15-Million-in-Series-A-Funding-Led-by-Hilco-Global)[November 7, 2023 / Retail Technology Innovation Hub“Fashion tech firm Zelig bags $15m in Series A funding for virtual try-on and styling experiences.”Read →](https://retailtechinnovationhub.com/home/2023/11/7/fashion-technology-company-zelig-bags-15-million-in-series-a-funding-for-virtual-try-on-and-styling-experiences) ## Press Kit Zelig press kit: logo files, founder headshots, product screenshots, and approved language available on request. Email . --- ### [Results](https://zelig.com/results/) **Published:** July 6, 2026 **Author:** zeligadmin **Content:** # Real Results: 9% item conversion. ## The setup This is what item conversion looks like when styling runs on a live catalog. A top online fashion catalog with 110,000 styles. 82% sold at full price. $1.1B in revenue in 2024. Their shoppers are millennial and Gen Z, fluent in fashion, demanding on the experience. They tested [Build a Look](/platform/) on half of their audience for three months. Quietly. No marketing push, no front-page placement. Just the feature live, and a control group seeing the legacy experience. ![](https://zelig.com/wp-content/uploads/2026/10/3A3FF5B5-F5A4-4CD1-BDFA-6FE54F513E99.png) ## What changed Four numbers moved across the test cohort. ### Session time: 3x Shoppers interacting with Build a Look stayed on the site three times longer than the control group. Time on site wasn’t friction. It was engagement. ### Item conversion: 9% against a 2.0% industry baseline Shoppers using Build a Look converted on items at four and a half times the industry baseline, which live market data puts [near 1.7%](https://www.irpcommerce.com/en/gb/ecommercemarketdata.aspx?Market=3). The styling layer surfaced adjacent purchases that the baseline experience missed. We break down how that works in our guide to the [outfit recommendation engine](/outfit-recommendation-engine/). ### Returns: down double digits Confidence at checkout went up. Returns dropped. In fashion, where returns can run 25 to 40%, that line moves directly to gross margin. ### Average order value: 1.5x The basket grew. When the platform shows a coat with the bottoms and shoes that finish the look, shoppers buy the look, not the coat. ## What launched A fashion retailer with over 54,000 SKUs available inside the styling experience launched Build a Look to its full audience in October 2025. Now, live on PLPs, PDPs and on dedicated landing pages across the site – serving literally millions of outfit combinations monthly. PDPs PLPs Landing Pages ![Zelig Build a Look implemented across the Revolve catalog](https://zelig.com/wp-content/uploads/2026/08/Revolve-Implementation-beigeBG-2.jpg) > “Zelig’s AI-powered styling brings our vision to life, enabling our customers to create an endless array of complete looks with confidence.” — Mike Karanikolas, Co-Founder and Co-CEO, REVOLVE --- ### [Platform](https://zelig.com/platform/) **Published:** July 6, 2026 **Author:** zeligadmin **Content:** # Zelig automatically turns your brand’s merchandising vision into personalized guidance for every online shopper. Zelig is an AI commerce platform that adds a styling layer to [the stack you already run](/integration/). No replatform, no new front-end. Your catalog, your brand voice, your rules. [Navigate to the next section](#) INTERACTIVE DEMO ## Build a Look The shopper picks one item. The widget assembles the rest of the outfit in real time using catalog and merchandising rules. The model on screen updates as pieces are swapped. Save the look. Share it. Add to bag. See what that did to [conversion at Revolve](/results/). The widget is easily integrated anywhere your customers engage: PDP PLP Landing Pages Email Paid Social ## Digital Closet Every shopper gets a closet on the site that learns what they like and how they wear it. Saves, favorites, and past purchases all live in one place, ready to be styled and bought. Returning shoppers come back to a personalized starting point instead of a homepage they have to re-navigate. ![Zelig Digital Closet Interface Example](https://zelig.com/wp-content/uploads/2026/08/digital_closet.png) ## Styling Intelligence, inside one AI commerce platform Stylists set the rules. Zelig applies that expertise across the entire catalog, matched to occasion, season, and silhouette, for every shopper who visits. That is what separates an AI commerce platform from a recommendation widget, in a category [McKinsey sizes at $400 to $660 billion a year](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier). Other virtual try-on solutions demonstrate customer engagement. Zelig surfaces how they’re styling: which pieces they pair, which combinations drive action, and where taste is trending. Every interaction generates first-party styling intelligence that feeds back into merchandising, buying, and your CRM. --- ### [Demo](https://zelig.com/demo/) **Published:** July 6, 2026 **Author:** zeligadmin **Content:** # See it on your catalog. This is an AI styling demo on your own catalog. Book a 20-minute call. Tell us which three categories matter most. We’ll run [Build a Look](/platform/) against a sample of your real SKUs and walk you through the conversion math live. ## What to expect from the AI styling demo 5 minutes: your team and ours, what you sell, who buys it. 10 minutes: Build a Look running on your categories. 5 minutes: integration timeline, pricing model, next steps. ## Who shows up from our side A platform lead and a senior engineer. The platform lead answers commercial and roadmap questions. The engineer answers integration and security questions. No SDR layer. The AI styling demo runs on your real SKUs, against an industry conversion baseline of [around 1.7%](https://www.irpcommerce.com/en/gb/ecommercemarketdata.aspx?Market=3). See the [production numbers](/results/) first if you prefer. Request a demo## Tell us what you sell. Tell us a bit about your store and we'll set up a time to show Zelig on your catalog. Something went wrong on our end. Email and we'll sort it out fast. First name \* Add your first name. Last name \* Add your last name. Company name \* Add your company name. Work email \* Add a valid work email. Phone optional Why Zelig for your store? optional We'll tailor the walkthrough to the categories and conversion problems you name here. Tell us a little about what you're looking to solve. Leave this field empty Request my demo → A real person replies, usually within one business day. ✓ ## Got it. You're on the list. A Zelig specialist will reach out **shortly** to lock a time and confirm which of your categories we'll run first. Usually within one business day. While you're here [ See how the platform works Build a Look, styling, and the conversion engine under the hood → ](/platform/) [ Look at the results What the conversion math looks like on real catalogs → ](/results/) [ Built for luxury How Zelig fits luxury houses and the buyer who arrives with intent → ](/for-retailers/luxury/) Explore by segment [Luxury →](/for-retailers/luxury/) [Brands →](/for-retailers/brands/) [Marketplaces →](/for-retailers/marketplaces/) --- ### [Company](https://zelig.com/about/) **Published:** July 6, 2026 **Author:** zeligadmin **Content:** # Built by people who run fashion. Zelig Technology was founded by Sandy Sholl and Adam Freede in 2020, and its work with REVOLVE is documented in our [results](/results/) and has been covered by [WWD](https://wwd.com/business-news/technology/revolve-enables-easy-outfitting-through-zelig-technology-1238288061/). Sandy is also Chairperson of MadaLuxe Group, North America’s leading luxury distributor. Adam is the CEO of MadaLuxe and serves as Co-Chair of The Sholl Freede Foundation. We came at AI commerce from inside the fashion business. The category needed both a deep understanding of how fashion actually operates and a clean-sheet view of what AI could do for shoppers. We built Zelig at that intersection. ## Why Zelig Technology exists Traditional eCommerce was designed when shoppers tolerated browsing. Product grids, filters, faceted search, recommendation rails. That model is exhausted.Today’s shopper knows what she likes. She wants help finding the things that fit her, look right on her, and pair with what she already owns. That is a styling problem, and styling is what fashion has always done best. Zelig brings styling intelligence to every shopper who visits your site. The product is new. The expertise behind it is decades old. ## Leadership ![Sandy Sholl headshot on greige background](https://zelig.com/wp-content/uploads/2026/07/Sandy-Sholl-Headshot.jpg) Sandy Sholl, CEO Sandy Sholl is the Founder of Zelig, where she is leading the transformation of the retail landscape by pioneering the industry’s first end-to-end AI Commerce styling platform. ![Dan Shao Headshot - Zelig](https://zelig.com/wp-content/uploads/2026/07/Zelig-Leadership-Dan-Shao.jpg) Dan Shao, CTO Dan Shao is an accomplished computer vision, machine learning, and AI leader with deep experience developing advanced perception systems across robotics, automation, and intelligent imaging. ![Ryan Cahill Headshot - Zelig](https://zelig.com/wp-content/uploads/2026/07/Zelig-Leadership-Ryan-Cahill.jpg) Ryan Cahill, CRO Ryan spent 15 years scaling revenue for the platforms that defined the last era of commerce, including Oracle, Selligent, and Stylitics, and joined Zelig to build the one defining the next. ![Adam Michael Headshot - Zelig](https://zelig.com/wp-content/uploads/2026/07/Zelig-Leadership-Adam-Michael.jpg) Adam Michaels, CPO Adam is a fashion tech and e-commerce executive who spent two decades at ZOZO and TechStyle Fashion Group (Fabletics, Savage X Fenty) building digital products, AI platforms, and global tech organizations. ## Funding and backing Zelig raised $15M in Series A funding in November 2023, led by Hilco Global. Total capital raised includes additional strategic investment. Company valuation at Series A was reported at $100M. ## Advisors Julie Wainwright Founder, The RealReal Michelle Lee AI Transformation and Policy Leader, AWS Mike Karanikolas Co-Founder and Co-CEO, REVOLVE Jeff Hechtman Founding CEO, Hilco Global Veronica Grazer Marketing Expert, SBE Kirk Posmantur Founder, Axcess Worldwide ## Press Zelig and our retail partners have been covered in WWD, Business of Fashion, Forbes, The Drum, and Business Wire. [See full press coverage](https://zelig.com/press/). WWD Business of Fashion Forbes The Drum Business Wire TechCrunch --- ### [For Retailers](https://zelig.com/for-retailers/) **Published:** July 6, 2026 **Author:** zeligadmin **Content:** # AI commerce for fashion retailers. AI commerce for fashion retailers is not one product dropped onto every catalog. A maison, a contemporary brand and a multi-brand marketplace each sell to a different shopper, under different rules. Zelig runs the same styling engine across all three, and lets your team decide what pairs with what. ## AI commerce for fashion retailers, three ways **[Luxury](/for-retailers/luxury/)** for maisons and luxury platforms, where the site is part of the brand and every adjacency has to read as if a head of merchandising signed off on it. **[Brands](/for-retailers/brands/)** for contemporary retailers selling to a faster, social-first shopper who taps, swipes and checks out. **[Marketplaces](/for-retailers/marketplaces/)** for multi-brand catalogs that need looks built across vendors rather than inside one label. Whichever one you are, the mechanism is the same. A shopper who builds a complete look buys more of it and sends less of it back. In production that meant 3x conversion, 1.5x average order value and a double-digit drop in returns, measured against an industry conversion baseline of [around 1.7%](https://www.irpcommerce.com/en/gb/ecommercemarketdata.aspx?Market=3). The full numbers are in our [results](/results/). --- ### [Brands](https://zelig.com/for-retailers/brands/) **Published:** July 6, 2026 **Author:** zeligadmin **Content:** # Built for the way contemporary shoppers buy. Contemporary fashion retailers are selling to a different shopper. Your audience is faster, more fluent on social. They don’t read the homepage. They tap, swipe, save, and check out. Zelig is built for that shopper. ## Outcomes contemporary fashion retailers care about ##### More Shoppers Buy When people see a full outfit instead of a single product, more of them add to bag and check out. ##### Bigger Orders Buy the whole look; top, bottoms, and outerwear, and the average order is larger than a single piece. ##### Fewer Returns Complete outfits are bought with real excitement and anticipation; no more second guesses that send pieces back. ##### Data You Own Every look a shopper builds shows you what sells together – first-party insight for buying and merchandising, and it stays yours. ## Proven in production In a deployment with a $3B-revenue fashion retailer, shoppers engaging with Build a Look spent 3x longer on site, converted at 3x the rate of the control group, drove 1.5x average order value, and reduced returns by double digits. For contemporary fashion retailers that matters most where [McKinsey attributes about 70% of fashion returns to fit and style](https://www.mckinsey.com/industries/retail/our-insights/returning-to-order-improving-returns-management-for-apparel-companies). [Read the full case study →](https://zelig.com/results/) ## Integrates with your stack in weeks Catalog ingest, model training, and front-end deployment in 6 to 8 weeks. See the full [integration path](/integration/). Works with Shopify Plus, Salesforce Commerce Cloud, BigCommerce, and custom stacks. You own deployment – a relatively effortless, single script that drives the entire experience. We own the styling work. Shopify Plus BigCommerce Salesforce Commerce Cloud Custom Stack ![A contemporary fashion shopper wearing a styled complete look](https://zelig.com/wp-content/uploads/2026/07/Contemporary2.png) ## Your data, your IP Every interaction with Build a Look generates first-party styling data. It belongs to you. Use it inside your CRM, your buying tool, your CDP. The use cases across your go-to-market and buying options are endless, enhancing customer retention and purchase experience. ![Contemporary knitwear styled into a full outfit](https://zelig.com/wp-content/uploads/2026/07/Contemporary3.png) --- ### [Luxury](https://zelig.com/for-retailers/luxury/) **Published:** July 7, 2026 **Author:** zeligadmin **Content:** # Styling at scale. On brand. On voice. AI styling for luxury retailers has to clear a higher bar. Luxury doesn’t tolerate the same shopping experience as everyone else. The site is part of the brand. Every recommendation, every adjacency, every interaction has to read as if a head of merchandising signed off on it. Zelig is built for that bar. ## Your stylists set the rules. The AI applies them. You decide what pairs with what. What never pairs. What seasons get featured. What categories sit adjacent. Zelig’s model is trained on your rules and applies them to every shopper interaction across your entire product catalog. The taste is yours. The reach is platform-scale. ![Digital Closet showing a shopper's saved outfits in a grid](https://zelig.com/wp-content/uploads/2026/07/Desktop-3-scaled.jpg) ## AI styling for luxury retailers, in your brand voice [Build a Look](/platform/) lives inside your site, in your typography, with your photography, in your voice. This extends across the model selection process – look, size, ethnicity, and style. The shopper never leaves your brand experience. That is what separates AI styling for luxury retailers from a generic recommendation widget. ![Zelig widget maintains your brand voice. Example of widget showing a formal night outfit.](https://zelig.com/wp-content/uploads/2026/07/BrandVoice-scaled.jpg) ## Outcomes that show up at the board meeting Higher average order value through complete-look purchasing, [proven in production at 1.5x](/results/). Lower return rates through confidence at checkout, where [McKinsey attributes about 70% of fashion returns to fit and style](https://www.mckinsey.com/industries/retail/our-insights/returning-to-order-improving-returns-management-for-apparel-companies). First-party styling data that informs buying and merchandising decisions. --- ### [Marketplaces](https://zelig.com/for-retailers/marketplaces/) **Published:** July 7, 2026 **Author:** zeligadmin **Content:** # Style across brands. AI styling for marketplaces starts with a simple problem. A big catalogue should feel like more options. Instead it feels like an endless grid of tops, scrolling past hundreds of near-identical products with no sense of where to stop. Shoppers don’t browse that. They abandon it. Zelig organizes around occasion instead of category. Rather than a wall of product, a shopper sees five pieces that work together for what they’re actually doing: a wedding, a first date, a Tuesday. The catalogue can be enormous. What the shopper sees never is. ## AI styling for marketplaces: one catalog, styled like a closet A top from one designer. Bottoms from another. Shoes from a third. Zelig assembles the look across your full vendor catalog, using your taxonomy and your brand adjacency rules to decide what belongs together. The shopper never sees a brand directory. They see a wardrobe someone put thought into. That’s what makes the inspiration feel relevant instead of random. Every look is built to feel like a personal shopper made the call, not an algorithm sorting by category. ![A shopper building a cross-brand outfit, the core of AI styling for marketplaces](https://zelig.com/wp-content/uploads/2026/07/Marketplace2.png) ## Better economics for every brand on the platform [Build a Look](/platform/) surfaces brands a shopper would not have reached through search or category browsing. See the [production numbers](/results/). The brands you carry sell more across categories. The marketplace earns more per shopper. That is the economics AI styling for marketplaces changes, in a category [McKinsey sizes at $400 to $660 billion a year](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier). ## Built for the entire catalog scale Zelig runs across catalogs of 50,000+ styles in production today and can scale well beyond one million. The platform is built for it, with continuous ingest, automated tagging, and real-time model updates as inventory shifts. ![Garments from several vendors styled together into one complete look](https://zelig.com/wp-content/uploads/2026/07/Marketplace3.png) > “At REVOLVE, we’re dedicated to reimagining how the next generation shops for fashion. Zelig’s AI-powered styling brings our vision to life, enabling customers to create complete looks with confidence and ease. This innovation underscores our commitment to building the most engaging digital platform in fashion, powered by technologies that will define the future of shopping.” — Mike Karanikolas, Co-Founder and Co-CEO, REVOLVE --- ### [Blog](https://zelig.com/blog/) **Published:** August 3, 2026 **Author:** zeligadmin **Content:** # Blog | Notes On AI Commerce The Zelig AI commerce blog, from the team behind the [Zelig platform](/platform/). Ideas from the intersection of fashion and AI – what we’re seeing in styling, discovery, and the way people shop now. [](https://zelig.com/nyc-dinner-retail-ai-priorities/)[![Retail leaders in discussion over lookbook prints and notes at a New York dinner, setting out their retail AI priorities](https://zelig.com/wp-content/uploads/2026/09/nyc-dinner-retail-leaders-in-discussion-600x403.webp)](https://zelig.com/nyc-dinner-retail-ai-priorities/) [AI Commerce](https://zelig.com/category/ai-commerce/)### What 20 Retail Leaders Talked About Over Dinner in New York On 16 September we put around twenty senior retail leaders around one table in New… [](https://zelig.com/shoppable-closet-what-shipped/)[![Pieces and lookbook prints laid out for review above the New York skyline, the raw material a digital closet draws on](https://zelig.com/wp-content/uploads/2026/09/shoppable-closet-merchandising-laydown-600x403.webp)](https://zelig.com/shoppable-closet-what-shipped/) [AI Commerce](https://zelig.com/category/ai-commerce/)### Shoppable Closet at Fashion Week: What Actually Shipped Fashion week produces a lot of announcements. Most of them are intentions with a date… [](https://zelig.com/increase-fashion-ecommerce-conversion-rate/)[![A shopper holds a white blouse against herself in a walk-in closet, the moment of doubt AI styling resolves to increase fashion ecommerce conversion rate](https://zelig.com/wp-content/uploads/2026/09/how-ai-styling-lifts-conversion-closet-decision-140x140.webp)](https://zelig.com/increase-fashion-ecommerce-conversion-rate/) [AI Commerce](https://zelig.com/category/ai-commerce/)##### How AI Styling Lifts Conversion [](https://zelig.com/zelig-shoppable-closet-revolve-app/)[![Lulus Build A Look powered by Zelig, mixing and matching tops, bottoms and outerwear into a complete outfit on a model](https://zelig.com/wp-content/uploads/2026/09/1-zelig-lulus-build-a-look-desktop-140x140.jpg)](https://zelig.com/zelig-shoppable-closet-revolve-app/) [Press Releases](https://zelig.com/category/press-releases/)##### Forbes: Retailers Are Spending Millions On AI. Can They Prove The ROI? [](https://zelig.com/lulus-ai-styling-zelig/)[![Zelig and Lulus announcement lockup](https://zelig.com/wp-content/uploads/2026/09/zelig-lulus-announcement-140x140.webp)](https://zelig.com/lulus-ai-styling-zelig/) [Press Releases](https://zelig.com/category/press-releases/)##### Lulus Brings AI Styling to Its Catalog with Zelig [](https://zelig.com/ai-commerce-for-fashion/)[![Woman Shopping On Tablet In Closet](https://zelig.com/wp-content/uploads/2026/09/WhatIsAiCommerceForFashionHero-140x140.jpg)](https://zelig.com/ai-commerce-for-fashion/) [AI Commerce](https://zelig.com/category/ai-commerce/)##### What Is AI Commerce? (for Fashion) [](https://zelig.com/your-pdp-is-working-against-you/)[![Why a static product detail page works against product detail page conversion](https://zelig.com/wp-content/uploads/2026/08/YourPDPIsWorkingAgainstYouHero-140x140.png)](https://zelig.com/your-pdp-is-working-against-you/) [AI Commerce](https://zelig.com/category/ai-commerce/)##### Your PDP is Working Against You [](https://zelig.com/styling-not-sizing-cutting-fashion-returns/)[![AI styling helping reduce fashion returns by showing a complete outfit before purchase](https://zelig.com/wp-content/uploads/2026/08/StylingNotSizinghero-140x140.png)](https://zelig.com/styling-not-sizing-cutting-fashion-returns/) [AI Commerce](https://zelig.com/category/ai-commerce/)##### Styling, Not Sizing – Cutting Fashion Returns [](https://zelig.com/outfit-recommendation-engine/)[![A shopper in a luxury boutique setting, where Zelig applies AI styling for luxury retailers](https://zelig.com/wp-content/uploads/2026/07/LuxuryHero-140x140.png)](https://zelig.com/outfit-recommendation-engine/) [AI Commerce](https://zelig.com/category/ai-commerce/)##### Outfit Recommendation Engine --- ### [Integration](https://zelig.com/integration/) **Published:** July 6, 2026 **Author:** zeligadmin **Content:** # Plugs into your catalog. Ships in weeks. AI styling integration should be boring. Zelig drops into an existing eCommerce stack. No replatform. No front-end rewrite. A truly painless integration. ## Catalog ingest, where AI styling integration starts Zelig reads your product catalog through your existing PIM, your Shopify Plus instance, your Salesforce Commerce Cloud setup, or a custom API. We ingest product imagery, attributes, and inventory state. Updates are continuous. ## Model training Our team trains the styling model on your catalog and your merchandising rules. Your stylists shape what works with what. We turn that into a model that runs at platform scale. Training takes as little as 2 weeks depending on catalog size. ## Data ownership Every interaction with [Build a Look](/platform/) is your first-party data, which is what makes an AI styling integration worth owning. See the [production results](/results/). Stored on your infrastructure or ours, your choice. Exportable to your CRM, CDP, and BI tools. ## Security posture SOC 2 Type II. PII handled per GDPR and CCPA. Penetration testing on every release. SSO available for retailer admins. ## Front-end deployment Build a Look ships as an embeddable module. Your team controls where it lives: PDP, PLP, dedicated landing pages, email modules, paid social creative. We provide working code, your team integrates it. [Talk to engineering](/demo/) --- ## Categories ### [AI Commerce](https://zelig.com/category/ai-commerce/) --- ### [Press Releases](https://zelig.com/category/press-releases/) ---