Top AI Tools in the Fashion Industry

Tools like The New Black and Ablo act as AI co-designers for fashion creatives. The New Black can take a simple text description or sketch and generate a polished clothing design image within minutes, helping designers rapidly ideate and visualize new concepts without needing a human illustrator.

Ablo goes further by helping aspiring brands with end-to-end label creation – from generating apparel designs to suggesting logos and graphics that fit a brand's aesthetic. These platforms often include trend analysis features and virtual try-on previews, enabling quick iteration and feedback during the design phase.

Established 3D design software like CLO 3D and Browzwear VStitcher have integrated AI enhancements to make virtual garments true to life. These programs allow designers to create detailed digital clothing and see how it drapes and moves on an avatar in real time.

CLO 3D is known for highly accurate fabric simulation and AI-assisted 3D garment modeling. Browzwear's VStitcher enables virtual fitting on various body types with physics-based accuracy. New entrants like Style3D offer comparable AI-powered 3D visualization and support AR/VR previews for immersive design reviews.

By leveraging AI to handle complex physics and pattern calculations, these tools drastically reduce the need for physical samples, saving time, material, and cost before production.

WGSN is a renowned trend forecasting service that has incorporated AI and data analytics into its predictions. Through a subscription platform, WGSN gathers data from runways, retail sales, social media and more, then uses algorithms alongside human experts to forecast upcoming styles, colors, and consumer sentiments.

The result is seasonal trend reports and analytics tools (like their TrendCurve AI) that give brands a "crystal ball" for planning future collections. Designers and merchandisers use WGSN's insights to make informed decisions on everything from silhouettes to optimal SKU mix, rather than relying on guesswork.

Paris-based Heuritech takes a tech-driven approach to trend forecasting by scanning what real people are wearing online. Its AI uses computer vision to analyze millions of social media images (Instagram, TikTok, etc.) and detect emerging patterns in apparel.

By quantifying organic streetstyle trends globally, Heuritech enables brands to anticipate demand and design accordingly before trends saturate the market. A brand might use Heuritech to see that pastel utility jackets are trending in East Asia, and incorporate that insight into their next line.

EDITED is a market intelligence tool that helps brands react to real-time retail data using AI. It tracks millions of products across e-commerce sites worldwide and uses machine learning to analyze pricing, discounting, and stock movements.

A fashion merchandiser can see if midi dresses in a certain style are selling out at a competitor, or if a rival brand just slashed prices on denim. EDITED's AI assists in forecasting demand and optimizing pricing strategy. The platform's assortment planning features highlight gaps or over-saturation in the market, helping retailers decide what to stock more of.

Stylumia overlaps trend insight with demand forecasting. Its machine learning models filter out "market noise" to reveal true underlying consumer demand. It can predict sales for new products even with no sales history, improving forecast accuracy by 20–40%.

Notably, Stylumia's ImaGenie feature generates new product design ideas aligned with identified trends, suggesting to designers what styles have a high chance of success. This marries the creative and analytical sides of fashion planning.

Nextail is a merchandise and inventory management solution that uses AI to allocate and redistribute stock in a granular way. Instead of treating all stores the same, Nextail's algorithms generate hyper-local demand forecasts at the SKU-by-store level.

This helps retailers know exactly which items to send to which store and in what quantity. Nextail automates allocation, replenishment and transfers, adapting to real-time sales data. Retailers using Nextail saw:

• ~30% reduction in inventory coverage
• 60% fewer stockouts
• Notable sales lifts

For smaller direct-to-consumer labels and Shopify-based stores, Prediko offers an AI-powered demand planning tool tailored to their needs. It plugs into a brand's e-commerce data and analyzes sales trends and seasonality to forecast demand for each product SKU.

Prediko then helps automate the restock ordering process – suggesting how many units of each variant to produce or reorder and when. This is invaluable when prepping for a new product launch or deciding how much inventory to buy for an upcoming season.

Singuli brings heavy-duty AI science to fashion demand forecasting. Developed by PhD data scientists, it delivers precise forecasts down to SKU, material and component levels. It accounts for complex factors (promotions, holidays, macro trends) and integrates with ERP systems.

Brands can run "what-if" simulations – for example, What if a planned marketing event doubles demand? – and the AI adjusts inventory plans accordingly. Singuli claims its AI improves forecast accuracy by over 10%, translating to significant savings and revenue lift.

Top fashion retailers have built or adopted AI for their own supply chain optimization:

• Zara leverages predictive analytics plus RFID tracking to monitor inventory and respond quickly to trends
• H&M uses AI-driven forecasting incorporating weather and social media trends
• Nike employs machine learning for demand sensing and inventory positioning
• Burberry intelligently redistributes inventory based on real-time demand signals

PICTOFiT by Reactive Reality is a leading platform for virtual try-on. It creates a personalized 3D avatar for each shopper using just a few photos. Instead of overlaying clothing on a generic model, PICTOFiT lets users see garments on a virtual body double matching their own body shape and measurements.

This significantly boosts confidence in fit and style while browsing online. Retailers using Reactive Reality's tech have noted higher engagement and lower return rates, since customers get a much more accurate sense of how an item will look before ordering.

Revery AI has made virtual try-ons accessible for smaller brands. Shoppers can select a model avatar matching their body shape or upload their own photo, then virtually try on clothes with realistic results.

The AI maps the garment onto the person's image, adjusting for different body dimensions and simulating fabric drape. For independent designers, this technology means offering a high-tech fitting experience akin to major retailers. Revery also enables showing each style on multiple body types without multiple photoshoots, promoting size inclusivity.

True Fit is one of the most widely used AI fit solutions, integrated into many apparel retailers' websites. It asks customers about their body shape and fit preferences, then predicts the best size for each product using machine learning trained on massive purchase and returns data.

Retailers using True Fit have seen notable reductions in fit-related returns. In an industry where return rates for online fashion can top 30%, such tools are invaluable for improving customer satisfaction and protecting margins.

Bold Metrics generates digital body doubles of shoppers using just a few inputs (height, weight, fit preferences). The AI creates a detailed predicted body profile covering more than 50 precise body measurements.

This "digital twin" is used to recommend the best size and provides insights to brands about how their customers' bodies actually measure. Bold Metrics has helped retailers measurably reduce fit-related returns while informing product design and grading decisions.

Fashion brands are using augmented reality – often powered by AI – to let customers visualize products. For example, Gucci introduced an AR sneaker try-on in its app: point your smartphone camera at your feet and the app overlays a digital 3D model of Gucci sneakers in real-time.

This computer-vision-based try-on employs AI to track the user's feet and adjust imagery, creating an engaging way to "try before you buy" that drives sales especially for younger, tech-savvy shoppers.

Vue.ai is a popular AI platform offering solutions for fashion e-commerce personalization. Its algorithms automatically tag products with detailed attributes (cut, pattern, neckline, color, etc.), helping retailers deal with thousands of SKUs.

With richer, AI-generated metadata, Vue.ai powers personalized product recommendations and complete outfit suggestions. It functions as a virtual stylist that learns customer preferences and curates looks they're most likely to love, increasing conversion rates and basket sizes.

Syte specializes in visual search and discovery for fashion. A customer can upload a photo (say, a dress from Instagram or a celebrity outfit screenshot) and the AI finds similar looking items in the retailer's inventory.

It can also suggest visually similar alternatives on a product page ("More like this" gallery powered by image recognition). On mobile, where typing descriptions is cumbersome, visual search makes discovering products far more intuitive.

Lily AI focuses on improving the depth and accuracy of product data, which powers better recommendations and site search. Lily's platform uses AI to analyze each product image and description, assigning rich attributes far beyond typical manual tagging.

With enhanced attribution, if a customer searches for "romantic summer dress," the site returns precise matches that fit that vibe. Lily AI essentially "speaks the customer's language" by connecting how shoppers describe items with how products are tagged in the catalog.

The rise of advanced language models has brought AI personal shoppers to fashion. DressX introduced DressX AI Agent, an interactive stylist that users chat with. Users enter their preferences into a "Style Passport" and message with the AI to get outfit ideas or find pieces across dozens of brands.

The North Face pioneered this with IBM Watson, creating a chatbot that asked users questions like "Where and when will you be using this jacket?" to recommend the perfect coat. As natural language AI improves, expect these virtual stylists to become more common and sophisticated.

Crescendo.ai offers an AI chat and voice assistant that answers shoppers' questions 24/7 – from product advice to order tracking – with high accuracy. By instantly responding to inquiries about size info, return policies, or style tips, such AI assistants enhance customer experience and free up human support teams.

They essentially replicate the experience of a helpful sales associate online, providing personal attention to thousands of customers simultaneously.

Creating high-quality visual content for e-commerce can be resource-intensive. PhotoRoom has become a game-changer by automating product photography post-processing and production. It can instantly remove backgrounds from product images and replace them with clean or themed backdrops.

It also enables virtual "on-model" imagery: upload a photo of a garment on a mannequin, and PhotoRoom generates realistic images of that garment on a model without needing a photoshoot. Tools like ZMO.ai let brands generate images of clothing on AI models of various body types, poses, and ethnicities using just product images as input.

Fashion is embracing generative AI for inspiration and content creation. Luxury label Moncler collaborated with an AI design studio to create the Moncler Genius "AI Jacket" and its accompanying campaign visuals. Designers like Hillary Taymour of Collina Strada feed their past design archives into generative models to brainstorm new garment ideas.

On the marketing side, brands use tools like DALL·E, Midjourney, or Adobe Firefly to create artistic imagery for mood boards, ads, and social media content with just a text prompt.

A futuristic crossover of fashion marketing and AI is the rise of AI-generated virtual influencers. These are completely digital characters who attract real social media followings and collaborate with brands. Lil Miquela is one such virtual influencer who has modeled for luxury brands like Prada and Calvin Klein.

Some retailers use AI models for product images on their websites. Levi's experimented with AI-generated models to showcase outfits on different body types and skin tones, aiming to broaden representation in e-commerce imagery.

AI plays a role in pricing strategy and resale markets. In the world of secondhand fashion, The RealReal employs AI tools to assist in authenticating luxury goods and setting optimal resale prices. "Vision" uses image recognition to flag potentially counterfeit items, while "Shield" analyzes item attributes and market demand to prioritize which consignment goods need human expert review.

AI algorithms can dynamically adjust prices for fashion items based on factors like current demand, stock levels, and broader trends – especially useful for resale marketplaces or off-price retailers.

An important tool in fashion e-commerce is AI-driven fraud prevention. Online fashion stores face fraud issues – from stolen credit cards to fake return claims. Solutions like Kount use machine learning to instantly evaluate the risk of each transaction or account activity.

Kount's system looks at user behavior patterns, device data, geolocation, and more to produce a risk score in milliseconds. Because it's AI-based, it continually adapts to new fraud patterns and catches subtler fraudulent behavior that static rules might miss.