Bring the in-store shopping experience online with our AI for fashion retail.
Our AI understands your catalog in depth and powers unique experiences based on every shopper’s tastes & preferences.
Trusted by over 150+ retailers across the globe
Vue.ai allows every retailer to customize AI personalization models to work best for their business. The systems build nuanced style profiles for every shopper by learning from their actions on the site and mapping it to rich product data. Using this, the AI finds, and brings to the surface through the shopping journey, the products in the catalog that the shopper would most likely purchase.
Vue.ai’s tagging solution automatically extracts and creates comprehensive and accurate tags from images and any unstructured text. This enriched data is then used to power everything on the site - from the search to recommendations.
Vue.ai’s solution enriches the product data with domain-specific taxonomy. The AI can be trained on custom ontology to support new categories while managing existing tags to follow the brand language and improve product discovery.
Vue.ai’s image moderation solution helps eCommerce marketplaces automate the assessment of product photos submitted by sellers or users to ensure they match their website guidelines. The solution works instantly and at scale for large volumes of images.
Vue.ai’s personalization solution provides recommendations in real-time based on each individual shopper’s activity on the site. These recommendations can be deployed across the site from the homepage to the cart page.
Recommendation curated especially for fashion retail websites:
Vue.ai’s uses rich product and shopper data to make category pages & search results accurate and personalized for every shopper - increasing conversions and reducing bounce rate.
The AI delivers relevant and personalized content to shoppers on their mails by unifying the 360-degree shopper profiles of every shopper with detailed product data. We integrate with all major email marketing platforms.
The recommendations are generated at the point of the mail being open to ensure:
The AI extracts and enhances product data that can then be mapped to shopper profiles, based on their site behavior, for highly personalized experiences.
Vue.ai uses computer vision and image recognition to detect, map, and identify visual attributes from product photos.
The NLP algorithms extract and enhance the textual information given by retailers and uses it to enrich catalog product data.
The algorithms are trained to extract data from multiple sources and predict data when there is no information available using computer vision, NLP, and historical data.
Vue.ai generates product tags, titles, and descriptions from the extracted data.
Vue.ai alters content real-time, to match every single shopper’s preferences and intent through their journey on the site.
The platform allows for a straightforward way of creating, testing, and measuring personalized experiences across different touchpoints.
The AI algorithms can be trained and customized for different retail strategies based on every business’ goals and priorities.
"Experimenting with Intelligent Retail Automation is something that’s been very exciting for us at Milaner. The big challenge has always been around how we can use data to inform our product design process. Through AI, we’re actually able to slice different types of shopping data to create amazing experiences for our shoppers."
Co-Founder & CTO
"Customers fill out a style profile, tell us what they like and upload moodboards from Pinterest. Then, combining Vue.ai’s technology plus our own stylists in the loop, we curate a box for them. Customers receive 10-12 items, try them and return what doesn’t work. It gives our customers convenience, freedom, privacy & flexibility so we found it a very powerful addition to the platform."
"With Vue.ai’s Personalization Suite, we saw 8x the engagement from users who had interacted with Vue.ai's features, compared to users who hadn‘t interacted with them. The average basket size per order via recommendations was 40% larger than that of non-engaged users. The Vue.ai team has also been really prompt with suggestions for optimizing the placement of widgets, for constantly improving results."
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