Vue.ai’s product tagging solution, VueTag, helps retailers create detailed tags and product metadata in a matter of minutes - saving a lot of the time and cost involved in manual labour.
Trusted by over 150+ retailers across the globe
Vue.ai’s product tagging solution, VueTag, uses data extraction and data prediction to enhance product data. With AI, it extracts and enriches product data from structured or unstructured visual and textual inputs. The automated process is instant, learns from feedback, and can learn customized tags. It helps save manual effort, time, and resources.
VueTag’s Image Recognition Engine helps detect and tag visual attributes in the products and assess the quality of product photos.
The NLP engine extracts tags from products and the catalog feed to enrich search, description, and discoverability with product content.
The algorithms are trained to predict data or tags when there is no information available using both image recognition & past available data.
When provided with EANs as a part of the input, VueTag can find the products on EAN lookup databases and list attributes and data linked there.
VueTag uses OCR (Optical Character Recognition) on product labels and packaging to extract attributes like product dimensions, wash instructions, ingredients list, etc.
For grocery retailers, VueTag can extract product attributes and other information from open market sources from the same segment and geography.
VueTag generates product content, titles and descriptions from the product data that was extracted from the images.
VueTag’s library contains thousands of tags and attribute values in the repository for all retail categories.
The engine continuously learns from QA input, creates new custom tags, and more.
VueTag can be trained to generate new or custom tags based on retailers’ requirements.
The engine has thousands of ML models - generated both by retailers and the team at Vue.ai, allowing new categories to be tagged anytime.
The SaaS & ML tools allow an easy way for users to give feedback, generate custom tags, view analytics & more.
VueTag can sort and visualize catalog data in different ways for retailers to be able to make better decisions.
The tool can integrate easily with most PIM solutions, and can also support custom integration requirements.
Use VueTag's exhaustive vertical-specific taxonomy and build on it for custom requirements to standardise tags and manage your catalog classification efficiently.
Map product attributes to tags and create titles, descriptions, and other text content with AI.
Offer nuanced filters and categories as products are tagged across a wide range of attributes.
Rich product data with detailed tags that ensures that every search shows accurate results.
Detailed product tags along with relevant titles and descriptions ensure that shoppers make informed shopping decisions.
Vue.ai’s tags are SEO friendly, ensuring products are discovered when shoppers search for them on the web.
Map nuances in shopper preferences with deep product data and show them exactly the products they’d love.
For retail teams that curate products for different channels, detailed A.I. tags can help find relevant products and styles easily.
Standardize and enrich your product data with a consistent, domain-specific taxonomy for improved data integration, data retrieval, and data cleansing.
Automatically group products with similar attributes together for better product discovery, filtering and navigation.
Vue.ai’s ML tagging tool enables retailers to structure and label product data efficiently. The tool can be used for building custom taxonomies and attribute structures based on business priorities. Through the tool, users can visualize the catalog spread and create AI models that can label, categorize and organize data at scale. Both the output structured product data and the trained models can be exported to fit into any existing systems.
The tool allows all users to build their own catalog taxonomies and attribute structures based on what works for their business.
Using CV and NLP, the tool processes and plots data points on a 2D canvas based on the extracted attribute vectors for users to visualize their products.
The users can drag and drop a few sample products into the attribute groups they match - from which the machine learns and clusters rest of the products instantly.
The tool facilitates easy exporting of labelled product data and predictions into a CSV file, which can be used across the eCommerce site or independently.
The trained models along with custom taxonomies can be exported to fit into any existing systems.
*Observed for a European customer using Vue.ai’s tagging solution
Diesel observed faster product digitization in less time and a shorter go-to-market period with VueTag. They also saw a decrease in the effort taken to manually tag products and an improvement in the efficiency of product onboarding. The depth of the meta tags allowed enhanced search and detailed filters leading to better product discovery.
“Dedicated and supportive team that enhanced our journey.”
The initial onboarding was really smooth and efficient. Their team is very friendly and committed to our success - monthly performance reports + quarterly check-ins + being able to reach out via Slack are all very helpful.
CIO, Ashley Stewart
We engaged with Vue.ai, which had the ability to actually reduce the headcount while increasing accuracy in increasing the throughput.
Anandamoy Roy Chowdhary,
Director of Technology,
Sequoia Capital India
The Vue.ai team is on a mission to put AI and intelligent automation in the hands of teams across the globe in ways that improve productivity and growth multi-fold.
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