VueTag uses AI to extract and enrich product data from structured or unstructured visual and textual inputs. The automated process is instant, learns from feedback, saving manual effort, time, and resources.
Trusted by over 150+ retailers across the globe
VueTag uses computer vision and Natural Language Processing to extract data from images and text, and power a wide range of solutions. The platform includes solutions to assess the quality of product images and text, build and enrich product tags and metadata, and sort data to give assortment and catalog insights. The AI systems are able to process thousands of products in mere minutes - saving time and resources, while improving quality.
*Observed on an average for VueTag customers across the globe
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. VueTag can extract product attributes and other information from open market sources from the same segment and geography.
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.
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.
“Excellent Product with easy-to-use UI.”
We are using VueTag for all the product tagging. This allows us to cut down on the internal team to tag our products in the back end. Since VueTag already has the product attributes built out, it’s pretty straightforward to map out these fields. The product tags help our team look at product trends, and our filters heavily depend on these tags.
CIO, Ashley Stewart
We engaged with Vue.ai, which had the ability to actually reduce the headcount while increasing accuracy in increasing the throughput.
“Great products for eComm/fashion + support from a dedicated team!”
Fast + accurate + stable item data tagging that resulted in cutting our fixed costs and increasing sales. We were able to double the tagging speed with Vue.ai. As a result, we were able to cut labor, spacing, equipment cost, etc (1/4 of what it used to be). Also, data standardization helped products searchability and improved sales.
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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