VueTag uses A.I. 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 A.I. 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 engines extract tags from products and the catalog feed to enrich search, description, 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.
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 Team Vue.ai, allowing new categories to be tagged anytime.
The SaaS & ML tools allow 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 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.
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.
The tagging tool generates detailed, high-quality product tags, and titles & descriptions instantly.
The tool gives you the flexibility to evaluate and modify tags for holistic QA.
With the Bulk Edit feature you can perform quality checks for multiple images uploaded and tagged - all in one go.
The tool provides gives you an overview of the number of images tagged, tags generated and verified, and tag accuracy.
The tool gives you a complete picture of catalog - at category, attribute, and batch levels.
The tool helps with QA by providing confidence scores and accuracy reports of tags in every batch.
Vue.ai’s tagging tool allows anybody to build Machine Learning (ML) models that label, classify and enrich raw data within minutes. With this, any team can teach the system and build custom, ready-to-deploy ML models for any data organization need. Whether you want simple no-code tools or the full developer suite, our tagging tool has got you covered!
Our Goal - To enable teams to deploy data organization ML models into production in < 24 hours.
Build your taxonomy - a hierarchy of attributes relevant to your data segment, business and use case.
CV and NLP models process your data, and plots data points on a 2D canvas based on the extracted feature vectors.
Drag and drop similar data points into labelled clusters. ML models learn and understand the intent behind each classification.
Export labels and predictions generated into a CSV file, which can be used across your business systems or independently.
Generate an API endpoint with the custom-built ML model deployed for inference.
"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."
"80% of our merchants actually end up using the tags that are auto-generated. It reduces the friction for small businesses to come online and at the same time, it improves quality to the end customer."
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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