Artificial intelligence (AI) can provide support for retail operations, increasing profits and optimizing business processes. AI services in the retail sector are predicted to increase from $5 billion to above $31 billion by 2028.

Why can retail and e-commerce businesses better serve their customers with the addition of artificial intelligence? This is because AI can aggregate, answer questions and make recommendations for customers across any market or segment. This allows employees to spend less time dealing with day-to-day tasks and more time serving customers better. That's why we provide high-quality training data.

With years of industry experience, we can provide the most up-to-date, accurate, and relevant training data to support our clients. All features needed for each market segment are met, and the required data is collected reasonably and legally.

You can efficiently deploy machine learning in each module with our high-quality training data. Autonomously complete all aspects, including search recommendations to supply chain management to bring a better shopping experience to customers.

Personalized Shopping

With the addition of AI, the e-commerce platform can provide customers with exclusive personalized discount recommendations based on their past purchase history. Thus, it can add value to orders for e-commerce shops and provide a customized experience like live shopping for customers.

Search Relevance

The platform can comprehensively understand and judge the products or services that customers need based on all their past searches through AI. And help personalize their predictions for customers to recommend products that match their preferences and tastes.

Visual Search

When users are not sure of the description of a product, such as its name, they cannot search for the product they want. Through the AI image analysis function, users can upload images of the product to the e-commerce store to recommend the best product recommendations that meet their requirements.

Shopping Cart Analysis

The e-commerce platform's AI system predicts and accurately analyzes customers' needs by examining the products in their shopping carts. It effectively provides customers with shopping convenience and significantly increases merchants' sales.

Inventory Management

By accurately tracking the number of products in stock, we can keep the supply of popular products. And accurately predict future product demand data to adjust inventory levels and prevent backlogs or out-of-stocks.

Virtual try-on

Artificial intelligence can significantly satisfy customers who want to experience product fitting before purchase. By uploading personal photos, computer vision can effectively generate the effect of the customer's upper body, as realistic as being in the actual situation.

Datasets recommend

144,810 Images Multi-class Fashion Item Detection Data

This dataset included 19,968 images of males and 124,842 images of females. The Fashion Items were divided into four parts based on the season (spring, autumn, summer, and winter). In terms of annotation, rectangular bounding boxes were adopted to annotate fashion items. The data can be used for tasks such as fashion item detection, fashion recommendation, and other charges.

18,880 Images of 466 People - 3D Instance Segmentation and 22 Landmarks Annotation Data of Human Body

The dataset diversity includes multiple scenes, light conditions, ages, shooting angles, and poses. In terms of annotation, we adopted instance segmentation annotations on the human body. Twenty-two landmarks were also annotated for each human body. The dataset can be used for human body instance segmentation and human behavior recognition tasks.

43,411 Images-464 Categories of Trademarks Data

The collecting environments include indoor and outdoor scenes. In this dataset, the image is clear without a watermark, and each image contains at least one trademark. The dataset can be used for scene recognition and trademark classification.

9,497 Images - OCR Data of 10 Types of Forms

Rectangular bounding boxes were adopted to annotate forms. The data can be used for tasks such as form detection.