Using AI for Personalization, Product Design, and Tagging

How can AI revolutionize the fashion consumer experience? From creating personalized shopping feeds to generating entirely new product designs, the use of AI in fashion is rapidly reshaping how consumers shop and how brands manage inventory, production, and product tagging.

LAAM, one of the largest South Asian fashion marketplaces with a catalog of nearly 100,000 products, is at the forefront of this transformation. By leveraging AI technologies like computer vision, natural language processing (NLP), and generative AI, LAAM delivers a superior shopping experience while optimizing its internal processes.

This tutorial will explore how AI is transforming personalization, product design, tagging, and supply chain management for fashion marketplaces like LAAM. By the end of this guide, you’ll understand how AI-driven approaches can:

  • Personalize user feeds to increase engagement and drive sales.

  • Enhance product tagging and categorization for better product discovery.

  • Use generative AI for new fashion designs.

  • Predict production and inventory needs to prevent overstocking and stockouts.

🚀 The Role of AI in the Fashion Industry

The fashion industry is undergoing a massive digital transformation, and AI is the catalyst for this change. With 100,000+ products in LAAM’s catalog, manual tagging, categorization, and inventory predictions are no longer scalable.

AI introduces the following benefits:

  1. Personalization: Personalized recommendations and search results.

  2. Product Understanding: Automated product tagging for attributes like color, fabric, pattern, and style.

  3. Generative AI: AI-generated fashion designs using models like DALL-E and Midjourney.

  4. Supply Chain Management: Forecast production and inventory needs to reduce overstock and prevent stockouts.

📘 1. Personalization: Customized Feeds and Search Optimization

Problem: Consumers expect personalized experiences, and a one-size-fits-all approach is no longer effective.

Solution: AI-driven personalized feeds provide users with tailored recommendations based on their browsing, purchase history, and demographic data.

How AI Personalizes User Feeds

  1. User Segmentation: AI analyzes user behavior (browsing patterns, clicks, and purchases) to segment users into categories (e.g., luxury shoppers vs. bargain hunters).

  2. Collaborative Filtering: Recommend items that similar users have viewed, liked, or purchased.

  3. Content-Based Recommendations: Based on product features (like style, color, and fabric) similar to those a user has liked.

  4. Multi-Armed Bandit Algorithms: Continuously learn from user interactions to improve recommendations.

AI Tools for Personalization

  • Recommendation Engines: TensorFlow Recommenders, Amazon Personalize.

  • Behavior Tracking: Mixpanel, Amplitude.

  • AI Models: Collaborative Filtering, Neural Collaborative Filtering (NCF).

Example: When a user clicks on a traditional "Shalwar Kameez" product, the AI learns to show similar "Shalwar Kameez" options in different colors, patterns, or price points in their personalized feed.

📘 2. AI-Driven Product Tagging and Categorization

Problem: Manually tagging 100,000+ products with labels like color, fabric, style, and pattern is time-consuming and error-prone.

Solution: Use AI-powered tagging systems based on computer vision and NLP to automatically categorize products.

How AI Enhances Product Tagging

  1. Image Recognition: AI models analyze product images and extract features like color, fabric type, and pattern (e.g., "red floral kurta").

  2. Text Extraction: Product descriptions are analyzed using NLP models to assign tags based on product descriptions.

  3. Multi-Label Classification: A single product (like a "Silk Saree") may have multiple tags: Silk, Traditional, Red, Wedding Wear.

  4. Object Detection: AI detects accessories (like jewelry or handbags) in fashion photos and tags them accordingly.

AI Tools for Tagging:

  • Computer Vision: OpenCV, YOLO (You Only Look Once), Detectron2.

  • NLP: spaCy, HuggingFace Transformers for extracting product features from descriptions.

Example: A model like YOLO can detect product elements (like lace, floral patterns, or buttons) and assign relevant tags. If the product is a cotton floral dress, it will be automatically tagged as:

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Tags: Cotton, Floral, Casual Wear, Women's Clothing

📘 3. Generative AI for Product Design

Problem: Creating new fashion designs is expensive, slow, and resource-intensive.

Solution: Use Generative AI models like DALL-E, Midjourney, or Stable Diffusion to design fashion products. Generative AI allows designers to quickly generate new styles, patterns, and fabric combinations.

How AI Generates Fashion Designs

  1. Text-to-Image Models: Use tools like DALL-E to generate fashion designs from text prompts (e.g., "floral silk saree with golden embroidery").

  2. Style Transfer: Apply a "style" from one image to another.

  3. GANs (Generative Adversarial Networks): Train a GAN to generate new clothing styles from an existing dataset of fashion images.

AI Tools for Design:

  • DALL-E / Midjourney: Text-to-image models for generating fashion concepts.

  • StyleGAN: For creating new fashion products from existing data.

Example: By inputting a prompt like "modern Shalwar Kameez for summer with floral pattern", DALL-E can generate a visual design of a new Shalwar Kameez. Fashion designers can use these visuals as inspiration for new collections.

📘 4. AI for Production and Inventory Management

Problem: Overstocking products or running out of stock can impact revenue.

Solution: Use AI to forecast production needs and optimize inventory. With demand forecasting models, businesses can predict when certain items (like wedding wear) will experience higher demand.

How AI Predicts Production and Inventory Needs

  1. Demand Forecasting: Use past sales data, seasonality, and promotional events to predict future demand.

  2. Inventory Management: AI tells you when to re-order products to avoid stockouts.

  3. Supply Chain Optimization: Detect bottlenecks in the production process and adjust production schedules.

AI Tools for Production Forecasting:

  • Time Series Models: ARIMA, Prophet, and LSTMs (Long Short-Term Memory models).

  • Predictive Analytics: Tools like AWS Forecast or Azure Machine Learning.

Example: LAAM can predict that red bridal sarees have higher demand during certain months. Based on this insight, production can be scaled up ahead of time to prevent stockouts.

📘 AI Tools and Technologies for Fashion Personalization

AI Tool/PlatformUse CaseTensorFlow RecommendersProduct recommendation enginesYOLO, Detectron2Product tagging (image recognition)DALL-E, MidjourneyGenerative fashion designAWS ForecastDemand forecasting and inventory optimizationspaCy, HuggingFaceNLP for extracting product tags from descriptions

🎉 Final Takeaways

AI is transforming the fashion industry, and LAAM is leading the charge by using AI for:

  • Personalized Feeds: Tailored recommendations using collaborative filtering and content-based models.

  • Product Tagging: Automatically tag 100,000+ products with color, fabric, and style tags using computer vision and NLP.

  • Generative Design: Use DALL-E and GANs to create unique fashion designs.

  • Supply Chain Optimization: Predict demand and optimize inventory to prevent stockouts.

These AI-driven approaches have created a superior consumer experience, making LAAM a trendsetting destination for South Asian fashion.

Personalisation, Tagging, Product Design & UX, ProductFrancesca Tabor