AI-Based Product Cataloging and Tagging System
Overview
A sophisticated AI-powered system that revolutionises e-commerce product management by automatically analysing, categorising, and tagging product images using advanced computer vision technology, enabling precise search capabilities and personalised shopping experiences.

Problem
- Manual product cataloguing is time-consuming and error-prone, with large e-commerce platforms managing millions of SKUs
- Inconsistent tagging leads to poor search results and missed sales opportunities
- Human-dependent categorisation creates bottlenecks in product listing workflows
- Scaling product operations across multiple categories requires significant human resources
- Inaccurate or missing product attributes affect recommendation engine performance
Solution
- Automatically detect and extract product attributes from images (colour, style, pattern, material)
- Generate accurate product tags and categories using multi-label classification
- Create standardised product descriptions based on visual analysis
- Enable bulk processing of product catalogs
- Integrate with existing e-commerce platforms through APIs
- Provide real-time validation and quality control
Key Impact

60% reduction in product cataloguing time

Decrease in manual tagging errors

40-70% faster new product onboarding

Improvement in search relevancy

Increase in conversion rates

25% reduction in product return rates

Enhancement in recommendation accuracy


Ideal Customer Profile (ICP)
Size
Mid to large-scale e-commerce companies
Annual Revenue
$50M+ annual revenue
Budget Owner
Chief Technology Officer or Head of E-commerce
Volume
Managing 10,000+ SKUs
Technology Maturity
Medium to High
Key Decision Makers
- E-commerce Operations Director
- Product Catalog Manager
- Chief Digital Officer
- Head of Merchandising