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CASE STUDY

AI Dataset Foundation for a Large-Scale Retail eCommerce Platform

Industry: Retail eCommerce · Dataset Scale: 1M+ Records · Type: Multimodal · Status: Confidential

1M+

Dataset Records

Multimodal

Text · Image · Video · Structured

Retail

eCommerce Industry

Global

Categories & Geographies

The Challenge

  • • No existing training dataset to bootstrap the platform
  • • Highly fragmented and noisy product data across multiple sources
  • • Significant taxonomy gaps across product categories
  • • Absence of platform-specific labeling standards
  • • Multimodal data complexity spanning text, images, video, and structured attributes

Customer Overview

An enterprise AI platform for Product Data Intelligence had its architecture in place but lacked the foundational dataset required to train, fine-tune, and validate AI models across product categories, geographies, and compliance requirements.

DXW’s Approach

  • • Designed a scalable dataset architecture aligned to retail KPIs, geographies, and demographics
  • • Created a domain-specific product taxonomy and attribute framework
  • • Sourced and curated multimodal data including text, images, structured and unstructured data, and compliance inputs
  • • Implemented validation and quality control workflows to address noisy data challenges
  • • Engineered datasets as continuous assets to support model iteration and long-term platform scalability

Dataset Scope

  • • Product text and descriptions
  • • Images and visual attributes
  • • Structured and unstructured product data
  • • Compliance and regulatory data
  • • Video content
  • • Retail-specific taxonomy structures

Scale: 100,000+ Records

Dataset Architecture

Designed scalable dataset structures aligned with retail KPIs, categories, and platform requirements.

Taxonomy & Classification

Built domain-specific product taxonomy and attribute frameworks for consistency and discoverability.

Multimodal Data Curation

Sourced and structured text, image, video, and compliance datasets across multiple sources.

Validation & Quality Control

Implemented workflows to clean noisy data and ensure high-quality, production-ready datasets.

Business Outcomes

96%

Model accuracy in core workflows

60–70%

Reduction in manual data handling

45–55%

Faster model iteration cycles

50%+

Improvement in data consistency

3×

Faster time-to-production

Metric Before DXW After DXW
Dataset Availability No foundational dataset 1M+ AI-ready records
Data Quality Fragmented, noisy, inconsistent Standardized, validated, production-grade
Taxonomy Structure Missing / incomplete Domain-specific B2B/B2C taxonomy
Model Accuracy Unreliable 96% accuracy in core workflows
Manual Effort Heavy manual handling 60–70% reduction
Model Iteration Speed Slow cycles 45–55% faster
Time to Production Delayed 3× faster deployment
Scalability Limited Enterprise-ready scale