0%

Reduction in false positive alerts

0%

Improvement in detection accuracy

0M+

Multi-modal annotations optimized

0+

Stores processed in retail deployment

Structured Data Across Every Retail AI Use Case

DXW enables retailers to build reliable AI systems across merchandising, store operations, and digital commerce by structuring and validating data across the entire lifecycle.

  • Product & Merchandising Intelligence through catalog structuring and demand forecasting datasets
  • Store & Customer Behavior Data across CV annotation, POS alignment, and journey tracking
  • Retail AI Validation with HITL oversight, bias monitoring, and feedback loops
  • Marketplace Operations including catalog normalization and taxonomy governance
Consistent product & catalog data

Standardized taxonomy improves discoverability and AI performance.

Real-time store & customer insights

Structured behavioral data supports better decisions and automation.

Accurate and reliable AI outputs

Validation layers ensure models perform consistently in production.

Scalable retail operations

Governed data enables seamless growth across stores, online, and marketplaces.

What DXW Does for Retail

DXW structures and validates retail data across merchandising, store operations, supply chains, and digital commerce, enabling reliable AI systems across the entire retail lifecycle.

  • ✓ Catalog structuring & enrichment
  • ✓ Computer vision annotation
  • ✓ Demand forecasting datasets
  • ✓ HITL validation frameworks
  • ✓ Taxonomy governance
  • ✓ Marketplace operations
01 STEP

Product & Merchandising Intelligence

Product catalog structuring, attribute enrichment, and pricing datasets enable demand forecasting, inventory optimization, and dynamic merchandising strategies.

02 STEP

Store & Customer Behavior Data

Computer vision annotation and customer journey datasets capture in-store activity, POS behavior, and digital interactions for actionable retail insights.


03 STEP

AI Validation & Oversight

Human-in-the-loop validation, bias monitoring, and feedback loops ensure retail AI models remain accurate, reliable, and production-ready.



04 STEP

Marketplace & Catalog Operations

Seller onboarding, product normalization, and taxonomy governance ensure catalog consistency and scalable operations across retail and marketplace ecosystems.


Frequently asked questions

DXW provides structured annotation and validation for computer vision systems used in shrink detection and loss prevention. This includes bounding box recalibration, instance segmentation for overlapping products, occlusion taxonomy, and cross-modal alignment between visual detections and POS transaction signals, eliminating the misclassification loops that inflate false positive rates.

DXW works across image and video data from in-store cameras, POS transaction records, inventory and supply chain datasets, product catalog data, customer journey and loyalty program data, and e-commerce behavioral signals. We also handle cross-modal datasets that combine multiple retail data types within a single training program.

Yes. DXW covers store operations including computer vision for foot traffic and shelf monitoring, as well as digital commerce including product catalog structuring, customer journey datasets, and marketplace catalog governance. Our operational coverage spans stores, online retail, and marketplace environments.

DXW designs and applies standardized taxonomies that create a unified product language across the catalog, aligning titles, attributes, categories, and metadata with AI model requirements. This improves search accuracy, recommendation relevance, and demand forecasting model performance.

Yes. Annotated and validated datasets are version-controlled and structured for ingestion into retraining pipelines, feature stores, and CI/CD deployment workflows. DXW has direct experience integrating with MLflow, SageMaker, and custom retail AI platforms.
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START YOUR AI JOURNEY

Build Retail AI That Performs Where It Counts.

Talk to a DXW retail data specialist about your AI program, data modalities, and the governance framework your models need.

Tell us your use case. We’ll design the right data strategy for it.

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