How Does EDITED Use Generative AI?

Definitions of Key Terms: 

Artificial Intelligence (AI)

AI is the foundation of our technology stack, simulating human intelligence to analyze retail data and market trends.

Machine Learning (ML)

Machine learning enables our systems to learn from data and improve over time. EDITED uses ML to identify patterns and make predictions based on historical retail information.

Generative AI

This technology creates new data using advanced models like GPT (Generative Pre-trained Transformer). EDITED uses generative AI to produce insights and summaries from complex retail data.

Key AI Components in EDITED Products

Classification/Categorization (Machine Learning)

Our classification system:

• Categorizes products by categories, colors, patterns, and sizes
• Uses specialized models for different tasks
• Works with labeled training data that's regularly updated to align with evolving fashion trends

MATCH (Deep Learning)

Our matching technology:

• Converts images and text into numerical representations called embeddings
• Finds similar options within a pre-defined universe of products
• Uses a bespoke architecture for enhanced matching capabilities

Internal Labeling Pipeline (Generative AI)

EDITED implements generative AI models to quickly produce labeled datasets to continuously keep our classification system up-to-date as new trends emerge.

AI Insights Summary (Generative AI)

• Uses Generative AI to summarize myEDITED dashboard data
• Produces concise summaries of insights for quick understanding

Applications of AI in EDITED Solutions

Assortment Opportunities

• Uses MATCH embeddings to identify product assortment gaps
• Uses generative AI to produce high-level summaries of these gaps

Pricing Opportunities

• Uses MATCH embeddings to analyze pricing trends
• Provides competitive pricing insights across the market

By combining these AI technologies, EDITED provides retailers with powerful analytics and insights to make data-driven decisions in an ever-changing market.