Acquia Personalization
The only personalization engine optimized for Drupal to deliver hyper-relevant digital experiences.
Transform customer shopping intent into mathematical style vectors for hyper-personalized retail journeys.
Fashion DNA, an advanced AI framework integrated within the AWS Personalize and SageMaker ecosystems, utilizes multi-modal deep learning to encode garment attributes and customer style preferences into a high-dimensional vector space. By 2026, the technology has evolved from a research-centric model into a production-ready API that harmonizes visual features (from product images) with structured metadata (taxonomy, material, brand) and historical behavioral signals. This 'DNA' profile allows retailers to transcend basic collaborative filtering, enabling sophisticated 'Complete the Look' engines and style-based discovery that understands the nuances of aesthetics—such as drapes, patterns, and silhouettes. Architecturally, it leverages Amazon's Titan Multi-modal Foundation Models to handle cold-start problems, ensuring new products are instantly recommendable based on visual style before a single transaction occurs. Positioned as a core component of the AWS Retail suite, it serves enterprise-scale merchants looking to reduce bounce rates and increase Average Order Value (AOV) through scientifically-backed aesthetic alignment.
Combines CNN-based visual feature extraction with NLP-based metadata analysis to create a unified vector.
The only personalization engine optimized for Drupal to deliver hyper-relevant digital experiences.
The market leader in AI-powered interactive Connected TV (CTV) advertising and personalization.
AI-driven personalization and market intelligence for the residential home building industry.
AI-Driven Behavioral Economics for Hyper-Personalized Retail Incentives and Yield Optimization.
Verified feedback from the global deployment network.
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Unsupervised clustering of products into 'themes' (e.g., 'Boho-Chic', 'Minimalist') without manual tagging.
Adjusts recommendations within a single session based on the last 3-5 clicks using a sequential RNN.
Integrates returns data to bias recommendations toward items likely to fit the user's profile.
Identifies items that 'go well together' based on color theory and style compatibility rather than similarity.
Analyzes the drift in vector clusters over time to identify emerging aesthetic trends.
Provides metadata 'tags' explaining why an item was recommended (e.g., 'Matches your preference for floral patterns').
Generic homepages lead to high bounce rates for first-time visitors.
Registry Updated:2/7/2026
Users struggle to find matching accessories for a primary garment.
Low engagement with generic 'New Arrival' emails.