Rosetta.ai

Analyzes product images and customer-uploaded photos using computer vision to extract visual attributes (color, style, material, fit) and infer purchase intent without relying on browsing history. The system builds a visual embedding space that maps customer imagery to product catalog features, enabling context-aware recommendations based on what customers are looking at rather than what they've clicked. This approach uses deep learning models trained on fashion/lifestyle datasets to recognize visual patterns that correlate with conversion.

Tracks customer interactions (clicks, hovers, time-on-product, scroll depth) and combines behavioral signals with visual recognition to dynamically adjust product layouts, recommendations, and content in real-time. Uses a multi-armed bandit or contextual bandit algorithm to optimize which products and layouts to show each visitor based on their visual preferences and behavioral patterns, with A/B testing built into the decision loop. The system maintains per-visitor state to enable consistent personalization across sessions.

Adjusts product recommendations and pricing in real-time based on current inventory levels, demand signals, and customer segments. The system models inventory as a constraint in the recommendation optimization function, deprioritizing low-stock items when better alternatives exist and surfacing high-inventory products to balance stock. Pricing adjustments are driven by demand elasticity models that estimate price sensitivity per customer segment, enabling margin-aware recommendations that maximize revenue rather than just conversion count.

Provides REST and webhook-based APIs to integrate Rosetta's personalization engine into existing ecommerce platforms (Shopify, WooCommerce, custom builds) without requiring months of professional services or platform migration. The system exposes endpoints for fetching personalized recommendations, tracking events, and retrieving visual analysis results, with SDKs available for common platforms. Integration follows a non-invasive pattern where Rosetta acts as a microservice that can be called on-demand rather than requiring deep platform customization.

Automatically extracts visual attributes (color, style, material, fit, pattern) from product images using computer vision and applies semantic tags to products without manual curation. The system learns attribute patterns from your catalog and can suggest tags for new products, reducing the manual data entry burden. Extracted attributes are stored as structured metadata that feeds into visual search, recommendations, and filtering, enabling customers to search and filter by visual characteristics.

Measures the impact of personalization on conversion rate, average order value, and other KPIs through built-in A/B testing and statistical analysis. The system automatically assigns visitors to control (non-personalized) and treatment (personalized) groups, tracks outcomes, and computes statistical significance using frequentist or Bayesian methods. Results are reported via dashboards showing lift estimates, confidence intervals, and segment-level performance breakdowns, enabling data-driven decisions about personalization strategy.

Extends personalization beyond the website to email campaigns, push notifications, and marketplace listings by providing a unified API for fetching personalized recommendations across channels. The system maintains cross-channel visitor identity (matching web sessions to email subscribers to app users) and ensures consistent personalization strategy across touchpoints. Recommendations can be customized per channel (e.g., email-optimized layouts vs. mobile app layouts) while maintaining coherent customer experience.

Automatically segments visitors into cohorts based on visual preferences, behavioral patterns, and purchase history without manual rule definition. The system uses clustering algorithms (k-means, hierarchical clustering) on visual embeddings and behavioral features to discover natural visitor groups, then labels segments with interpretable characteristics (e.g., 'minimalist style preference', 'price-sensitive'). Segments are continuously updated as new data arrives, enabling dynamic personalization based on evolving customer preferences.