How PSYKHE AI Computes Taste

PSYKHE AI is powered by a patented psychographic world model to personalize product recommendations for each visitor.

Personalization Approach

Unlike traditional recommendation engines that rely solely on collaborative filtering ("customers who bought X also bought Y"), PSYKHE AI builds a real-time taste profile for each visitor by combining psychographic signals from browsing behavior with other latent and non-latent signals.

This means:

• Anonymous visitors start getting personalized results from the first tracked interaction - a single click is enough to begin shaping the session profile
• Returning users benefit from both real-time session signals and longer-term preference data
• Ranking is dynamic and adapts as user behavior changes during a session

How It Works

1. Product Intelligence - We ingest your catalog and use computer vision and NLP to ascribe multi-dimensional embeddings fine-tuned to your catalog
2. Behavior Collection - The Tracking API captures user interactions: page views, list views, clicks, add-to-cart, dwell time, and hovers
3. Profile Building - PSYKHE AI's models combine psychographic signals with other latent and non-latent signals to identify the taste patterns and drivers behind each decision
4. Real-Time Ranking - When a product list is requested, the Recommendation API ranks products in real time based on the user's profile
5. Continuous Learning - Each interaction refines the profile, improving recommendations throughout the session

Surfaces

PSYKHE AI can power per-user real-time personalization anywhere products appear, but broadly across three commerce surfaces:

• Browse (Category) - Product listing pages ranked per-user in real time
• Search - Search results calibrated by personal relevance
• Carousels - Recommendation widgets like "You May Also Like", "More Like This", and "Not Found" (personalized suggestions for empty search or 404 pages)

Learn More

• Methodology - Deep dive into the science behind PSYKHE AI
• Case Studies - Real-world results from PSYKHE AI deployments
• API Reference - Technical details of the Recommendation API