Data Capture
J'ko AIJ'ko captures the complete customer journey through virtual try-on interactions, generating rich behavioral data.
Each product in the Oraklex ecosystem captures unique data that feeds the Data Oracle - creating a vertically integrated commerce intelligence platform.
J'ko captures the complete customer journey through virtual try-on interactions, generating rich behavioral data.
HyperC processes the data to generate inventory, pricing, and campaign recommendations.
Anonymized, structured datasets are packaged and sold to LLM companies and analytics platforms.
J'ko combines computer vision, conversational AI, and commerce integration to create the complete virtual try-on experience. Customers send a selfie via Instagram DM, and our AI renders them wearing your products in under 20 seconds.
Comment triggers, automated responses, and seamless conversation flows that guide customers to try-on.
Photo-realistic garment rendering using SDXL and ControlNet. Preserves pose, lighting, and body language.
Live inventory sync, one-tap checkout links, and complete purchase attribution tracking.
Track every step: Instagram post → DM → Try-on → Purchase. Know exactly what drives revenue.
Captures complete conversion funnels, conversational commerce patterns, and product-purchase attribution - the core data for the Data Oracle.
Anonymized, privacy-compliant datasets that show the complete customer journey from social discovery to purchase. Exactly what LLM companies need to train models that understand buying behavior.
Complete journey from social impression to purchase with attributed outcomes.
Rich behavioral signals that show what customers want before they buy.
Privacy-first design with differential privacy, k-anonymity, and GDPR compliance.
RESTful API for real-time data access and batch exports for training.
The data monetization layer - packaging the insights from all Oraklex products for LLM training and analytics.
Andrew Gree's HyperC technology brings mathematical optimization to e-commerce. The Large Retail Model (LRM) uses automated theorem proving and type theory to calculate optimal inventory levels, pricing, and campaign timing.
Predictive replenishment using demand signals from try-on and purchase data.
Efficient frontier calculations for optimal price points based on demand and competition.
AI-powered scheduling for product launches, promotions, and content timing.
Predict future demand based on historical patterns and real-time signals.
Adds AI planning and optimization to the data layer, creating actionable intelligence from raw behavioral data.