Exoticz leverages cutting-edge artificial intelligence to create a personalized, intuitive shopping experience that adapts to your unique preferences and needs.
Multi-layered neural network with embedding layers for user preferences and product characteristics
Hybrid recommendation system combining collaborative filtering, content-based filtering, and contextual bandits
Canvas-based WebGL acceleration with dynamic 3D perspective and parametric equations
Event-driven architecture with spatial partitioning for hit detection
WebSpeech API with custom acoustic model tuning and domain-specific vocabulary
Intent classification using transformer-based models with context-aware templating
Our platform implements a sophisticated contextual awareness system that tracks multiple dimensions of user context to deliver highly relevant recommendations that adapt to changing needs.
Analyzes current browsing patterns and interaction history to understand immediate interests and intent.
Considers time of day, day of week, and seasonal factors to provide timely recommendations.
Combines explicitly stated and implicitly derived preferences to build a comprehensive user profile.
Incorporates symptom profiles and treatment goals to recommend products with appropriate effects.
| Feature | Metric | Performance |
|---|---|---|
| Recommendation Generation | Latency | 150-250ms |
| Recommendation Generation | Relevance Score | 0.87 (vs. industry avg 0.72) |
| Terpene Explorer | Frame Rate | 58-60 FPS |
| Terpene Explorer | Memory Usage | 4.8MB |
| Voice Recognition | Word Error Rate | 4.2% |
| Voice Recognition | Intent Recognition | 92.7% accuracy |
| Overall Platform | Time to Interactive | 1.2s |
| Overall Platform | Lighthouse Performance | 94/100 |
Traditional systems use 3-5 variables for recommendations; Exoticz uses 27+ variables for significantly more accurate matching.
Continuous improvement through reinforcement learning versus static rule-based systems used by competitors.
Voice, visual, and text interfaces integrated seamlessly for a natural, intuitive user experience.
Recommendations update instantly based on user behavior, unlike traditional systems with delayed batch processing.
Transparent reasoning for recommendations versus black-box approaches, building user trust and understanding.
On-device model training for enhanced privacy and personalization without compromising user data.
Camera-based product recognition and visual search capabilities for intuitive shopping experiences.
Augmented reality visualization of effects and terpene profiles for immersive product exploration.
Optional integration with wearable devices for effect tracking and personalized dosage recommendations.
Zero-shot learning for handling novel user queries without explicit training examples.