The Challenge

Consumers sought food experiences aligned with health goals, allergies, and preferences.
Existing restaurant applications offered generic results, while menu data lacked semantic structure.

The Solution

Regrev created DineIntel, a personalization platform that merges culinary data, nutrition science, and contextual inference.
It converts raw menu data into structured knowledge that drives adaptive and transparent recommendations.

Core Architecture Highlights

• Unified data model built on semantic menu, nutrition, and ingredient metadata
  
  

• Personalization engine that aligns dish profiles with user health and preference vectors
  
  

• Adaptive modification layer that recommends ingredient adjustments to meet dietary goals
  
  

• Contextual retrieval system matching local offerings to user intent in real time
  
  

• Analytics framework monitoring retention, satisfaction, and behavioral patterns
  
  

• Continuous evaluation loop scoring personalization accuracy and recommendation novelty

The Result

• 50 percent increase in user retention within the first quarter

• Improved satisfaction through context-aware personalization

• Clear differentiation as a pioneer in intelligent dining personalization

Regrev Impact

Regrev demonstrated how semantic modeling, adaptive inference, and responsible personalization can merge to create meaningful consumer intelligence systems.