3.1 Pre-Call Intelligence Before answering a call: Identify caller Pull history Predict intent Prepare response Example: “Likely booking — Friday evening pattern detected” 3.2 Life Pattern Engine Tracks: Time-based habits Frequency Behavioral cycles Examples: Weekly bookings Monthly services Time-of-day preferences 3.3 Taste Graph Builds a structured preference model: Likes / dislikes Spending patterns Product/service choices Used for: Recommendations Upsells Personalization 3.4 Decision Compression Engine Instead of: “Here are 10 options” Recepcia: “Here’s the best option for you” This: Reduces friction Speeds up interactions Increases conversions 3.5 Event Detection Engine Detects contextual signals: Examples: “My parents are visiting” “We’re celebrating” “I’m in a rush” Triggers: Adjusted recommendations Priority handling Context-aware responses