AI Services Architecture

This document outlines the AI services architecture for Stayzr's intelligent guest communication platform, enabling automated, personalized, and context-aware interactions throughout the guest journey.

Core AI Services

The AI services platform provides intelligent automation for guest communication, service recommendations, and operational optimization across the hospitality ecosystem.

Intelligent Communication Engine

The core AI communication system that manages guest interactions across multiple channels with natural language understanding and contextual responses.

Key Components:

• Natural Language Processing (NLP): Understands guest intent, sentiment, and context
• Multi-language Support: Automatic language detection and translation
• Context Management: Maintains conversation history and guest preferences
• Response Generation: Creates personalized, brand-appropriate responses

Service Recommendation Engine

AI-powered recommendation system that suggests relevant hotel services, amenities, and local experiences based on guest profiles and behavior.

Capabilities:

• Personalized Recommendations: Based on guest preferences, stay history, and demographics
• Real-time Suggestions: Context-aware recommendations during the guest journey
• Upselling Intelligence: Revenue optimization through intelligent service suggestions
• Local Experience Curation: Dynamic recommendations for nearby attractions and services

Automated Workflow Engine

Intelligent workflow automation that orchestrates guest communication and service delivery throughout the stay lifecycle.

Features:

• Pre-arrival Automation: Welcome messages, check-in preparation, preference collection
• During-stay Workflows: Service requests, proactive notifications, issue resolution
• Post-stay Engagement: Feedback collection, loyalty program enrollment, future booking incentives
• Smart Escalation: Automatic handoff to human agents when complex issues arise

System Architecture

Data Models

PostgreSQL Schema (Guest Communication Data)

The primary PostgreSQL database stores structured data for guest interactions, preferences, and communication workflows.

Guest Communication Profiles:

CREATE TABLE guest_communication_profiles (
    id SERIAL PRIMARY KEY,
    organization_id INTEGER NOT NULL REFERENCES organizations(id),
    guest_id INTEGER NOT NULL REFERENCES guests(id),
    
    -- Communication preferences
    preferred_language VARCHAR(10) DEFAULT 'en',
    preferred_channels TEXT[] DEFAULT '{"whatsapp","sms","email"}',
    communication_frequency communication_frequency_enum DEFAULT 'normal',
    
    -- AI personalization data
    interests TEXT[],
    service_preferences JSONB DEFAULT '{}',
    dietary_preferences TEXT[],
    accessibility_needs TEXT[],
    
    -- Behavior analytics
    response_patterns JSONB DEFAULT '{}',
    service_usage_history JSONB DEFAULT '{}',
    satisfaction_scores DECIMAL(3,2)[],
    
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

AI Conversation Context:

CREATE TABLE ai_conversations (
    id SERIAL PRIMARY KEY,
    organization_id INTEGER NOT NULL REFERENCES organizations(id),
    guest_id INTEGER NOT NULL REFERENCES guests(id),
    reservation_id INTEGER REFERENCES reservations(id),
    
    -- Conversation metadata
    channel VARCHAR(50) NOT NULL, -- 'whatsapp', 'sms', 'web_chat', 'mobile_app'
    conversation_status VARCHAR(50) DEFAULT 'active',
    language VARCHAR(10) DEFAULT 'en',
    
    -- AI context
    intent_classification VARCHAR(100),
    sentiment_score DECIMAL(3,2), -- -1.0 to 1.0
    urgency_level urgency_level_enum DEFAULT 'low',
    requires_human_handoff BOOLEAN DEFAULT false,
    
    -- Conversation summary
    topic_categories TEXT[],
    resolved_issues TEXT[],
    pending_actions JSONB DEFAULT '[]',
    
    started_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    last_message_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    resolved_at TIMESTAMP WITH TIME ZONE
);

Service Recommendations:

CREATE TABLE ai_service_recommendations (
    id SERIAL PRIMARY KEY,
    organization_id INTEGER NOT NULL REFERENCES organizations(id),
    guest_id INTEGER NOT NULL REFERENCES guests(id),
    
    -- Recommendation details
    service_type VARCHAR(100) NOT NULL,
    service_name VARCHAR(255) NOT NULL,
    description TEXT,
    confidence_score DECIMAL(3,2), -- 0.0 to 1.0
    
    -- Personalization factors
    recommendation_factors JSONB DEFAULT '{}',
    guest_context JSONB DEFAULT '{}',
    
    -- Tracking
    presented_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    guest_response VARCHAR(50), -- 'accepted', 'declined', 'ignored'
    booking_converted BOOLEAN DEFAULT false,
    
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

Redis Cache Schema (Real-time Data)

Redis stores real-time conversation state, session data, and frequently accessed guest preferences.

Active Conversation State:

{
  "conversation:{guest_id}:{channel}": {
    "current_intent": "room_service_request",
    "context_variables": {
      "room_number": "205",
      "dietary_restrictions": ["vegetarian"],
      "last_service_request": "housekeeping"
    },
    "conversation_flow_state": "awaiting_confirmation",
    "ai_confidence": 0.89,
    "escalation_threshold": 0.3,
    "session_start": "2024-01-15T14:30:00Z",
    "last_activity": "2024-01-15T14:45:00Z"
  }
}

Guest Preference Cache:

{
  "guest_prefs:{guest_id}": {
    "language": "en",
    "timezone": "America/New_York",
    "preferred_services": ["room_service", "concierge", "spa"],
    "communication_style": "formal",
    "response_time_preference": "immediate",
    "cached_at": "2024-01-15T14:00:00Z",
    "ttl": 3600
  }
}

AI Service Capabilities

The AI services platform provides the following core capabilities for intelligent guest communication:

Natural Language Processing

• Intent Recognition: Automatically identify guest requests (room service, housekeeping, concierge, complaints)
• Entity Extraction: Extract key information (room numbers, dates, service types, preferences)
• Sentiment Analysis: Monitor guest satisfaction and emotional state throughout conversations
• Language Detection: Automatically detect guest language and provide appropriate responses
• Context Understanding: Maintain conversation context across multiple interactions

Intelligent Response Generation

• Personalized Responses: Generate contextually appropriate responses based on guest history and preferences
• Brand Voice Consistency: Maintain hotel brand personality across all AI interactions
• Multi-channel Adaptation: Adapt response format for WhatsApp, SMS, web chat, and mobile app
• Escalation Intelligence: Recognize when to transfer conversations to human staff
• Template Management: Smart use of pre-approved response templates with dynamic personalization

Service Recommendation Engine

• Behavioral Analysis: Analyze guest behavior patterns to suggest relevant services
• Contextual Recommendations: Provide time-appropriate suggestions (breakfast, spa, dining)
• Revenue Optimization: Intelligent upselling based on guest preferences and hotel capacity
• Local Experience Integration: Recommend nearby attractions, restaurants, and activities
• Preference Learning: Continuously learn from guest interactions to improve recommendations

Operational Intelligence

• Workload Distribution: Automatically route requests to appropriate hotel departments
• Priority Assessment: Evaluate urgency and priority of guest requests
• Capacity Management: Monitor service availability and suggest alternatives when needed
• Performance Analytics: Track AI effectiveness and guest satisfaction metrics
• Continuous Learning: Improve responses based on guest feedback and successful interactions

Integration Capabilities

The AI services seamlessly integrate with hotel operations through:

Hotel Management Systems

• PMS Integration: Real-time access to reservation data, guest profiles, and room status
• Service Request Systems: Automatic creation and tracking of maintenance and housekeeping requests
• Billing Integration: Process service bookings and charges directly through hotel systems
• Staff Coordination: Route complex requests to appropriate departments with full context

Communication Channels

• WhatsApp Business API: Rich messaging with media, location sharing, and quick replies
• SMS Integration: Fallback communication for guests without WhatsApp
• Web Chat Widget: Embedded chat for hotel websites and guest portals
• Mobile App SDK: Native integration for hotel mobile applications

External Services

• Translation Services: Real-time translation for international guests
• Maps and Directions: Integration with mapping services for local recommendations
• Weather Services: Contextual recommendations based on weather conditions
• Event Platforms: Integration with local event and booking platforms

Performance and Scalability

Response Time Targets

• Intent Recognition: < 200ms
• Response Generation: < 500ms
• Recommendation Engine: < 1000ms
• Complex Query Processing: < 2000ms

Scalability Features

• Horizontal Scaling: Auto-scaling based on conversation volume
• Load Balancing: Intelligent routing of AI requests
• Caching Strategy: Redis-based caching for frequently accessed data
• Queue Management: Message queuing for high-volume processing

Security and Privacy

Data Protection

• Data Encryption: End-to-end encryption for all guest communications
• PII Handling: Secure processing and storage of personally identifiable information
• Tenant Isolation: Multi-tenant architecture with strict data separation
• Audit Logging: Comprehensive logging of all AI interactions

Compliance

• GDPR Compliance: Full compliance with European data protection regulations
• CCPA Compliance: California Consumer Privacy Act adherence
• Industry Standards: SOC 2 Type II and ISO 27001 compliance
• Data Retention: Automated data lifecycle management

Implementation Guidelines

Configuration

1. AI Service Setup: Configure AI service endpoints and authentication
2. Model Training: Train hotel-specific language models with historical data
3. Integration Testing: Comprehensive testing of AI service integrations
4. Performance Tuning: Optimize response times and accuracy

Monitoring

• Real-time Metrics: Monitor AI service performance and accuracy
• Guest Satisfaction: Track guest satisfaction with AI interactions
• Error Handling: Robust error handling and fallback mechanisms
• Continuous Improvement: Regular model updates and optimization

Related Documentation

• System Architecture Overview
• Security Architecture
• Database Schema
• Multi-tenant Architecture