Developers

Platform Overview

Comprehensive overview of MindPeeker's psychic intelligence platform architecture and capabilities

Platform Architecture

MindPeeker's platform is built on a microservices architecture designed for scalability, reliability, and security. Our system coordinates certified psychic talent with advanced AI analysis to deliver actionable intelligence through RESTful APIs and real-time communication protocols.

System Architecture

<UAlert icon="i-heroicons-squares-plus" title="Microservices Architecture" description="Scalable, resilient platform design for psychic intelligence delivery" color="blue"

Core Services

Remote Viewing Engine

Dowsing Services

Analytics Platform

AI Enhancement Layer

Infrastructure

Load Balancing

Auto-scaling Groups

Database Clusters

Content Delivery Network

Security

End-to-End Encryption

Identity Management

Threat Detection

Compliance Monitoring

Core Services

Remote Viewing Engine

Session Management:

Multi-viewer coordination and synchronization
Real-time session monitoring and control
Protocol automation and standardization
Quality assurance and validation

Target Processing:

Target material analysis and optimization
Reference image and data processing
Coordinate and location intelligence
Historical and temporal target support

Result Integration:

Multi-viewer data synthesis
Cross-validation and consensus building
Confidence scoring and probability assessment
Structured result formatting

Dowsing Services

Map Dowsing:

Geographic location intelligence
Coordinate precision and accuracy
Multi-scale mapping support
GIS integration and visualization

Energy Detection:

Energy signature analysis
Residual pattern recognition
Emotional and psychic energy mapping
Environmental energy assessment

Information Dowsing:

Question-answer intelligence
Binary and categorical responses
Confidence level assessment
Validation and verification

Analytics Platform

Pattern Recognition:

Machine learning for pattern detection
Historical data analysis and correlation
Predictive modeling and forecasting
Anomaly detection and alerting

Performance Metrics:

Real-time performance monitoring
Accuracy and success rate tracking
Viewer performance analysis
System health and availability

Business Intelligence:

Usage analytics and reporting
Customer insights and behavior
Revenue and cost analysis
Market trend identification

AI Enhancement Layer

Natural Language Processing:

Result text analysis and summarization
Entity recognition and extraction
Sentiment analysis and classification
Language translation and localization

Computer Vision:

Image and video analysis
Object detection and recognition
Facial recognition and matching
Scene understanding and context

Predictive Analytics:

Outcome prediction and probability
Risk assessment and scoring
Trend analysis and forecasting
Recommendation engine

Data Flow Architecture

Request Processing Pipeline

1. Request Reception:

graph LR
    A[Client Request] --> B[API Gateway]
    B --> C[Authentication Service]
    C --> D[Rate Limiting]
    D --> E[Request Validation]

2. Session Creation:

graph LR
    E --> F[Session Manager]
    F --> G[Viewer Assignment]
    G --> H[Target Processing]
    H --> I[Protocol Selection]

3. Intelligence Generation:

graph LR
    I --> J[Remote Viewing Engine]
    J --> K[Multiple Viewers]
    K --> L[Result Collection]
    L --> M[AI Enhancement]

4. Result Delivery:

graph LR
    M --> N[Quality Assurance]
    N --> O[Result Formatting]
    O --> P[Response Delivery]
    P --> Q[Client Notification]

Data Storage Architecture

Primary Storage:

PostgreSQL: Transactional data and user information
MongoDB: Flexible document storage for session data
Redis: Caching and session state management
Elasticsearch: Search and analytics indexing

Object Storage:

AWS S3: Target materials and result files
CloudFront CDN: Global content delivery
Glacier: Long-term archival storage
Backup Systems: Multi-region redundancy

Analytics Storage:

TimescaleDB: Time-series performance data
Snowflake: Data warehouse and analytics
Spark: Big data processing and ML
Tableau: Business intelligence visualization

Security Architecture

Authentication and Authorization

OAuth 2.0 Implementation:

Client credentials flow for server-to-server
Authorization code flow for user applications
JWT tokens with short expiration
Refresh token rotation and revocation

Role-Based Access Control:

Granular permission system
Resource-level access control
API key scoping and limitations
Audit logging and monitoring

Data Protection

Encryption Standards:

TLS 1.3 for all communications
AES-256 encryption for data at rest
End-to-end encryption for sensitive data
Key management with AWS KMS

Privacy Compliance:

GDPR compliance for EU customers
CCPA compliance for California residents
Data minimization and purpose limitation
Right to deletion and data portability

Infrastructure Security

Network Security:

VPC with private subnets
Security groups and NACLs
DDoS protection and mitigation
Web Application Firewall (WAF)

Application Security:

Input validation and sanitization
SQL injection prevention
XSS protection and CSP headers
Dependency vulnerability scanning

Performance and Scalability

Auto-Scaling Architecture

Horizontal Scaling:

Container orchestration with Kubernetes
Auto-scaling based on CPU/memory metrics
Queue-based processing for async operations
Database read replicas and sharding

Load Balancing:

Application Load Balancer (ALB)
Geographic routing with Route 53
Health checks and failover
Session affinity and sticky sessions

Performance Optimization

Caching Strategy:

Redis for session and API caching
CDN for static content delivery
Database query result caching
Application-level caching with TTL

Database Optimization:

Connection pooling and query optimization
Indexing strategy and performance tuning
Read replicas for query distribution
Partitioning for large datasets

Monitoring and Observability

Application Monitoring:

Prometheus metrics collection
Grafana dashboards and alerting
Distributed tracing with Jaeger
Log aggregation with ELK stack

Infrastructure Monitoring:

CloudWatch for AWS resources
Custom metrics and business KPIs
Error tracking and alerting
Performance baseline and anomaly detection

Integration Patterns

Real-Time Integration

WebSocket Connections:

const ws = new WebSocket('wss://api.mindpeeker.com/v1/sessions/ws');

ws.onmessage = (event) => {
  const data = JSON.parse(event.data);
  console.log('Session update:', data);
};

Server-Sent Events:

const eventSource = new EventSource(
  'https://api.mindpeeker.com/v1/sessions/events'
);

eventSource.onmessage = (event) => {
  const update = JSON.parse(event.data);
  handleSessionUpdate(update);
};

Batch Processing

Asynchronous Operations:

const session = await client.sessions.create({
  targetType: 'location',
  async: true,
  callbackUrl: 'https://your-app.com/webhook/results'
});

// Check status later
const status = await client.sessions.getStatus(session.id);

Bulk Operations:

const bulkRequest = {
  sessions: [
    { targetType: 'location', targetData: {...} },
    { targetType: 'person', targetData: {...} },
    { targetType: 'object', targetData: {...} }
  ]
};

const results = await client.sessions.createBulk(bulkRequest);