Technical Architecture Deck

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Advanced AI Mental Health Platform

Version 2.1.0 • Confidential Technical Documentation

01

System Architecture Overview

Microservices Architecture

Frontend Layer

Next.js 15 with App Router
TypeScript for type safety
Tailwind CSS + Framer Motion
Real-time WebSocket connections
Progressive Web App (PWA)

AI Processing Engine

Google Gemini Pro for reasoning
Crisis detection algorithms
Sentiment analysis pipeline
NLP preprocessing
Context-aware responses

Backend Infrastructure

Supabase PostgreSQL database
Real-time subscriptions
Row-level security (RLS)
Edge functions for AI calls
Automatic backups & scaling

Security & Compliance

HIPAA compliance ready
End-to-end encryption
OAuth 2.0 authentication
Audit logging
Data anonymization

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FRANK AI Engine

94% Crisis Detection Accuracy

0.3s Average Response Time

24/7 Availability

Crisis Detection Algorithm

interface CrisisIndicator {
  severity: 'low' | 'medium' | 'high' | 'critical';
  confidence: number;
  keywords: string[];
  sentiment_score: number;
  risk_factors: string[];
  immediate_action_required: boolean;
}

async function detectCrisisIndicators(text: string): Promise<CrisisIndicator> {
  const analysis = await geminiPro.analyze({
    input: text,
    context: conversationHistory,
    userProfile: riskAssessment
  });
  
  return {
    severity: calculateSeverity(analysis),
    confidence: analysis.confidence,
    keywords: extractCrisisKeywords(text),
    sentiment_score: analysis.sentiment,
    risk_factors: identifyRiskFactors(analysis),
    immediate_action_required: analysis.severity === 'critical'
  };
}
            

Response Generation Pipeline

Context Analysis: Understanding conversation history and user state
Empathy Modeling: Generating compassionate, human-like responses
Safety Filtering: Ensuring responses are therapeutically appropriate
Crisis Escalation: Automatic routing to human counselors when needed

03

Data Architecture & Security

Data Flow Diagram

                    User Input → Encryption → AI Processing → Response Generation → Audit Log → Encrypted Storage
                

Database Schema

-- Users table with RLS
CREATE TABLE profiles (
  id UUID PRIMARY KEY,
  email TEXT UNIQUE NOT NULL,
  risk_level risk_level_enum,
  created_at TIMESTAMPTZ,
  updated_at TIMESTAMPTZ
);

-- Messages with crisis indicators
CREATE TABLE messages (
  id UUID PRIMARY KEY,
  user_id UUID REFERENCES profiles(id),
  role message_role_enum,
  content TEXT NOT NULL,
  crisis_indicators JSONB,
  sentiment_score DECIMAL,
  created_at TIMESTAMPTZ
);

-- Mood tracking
CREATE TABLE mood_entries (
  id UUID PRIMARY KEY,
  user_id UUID REFERENCES profiles(id),
  mood_score INTEGER CHECK (mood_score BETWEEN 1 AND 5),
  emotions TEXT[],
  notes TEXT,
  created_at TIMESTAMPTZ
);
                    

Security Measures

AES-256 encryption at rest
TLS 1.3 for data in transit
Row-level security policies
Regular security audits
GDPR compliance
Automated backup encryption
Zero-trust architecture

04

Performance & Scalability

10M+ Concurrent Users Supported

99.9% Uptime SLA

<100ms Database Query Time

Scalability Strategy

Horizontal Scaling

Auto-scaling edge functions
Load balancing across regions
CDN for static assets
Database read replicas
Redis caching layer

Performance Monitoring

Real-time performance metrics
AI response time tracking
Database performance analysis
User experience monitoring
Automated alerting system

Infrastructure as Code

// Vercel deployment configuration
export default {
  name: 'frank-robotics',
  version: 2,
  builds: [
    { src: 'package.json', use: '@vercel/next' }
  ],
  env: {
    SUPABASE_URL: '@supabase-url',
    GEMINI_API_KEY: '@gemini-api-key',
    NEXT_PUBLIC_SUPABASE_ANON_KEY: '@supabase-anon-key'
  },
  functions: {
    'app/api/chat/route.ts': {
      maxDuration: 30
    }
  }
};
            

05

Development & Testing Strategy

Test-Driven Development (TDD)

// Crisis detection testing
describe('Crisis Detection Engine', () => {
  test('should detect high-risk suicide ideation', async () => {
    const result = await detectCrisisIndicators('I want to kill myself');
    expect(result.severity).toBe('critical');
    expect(result.confidence).toBeGreaterThan(0.9);
    expect(result.immediate_action_required).toBe(true);
  });
  
  test('should respond within performance benchmarks', async () => {
    const start = Date.now();
    await getFrankResponse('I need help', [], mockProfile);
    const duration = Date.now() - start;
    expect(duration).toBeLessThan(500);
  });
});
            

Testing Coverage

Unit tests for AI algorithms
Integration tests for API endpoints
End-to-end crisis scenario testing
Performance benchmark testing
Security penetration testing
Accessibility compliance testing

CI/CD Pipeline

Automated testing on every commit
Deployment to staging environment
AI model validation checks
Security scanning
Performance regression testing
Blue-green deployment strategy

06

Technical Roadmap

Q2 2025 - Enhanced AI

Multi-modal AI (voice, text, video)
Improved crisis prediction models
Personalized therapy recommendations
Integration with wearable devices

Q3 2025 - Platform Expansion

Mobile native applications
Telehealth provider integrations
Group therapy session support
Advanced analytics dashboard

Q4 2025 - Enterprise Features

White-label solutions
Healthcare system integrations
Advanced reporting & compliance
Multi-language support

2026 - Advanced Research

Predictive mental health modeling
Brain-computer interface research
Quantum computing applications
Global mental health initiatives

Our Mission

To build the world's most advanced AI mental health platform, saving lives through technology, empathy, and innovation.

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