Kaizen AI's frontend architecture leverages modern web technologies to deliver a responsive, real-time user experience across web and mobile platforms. The architecture emphasizes performance, accessibility, and seamless integration with the backend analytical services through WebSocket connections and RESTful APIs.
Technology Stack:
Framework: Next.js 14 with App Router for server-side rendering and optimization
Styling: TailwindCSS 3.4 for utility-first CSS with custom design system
UI Components: ShadCN/UI for consistent, accessible component library
State Management: Zustand for lightweight global state management
Real-time Communication: Socket.io for live updates and chat functionality
Charts & Visualizations: Recharts and D3.js for data visualization
The backend infrastructure of Kaizen AI is built on a microservices architecture using Node.js and Fastify, designed for high performance, scalability, and maintainability. The services are containerized using Docker and orchestrated through Google Kubernetes Engine (GKE) for optimal resource management and horizontal scaling.
Technology Stack:
Runtime: Node.js 20 LTS with TypeScript for type safety
Framework: Fastify 4.x for high-performance HTTP server
Database: PostgreSQL 15 for relational data, TimescaleDB for time-series
Cache: Redis 7 for session management and high-speed data access
Message Queue: BullMQ with Redis for job processing
Container: Docker with multi-stage builds for optimization
Orchestration: Google Kubernetes Engine (GKE) with Istio service mesh
Microservices Architecture
Service Decomposition
Fastify Application Setup
Analysis Service Implementation
Core Analysis Service
Database Plugin
PostgreSQL Integration
Database Design
Schema Architecture
Kaizen AI utilizes a hybrid database architecture combining PostgreSQL for relational data, TimescaleDB for time-series blockchain data, and Redis for high-performance caching and session management.
Database Structure Overview:
Performance Optimization
Database Optimization Strategies
Message Queue System
BullMQ Implementation
Queue Architecture and Configuration
Caching Strategy
Redis Implementation
Multi-Layer Caching Architecture
This comprehensive technical infrastructure documentation provides the foundation for understanding, deploying, and maintaining Kaizen AI's scalable architecture across frontend, backend, database, messaging, and caching layers.
-- Core database schema for Kaizen AI
-- Users and Authentication
CREATE TABLE users (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
email VARCHAR(255) UNIQUE NOT NULL,
username VARCHAR(50) UNIQUE,
password_hash VARCHAR(255) NOT NULL,
profile JSONB DEFAULT '{}',
preferences JSONB DEFAULT '{}',
subscription_tier VARCHAR(20) DEFAULT 'free',
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
last_login TIMESTAMP WITH TIME ZONE,
is_verified BOOLEAN DEFAULT FALSE,
is_active BOOLEAN DEFAULT TRUE
);
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_username ON users(username);
CREATE INDEX idx_users_subscription ON users(subscription_tier);
-- Project tracking
CREATE TABLE projects (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
contract_address VARCHAR(42) NOT NULL,
network VARCHAR(20) NOT NULL,
token_symbol VARCHAR(20),
token_name VARCHAR(100),
project_metadata JSONB DEFAULT '{}',
first_seen TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
last_updated TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
is_active BOOLEAN DEFAULT TRUE,
UNIQUE(contract_address, network)
);
CREATE INDEX idx_projects_contract_network ON projects(contract_address, network);
CREATE INDEX idx_projects_symbol ON projects(token_symbol);
CREATE INDEX idx_projects_network ON projects(network);
-- Analysis results storage
CREATE TABLE analysis_results (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
project_id UUID REFERENCES projects(id) ON DELETE CASCADE,
user_id UUID REFERENCES users(id) ON DELETE SET NULL,
kaizen_score INTEGER NOT NULL CHECK (kaizen_score >= 0 AND kaizen_score <= 100),
risk_level VARCHAR(20) NOT NULL,
confidence DECIMAL(3,2) NOT NULL CHECK (confidence >= 0 AND confidence <= 1),
-- Score breakdown
technical_score INTEGER NOT NULL,
economic_score INTEGER NOT NULL,
social_score INTEGER NOT NULL,
governance_score INTEGER NOT NULL,
-- Analysis metadata
analysis_depth VARCHAR(20) DEFAULT 'standard',
processing_time_ms INTEGER,
data_quality_score DECIMAL(3,2),
-- Full analysis data
full_analysis JSONB NOT NULL,
alerts JSONB DEFAULT '[]',
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
CREATE INDEX idx_analysis_project ON analysis_results(project_id);
CREATE INDEX idx_analysis_user ON analysis_results(user_id);
CREATE INDEX idx_analysis_score ON analysis_results(kaizen_score);
CREATE INDEX idx_analysis_created ON analysis_results(created_at);
CREATE INDEX idx_analysis_risk_level ON analysis_results(risk_level);
-- Time-series tables for blockchain data (TimescaleDB)
CREATE TABLE blockchain_events (
time TIMESTAMP WITH TIME ZONE NOT NULL,
network VARCHAR(20) NOT NULL,
contract_address VARCHAR(42) NOT NULL,
event_type VARCHAR(50) NOT NULL,
block_number BIGINT NOT NULL,
transaction_hash VARCHAR(66) NOT NULL,
event_data JSONB NOT NULL,
gas_used INTEGER,
gas_price BIGINT
);
-- Convert to hypertable for time-series optimization
SELECT create_hypertable('blockchain_events', 'time');
CREATE INDEX idx_blockchain_events_contract ON blockchain_events(contract_address, time DESC);
CREATE INDEX idx_blockchain_events_type ON blockchain_events(event_type, time DESC);
CREATE INDEX idx_blockchain_events_network ON blockchain_events(network, time DESC);
-- Token price and market data
CREATE TABLE token_prices (
time TIMESTAMP WITH TIME ZONE NOT NULL,
contract_address VARCHAR(42) NOT NULL,
network VARCHAR(20) NOT NULL,
price_usd DECIMAL(20,8),
volume_24h DECIMAL(20,2),
market_cap DECIMAL(20,2),
liquidity_usd DECIMAL(20,2),
holder_count INTEGER,
data_source VARCHAR(50) NOT NULL
);
SELECT create_hypertable('token_prices', 'time');
CREATE INDEX idx_token_prices_contract ON token_prices(contract_address, time DESC);
CREATE INDEX idx_token_prices_network ON token_prices(network, time DESC);
-- Social media data
CREATE TABLE social_mentions (
time TIMESTAMP WITH TIME ZONE NOT NULL,
contract_address VARCHAR(42) NOT NULL,
platform VARCHAR(20) NOT NULL,
post_id VARCHAR(255) NOT NULL,
author_id VARCHAR(255),
content TEXT,
sentiment_score DECIMAL(3,2),
engagement_metrics JSONB,
is_suspicious BOOLEAN DEFAULT FALSE,
UNIQUE(platform, post_id)
);
SELECT create_hypertable('social_mentions', 'time');
CREATE INDEX idx_social_contract ON social_mentions(contract_address, time DESC);
CREATE INDEX idx_social_platform ON social_mentions(platform, time DESC);
CREATE INDEX idx_social_sentiment ON social_mentions(sentiment_score);
-- Intelligence and attribution data
CREATE TABLE wallet_intelligence (
wallet_address VARCHAR(42) PRIMARY KEY,
entity_name VARCHAR(255),
entity_type VARCHAR(50),
risk_score INTEGER CHECK (risk_score >= 0 AND risk_score <= 100),
labels TEXT[],
intelligence_sources JSONB,
confidence DECIMAL(3,2),
first_seen TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
last_updated TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
CREATE INDEX idx_wallet_intel_type ON wallet_intelligence(entity_type);
CREATE INDEX idx_wallet_intel_risk ON wallet_intelligence(risk_score);
CREATE INDEX idx_wallet_intel_labels ON wallet_intelligence USING GIN(labels);
-- User watchlists
CREATE TABLE user_watchlists (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
name VARCHAR(100) NOT NULL,
description TEXT,
is_public BOOLEAN DEFAULT FALSE,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
CREATE INDEX idx_watchlists_user ON user_watchlists(user_id);
-- Watchlist items
CREATE TABLE watchlist_items (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
watchlist_id UUID REFERENCES user_watchlists(id) ON DELETE CASCADE,
project_id UUID REFERENCES projects(id) ON DELETE CASCADE,
alert_threshold INTEGER,
notes TEXT,
added_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
UNIQUE(watchlist_id, project_id)
);
CREATE INDEX idx_watchlist_items_list ON watchlist_items(watchlist_id);
CREATE INDEX idx_watchlist_items_project ON watchlist_items(project_id);
-- User activity logs
CREATE TABLE user_activity (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
activity_type VARCHAR(50) NOT NULL,
activity_data JSONB,
ip_address INET,
user_agent TEXT,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
CREATE INDEX idx_activity_user ON user_activity(user_id, created_at DESC);
CREATE INDEX idx_activity_type ON user_activity(activity_type, created_at DESC);
-- API usage tracking
CREATE TABLE api_usage (
time TIMESTAMP WITH TIME ZONE NOT NULL,
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
endpoint VARCHAR(255) NOT NULL,
method VARCHAR(10) NOT NULL,
status_code INTEGER NOT NULL,
response_time_ms INTEGER,
request_size INTEGER,
response_size INTEGER
);
SELECT create_hypertable('api_usage', 'time');
CREATE INDEX idx_api_usage_user ON api_usage(user_id, time DESC);
CREATE INDEX idx_api_usage_endpoint ON api_usage(endpoint, time DESC);
-- Performance optimization views and functions
-- Materialized view for project statistics
CREATE MATERIALIZED VIEW project_stats AS
SELECT
p.id,
p.contract_address,
p.network,
p.token_symbol,
COUNT(ar.id) as analysis_count,
AVG(ar.kaizen_score) as avg_score,
MAX(ar.created_at) as last_analysis,
COUNT(DISTINCT ar.user_id) as unique_analyzers
FROM projects p
LEFT JOIN analysis_results ar ON p.id = ar.project_id
WHERE p.is_active = TRUE
GROUP BY p.id, p.contract_address, p.network, p.token_symbol;
CREATE UNIQUE INDEX idx_project_stats_id ON project_stats(id);
CREATE INDEX idx_project_stats_score ON project_stats(avg_score);
-- Function to refresh project stats
CREATE OR REPLACE FUNCTION refresh_project_stats()
RETURNS VOID AS $$
BEGIN
REFRESH MATERIALIZED VIEW CONCURRENTLY project_stats;
END;
$$ LANGUAGE plpgsql;
-- Automated refresh trigger
CREATE OR REPLACE FUNCTION trigger_refresh_project_stats()
RETURNS TRIGGER AS $$
BEGIN
-- Refresh stats after significant changes
PERFORM pg_notify('refresh_stats', 'project_stats');
RETURN NULL;
END;
$$ LANGUAGE plpgsql;
CREATE TRIGGER analysis_results_refresh_stats
AFTER INSERT OR UPDATE OR DELETE ON analysis_results
FOR EACH STATEMENT
EXECUTE FUNCTION trigger_refresh_project_stats();
-- Partitioning strategy for large tables
-- Partition blockchain_events by month
CREATE TABLE blockchain_events_template (
LIKE blockchain_events INCLUDING ALL
);
-- Function to create monthly partitions
CREATE OR REPLACE FUNCTION create_monthly_partition(table_name TEXT, start_date DATE)
RETURNS VOID AS $$
DECLARE
partition_name TEXT;
end_date DATE;
BEGIN
partition_name := table_name || '_' || to_char(start_date, 'YYYY_MM');
end_date := start_date + INTERVAL '1 month';
EXECUTE format('CREATE TABLE IF NOT EXISTS %I PARTITION OF %I
FOR VALUES FROM (%L) TO (%L)',
partition_name, table_name, start_date, end_date);
END;
$$ LANGUAGE plpgsql;
-- Performance monitoring queries
CREATE OR REPLACE VIEW slow_queries AS
SELECT
query,
calls,
total_time,
mean_time,
rows,
100.0 * shared_blks_hit / nullif(shared_blks_hit + shared_blks_read, 0) AS hit_percent
FROM pg_stat_statements
WHERE mean_time > 100 -- Queries taking more than 100ms on average
ORDER BY mean_time DESC;
-- Index usage monitoring
CREATE OR REPLACE VIEW unused_indexes AS
SELECT
schemaname,
tablename,
indexname,
idx_scan,
pg_size_pretty(pg_relation_size(i.indexrelid)) AS index_size
FROM pg_stat_user_indexes ui
JOIN pg_index i ON ui.indexrelid = i.indexrelid
WHERE idx_scan = 0
AND NOT i.indisunique
AND NOT i.indisprimary
ORDER BY pg_relation_size(i.indexrelid) DESC;
