Implementation & Infrastructure

Expert Fabric's implementation strategy focuses on building a robust, scalable platform that can evolve from MVP to global-scale operation while maintaining security, performance, and reliability.

Technology Stack

Core Platform Architecture

Backend Services

# Primary technology choices
Language: TypeScript/Node.js
Framework: Express.js with Fastify optimization
API Layer: GraphQL (primary) + REST (compatibility)
Message Queue: Apache Kafka
Task Queue: Bull/BullMQ with Redis
Process Management: PM2 with clustering

Microservices Design

// Service registry pattern
interface ServiceConfig {
  name: string;
  version: string;
  instances: number;
  healthEndpoint: string;
  dependencies: string[];
  resources: ResourceRequirements;
}

const services: ServiceConfig[] = [
  {
    name: 'task-orchestrator',
    version: '1.0.0',
    instances: 3,
    healthEndpoint: '/health',
    dependencies: ['vector-db', 'expert-matcher'],
    resources: { cpu: '500m', memory: '1Gi' }
  },
  {
    name: 'expert-matcher',
    version: '1.0.0', 
    instances: 2,
    healthEndpoint: '/health',
    dependencies: ['vector-db', 'expert-registry'],
    resources: { cpu: '250m', memory: '512Mi' }
  }
  // Additional services...
];

Data Layer

Database Architecture

-- Primary PostgreSQL schema
CREATE SCHEMA IF NOT EXISTS expertfabric;

-- Core entities
CREATE TABLE expertfabric.organizations (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name VARCHAR(255) NOT NULL,
  tier VARCHAR(50) NOT NULL,
  created_at TIMESTAMP DEFAULT NOW(),
  settings JSONB DEFAULT '{}'
);

CREATE TABLE expertfabric.tasks (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  organization_id UUID REFERENCES expertfabric.organizations(id),
  title VARCHAR(500) NOT NULL,
  description TEXT,
  status VARCHAR(50) NOT NULL DEFAULT 'pending',
  priority INTEGER DEFAULT 3,
  estimated_cost DECIMAL(10,2),
  actual_cost DECIMAL(10,2),
  created_at TIMESTAMP DEFAULT NOW(),
  completed_at TIMESTAMP,
  metadata JSONB DEFAULT '{}'
);

-- Partitioning for scale
CREATE TABLE expertfabric.task_results (
  task_id UUID,
  subtask_id UUID,
  expert_node_id VARCHAR(255),
  result_data JSONB,
  quality_score FLOAT,
  execution_time_ms INTEGER,
  created_at TIMESTAMP DEFAULT NOW()
) PARTITION BY RANGE (created_at);

-- Create monthly partitions
CREATE TABLE expertfabric.task_results_2025_06 
PARTITION OF expertfabric.task_results
FOR VALUES FROM ('2025-06-01') TO ('2025-07-01');

Vector Database Integration

// Pinecone configuration
const pineconeConfig = {
  environment: process.env.PINECONE_ENVIRONMENT,
  apiKey: process.env.PINECONE_API_KEY,
  indexName: 'expert-fabric-knowledge',
  dimension: 1536,
  metric: 'cosine'
};

class VectorStore {
  private pinecone: PineconeClient;
  
  constructor() {
    this.pinecone = new PineconeClient({
      apiKey: pineconeConfig.apiKey,
      environment: pineconeConfig.environment
    });
  }
  
  async upsertTaskEmbedding(
    taskId: string, 
    embedding: number[], 
    metadata: any
  ): Promise<void> {
    const index = this.pinecone.Index(pineconeConfig.indexName);
    
    await index.upsert({
      upsertRequest: {
        vectors: [{
          id: taskId,
          values: embedding,
          metadata: {
            ...metadata,
            type: 'task',
            timestamp: Date.now()
          }
        }]
      }
    });
  }
  
  async findSimilarTasks(
    embedding: number[], 
    topK: number = 10
  ): Promise<SimilarTask[]> {
    const index = this.pinecone.Index(pineconeConfig.indexName);
    
    const queryResponse = await index.query({
      queryRequest: {
        vector: embedding,
        topK,
        filter: { type: { $eq: 'task' } },
        includeMetadata: true
      }
    });
    
    return queryResponse.matches?.map(match => ({
      taskId: match.id,
      similarity: match.score || 0,
      metadata: match.metadata
    })) || [];
  }
}

Infrastructure & Deployment

Kubernetes Configuration

# Namespace
apiVersion: v1
kind: Namespace
metadata:
  name: expert-fabric
  labels:
    name: expert-fabric

---
# Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: task-orchestrator
  namespace: expert-fabric
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  selector:
    matchLabels:
      app: task-orchestrator
  template:
    metadata:
      labels:
        app: task-orchestrator
    spec:
      containers:
      - name: orchestrator
        image: expertfabric/task-orchestrator:v1.0.0
        ports:
        - containerPort: 3000
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: database-secret
              key: url
        - name: REDIS_URL
          valueFrom:
            secretKeyRef:
              name: redis-secret
              key: url
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 3000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 3000
          initialDelaySeconds: 5
          periodSeconds: 5

---
# Service
apiVersion: v1
kind: Service
metadata:
  name: task-orchestrator-service
  namespace: expert-fabric
spec:
  selector:
    app: task-orchestrator
  ports:
  - port: 80
    targetPort: 3000
  type: ClusterIP

---
# Horizontal Pod Autoscaler
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: task-orchestrator-hpa
  namespace: expert-fabric
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: task-orchestrator
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

Infrastructure as Code (Terraform)

# AWS EKS Cluster
module "eks" {
  source = "terraform-aws-modules/eks/aws"
  
  cluster_name    = "expert-fabric-${var.environment}"
  cluster_version = "1.28"
  
  vpc_id     = module.vpc.vpc_id
  subnet_ids = module.vpc.private_subnets
  
  node_groups = {
    main = {
      desired_capacity = 3
      max_capacity     = 10
      min_capacity     = 3
      
      instance_types = ["t3.large"]
      
      k8s_labels = {
        Environment = var.environment
        Application = "expert-fabric"
      }
    }
    
    compute_intensive = {
      desired_capacity = 1
      max_capacity     = 5
      min_capacity     = 0
      
      instance_types = ["c5.2xlarge"]
      
      k8s_labels = {
        Environment = var.environment
        Application = "expert-fabric"
        NodeType    = "compute"
      }
      
      taints = [{
        key    = "compute-intensive"
        value  = "true"
        effect = "NO_SCHEDULE"
      }]
    }
  }
}

# RDS PostgreSQL
resource "aws_db_instance" "main" {
  identifier = "expert-fabric-${var.environment}"
  
  engine         = "postgres"
  engine_version = "15.4"
  instance_class = "db.r5.large"
  
  allocated_storage     = 100
  max_allocated_storage = 1000
  storage_encrypted     = true
  
  db_name  = "expertfabric"
  username = var.db_username
  password = var.db_password
  
  vpc_security_group_ids = [aws_security_group.rds.id]
  db_subnet_group_name   = aws_db_subnet_group.main.name
  
  backup_retention_period = 7
  backup_window          = "03:00-04:00"
  maintenance_window     = "Mon:04:00-Mon:05:00"
  
  performance_insights_enabled = true
  monitoring_interval         = 60
  
  tags = {
    Environment = var.environment
    Application = "expert-fabric"
  }
}

# ElastiCache Redis
resource "aws_elasticache_replication_group" "main" {
  replication_group_id       = "expert-fabric-${var.environment}"
  description                = "Expert Fabric Redis cluster"
  
  num_cache_clusters         = 3
  node_type                 = "cache.r5.large"
  port                      = 6379
  parameter_group_name      = "default.redis7"
  
  subnet_group_name = aws_elasticache_subnet_group.main.name
  security_group_ids = [aws_security_group.redis.id]
  
  at_rest_encryption_enabled = true
  transit_encryption_enabled = true
  
  tags = {
    Environment = var.environment
    Application = "expert-fabric"
  }
}

CI/CD Pipeline

GitHub Actions Workflow

name: Deploy Expert Fabric

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

env:
  REGISTRY: ghcr.io
  IMAGE_NAME: expertfabric

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
    
    - name: Setup Node.js
      uses: actions/setup-node@v4
      with:
        node-version: '18'
        cache: 'npm'
    
    - name: Install dependencies
      run: npm ci
    
    - name: Run tests
      run: npm test
    
    - name: Run integration tests
      run: npm run test:integration
      env:
        DATABASE_URL: postgresql://test:test@localhost:5432/test
        REDIS_URL: redis://localhost:6379
    
    - name: Security scan
      run: npm audit --audit-level high

  build:
    needs: test
    runs-on: ubuntu-latest
    outputs:
      image-tag: ${{ steps.meta.outputs.tags }}
      image-digest: ${{ steps.build.outputs.digest }}
    
    steps:
    - uses: actions/checkout@v4
    
    - name: Setup Docker Buildx
      uses: docker/setup-buildx-action@v3
    
    - name: Login to Container Registry
      uses: docker/login-action@v3
      with:
        registry: ${{ env.REGISTRY }}
        username: ${{ github.actor }}
        password: ${{ secrets.GITHUB_TOKEN }}
    
    - name: Extract metadata
      id: meta
      uses: docker/metadata-action@v5
      with:
        images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
    
    - name: Build and push
      id: build
      uses: docker/build-push-action@v5
      with:
        context: .
        push: true
        tags: ${{ steps.meta.outputs.tags }}
        labels: ${{ steps.meta.outputs.labels }}
        cache-from: type=gha
        cache-to: type=gha,mode=max

  deploy:
    needs: build
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    
    steps:
    - uses: actions/checkout@v4
    
    - name: Configure AWS credentials
      uses: aws-actions/configure-aws-credentials@v4
      with:
        aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
        aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
        aws-region: us-west-2
    
    - name: Update kubeconfig
      run: aws eks update-kubeconfig --name expert-fabric-prod
    
    - name: Deploy to Kubernetes
      run: |
        kubectl set image deployment/task-orchestrator \
          orchestrator=${{ needs.build.outputs.image-tag }} \
          -n expert-fabric
        
        kubectl rollout status deployment/task-orchestrator -n expert-fabric

Monitoring & Observability

Prometheus Configuration

# Prometheus scrape config
global:
  scrape_interval: 15s
  evaluation_interval: 15s

rule_files:
  - "expert_fabric_rules.yml"

scrape_configs:
  - job_name: 'expert-fabric-services'
    kubernetes_sd_configs:
    - role: pod
      namespaces:
        names:
        - expert-fabric
    relabel_configs:
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
      action: keep
      regex: true
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
      action: replace
      target_label: __metrics_path__
      regex: (.+)

alerting:
  alertmanagers:
  - static_configs:
    - targets:
      - alertmanager:9093

# Alert rules
groups:
- name: expert-fabric
  rules:
  - alert: HighErrorRate
    expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.1
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "High error rate detected"
      description: "Error rate is {{ $value }} errors per second"
  
  - alert: TaskProcessingBacklog
    expr: task_queue_length > 100
    for: 10m
    labels:
      severity: warning
    annotations:
      summary: "Task processing backlog"
      description: "{{ $value }} tasks waiting in queue"

Application Metrics

import { register, Counter, Histogram, Gauge } from 'prom-client';

// Task metrics
export const taskCounter = new Counter({
  name: 'tasks_total',
  help: 'Total number of tasks processed',
  labelNames: ['status', 'type', 'organization']
});

export const taskDuration = new Histogram({
  name: 'task_duration_seconds',
  help: 'Task processing duration',
  labelNames: ['type', 'expert_type'],
  buckets: [0.1, 0.5, 1, 5, 10, 30, 60, 300, 600]
});

export const activeExperts = new Gauge({
  name: 'active_experts_total',
  help: 'Number of active expert nodes',
  labelNames: ['type', 'specialization']
});

// Expert node metrics
export const expertUtilization = new Gauge({
  name: 'expert_utilization_ratio',
  help: 'Expert node utilization ratio',
  labelNames: ['expert_id', 'type']
});

export const expertQuality = new Gauge({
  name: 'expert_quality_score',
  help: 'Expert node quality score',
  labelNames: ['expert_id', 'specialization']
});

// System metrics
export const databaseConnections = new Gauge({
  name: 'database_connections_active',
  help: 'Active database connections'
});

export const redisOperations = new Counter({
  name: 'redis_operations_total',
  help: 'Total Redis operations',
  labelNames: ['operation', 'status']
});

Security Implementation

Authentication Service

import jwt from 'jsonwebtoken';
import bcrypt from 'bcrypt';
import { RateLimiter } from 'limiter';

class AuthenticationService {
  private rateLimiter = new RateLimiter(5, 'minute'); // 5 attempts per minute
  
  async authenticate(email: string, password: string): Promise<AuthResult> {
    // Rate limiting
    if (!this.rateLimiter.tryRemoveTokens(1)) {
      throw new Error('Rate limit exceeded');
    }
    
    // Get user from database
    const user = await this.getUserByEmail(email);
    if (!user) {
      throw new Error('Invalid credentials');
    }
    
    // Verify password
    const isValid = await bcrypt.compare(password, user.passwordHash);
    if (!isValid) {
      await this.logFailedAttempt(email);
      throw new Error('Invalid credentials');
    }
    
    // Generate tokens
    const accessToken = this.generateAccessToken(user);
    const refreshToken = this.generateRefreshToken(user);
    
    return {
      accessToken,
      refreshToken,
      user: this.sanitizeUser(user)
    };
  }
  
  private generateAccessToken(user: User): string {
    return jwt.sign(
      { 
        userId: user.id, 
        orgId: user.organizationId,
        role: user.role 
      },
      process.env.JWT_SECRET!,
      { expiresIn: '15m' }
    );
  }
  
  async validateToken(token: string): Promise<TokenPayload> {
    try {
      return jwt.verify(token, process.env.JWT_SECRET!) as TokenPayload;
    } catch (error) {
      throw new Error('Invalid token');
    }
  }
}

This implementation provides a solid foundation for Expert Fabric's technical infrastructure, enabling reliable operation from MVP through to enterprise scale while maintaining security, performance, and observability standards.