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System Architecture & Design

Expert Fabric's architecture is built on modern microservices principles with a focus on scalability, modularity, and extensibility. The system combines AI orchestration, human expert coordination, and knowledge management in a unified platform.

High-Level Architecture​

Core Components​

1. Task Orchestration Engine​

The orchestration engine is the brain of Expert Fabric, responsible for managing the entire task lifecycle.

Task Decomposer​

  • Function: Breaks complex tasks into logical subtasks
  • Technology: LLM-powered analysis with rule-based validation
  • Input: Natural language task descriptions, attached data
  • Output: Structured subtask definitions with requirements and dependencies

Expert Matcher​

  • Function: Assigns optimal expert nodes to each subtask
  • Algorithm: Vector similarity + availability + performance scoring
  • Factors: Domain expertise, cost, historical performance, current load
  • Output: Expert-subtask assignments with confidence scores

Quality Assurance Engine​

  • Function: Validates outputs and ensures coherence
  • Process: Automated checks + human review triggers
  • Criteria: Completeness, accuracy, consistency, formatting
  • Output: Quality scores and improvement recommendations

2. Modular Expert Node Framework​

Built on the Model Context Protocol (MCP) standard for maximum flexibility and extensibility.

AI Expert Nodes​

interface AIExpertNode {
  id: string;
  specialization: string[];
  capabilities: string[];
  modelEndpoint: string;
  costPerToken: number;
  averageResponseTime: number;
  qualityScore: number;
}

Human Expert Nodes​

interface HumanExpertNode {
  id: string;
  expertId: string;
  specializations: string[];
  availability: AvailabilitySchedule;
  hourlyRate: number;
  reputation: ReputationScore;
  languages: string[];
}

MCP Integration​

  • Standardized interfaces for connecting diverse expert types
  • Plugin architecture for extending capabilities
  • Tool access via MCP connectors (GitHub, Google Drive, APIs)
  • Context sharing between nodes within task workflows

3. Knowledge Management System​

Vector Database Architecture​

-- Task embeddings for similarity matching
CREATE TABLE task_embeddings (
  task_id UUID PRIMARY KEY,
  embedding VECTOR(1536),
  domain VARCHAR(100),
  complexity_score FLOAT,
  created_at TIMESTAMP
);

-- Expert knowledge vectors
CREATE TABLE expert_knowledge (
  expert_id UUID,
  knowledge_vector VECTOR(1536),
  domain VARCHAR(100),
  confidence_score FLOAT,
  last_updated TIMESTAMP
);

Context Capture & RAG​

  • Embedding generation using latest embedding models
  • Semantic search for relevant historical context
  • Context injection into AI model prompts
  • Knowledge graph relationships between concepts and experts

4. Collaboration Infrastructure​

Real-time Workspace​

  • WebSocket connections for live collaboration
  • Document versioning with conflict resolution
  • Comment and annotation system for expert feedback
  • Progress tracking with milestone notifications

Communication Layer​

  • Message queuing (Apache Kafka) for asynchronous processing
  • Event streaming for real-time updates
  • Notification service for task status changes
  • API rate limiting and request prioritization

Scalability Architecture​

Horizontal Scaling Strategy​

Microservices Deployment​

# Kubernetes deployment example
apiVersion: apps/v1
kind: Deployment
metadata:
  name: task-orchestrator
spec:
  replicas: 3
  selector:
    matchLabels:
      app: task-orchestrator
  template:
    metadata:
      labels:
        app: task-orchestrator
    spec:
      containers:
      - name: orchestrator
        image: expertfabric/orchestrator:latest
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"

Auto-scaling Configuration​

  • CPU-based scaling for compute-intensive operations
  • Queue depth scaling for task processing
  • Custom metrics based on expert availability and load
  • Geographic distribution for reduced latency

Data Layer Scaling​

Database Sharding Strategy​

  • Task sharding by client organization
  • Knowledge sharding by domain/specialization
  • Expert sharding by geographic region
  • Cross-shard queries via federation layer

Caching Architecture​

# Redis cluster configuration
cluster-enabled yes
cluster-config-file nodes.conf
cluster-node-timeout 5000
appendonly yes

# Expert availability cache
SET expert:availability:123 "available" EX 300
ZADD expert:load:finance 85 expert:123
HSET expert:profile:123 specialization "financial-analysis"

Security & Privacy Architecture​

Data Protection​

Encryption Strategy​

  • At rest: AES-256 encryption for all stored data
  • In transit: TLS 1.3 for all API communications
  • Key management: Hardware Security Modules (HSM)
  • Client isolation: Tenant-specific encryption keys

Privacy Controls​

interface DataGovernancePolicy {
  clientId: string;
  dataRetention: number; // days
  geographicRestrictions: string[];
  expertAccessLevel: 'public' | 'private' | 'organization';
  aiModelRestrictions: string[];
}

Authentication & Authorization​

Multi-factor Authentication​

  • OAuth 2.0/OIDC integration with enterprise identity providers
  • MFA enforcement for sensitive operations
  • API key management with rotation and scoping
  • Session management with secure token handling

Role-Based Access Control (RBAC)​

{
  "roles": {
    "client_admin": {
      "permissions": ["task.create", "task.view", "billing.view"],
      "resources": ["organization/*"]
    },
    "expert": {
      "permissions": ["task.contribute", "profile.manage"],
      "resources": ["expert/self", "task/assigned"]
    },
    "platform_admin": {
      "permissions": ["*"],
      "resources": ["*"]
    }
  }
}

Technology Stack​

Core Platform​

  • Backend: Node.js/TypeScript with Express.js
  • Message Queue: Apache Kafka for event streaming
  • Task Queue: Bull/BullMQ with Redis
  • API Layer: GraphQL + REST endpoints

Data Storage​

  • Primary Database: PostgreSQL with read replicas
  • Vector Database: Pinecone or Weaviate for embeddings
  • Cache: Redis Cluster for session and application caching
  • Object Storage: AWS S3 with CloudFront CDN

AI & ML Infrastructure​

  • Model Serving: FastAPI with GPU instances
  • Model Management: MLflow for experiment tracking
  • Feature Store: Feast for ML feature management
  • Model Gateway: LiteLLM for multi-provider AI access

Infrastructure & DevOps​

  • Container Orchestration: Kubernetes (EKS/GKE)
  • Service Mesh: Istio for traffic management and security
  • Monitoring: Prometheus + Grafana + Jaeger for observability
  • CI/CD: GitHub Actions with ArgoCD for GitOps

Performance Optimization​

Latency Reduction​

  • Edge caching for static content and common queries
  • Connection pooling for database and external API calls
  • Async processing for non-blocking operations
  • Predictive scaling based on usage patterns

Cost Optimization​

  • AI model caching to reduce API calls
  • Intelligent routing to cost-effective expert nodes
  • Resource scheduling for optimal infrastructure utilization
  • Compression for data transfer and storage

This architecture provides the foundation for Expert Fabric to scale from hundreds to millions of tasks while maintaining security, performance, and reliability standards required for enterprise adoption.