Architecture Overview

TietAI's platform is built on a modern microservices architecture designed for healthcare-grade reliability, security, and scalability. Our architecture enables seamless integration of disparate healthcare systems while maintaining HIPAA compliance and supporting real-time AI processing.

High-Level Architecture

Data Sources

HL7, FHIR, X12, APIs

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Integration Layer

Connectors & Adapters

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Processing Core

AI & Transformation

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Data Lake

Unified Storage

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Applications

APIs & UI

Core Architectural Principles

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Security First

End-to-end encryption, zero-trust architecture, and comprehensive audit logging for all PHI access

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Event-Driven

Asynchronous processing with event sourcing for reliability and real-time data synchronization

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Cloud-Native

Containerized microservices with Kubernetes orchestration for elastic scaling

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AI-Powered

Embedded machine learning models for intelligent mapping, validation, and anomaly detection

Microservices Architecture

Core Services

Service Purpose Technology Stack
Integration Engine Manages data ingestion from various sources Node.js, TypeScript, Apache Kafka
Transformation Service Converts between healthcare data formats Python, Apache Beam, TensorFlow
Validation Service Ensures data integrity and compliance Go, PostgreSQL, Redis
AI Processing Machine learning inference and training Python, PyTorch, CUDA, Triton
API Gateway Unified entry point for all clients Kong, GraphQL, REST
Workflow Orchestrator Manages complex multi-step processes Apache Airflow, Temporal
Audit Service Comprehensive logging and compliance Elasticsearch, Logstash, Kibana
lightbulb Design Pattern: Each microservice is independently deployable, has its own database, and communicates via well-defined APIs or message queues.

Data Flow Architecture

Ingestion Pipeline

1. Data Source → Connector (Protocol-specific)
2. Connector → Message Queue (Kafka/NATS)
3. Message Queue → Validation Service
4. Validation → Transformation Engine
5. Transformation → AI Enhancement
6. AI Enhancement → Data Lake Storage
7. Storage → API/Application Layer

Real-time Processing

  • Stream Processing: Apache Kafka for high-throughput event streaming
  • Message Bus: NATS for lightweight pub/sub messaging
  • Cache Layer: Redis for session management and hot data
  • CDC: Debezium for change data capture from databases

Batch Processing

  • ETL Jobs: Apache Airflow for scheduled workflows
  • Data Processing: Apache Spark for large-scale analytics
  • ML Training: Kubernetes Jobs for model training pipelines

AI & Machine Learning Layer

Model Architecture

Schema Detection

Transformer models for automatic format recognition

BERT Custom NER

Field Mapping

Similarity matching and semantic understanding

Sentence-BERT FastText

Anomaly Detection

Identify data quality issues and outliers

Isolation Forest LSTM

Clinical NLP

Extract entities from unstructured text

BioBERT ScispaCy

Model Serving Infrastructure

# GPU-accelerated inference
- NVIDIA Triton Inference Server
- TensorRT optimization
- Model versioning and A/B testing
- Automatic scaling based on load
- Request batching for efficiency

Security Architecture

Defense in Depth

Layer Security Measures
Network VPC isolation, WAF, DDoS protection, TLS 1.3
Application OAuth 2.0, JWT tokens, RBAC, API rate limiting
Data AES-256 encryption at rest, TLS in transit, field-level encryption
Infrastructure Kubernetes RBAC, Pod Security Policies, Network Policies
Monitoring SIEM integration, audit logging, intrusion detection
HIPAA Compliance: All components are designed to meet HIPAA Technical Safeguards including access controls, audit controls, integrity controls, and transmission security.

Scalability Patterns

Horizontal Scaling

  • Auto-scaling: Kubernetes HPA based on CPU/memory/custom metrics
  • Load Balancing: NGINX Ingress with session affinity
  • Database Sharding: Partition by tenant/region for multi-tenancy
  • Caching Strategy: Multi-tier caching with Redis and CDN

Performance Optimization

# Key Metrics & Targets
- API Response Time: p99 < 200ms
- Data Processing: 10,000 messages/second
- Model Inference: p95 < 50ms
- Availability: 99.99% uptime SLA
- RPO: 1 hour, RTO: 4 hours

Deployment Architecture

Infrastructure as Code

Terraform Helm Charts ArgoCD GitOps

Deployment Environments

Environment Purpose Configuration
Development Feature development and testing Single-node, synthetic data
Staging Integration testing and UAT Multi-node, anonymized data
Production Live customer workloads Multi-region, HA configuration
DR Site Disaster recovery Warm standby, automated failover

CI/CD Pipeline

1. Code Commit → GitHub
2. Automated Tests → GitHub Actions
3. Security Scan → Snyk/SonarQube
4. Container Build → Docker/Buildkit
5. Registry Push → ECR/GCR
6. Deploy Staging → ArgoCD
7. Integration Tests → Automated Suite
8. Production Deploy → Blue/Green or Canary
9. Health Checks → Monitoring

Observability & Monitoring

Three Pillars of Observability

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Metrics

Prometheus + Grafana for system and application metrics

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Logging

ELK Stack for centralized log aggregation and analysis

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Tracing

Jaeger for distributed tracing across microservices

Key Dashboards

  • System Health: CPU, memory, disk, network metrics
  • Application Performance: Request rates, latencies, error rates
  • Business Metrics: Messages processed, integrations active, data quality
  • Security: Failed auth attempts, access patterns, compliance violations
  • Cost Optimization: Resource utilization, scaling events, cloud spend
warning Alerting: PagerDuty integration with intelligent routing based on severity and on-call schedules. Runbook automation for common issues.
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