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CORTX Platform - Functional & Technical Design Document

Version: 1.0.0 Date: 2025-09-30 Status: Living Document Organization: Sinergy Solutions LLC

1. Executive Summary

1.1 Platform Purpose

CORTX (Compliance Operations & Rule-based Transformation Execution) is an AI-powered orchestration and compliance automation platform designed for highly regulated industries. CORTX enables organizations to automate complex business processes while maintaining strict compliance with regulatory frameworks including FedRAMP, HIPAA, NIST 800-53, and SOC 2.

1.2 Core Value Proposition

  • Compliance-First Architecture: Built-in audit trails, immutable logging, and regulatory control mapping
  • AI Orchestration: Intelligent workflow execution with LLM-powered explanations and recommendations
  • Hierarchical RAG: 4-level knowledge architecture (Platform → Suite → Module → Entity) with scoped retrieval and specificity boosts
  • RulePack/WorkflowPack Model: Externalized, version-controlled compliance logic (JSON/YAML artifacts)
  • Multi-Tenant SaaS: Schema-per-tenant isolation with dedicated and on-prem deployment options
  • Marketplace Ecosystem: "GitHub for Compliance Workflows" with Pack sharing and certification

1.3 Platform Architecture

CORTX operates as a microservices platform with three architectural layers:

┌─────────────────────────────────────────────────────────────────┐
│                     CORTX ECOSYSTEM                             │
├─────────────────────────────────────────────────────────────────┤
│  Design Layer                                                   │
│  ├── BPM Designer (Visual workflow builder)                     │
│  ├── AI Assistant (Natural language → workflows)                │
│  └── Designer ↔ Platform RAG (UI hooks into svc-rag)           │
├─────────────────────────────────────────────────────────────────┤
│  Execution Layer (Platform Services)                            │
│  ├── Gateway (8080)         - API routing, rate limiting        │
│  ├── Identity (8082)        - Auth & authorization (JWT)        │
│  ├── AI Broker (8085)       - LLM routing, RAG, inference       │
│  ├── Schemas (8084)         - Schema registry & validation      │
│  ├── Validation (8083)      - RulePack execution engine         │
│  ├── Compliance (8135)      - Audit logging, trails             │
│  ├── Ledger (8136)         - Append-only, hash-chained events  │
│  ├── OCR (8137)            - Doc → text/fields (Tesseract/DocAI)│
│  ├── RAG (8138)            - Hierarchical retrieval + indexing  │
│  └── Workflow (8130)        - WorkflowPack orchestration        │
├─────────────────────────────────────────────────────────────────┤
│  Domain Layer (Vertical Suites)                                │
│  ├── FedSuite (8081)        - Federal financial compliance      │
│  ├── CorpSuite              - Real estate & procurement         │
│  ├── MedSuite               - Healthcare compliance             │
│  └── GovSuite               - Government operations             │
├─────────────────────────────────────────────────────────────────┤
│  Infrastructure Layer                                           │
│  ├── GCP Cloud Run          - Serverless compute                │
│  ├── PostgreSQL/Supabase    - Multi-tenant data                 │
│  ├── Redis                  - Event bus, caching                │
│  ├── Cloud Storage          - Artifact storage                  │
│  └── Terraform              - Infrastructure as Code            │
└─────────────────────────────────────────────────────────────────┘

1.4 Key Regulatory Frameworks

Framework Status Scope
FedRAMP Phase I (20x controls) Moderate ATO target Q4 2026
HIPAA Controls implemented 3rd party audit Q1 2026
NIST 800-53 175/325 controls mapped Rev 5 compliance
SOC 2 Type II In progress Audit scheduled Q2 2026
FISMA Moderate ready Continuous monitoring active
OMB A-136 FedSuite compliant Treasury financial reporting

2. Functional Requirements

2.1 Platform Capabilities

2.1.1 RulePack Execution

  • Purpose: Execute externalized validation rules and compliance policies
  • Format: JSON-based rule definitions with versioning
  • Features:
  • Safe operators (no eval/exec): ==, !=, <, >, in, matches, etc.
  • Severity levels: FATAL (blocking), WARNING (review required), INFO (logged)
  • Field-level validation with contextual error messages
  • Batch processing support (1M+ records)
  • Rule composition and chaining

Example RulePack Structure:

{
  "metadata": {
    "pack_id": "federal-gtas-v1",
    "version": "1.0.0",
    "compliance": ["OMB-A-136", "GTAS-2024"]
  },
  "rules": [
    {
      "rule_id": "GTAS-001",
      "type": "FATAL",
      "field": "TAS",
      "operator": "matches",
      "pattern": "^[0-9]{3}-[0-9]{4}$",
      "error_message": "Invalid TAS format. Expected: ###-####"
    }
  ]
}

2.1.2 WorkflowPack Orchestration

  • Purpose: Define and execute multi-step business processes
  • Format: YAML-based workflow definitions
  • Features:
  • Node types: Data source, validation, AI inference, decision, approval, data sink
  • Human-in-the-loop approval gates
  • Saga pattern for distributed transactions
  • Automatic compensation on failures
  • Parallel execution support

Example WorkflowPack Structure:

workflow_id: gtas-monthly-submission
version: 1.0.0
steps:
  - id: ingest
    type: data-source
    config:
      format: csv
      schema: trial-balance-v1

  - id: validate
    type: validation
    config:
      rulepack: federal-gtas-v1
      on_failure: halt

  - id: reconcile
    type: calculation
    config:
      formula: sum(debits) - sum(credits)

  - id: approve
    type: approval
    config:
      role: certifying_official
      timeout_hours: 48

  - id: submit
    type: data-sink
    config:
      endpoint: https://gtas.treasury.gov/api/submit
      method: POST

2.1.3 AI Orchestration

  • Purpose: Provide intelligent assistance and automation
  • Capabilities:
  • Model Router: Select optimal model (cost, speed, compliance)
  • RAG (Retrieval-Augmented Generation): Vector store with compliance knowledge
  • PII Redaction: Automatic sensitive data scrubbing before LLM calls
  • Explainability: Generate plain-language explanations for rule failures
  • Smart Recommendations: Suggest corrections for compliance violations

Supported AI Models:

  • Production: Google Gemini 1.5 Pro/Flash (via Vertex AI)
  • Roadmap: Claude 3.5 Sonnet, GPT-4 Turbo, AWS Bedrock, Hugging Face local models

2.1.4 Multi-Tenant Architecture

  • Tenant Isolation:
  • PostgreSQL: Schema-per-tenant with Row-Level Security (RLS)
  • Kubernetes: Namespace-per-tenant (enterprise tier)
  • Redis: Key prefixing by tenant ID
  • Storage: GCS buckets with tenant-scoped access
  • Deployment Modes:
  • SaaS Multi-Tenant: Shared platform, schema isolation ($10k/mo base)
  • SaaS Dedicated: Dedicated cluster per tenant ($50k/mo+)
  • On-Prem/Private: Customer infrastructure ($200k/yr license)

2.1.5 RAG (Retrieval-Augmented Generation)

  • Purpose: Provide contextual, compliance-aware knowledge retrieval for AI and UI workflows
  • Architecture: 4-level hierarchy (Platform → Suite → Module → Entity) with scope-based retrieval
  • Features:
  • Scoped search: Retrieve knowledge at desired specificity (e.g., suite, module, entity)
  • Boosting: Relevance scoring based on context, recency, and compliance tags
  • Real-time ingestion: New docs, policies, and evidence can be indexed on demand
  • Admin UI: Upload, manage, and visualize knowledge graph
  • Integration: RAG available via API and UI hooks (Designer, AI Assistant)
  • Sample Use Cases:
  • Explain compliance rules with traceable source references
  • Retrieve agency-specific guidance for workflow steps
  • Power AI Assistant with up-to-date, certified knowledge

2.1.6 OCR (Optical Character Recognition)

  • Purpose: Extract structured data and text from scanned documents and images
  • Features:
  • Multi-engine support: Tesseract (open source), Google DocAI (cloud)
  • Field extraction: Map document zones to schema fields (template-based)
  • Batch processing: Handle large volumes (1000+ docs per batch)
  • Confidence scoring: Per-field and per-page extraction quality
  • Redaction: Mask PII/PHI on output if enabled
  • Supported Formats: PDF, TIFF, PNG, JPEG
  • Sample Use Cases:
  • Ingest scanned financial reports for validation
  • Extract fields from government forms (e.g., SF-133)
  • Pre-process documents for RAG indexing

2.1.7 Ledger (Immutable Audit Ledger)

  • Purpose: Provide tamper-evident, append-only event logging for compliance and audit
  • Features:
  • SHA-256 hash-chain: Each event links to previous for immutability
  • Append-only API: No updates or deletes permitted
  • Periodic verification: Automated detection of drift or tampering
  • Integration: Compliance service logs to ledger for all critical events
  • Export: Downloadable for 3rd party audits
  • Sample Use Cases:
  • Store evidence of workflow execution and approvals
  • Provide audit trail for regulatory certification
  • Detect unauthorized event modification attempts

2.2 User Roles & RBAC

Role Permissions UI Access
PLATFORM_VIEWER Read-only platform status Dashboards, logs
PACK_AUTHOR Create/edit RulePacks & WorkflowPacks Designer, testing
PACK_REVIEWER Approve packs for deployment Review queue, annotations
COMPLIANCE_OFFICER Certify compliance, audit access Audit logs, reports
SUITE_OPERATOR Execute workflows, upload data Suite dashboards, data entry
SUITE_ADMIN Manage suite configuration Suite settings, integrations
PLATFORM_ADMIN Full platform administration All features, tenant management

2.3 Audit & Compliance Logging

Required Events (NIST 800-53 AU-2, AU-3):

  • User authentication (login, logout, failures)
  • Pack creation, modification, deployment
  • Workflow execution (start, steps, end, errors)
  • Data access (read, write, delete)
  • Permission changes (role grants, revocations)
  • AI inference calls (model, prompt hash, response)
  • Configuration changes (platform, tenant, suite)

Log Format (JSON):

{
  "timestamp": "2025-09-30T14:23:45Z",
  "event_type": "workflow_executed",
  "tenant_id": "agency-dod-001",
  "user_id": "jane.doe@dod.gov",
  "session_id": "sess_abc123",
  "correlation_id": "wf_xyz789",
  "details": {
    "workflow_id": "gtas-monthly-submission",
    "status": "completed",
    "duration_ms": 3542
  },
  "compliance_tags": ["FISMA", "GTAS", "OMB-A-136"]
}

Retention:

  • Audit logs: 7 years (regulatory requirement)
  • Workflow execution logs: 3 years
  • Access logs: 1 year
  • Performance logs: 90 days

3. Technical Architecture

3.1 Platform Services

3.1.1 Gateway Service (Port 8080)

Technology: FastAPI (Python 3.11), Uvicorn, Redis Purpose: API routing, rate limiting, request/response transformation

Capabilities:

  • Reverse proxy to all platform services
  • Rate limiting (100 req/sec per tenant, burst 200)
  • Request authentication (JWT validation)
  • CORS handling
  • Request/response logging
  • Circuit breaker for downstream services

Endpoints:

GET  /health                    # Health check
POST /api/v1/rulepacks/execute  # Execute RulePack
POST /api/v1/workflows/execute  # Execute WorkflowPack
GET  /api/v1/services           # Service discovery

3.1.2 Identity Service (Port 8082)

Technology: FastAPI, PostgreSQL, JWT, Supabase Auth Purpose: Authentication, authorization, tenant management

Features:

  • OAuth 2.0 / OpenID Connect support
  • Multi-factor authentication (MFA)
  • JWT token issuance (15min access, 7day refresh)
  • Role-Based Access Control (RBAC)
  • Tenant onboarding automation

Claims in JWT:

{
  "sub": "user_123",
  "tenant_id": "agency-dod-001",
  "roles": ["SUITE_OPERATOR", "PACK_AUTHOR"],
  "permissions": ["execute:workflows", "create:packs"],
  "exp": 1696089825
}

3.1.3 AI Broker Service (Port 8085)

Technology: FastAPI, LangChain, Google Vertex AI, Redis (cache) Purpose: AI model orchestration, RAG, PII protection

Components:

  • Model Router: Select model based on cost, latency, compliance requirements
  • Prompt Manager: Template management with variable injection
  • RAG Engine: Vector search (384-dim embeddings, cosine similarity)
  • PII Redactor: Regex + NER-based sensitive data removal

RAG Vector Store:

  • 15+ embedded documents (Treasury rules, HIPAA guidelines, etc.)
  • Semantic search with keyword boosting
  • Top-k retrieval (default k=5, threshold=0.5)
  • Real-time knowledge base updates

API Endpoints:

POST /api/ai/inference          # Generate AI response
POST /api/ai/explain            # Explain rule failure
POST /api/ai/generate-workflow  # NL → WorkflowPack
GET  /api/ai/models             # Available models
POST /api/ai/rag/search         # Search knowledge base

3.1.4 Validation Service (Port 8083)

Technology: FastAPI, Pydantic, Safe eval engine Purpose: RulePack execution, data validation

Safe Operators (no code injection):

  • Comparison: ==, !=, <, <=, >, >=
  • Membership: in, not_in
  • String: contains, starts_with, ends_with, matches (regex)
  • Null checks: is_null, is_not_null

Validation Flow:

  1. Load RulePack from registry
  2. Parse input data (JSON, CSV, XML)
  3. Apply rules sequentially
  4. Collect violations (FATAL, WARNING, INFO)
  5. Return results with contextual errors

3.1.5 Workflow Service (Port 8130)

Technology: FastAPI, Temporal.io (planned), Redis (current) Purpose: WorkflowPack orchestration, saga pattern

Features:

  • Sequential and parallel step execution
  • Conditional branching (decision nodes)
  • Human-in-the-loop approval gates
  • Automatic compensation on failure
  • Event-driven cross-suite workflows

Saga Pattern (distributed transactions):

saga:
  steps:
    - id: step1
      compensate: rollback_step1
    - id: step2
      compensate: rollback_step2

  on_failure:
    - execute: rollback_step2
    - execute: rollback_step1
    - notify: failure_alert

3.1.6 Compliance Service (Port 8135)

Technology: FastAPI, PostgreSQL (time-series), Cloud Logging Purpose: Audit logging, compliance reporting

Capabilities:

  • Immutable audit trail (append-only)
  • Compliance report generation (FISMA, FedRAMP, HIPAA)
  • NIST 800-53 control evidence collection
  • Automated compliance attestation
  • Retention policy enforcement

3.1.7 Schema Service (Port 8084)

Technology: FastAPI, JSON Schema, YAML validation Purpose: Schema registry, RulePack/WorkflowPack validation

Schemas:

  • RulePack schema (JSON Schema draft-07)
  • WorkflowPack schema (YAML with JSON Schema validation)
  • Data schema registry (for input/output validation)
  • Version management (semantic versioning)

3.1.8 RAG Service (Port 8138)

Technology: FastAPI, Qdrant/Weaviate, LangChain, PostgreSQL Purpose: Hierarchical knowledge retrieval, indexing, and management

Features:

  • 4-level scope: platform, suite, module, entity
  • Embedding: 384-dim, OpenAI or Vertex AI
  • Metadata: Compliance tags, source, recency, access control
  • Scoped search: scope=platform|suite|module|entity
  • Admin UI: Upload, browse, tag, delete knowledge
  • Real-time updates: Sync with RulePack/WorkflowPack/Docs

API Endpoints:

POST /api/rag/query
{
  "query": "What is GTAS reporting?",
  "scope": "suite",
  "suite_id": "fedsuite",
  "top_k": 5
}

POST /api/rag/index
{
  "doc_id": "fed-guide-2025",
  "content": "...",
  "scope": "suite",
  "suite_id": "fedsuite",
  "tags": ["FedRAMP", "GTAS"]
}

GET /api/rag/docs?suite_id=fedsuite&scope=suite

3.1.9 OCR Service (Port 8137)

Technology: FastAPI, Tesseract, Google DocAI, Celery Purpose: Document OCR and field extraction

Features:

  • Multi-engine: engine=tesseract|docai
  • Template-based field mapping (JSON)
  • Batch and async processing
  • Output: JSON (text, fields, confidence)
  • Redaction: Optional PII masking

API Endpoints:

POST /api/ocr/extract
{
  "engine": "docai",
  "files": [ ... ],
  "template_id": "sf133-v1"
}

GET /api/ocr/results/{job_id}

3.1.10 Ledger Service (Port 8136)

Technology: FastAPI, PostgreSQL (append-only), SHA-256, Celery Purpose: Tamper-evident, immutable event ledger for compliance

Features:

  • Append-only API (no updates/deletes)
  • SHA-256 hash-chain per event
  • Verification endpoint (drift detection)
  • Export for audit
  • Integrated with Compliance service

API Endpoints:

POST /api/ledger/append
{
  "event_type": "workflow_executed",
  "payload": { ... }
}

GET /api/ledger/verify
GET /api/ledger/events?since=2025-09-01

3.2 Data Flow

Typical Execution Flow:

sequenceDiagram
    participant User
    participant Gateway
    participant Identity
    participant Workflow
    participant Validation
    participant AIBroker
    participant Compliance

    User->>Gateway: POST /api/v1/workflows/execute
    Gateway->>Identity: Validate JWT
    Identity-->>Gateway: User context + tenant_id
    Gateway->>Workflow: Execute WorkflowPack
    Workflow->>Validation: Execute RulePack (step 1)
    Validation-->>Workflow: Validation results
    Workflow->>AIBroker: Get AI recommendation (step 2)
    AIBroker-->>Workflow: AI response
    Workflow->>Compliance: Log execution
    Workflow-->>Gateway: Workflow results
    Gateway-->>User: Response with audit trail

OCR → RAG → AI Sequence:

sequenceDiagram
    participant User
    participant OCR
    participant RAG
    participant AI
    participant Workflow

    User->>OCR: Upload scanned document
    OCR->>OCR: Extract text + fields (Tesseract/DocAI)
    OCR-->>User: Structured data + confidence
    OCR->>RAG: Index extracted content (optional)
    User->>RAG: Query knowledge (with context)
    RAG-->>User: Relevant docs/snippets
    User->>AI: Generate explanation/recommendation (with RAG context)
    AI-->>User: AI response (with source citations)
    User->>Workflow: Use OCR/RAG/AI output in process

3.3 Security Architecture

3.3.1 Network Security

  • TLS 1.3: All service-to-service communication
  • mTLS: Optional for high-security tenants
  • VPC: Private network for service mesh
  • WAF: Cloud Armor for DDoS protection
  • API Gateway: Rate limiting, IP allowlisting

3.3.2 Data Security

  • Encryption at Rest: AES-256 for databases, Cloud KMS for keys
  • Encryption in Transit: TLS 1.3 with perfect forward secrecy
  • PII Protection: Automatic redaction before LLM calls
  • Data Minimization: Collect only required fields
  • Right to Deletion: GDPR-compliant data purge
  • Tamper Evidence: Ledger service computes SHA-256 hash-chain for compliance events; periodic verification jobs detect drift

3.3.3 Access Control

  • RBAC: Role-based with least privilege
  • Attribute-Based: Context-aware policies (time, location, risk)
  • MFA: Required for admin roles
  • Session Management: 15min idle timeout, concurrent session limits
  • Audit: All permission changes logged

3.3.4 Compliance Controls (NIST 800-53)

Control Family Implemented Controls Status
AC (Access Control) AC-2, AC-3, AC-7 ✅ Complete
AU (Audit) AU-2, AU-3, AU-12 ✅ Complete
IA (Identification & Auth) IA-2, IA-4, IA-5 ✅ Complete
SC (System & Comm) SC-7, SC-8, SC-13 ✅ Complete
SI (System Integrity) SI-3, SI-4, SI-7 🚧 In Progress
CA (Assessment) CA-8 (pen testing) 📋 Planned Q1 2026

3.4 Deployment Architecture

3.4.1 GCP Cloud Run (Primary)

services:
  gateway:
    image: gcr.io/cortx-platform/gateway:latest
    resources:
      cpu: 2
      memory: 4Gi
    scaling:
      min_instances: 2
      max_instances: 100
      target_cpu: 70%

  ai-broker:
    image: gcr.io/cortx-platform/ai-broker:latest
    resources:
      cpu: 4
      memory: 8Gi
    env:
      - GOOGLE_API_KEY: secret:gemini-api-key

  rag:
    image: gcr.io/cortx-platform/rag:latest
    resources:
      cpu: 2
      memory: 4Gi
    env:
      - QDRANT_URL: http://qdrant:6333
      - EMBEDDING_MODEL: vertexai

  ocr:
    image: gcr.io/cortx-platform/ocr:latest
    resources:
      cpu: 2
      memory: 4Gi
    env:
      - DOC_AI_KEY: secret:docai-key
      - OCR_ENGINE: tesseract

  ledger:
    image: gcr.io/cortx-platform/ledger:latest
    resources:
      cpu: 1
      memory: 2Gi
    env:
      - LEDGER_DB_URL: secret:ledger-db-url

3.4.2 Kubernetes (Enterprise/On-Prem)

namespaces:
  - cortx-platform      # Core services
  - tenant-dod-001      # Dedicated tenant namespace
  - tenant-hhs-002

ingress:
  nginx:
    tls: true
    rate_limit: 1000/min

storage:
  class: ssd-persistent
  backup: daily
  retention: 30d

3.4.3 Multi-Region (Planned Phase 2)

  • Primary: us-central1 (Iowa)
  • DR: us-east1 (South Carolina)
  • Replication: Cross-region PostgreSQL, GCS
  • Failover: Automated DNS switching, <5 min RTO

4. Pack Schemas & Governance

4.1 RulePack JSON Schema

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "RulePack",
  "type": "object",
  "required": ["metadata", "rules"],
  "properties": {
    "metadata": {
      "type": "object",
      "required": ["pack_id", "version"],
      "properties": {
        "pack_id": {"type": "string"},
        "version": {"type": "string", "pattern": "^\\d+\\.\\d+\\.\\d+$"},
        "compliance": {"type": "array", "items": {"type": "string"}},
        "created_by": {"type": "string"},
        "created_at": {"type": "string", "format": "date-time"}
      }
    },
    "rules": {
      "type": "array",
      "items": {
        "type": "object",
        "required": ["rule_id", "type", "field", "operator"],
        "properties": {
          "rule_id": {"type": "string"},
          "type": {"enum": ["FATAL", "WARNING", "INFO"]},
          "field": {"type": "string"},
          "operator": {"enum": ["==", "!=", "<", "<=", ">", ">=", "in", "not_in", "contains", "matches"]},
          "value": {},
          "error_message": {"type": "string"}
        }
      }
    }
  }
}

4.2 WorkflowPack YAML Schema

$schema: http://json-schema.org/draft-07/schema#
title: WorkflowPack
type: object
required: [workflow_id, version, steps]
properties:
  workflow_id:
    type: string
  version:
    type: string
    pattern: ^\d+\.\d+\.\d+$
  steps:
    type: array
    items:
      type: object
      required: [id, type]
      properties:
        id: {type: string}
        type:
          enum: [data-source, validation, calculation, decision, approval, ai-inference, data-sink]
        config:
          type: object

4.3 Pack Governance

Approval Workflow:

  1. Draft: Author creates pack in designer
  2. Review: Business analyst reviews functional correctness
  3. Compliance: Compliance officer certifies regulatory alignment
  4. Testing: QA validates with test data
  5. Approval: Admin approves for deployment
  6. Deployment: Pack pushed to production registry

Versioning:

  • Semantic versioning: MAJOR.MINOR.PATCH
  • Breaking changes: MAJOR increment, requires re-certification
  • Backward-compatible features: MINOR increment
  • Bug fixes: PATCH increment

Marketplace Tiers:

  • Official: Treasury/IRS verified, free (government funded)
  • Certified: Community tested, 70/30 revenue split
  • Community: User contributed, as-is
  • Private: Agency-specific, enterprise licensing

5. Integration Points

5.1 Suite Integration

API Contract:

# Suite registration
POST /api/v1/suites/register
{
  "suite_id": "fedsuite",
  "base_url": "http://fedsuite:8081",
  "capabilities": ["gtas", "cars", "sf133"],
  "health_endpoint": "/health"
}

# Suite execution
POST /api/v1/suites/{suite_id}/execute
{
  "workflow_id": "gtas-monthly",
  "input_data": {...},
  "tenant_id": "agency-dod-001"
}

Event Bus (Redis Streams):

# Publish event
XADD cortx:events *
  event_type "workflow_completed"
  tenant_id "agency-dod-001"
  workflow_id "gtas-monthly"
  status "success"

# Subscribe
XREAD COUNT 10 STREAMS cortx:events 0

5.2 External Systems

Treasury Systems (FedSuite):

  • GTAS API: https://gtas.treasury.gov/api/submit
  • CARS API: https://cars.treasury.gov/api/daily-position
  • Authentication: Client certificate (mTLS)

Healthcare Systems (MedSuite):

  • CMS FHIR API: https://api.cms.gov/fhir/v1
  • NPI Registry: https://npiregistry.cms.hhs.gov/api
  • Authentication: OAuth 2.0

Real Estate Systems (CorpSuite):

  • Maryland SDAT: Licensed bulk files (SFTP)
  • MDLandRec: API integration (API key)
  • Compliance: AUP restrictions, human-in-loop required

5.3 AI Model APIs

Current:

  • Google Vertex AI: Gemini 1.5 Pro/Flash
  • LangChain: Orchestration framework

Planned:

  • Anthropic Claude API
  • OpenAI GPT-4 API
  • AWS Bedrock
  • Hugging Face Inference API (on-prem)

6. Testing Strategy

6.1 Platform Service Tests

Unit Tests (pytest, >85% coverage):

  • Service logic (validation, workflow, AI broker)
  • Data models (Pydantic schemas)
  • Utility functions

Integration Tests:

  • Service-to-service communication
  • Database interactions (PostgreSQL, Redis)
  • External API mocking (Treasury, CMS)

Contract Tests (Pact):

  • API contracts between services
  • Suite integration contracts

6.2 Pack Execution Tests

RulePack Tests:

  • Rule accuracy (known input → expected output)
  • Edge cases (null values, malformed data)
  • Performance (1M records in <30 seconds)

WorkflowPack Tests:

  • End-to-end workflow execution
  • Failure scenarios (compensation testing)
  • Human-in-loop approval gates

6.3 AI/ML Tests

Reproducibility (>95% threshold):

  • Same prompt → same response (temperature=0)
  • Model version pinning
  • Regression test suite

RAG Tests:

  • Knowledge retrieval accuracy
  • Context relevance scoring
  • Embedding drift detection

6.4 Security Tests

OWASP Top 10:

  • Injection attacks (SQL, NoSQL, command)
  • Broken authentication
  • Sensitive data exposure
  • XML external entities (XXE)
  • Broken access control

Penetration Testing:

  • Annual 3rd party assessment
  • Vulnerability scanning (weekly)
  • Dependency scanning (CI/CD)

6.5 Compliance Tests

FedRAMP/NIST 800-53:

  • Control implementation validation
  • Evidence collection automation
  • Continuous monitoring tests

HIPAA:

  • PHI access logging
  • Encryption verification
  • Breach notification procedures

7. Deployment & Operations

7.0 Environment Strategy

Env Purpose Data Isolation Compliance Level Notes
dev Developer testing Shared Low Rapid iteration, resettable
staging Pre-prod validation Per-tenant Moderate Near-prod config, test data
prod Production Per-tenant High Full compliance, audit logs
sandbox Customer demos/trials Per-tenant Moderate Isolated, short-lived

7.1 CI/CD Pipeline

stages:
  - name: lint
    tools: [ruff, mypy, eslint]

  - name: test
    parallel:
      - unit_tests: pytest --cov=80
      - integration_tests: pytest tests/integration
      - security_scan: trivy, snyk

  - name: build
    artifacts:
      - docker_images: gcr.io/cortx-platform/*
      - helm_charts: charts/*

  - name: deploy
    environments: [dev, staging, prod]
    approval_required: [staging, prod]

7.2 Observability

Metrics (Prometheus):

  • Request rate, latency (p50, p95, p99)
  • Error rate (4xx, 5xx)
  • Pack execution duration
  • AI inference latency
  • Database connection pool

Dashboards (Grafana):

  • Platform health overview
  • Per-tenant usage metrics
  • Compliance audit metrics
  • Cost attribution (GCP billing)

Alerts (Cloud Monitoring):

  • Error rate >1% (5min window)
  • Latency p99 >1000ms
  • Database connections >80%
  • Failed compliance checks

7.3 Incident Response

Runbook:

  1. Detection: Automated alerts via PagerDuty
  2. Triage: On-call engineer assesses severity
  3. Mitigation: Rollback, scale, failover
  4. Resolution: Root cause analysis
  5. Post-Mortem: Document lessons learned

SLA Targets:

  • P0 (Platform down): 15min response, 1hr resolution
  • P1 (Suite degraded): 1hr response, 4hr resolution
  • P2 (Feature issue): 4hr response, 24hr resolution

8. Quality & Success Metrics

8.1 Technical Metrics

Metric Target Current Status
Test coverage ≥85% 78% 🚧 In progress
API latency (p99) <500ms 342ms ✅ Met
Platform uptime 99.9% 99.87% 🚧 Close
AI accuracy ≥95% 96.3% ✅ Met
Pack execution <30s/1M records 18s ✅ Met

8.2 Compliance Metrics

Framework Controls Mapped Evidence Collected Status
FedRAMP 175/325 (54%) 120/175 (69%) 🚧 Phase I
HIPAA 48/48 (100%) 45/48 (94%) ✅ Ready for audit
NIST 800-53 175/325 (54%) 120/175 (69%) 🚧 In progress
SOC 2 64/64 (100%) 58/64 (91%) 🚧 Audit Q2 2026

8.3 Business Metrics

  • Platform adoption: 12 tenants (target: 50 by EOY 2026)
  • Pack marketplace: 23 certified packs (target: 100)
  • Time savings: 75% reduction in manual reconciliation (FedSuite)
  • Error reduction: 90% fewer compliance violations (MedSuite)

9. Roadmap

Phase 1 (Complete) ✅

  • Core platform services (7 microservices)
  • RulePack/WorkflowPack execution
  • Multi-tenant SaaS deployment
  • FedSuite production (GTAS reconciliation)

Phase 2 (Q1-Q2 2026) 🚧

  • AI model expansion (Claude, GPT-4, Bedrock)
  • BPM Designer RAG enhancements
  • HIPAA 3rd party audit completion
  • CorpSuite production (PropVerify)
  • OCR Service GA (8137)
  • Ledger Service GA (8136)
  • RAG Service GA (8138)
  • RAG Admin UI (knowledge management)

Phase 3 (Q3-Q4 2026) 📋

  • Pack Marketplace launch
  • Multi-region deployment (DR)
  • FedRAMP ATO achievement
  • Cross-suite saga orchestration (Kafka migration)
  • Multi-level RAG quality evaluations (retrieval accuracy, explainability, compliance trace)

Phase 4 (2027+) 🔮

  • International expansion (UK, Canada, Australia)
  • On-prem appliance offering
  • Advanced AI (fine-tuned compliance models)
  • Blockchain-based audit trails (immutable ledger)

10. Appendices

A. Glossary

  • CORTX: Compliance Operations & Rule-based Transformation Execution
  • RulePack: JSON-based validation rules and compliance policies
  • WorkflowPack: YAML-based process orchestration definitions
  • RAG: Retrieval-Augmented Generation (AI + knowledge base)
  • Saga: Distributed transaction pattern with compensation
  • Pack: Generic term for RulePack or WorkflowPack
  • Suite: Domain-specific vertical application (FedSuite, MedSuite, etc.)

B. References

C. Contact Information

  • Platform Owner: Sinergy Solutions LLC
  • Technical Lead: [Contact via GitHub]
  • Security Officer: [Contact for compliance inquiries]
  • Support: support@sinergysolutions.ai

Document Control

  • Version: 1.0.0
  • Last Updated: 2025-09-30
  • Review Cycle: Quarterly
  • Classification: Internal Use / Proprietary
  • Approvers: Platform Architecture Team

This document is a living specification and will evolve with the CORTX platform. All changes are tracked in version control.