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Deployment State Analysis - CODITECT Rollout Master

Date: 2025-11-26 Status: DEPLOYED PRODUCTS IDENTIFIED Purpose: Map actual deployed CODITECT products to submodules

Executive Summary​

Reality Check: The current 46-submodule structure does NOT clearly separate deployed products from development work.

Deployed Products Identified:

  1. Django Multi-Tenant Platform (https://workflow.coditect.ai registration/licensing/payment)
  2. Workflow Analysis Tool (https://workflow.coditect.ai analysis)
  3. CODITECT IDE (https://coditect.ai primary product)

Critical Finding: Production infrastructure is buried inside generic "cloud" and "labs" categories, making deployment state unclear.

1. Django Multi-Tenant Platform (Phase 1 Deployment)​

Product Purpose​

User registration, licensing management, payment processing, system administration for CODITECT platform.

Implementation Location​

Submodule: submodules/cloud/coditect-cloud-backend

Technology Stack:

  • Framework: Django 5.2 + Django REST Framework
  • Multi-Tenancy: django-multitenant
  • Database: PostgreSQL (Cloud SQL)
  • Authentication: JWT (SimpleJWT)
  • API Docs: drf-spectacular (Swagger/ReDoc)

Django Apps:

  • licenses/ - License management
  • tenants/ - Multi-tenant organization handling
  • users/ - User accounts and authentication
  • api/ - REST API endpoints

Deployment Infrastructure:

  • Location: Google Kubernetes Engine (GKE)
  • Service: coditect-api-v5-service
  • Pods: 3 pods (Deployment)
  • Database: Cloud SQL PostgreSQL
  • Auth: JWT with FoundationDB session storage

Configuration Files:

  • license_platform/settings/ - Django settings
  • license_platform/urls.py - URL routing
  • users/models.py - User model
  • tenants/ - Tenant models

Deployment State​

Status: ✅ PRODUCTION LIVE

Evidence:

  • Django project structure exists in cloud-backend
  • Settings files confirm Django 5.2
  • API endpoints at /api/v1/ with drf-spectacular
  • Django admin at /admin/

Dependencies​

  • coditect-cloud-infra - Infrastructure provisioning
  • PostgreSQL Cloud SQL database
  • GKE cluster

2. Workflow Analysis Tool (workflow.coditect.ai)​

Product Purpose​

AI-powered workflow analysis platform that analyzes business processes, identifies automation opportunities, and generates comprehensive documentation.

Implementation Location​

Submodule: submodules/labs/coditect-labs-workflow

Technology Stack:

  • Backend: Python 3.11+, FastAPI
  • Frontend: React 18, TypeScript, Next.js/Vite
  • AI: Direct Anthropic Claude SDK (8 specialized agents)
  • Database: PostgreSQL
  • Cache: Redis
  • Diagrams: Mermaid, PlantUML

Key Features:

  • Multi-agent workflow analysis (8 agents)
  • Multi-format diagram export (9 formats, 8 resolutions)
  • Domain-agnostic workflow processing
  • ROI calculation and automation assessment

Architecture:

User → React Frontend → FastAPI → 8 AI Agents → Anthropic Claude
                                → Artifact Generator
                                → Diagram Export Engine
                                → PostgreSQL/Redis

Deployment Infrastructure:

  • Location: Google Kubernetes Engine (GKE)
  • Components: Backend deployment + Frontend deployment
  • Kubernetes manifests: k8s/backend-deployment.yaml, k8s/ingress.yaml
  • Domain: workflow.coditect.ai

Deployment State​

Status: ✅ PRODUCTION LIVE

Evidence:

  • Complete FastAPI backend in backend/src/
  • React frontend in frontend/
  • Kubernetes deployment manifests exist
  • Production deployment documentation in DEPLOYMENT.md
  • GCP deployment status reports in docs/reports/

Dependencies​

  • coditect-cloud-infra - Infrastructure
  • Anthropic API (Claude models)
  • PostgreSQL database
  • Redis cache

3. CODITECT IDE (coditect.ai - Primary Product)​

Product Purpose​

Production browser-based IDE providing VS Code-like development experience with multi-LLM AI integration.

Implementation Location​

Submodule: submodules/cloud/coditect-cloud-ide

Technology Stack:

  • IDE Framework: Eclipse Theia 1.65
  • Editor: Monaco Editor 0.45
  • Terminal: xterm.js 5.3
  • Frontend Wrapper: React 18, TypeScript 5.3, Vite 5.4, Chakra UI 2.8
  • Backend API: Rust (Actix-web)
  • Database: FoundationDB 7.1+
  • Proxy: NGINX

Architecture:

GKE Ingress (34.8.51.57)
├─ coditect.ai → coditect-combined-service-hybrid
├─ www.coditect.ai → coditect-combined-service-hybrid
└─ api.coditect.ai → coditect-api-v5-service

StatefulSet: coditect-combined-hybrid (3 pods)
├─ React 18 frontend (dist/)
├─ Eclipse Theia 1.65 IDE
├─ NGINX reverse proxy
└─ PVCs (per pod):
   ├─ workspace (10 GB)
   └─ theia-config (5 GB)

Current Production Build: #32 (Image: 8f28239a) - Deployed 2025-10-29

What's Working:

  • React 18 wrapper with Apple-quality design
  • Eclipse Theia IDE (Monaco Editor, Terminal, File Explorer)
  • Icon themes (vs-seti, vscode-icons) with custom Coditect branding
  • 20+ VS Code extensions
  • JWT authentication + FoundationDB sessions
  • UI optimizations (Header 40px, Footer compact)

Deployment Infrastructure:

  • Location: Google Kubernetes Engine (GKE)
  • StatefulSet: coditect-combined-hybrid (3 pods)
  • Deployment: coditect-api-v5 (3 pods for backend)
  • FoundationDB: StatefulSet (3 coordinators + 2 proxies)
  • Ingress IP: 34.8.51.57
  • Storage: 45 GB total (10 GB workspace + 5 GB config per pod)
  • Cost Savings: $291.60/year (75% reduction via hybrid storage)

Deployment State​

Status: ✅ PRODUCTION OPERATIONAL (Build #32)

Evidence:

  • Live at https://coditect.ai
  • 3/3 pods running (verified in README)
  • Recent deployment (Build #32 - 2025-10-29)
  • Comprehensive deployment documentation
  • Health check endpoints operational

Dependencies​

  • coditect-cloud-backend - User authentication
  • coditect-cloud-infra - GKE infrastructure
  • FoundationDB cluster
  • NGINX ingress

4. Supporting Infrastructure Submodules​

4.1 coditect-cloud-infra​

Purpose: Terraform/IaC for GCP infrastructure provisioning

Provides:

  • Google Kubernetes Engine (GKE) cluster
  • Cloud SQL PostgreSQL instances
  • FoundationDB StatefulSets
  • Ingress controllers and load balancers
  • Networking (VPC, subnets, firewall rules)
  • GCP Secret Manager integration

Status: ✅ CRITICAL INFRASTRUCTURE (supports all deployed products)

Deployment Reality:

  • Infrastructure-as-Code for all GCP resources
  • Terraform configurations for production environment
  • Kubernetes manifest templates

4.2 coditect-cloud-frontend​

Purpose: Admin dashboard for platform management

Technology: React, TypeScript, Chakra UI

Functionality:

  • User management interface
  • Organization administration
  • License tracking
  • Project management

Status: 🟡 DEVELOPMENT (separate from deployed products)

Note: This is the admin dashboard, NOT the user-facing IDE (which is in cloud-ide).

4.3 coditect-core (framework)​

Purpose: Distributed intelligence framework (the "brain")

Provides:

  • 52 specialized AI agents
  • 81 slash commands
  • 26 production skills (254+ reusable assets)
  • MEMORY-CONTEXT system
  • Project management templates

Status: ✅ FRAMEWORK DEPENDENCY (all products use via .coditect symlinks)

Deployment Reality:

  • NOT a deployed service
  • Distributed via symlinks to all submodules
  • Provides development and operational intelligence

4.4 coditect-ops-license​

Purpose: License validation server

Technology: License key generation, activation tracking, subscription management

Status: ⚠️ PRODUCTION CRITICAL (needed for commercial operations)

Deployment Reality:

  • Integrated with Django multi-tenant backend
  • License validation API
  • Must be deployed for commercial launch

5. Development Submodules (Not Deployed)​

5.1 Research/Labs Category​

Submodules:

  • coditect-labs-agent-standards
  • coditect-labs-agents-research
  • coditect-labs-claude-research
  • coditect-labs-cli-web-arch
  • coditect-labs-first-principles
  • coditect-labs-learning
  • coditect-labs-mcp-auth
  • coditect-labs-multi-agent-rag
  • coditect-labs-screenshot
  • coditect-labs-v4-archive
  • coditect-nested-learning
  • coditect-next-generation

Status: 🔬 RESEARCH/EXPERIMENTAL

Deployment Reality: None deployed. These are R&D, prototypes, and archived reference materials.

5.2 Development Tools Category​

Submodules:

  • coditect-analytics
  • coditect-automation
  • coditect-cli
  • coditect-dev-audio2text
  • coditect-dev-context
  • coditect-dev-intelligence
  • coditect-dev-pdf
  • coditect-dev-qrcode

Status: 🛠️ DEVELOPMENT TOOLS

Deployment Reality: Internal tools, not customer-facing products.

5.3 Go-To-Market Category​

Submodules:

  • coditect-gtm-comms
  • coditect-gtm-crm
  • coditect-gtm-customer-clipora
  • coditect-gtm-investor
  • coditect-gtm-legitimacy
  • coditect-gtm-personas
  • coditect-gtm-strategy

Status: 📢 MARKETING/SALES MATERIALS

Deployment Reality: Not deployed infrastructure. Content repositories.

5.4 Documentation Category​

Submodules:

  • coditect-docs-blog
  • coditect-docs-main
  • coditect-docs-setup
  • coditect-docs-training
  • coditect-legal

Status: 📚 DOCUMENTATION

Deployment Reality: Static content, may be deployed to documentation sites.

5.5 Market Intelligence Category​

Submodules:

  • coditect-market-activity
  • coditect-market-agents

Status: 📊 MARKET RESEARCH

Deployment Reality: Not deployed. Research and analysis repositories.

5.6 Operations Category​

Submodules:

  • coditect-ops-compliance
  • coditect-ops-distribution
  • coditect-ops-estimation-engine
  • coditect-ops-projects

Status: ⚙️ OPERATIONAL TOOLING

Deployment Reality:

  • ops-license - ✅ MUST DEPLOY (critical for commercial operations)
  • Others - Internal tools, may or may not be deployed

6. Core Architecture Submodules​

coditect-core-architecture​

Purpose: Architecture documentation and standards

Status: 📐 DOCUMENTATION

Deployment Reality: Not deployed. Reference documentation.

coditect-core-framework​

Purpose: Core framework implementation

Status: ⚙️ FRAMEWORK

Deployment Reality: Not deployed as standalone service. Integrated into products via symlinks.

7. Deployment Matrix Summary​

SubmoduleCategoryDeployed ProductStatusPriority
cloud-backendcloudDjango Multi-Tenant Platform✅ LIVEP0
cloud-idecloudCODITECT IDE (coditect.ai)✅ LIVEP0
labs-workflowlabsWorkflow Analysis Tool✅ LIVEP0
cloud-infracloudInfrastructure✅ SUPPORTINGP0
ops-licenseopsLicense Server⚠️ CRITICAL (not yet deployed)P0
cloud-frontendcloudAdmin Dashboard🟡 DEVP1
coditect-corecoreFramework✅ FRAMEWORKP0
core-architecturecoreDocumentation📐 REFERENCEP2
core-frameworkcoreFramework Implementation⚙️ FRAMEWORKP1
dev-*devDevelopment Tools🛠️ TOOLSP2-P3
gtm-*gtmMarketing Materials📢 CONTENTP2-P3
docs-*docsDocumentation📚 CONTENTP2-P3
labs-*labsResearch/Prototypes🔬 R&DP3
market-*marketMarket Research📊 RESEARCHP3
ops-*opsOperational Tools⚙️ TOOLSP2

8. Critical Findings​

Issue 1: Deployed Products Hidden in Generic Categories​

Problem: Production systems are buried inside "cloud" and "labs" folders, making deployment state non-obvious.

Examples:

  • labs/coditect-labs-workflow sounds experimental, but it's LIVE IN PRODUCTION
  • cloud/coditect-cloud-ide doesn't indicate it's the PRIMARY PRODUCT

Impact: New team members can't easily identify what's deployed vs. in development.

Issue 2: No Clear Separation of Deployed vs Development​

Problem: Mix of deployed products, development tools, research, and marketing in flat structure.

Current Structure:

submodules/
├── cloud/  (mix of deployed + development)
├── labs/   (mix of deployed + research)
├── dev/    (all development)
├── gtm/    (all marketing)
└── ...

Ideal Structure:

products/          # Deployed customer-facing products
platform/          # Core platform (authentication, licensing)
infrastructure/    # Deployment configs and IaC
services/          # Backend microservices
development/       # In-progress features
research/          # Experimental/POC

Issue 3: Infrastructure Components Not Clearly Identified​

Problem: Critical infrastructure like FoundationDB, PostgreSQL, NGINX not clearly mapped to products.

Missing Clarity:

  • Which products depend on FoundationDB?
  • Which use PostgreSQL vs. FoundationDB?
  • What's shared vs. product-specific infrastructure?

Issue 4: Django Multi-Tenant Buried Inside FastAPI Project​

Problem: Django implementation is inside cloud-backend which README claims is FastAPI!

Reality:

  • README says: "CODITECT Cloud Backend is the primary API server...built with FastAPI"
  • Actual code: Django 5.2 project with license_platform/ Django app

Impact: Documentation doesn't match reality, confusing for developers.

9. Deployment Dependencies Graph​

Products (Deployed):
├─ coditect.ai (IDE)
│  ├─ Depends on: cloud-infra (GKE)
│  ├─ Depends on: cloud-backend (auth)
│  └─ Depends on: FoundationDB (sessions)
│
├─ workflow.coditect.ai (Workflow Analysis)
│  ├─ Depends on: cloud-infra (GKE)
│  ├─ Depends on: PostgreSQL (storage)
│  ├─ Depends on: Redis (cache)
│  └─ Depends on: Anthropic API (AI)
│
└─ Django Multi-Tenant Platform
   ├─ Depends on: cloud-infra (GKE)
   ├─ Depends on: Cloud SQL PostgreSQL (database)
   ├─ Depends on: ops-license (license validation)
   └─ Depends on: FoundationDB (session storage)

Infrastructure (Supporting):
├─ GKE Cluster (cloud-infra)
├─ Cloud SQL PostgreSQL (cloud-infra)
├─ FoundationDB StatefulSet (cloud-infra)
├─ NGINX Ingress (cloud-infra)
└─ GCP Secret Manager (cloud-infra)

Framework (Distributed):
└─ coditect-core (via .coditect symlinks to all products)

10. Recommendations​

Immediate Actions (Don't Break Production)​

  1. Document current deployment state ✅ (THIS DOCUMENT)
  2. Create deployment dependency map ✅ (Section 9)
  3. Identify all infrastructure components ✅ (Section 4)
  4. Mark deployed submodules as PRODUCTION (next step)

Phase 1: Clarify Without Moving​

  1. Update README files to clearly state deployment status
  2. Add DEPLOYMENT-STATUS.md to each submodule
  3. Document infrastructure dependencies
  4. Fix documentation mismatches (FastAPI vs. Django)

Phase 2: Safe Reorganization​

  1. Create products/ category for deployed products
  2. Move deployment configs to infrastructure/
  3. Separate development from production code
  4. Archive experimental/unused submodules

Phase 3: Optimize Structure​

  1. Consolidate related microservices
  2. Eliminate redundant submodules
  3. Streamline development workflows
  4. Document new structure

11. Next Steps​

  1. ✅ Complete: Deployment state analysis (this document)
  2. ⏳ Next: Create submodule-to-product mapping matrix
  3. ⏳ Next: Design deployment-aware reorganization plan
  4. ⏳ Next: Propose migration strategy (safe, incremental changes)

Status: ANALYSIS COMPLETE Confidence: HIGH (verified via README files, configuration files, and deployment evidence) Risk Level: LOW (no changes proposed yet, purely documentation)

Generated: 2025-11-26 Author: Claude Code (Deployment-Focused Analysis)