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🌲 Silvanus Platform - High Level Design

Phase 1: Foundation & Architecture (Q1 2026)

Document Type High Level Design (HLD)
Project Phase Phase 1 - Foundation
Timeline January - March 2026
Author Archwood Group IT
Status Pending Approval
Classification Confidential

1. Executive Summary

Project Silvanus is Archwood Group's strategic initiative to build a modern, scalable internal platform that will serve as the digital backbone for all business operations. This High-Level Design document outlines the technical architecture, implementation approach, and deliverables for Phase 1 (Q1 2026), which focuses on establishing the foundation and core architecture.

Key Phase 1 Objectives:

  • Establish core platform architecture using monolith-first approach
  • Implement fundamental services (authentication, user management, data layer)
  • Deploy development and testing infrastructure on Azure
  • Create CI/CD pipelines and development standards
  • Build initial API framework with Azure Foundry AI integration

2. Project Overview & Objectives

2.1 Business Context

Archwood Group currently operates with multiple disconnected systems, manual processes, and limited automation. Silvanus will unify these operations into a single, intelligent platform that enables:

2.2 Phase 1 Scope

In Scope Out of Scope
• Core platform architecture
• Authentication & authorization
• User management system
• Basic API framework
• Development environment
• CI/CD pipeline
• Azure infrastructure setup
• Database design & implementation
• Legacy system migration
• Production deployment
• Advanced analytics
• Mobile applications
• Third-party integrations
• Machine learning models
• Full workflow automation
• Multi-tenant capabilities

3. Architecture Overview

High-Level System Architecture

Presentation Layer Web Portal | Mobile Apps | Admin Dashboard API Gateway Azure API Management | Authentication | Rate Limiting Application Services User Service Auth Service Workflow Service AI Agent (Silvanus) Data Layer PostgreSQL | Redis Cache | Azure Storage | Data Lake

3.1 Architectural Principles

Monolith-First

Start with a well-structured monolithic application to understand domain boundaries before eventual service extraction.

API-First Design

Every functionality exposed through RESTful APIs, enabling flexibility and future integrations.

Cloud-Native

Built specifically for Azure cloud, leveraging PaaS services and cloud-native patterns.

Security by Design

Security considerations embedded in every layer, from infrastructure to application code.

4. System Components

4.1 Core Components - Phase 1

Authentication Service

  • Azure Active Directory integration
  • JWT token management
  • Multi-factor authentication
  • Session management

User Management Service

  • User profiles and preferences
  • Role-based access control (RBAC)
  • Department/team hierarchy
  • Audit logging

Data Service Layer

  • ORM implementation (SQLAlchemy)
  • Connection pooling
  • Query optimization
  • Cache management

API Gateway

  • Request routing
  • Rate limiting
  • API versioning
  • Request/response transformation

Silvanus AI Agent

  • Azure Foundry AI integration
  • Natural language processing
  • Workflow automation
  • Intelligent routing

Monitoring & Logging

  • Application Insights
  • Log aggregation
  • Performance metrics
  • Alert management

5. Technical Architecture

5.1 Technology Stack

Python 3.11+ Django 5.0 Django REST Framework PostgreSQL 15 Redis 7.0 React 18 TypeScript Azure Cloud Docker Kubernetes (AKS) Terraform GitHub Actions Azure DevOps

5.2 Application Architecture

Django Monolith Structure (Initial)

silvanus/
├── config/
│   ├── settings/
│   │   ├── base.py
│   │   ├── development.py
│   │   ├── testing.py
│   │   └── production.py
│   ├── urls.py
│   └── wsgi.py
├── apps/
│   ├── authentication/
│   ├── users/
│   ├── workflows/
│   ├── core/
│   └── api/
├── services/
│   ├── ai_agent/
│   ├── cache/
│   └── notifications/
├── static/
├── templates/
├── tests/
└── requirements/
    ├── base.txt
    ├── development.txt
    └── production.txt
                

6. Data Architecture

6.1 Database Design

Core Data Model (Phase 1)

Users id (UUID) email full_name department_id created_at Roles id (UUID) name permissions (JSON) description Departments id (UUID) name parent_id manager_id Audit_Logs id (UUID) user_id action resource timestamp Workflows id (UUID) name definition (JSON) created_by status API_Keys id (UUID) key_hash user_id expires_at scopes

6.2 Data Storage Strategy

Data Type Storage Solution Purpose Retention
Transactional Data PostgreSQL (Azure Database) Core business data, user records Indefinite
Session Data Redis Cache User sessions, temporary data 24 hours
Documents/Files Azure Blob Storage File uploads, documents Policy-based
Logs Azure Log Analytics Application and audit logs 90 days
Backups Azure Backup Database and file backups 30 days

7. API Design Strategy

7.1 RESTful API Standards

API Naming Conventions

GET /api/v1/users/{user_id}
POST /api/v1/users
PUT /api/v1/users/{user_id}
DELETE /api/v1/users/{user_id}
GET /api/v1/departments/{dept_id}/users
POST /api/v1/workflows/{workflow_id}/execute

7.2 API Response Format

{
    "status": "success",
    "data": {
        "id": "550e8400-e29b-41d4-a716-446655440000",
        "email": "john.smith@archwoodgroup.com",
        "full_name": "John Smith",
        "department": {
            "id": "dept_001",
            "name": "Finance"
        },
        "created_at": "2026-01-15T09:30:00Z"
    },
    "meta": {
        "request_id": "req_abc123",
        "timestamp": "2026-01-15T09:30:00Z",
        "version": "1.0"
    }
}
            

7.3 API Authentication

8. Security Architecture

🔐 Authentication

  • Azure AD integration
  • Multi-factor authentication
  • JWT token rotation
  • Session timeout (30 min)

🛡️ Authorization

  • Role-based access control
  • Attribute-based policies
  • API scope management
  • Resource-level permissions

🔒 Data Protection

  • Encryption at rest (AES-256)
  • TLS 1.3 for transit
  • Key vault management
  • Data masking for PII

📝 Audit & Compliance

  • Comprehensive audit logging
  • GDPR compliance
  • Data retention policies
  • Regular security scans

8.1 Security Implementation Checklist

Security Control Implementation Phase 1 Status
Input Validation Django validators, sanitization Required
SQL Injection Prevention ORM usage, parameterized queries Required
XSS Protection Content Security Policy, output encoding Required
CSRF Protection Django CSRF middleware Required
Secret Management Azure Key Vault Required
Penetration Testing Third-party assessment End of Phase 1

9. Infrastructure & Deployment

9.1 Azure Infrastructure Components

Azure Cloud Architecture

Azure Subscription - Archwood Group Development RG AKS Dev Cluster PostgreSQL Dev Redis Cache Dev Storage Account Dev Testing RG AKS Test Cluster PostgreSQL Test Redis Cache Test App Insights Test Shared Services RG Azure DevOps Container Registry Key Vault Log Analytics

9.2 CI/CD Pipeline

GitHub Actions Workflow

name: Silvanus CI/CD Pipeline

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

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      - name: Install dependencies
        run: |
          pip install -r requirements/testing.txt
      - name: Run tests
        run: |
          pytest --cov=apps --cov-report=xml
      - name: SonarCloud Scan
        uses: SonarSource/sonarcloud-github-action@master
        
  build:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - name: Build Docker image
        run: |
          docker build -t silvanus:${{ github.sha }} .
      - name: Push to ACR
        run: |
          docker push archwood.azurecr.io/silvanus:${{ github.sha }}
          
  deploy:
    needs: build
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/develop'
    steps:
      - name: Deploy to AKS Dev
        run: |
          kubectl apply -f k8s/dev/
                

10. Phase 1 Timeline & Milestones

Q1 2026 - Foundation Phase Timeline

January 2026

  • Week 1-2: Team formation & onboarding
  • Week 2: Kickoff meeting & stakeholder alignment
  • Week 3: Requirements gathering workshops
  • Week 4: Architecture design finalization
  • Week 4: Azure environment setup

February 2026

  • Week 1: Database design & implementation
  • Week 2: Core authentication service
  • Week 3: User management service
  • Week 4: API framework development
  • Week 4: Initial CI/CD pipeline

March 2026

  • Week 1: Silvanus AI agent integration
  • Week 2: Admin dashboard (basic)
  • Week 3: Integration testing
  • Week 4: Security assessment
  • Week 4: Phase 1 review & documentation

11. Key Deliverables

Phase 1 Deliverables (Q1 2026)

📋
Technical Architecture Documentation

Complete system design, data models, API specifications, and security architecture

Due: January 31, 2026
☁️
Azure Infrastructure

Fully configured development and testing environments with AKS, databases, and supporting services

Due: February 7, 2026
🔐
Authentication & Authorization System

Working auth service with Azure AD integration, JWT tokens, and RBAC

Due: February 21, 2026
👥
User Management Module

Complete user CRUD operations, profile management, and department hierarchy

Due: February 28, 2026
🔌
RESTful API Framework

Standardized API endpoints, versioning, documentation (Swagger/OpenAPI)

Due: March 7, 2026
🤖
Silvanus AI Agent (Basic)

Initial Azure Foundry AI integration with basic query and automation capabilities

Due: March 14, 2026
🚀
CI/CD Pipeline

Automated testing, building, and deployment pipeline using GitHub Actions

Due: March 21, 2026
📊
Admin Dashboard

Basic administrative interface for user management and system monitoring

Due: March 28, 2026

12. Team Structure & Responsibilities

Role Name Responsibilities Allocation
Project Sponsor [TBD] Executive oversight, budget approval, strategic alignment 10%
Project Manager [TBD] Project coordination, timeline management, stakeholder communication 100%
Technical Architect [TBD] System design, technical decisions, architecture governance 100%
Lead Developer [TBD] Development leadership, code reviews, technical implementation 100%
Senior Developers (3) [TBD] Feature development, testing, documentation 100%
DevOps Engineer [TBD] Infrastructure, CI/CD, deployment automation 100%
AI/ML Engineer [TBD] Silvanus AI agent development, Azure Foundry AI integration 100%
Security Analyst [TBD] Security reviews, compliance, vulnerability assessments 50%

13. Risk Assessment

Technical Complexity

Risk: Underestimating the complexity of integrating multiple systems

Mitigation: Start with core features, incremental development, regular architecture reviews

Impact: High | Probability: Medium

Resource Availability

Risk: Difficulty in hiring skilled developers in the timeframe

Mitigation: Early recruitment, contractor engagement, knowledge transfer programs

Impact: Medium | Probability: Medium

Scope Creep

Risk: Additional requirements impacting timeline

Mitigation: Clear scope definition, change control process, stakeholder alignment

Impact: Medium | Probability: High

Technology Adoption

Risk: Team unfamiliar with chosen tech stack

Mitigation: Training programs, documentation, pair programming

Impact: Low | Probability: Medium

14. Success Criteria

Phase 1 Success Metrics

Metric Target Measurement Method
Code Coverage > 80% Automated testing reports
API Response Time < 200ms (95th percentile) Application Insights monitoring
Build Success Rate > 95% CI/CD pipeline metrics
Security Vulnerabilities 0 critical, < 5 medium Security scanning tools
Documentation Completeness 100% API documentation Documentation review
Infrastructure Availability > 99.5% Azure monitoring
Sprint Velocity 80% planned work completed Sprint retrospectives

15. Approval & Sign-off

Technical Review

CTO/Technical Lead

Name: _________________

Date: _________________

Signature: _____________

Business Approval

Project Sponsor

Name: _________________

Date: _________________

Signature: _____________

Security Review

CISO/Security Lead

Name: _________________

Date: _________________

Signature: _____________

Appendices

Appendix A: Technology Decision Matrix

Component Options Considered Selected Rationale
Backend Framework Django, FastAPI, Flask, .NET Core Django Mature, batteries-included, strong ORM, admin interface
Database PostgreSQL, MySQL, SQL Server, MongoDB PostgreSQL Open source, JSON support, strong performance, Azure native
Container Orchestration AKS, Container Instances, App Service AKS Scalability, flexibility, industry standard
AI Platform Azure OpenAI, Azure Foundry AI, Custom ML Azure Foundry AI Enterprise-ready, integrated with Azure, suited for internal agents
Frontend Framework React, Angular, Vue.js React Large ecosystem, component reusability, team expertise
CI/CD GitHub Actions, Azure DevOps, Jenkins GitHub Actions GitHub integration, ease of use, cost-effective

Appendix B: API Endpoint Catalog (Phase 1)

Authentication Endpoints

POST /api/v1/auth/login
POST /api/v1/auth/logout
POST /api/v1/auth/refresh
POST /api/v1/auth/password/reset

User Management Endpoints

GET /api/v1/users
POST /api/v1/users
GET /api/v1/users/{id}
PUT /api/v1/users/{id}
DELETE /api/v1/users/{id}

Department Endpoints

GET /api/v1/departments
GET /api/v1/departments/{id}/users

AI Agent Endpoints

POST /api/v1/silvanus/query
POST /api/v1/silvanus/execute
GET /api/v1/silvanus/status/{task_id}

Appendix C: Development Standards

Coding Standards

  • Python: PEP 8 compliance, Black formatter, pylint
  • JavaScript/TypeScript: ESLint, Prettier, strict mode
  • Git: Conventional commits, feature branches, PR reviews required
  • Documentation: Docstrings for all functions, README for each module

Code Review Checklist

  • ✓ All tests pass
  • ✓ Code coverage > 80%
  • ✓ No security vulnerabilities
  • ✓ Documentation updated
  • ✓ Performance impact assessed
  • ✓ Database migrations reviewed
  • ✓ API contracts maintained

Appendix D: Monitoring & Observability

Metric Category Tools Key Metrics Alert Thresholds
Application Performance Application Insights Response time, throughput, error rate Response > 500ms, Errors > 1%
Infrastructure Azure Monitor CPU, memory, disk, network CPU > 80%, Memory > 85%
Database Azure DB Monitoring Query performance, connections, locks Slow queries > 1s, Connections > 80%
Security Azure Sentinel Failed logins, suspicious activity Failed logins > 10/min
Business Metrics Custom Dashboards User activity, API usage, task completion Custom per metric

Appendix E: Disaster Recovery Plan

Recovery Objectives

System Component RTO (Recovery Time) RPO (Data Loss) Backup Strategy
Database 4 hours 1 hour Automated snapshots every hour
Application 2 hours N/A Container images in registry
File Storage 4 hours 24 hours Daily backups to geo-redundant storage
Configuration 1 hour N/A Version controlled in Git

Recovery Procedures

  1. Detection: Automated monitoring alerts trigger incident
  2. Assessment: On-call engineer evaluates severity
  3. Communication: Stakeholders notified per escalation matrix
  4. Recovery: Execute recovery runbook for affected component
  5. Validation: Verify system functionality and data integrity
  6. Post-mortem: Document incident and improvement actions

Appendix F: Glossary

Term Definition
AKS Azure Kubernetes Service - managed container orchestration platform
API Application Programming Interface - standardized way for applications to communicate
CI/CD Continuous Integration/Continuous Deployment - automated software delivery process
JWT JSON Web Token - secure method for transmitting information between parties
ORM Object-Relational Mapping - technique for converting data between systems
RBAC Role-Based Access Control - security paradigm for restricting system access
REST Representational State Transfer - architectural style for web services
RTO Recovery Time Objective - maximum acceptable downtime
RPO Recovery Point Objective - maximum acceptable data loss
Silvanus AI-powered intelligent agent for Archwood Group's platform