📑 Table of Contents
- Executive Summary
- Project Overview & Objectives
- Architecture Overview
- System Components
- Technical Architecture
- Data Architecture
- API Design Strategy
- Security Architecture
- Infrastructure & Deployment
- Phase 1 Timeline & Milestones
- Key Deliverables
- Team Structure & Responsibilities
- Risk Assessment
- Success Criteria
- Approval & Sign-off
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:
- Automated workflow processing and approvals
- Real-time data insights and analytics
- Self-service capabilities for employees
- Seamless integration between business units
- AI-powered decision support via Azure Foundry AI
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
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
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)
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
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
- JWT Tokens: Primary authentication mechanism
- API Keys: For service-to-service communication
- OAuth 2.0: For third-party integrations (Phase 2)
- Rate Limiting: 1000 requests per hour per user
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
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)
Complete system design, data models, API specifications, and security architecture
Fully configured development and testing environments with AKS, databases, and supporting services
Working auth service with Azure AD integration, JWT tokens, and RBAC
Complete user CRUD operations, profile management, and department hierarchy
Standardized API endpoints, versioning, documentation (Swagger/OpenAPI)
Initial Azure Foundry AI integration with basic query and automation capabilities
Automated testing, building, and deployment pipeline using GitHub Actions
Basic administrative interface for user management and system monitoring
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
User Management Endpoints
Department Endpoints
AI Agent Endpoints
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
- Detection: Automated monitoring alerts trigger incident
- Assessment: On-call engineer evaluates severity
- Communication: Stakeholders notified per escalation matrix
- Recovery: Execute recovery runbook for affected component
- Validation: Verify system functionality and data integrity
- 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 |