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Created a detailed design specification for the post-onboarding setup wizard that guides users through adding suppliers, inventory, recipes, quality checks, and team members. Key features of the specification: **Wizard Structure (7 Steps)**: - Step 5: Welcome & Setup Overview - Step 6: Add Suppliers (≥1 required) - Step 7: Set Up Inventory Items (≥3 required) - Step 8: Create Recipes (≥1 required) - Step 9: Define Quality Standards (≥2 required) - Step 10: Add Team Members (optional) - Step 11: Review & Launch **Design Principles**: - Guide, don't block (flexible but directed) - Explain, don't assume (plain language, contextual help) - Validate early, fail friendly (real-time validation) - Progress over perfection (good enough to move forward) - Show value early (unlock features as you progress) **Smart Features**: 1. Intelligent templates (starter inventory, recipe templates, quality checks) 2. Auto-suggestions & smart defaults (ML-powered category detection) 3. Bulk import & export (CSV/Excel support) 4. Contextual help system (tooltips, video tutorials, inline examples) 5. Progress celebrations & motivation (milestone animations) 6. Intelligent validation warnings (non-blocking soft warnings) **Technical Implementation**: - Component architecture and file structure - Reusing OnboardingWizard and AddModal patterns - Backend API requirements (bulk endpoints, templates, smart suggestions) - State management approach - Performance considerations (lazy loading, caching, optimistic updates) - Accessibility and internationalization support **Progress Tracking**: - Weighted progress calculation (by step complexity) - Save & resume functionality - Mobile and desktop navigation patterns - Auto-save behavior **Validation & Error Handling**: - Field-level, cross-field, and step-level validation - Helpful error messages (not technical jargon) - Dependency enforcement (suppliers → inventory → recipes) - Error recovery strategies **Success Metrics**: - Leading: Completion rate (≥80%), time to complete (15-25 min), data quality (≥90%) - Lagging: Feature adoption (≥70%), NPS (≥40), time to first value (≤3 days) - Business impact: Waste reduction (15-20%), cost visibility (100%), quality compliance (≥80%) **Implementation Roadmap**: - Phase 1: Foundation (Week 1-2) - Phase 2: Core Steps (Week 3-5) - Phase 3: Advanced Features (Week 6-7) - Phase 4: Polish & Smart Features (Week 8) - Phase 5: Testing & Iteration (Week 9-10) - Phase 6: Launch & Monitor (Week 11+) Estimated completion time: 15-20 minutes for users Target completion rate: ≥80% Based on JTBD analysis in docs/jtbd-analysis-inventory-setup.md
🍞 Bakery IA - Multi-Service Architecture
Welcome to Bakery IA, an advanced AI-powered platform for bakery management and optimization. This project implements a microservices architecture with multiple interconnected services to provide comprehensive bakery management solutions.
🚀 Quick Start
Prerequisites
- Docker Desktop with Kubernetes enabled
- Docker Compose
- Node.js (for frontend development)
Running the Application
-
Clone the repository:
git clone <repository-url> cd bakery-ia -
Set up environment variables:
cp .env.example .env # Edit .env with your specific configuration -
Run with Docker Compose:
docker-compose up --build -
Or run with Kubernetes (Docker Desktop):
# Enable Kubernetes in Docker Desktop # Run the setup script ./scripts/setup-kubernetes-dev.sh
🏗️ Architecture Overview
The project follows a microservices architecture with the following main components:
- Frontend: React-based dashboard for user interaction
- Gateway: API gateway handling authentication and routing
- Services: Multiple microservices handling different business domains
- Infrastructure: Redis, RabbitMQ, PostgreSQL databases
🐳 Kubernetes Infrastructure
🛠️ Services
The project includes multiple services:
- Auth Service: Authentication and authorization
- Tenant Service: Multi-tenancy management
- Sales Service: Sales processing
- External Service: Integration with external systems
- Training Service: AI model training
- Forecasting Service: Demand forecasting
- Notification Service: Notifications and alerts
- Inventory Service: Inventory management
- Recipes Service: Recipe management
- Suppliers Service: Supplier management
- POS Service: Point of sale
- Orders Service: Order management
- Production Service: Production planning
- Alert Processor: Background alert processing
📊 Monitoring
The system includes comprehensive monitoring with:
- Prometheus for metrics collection
- Grafana for visualization
- ELK stack for logging (planned)
🚀 Production Deployment
For production deployment on clouding.io with Kubernetes:
- Set up your clouding.io Kubernetes cluster
- Update image references to your container registry
- Configure production-specific values
- Deploy using the production kustomization:
kubectl apply -k infrastructure/kubernetes/environments/production/
🤝 Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Submit a pull request
📄 License
This project is licensed under the MIT License.