997 lines
34 KiB
Markdown
997 lines
34 KiB
Markdown
# Bakery-IA: Complete Technical Documentation Summary
|
||
|
||
**For VUE Madrid (Ventanilla Única Empresarial) Business Plan Submission**
|
||
|
||
---
|
||
|
||
## Executive Summary
|
||
|
||
Bakery-IA is an **AI-powered SaaS platform** designed specifically for the Spanish bakery market, combining advanced machine learning forecasting with comprehensive operational management. The platform reduces food waste by 20-40%, saves €500-2,000 monthly per bakery, and provides 70-85% demand forecast accuracy using Facebook's Prophet algorithm integrated with Spanish weather data, Madrid traffic patterns, and local holiday calendars.
|
||
|
||
## Platform Architecture Overview
|
||
|
||
### System Design
|
||
- **Architecture Pattern**: Microservices (21 independent services)
|
||
- **API Gateway**: Centralized routing with JWT authentication
|
||
- **Frontend**: React 18 + TypeScript progressive web application
|
||
- **Database Strategy**: PostgreSQL 17 per service (database-per-service pattern)
|
||
- **Caching Layer**: Redis 7.4 for performance optimization
|
||
- **Message Queue**: RabbitMQ 4.1 for event-driven architecture
|
||
- **Deployment**: Kubernetes on VPS infrastructure
|
||
|
||
### Technology Stack Summary
|
||
|
||
**Backend Technologies:**
|
||
- Python 3.11+ with FastAPI (async)
|
||
- SQLAlchemy 2.0 (async ORM)
|
||
- Prophet (Facebook's ML forecasting library)
|
||
- Pandas, NumPy for data processing
|
||
- Prometheus metrics, Structlog logging
|
||
|
||
**Frontend Technologies:**
|
||
- React 18.3, TypeScript 5.3, Vite 5.0
|
||
- Zustand state management
|
||
- TanStack Query for API calls
|
||
- Tailwind CSS, Radix UI components
|
||
- Server-Sent Events (SSE) + WebSocket for real-time
|
||
|
||
**Infrastructure:**
|
||
- Docker containers, Kubernetes orchestration
|
||
- PostgreSQL 17, Redis 7.4, RabbitMQ 4.1
|
||
- **SigNoz unified observability platform** - Traces, metrics, logs
|
||
- OpenTelemetry instrumentation across all services
|
||
- HTTPS with automatic certificate renewal
|
||
|
||
---
|
||
|
||
## Service Documentation Index
|
||
|
||
### 📚 Comprehensive READMEs Created (15/21)
|
||
|
||
**Fully Documented Services:**
|
||
1. API Gateway (700+ lines)
|
||
2. Frontend Dashboard (800+ lines)
|
||
3. Forecasting Service (1,095+ lines)
|
||
4. Training Service (850+ lines)
|
||
5. AI Insights Service (enhanced)
|
||
6. Sales Service (493+ lines)
|
||
7. Inventory Service (1,120+ lines)
|
||
8. Production Service (394+ lines)
|
||
9. Orders Service (833+ lines)
|
||
10. Procurement Service (1,343+ lines)
|
||
11. Distribution Service (961+ lines)
|
||
12. Alert Processor Service (1,800+ lines)
|
||
13. Orchestrator Service (enhanced)
|
||
14. Demo Session Service (708+ lines)
|
||
15. Alert System Architecture (2,800+ lines standalone doc)
|
||
|
||
### 🎯 **New: Alert System Architecture** ([docs/ALERT-SYSTEM-ARCHITECTURE.md](./ALERT-SYSTEM-ARCHITECTURE.md))
|
||
**2,800+ lines | Complete Alert System Documentation**
|
||
|
||
**Comprehensive Guide Covering:**
|
||
- **Alert System Philosophy**: Context over noise, smart prioritization, user agency
|
||
- **Three-Tier Enrichment Strategy**:
|
||
- Tier 1: ALERTS (Full enrichment, 500-800ms) - Actionable items requiring user intervention
|
||
- Tier 2: NOTIFICATIONS (Lightweight, 20-30ms, 80% faster) - Informational updates
|
||
- Tier 3: RECOMMENDATIONS (Moderate, 50-80ms) - Advisory suggestions
|
||
- **Multi-Factor Priority Scoring** (0-100):
|
||
- Business Impact (40%): Financial consequences, affected orders
|
||
- Urgency (30%): Time sensitivity, deadlines
|
||
- User Agency (20%): Can user take action?
|
||
- AI Confidence (10%): Prediction certainty
|
||
- **Alert Escalation System**: Time-based priority boosts (+10 at 48h, +20 at 72h, +30 near deadline)
|
||
- **Alert Chaining**: Causal relationships (stock shortage → production delay → order risk)
|
||
- **Deduplication**: Prevent alert spam by merging similar events
|
||
- **18 Custom React Hooks**: Domain-specific alert/notification/recommendation hooks
|
||
- **Redis Pub/Sub Architecture**: Channel-based event streaming with 70% traffic reduction
|
||
- **Smart Actions**: Phone calls, navigation, modals, API calls - all context-aware
|
||
- **Real-Time SSE Integration**: Multi-channel subscription with wildcard support
|
||
- **CronJob Architecture**: Delivery tracking, priority recalculation - why cronjobs vs events
|
||
- **Frontend Integration Patterns**: Complete migration guide with examples
|
||
|
||
**Business Value:**
|
||
- 80% faster notification processing (20-30ms vs 200-300ms)
|
||
- 70% less SSE traffic on domain pages
|
||
- 92% API call reduction (event-driven vs polling)
|
||
- Complete semantic separation of alerts/notifications/recommendations
|
||
|
||
**Technology:** Python, FastAPI, PostgreSQL, Redis, RabbitMQ, React, TypeScript, SSE
|
||
|
||
---
|
||
|
||
#### 1. **API Gateway** ([gateway/README.md](../gateway/README.md))
|
||
**700+ lines | Centralized Entry Point**
|
||
|
||
**Key Features:**
|
||
- Single API endpoint for 21 microservices
|
||
- JWT authentication with 15-minute token cache
|
||
- Rate limiting (300 req/min per client)
|
||
- Server-Sent Events (SSE) for real-time alerts
|
||
- WebSocket proxy for ML training updates
|
||
- Request ID tracing for distributed debugging
|
||
- 95%+ token cache hit rate
|
||
|
||
**Business Value:**
|
||
- Simplifies client integration
|
||
- Enterprise-grade security
|
||
- 60-70% backend load reduction through caching
|
||
- Scalable to thousands of concurrent users
|
||
|
||
**Technology:** FastAPI, Redis, HTTPx, Prometheus metrics
|
||
|
||
---
|
||
|
||
#### 2. **Frontend Dashboard** ([frontend/README.md](../frontend/README.md))
|
||
**800+ lines | Modern React Application**
|
||
|
||
**Key Features:**
|
||
- AI-powered demand forecasting visualization
|
||
- **Panel de Control (Dashboard Redesign - NEW)**:
|
||
- **GlanceableHealthHero**: Traffic light status system (🟢🟡🔴) - understand bakery state in 3 seconds
|
||
- **SetupWizardBlocker**: Full-page setup wizard (<50% blocks access) - progressive onboarding
|
||
- **CollapsibleSetupBanner**: Compact reminder (50-99% progress) - dismissible for 7 days
|
||
- **UnifiedActionQueueCard**: Time-based grouping (Urgent/Today/This Week) - 60% faster resolution
|
||
- **ExecutionProgressTracker**: Plan vs actual tracking - production, deliveries, approvals
|
||
- **IntelligentSystemSummaryCard**: AI insights dashboard - what AI did and why
|
||
- **StockReceiptModal Integration**: Delivery receipt workflow - HACCP compliance
|
||
- **Three-State Setup Flow**: Blocker (<50%) → Banner (50-99%) → Hidden (100%)
|
||
- **Design Principles**: Glanceable First, Mobile-First, Progressive Disclosure, Outcome-Focused
|
||
- **Enriched Alert System UI**:
|
||
- AI Impact Showcase - Celebrate AI wins with metrics
|
||
- 3-Tab Alert Hub - Organized navigation (All/For Me/Archived)
|
||
- Auto-Action Countdown - Real-time timer with cancel
|
||
- Priority Score Explainer - Educational transparency modal
|
||
- Trend Visualizations - Inline sparklines for pattern warnings
|
||
- Action Consequence Previews - See outcomes before acting
|
||
- Response Time Gamification - Track performance metrics
|
||
- Full i18n - English, Spanish, Basque translations
|
||
- Real-time operational dashboard with SSE alerts
|
||
- Inventory management with expiration tracking
|
||
- Production planning and batch tracking
|
||
- Multi-tenant administration
|
||
- ML model training with live WebSocket updates
|
||
- Mobile-first responsive design (44x44px min touch targets)
|
||
- WCAG 2.1 AA accessibility compliant
|
||
|
||
**Business Value:**
|
||
- 15-20 hours/week time savings on manual planning
|
||
- 60% faster alert resolution with smart actions
|
||
- 70% fewer false alarms through intelligent filtering
|
||
- 3-second dashboard comprehension (5 AM Test)
|
||
- One-handed mobile operation (thumb zone CTAs)
|
||
- No training required - intuitive JTBD-aligned interface
|
||
- Real-time updates keep users engaged
|
||
- Progressive onboarding reduces setup friction
|
||
|
||
**Technology:** React 18, TypeScript, Vite, Zustand, TanStack Query, Tailwind CSS, Chart.js
|
||
|
||
---
|
||
|
||
#### 2b. **Demo Onboarding System** ([frontend/src/features/demo-onboarding/README.md](../frontend/src/features/demo-onboarding/README.md))
|
||
**210+ lines | Interactive Demo Tour & Conversion**
|
||
|
||
**Key Features:**
|
||
- **Interactive guided tour** - 12-step desktop, 8-step mobile (Driver.js)
|
||
- **Demo banner** with live session countdown and time remaining
|
||
- **Exit modal** with benefits reminder and conversion messaging
|
||
- **State persistence** - Auto-resume tour with sessionStorage
|
||
- **Analytics tracking** - Google Analytics & Plausible integration
|
||
- **Full localization** - Spanish and English translations
|
||
- **Mobile-responsive** - Optimized for thumb zone navigation
|
||
|
||
**Tour Steps Coverage:**
|
||
- Welcome → Metrics Dashboard → Pending Approvals → System Actions
|
||
- Production Plan → Database Nav → Operations → Analytics → Multi-Bakery
|
||
- Demo Limitations → Final CTA
|
||
|
||
**Tracked Events:**
|
||
- `tour_started`, `tour_step_completed`, `tour_dismissed`
|
||
- `tour_completed`, `conversion_cta_clicked`
|
||
|
||
**Business Value:**
|
||
- Guided onboarding reduces setup friction
|
||
- Auto-resume increases completion rates
|
||
- Conversion CTAs throughout demo journey
|
||
- Session countdown creates urgency
|
||
- 3-second comprehension with progressive disclosure
|
||
|
||
**Technology:** Driver.js, React, TypeScript, SessionStorage
|
||
|
||
---
|
||
|
||
#### 3. **Forecasting Service** ([services/forecasting/README.md](../services/forecasting/README.md))
|
||
**850+ lines | AI Demand Prediction Core**
|
||
|
||
**Key Features:**
|
||
- **Prophet algorithm** - Facebook's time series forecasting
|
||
- Multi-day forecasts up to 30 days ahead
|
||
- **Spanish integration:** AEMET weather, Madrid traffic, Spanish holidays
|
||
- 20+ engineered features (temporal, weather, traffic, holidays)
|
||
- Confidence intervals (95%) for risk assessment
|
||
- Redis caching (24h TTL, 85-90% hit rate)
|
||
- Automatic low/high demand alerting
|
||
- Business rules engine for Spanish bakery patterns
|
||
|
||
**AI/ML Capabilities:**
|
||
```python
|
||
# Prophet Model Configuration
|
||
seasonality_mode='additive' # Optimized for bakery patterns
|
||
daily_seasonality=True # Breakfast/lunch peaks
|
||
weekly_seasonality=True # Weekend differences
|
||
yearly_seasonality=True # Holiday/seasonal effects
|
||
country_holidays='ES' # Spanish national holidays
|
||
```
|
||
|
||
**Performance Metrics:**
|
||
- **MAPE**: 15-25% (industry standard)
|
||
- **R² Score**: 0.70-0.85
|
||
- **Accuracy**: 70-85% typical
|
||
- **Response Time**: <10ms (cached), <2s (computed)
|
||
|
||
**Business Value:**
|
||
- **Waste Reduction**: 20-40% through accurate predictions
|
||
- **Cost Savings**: €500-2,000/month per bakery
|
||
- **Revenue Protection**: Never run out during high demand
|
||
- **Labor Optimization**: Plan staff based on forecasts
|
||
|
||
**Technology:** FastAPI, Prophet, PostgreSQL, Redis, RabbitMQ, NumPy/Pandas
|
||
|
||
---
|
||
|
||
#### 4. **Training Service** ([services/training/README.md](../services/training/README.md))
|
||
**850+ lines | ML Model Management**
|
||
|
||
**Key Features:**
|
||
- One-click model training for all products
|
||
- Background job queue with progress tracking
|
||
- **Real-time WebSocket updates** - Live training progress
|
||
- Automatic model versioning and artifact storage
|
||
- Performance metrics tracking (MAE, RMSE, R², MAPE)
|
||
- Feature engineering with 20+ features
|
||
- Historical data aggregation from sales
|
||
- External data integration (weather, traffic, holidays)
|
||
|
||
**ML Pipeline:**
|
||
```
|
||
Data Collection → Feature Engineering → Prophet Training
|
||
→ Model Validation → Artifact Storage → Registration
|
||
→ Deployment → Notification
|
||
```
|
||
|
||
**Training Capabilities:**
|
||
- Concurrent job control (3 parallel jobs)
|
||
- 30-minute timeout handling
|
||
- Joblib model serialization
|
||
- Model performance comparison
|
||
- Automatic best model selection
|
||
|
||
**Business Value:**
|
||
- **Continuous Improvement**: Models auto-improve with data
|
||
- **No ML Expertise**: One-click training
|
||
- **Self-Learning**: Weekly automatic retraining
|
||
- **Transparent Performance**: Clear accuracy metrics
|
||
|
||
**Technology:** FastAPI, Prophet, Joblib, WebSocket, PostgreSQL, RabbitMQ
|
||
|
||
---
|
||
|
||
#### 5. **AI Insights Service** ([services/ai_insights/README.md](../services/ai_insights/README.md))
|
||
**Enhanced | Intelligent Recommendations**
|
||
|
||
**Key Features:**
|
||
- Intelligent recommendations across inventory, production, procurement, sales
|
||
- Confidence scoring (0-100%) with multi-factor analysis
|
||
- Impact estimation (cost savings, revenue increase, waste reduction)
|
||
- Feedback loop for closed-loop learning
|
||
- Cross-service intelligence and correlation detection
|
||
- Priority-based categorization (critical, high, medium, low)
|
||
- Actionable insights with recommended actions
|
||
|
||
**Insight Categories:**
|
||
- **Inventory Optimization**: Reorder points, stock level adjustments
|
||
- **Production Planning**: Batch size, scheduling optimization
|
||
- **Procurement**: Supplier selection, order timing
|
||
- **Sales Opportunities**: Trending products, underperformers
|
||
- **Cost Reduction**: Waste reduction opportunities
|
||
- **Quality Improvements**: Pattern-based quality insights
|
||
|
||
**Business Value:**
|
||
- **Proactive Management**: Recommendations before problems occur
|
||
- **Cost Savings**: €300-1,000/month identified opportunities
|
||
- **Time Savings**: 5-10 hours/week on manual analysis
|
||
- **ROI Tracking**: Measurable impact of applied insights
|
||
|
||
**Technology:** FastAPI, PostgreSQL, Pandas, Scikit-learn, Redis
|
||
|
||
---
|
||
|
||
#### 6. **Sales Service** ([services/sales/README.md](../services/sales/README.md))
|
||
**800+ lines | Data Foundation**
|
||
|
||
**Key Features:**
|
||
- Historical sales recording and management
|
||
- Bulk CSV/Excel import (15,000+ records in minutes)
|
||
- Real-time sales tracking from multiple channels
|
||
- Comprehensive sales analytics and reporting
|
||
- Data validation and duplicate detection
|
||
- Revenue tracking (daily, weekly, monthly, yearly)
|
||
- Product performance analysis
|
||
- Trend analysis and comparative analytics
|
||
|
||
**Import Capabilities:**
|
||
- CSV and Excel (.xlsx) support
|
||
- Column mapping for flexible data import
|
||
- Batch processing (1000 rows per transaction)
|
||
- Error handling with detailed reports
|
||
- Progress tracking for large imports
|
||
|
||
**Analytics Features:**
|
||
- Revenue by period and product
|
||
- Best sellers and slow movers
|
||
- Period-over-period comparisons
|
||
- Customer insights (frequency, average transaction value)
|
||
- Export for accounting/tax compliance
|
||
|
||
**Business Value:**
|
||
- **Time Savings**: 5-8 hours/week on manual tracking
|
||
- **Accuracy**: 99%+ vs. manual entry
|
||
- **ML Foundation**: Clean data improves forecast accuracy 15-25%
|
||
- **Easy Migration**: Import historical data in minutes
|
||
|
||
**Technology:** FastAPI, PostgreSQL, Pandas, openpyxl, Redis, RabbitMQ
|
||
|
||
---
|
||
|
||
## Remaining Services (Brief Overview)
|
||
|
||
### Core Business Services
|
||
|
||
**7. Inventory Service** ([services/inventory/README.md](../services/inventory/README.md))
|
||
**1,120+ lines | Stock Management & Food Safety Compliance**
|
||
|
||
**Key Features:**
|
||
- Comprehensive ingredient management with FIFO consumption and batch tracking
|
||
- Automatic stock updates from delivery events with batch/expiry tracking
|
||
- HACCP-compliant food safety monitoring with temperature logging
|
||
- Expiration management with automated FIFO rotation and waste tracking
|
||
- Multi-location inventory tracking across storage locations
|
||
- Enterprise: Automatic inventory transfer processing for internal shipments
|
||
- **Stock Receipt System**:
|
||
- Lot-level tracking with expiration dates (food safety requirement)
|
||
- Purchase order integration with discrepancy tracking
|
||
- Draft/Confirmed receipt workflow with line item validation
|
||
- Alert integration and automatic resolution on confirmation
|
||
- Atomic transactions for stock updates and PO status changes
|
||
|
||
**Alert Types Published:**
|
||
- Low stock alerts (below reorder point)
|
||
- Expiring soon alerts (within threshold days)
|
||
- Food safety alerts (temperature violations)
|
||
|
||
**Business Value:**
|
||
- Waste Reduction: 20-40% through FIFO and expiry management
|
||
- Cost Savings: €200-600/month from reduced waste
|
||
- Time Savings: 8-12 hours/week on manual tracking
|
||
- Compliance: 100% HACCP compliance (avoid €5,000+ fines)
|
||
- Inventory Accuracy: 95%+ vs. 70-80% manual
|
||
|
||
**Technology:** FastAPI, PostgreSQL, Redis, RabbitMQ, SQLAlchemy
|
||
|
||
**8. Production Service** ([services/production/README.md](../services/production/README.md))
|
||
**394+ lines | Manufacturing Operations Core**
|
||
|
||
**Key Features:**
|
||
- Automated forecast-driven scheduling (7-day advance planning)
|
||
- Real-time batch tracking with FIFO stock deduction and yield monitoring
|
||
- Digital quality control with standardized templates and metrics
|
||
- Equipment management with preventive maintenance tracking
|
||
- Production analytics with OEE and cost analysis
|
||
- Multi-day scheduling with automatic equipment allocation
|
||
|
||
**Alert Types Published (8 types):**
|
||
- Production delays, equipment failures, capacity overload
|
||
- Quality issues, missing ingredients, maintenance due
|
||
- Batch start delays, production start notifications
|
||
|
||
**Business Value:**
|
||
- Time Savings: 10-15 hours/week on planning
|
||
- Waste Reduction: 15-25% through optimization
|
||
- Quality Improvement: 20-30% fewer defects
|
||
- Capacity Utilization: 85%+ vs 65-70% manual
|
||
|
||
**Technology:** FastAPI, PostgreSQL, Redis, RabbitMQ, SQLAlchemy
|
||
|
||
---
|
||
|
||
**9. Recipes Service**
|
||
- Recipe management with versioning
|
||
- Ingredient quantities and scaling
|
||
- Batch size calculation
|
||
- Cost estimation and margin analysis
|
||
- Production instructions
|
||
|
||
---
|
||
|
||
**10. Orders Service** ([services/orders/README.md](../services/orders/README.md))
|
||
**833+ lines | Customer Order Management**
|
||
|
||
**Key Features:**
|
||
- Multi-channel order management (in-store, phone, online, wholesale)
|
||
- Comprehensive customer database with RFM analysis
|
||
- B2B wholesale management with custom pricing
|
||
- Automated invoicing with payment tracking
|
||
- Order fulfillment integration with production and inventory
|
||
- Customer analytics and segmentation
|
||
|
||
**Alert Types Published (5 types):**
|
||
- POs pending approval, approval reminders
|
||
- Critical PO escalation, auto-approval summaries
|
||
- PO approval confirmations
|
||
|
||
**Business Value:**
|
||
- Revenue Growth: 10-20% through improved B2B
|
||
- Time Savings: 5-8 hours/week on management
|
||
- Order Accuracy: 99%+ vs. 85-90% manual
|
||
- Payment Collection: 30% faster with reminders
|
||
|
||
**Technology:** FastAPI, PostgreSQL, Redis, RabbitMQ, Pydantic
|
||
|
||
---
|
||
|
||
**11. Procurement Service** ([services/procurement/README.md](../services/procurement/README.md))
|
||
**1,343+ lines | Intelligent Purchasing Automation**
|
||
|
||
**Key Features:**
|
||
- Intelligent forecast-driven replenishment (7-30 day projections)
|
||
- Automated PO generation with smart supplier selection
|
||
- Dashboard-integrated approval workflow with email notifications
|
||
- Delivery tracking with automatic stock updates
|
||
- EOQ and reorder point calculation
|
||
- Enterprise: Internal transfers with cost-based pricing
|
||
|
||
**Alert Types Published (7 types):**
|
||
- Stock shortages, delivery overdue, supplier performance issues
|
||
- Price increases, partial deliveries, quality issues
|
||
- Low supplier ratings
|
||
|
||
**Business Value:**
|
||
- Stockout Prevention: 85-95% reduction
|
||
- Cost Savings: 5-15% through optimized ordering
|
||
- Time Savings: 8-12 hours/week
|
||
- Inventory Reduction: 20-30% lower levels
|
||
|
||
**Technology:** FastAPI, PostgreSQL, Redis, RabbitMQ, Pydantic
|
||
|
||
**12. Suppliers Service**
|
||
- Supplier database
|
||
- Performance tracking
|
||
- Quality reviews
|
||
- Price lists
|
||
|
||
### Integration Services
|
||
|
||
**13. POS Service**
|
||
- Square, Toast, Lightspeed integration
|
||
- Transaction sync
|
||
- Webhook handling
|
||
|
||
**14. External Service**
|
||
- AEMET weather API
|
||
- Madrid traffic data
|
||
- Spanish holiday calendar
|
||
|
||
**15. Notification Service**
|
||
- Email (SMTP)
|
||
- WhatsApp (Twilio)
|
||
- Multi-channel routing
|
||
|
||
**16. Alert Processor Service** ([services/alert_processor/README.md](../services/alert_processor/README.md))
|
||
**1,800+ lines | Unified Enriched Alert System**
|
||
|
||
**Waves 3-6 Complete + Escalation & Chaining - Production Ready**
|
||
|
||
**Key Features:**
|
||
- **Multi-Dimensional Priority Scoring** - 0-100 score with 4 weighted factors
|
||
- Business Impact (40%): Financial consequences, affected orders
|
||
- Urgency (30%): Time sensitivity, deadlines
|
||
- User Agency (20%): Can user take action?
|
||
- AI Confidence (10%): Prediction certainty
|
||
- **Smart Alert Classification** - 5 types for clear user intent
|
||
- ACTION_NEEDED, PREVENTED_ISSUE, TREND_WARNING, ESCALATION, INFORMATION
|
||
- **Alert Escalation System (NEW)**:
|
||
- Time-based priority boosts (+10 at 48h, +20 at 72h)
|
||
- Deadline proximity boosting (+15 at 24h, +30 at 6h)
|
||
- Hourly priority recalculation cronjob
|
||
- Escalation metadata and history tracking
|
||
- Redis cache invalidation for real-time updates
|
||
- **Alert Chaining (NEW)**:
|
||
- Causal chains (stock shortage → production delay → order risk)
|
||
- Related entity chains (same PO: approval → overdue → receipt incomplete)
|
||
- Temporal chains (same issue over time)
|
||
- Parent/child relationship detection
|
||
- Chain visualization in frontend
|
||
- **Deduplication (NEW)**:
|
||
- Prevent alert spam by merging similar events
|
||
- 24-hour deduplication window
|
||
- Occurrence counting and trend tracking
|
||
- Context merging for historical analysis
|
||
- **Email Digest Service** - Celebration-first daily/weekly summaries
|
||
- **Auto-Action Countdown** - Real-time timer for escalation alerts
|
||
- **Response Time Gamification** - Track performance by priority level
|
||
- **Full API Documentation** - Complete reference guide with examples
|
||
- **Database Migration** - Clean break from legacy `severity`/`actions` fields
|
||
- **Backfill Script** - Enriches existing alerts with missing data
|
||
- **Integration Tests** - Comprehensive test suite
|
||
|
||
**Business Value:**
|
||
- 90% faster issue detection (real-time vs. hours/days)
|
||
- 70% fewer false alarms through intelligent filtering
|
||
- 60% faster resolution with smart actions
|
||
- €500-2,000/month cost avoidance (prevented issues)
|
||
- 85%+ of alerts include AI reasoning
|
||
- 95% reduction in alert spam through deduplication
|
||
- Zero stale alerts (automatic escalation)
|
||
|
||
**Technology:** FastAPI, PostgreSQL, Redis, RabbitMQ, Server-Sent Events, Kubernetes CronJobs
|
||
|
||
### Platform Services
|
||
|
||
**17. Auth Service**
|
||
- JWT authentication
|
||
- User registration
|
||
- GDPR compliance
|
||
- Audit logging
|
||
|
||
**18. Tenant Service**
|
||
- Multi-tenant management
|
||
- Stripe subscriptions
|
||
- Team member management
|
||
|
||
**19. Orchestrator Service** ([services/orchestrator/README.md](../services/orchestrator/README.md))
|
||
**Enhanced | Workflow Automation & Delivery Tracking**
|
||
|
||
**Key Features:**
|
||
- Daily workflow automation
|
||
- Scheduled forecasting and production planning
|
||
- **Delivery Tracking Service (NEW)**:
|
||
- Proactive delivery monitoring with time-based alerts
|
||
- Hourly cronjob checks expected deliveries
|
||
- DELIVERY_ARRIVING_SOON (T-2 hours) - Prepare for receipt
|
||
- DELIVERY_OVERDUE (T+30 min) - Critical escalation
|
||
- STOCK_RECEIPT_INCOMPLETE (T+2 hours) - Reminder
|
||
- Procurement service integration
|
||
- Automatic alert resolution on stock receipt
|
||
- **Architecture Decision**: CronJob vs Event System comparison matrix
|
||
|
||
**Business Value:**
|
||
- 90% on-time delivery detection
|
||
- Proactive warnings prevent stockouts
|
||
- 60% faster supplier issue resolution
|
||
|
||
**Technology:** FastAPI, PostgreSQL, RabbitMQ, Kubernetes CronJobs
|
||
|
||
**20. Demo Session Service** ([services/demo_session/README.md](../services/demo_session/README.md))
|
||
**708+ lines | Demo Environment Management**
|
||
|
||
**Key Features:**
|
||
- Direct database loading approach (eliminates Kubernetes Jobs)
|
||
- XOR-based deterministic ID transformation for tenant isolation
|
||
- Temporal determinism with dynamic date adjustment
|
||
- Per-service cloning progress tracking with JSONB metadata
|
||
- Session lifecycle management (PENDING → READY → EXPIRED → DESTROYED)
|
||
- Professional (~40s) and Enterprise (~75s) demo profiles
|
||
- Frontend polling mechanism for status updates
|
||
- Session extension and retry capabilities
|
||
|
||
**Session Statuses:**
|
||
- PENDING: Data cloning in progress
|
||
- READY: All data loaded, ready to use
|
||
- PARTIAL: Some services failed, others succeeded
|
||
- FAILED: Cloning failed
|
||
- EXPIRED: Session TTL exceeded
|
||
- DESTROYED: Session terminated
|
||
|
||
**Business Value:**
|
||
- 60-70% performance improvement (5-15s vs 30-40s)
|
||
- 100% reduction in Kubernetes Jobs (30+ → 0)
|
||
- Deterministic data loading with zero ID collisions
|
||
- Complete session isolation for demo accounts
|
||
|
||
**Technology:** FastAPI, PostgreSQL, Redis, Async background tasks
|
||
|
||
---
|
||
|
||
**21. Distribution Service** ([services/distribution/README.md](../services/distribution/README.md))
|
||
**961+ lines | Enterprise Fleet Management & Route Optimization**
|
||
|
||
**Key Features:**
|
||
- VRP-based route optimization using Google OR-Tools
|
||
- Real-time shipment tracking with GPS and proof of delivery
|
||
- Delivery scheduling with recurring patterns
|
||
- Haversine distance calculation for accurate routing
|
||
- Parent-child tenant hierarchy integration
|
||
- Enterprise subscription gating with tier validation
|
||
|
||
**Event Types Published:**
|
||
- Distribution plan created
|
||
- Shipment status updated
|
||
- Delivery completed with proof
|
||
|
||
**Business Value:**
|
||
- Route Efficiency: 20-30% distance reduction
|
||
- Fuel Savings: €200-500/month per vehicle
|
||
- Delivery Success Rate: 95-98% on-time delivery
|
||
- Time Savings: 10-15 hours/week on route planning
|
||
- ROI: 250-400% within 12 months for 5+ locations
|
||
|
||
**Technology:** FastAPI, PostgreSQL, Google OR-Tools, RabbitMQ, NumPy
|
||
|
||
---
|
||
|
||
## Business Value Summary
|
||
|
||
### Quantifiable ROI Metrics
|
||
|
||
**Cost Savings:**
|
||
- €500-2,000/month per bakery (average: €1,100)
|
||
- 20-40% waste reduction
|
||
- 15-25% improved forecast accuracy = better inventory management
|
||
|
||
**Time Savings:**
|
||
- 15-20 hours/week on manual planning
|
||
- 5-8 hours/week on sales tracking
|
||
- 10-15 hours/week on manual forecasting
|
||
- **Total: 30-43 hours/week saved**
|
||
|
||
**Revenue Protection:**
|
||
- 85-95% stockout prevention
|
||
- Never miss high-demand days
|
||
- Optimize pricing based on demand
|
||
|
||
**Operational Efficiency:**
|
||
- 70-85% forecast accuracy
|
||
- Real-time alerts and notifications
|
||
- Automated daily workflows
|
||
|
||
### Target Market: Spanish Bakeries
|
||
|
||
**Market Size:**
|
||
- 10,000+ bakeries in Spain
|
||
- 2,000+ in Madrid metropolitan area
|
||
- €5 billion annual bakery market
|
||
|
||
**Spanish Market Integration:**
|
||
- AEMET weather API (official Spanish meteorological agency)
|
||
- Madrid traffic data (Open Data Madrid)
|
||
- Spanish holiday calendar (national + regional)
|
||
- Euro currency, Spanish date formats
|
||
- Spanish UI language (default)
|
||
|
||
---
|
||
|
||
## Technical Innovation Highlights
|
||
|
||
### AI/ML Capabilities
|
||
|
||
**1. Prophet Forecasting Algorithm**
|
||
- Industry-leading time series forecasting
|
||
- Automatic seasonality detection
|
||
- Confidence interval calculation
|
||
- Handles missing data and outliers
|
||
|
||
**2. Feature Engineering**
|
||
- 20+ engineered features
|
||
- Weather impact analysis
|
||
- Traffic correlation
|
||
- Holiday effects
|
||
- Business rule adjustments
|
||
|
||
**3. Continuous Learning**
|
||
- Weekly automatic model retraining
|
||
- Performance tracking and comparison
|
||
- Feedback loop for improvement
|
||
- Model versioning and rollback
|
||
|
||
### Real-Time Architecture
|
||
|
||
**1. Server-Sent Events (SSE)**
|
||
- Real-time alert streaming to dashboard
|
||
- Tenant-isolated channels
|
||
- Auto-reconnection support
|
||
- Scales across gateway instances
|
||
|
||
**2. WebSocket Communication**
|
||
- Live ML training progress
|
||
- Bidirectional updates
|
||
- Connection management
|
||
- JWT authentication
|
||
|
||
**3. Event-Driven Design**
|
||
- RabbitMQ message queue
|
||
- Publish-subscribe pattern
|
||
- Service decoupling
|
||
- Asynchronous processing
|
||
|
||
**4. Distributed Tracing (OpenTelemetry)**
|
||
- End-to-end request tracking across all 18 microservices
|
||
- Automatic instrumentation for FastAPI, HTTPX, SQLAlchemy, Redis
|
||
- Performance bottleneck identification
|
||
- Database query performance analysis
|
||
- External API call monitoring
|
||
- Error tracking with full context
|
||
|
||
### Scalability & Performance
|
||
|
||
**1. Microservices Architecture**
|
||
- 18 independent services
|
||
- Database per service
|
||
- Horizontal scaling
|
||
- Fault isolation
|
||
|
||
**2. Caching Strategy**
|
||
- Redis for token validation (95%+ hit rate)
|
||
- Prediction cache (85-90% hit rate)
|
||
- Analytics cache (60 min TTL)
|
||
- 60-70% backend load reduction
|
||
|
||
**3. Performance Metrics**
|
||
- <10ms API response (cached)
|
||
- <2s forecast generation
|
||
- 1,000+ req/sec per gateway instance
|
||
- 10,000+ concurrent connections
|
||
|
||
**4. Observability & Monitoring**
|
||
- **SigNoz Platform**: Unified traces, metrics, and logs
|
||
- **Auto-Instrumentation**: Zero-code instrumentation via OpenTelemetry
|
||
- **Application Monitoring**: All 18 services reporting metrics
|
||
- **Infrastructure Monitoring**: 18 PostgreSQL databases, Redis, RabbitMQ
|
||
- **Kubernetes Monitoring**: Node, pod, container metrics
|
||
- **Log Aggregation**: Centralized logs with trace correlation
|
||
- **Real-Time Alerting**: Email and Slack notifications
|
||
- **Query Performance**: ClickHouse backend for fast analytics
|
||
|
||
---
|
||
|
||
## Security & Compliance
|
||
|
||
### Security Measures
|
||
|
||
**Authentication & Authorization:**
|
||
- JWT token-based authentication
|
||
- Refresh token rotation
|
||
- Role-based access control (RBAC)
|
||
- Multi-factor authentication (planned)
|
||
|
||
**Data Protection:**
|
||
- Tenant isolation at all levels
|
||
- HTTPS-only (production)
|
||
- SQL injection prevention
|
||
- XSS protection
|
||
- Input validation (Pydantic schemas)
|
||
|
||
**Infrastructure Security:**
|
||
- Rate limiting (300 req/min)
|
||
- CORS restrictions
|
||
- API request signing
|
||
- Audit logging
|
||
|
||
### GDPR Compliance
|
||
|
||
**Data Subject Rights:**
|
||
- Right to access (data export)
|
||
- Right to erasure (account deletion)
|
||
- Right to rectification (data updates)
|
||
- Right to data portability (CSV/JSON export)
|
||
|
||
**Compliance Features:**
|
||
- User consent management
|
||
- Consent history tracking
|
||
- Anonymization capabilities
|
||
- Data retention policies
|
||
- Privacy by design
|
||
|
||
---
|
||
|
||
## Deployment & Infrastructure
|
||
|
||
### Development Environment
|
||
- Docker Compose
|
||
- Local services
|
||
- Hot reload
|
||
- Development databases
|
||
|
||
### Production Environment
|
||
- **Cloud Provider**: clouding.io VPS
|
||
- **Orchestration**: Kubernetes
|
||
- **Ingress**: NGINX Ingress Controller
|
||
- **Certificates**: Let's Encrypt (auto-renewal)
|
||
- **Observability**: SigNoz (unified traces, metrics, logs)
|
||
- **Distributed Tracing**: OpenTelemetry auto-instrumentation (FastAPI, HTTPX, SQLAlchemy, Redis)
|
||
- **Application Metrics**: RED metrics (Rate, Error, Duration) from all 18 services
|
||
- **Infrastructure Metrics**: PostgreSQL (18 databases), Redis, RabbitMQ, Kubernetes cluster
|
||
- **Log Management**: Centralized logs with trace correlation and Kubernetes metadata
|
||
- **Alerting**: Multi-channel notifications (email, Slack) via AlertManager
|
||
- **Telemetry Backend**: ClickHouse for high-performance time-series storage
|
||
|
||
### CI/CD Pipeline
|
||
1. Code push to GitHub
|
||
2. Automated tests (pytest)
|
||
3. Docker image build
|
||
4. Push to container registry
|
||
5. Kubernetes deployment
|
||
6. Health check validation
|
||
7. Rollback on failure
|
||
|
||
### Scalability Strategy
|
||
- **Horizontal Pod Autoscaling (HPA)**
|
||
- CPU-based scaling triggers
|
||
- Min 2 replicas, max 10 per service
|
||
- Load balancing across pods
|
||
- Database connection pooling
|
||
|
||
---
|
||
|
||
## Competitive Advantages
|
||
|
||
### 1. Spanish Market Focus
|
||
- AEMET weather integration (official data)
|
||
- Madrid traffic patterns
|
||
- Spanish holiday calendar (national + regional)
|
||
- Euro currency, Spanish formats
|
||
- Spanish UI language
|
||
|
||
### 2. AI-First Approach
|
||
- Automated forecasting (no manual input)
|
||
- Self-learning system
|
||
- Predictive vs. reactive
|
||
- 70-85% accuracy
|
||
|
||
### 3. Complete ERP Solution
|
||
- Not just forecasting
|
||
- Sales → Inventory → Production → Procurement
|
||
- All-in-one platform
|
||
- Single vendor
|
||
|
||
### 4. Multi-Tenant SaaS
|
||
- Scalable architecture
|
||
- Subscription revenue model
|
||
- Stripe integration
|
||
- Automated billing
|
||
|
||
### 5. Real-Time Operations & Observability
|
||
- SSE for instant alerts
|
||
- WebSocket for live updates
|
||
- Sub-second dashboard refresh
|
||
- Always up-to-date data
|
||
- **Full-stack observability** with SigNoz
|
||
- Distributed tracing for performance debugging
|
||
- Real-time metrics from all layers (app, DB, cache, queue, cluster)
|
||
|
||
### 6. Developer-Friendly
|
||
- RESTful APIs
|
||
- OpenAPI documentation
|
||
- Webhook support
|
||
- Easy third-party integration
|
||
|
||
---
|
||
|
||
## Market Differentiation
|
||
|
||
### vs. Traditional Bakery Software
|
||
- ❌ Traditional: Manual forecasting, static reports
|
||
- ✅ Bakery-IA: AI-powered predictions, real-time analytics
|
||
|
||
### vs. Generic ERP Systems
|
||
- ❌ Generic: Not bakery-specific, complex, expensive
|
||
- ✅ Bakery-IA: Bakery-optimized, intuitive, affordable
|
||
|
||
### vs. Spreadsheets
|
||
- ❌ Spreadsheets: Manual, error-prone, no forecasting
|
||
- ✅ Bakery-IA: Automated, accurate, AI-driven
|
||
|
||
---
|
||
|
||
## Financial Projections
|
||
|
||
### Pricing Strategy
|
||
|
||
**Subscription Tiers:**
|
||
- **Free**: 1 location, basic features, community support
|
||
- **Pro**: €49/month - 3 locations, full features, email support
|
||
- **Enterprise**: €149/month - Unlimited locations, priority support, custom integration
|
||
|
||
**Target Customer Acquisition:**
|
||
- Year 1: 100 paying customers
|
||
- Year 2: 500 paying customers
|
||
- Year 3: 2,000 paying customers
|
||
|
||
**Revenue Projections:**
|
||
- Year 1: €60,000 (100 customers × €50 avg)
|
||
- Year 2: €360,000 (500 customers × €60 avg)
|
||
- Year 3: €1,800,000 (2,000 customers × €75 avg)
|
||
|
||
### Customer ROI
|
||
|
||
**Investment:** €49-149/month
|
||
**Savings:** €500-2,000/month
|
||
**ROI:** 300-1,300%
|
||
**Payback Period:** <1 month
|
||
|
||
---
|
||
|
||
## Roadmap & Future Enhancements
|
||
|
||
### Q1 2026
|
||
- Mobile apps (iOS/Android)
|
||
- Advanced analytics dashboard
|
||
- Multi-currency support
|
||
- Voice commands integration
|
||
|
||
### Q2 2026
|
||
- Deep learning models (LSTM)
|
||
- Customer segmentation
|
||
- Promotion impact modeling
|
||
- Blockchain audit trail
|
||
|
||
### Q3 2026
|
||
- Multi-language support (English, French, Portuguese)
|
||
- European market expansion
|
||
- Bank API integration
|
||
- Advanced supplier marketplace
|
||
|
||
### Q4 2026
|
||
- Franchise management features
|
||
- B2B ordering portal
|
||
- IoT sensor integration
|
||
- Predictive maintenance
|
||
|
||
---
|
||
|
||
## Technical Contact & Support
|
||
|
||
**Development Team:**
|
||
- Lead Architect: System design and AI/ML
|
||
- Backend Engineers: Microservices development
|
||
- Frontend Engineers: React dashboard
|
||
- DevOps Engineers: Kubernetes infrastructure
|
||
|
||
**Documentation:**
|
||
- Technical docs: See individual service READMEs
|
||
- API docs: Swagger UI at `/docs` endpoints
|
||
- User guides: In-app help system
|
||
|
||
**Support Channels:**
|
||
- Email: support@bakery-ia.com
|
||
- Documentation: https://docs.bakery-ia.com
|
||
- Status page: https://status.bakery-ia.com
|
||
|
||
---
|
||
|
||
## Conclusion for VUE Madrid Submission
|
||
|
||
Bakery-IA represents a **complete, production-ready AI-powered SaaS platform** specifically designed for the Spanish bakery market. The platform demonstrates:
|
||
|
||
✅ **Technical Innovation**: Prophet ML algorithm, real-time architecture, microservices
|
||
✅ **Market Focus**: Spanish weather, traffic, holidays, currency, language
|
||
✅ **Proven ROI**: €500-2,000/month savings, 30-43 hours/week time savings
|
||
✅ **Scalability**: Multi-tenant SaaS architecture for 10,000+ bakeries
|
||
✅ **Sustainability**: 20-40% waste reduction supports SDG goals
|
||
✅ **Compliance**: GDPR-ready, audit trails, data protection
|
||
|
||
**Investment Ask**: €150,000 for:
|
||
- Marketing and customer acquisition
|
||
- Sales team expansion
|
||
- Enhanced AI/ML features
|
||
- European market expansion
|
||
|
||
**Expected Outcome**: 2,000 customers by Year 3, €1.8M annual revenue, profitable operations
|
||
|
||
---
|
||
|
||
**Document Version**: 3.0
|
||
**Last Updated**: December 19, 2025
|
||
**Prepared For**: VUE Madrid (Ventanilla Única Empresarial)
|
||
**Company**: Bakery-IA
|
||
|
||
**Copyright © 2025 Bakery-IA. All rights reserved.**
|