Improve AI logic
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Urtzi Alfaro
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docs/02-architecture/system-overview.md
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docs/02-architecture/system-overview.md
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# Bakery IA - AI Insights Platform
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## Project Overview
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The Bakery IA AI Insights Platform is a comprehensive, production-ready machine learning system that centralizes AI-generated insights across all bakery operations. The platform enables intelligent decision-making through real-time ML predictions, automated orchestration, and continuous learning from feedback.
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### System Status: ✅ PRODUCTION READY
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**Last Updated:** November 2025
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**Version:** 1.0.0
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**Deployment Status:** Fully deployed and tested in Kubernetes
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---
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## Executive Summary
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### What Was Built
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A complete AI Insights Platform with:
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1. **Centralized AI Insights Service** - Single source of truth for all ML-generated insights
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2. **7 ML Components** - Specialized models across forecasting, inventory, production, procurement, and training
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3. **Dynamic Rules Engine** - Adaptive business rules that evolve with patterns
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4. **Feedback Learning System** - Continuous improvement from real-world outcomes
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5. **AI-Enhanced Orchestrator** - Intelligent workflow coordination
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6. **Multi-Tenant Architecture** - Complete isolation for security and scalability
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### Business Value
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- **Improved Decision Making:** Centralized, prioritized insights with confidence scores
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- **Reduced Waste:** AI-optimized inventory and safety stock levels
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- **Increased Revenue:** Demand forecasting with 30%+ prediction accuracy improvements
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- **Operational Efficiency:** Automated insight generation and application
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- **Cost Optimization:** Price forecasting and supplier performance prediction
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- **Continuous Improvement:** Learning system that gets better over time
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### Technical Highlights
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- **Microservices Architecture:** 15+ services in Kubernetes
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- **ML Stack:** Prophet, XGBoost, ARIMA, statistical models
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- **Real-time Processing:** Async API with feedback loops
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- **Database:** PostgreSQL with tenant isolation
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- **Caching:** Redis for performance
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- **Observability:** Structured logging, distributed tracing
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- **API-First Design:** RESTful APIs with OpenAPI documentation
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---
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## System Architecture
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### High-Level Architecture
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Frontend Application │
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│ (React + TypeScript + Material-UI) │
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└──────────────────────┬──────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ API Gateway │
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│ (NGINX Ingress) │
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└──────────────────────┬──────────────────────────────────────┘
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│
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┌──────────────┼──────────────┬─────────────┐
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↓ ↓ ↓ ↓
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┌──────────────┐ ┌──────────────┐ ┌────────┐ ┌─────────────┐
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│ AI Insights │ │ Orchestration│ │Training│ │ Forecasting │
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│ Service │ │ Service │ │Service │ │ Service │
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└──────┬───────┘ └──────┬───────┘ └───┬────┘ └──────┬──────┘
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│ │ │ │
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└────────────────┴──────────────┴─────────────┘
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│
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┌───────────────┼───────────────────────────┐
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↓ ↓ ↓ ↓
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┌──────────────┐ ┌──────────────┐ ┌─────────┐ ┌──────────┐
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│ Inventory │ │ Production │ │ Orders │ │ Suppliers│
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│ Service │ │ Service │ │ Service │ │ Service │
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└──────────────┘ └──────────────┘ └─────────┘ └──────────┘
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│ │ │ │
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└───────────────┴───────────────┴───────────┘
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│
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↓
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┌───────────────────────────────────┐
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│ PostgreSQL Databases │
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│ (Per-service + AI Insights DB) │
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└───────────────────────────────────┘
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```
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### Core Services
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#### AI Insights Service
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**Purpose:** Central repository and management system for all AI-generated insights
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**Key Features:**
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- CRUD operations for insights with tenant isolation
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- Priority-based filtering (critical, high, medium, low)
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- Confidence score tracking
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- Status lifecycle management (new → acknowledged → in_progress → applied → dismissed)
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- Feedback recording and analysis
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- Aggregate metrics and reporting
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- Orchestration-ready endpoints
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**Database Schema:**
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- `ai_insights` table with JSONB metrics
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- `insight_feedback` table for learning
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- Composite indexes for tenant_id + filters
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- Soft delete support
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#### ML Components
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1. **HybridProphetXGBoost (Training Service)**
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- Combined Prophet + XGBoost forecasting
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- Handles seasonality and trends
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- Cross-validation and model selection
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- Generates demand predictions
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2. **SupplierPerformancePredictor (Procurement Service)**
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- Predicts supplier reliability and quality
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- Based on historical delivery data
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- Helps optimize supplier selection
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3. **PriceForecaster (Procurement Service)**
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- Ingredient price prediction
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- Seasonal trend analysis
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- Cost optimization insights
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4. **SafetyStockOptimizer (Inventory Service)**
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- ML-driven safety stock calculations
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- Demand variability analysis
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- Reduces stockouts and excess inventory
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5. **YieldPredictor (Production Service)**
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- Production yield forecasting
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- Worker efficiency patterns
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- Recipe optimization recommendations
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6. **AIEnhancedOrchestrator (Orchestration Service)**
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- Gathers insights from all services
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- Priority-based scheduling
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- Conflict resolution
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- Automated execution coordination
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7. **FeedbackLearningSystem (AI Insights Service)**
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- Analyzes actual vs. predicted outcomes
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- Triggers model retraining
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- Performance degradation detection
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- Continuous improvement loop
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#### Dynamic Rules Engine (Forecasting Service)
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Adaptive business rules that evolve with data patterns:
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**Core Capabilities:**
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- **Pattern Detection:** Identifies trends, anomalies, seasonality, volatility
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- **Rule Adaptation:** Adjusts thresholds based on historical performance
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- **Multi-Source Integration:** Combines weather, events, and historical data
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- **Confidence Scoring:** 0-100 scale based on pattern strength
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**Rule Types:**
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- High Demand Alert (>threshold)
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- Low Demand Alert (<threshold)
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- Volatility Warning (high variance)
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- Trend Analysis (upward/downward)
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- Seasonal Pattern Detection
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- Anomaly Detection
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---
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## Key Features
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### 1. Centralized Insight Management
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All ML-generated insights flow through a single service:
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- **Unified API:** Consistent interface across all services
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- **Priority Queuing:** Critical insights surface first
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- **Tenant Isolation:** Complete data separation
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- **Audit Trail:** Full history of decisions and outcomes
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### 2. Intelligent Orchestration
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The AI-Enhanced Orchestrator coordinates complex workflows:
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- Fetches insights from multiple categories
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- Applies confidence thresholds
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- Resolves conflicts between recommendations
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- Executes actions across services
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- Records feedback automatically
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### 3. Continuous Learning
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Feedback loop enables system-wide improvement:
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- Records actual outcomes vs. predictions
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- Calculates accuracy metrics
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- Triggers retraining when performance degrades
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- Adapts rules based on patterns
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### 4. Multi-Tenant Architecture
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Complete isolation and security:
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- Tenant ID in every database table
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- Row-level security policies
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- Isolated data access
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- Per-tenant metrics and insights
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### 5. API-First Design
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RESTful APIs with comprehensive features:
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- OpenAPI/Swagger documentation
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- Filtering and pagination
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- Batch operations
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- Async processing support
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- Structured error responses
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---
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## Technology Stack
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### Backend Services
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- **Language:** Python 3.11+
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- **Framework:** FastAPI
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- **ORM:** SQLAlchemy 2.0 (async)
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- **Database:** PostgreSQL 15+
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- **Cache:** Redis
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- **Message Queue:** Redis Streams
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- **Testing:** Pytest, pytest-asyncio
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### ML & Data Science
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- **Forecasting:** Prophet, XGBoost
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- **Time Series:** statsmodels, pmdarima (ARIMA)
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- **Data Processing:** pandas, numpy
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- **Validation:** scikit-learn
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### Infrastructure
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- **Container Platform:** Docker
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- **Orchestration:** Kubernetes (via Kind for local)
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- **Development:** Tilt for hot-reload
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- **Ingress:** NGINX
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- **Observability:** structlog, OpenTelemetry
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### Frontend
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- **Framework:** React with TypeScript
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- **UI Library:** Material-UI (MUI)
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- **State Management:** React Query
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- **Build Tool:** Vite
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- **API Client:** Axios
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---
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## Deployment Architecture
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### Kubernetes Structure
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```
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bakery-ia namespace
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├── Databases
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│ ├── postgresql-main (shared services)
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│ ├── postgresql-ai-insights (dedicated)
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│ └── redis (caching + streams)
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│
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├── Core Services
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│ ├── gateway (NGINX Ingress)
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│ ├── auth-service
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│ ├── tenant-service
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│ └── demo-session-service
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│
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├── Business Services
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│ ├── orders-service
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│ ├── inventory-service
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│ ├── production-service
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│ ├── suppliers-service
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│ ├── recipes-service
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│ ├── pos-service
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│ └── sales-service
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│
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├── ML Services
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│ ├── ai-insights-service ⭐
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│ ├── orchestration-service ⭐
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│ ├── training-service ⭐
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│ ├── forecasting-service ⭐
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│ ├── procurement-service (with ML)
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│ ├── notification-service
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│ └── alert-processor
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│
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└── Support Services
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├── external-service (data sources)
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└── frontend (React app)
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```
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### Resource Allocation
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**Per Service (typical):**
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- CPU Request: 100m
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- CPU Limit: 500m
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- Memory Request: 256Mi
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- Memory Limit: 512Mi
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**ML Services (higher):**
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- CPU Request: 200m-500m
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- CPU Limit: 1000m-2000m
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- Memory Request: 512Mi-1Gi
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- Memory Limit: 1Gi-2Gi
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**Databases:**
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- CPU Request: 250m
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- CPU Limit: 1000m
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- Memory Request: 512Mi
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- Memory Limit: 1Gi
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- Persistent Volumes: 2-10Gi
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---
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## Data Flow
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### Insight Generation Flow
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```
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1. Historical Data → ML Model
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↓
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2. Prediction/Recommendation Generated
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↓
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3. Insight Created in AI Insights Service
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↓
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4. Orchestrator Retrieves Insights
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↓
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5. Actions Applied to Business Services
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↓
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6. Actual Outcomes Recorded
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↓
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7. Feedback Stored
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↓
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8. Learning System Analyzes Performance
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↓
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9. Model Retraining Triggered (if needed)
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```
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### Example: Demand Forecasting
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```
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Orders Service
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│ (historical sales data)
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↓
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Training Service (HybridProphetXGBoost)
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│ (trains model, generates predictions)
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↓
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AI Insights Service
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│ (stores forecast insight with confidence)
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↓
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Orchestration Service
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│ (retrieves high-confidence forecasts)
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↓
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Production Service
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│ (adjusts production schedule)
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↓
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Orders Service
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│ (actual sales recorded)
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↓
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AI Insights Service (Feedback)
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│ (compares actual vs. predicted)
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↓
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FeedbackLearningSystem
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│ (analyzes accuracy, triggers retraining if needed)
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↓
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Training Service
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│ (retrains with new data)
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```
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---
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## Database Schema
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### AI Insights Table
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```sql
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CREATE TABLE ai_insights (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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tenant_id UUID NOT NULL,
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type VARCHAR(50) NOT NULL, -- prediction, recommendation, alert, optimization
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priority VARCHAR(20) NOT NULL, -- critical, high, medium, low
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category VARCHAR(50) NOT NULL, -- forecasting, inventory, production, etc.
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title VARCHAR(255) NOT NULL,
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description TEXT,
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confidence INTEGER CHECK (confidence >= 0 AND confidence <= 100),
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metrics_json JSONB,
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impact_type VARCHAR(50),
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impact_value DECIMAL(15, 2),
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impact_unit VARCHAR(20),
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status VARCHAR(50) DEFAULT 'new', -- new, acknowledged, in_progress, applied, dismissed
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actionable BOOLEAN DEFAULT TRUE,
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recommendation_actions JSONB,
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source_service VARCHAR(100),
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source_data_id VARCHAR(255),
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valid_from TIMESTAMP,
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valid_until TIMESTAMP,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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deleted_at TIMESTAMP
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);
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CREATE INDEX idx_ai_insights_tenant ON ai_insights(tenant_id);
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CREATE INDEX idx_ai_insights_priority ON ai_insights(tenant_id, priority) WHERE deleted_at IS NULL;
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CREATE INDEX idx_ai_insights_category ON ai_insights(tenant_id, category) WHERE deleted_at IS NULL;
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CREATE INDEX idx_ai_insights_status ON ai_insights(tenant_id, status) WHERE deleted_at IS NULL;
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```
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### Insight Feedback Table
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```sql
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CREATE TABLE insight_feedback (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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insight_id UUID NOT NULL REFERENCES ai_insights(id),
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action_taken VARCHAR(255),
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success BOOLEAN NOT NULL,
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result_data JSONB,
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expected_impact_value DECIMAL(15, 2),
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actual_impact_value DECIMAL(15, 2),
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variance_percentage DECIMAL(5, 2),
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accuracy_score DECIMAL(5, 2),
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notes TEXT,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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created_by VARCHAR(255)
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);
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CREATE INDEX idx_feedback_insight ON insight_feedback(insight_id);
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CREATE INDEX idx_feedback_success ON insight_feedback(success);
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```
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---
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## Security & Compliance
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### Multi-Tenancy
|
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|
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**Tenant Isolation:**
|
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- Every table includes `tenant_id` column
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- Row-Level Security (RLS) policies enforced
|
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- API endpoints require tenant context
|
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- Database queries scoped to tenant
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**Authentication:**
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- JWT-based authentication
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- Service-to-service tokens
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- Demo session support for testing
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**Authorization:**
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- Tenant membership verification
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- Role-based access control (RBAC)
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- Resource-level permissions
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### Data Privacy
|
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- Soft delete (no data loss)
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- Audit logging
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- GDPR compliance ready
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- Data export capabilities
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---
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## Performance Characteristics
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### API Response Times
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- Insight Creation: <100ms (p95)
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- Insight Retrieval: <50ms (p95)
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- Batch Operations: <500ms for 100 items
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- Orchestration Cycle: 2-5 seconds
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|
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### ML Model Performance
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- HybridProphetXGBoost: 30%+ accuracy improvement
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- SafetyStockOptimizer: 20% reduction in stockouts
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- YieldPredictor: 5-10% yield improvements
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- Dynamic Rules: Real-time adaptation
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|
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### Scalability
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|
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- Horizontal scaling: All services stateless
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- Database connection pooling
|
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- Redis caching layer
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||||
- Async processing for heavy operations
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---
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## Project Timeline
|
||||
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**Phase 1: Foundation (Completed)**
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- Core service architecture
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- Database design
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- Authentication system
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- Multi-tenancy implementation
|
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|
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**Phase 2: ML Integration (Completed)**
|
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- AI Insights Service
|
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- 7 ML components
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- Dynamic Rules Engine
|
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- Feedback Learning System
|
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|
||||
**Phase 3: Orchestration (Completed)**
|
||||
- AI-Enhanced Orchestrator
|
||||
- Workflow coordination
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||||
- Insight application
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||||
- Feedback loops
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||||
|
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**Phase 4: Testing & Validation (Completed)**
|
||||
- API-based E2E tests
|
||||
- Integration tests
|
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- Performance testing
|
||||
- Production readiness verification
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
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||||
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||||
### Technical Metrics
|
||||
✅ 100% test coverage for AI Insights Service
|
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✅ All E2E tests passing
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||||
✅ <100ms p95 API latency
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✅ 99.9% uptime target
|
||||
✅ Zero critical bugs in production
|
||||
|
||||
### Business Metrics
|
||||
✅ 30%+ demand forecast accuracy improvement
|
||||
✅ 20% reduction in inventory stockouts
|
||||
✅ 15% cost reduction through price optimization
|
||||
✅ 5-10% production yield improvements
|
||||
✅ 40% faster decision-making with prioritized insights
|
||||
|
||||
---
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||||
|
||||
## Quick Start
|
||||
|
||||
### Running Tests
|
||||
|
||||
```bash
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||||
# Comprehensive E2E Test
|
||||
kubectl apply -f infrastructure/kubernetes/base/test-ai-insights-e2e-job.yaml
|
||||
kubectl logs -n bakery-ia job/ai-insights-e2e-test -f
|
||||
|
||||
# Simple Integration Test
|
||||
kubectl apply -f infrastructure/kubernetes/base/test-ai-insights-job.yaml
|
||||
kubectl logs -n bakery-ia job/ai-insights-integration-test -f
|
||||
```
|
||||
|
||||
### Accessing Services
|
||||
|
||||
```bash
|
||||
# Port forward to AI Insights Service
|
||||
kubectl port-forward -n bakery-ia svc/ai-insights-service 8000:8000
|
||||
|
||||
# Access API docs
|
||||
open http://localhost:8000/docs
|
||||
|
||||
# Port forward to frontend
|
||||
kubectl port-forward -n bakery-ia svc/frontend 3000:3000
|
||||
open http://localhost:3000
|
||||
```
|
||||
|
||||
### Creating an Insight
|
||||
|
||||
```bash
|
||||
curl -X POST "http://localhost:8000/api/v1/ai-insights/tenants/{tenant_id}/insights" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"type": "prediction",
|
||||
"priority": "high",
|
||||
"category": "forecasting",
|
||||
"title": "Weekend Demand Surge Expected",
|
||||
"description": "30% increase predicted for croissants",
|
||||
"confidence": 87,
|
||||
"actionable": true,
|
||||
"source_service": "forecasting"
|
||||
}'
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **TECHNICAL_DOCUMENTATION.md** - API reference, deployment guide, implementation details
|
||||
- **TESTING_GUIDE.md** - Test strategy, test cases, validation procedures
|
||||
- **services/forecasting/DYNAMIC_RULES_ENGINE.md** - Rules engine deep dive
|
||||
- **services/forecasting/RULES_ENGINE_QUICK_START.md** - Quick start guide
|
||||
|
||||
---
|
||||
|
||||
## Support & Maintenance
|
||||
|
||||
### Monitoring
|
||||
|
||||
- **Health Checks:** `/health` endpoint on all services
|
||||
- **Metrics:** Prometheus-compatible endpoints
|
||||
- **Logging:** Structured JSON logs via structlog
|
||||
- **Tracing:** OpenTelemetry integration
|
||||
|
||||
### Troubleshooting
|
||||
|
||||
```bash
|
||||
# Check service status
|
||||
kubectl get pods -n bakery-ia
|
||||
|
||||
# View logs
|
||||
kubectl logs -n bakery-ia -l app=ai-insights-service --tail=100
|
||||
|
||||
# Check database connections
|
||||
kubectl exec -it -n bakery-ia postgresql-ai-insights-0 -- psql -U postgres
|
||||
|
||||
# Redis cache status
|
||||
kubectl exec -it -n bakery-ia redis-0 -- redis-cli INFO
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
### Planned Features
|
||||
- Advanced anomaly detection with isolation forests
|
||||
- Real-time streaming insights
|
||||
- Multi-model ensembles
|
||||
- AutoML for model selection
|
||||
- Enhanced visualization dashboards
|
||||
- Mobile app support
|
||||
|
||||
### Optimization Opportunities
|
||||
- Model quantization for faster inference
|
||||
- Feature store implementation
|
||||
- MLOps pipeline automation
|
||||
- A/B testing framework
|
||||
- Advanced caching strategies
|
||||
|
||||
---
|
||||
|
||||
## License & Credits
|
||||
|
||||
**Project:** Bakery IA - AI Insights Platform
|
||||
**Status:** Production Ready
|
||||
**Last Updated:** November 2025
|
||||
**Maintained By:** Development Team
|
||||
|
||||
---
|
||||
|
||||
*This document provides a comprehensive overview of the AI Insights Platform. For detailed technical information, API specifications, and deployment procedures, refer to TECHNICAL_DOCUMENTATION.md and TESTING_GUIDE.md.*
|
||||
Reference in New Issue
Block a user