feat(boc): Complete Business Operations Center v1.0

- Go backend API with full CRUD for all modules (CRM, Sales, Finance, HR, Legal, Marketing, Support, Purchase, Inventory, Projects, Automation, Analytics)
- Rust analytics service with parallel report generation
- C runtime with POSIX shared memory IPC
- PostgreSQL schema with 30+ tables, full migrations
- Redis cache, sessions, pub/sub
- Kafka event streaming with Zookeeper
- WebSocket hub for real-time updates
- Automation engine with cron jobs, workflows, event triggers
- JWT authentication, multi-tenant from start
- Docker Compose with all services
- Nginx reverse proxy with rate limiting
- Integration tests passing
- Feature gap analysis against Fortnox/Odoo/Visma

Refs: BOC-001

vims-core/instances/retail-analytics/README.md

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+# Retail Site Analytics
+
+VIMS instance for retail-analytics.
+
+## Related Article
+[/insights/retail-site-selection-data/](https://landvex.com/insights/retail-site-selection-data/)
+
+## Anomaly Classes
+- foot_traffic
+- storefront_condition
+- signage
+- parking
+- competitor_presence
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/retail-analytics/predict`
+- WebSocket: `ws://host/ws/retail-analytics/alerts`

vims-core/instances/retail-analytics/config.yaml

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+# retail-analytics configuration
+
+topic: retail-analytics
+display_name: Retail Site Analytics
+article_url: /insights/retail-site-selection-data/
+
+anomaly_classes:
+  - foot_traffic
+  - storefront_condition
+  - signage
+  - parking
+  - competitor_presence
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/retail-analytics/src/database.py

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+"""
+Database setup for Retail Site Analytics
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for retail-analytics."""
+    db = VIMSDatabase("retail-analytics")
+    db.create_schema(anomaly_classes=['foot_traffic', 'storefront_condition', 'signage', 'parking', 'competitor_presence'])
+    print(f"Database initialized for Retail Site Analytics")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/retail-analytics/src/detector.py

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+"""
+retail-analytics Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/retail-site-selection-data/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class RetailAnalyticsDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Retail Site Analytics.
+    
+    Article: /insights/retail-site-selection-data/
+    """
+    
+    TOPIC = "retail-analytics"
+    CLASS_NAMES = {
+        0: "foot_traffic", 1: "storefront_condition", 2: "signage", 3: "parking", 4: "competitor_presence"
+    }
+    
+    SEVERITY_MAP = {
+        "foot_traffic": 3, "storefront_condition": 3, "signage": 3, "parking": 3, "competitor_presence": 3
+    }
+    
+    def preprocess(self, image):
+        """retail-analytics-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """retail-analytics-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("retail-analytics", RetailAnalyticsDetector)