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/urban-decay/README.md

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+# Urban Decay Measurement
+
+VIMS instance for urban-decay.
+
+## Related Article
+[/insights/how-to-measure-urban-decay/](https://landvex.com/insights/how-to-measure-urban-decay/)
+
+## Anomaly Classes
+- building_deterioration
+- abandoned_property
+- trash_accumulation
+- broken_infrastructure
+- illegal_dumping
+
+## 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/urban-decay/predict`
+- WebSocket: `ws://host/ws/urban-decay/alerts`

vims-core/instances/urban-decay/config.yaml

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+# urban-decay configuration
+
+topic: urban-decay
+display_name: Urban Decay Measurement
+article_url: /insights/how-to-measure-urban-decay/
+
+anomaly_classes:
+  - building_deterioration
+  - abandoned_property
+  - trash_accumulation
+  - broken_infrastructure
+  - illegal_dumping
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/urban-decay/src/database.py

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+"""
+Database setup for Urban Decay Measurement
+"""
+
+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 urban-decay."""
+    db = VIMSDatabase("urban-decay")
+    db.create_schema(anomaly_classes=['building_deterioration', 'abandoned_property', 'trash_accumulation', 'broken_infrastructure', 'illegal_dumping'])
+    print(f"Database initialized for Urban Decay Measurement")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/urban-decay/src/detector.py

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+"""
+urban-decay Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/how-to-measure-urban-decay/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class UrbanDecayDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Urban Decay Measurement.
+    
+    Article: /insights/how-to-measure-urban-decay/
+    """
+    
+    TOPIC = "urban-decay"
+    CLASS_NAMES = {
+        0: "building_deterioration", 1: "abandoned_property", 2: "trash_accumulation", 3: "broken_infrastructure", 4: "illegal_dumping"
+    }
+    
+    SEVERITY_MAP = {
+        "building_deterioration": 3, "abandoned_property": 3, "trash_accumulation": 3, "broken_infrastructure": 3, "illegal_dumping": 3
+    }
+    
+    def preprocess(self, image):
+        """urban-decay-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """urban-decay-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("urban-decay", UrbanDecayDetector)