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/data-quality/README.md

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+# Field Data Quality Assessment
+
+VIMS instance for data-quality.
+
+## Related Article
+[/insights/crowdsourced-data-quality/](https://landvex.com/insights/crowdsourced-data-quality/)
+
+## Anomaly Classes
+- blurry_image
+- poor_lighting
+- wrong_angle
+- missing_context
+- gps_inaccurate
+
+## 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/data-quality/predict`
+- WebSocket: `ws://host/ws/data-quality/alerts`

vims-core/instances/data-quality/config.yaml

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+# data-quality configuration
+
+topic: data-quality
+display_name: Field Data Quality Assessment
+article_url: /insights/crowdsourced-data-quality/
+
+anomaly_classes:
+  - blurry_image
+  - poor_lighting
+  - wrong_angle
+  - missing_context
+  - gps_inaccurate
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/data-quality/src/database.py

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+"""
+Database setup for Field Data Quality Assessment
+"""
+
+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 data-quality."""
+    db = VIMSDatabase("data-quality")
+    db.create_schema(anomaly_classes=['blurry_image', 'poor_lighting', 'wrong_angle', 'missing_context', 'gps_inaccurate'])
+    print(f"Database initialized for Field Data Quality Assessment")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/data-quality/src/detector.py

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+"""
+data-quality Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/crowdsourced-data-quality/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class DataQualityDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Field Data Quality Assessment.
+    
+    Article: /insights/crowdsourced-data-quality/
+    """
+    
+    TOPIC = "data-quality"
+    CLASS_NAMES = {
+        0: "blurry_image", 1: "poor_lighting", 2: "wrong_angle", 3: "missing_context", 4: "gps_inaccurate"
+    }
+    
+    SEVERITY_MAP = {
+        "blurry_image": 3, "poor_lighting": 3, "wrong_angle": 3, "missing_context": 3, "gps_inaccurate": 3
+    }
+    
+    def preprocess(self, image):
+        """data-quality-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """data-quality-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("data-quality", DataQualityDetector)