Add real data detection QA workflow smoke

docs/AI_PIPELINES.md

@@ -137,6 +137,34 @@ detector fixtures or download weights. A zero detection count is acceptable on
 the synthetic demo raster; production usefulness still requires validation on
 real georeferenced orthophotos and reference vectors.
 
+### Real-data detection and QA validation
+
+The real operational validation path uses operator-provided files rather than
+demo fixtures:
+
+```bash
+REAL_RASTER_PATH=/mnt/user/appdata/geointel/data/orthophoto.tif \
+REAL_REFERENCE_VECTOR_PATH=/mnt/user/appdata/geointel/data/reference-buildings.geojson \
+bash scripts/verify_real_data_detection_qa_workflow.sh http://192.168.10.150:1202
+```
+
+The script verifies the full persisted chain:
+
+- source raster upload with CRS and bounds metadata;
+- reference building vector upload as `dataset_role=reference`;
+- raster inspect and tile manifest generation;
+- local model asset selection and read-only YOLO preflight;
+- configured-YOLO detection run through Job, AnalysisRun and Detection rows;
+- detection GeoJSON generated from persisted geometry;
+- detection QA against persisted reference `vector_features` with persisted
+  `QualityCheck` and `Metric` rows;
+- detection run GeoJSON export.
+
+It refuses to run without a real GeoTIFF-style raster and GeoJSON/JSON reference
+vector. It does not seed demo data, use `fixture_mode`, fetch live providers or
+download model weights. A zero detection count is valid as runtime evidence but
+does not prove the model is useful for the target imagery.
+
 ### Sprint 8C detection visualization and QA status
 
 Sprint 8C makes persisted detections reviewable: