M3: implement Data Quality Workbench

Five rule scanners (duplicate customers, missing fields, odometer regression, booking overlap, status conflict) run automatically after seed and via an explicit scan endpoint. Issue defer/reject/merge-customers endpoints with transactional customer merge (booking rewiring, tombstone, audit). Data Quality nav + workbench UI with two-column duplicate comparison and inline (non-native) confirm. Dashboard attention items now link to issues. 35 backend tests passing, ruff clean. Fixed a real false-positive bug in odometer-regression detection found through iteration on seed data, and two TS narrowing errors. Verified end-to-end via browser: S2 merge and S4 overlap scenarios.

backend/app/seed_loader.py

@@ -260,7 +260,11 @@ def load_seed(db: Session) -> SeedResult:
 
 
 def reset_and_seed(db: Session) -> SeedResult:
+    from app.services.data_quality import run_scan
+
     clear_all(db)
     result = load_seed(db)
     db.commit()
+    scan = run_scan(db)
+    result.counts["data_quality_issues"] += sum(scan.created.values())
     return result