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/schemas.py

@@ -106,6 +106,27 @@ class DataQualityIssueOut(BaseModel):
     resolved_at: datetime | None = None
 
 
+class DataQualityIssueDetailOut(DataQualityIssueOut):
+    entity_snapshot: dict[str, Any] | None = None
+    related_snapshots: list[dict[str, Any]] = Field(default_factory=list)
+
+
+class MergeCustomersRequest(BaseModel):
+    survivor_ref: str
+    field_overrides: dict[str, str] | None = None
+
+
+class MergeCustomersResult(BaseModel):
+    issue_ref: str
+    survivor_ref: str
+    loser_ref: str
+    rewired_bookings: int
+
+
+class ScanResultOut(BaseModel):
+    created: dict[str, int]
+
+
 class VehicleDetailOut(VehicleOut):
     bookings: list[BookingSummaryOut] = Field(default_factory=list)
     inspections: list[InspectionOut] = Field(default_factory=list)
@@ -130,6 +151,7 @@ class AttentionItem(BaseModel):
     detail: str
     link_type: Literal["vehicle", "booking", "customer"]
     link_ref: str
+    issue_ref: str | None = None
 
 
 class TodayItem(BaseModel):