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.

frontend/src/api/types.ts

@@ -92,6 +92,7 @@ export interface AttentionItem {
   detail: string;
   link_type: "vehicle" | "booking" | "customer";
   link_ref: string;
+  issue_ref: string | null;
 }
 
 export interface TodayItem {
@@ -144,6 +145,28 @@ export interface RegisterReturnResult {
   next_booking_risk: NextBookingRisk | null;
 }
 
+export interface EntitySnapshot {
+  public_ref: string;
+  [key: string]: unknown;
+}
+
+export interface DataQualityIssueDetail extends DataQualityIssue {
+  entity_snapshot: EntitySnapshot | null;
+  related_snapshots: EntitySnapshot[];
+}
+
+export interface MergeCustomersRequest {
+  survivor_ref: string;
+  field_overrides?: Record<string, string>;
+}
+
+export interface MergeCustomersResult {
+  issue_ref: string;
+  survivor_ref: string;
+  loser_ref: string;
+  rewired_bookings: number;
+}
+
 export interface AuditEvent {
   id: string;
   actor_type: string;