Build duplicate detection service layer
epic-organizational-hierarchy-management-duplicate-detection-task-010 — Implement the DuplicateActivityDetectorService in Dart that wraps the repository and provides business logic: loading pending duplicates for admin review, submitting resolution decisions (confirmed duplicate vs false positive), and reading detection configuration. Expose as a Riverpod provider for consumption by UI and BLoC layers.
Acceptance Criteria
Technical Requirements
Execution Context
Tier 4 - 323 tasks
Can start after Tier 3 completes
Implementation Notes
Use Riverpod's `ref.watch` pattern for the provider. Define `duplicateActivityDetectorRepositoryProvider` first (from task-009), then define `duplicateActivityDetectorServiceProvider` as a `Provider, String>` taking orgId as the family argument. The AlreadyResolvedException should be a simple Dart class extending Exception with a descriptive message.
Avoid adding caching or debouncing at this layer — that concern belongs in the BLoC or UI layer. Keep service methods thin: validate business rules, delegate to repository, return results.
Testing Requirements
Write unit tests with flutter_test and a mocked DuplicateActivityDetectorRepository. Test scenarios: (1) getPendingDuplicates delegates to repository and returns mapped list, (2) resolveAsDuplicate calls updateReviewStatus with confirmedDuplicate status and sets resolved_at timestamp, (3) markAsFalsePositive calls updateReviewStatus with falsePositive status, (4) calling resolveAsDuplicate on an already-resolved record throws AlreadyResolvedException, (5) repository exception propagates as-is from service methods, (6) getDetectionConfig returns null when repository returns null without throwing. Test Riverpod provider initialization in a ProviderContainer to verify provider graph builds without errors.
Fingerprint-based similarity matching may produce high false-positive rates for common activity types (e.g., weekly group sessions with the same participants), causing alert fatigue among coordinators and undermining trust in the detection system.
Mitigation & Contingency
Mitigation: Start with conservative, high-confidence thresholds (exact peer mentor match + same date + same activity type) before adding looser fuzzy matching. Allow NHF administrators to tune thresholds based on observed false-positive rates. Log all detection decisions for retrospective threshold calibration.
Contingency: Introduce a snooze mechanism allowing coordinators to dismiss false positives for a configurable period. Track dismissal rates per activity type and automatically raise the similarity threshold for activity types with high dismissal rates.
A database trigger on the activities insert path adds synchronous overhead to every activity registration. For HLF peer mentors with 380 annual registrations or coordinators doing bulk proxy registration, this could create perceptible latency or lock contention.
Mitigation & Contingency
Mitigation: Implement the trigger as a DEFERRED constraint trigger (fires after the transaction commits) or replace it with a LISTEN/NOTIFY pattern that queues detection work asynchronously via an Edge Function, completely decoupling detection from the registration write path.
Contingency: Disable the synchronous trigger entirely and rely solely on the scheduled Edge Function for batch detection. Accept a detection delay of up to the scheduling interval (e.g., 15 minutes) in exchange for zero impact on registration latency.
The duplicate detection logic must be validated and approved by NHF before go-live, including agreement on threshold values and the review workflow. NHF stakeholder availability for sign-off may delay this epic's release independently of technical readiness.
Mitigation & Contingency
Mitigation: Gate the feature behind the NHF-specific feature flag so technical deployment can proceed independently of business approval. Involve an NHF administrator in threshold calibration sessions during QA, reducing the formal sign-off surface to policy and workflow rather than technical details.
Contingency: Release the detection system in 'silent mode' — flagging duplicates internally without surfacing notifications to coordinators — until NHF approves the workflow. Use the silent period to collect real data on false-positive rates and refine thresholds before activating notifications.