diff --git a/mobile/lib/face/db.dart b/mobile/lib/face/db.dart
index 5945175470..905c2a3c49 100644
--- a/mobile/lib/face/db.dart
+++ b/mobile/lib/face/db.dart
@@ -846,6 +846,21 @@ class FaceMLDataDB {
     return result;
   }
 
+  Future<Map<int, (Uint8List, int)>> getClusterToClusterSummary(Iterable<int> clusterIDs) async {
+    final db = await instance.sqliteAsyncDB;
+    final Map<int, (Uint8List, int)> result = {};
+    final rows = await db.getAll(
+      'SELECT * FROM $clusterSummaryTable WHERE $clusterIDColumn IN (${clusterIDs.join(",")})',
+    );
+    for (final r in rows) {
+      final id = r[clusterIDColumn] as int;
+      final avg = r[avgColumn] as Uint8List;
+      final count = r[countColumn] as int;
+      result[id] = (avg, count);
+    }
+    return result;
+  }
+
   Future<Map<int, String>> getClusterIDToPersonID() async {
     final db = await instance.database;
     final List<Map<String, dynamic>> maps = await db.rawQuery(
diff --git a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
index 10f7b90d66..c6c185741c 100644
--- a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
+++ b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
@@ -877,21 +877,21 @@ class ClusterFeedbackService {
     final faceMlDb = FaceMLDataDB.instance;
 
     // Get the cluster averages for the person's clusters and the suggestions' clusters
-    final Map<int, (Uint8List, int)> clusterToSummary =
-        await faceMlDb.getAllClusterSummary();
+    final personClusters = await faceMlDb.getPersonClusterIDs(person.remoteID);
+    final Map<int, (Uint8List, int)> personClusterToSummary =
+        await faceMlDb.getClusterToClusterSummary(personClusters);
     final clusterSummaryCallTime = DateTime.now();
 
     // Calculate the avg embedding of the person
-    final personClusters = await faceMlDb.getPersonClusterIDs(person.remoteID);
     final personEmbeddingsCount = personClusters
-        .map((e) => clusterToSummary[e]!.$2)
+        .map((e) => personClusterToSummary[e]!.$2)
         .reduce((a, b) => a + b);
     final List<double> personAvg = List.filled(192, 0);
     for (final personClusterID in personClusters) {
-      final personClusterBlob = clusterToSummary[personClusterID]!.$1;
+      final personClusterBlob = personClusterToSummary[personClusterID]!.$1;
       final personClusterAvg = EVector.fromBuffer(personClusterBlob).values;
       final clusterWeight =
-          clusterToSummary[personClusterID]!.$2 / personEmbeddingsCount;
+          personClusterToSummary[personClusterID]!.$2 / personEmbeddingsCount;
       for (int i = 0; i < personClusterAvg.length; i++) {
         personAvg[i] += personClusterAvg[i] *
             clusterWeight; // Weighted sum of the cluster averages