diff --git a/web/packages/new/photos/services/ml/cluster-new.ts b/web/packages/new/photos/services/ml/cluster-new.ts
new file mode 100644
index 0000000000..e1adb96500
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+++ b/web/packages/new/photos/services/ml/cluster-new.ts
@@ -0,0 +1,50 @@
+import log from "@/base/log";
+import type { FaceIndex } from "./face";
+
+/**
+ * A cluster is an set of faces.
+ *
+ * Each cluster has an id so that a Person (a set of clusters) can refer to it.
+ */
+export interface Cluster {
+    /** A unique nanoid to identify this cluster. */
+    id: string;
+    /**
+     * An unordered set of ids of the faces that belong to the cluster.
+     *
+     * For ergonomics of transportation and persistence this is an array but it
+     * should conceptually be thought of as a set.
+     */
+    faceIDs: string[];
+}
+
+/**
+ * A Person is a set of clusters, with some attached metadata.
+ *
+ * The person is the user visible concept. It consists of a set of clusters,
+ * each of which itself is a set of faces.
+ */
+export interface Person {
+    /** A unique nanoid to identify this person. */
+    id: string;
+    /**
+     * An unordered set of ids of the clusters that belong to this person.
+     * For ergonomics of transportation and persistence this is an array but it
+     * should conceptually be thought of as a set.
+     */
+    clusterIDs: string[];
+}
+
+/**
+ * Cluster faces into groups.
+ *
+ * [Note: Face clustering algorithm]
+ *
+ * 1. clusters = []
+ * 2. For each face, find its nearest neighbour in the embedding space. If no
+ *    such neighbour is found within our threshold, create a new c
+ */
+export const clusterFaces = (faceIndices: FaceIndex[]) => {
+    log.debug(() => ["Clustering", faceIndices]);
+    return undefined;
+};