diff --git a/graphviz/processing/create_clusters.py b/graphviz/processing/create_clusters.py
new file mode 100644
index 0000000..c7975ff
--- /dev/null
+++ b/graphviz/processing/create_clusters.py
@@ -0,0 +1,178 @@
+import csv
+import json
+import uuid
+from typing import List, Dict
+
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.metrics.pairwise import cosine_similarity
+from tqdm import tqdm
+
+
+INPUT_CSV = "../../data/dataset-dev.csv"
+OUTPUT_JSON = "../../data/clustered_output.json"
+MODEL_NAME = "all-MiniLM-L6-v2"
+SIMILARITY_THRESHOLD = 0.65
+
+def generate_guid():
+    return str(uuid.uuid4())
+
+
+def read_csv(file_path: str):
+    """
+    Expected format per row:
+    [claim, event1, event2, event3, ...]
+    """
+    data = []
+
+    with open(file_path, newline='', encoding='utf-8') as f:
+        reader = csv.reader(f)
+        for row in tqdm(reader, desc="Reading CSV"):
+            row = [r.strip() for r in row if r.strip()]
+            if not row:
+                continue
+
+            claim = row[0]
+            events = row[1:]
+
+            claim_id = generate_guid()
+
+            event_objects = []
+            for e in events:
+                event_objects.append({
+                    "id": generate_guid(),
+                    "text": e
+                })
+
+            data.append({
+                "claim": {
+                    "id": claim_id,
+                    "text": claim
+                },
+                "events": event_objects
+            })
+
+    return data
+
+def embed_texts(model, texts: List[str], desc="Embedding"):
+    embeddings = []
+    for t in tqdm(texts, desc=desc):
+        emb = model.encode(t, normalize_embeddings=True)
+        embeddings.append(emb)
+    return np.array(embeddings)
+
+
+def cluster_embeddings(embeddings, threshold=0.75, desc="Clustering"):
+    """
+    Uses Agglomerative clustering with cosine distance
+    """
+    distance_matrix = 1 - cosine_similarity(embeddings)
+
+    clustering = AgglomerativeClustering(
+        metric='precomputed',
+        linkage='average',
+        distance_threshold=1 - threshold,
+        n_clusters=None
+    )
+
+    labels = clustering.fit_predict(distance_matrix)
+    return labels
+
+def main():
+    print("Loading model...")
+    model = SentenceTransformer(MODEL_NAME)
+
+    data = read_csv(INPUT_CSV)
+
+    # Collect all claims and events separately
+    claim_texts = []
+    claim_ids = []
+
+    event_texts = []
+    event_ids = []
+
+    links = []  # claim -> events
+
+    for entry in tqdm(data, desc="Processing rows"):
+        claim = entry["claim"]
+        claim_ids.append(claim["id"])
+
+        # Context-enhanced claim
+        claim_texts.append(f"Claim: {claim['text']}")
+
+        for event in entry["events"]:
+            event_ids.append(event["id"])
+
+            # Context-enhanced event
+            event_texts.append(f"Event: {event['text']}")
+
+            links.append({
+                "claim_id": claim["id"],
+                "event_id": event["id"]
+            })
+
+    # Embed
+    print("Embedding claims...")
+    claim_embeddings = embed_texts(model, claim_texts, desc="Claims")
+
+    print("Embedding events...")
+    event_embeddings = embed_texts(model, event_texts, desc="Events")
+
+    # Cluster
+    print("Clustering claims...")
+    claim_labels = cluster_embeddings(claim_embeddings, SIMILARITY_THRESHOLD)
+
+    print("Clustering events...")
+    event_labels = cluster_embeddings(event_embeddings, SIMILARITY_THRESHOLD)
+
+    # Build cluster structures
+    claim_clusters: Dict[int, List[str]] = {}
+    for cid, label in zip(claim_ids, claim_labels):
+        claim_clusters.setdefault(int(label), []).append(cid)
+
+    event_clusters: Dict[int, List[str]] = {}
+    for eid, label in zip(event_ids, event_labels):
+        event_clusters.setdefault(int(label), []).append(eid)
+
+    # Build cluster-level links
+    cluster_links = []
+    for link in links:
+        claim_cluster = int(claim_labels[claim_ids.index(link["claim_id"])])
+        event_cluster = int(event_labels[event_ids.index(link["event_id"])])
+
+        cluster_links.append({
+            "claim_cluster": claim_cluster,
+            "event_cluster": event_cluster
+        })
+
+    # Output structure
+    output = {
+        "claims": [
+            {"id": cid, "text": txt.replace("Claim: ", "")}
+            for cid, txt in zip(claim_ids, claim_texts)
+        ],
+        "events": [
+            {"id": eid, "text": txt.replace("Event: ", "")}
+            for eid, txt in zip(event_ids, event_texts)
+        ],
+        "claim_clusters": [
+            {"cluster_id": int(k), "members": v}
+            for k, v in claim_clusters.items()
+        ],
+        "event_clusters": [
+            {"cluster_id": int(k), "members": v}
+            for k, v in event_clusters.items()
+        ],
+        "links": links,
+        "cluster_links": cluster_links
+    }
+
+    with open(OUTPUT_JSON, "w", encoding="utf-8") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+
+    print(f"Saved output to {OUTPUT_JSON}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/graphviz/processing/requirements.txt b/graphviz/processing/requirements.txt
new file mode 100644
index 0000000..470db3c
--- /dev/null
+++ b/graphviz/processing/requirements.txt
@@ -0,0 +1 @@
+sentence_transformers