diff --git a/backend/postcode_splitter/hackney.xlsx b/backend/postcode_splitter/hackney.xlsx
deleted file mode 100644
index 64892f3a..00000000
Binary files a/backend/postcode_splitter/hackney.xlsx and /dev/null differ
diff --git a/backend/postcode_splitter/main.py b/backend/postcode_splitter/main.py
index d417c8f1..d55f618a 100644
--- a/backend/postcode_splitter/main.py
+++ b/backend/postcode_splitter/main.py
@@ -1,10 +1,12 @@
 import pandas as pd
 import requests
-from backend.address2UPRN.main import resolve_uprns_for_postcode_group, get_epc_data_with_postcode
+from backend.address2UPRN.main import (
+    resolve_uprns_for_postcode_group,
+    get_epc_data_with_postcode,
+)
 from tqdm import tqdm
 
 
-
 def sanitise_postcode(postcode: str) -> str | None:
     """
     Normalise postcode for grouping.
@@ -51,11 +53,7 @@ def main():
     # --- validate AFTER grouping (save API calls) ---
 
     # Get unique, non-null postcodes
-    unique_postcodes = (
-        df["postcode_clean"]
-        .dropna()
-        .unique()
-    )
+    unique_postcodes = df["postcode_clean"].dropna().unique()
 
     # Validate each postcode once, TODOadd a progress bar
     postcode_validity = {
@@ -66,7 +64,6 @@ def main():
     # Map validity back onto dataframe
     df["postcode_valid"] = df["postcode_clean"].map(postcode_validity)
 
-
     results = []
 
     for postcode, group_df in tqdm(
@@ -98,17 +95,33 @@ def main():
             results.append(tmp)
 
     final_df = pd.concat(results, ignore_index=True)
-    a = final_df[[
-            "best_match_lexiscore","Address 1",
-            "best_match_address", "Postcode",
-            "UPRN", "best_match_uprn"
-        ]] # add levi score to viewing
-    b = final_df[final_df["best_match_lexiscore"]>0] # add levi score to viewing
-    b = b[[
-            "best_match_lexiscore","Address 1",
-            "best_match_address", "Postcode",
-            "UPRN", "best_match_uprn"
-        ]] 
+    a = final_df[
+        [
+            "best_match_lexiscore",
+            "Address 1",
+            "best_match_address",
+            "Postcode",
+            "UPRN",
+            "best_match_uprn",
+        ]
+    ]  # add levi score to viewing
+    b = final_df[final_df["best_match_lexiscore"] > 0]  # add levi score to viewing
+    b = b[
+        [
+            "best_match_lexiscore",
+            "Address 1",
+            "best_match_address",
+            "Postcode",
+            "UPRN",
+            "best_match_uprn",
+        ]
+    ]
+
+
+def handler(event, context):
+    print("hello Postcode splitter world")
+    return {"statusCode": 200, "body": "hello world"}
+
 
 if __name__ == "__main__":
     main()
diff --git a/infrastructure/terraform/shared/main.tf b/infrastructure/terraform/shared/main.tf
index 3ba78ef3..fa1007f2 100644
--- a/infrastructure/terraform/shared/main.tf
+++ b/infrastructure/terraform/shared/main.tf
@@ -311,4 +311,29 @@ module "address2uprn_registry" {
 
 output "address2uprn_repository_url" {
   value = module.address2uprn_registry.repository_url
+}
+
+
+################################################
+# Postcode Splitter – Lambda ECR
+################################################
+module "postcodeSplitter_state_bucket" {
+  source      = "../modules/tf_state_bucket"
+  bucket_name = "postcodeSplitter-terraform-state"
+
+}
+
+output "postcodeSplitter_state_bucket_name" {
+  value = module.postcodeSplitter_state_bucket.bucket_name
+}
+
+module "postcodeSplitter_registry" {
+  source = "../modules/container_registry"
+  name   = "postcodeSplitter"
+  stage = var.stage
+
+}
+
+output "postcodeSplitter_repository_url" {
+  value = module.postcodeSplitter_registry.repository_url
 }
\ No newline at end of file