diff --git a/backend/app/BatterySapScorer.py b/backend/app/BatterySapScorer.py
index f5e485c4..923c5498 100644
--- a/backend/app/BatterySapScorer.py
+++ b/backend/app/BatterySapScorer.py
@@ -25,6 +25,6 @@ class BatterySAPScorer:
             + cls.COEF_PV_SIZE * pv_size
         )
 
-        # Round + clamp to [0,5]
-        sap_uplift = int(np.round(np.clip(sap_uplift, 0, 5)))
+        # Round + clamp to [1,5] - there are only a small number of cases with 0 points
+        sap_uplift = int(np.round(np.clip(sap_uplift, 1, 5)))
         return sap_uplift
diff --git a/backend/engine/engine.py b/backend/engine/engine.py
index b04cb2f5..1f2b3976 100644
--- a/backend/engine/engine.py
+++ b/backend/engine/engine.py
@@ -1136,7 +1136,12 @@ async def model_engine(body: PlanTriggerRequest):
                     # This is the uplift score ABS
                     uplift_project_score = optimal_solution["total_uplift_score"]
                     # This is the SAP score associated to a battery
-                    battery_sap_score = optimal_solution["battery_sap_uplift"]
+                    pv_size = next(
+                        (m["array_size"] for m in optimal_solution["items"] if m["type"] == "solar_pv"), 0
+                    )
+                    battery_sap_score = BatterySAPScorer.score(
+                        starting_sap=optimal_solution["ending_sap"], pv_size=pv_size
+                    )
             else:
                 # We optimise and then we determine eligibility for funding, based on the measures selected
                 optimiser = (
diff --git a/etl/customers/lincs_rural/prepare_data.py b/etl/customers/lincs_rural/prepare_data.py
new file mode 100644
index 00000000..db7a9087
--- /dev/null
+++ b/etl/customers/lincs_rural/prepare_data.py
@@ -0,0 +1,26 @@
+"""
+Rough script to prepare the data for Lincs Rural project
+"""
+import pandas as pd
+from etl.find_my_epc.RetrieveFindMyEpc import RetrieveFindMyEpc
+
+data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Downloads/MASTER LIST EPCS UPDATED November 2025 Domna Homes.xlsx",
+    sheet_name="PROPERTY EPC RATINGS"
+)
+
+# We have property RRNs - we need UPRN
+
+for _, x in data.iterrows():
+    rrn = x["EPC Ref."]
+
+    # Fetch from find my epc
+    retriever = RetrieveFindMyEpc(
+        address="",
+        postcode="",
+        rrn=rrn,
+        address_postal_town="",
+        sap_rating=x["Actual"]
+    )
+
+    find_epc_data = retriever.retrieve_all_find_my_epc_data()
diff --git a/recommendations/optimiser/funding_optimiser.py b/recommendations/optimiser/funding_optimiser.py
index 0aa69f39..a8b998ae 100644
--- a/recommendations/optimiser/funding_optimiser.py
+++ b/recommendations/optimiser/funding_optimiser.py
@@ -533,18 +533,19 @@ def optimise_with_funding_paths(
     # For properties that are including batteries, we need to adjust the starting SAP to include the battery SAP uplift
     # Note: We score on ending sap, as the battery SAP uplift is based on the ending SAP after fabric/heat/solar
     # upgrades of each package is applied
+    # NB: The battery SAP uplift is used to potentially prioritise packages that include batteries, it does NOT impact
+    # the eventual SAP score at this point. Once the package is included, we'll re-calculate battery SAP score outside
+    # of this. This is because
     solutions["battery_sap_uplift"] = solutions.apply(
         lambda x: BatterySAPScorer.score(starting_sap=x["ending_sap"], pv_size=x["array_size"])
         if x["has_battery"] else 0,
         axis=1
     )
-    # We add this on to ending SAP
-    solutions["ending_sap"] = solutions["ending_sap"] + solutions["battery_sap_uplift"]
 
     solutions["starting_band"] = (solutions["starting_sap"] + solutions["already_installed_gain"]).apply(
         funding.get_sap_band
     )
-    solutions["ending_band"] = solutions["ending_sap"].apply(funding.get_sap_band)
+    solutions["ending_band"] = (solutions["ending_sap"] + solutions["battery_sap_uplift"]).apply(funding.get_sap_band)
     solutions["floor_area_band"] = solutions["floor_area"].apply(funding.get_floor_area_band)
     solutions["project_score"] = solutions.apply(
         lambda x: funding._calculate_full_project_abs(
diff --git a/recommendations/optimiser/optimiser_functions.py b/recommendations/optimiser/optimiser_functions.py
index d7705456..0eec35dc 100644
--- a/recommendations/optimiser/optimiser_functions.py
+++ b/recommendations/optimiser/optimiser_functions.py
@@ -371,11 +371,14 @@ def flatten_recommendations_with_defaults(property_id, recommendations, selected
 
     final_recommendations = []
     for recommendations_by_type in recommendations[property_id]:
+        recs_by_type = []
         for rec in recommendations_by_type:
             rec_copy = {**rec, "default": rec["recommendation_id"] in selected}
             if rec_copy.get("has_battery", False):
                 rec_copy["sap_points"] += battery_sap_score
-            final_recommendations.append(rec_copy)
+            recs_by_type.append(rec_copy)
+
+        final_recommendations.append(recs_by_type)
 
     # Flatten the nested list of lists into a single list
     return [rec for recommendations_by_type in final_recommendations for rec in recommendations_by_type]