diff --git a/recommendations/optimiser/optimiser_functions.py b/recommendations/optimiser/optimiser_functions.py
index 5fd6e0ee..94190bdd 100644
--- a/recommendations/optimiser/optimiser_functions.py
+++ b/recommendations/optimiser/optimiser_functions.py
@@ -1,7 +1,7 @@
 import pandas as pd
 import backend.app.assumptions as assumptions
-from Property import Property
-from app.plan.schemas import PlanTriggerRequest
+from backend.Property import Property
+from backend.app.plan.schemas import PlanTriggerRequest
 from backend.app.utils import epc_to_sap_lower_bound
 from recommendations.optimiser.CostOptimiser import CostOptimiser
 
diff --git a/recommendations/tests/test_data/measures_to_optimise.py b/recommendations/tests/test_data/measures_to_optimise.py
new file mode 100644
index 00000000..cefd36e4
--- /dev/null
+++ b/recommendations/tests/test_data/measures_to_optimise.py
@@ -0,0 +1,350 @@
+import datetime
+import numpy as np
+from numpy import nan
+from pandas import Timestamp
+
+measures_to_optimise = [
+    [{'phase': 0, 'parts': [{'id': 2466, 'type': 'external_wall_insulation',
+                             'description': 'EWI Pro EPS external wall insulation system with '
+                                            'Brick Slip finish',
+                             'depth': 150.0, 'depth_unit': 'mm', 'cost': None,
+                             'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.02631579,
+                             'r_value_unit': 'square_meter_kelvin_per_watt',
+                             'thermal_conductivity': 0.038,
+                             'thermal_conductivity_unit': 'watt_per_meter_kelvin', 'link': 'SCIS',
+                             'created_at': Timestamp('2025-03-16 15:26:22.379496'),
+                             'is_active': True, 'prime_material_cost': None, 'material_cost': 0.0,
+                             'labour_cost': 0.0, 'labour_hours_per_unit': 0.0, 'plant_cost': 0.0,
+                             'total_cost': 298.35, 'notes': 'This is the quoted value from SCIS',
+                             'is_installer_quote': True, 'quantity': 63.98796761892035,
+                             'quantity_unit': 'm2', 'total': 19090.810139104888,
+                             'labour_hours': 0.0, 'labour_days': 0.0}],
+      'type': 'external_wall_insulation', 'measure_type': 'external_wall_insulation',
+      'description': 'Install 150mm EWI Pro EPS external wall insulation system with Brick Slip '
+                     'finish on external walls',
+      'starting_u_value': 1.7, 'new_u_value': 0.32, 'already_installed': False,
+      'sap_points': np.float64(9.6),
+      'simulation_config': {'is_as_built_ending': False, 'walls_is_assumed_ending': False,
+                            'walls_insulation_thickness_ending': 'average',
+                            'external_insulation_ending': True, 'walls_energy_eff_ending': 'Good',
+                            'walls_thermal_transmittance_ending': 0.23},
+      'description_simulation': {'walls-description': 'Solid brick, with external insulation',
+                                 'walls-energy-eff': 'Good'}, 'total': 19090.810139104888,
+      'labour_hours': 0.0, 'labour_days': 0.0, 'survey': False, 'recommendation_id': '0_phase=0',
+      'efficiency': 11229.568317120522, 'co2_equivalent_savings': np.float64(0.5),
+      'heat_demand': np.float64(37.099999999999994), 'kwh_savings': np.float64(1813.199999999999),
+      'energy_cost_savings': np.float64(135.03007058823516)}, {'phase': 0, 'parts': [
+        {'id': 2373, 'type': 'internal_wall_insulation', 'description': 'SWIP EcoBatt & Plastered finish',
+         'depth': 95.0,
+         'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.03125,
+         'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': 0.032,
+         'thermal_conductivity_unit': None,
+         'link': 'SCIS', 'created_at': Timestamp('2025-03-16 15:26:22.379496'), 'is_active': True,
+         'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 2.1,
+         'plant_cost': 0.0, 'total_cost': 89.0, 'notes': None, 'is_installer_quote': True,
+         'quantity': 63.98796761892035,
+         'quantity_unit': 'm2', 'total': 5694.929118083911, 'labour_hours': 134.37473199973275,
+         'labour_days': 4.199210374991648}], 'type': 'internal_wall_insulation',
+                                                               'measure_type': 'internal_wall_insulation',
+                                                               'description': 'Install 95mm SWIP '
+                                                                              'EcoBatt & '
+                                                                              'Plastered finish '
+                                                                              'on internal walls',
+                                                               'starting_u_value': 1.7,
+                                                               'new_u_value': 0.32,
+                                                               'already_installed': False,
+                                                               'sap_points': 6,
+                                                               'simulation_config': {
+                                                                   'is_as_built_ending': False,
+                                                                   'walls_is_assumed_ending': False,
+                                                                   'walls_insulation_thickness_ending': 'average',
+                                                                   'internal_insulation_ending':
+                                                                       True,
+                                                                   'walls_energy_eff_ending':
+                                                                       'Good',
+                                                                   'walls_thermal_transmittance_ending': 0.29},
+                                                               'description_simulation': {
+                                                                   'walls-description': 'Solid '
+                                                                                        'brick, '
+                                                                                        'with '
+                                                                                        'internal '
+                                                                                        'insulation',
+                                                                   'walls-energy-eff': 'Good'},
+                                                               'total': 5694.929118083911,
+                                                               'labour_hours': 134.37473199973275,
+                                                               'labour_days': 4.199210374991648,
+                                                               'survey': True,
+                                                               'recommendation_id': '1_phase=0',
+                                                               'efficiency': 3349.6383047552417,
+                                                               'co2_equivalent_savings': np.float64(
+                                                                   0.5), 'heat_demand': np.float64(
+            35.30000000000001), 'kwh_savings': np.float64(1424.699999999999), 'energy_cost_savings': np.float64(
+            106.09824705882352)}], [{'phase': 1, 'parts': [
+        {'id': 2351, 'type': 'loft_insulation', 'description': 'Knauf Loft Roll 44 glass fibre roll', 'depth': 300.0,
+         'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.022727273,
+         'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': 0.044,
+         'thermal_conductivity_unit': 'watt_per_meter_kelvin', 'link': 'SCIS',
+         'created_at': Timestamp('2025-03-16 15:26:22.379496'), 'is_active': True, 'prime_material_cost': None,
+         'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 0.11, 'plant_cost': 0.0, 'total_cost': 15.0,
+         'notes': 'This is the cost if there is less than 100mm existing insulation', 'is_installer_quote': True,
+         'quantity': 63.98796761892035, 'quantity_unit': 'm2', 'total': 645.0, 'labour_hours': 8, 'labour_days': 1}],
+                                     'type': 'loft_insulation', 'measure_type': 'loft_insulation',
+                                     'description': 'Install 300mm of Knauf Loft Roll 44 glass fibre roll in your loft',
+                                     'starting_u_value': 2.3, 'new_u_value': 2.3, 'sap_points': np.float64(2.4),
+                                     'already_installed': False,
+                                     'simulation_config': {'is_loft_ending': True, 'roof_is_assumed_ending': False,
+                                                           'roof_insulation_thickness_ending': '300',
+                                                           'roof_thermal_transmittance_ending': 2.3,
+                                                           'roof_energy_eff_ending': 'Very Good'},
+                                     'description_simulation': {'roof-description': 'Pitched, 300mm loft insulation',
+                                                                'roof-energy-eff': 'Very Good'}, 'total': 645.0,
+                                     'labour_hours': 8, 'labour_days': 1, 'survey': False,
+                                     'recommendation_id': '2_phase=1',
+                                     'efficiency': 278.1347826086957,
+                                     'co2_equivalent_savings': np.float64(0.10000000000000009),
+                                     'heat_demand': np.float64(1.5), 'kwh_savings': np.float64(572.5500000000002),
+                                     'energy_cost_savings': np.float64(42.638135294117774)}], [{'phase': 2, 'parts': [
+        {'id': 2329, 'type': 'mechanical_ventilation', 'description': 'Mechanical Extract Ventilation', 'depth': 0.0,
+         'depth_unit': None, 'cost': None, 'cost_unit': 'gbp_per_unit', 'r_value_per_mm': nan,
+         'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': None,
+         'thermal_conductivity_unit': None,
+         'link': 'SCIS', 'created_at': datetime.datetime(2025, 3, 16, 15, 26, 22, 379496), 'is_active': True,
+         'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 0.0,
+         'plant_cost': 0.0, 'total_cost': 350.0, 'notes': None, 'is_installer_quote': True, 'total': 700.0,
+         'quantity': 2,
+         'quantity_unit': 'part'}
+    ], 'type': 'mechanical_ventilation', 'measure_type': 'mechanical_ventilation',
+                                                                                                'description':
+                                                                                                    'Install 2 '
+                                                                                                    'Mechanical '
+                                                                                                    'Extract '
+                                                                                                    'Ventilation units',
+                                                                                                'starting_u_value': None,
+                                                                                                'new_u_value': None,
+                                                                                                'already_installed': False,
+                                                                                                'sap_points': np.float64(
+                                                                                                    -0.10000000000000142),
+                                                                                                'heat_demand': np.float64(
+                                                                                                    -3.3999999999999773),
+                                                                                                'kwh_savings': np.float64(
+                                                                                                    -45.899999999999636),
+                                                                                                'co2_equivalent_savings': np.float64(
+                                                                                                    0.0),
+                                                                                                'energy_cost_savings': np.float64(
+                                                                                                    -3.4181999999999846),
+                                                                                                'total': 700.0,
+                                                                                                'labour_hours': 8,
+                                                                                                'labour_days': 1.0,
+                                                                                                'simulation_config': {
+                                                                                                    'mechanical_ventilation_ending': 'mechanical, extract only'},
+                                                                                                'description_simulation': {
+                                                                                                    'mechanical-ventilation': 'mechanical, extract only'},
+                                                                                                'recommendation_id': '3_phase=2',
+                                                                                                'efficiency': 0}
+                                                                                               ], [
+        {'phase': 3, 'parts': [{'id': 2409, 'type': 'suspended_floor_insulation',
+                                'description': 'Q-bot underfloor insulation', 'depth': 75.0,
+                                'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2',
+                                'r_value_per_mm': 0.045454547,
+                                'r_value_unit': 'square_meter_kelvin_per_watt',
+                                'thermal_conductivity': 0.022,
+                                'thermal_conductivity_unit': 'watt_per_meter_kelvin',
+                                'link': 'SCIS',
+                                'created_at': Timestamp('2025-03-16 15:26:22.379496'),
+                                'is_active': True, 'prime_material_cost': None,
+                                'material_cost': 0.0, 'labour_cost': 0.0,
+                                'labour_hours_per_unit': 1.63, 'plant_cost': 0.0,
+                                'total_cost': 93.75,
+                                'notes': 'Linearly interpolated based on Qbot costs',
+                                'is_installer_quote': True, 'quantity': 43.0, 'quantity_unit': 'm2',
+                                'total': 4031.25, 'labour_hours': 70.08999999999999,
+                                'labour_days': 2.920416666666666}],
+         'type': 'suspended_floor_insulation', 'measure_type': 'suspended_floor_insulation',
+         'description': 'Install 75mm Q-bot underfloor insulation insulation in suspended floor',
+         'starting_u_value': 0.83, 'new_u_value': 0.22, 'sap_points': 2, 'survey': True,
+         'already_installed': False, 'simulation_config': {'floor_is_assumed_ending': False,
+                                                           'floor_insulation_thickness_ending':
+                                                               'average',
+                                                           'floor_thermal_transmittance_ending':
+                                                               0.685593},
+         'description_simulation': {'floor-description': 'Suspended, insulated'}, 'total': 4031.25,
+         'labour_hours': 70.08999999999999, 'labour_days': 2.920416666666666,
+         'recommendation_id': '4_phase=3', 'efficiency': 4856.707710843373,
+         'co2_equivalent_savings': np.float64(0.20000000000000018), 'heat_demand': np.float64(33.5),
+         'kwh_savings': np.float64(1018.0999999999995),
+         'energy_cost_savings': np.float64(75.8185058823529)}], [
+        {'phase': 4, 'parts': [], 'type': 'low_energy_lighting',
+         'measure_type': 'low_energy_lighting',
+         'description': 'Install low energy lighting in 14 outlets', 'starting_u_value': None,
+         'new_u_value': None, 'already_installed': False, 'sap_points': 2, 'kwh_savings': 766.5,
+         'energy_cost_savings': 197.22044999999997,
+         'co2_equivalent_savings': np.float64(0.09999999999999964),
+         'description_simulation': {'lighting-energy-eff': 'Very Good',
+                                    'lighting-description': 'Low energy lighting in all fixed '
+                                                            'outlets',
+                                    'low-energy-lighting': 100}, 'total': 58.8, 'labour_hours': 1,
+         'labour_days': 0.125, 'survey': True, 'recommendation_id': '5_phase=4', 'efficiency': 29.4,
+         'heat_demand': np.float64(5.099999999999994)}], [
+        {'type': 'heating', 'phase': 5, 'measure_type': 'time_temperature_zone_control',
+         'parts': [],
+         'description': 'Upgrade heating controls to Smart Thermostats, room sensors and smart '
+                        'radiator valves (time & temperature zone control)',
+         'total': 739.576, 'subtotal': 700.48, 'vat': 39.096000000000004,
+         'labour_hours': 3.6199999999999997, 'labour_days': np.float64(1.0),
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(2.9),
+         'already_installed': False,
+         'simulation_config': {'thermostatic_control_ending': 'time and temperature zone control',
+                               'switch_system_ending': None, 'trvs_ending': None,
+                               'mainheatc_energy_eff_ending': 'Very Good'},
+         'description_simulation': {'mainheatcont-description': 'Time and temperature zone control',
+                                    'mainheatc-energy-eff': 'Very Good'},
+         'recommendation_id': '6_phase=5', 'efficiency': 739.576,
+         'co2_equivalent_savings': np.float64(0.30000000000000027),
+         'heat_demand': np.float64(6.599999999999994), 'kwh_savings': np.float64(853.6999999999998),
+         'energy_cost_savings': np.float64(63.57554117647055)}], [
+        {'phase': 6, 'parts': [], 'type': 'secondary_heating', 'measure_type': 'secondary_heating',
+         'description': 'Remove the secondary heating system', 'starting_u_value': None,
+         'new_u_value': None, 'sap_points': np.float64(3.6), 'already_installed': False,
+         'total': 30.0, 'subtotal': 25.0, 'vat': 5.0, 'labour_hours': 3.0,
+         'labour_days': np.float64(1.0),
+         'simulation_config': {'secondheat_description_ending': 'None'},
+         'description_simulation': {'secondheat-description': 'None'},
+         'recommendation_id': '7_phase=6', 'efficiency': 30.0,
+         'co2_equivalent_savings': np.float64(0.10000000000000009),
+         'heat_demand': np.float64(15.400000000000006),
+         'kwh_savings': np.float64(202.30000000000018),
+         'energy_cost_savings': np.float64(15.065400000000011)}], [
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
+         'already_installed': False, 'total': 6013.139999999999, 'subtotal': 5010.95, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
+         'has_battery': False, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
+         'description_simulation': {'photo-supply': np.float64(65.0)},
+         'recommendation_id': '8_phase=7', 'efficiency': np.float64(462.54923076923075),
+         'co2_equivalent_savings': np.float64(0.47347873833399995),
+         'heat_demand': np.float64(88.69999999999999),
+         'kwh_savings': np.float64(2040.8566307499998),
+         'energy_cost_savings': np.float64(525.1124110919749)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system, with a battery.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
+         'already_installed': False, 'total': 10537.008, 'subtotal': 8780.84, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
+         'has_battery': True, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
+         'description_simulation': {'photo-supply': np.float64(65.0)},
+         'recommendation_id': '9_phase=7', 'efficiency': np.float64(810.5390769230769),
+         'co2_equivalent_savings': np.float64(0.6628702336675999),
+         'heat_demand': np.float64(88.69999999999999),
+         'kwh_savings': np.float64(2857.1992830499994),
+         'energy_cost_savings': np.float64(735.1573755287648)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
+         'already_installed': False, 'total': 5826.491999999999, 'subtotal': 4855.41, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
+         'has_battery': False, 'initial_ac_kwh_per_year': np.float64(3692.66794),
+         'description_simulation': {'photo-supply': np.float64(60.0)},
+         'recommendation_id': '10_phase=7', 'efficiency': np.float64(485.54099999999994),
+         'co2_equivalent_savings': np.float64(0.42834948104),
+         'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(1846.33397),
+         'energy_cost_savings': np.float64(475.0617304809999)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system, with a battery.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
+         'already_installed': False, 'total': 10350.359999999999, 'subtotal': 8625.3, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
+         'has_battery': True, 'initial_ac_kwh_per_year': np.float64(3692.66794),
+         'description_simulation': {'photo-supply': np.float64(60.0)},
+         'recommendation_id': '11_phase=7', 'efficiency': np.float64(862.5299999999999),
+         'co2_equivalent_savings': np.float64(0.599689273456),
+         'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(2584.867558),
+         'energy_cost_savings': np.float64(665.0864226734)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
+         'already_installed': False, 'total': 5642.604, 'subtotal': 4702.17, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(55.0),
+         'has_battery': False, 'initial_ac_kwh_per_year': np.float64(3300.5416548),
+         'description_simulation': {'photo-supply': np.float64(55.0)},
+         'recommendation_id': '12_phase=7', 'efficiency': np.float64(512.964),
+         'co2_equivalent_savings': np.float64(0.3828628319568), 'heat_demand': np.float64(78.3),
+         'kwh_savings': np.float64(1650.2708274),
+         'energy_cost_savings': np.float64(424.61468389001993)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system, with a battery.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
+         'already_installed': False, 'total': 10166.472, 'subtotal': 8472.06, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(55.0),
+         'has_battery': True, 'initial_ac_kwh_per_year': np.float64(3300.5416548),
+         'description_simulation': {'photo-supply': np.float64(55.0)},
+         'recommendation_id': '13_phase=7', 'efficiency': np.float64(924.2247272727273),
+         'co2_equivalent_savings': np.float64(0.53600796473952), 'heat_demand': np.float64(78.3),
+         'kwh_savings': np.float64(2310.3791583599996),
+         'energy_cost_savings': np.float64(594.4605574460278)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
+         'already_installed': False, 'total': 5458.727999999999, 'subtotal': 4548.94, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(45.0),
+         'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2907.1867812),
+         'description_simulation': {'photo-supply': np.float64(45.0)},
+         'recommendation_id': '14_phase=7', 'efficiency': np.float64(606.5253333333333),
+         'co2_equivalent_savings': np.float64(0.3372336666192), 'heat_demand': np.float64(64.0),
+         'kwh_savings': np.float64(1453.5933906),
+         'energy_cost_savings': np.float64(374.00957940138)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system, with a battery.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
+         'already_installed': False, 'total': 9982.596, 'subtotal': 8318.83, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(45.0),
+         'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2907.1867812),
+         'description_simulation': {'photo-supply': np.float64(45.0)},
+         'recommendation_id': '15_phase=7', 'efficiency': np.float64(1109.1773333333333),
+         'co2_equivalent_savings': np.float64(0.47212713326688), 'heat_demand': np.float64(64.0),
+         'kwh_savings': np.float64(2035.03074684),
+         'energy_cost_savings': np.float64(523.6134111619319)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
+         'already_installed': False, 'total': 5274.852, 'subtotal': 4395.71, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(40.0),
+         'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2510.25188),
+         'description_simulation': {'photo-supply': np.float64(40.0)},
+         'recommendation_id': '16_phase=7', 'efficiency': np.float64(659.3565),
+         'co2_equivalent_savings': np.float64(0.29118921808), 'heat_demand': np.float64(54.3),
+         'kwh_savings': np.float64(1255.12594),
+         'energy_cost_savings': np.float64(322.94390436199996)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system, with a battery.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
+         'already_installed': False, 'total': 9798.72, 'subtotal': 8165.6, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(40.0),
+         'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2510.25188),
+         'description_simulation': {'photo-supply': np.float64(40.0)},
+         'recommendation_id': '17_phase=7', 'efficiency': np.float64(1224.84),
+         'co2_equivalent_savings': np.float64(0.40766490531199995), 'heat_demand': np.float64(54.3),
+         'kwh_savings': np.float64(1757.1763159999998),
+         'energy_cost_savings': np.float64(452.1214661067999)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
+         'already_installed': False, 'total': 5090.976, 'subtotal': 4242.48, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(35.0),
+         'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2096.682636),
+         'description_simulation': {'photo-supply': np.float64(35.0)},
+         'recommendation_id': '18_phase=7', 'efficiency': np.float64(727.2822857142856),
+         'co2_equivalent_savings': np.float64(0.243215185776), 'heat_demand': np.float64(48.5),
+         'kwh_savings': np.float64(1048.341318), 'energy_cost_savings': np.float64(269.7382211214)},
+        {'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
+         'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system, with a battery.',
+         'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
+         'already_installed': False, 'total': 9614.844, 'subtotal': 8012.369999999999, 'vat': 0,
+         'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(35.0),
+         'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2096.682636),
+         'description_simulation': {'photo-supply': np.float64(35.0)},
+         'recommendation_id': '19_phase=7', 'efficiency': np.float64(1373.5491428571427),
+         'co2_equivalent_savings': np.float64(0.3405012600864), 'heat_demand': np.float64(48.5),
+         'kwh_savings': np.float64(1467.6778451999999),
+         'energy_cost_savings': np.float64(377.6335095699599)}]
+]
diff --git a/recommendations/tests/test_optimiser_functions.py b/recommendations/tests/test_optimiser_functions.py
index 197b3263..b8146c6b 100644
--- a/recommendations/tests/test_optimiser_functions.py
+++ b/recommendations/tests/test_optimiser_functions.py
@@ -1,6 +1,10 @@
 import pytest
+import numpy as np
 from types import SimpleNamespace
+from recommendations.tests.test_data.measures_to_optimise import measures_to_optimise
 from recommendations.optimiser import optimiser_functions
+from recommendations.optimiser.GainOptimiser import GainOptimiser
+from recommendations.optimiser.CostOptimiser import CostOptimiser
 
 
 class TestPrepareInputMeasures:
@@ -133,3 +137,129 @@ class TestFlattenRecommendationsWithDefaults:
         assert all("default" in rec for rec in result)
         assert next(r for r in result if r["recommendation_id"] == "b")["default"] is True
         assert next(r for r in result if r["recommendation_id"] == "a")["default"] is False
+
+
+class TestIncreasingEpcE2e:
+    """
+    Test out the classic increasing EPC optimisation flow end-to-end.
+    We have a goal (Increasing EPC), no budget, and we expect the optimiser to choose
+    the best set of measures and include best-practice ventilation.
+    """
+
+    @pytest.fixture
+    def setup_case(self):
+        # ✅ Dummy property object
+        p = SimpleNamespace(
+            id="P1",
+            has_ventilation=False,
+            data={"current-energy-efficiency": "52"},
+        )
+
+        # ✅ Dummy request body
+        body = SimpleNamespace(
+            goal="Increasing EPC",
+            goal_value="C",
+            optimise=True,
+            budget=None,
+            simulate_sap_10=False,
+            required_measures=[]
+        )
+
+        # ✅ Use your massive measures_to_optimise list
+
+        recommendations = {"P1": measures_to_optimise}
+
+        return p, body, recommendations
+
+    def test_end_to_end_increasing_epc(self, setup_case, monkeypatch):
+        p, body, recommendations = setup_case
+
+        # ✅ Patch assumptions to simplify behaviour
+        monkeypatch.setattr(optimiser_functions.assumptions, "measures_needing_ventilation",
+                            ["external_wall_insulation", "internal_wall_insulation"])
+
+        # ✅ Patch CostOptimiser.calculate_sap_gain_with_slack so we don't need SAP logic
+        monkeypatch.setattr(CostOptimiser, "calculate_sap_gain_with_slack", staticmethod(lambda x: x))
+
+        # ✅ Patch epc_to_sap_lower_bound for a known SAP target
+        monkeypatch.setattr(optimiser_functions, "epc_to_sap_lower_bound", lambda goal: 69)  # EPC C lower bound ~69 SAP
+
+        # ---------------------
+        # RUN THE OPTIMISATION LOOP
+        # ---------------------
+
+        property_measure_types = {rec["type"] for recs in recommendations[p.id] for rec in recs}
+        property_required_measures = [m for m in recommendations[p.id] if m[0]["type"] in body.required_measures]
+        measures_to_optimise = [m for m in recommendations[p.id] if m[0]["type"] not in body.required_measures]
+
+        # ventilation flag
+        needs_ventilation = any(
+            x in property_measure_types for x in optimiser_functions.assumptions.measures_needing_ventilation
+        ) and not p.has_ventilation
+
+        assert needs_ventilation
+
+        input_measures = optimiser_functions.prepare_input_measures(measures_to_optimise, body.goal, needs_ventilation)
+
+        assert input_measures, "Expected measures to optimise"
+        assert len(input_measures) == 7
+
+        fixed_gain = optimiser_functions.calculate_fixed_gain(
+            property_required_measures, recommendations, p, needs_ventilation
+        )
+        assert fixed_gain == 0, "No required measures should mean fixed gain is 0"
+
+        gain = optimiser_functions.calculate_gain(body=body, p=p, fixed_gain=fixed_gain)
+
+        assert gain == 18.5, "Expected gain to be calculated correctly based on fixed gain and SAP target"
+
+        optimiser = (
+            GainOptimiser(
+                input_measures, max_cost=body.budget, max_gain=gain,
+                allow_slack=body.goal == "Increasing EPC"
+            ) if body.budget else CostOptimiser(input_measures, min_gain=gain)
+        )
+        optimiser.setup()
+        optimiser.solve()
+        solution = optimiser.solution
+
+        # ✅ Validate solution makes sense
+        assert solution, "Optimiser should return a non-empty solution"
+        assert all("id" in m for m in solution)
+        assert any("solar_pv" in m["type"] for m in solution), "Expected solar PV to be included"
+
+        # ✅ Collect selected measure IDs
+        selected = {r["id"] for r in solution}
+
+        assert selected == {'8_phase=7', '5_phase=4', '7_phase=6'}
+
+        # ✅ Add required measures (none here)
+        solution = optimiser_functions.add_required_measures(
+            property_id=p.id, property_required_measures=property_required_measures,
+            recommendations=recommendations, selected=selected,
+        )
+
+        assert solution == [
+            {'id': '5_phase=4', 'cost': 58.8, 'gain': 2, 'type': 'low_energy_lighting'},
+            {'id': '7_phase=6', 'cost': 30.0, 'gain': np.float64(3.6), 'type': 'secondary_heating'},
+            {'id': '8_phase=7', 'cost': 6013.139999999999, 'gain': np.float64(13.0), 'type': 'solar_pv'}
+        ]
+
+        total_optimised_gain = sum(m["gain"] for m in solution)
+        assert total_optimised_gain == 18.6, "Total gain of optimised measures should meet or exceed target gain"
+
+        selected = optimiser_functions.add_best_practice_measures(p.id, solution, recommendations, selected)
+
+        # ✅ Flatten recommendations for output
+        flattened = optimiser_functions.flatten_recommendations_with_defaults(p.id, recommendations, selected)
+
+        # ---------------------
+        # FINAL ASSERTIONS
+        # ---------------------
+        assert isinstance(flattened, list)
+        assert all("default" in rec for rec in flattened)
+        assert any(rec["default"] for rec in flattened), "Some measures should be marked as default"
+
+        # We don't add ventilation as major insulation work isn't done
+        ventilation_added = any(rec["recommendation_id"] == "3_phase=2" and rec["default"] for rec in flattened)
+        assert not ventilation_added, "Ventilation should not be added without major insulation work"