diff --git a/etl/hubSpotClient/types.py b/etl/hubSpotClient/types.py
index 35afaa0..6131136 100644
--- a/etl/hubSpotClient/types.py
+++ b/etl/hubSpotClient/types.py
@@ -18,6 +18,8 @@ def string_to_installer(installer):
         return SharePointInstaller.SOUTH_COAST_INSULATION
     elif installer.upper() == "SGEC":
         return SharePointInstaller.JJC
+    elif installer.upper() == "WARM FRONT":
+        return SharePointInstaller.BAXTER_KELLY
     else:
         return None
 
diff --git a/etl/month_end_automation_wave_2_no_11.py b/etl/month_end_automation_wave_2_no_11.py
index f05ef13..41f4438 100644
--- a/etl/month_end_automation_wave_2_no_11.py
+++ b/etl/month_end_automation_wave_2_no_11.py
@@ -8,9 +8,10 @@ import json
 
 monday_key = "eyJhbGciOiJIUzI1NiJ9.eyJ0aWQiOjQ5ODc2ODQxOCwiYWFpIjoxMSwidWlkIjozNjE3ODAzNCwiaWFkIjoiMjAyNS0wNC0xMVQxMToyMzoxNy40NjdaIiwicGVyIjoibWU6d3JpdGUiLCJhY3RpZCI6MTM5OTc4MjMsInJnbiI6InVzZTEifQ.-2Lit4s46ZF6AXuMW9t0TxIaFLkHqD4Yo-PyM9i2XZY"
 monday = MondayClient(monday_key)
-# Northumberland LAD2 & HUG2
-board_ids = ["5121300882"]
 
+board_ids = ["5121300882"]
+emp = "rate card is empty"
+check_wall_type = "check wall type"
 rate_card_data = {
     "job_type": [
         "RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
@@ -19,10 +20,10 @@ rate_card_data = {
         "RA no show", "ATT no show", "post EPC no show"
     ],
     "rate": [
-        259, 125, 280, 125, 125,
-        650, 195, 175, 135,
-        120, "Post EPR Please Marianne", 85, 125, 60,
-        25, 25, 25
+        225, 92.65, check_wall_type, check_wall_type, check_wall_type,
+        check_wall_type, check_wall_type, 165, 135,
+        120, "60 - check", 48.6, 85.55, 49.6,
+        125, 125, 125
     ]
 }
 
@@ -92,7 +93,7 @@ def get_df(df, column_name, success_critera, job_name=None):
 
 
 # RA
-ra = get_df(df, "ra", ["completed rdsap 9.9", "completed rd sap 10"], "RA")
+ra = get_df(df, "ra", ["completed rdsap 10", "completed rdsap 9.9"], "RA")
 filtered_dfs.append(ra)
 
 
diff --git a/etl/month_end_automation_wave_2_no_14.py b/etl/month_end_automation_wave_2_no_14.py
index 4a644ee..3a670b9 100644
--- a/etl/month_end_automation_wave_2_no_14.py
+++ b/etl/month_end_automation_wave_2_no_14.py
@@ -21,8 +21,8 @@ rate_card_data_example = {
     ],
     "rate": [
         259, 125, 280, 125,
-        260, 126, 281, 126,
-        262, 127, 282, 127,
+        135, "60 to check", 85, 125,
+        60, 45, 45, 45,
     ]
 }
 
@@ -193,4 +193,14 @@ filtered_dfs.append(epc_ns)
 
 final_df = pd.concat(filtered_dfs).reset_index(drop=True)
 
-final_df[['address', 'client', 'job_type']]
\ No newline at end of file
+
+final_df["job_type"] = final_df["job_type"].str.lower()
+rate_card_df["job_type"] = rate_card_df["job_type"].str.lower()
+
+# Now perform the merge
+combined_with_rates = final_df.merge(rate_card_df, on="job_type", how="left")
+import datetime
+timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')
+
+attribute = ['address', 'client', 'job_type', 'rate']
+combined_with_rates[attribute].to_excel(f'Decent Homes Stonewater - Operations_{timestamp}.xlsx', index=False)
diff --git a/etl/month_end_automation_wave_3_layout.py b/etl/month_end_automation_wave_3_layout.py
index 673ea66..d5649f6 100644
--- a/etl/month_end_automation_wave_3_layout.py
+++ b/etl/month_end_automation_wave_3_layout.py
@@ -10,15 +10,15 @@ import json
 monday_key = "eyJhbGciOiJIUzI1NiJ9.eyJ0aWQiOjQ5ODc2ODQxOCwiYWFpIjoxMSwidWlkIjozNjE3ODAzNCwiaWFkIjoiMjAyNS0wNC0xMVQxMToyMzoxNy40NjdaIiwicGVyIjoibWU6d3JpdGUiLCJhY3RpZCI6MTM5OTc4MjMsInJnbiI6InVzZTEifQ.-2Lit4s46ZF6AXuMW9t0TxIaFLkHqD4Yo-PyM9i2XZY"
 monday = MondayClient(monday_key)
 board_ids = [
-#     "9349630181", # WCHG Walkups-Operations
-    # "8829428746", # 2502 Accent Housing
+    "9349630181", # WCHG Walkups-Operations
     # "8830772914", # "L&Q London"
     # "9601691730", # Cardo Wales & West - Wave 3
-    "9660895490", # Northumberland County SHDF Wave 3
+    # "9660895490", # Northumberland County SHDF Wave 3
+    # "9641491000", #Watford Warm Homes
 ]
 
 empty = "Rate card info missing"
-rate_card_data_example = {
+rate_card_data_watford_warm_homes = {
     "job_type": [
         "RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
         "Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
@@ -26,10 +26,10 @@ rate_card_data_example = {
         "RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
     ],
     "rate": [
-        259, 125, 280, 125, 125,
-        650, 415, 195, 175, 135,
-        120, "Post EPR Please Marianne", 85, 125, 60,
-        25, 25, 25, "Mariann please input full cost mtp", "Marianne please input measure modelling"
+        165, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
     ]
 }
 
@@ -76,16 +76,31 @@ rate_card_data_northhumberland_country_shdf_wave_3 = {
         empty, empty, empty, empty, empty,
         empty, empty, empty, empty, empty,
         empty, empty, empty, empty, empty,
-        empty, empty, empty, 280, 150
+        empty, empty, empty, 280, 130,
     ]
 }
 
+rate_card_data_walk_ups = {
+    "job_type": [
+        "RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
+        "Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
+        "Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
+        "RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
+    ],
+    "rate": [
+        259, 125, 280, 125, 125,
+        650, 415, 195, 175, 135,
+        120, "60 to check", 85, 125, 60,
+        45, 45, 45, empty, empty,
+    ]
+}
 
 
 # rate_card_df = pd.DataFrame(rate_card_data_example)
 # rate_card_df = pd.DataFrame(rate_card_data_2502_accent_housing)
 # rate_card_df = pd.DataFrame(rate_card_data_l_and_q_london)
-rate_card = pd.DataFrame(rate_card_data_northhumberland_country_shdf_wave_3)
+# rate_card_df = pd.DataFrame(rate_card_data_northhumberland_country_shdf_wave_3)
+rate_card_df = pd.DataFrame(rate_card_data_walk_ups)
 
 
 for board in tqdm(board_ids):
@@ -264,4 +279,286 @@ import datetime
 timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')
 
 attribute = ['address', 'client', 'job_type', 'rate']
-combined_with_rates[attribute].to_excel(f'L&Q London {timestamp}.xlsx', index=False)
+combined_with_rates[attribute].to_excel(f'WCHG Walk up {timestamp}.xlsx', index=False)
+# Wave 3's month end automation
+
+from tqdm import tqdm
+from monday import MondayClient
+from etl.osmosis_complaince_address_to_files import get_all_items, extract_asset_ids
+from pprint import pprint
+import pandas as pd
+import json
+
+monday_key = "eyJhbGciOiJIUzI1NiJ9.eyJ0aWQiOjQ5ODc2ODQxOCwiYWFpIjoxMSwidWlkIjozNjE3ODAzNCwiaWFkIjoiMjAyNS0wNC0xMVQxMToyMzoxNy40NjdaIiwicGVyIjoibWU6d3JpdGUiLCJhY3RpZCI6MTM5OTc4MjMsInJnbiI6InVzZTEifQ.-2Lit4s46ZF6AXuMW9t0TxIaFLkHqD4Yo-PyM9i2XZY"
+monday = MondayClient(monday_key)
+board_ids = [
+    "9349630181", # WCHG Walkups-Operations
+    # "8830772914", # "L&Q London"
+    # "9601691730", # Cardo Wales & West - Wave 3
+    # "9660895490", # Northumberland County SHDF Wave 3
+    # "9641491000", #Watford Warm Homes
+]
+
+empty = "Rate card info missing"
+rate_card_data_watford_warm_homes = {
+    "job_type": [
+        "RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
+        "Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
+        "Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
+        "RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
+    ],
+    "rate": [
+        165, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+    ]
+}
+
+
+rate_card_data_2502_accent_housing = {
+    "job_type": [
+        "RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
+        "Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
+        "Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
+        "RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
+    ],
+    "rate": [
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, 280, 150
+    ]
+}
+
+rate_card_data_l_and_q_london = {
+    "job_type": [
+        "RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
+        "Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
+        "Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
+        "RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
+    ],
+    "rate": [
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, 280, 150
+    ]
+}
+
+
+rate_card_data_northhumberland_country_shdf_wave_3 = {
+    "job_type": [
+        "RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
+        "Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
+        "Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
+        "RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
+    ],
+    "rate": [
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, empty, empty,
+        empty, empty, empty, 280, 130,
+    ]
+}
+
+rate_card_data_walk_ups = {
+    "job_type": [
+        "RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
+        "Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
+        "Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
+        "RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
+    ],
+    "rate": [
+        259, 125, 280, 125, 125,
+        650, 415, 195, 175, 135,
+        120, "60 to check", 85, 125, 60,
+        45, 45, 45, empty, empty,
+    ]
+}
+
+
+# rate_card_df = pd.DataFrame(rate_card_data_example)
+# rate_card_df = pd.DataFrame(rate_card_data_2502_accent_housing)
+# rate_card_df = pd.DataFrame(rate_card_data_l_and_q_london)
+# rate_card_df = pd.DataFrame(rate_card_data_northhumberland_country_shdf_wave_3)
+rate_card_df = pd.DataFrame(rate_card_data_walk_ups)
+
+
+for board in tqdm(board_ids):
+    print(f"working on board {board}")
+    board_data = monday.boards.fetch_boards_by_id(board)
+    columns = board_data["data"]["boards"][0]["columns"]
+    col_id_map = {col["title"].lower(): col["id"] for col in columns}
+    reversed_col_id_map = {v: k for k, v in col_id_map.items()}
+    
+    
+    items = get_all_items(board, monday)
+
+    all_records = []
+    for row in tqdm(items):
+        data = {}
+        data.update({"address": row['name']})
+        data.update({"client": row['group']['title']})
+        for col in row.get("column_values", []):
+            if col.get("id") in reversed_col_id_map:
+                if col.get("type") == "file":
+                    value = col.get("value")
+                    no_of_files = 0
+
+                    if value:
+                        value = json.loads(col["value"])
+                        no_of_files = len(value.get('files', []))
+                    data.update({reversed_col_id_map[col.get("id")]: no_of_files})
+                else:
+                    data.update({
+                        reversed_col_id_map[col.get("id")]: col.get("text")
+                    })
+        all_records.append(data)
+
+# Convert to DataFrame
+df = pd.DataFrame(all_records)
+
+filtered_dfs = []
+
+
+def get_df(df, column_name, success_critera, job_name=None):
+    _ = pd.DataFrame()
+    if column_name in col_id_map:
+        _ = df[
+            df[column_name].str.lower().isin(success_critera)
+        ].copy()
+        if job_name:
+            _["job_type"] = job_name
+    
+    
+    return _
+
+
+# RA
+ra = get_df(df, "ra invoicing status", ["to invoice"], "RA")
+if not ra.empty:
+    filtered_dfs.append(ra)
+
+
+att = get_df(df, "post att invoicing status", ["to invoice"], "ATT")
+if not att.empty:
+    filtered_dfs.append(att)
+
+modeling = get_df(df, "mtp invoicing status", ["modelling to invoice"], "Measure Modelling")
+if not modeling.empty:
+    filtered_dfs.append(modeling)
+
+try:
+    # Only needed for one board in wave 3
+    full_cost = get_df(df, "mtp invoicing status", ["(V1) Full cost MTP to invoice (no previous modelling)".lower()], "full cost mtp")
+    if not full_cost.empty:
+        filtered_dfs(full_cost)
+except Exception as e:
+    print(e)
+
+v1 = get_df(df, "mtp invoicing status", ["(v1) ioe/mtp to invoice"], "Coordination Stage 1 v1")
+if not v1.empty:
+    filtered_dfs.append(v1)
+
+v2 = get_df(df, "mtp invoicing status", ["(v2) ioe/mtp to invoice"], "Coordination Stage 1 v2 remodel")
+if not v2.empty:
+    filtered_dfs.append(v2)
+
+v3 = get_df(df, "mtp invoicing status", ["(v3) ioe/mtp to invoice"], "Coordination Stage 1 v3 remodel")
+if not v3.empty:
+    filtered_dfs.append(v3)
+
+# Coordination stage 2 Please complete
+cors2 = get_df(df, "rc stage 2", ["to invoice"], "Coordination Stage 2")
+if not cors2.empty:
+    filtered_dfs.append(cors2)
+
+# Design archetype complex
+design = get_df(df, "design invoicing status", ["to invoice"])
+design1 = get_df(design, "design invoice type", ["archetype (complex)"], "Design Archetype Complex")
+if not design1.empty :
+    filtered_dfs.append(design1)
+
+# Design archetype simple
+design1 = get_df(design, "design invoice type", ["archetype (simple)"], "Design Archetype Simple")
+if not design1.empty:
+    filtered_dfs.append(design1)
+
+# Design repetitive simple
+design1 = get_df(design, "design invoice type", ["archetype (simple)"], "Design Archetype repetitive")
+if not design1.empty:
+    filtered_dfs.append(design1)
+
+# Design repetitive complex
+design1 = get_df(design, "design invoice type", ["archetype (complex)"], "Design Archetype complex")
+if not design1.empty:
+    filtered_dfs.append(design1)
+
+# Design Revision
+revision_letter = ['a', 'b', 'c', 'd']
+for letter in revision_letter:
+    design = get_df(df, "design revision invoice", [f"rev. {letter} to invoice"], "Design Revision")
+    if not design.empty:
+        filtered_dfs.append(design)
+
+# Lodgement Phase 1
+lodg1  = get_df(df, "lodgement phase 1 invoicing status", ["to invoice"], "Lodgement Phase 1")
+if not lodg1.empty:
+    filtered_dfs(lodg1)
+
+# Full Lodgement Phase
+lodg2 = get_df(df, "full lodgement invoicing status", ["to invoice"], "Full lodgement phase 2")
+if not lodg2.empty:
+    filtered_dfs.append(lodg2)
+
+# POST EPC
+post_epc = get_df(df, "post epc & eval. invoicing status", ["epc to invoice"], "POST EPC")
+if not post_epc.empty:
+    filtered_dfs.append(post_epc)
+
+
+# POST EPR
+post_epr = get_df(df, "post epc & eval. invoicing status", ["epr to invoice"], "POST EPR")
+if not post_epr.empty:
+    filtered_dfs.append(post_epr)
+
+# post att
+post_att = get_df(df, "post att invoicing status", ["to invoice"], "POST ATT")
+if not post_att.empty:
+    filtered_dfs.append(post_epc)
+
+# Retrofit Evaluation
+rc = get_df(df, "rc stage 2 invoicing status", ["to invoice"], "retrofit evaluation")
+if not rc.empty:
+    filtered_dfs.append(rc)
+
+# RA NO Show
+ra_ns  = get_df(df,"ra no show invoice", ["to invoice","to invoice (+1 previous no show)", "to invoice (+2 previous no shows)"], "RA NO SHOW")
+if not ra_ns.empty:
+    filtered_dfs.append(ra_ns)
+
+
+# ATT NO Show
+att_ns = get_df(df, "pre att no show invoice", ["to invoice","to invoice (+1 previous no show)", "to invoice (+2 previous no shows)"], "ATT NO SHOW")
+if not att_ns.empty:
+    filtered_dfs.append(att_ns)
+
+
+# Post visit no show
+epc_ns = get_df(df, "post works no show invoice", ["to invoice","to invoice (+1 previous no show)", "to invoice (+2 previous no shows)"], "post EPC NO SHOW")
+if not epc_ns.empty:
+    filtered_dfs.append(epc_ns)
+
+final_df = pd.concat(filtered_dfs).reset_index(drop=True)
+
+final_df["job_type"] = final_df["job_type"].str.lower()
+rate_card_df["job_type"] = rate_card_df["job_type"].str.lower()
+
+# Now perform the merge
+combined_with_rates = final_df.merge(rate_card_df, on="job_type", how="left")
+import datetime
+timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')
+
+attribute = ['address', 'client', 'job_type', 'rate']
+combined_with_rates[attribute].to_excel(f'WCHG Walk up {timestamp}.xlsx', index=False)
diff --git a/etl/scraper/scraper.py b/etl/scraper/scraper.py
index fb289cd..b4f0691 100644
--- a/etl/scraper/scraper.py
+++ b/etl/scraper/scraper.py
@@ -20,8 +20,8 @@ WEEK_COMMENCING = os.getenv("WEEK_COMMENCING", previous_monday())
 
 class SharePointInstaller(Enum):
     # https//{tenant}.sharepoint.com/sites/{site}/_api/site/id
-    SOUTH_COAST_INSULATION = os.getenv("SOUTH_COAST_INSULATION_SERVICE_SHAREPOINT_ID", None)
-    JJC = os.getenv("JJC_SERVICE_SHAREPOINT_ID", None)
+    SOUTH_COAST_INSULATION = os.getenv("SOUTH_COAST_INSULATION_SERVICE_SHAREPOINT_ID", "b5a51507-9427-4ee0-b03e-90ec7681e2d3")
+    JJC = os.getenv("JJC_SERVICE_SHAREPOINT_ID", "7fdd0485-bbf3-4b29-b30f-98c81c2a6284")
     SGEC = os.getenv("SGEC_SERVICE_SHAREPOINT_ID", "52018e5c-3215-4fe4-a4e3-bbf0d0aa7cd9")
     BAXTER_KELLY = os.getenv("BAXTER_KELLY_SERVICE_SHAREPOINT_ID", "6f930bf3-572d-4f91-b1ae-ec536fa319e2")
     DOMNA = os.getenv("DOMNA_SHAREPOINT_ID", "8ab64924-ccde-4b56-b0dc-4e11596446e4")