diff --git a/ops_scripts/generic/new_textract_file_reconciliation.py b/ops_scripts/generic/new_textract_file_reconciliation.py
new file mode 100644
index 0000000..ced41bf
--- /dev/null
+++ b/ops_scripts/generic/new_textract_file_reconciliation.py
@@ -0,0 +1,70 @@
+import boto3
+import os
+import pandas as pd
+from datetime import datetime
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from io import StringIO
+
+PROFILE_NAME = 'temp_cred'
+SOURCE_BUCKET = 'doczyai-use2-u-cn1-s3-textract-processing-001'
+DESTINATION_BUCKET = 'healthnet-json-files'
+ERROR_LOG_FILE = "error_log_files.txt"
+MAX_WORKERS = 200
+CSV_FILE_PATH = "processed_batch_ids/large-pdf-files-processed-3.csv"
+DESTINATION_PATHS = {
+    "text": 'new-batch6/large-pdf-txt-files/',
+    "stuck": 'new-batch6/stuck-files/',
+    "pdf": 'new-batch6/large-pdf-processed-files/',
+    "invalid": 'new-batch6/invalid-files/',
+    "unprocessed": 'new-batch6/unprocessed-files/'
+}
+
+session = boto3.Session(profile_name=PROFILE_NAME)
+s3_client = session.client('s3')
+
+def log_error_to_file(key, error_message):
+    with open(ERROR_LOG_FILE, 'a') as f:
+        f.write(f"Error with file: {key} - {error_message}\n")
+
+def copy_single_file(key, destination_prefix):
+    destination_prefix = DESTINATION_PATHS[destination_prefix]
+    try:
+        copy_source = {'Bucket': SOURCE_BUCKET, 'Key': key}
+        destination_key = os.path.join(destination_prefix, os.path.basename(key))
+        s3_client.copy(copy_source, DESTINATION_BUCKET, destination_key)
+        print(f"Copied {key} to {destination_key}")
+    except Exception as e:
+        print(f"Error copying {key} to {destination_key}")
+        log_error_to_file(key, str(e))
+
+def main():
+    response = s3_client.get_object(Bucket=DESTINATION_BUCKET, Key=CSV_FILE_PATH)
+    csv_content = response['Body'].read().decode('utf-8')
+    batch_ids_df = pd.read_csv(StringIO(csv_content))
+    batch_ids = batch_ids_df['batch_id'].unique()
+    contract_names = batch_ids_df['contract_name']
+    
+    print(f"Total number of batches: {len(batch_ids)}")
+    file_keys = []
+    for batch_id, contract_name in zip(batch_ids_df['batch_id'], contract_names):
+        contract_name = contract_name[:-4]
+        txt_file_key = f'contract-text-file/{batch_id}/{contract_name}.txt'
+        stuck_file_key = f'all-pdfs/{batch_id}/{contract_name}.pdf'
+        pdf_file_key = f'textract-receiver-processed-pdfs/{batch_id}/{contract_name}.pdf'
+        invalid_file_key = f'invalid-pdfs/{batch_id}/{contract_name}.pdf'
+        unprocessed_file_key = f'textract-sender-staging-pdfs/{batch_id}/{contract_name}.pdf'
+        file_keys.append((txt_file_key, 'text'))
+        file_keys.append((stuck_file_key, 'stuck'))
+        file_keys.append((pdf_file_key, 'pdf'))
+        file_keys.append((invalid_file_key, 'invalid'))
+        file_keys.append((unprocessed_file_key, 'unprocessed'))
+
+    with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
+        futures = [executor.submit(copy_single_file, key, dest) for key, dest in file_keys]
+        for future in as_completed(futures):
+            future.result()
+    
+    print("Copy operation completed.")
+
+if __name__ == '__main__':
+    main()
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