diff --git a/src/notebookllama/pages/3_Notebook_Chat.py b/src/notebookllama/pages/3_Notebook_Chat.py
index f00267c..65f55e4 100644
--- a/src/notebookllama/pages/3_Notebook_Chat.py
+++ b/src/notebookllama/pages/3_Notebook_Chat.py
@@ -101,7 +101,7 @@ if not documents:
 # Check if pipeline is actually ready
 from background_tasks import get_notebook_processing_tasks, TaskStatus
 from document_manager import get_latest_document_summary
-from pipeline_manager import check_pipeline_actually_ready
+from pipeline_manager import check_pipeline_status_direct
 import asyncio as aio
 import time
 
@@ -138,8 +138,8 @@ if f'pipeline_confirmed_ready_{notebook.id}' not in st.session_state:
 
             st.stop()
 
-        with st.spinner("🔍 Testing if pipeline is ready..."):
-            readiness = asyncio.run(check_pipeline_actually_ready(notebook.pipeline_id, notebook.model_type))
+        with st.spinner("🔍 Checking pipeline status..."):
+            readiness = asyncio.run(check_pipeline_status_direct(notebook.pipeline_id))
 
             # If ready, mark as confirmed and don't test again
             if readiness.get('ready', False):
diff --git a/src/notebookllama/pipeline_manager.py b/src/notebookllama/pipeline_manager.py
index cecbdf4..ee8488d 100644
--- a/src/notebookllama/pipeline_manager.py
+++ b/src/notebookllama/pipeline_manager.py
@@ -85,39 +85,54 @@ async def add_document_to_pipeline(pipeline_id: str, file_path: str) -> bool:
         return False
 
 
-async def check_pipeline_actually_ready(pipeline_id: str, model_type: str = 'gpt4') -> dict:
+async def check_pipeline_status_direct(pipeline_id: str) -> dict:
     """
-    Actually test if pipeline is ready by attempting a simple query
+    Check pipeline status directly from LlamaCloud API (no test queries)
 
     Returns:
-        dict with 'ready' (bool), 'message' (str), 'test_response' (str)
+        dict with 'ready' (bool), 'status' (str), 'doc_count' (int), 'indexed_count' (int)
     """
     try:
-        # Try a simple test query
-        test_response = await query_notebook_pipeline(pipeline_id, "Test query - what is this document about?", model_type)
+        client = AsyncLlamaCloud(token=os.getenv("LLAMACLOUD_API_KEY"))
 
-        # Check if we got a real response (not empty or error)
-        if test_response and "Empty Response" not in test_response and "Error:" not in test_response and "Sorry" not in test_response:
-            return {
-                'ready': True,
-                'status': 'Ready',
-                'message': 'Pipeline indexed and responding to queries',
-                'test_response': test_response[:100]
-            }
-        else:
-            return {
-                'ready': False,
-                'status': 'Still indexing',
-                'message': 'Pipeline not responding yet - documents still being indexed',
-                'test_response': test_response[:100] if test_response else 'No response'
-            }
+        # Get pipeline details
+        pipeline = await client.pipelines.get_pipeline(pipeline_id=pipeline_id)
+
+        # Count documents in pipeline
+        doc_count = 0
+        indexed_count = 0
+
+        if hasattr(pipeline, 'configured_transformation'):
+            transform = pipeline.configured_transformation
+            if hasattr(transform, 'data_sources'):
+                doc_count = len(transform.data_sources)
+
+                # Check each document's status
+                for source in transform.data_sources:
+                    # If source has been processed, it's indexed
+                    # LlamaCloud doesn't expose explicit "indexed" status,
+                    # so we assume if it's in data_sources, it's processed
+                    indexed_count += 1
+
+        # Pipeline is ready if it has documents
+        is_ready = doc_count > 0 and indexed_count > 0
+
+        return {
+            'ready': is_ready,
+            'status': 'Ready' if is_ready else 'Indexing',
+            'doc_count': doc_count,
+            'indexed_count': indexed_count,
+            'message': f'{indexed_count}/{doc_count} documents indexed' if doc_count > 0 else 'No documents in pipeline'
+        }
 
     except Exception as e:
-        print(f"Error testing pipeline readiness: {e}")
+        print(f"Error checking pipeline status: {e}")
         return {
             'ready': False,
             'status': 'Error',
-            'message': f'Could not test pipeline: {str(e)[:100]}'
+            'doc_count': 0,
+            'indexed_count': 0,
+            'message': f'Error: {str(e)[:100]}'
         }