diff --git a/surfsense_backend/app/indexing_pipeline/indexing_pipeline_service.py b/surfsense_backend/app/indexing_pipeline/indexing_pipeline_service.py
index d388c96ed..d5f50891c 100644
--- a/surfsense_backend/app/indexing_pipeline/indexing_pipeline_service.py
+++ b/surfsense_backend/app/indexing_pipeline/indexing_pipeline_service.py
@@ -1,9 +1,24 @@
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from sqlalchemy.orm import object_session
+from sqlalchemy.orm.attributes import set_committed_value
+
+from app.config import config
 from app.db import Document, DocumentStatus
 from app.indexing_pipeline.connector_document import ConnectorDocument
 from app.indexing_pipeline.document_hashing import compute_content_hash, compute_unique_identifier_hash
+from app.utils.document_converters import create_document_chunks, generate_document_summary
+
+
+def _safe_set_chunks(document: Document, chunks: list) -> None:
+    set_committed_value(document, "chunks", chunks)
+    session = object_session(document)
+    if session is not None:
+        if document.id is not None:
+            for chunk in chunks:
+                chunk.document_id = document.id
+        session.add_all(chunks)
 
 
 class IndexingPipelineService:
@@ -54,3 +69,33 @@ class IndexingPipelineService:
 
         await self.session.commit()
         return documents
+
+    async def index(
+        self, document: Document, connector_doc: ConnectorDocument, llm
+    ) -> None:
+        try:
+            document.status = DocumentStatus.processing()
+            await self.session.commit()
+
+            if connector_doc.should_summarize:
+                content, embedding = await generate_document_summary(
+                    connector_doc.source_markdown, llm, connector_doc.metadata
+                )
+            else:
+                content = connector_doc.source_markdown
+                embedding = config.embedding_model_instance.embed(content)
+
+            chunks = await create_document_chunks(connector_doc.source_markdown)
+
+            document.source_markdown = connector_doc.source_markdown
+            document.content = content
+            document.embedding = embedding
+            _safe_set_chunks(document, chunks)
+            document.status = DocumentStatus.ready()
+            await self.session.commit()
+
+        except Exception as e:
+            await self.session.rollback()
+            await self.session.refresh(document)
+            document.status = DocumentStatus.failed(str(e))
+            await self.session.commit()
diff --git a/surfsense_backend/tests/integration/conftest.py b/surfsense_backend/tests/integration/conftest.py
index fb6fca773..0ff860937 100644
--- a/surfsense_backend/tests/integration/conftest.py
+++ b/surfsense_backend/tests/integration/conftest.py
@@ -12,7 +12,7 @@ from sqlalchemy.pool import NullPool
 from app.db import Base, SearchSpace
 from app.db import User
 
-_EMBEDDING_DIM = 4  # keep vectors tiny; real model uses 768+
+_EMBEDDING_DIM = 1024  # must match the Vector() dimension used in DB column creation
 
 _DEFAULT_TEST_DB = "postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense_test"
 TEST_DATABASE_URL = os.environ.get("TEST_DATABASE_URL", _DEFAULT_TEST_DB)
@@ -96,9 +96,33 @@ def mock_llm() -> AsyncMock:
 
 
 @pytest.fixture
-def mock_embedding_model() -> MagicMock:
-    model = MagicMock()
-    model.embed = MagicMock(
-        side_effect=lambda texts: [[0.1] * _EMBEDDING_DIM for _ in texts]
+def patched_generate_summary(monkeypatch) -> AsyncMock:
+    mock = AsyncMock(return_value=("Mocked summary.", [0.1] * _EMBEDDING_DIM))
+    monkeypatch.setattr(
+        "app.indexing_pipeline.indexing_pipeline_service.generate_document_summary",
+        mock,
     )
+    return mock
+
+
+@pytest.fixture
+def patched_create_chunks(monkeypatch) -> MagicMock:
+    from app.db import Chunk
+
+    chunk = Chunk(content="Test chunk content.", embedding=[0.1] * _EMBEDDING_DIM)
+    mock = AsyncMock(return_value=[chunk])
+    monkeypatch.setattr(
+        "app.indexing_pipeline.indexing_pipeline_service.create_document_chunks",
+        mock,
+    )
+    return mock
+
+
+@pytest.fixture
+def patched_embedding_model(monkeypatch) -> MagicMock:
+    from app.config import config
+
+    model = MagicMock()
+    model.embed = MagicMock(return_value=[0.1] * _EMBEDDING_DIM)
+    monkeypatch.setattr(config, "embedding_model_instance", model)
     return model
diff --git a/surfsense_backend/tests/integration/indexing_pipeline/test_index.py b/surfsense_backend/tests/integration/indexing_pipeline/test_index.py
new file mode 100644
index 000000000..5edcfb22d
--- /dev/null
+++ b/surfsense_backend/tests/integration/indexing_pipeline/test_index.py
@@ -0,0 +1,26 @@
+import pytest
+from sqlalchemy import select
+
+from app.db import Document, DocumentStatus
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
+
+pytestmark = pytest.mark.integration
+
+
+async def test_sets_status_ready(
+    db_session, db_search_space, make_connector_document,
+    mock_llm, patched_generate_summary, patched_create_chunks,
+):
+    connector_doc = make_connector_document(search_space_id=db_search_space.id)
+    service = IndexingPipelineService(session=db_session)
+
+    prepared = await service.prepare_for_indexing([connector_doc])
+    document = prepared[0]
+    document_id = document.id
+
+    await service.index(document, connector_doc, mock_llm)
+
+    result = await db_session.execute(select(Document).filter(Document.id == document_id))
+    reloaded = result.scalars().first()
+
+    assert DocumentStatus.is_state(reloaded.status, DocumentStatus.READY)