diff --git a/src/deepsparse/server/helpers.py b/src/deepsparse/server/helpers.py
index 0a86748af7..2163365500 100644
--- a/src/deepsparse/server/helpers.py
+++ b/src/deepsparse/server/helpers.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from http import HTTPStatus
-from typing import Any, Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 import numpy
 from pydantic import BaseModel
@@ -75,23 +75,35 @@ def server_logger_from_config(config: ServerConfig) -> BaseLogger:
     )
 
 
-def prep_outputs_for_serialization(pipeline_outputs: Any):
+def prep_outputs_for_serialization(
+    pipeline_outputs: Union[BaseModel, numpy.ndarray, list]
+) -> Union[BaseModel, list]:
     """
     Prepares a pipeline output for JSON serialization by converting any numpy array
     field to a list. For large numpy arrays, this operation will take a while to run.
 
-    :param pipeline_outputs: output data to clean
-    :return: cleaned pipeline_outputs
+    :param pipeline_outputs: output data to that is to be processed before
+        serialisation. Nested objects are supported.
+    :return: Pipeline_outputs with potential numpy arrays
+        converted to lists
     """
     if isinstance(pipeline_outputs, BaseModel):
         for field_name in pipeline_outputs.__fields__.keys():
             field_value = getattr(pipeline_outputs, field_name)
-            if isinstance(field_value, numpy.ndarray):
-                # numpy arrays aren't JSON serializable
-                setattr(pipeline_outputs, field_name, field_value.tolist())
+            if isinstance(field_value, (numpy.ndarray, BaseModel, list)):
+                setattr(
+                    pipeline_outputs,
+                    field_name,
+                    prep_outputs_for_serialization(field_value),
+                )
+
     elif isinstance(pipeline_outputs, numpy.ndarray):
         pipeline_outputs = pipeline_outputs.tolist()
 
+    elif isinstance(pipeline_outputs, list):
+        for i, value in enumerate(pipeline_outputs):
+            pipeline_outputs[i] = prep_outputs_for_serialization(value)
+
     return pipeline_outputs
 
 
diff --git a/tests/server/test_helpers.py b/tests/server/test_helpers.py
index 460c75fc83..289e8ba5a2 100644
--- a/tests/server/test_helpers.py
+++ b/tests/server/test_helpers.py
@@ -12,16 +12,67 @@
 # # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # # See the License for the specific language governing permissions and
 # # limitations under the License.
+from typing import Any
+
+import numpy as np
 import yaml
+from pydantic import BaseModel
 
 import pytest
 from deepsparse.loggers import AsyncLogger, MultiLogger, PythonLogger
 from deepsparse.server.config import ServerConfig
-from deepsparse.server.helpers import server_logger_from_config
+from deepsparse.server.helpers import (
+    prep_outputs_for_serialization,
+    server_logger_from_config,
+)
 from tests.deepsparse.loggers.helpers import fetch_leaf_logger
 from tests.helpers import find_free_port
 
 
+class DummyOutputSchema(BaseModel):
+    field_1: Any
+    field_2: Any
+    field_3: Any
+
+
+@pytest.mark.parametrize(
+    "unserialized_output, target_serialized_output",
+    [
+        (
+            DummyOutputSchema(
+                field_1=[np.array([[1, 2, 3]])],
+                field_2={"key_1": np.array([[[1, 2, 3]]])},
+                field_3=DummyOutputSchema(field_1=np.array([0])),
+            ),
+            DummyOutputSchema(
+                field_1=[[[1, 2, 3]]],
+                field_2={"key_1": [[[1, 2, 3]]]},
+                field_3=DummyOutputSchema(field_1=[0]),
+            ),
+        )
+    ],
+)
+def test_prep_outputs_for_serialization(unserialized_output, target_serialized_output):
+    def check_dict_equality(dict_1, dict_2):
+        for key, value in dict_1.items():
+            if isinstance(value, BaseModel):
+                value = value.dict()
+                check_dict_equality(value, dict_2[key].dict())
+            elif isinstance(value, dict):
+                check_dict_equality(value, dict_2[key])
+            elif isinstance(value, list):
+                equal = value == dict_2[key]
+                equal = equal if isinstance(equal, bool) else equal.all()
+                assert equal
+            else:
+                assert value == dict_2[key]
+
+    serialized_output = prep_outputs_for_serialization(unserialized_output)
+    serialized_output = serialized_output.dict()
+    target_serialized_output = target_serialized_output.dict()
+    check_dict_equality(target_serialized_output, serialized_output)
+
+
 yaml_config_1 = """
 loggers:
     python: