diff --git a/examples/foundational/99-content-moderation.py b/examples/foundational/99-content-moderation.py
index cd5431c65..210840b8f 100644
--- a/examples/foundational/99-content-moderation.py
+++ b/examples/foundational/99-content-moderation.py
@@ -10,7 +10,7 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import TextFrame
+from pipecat.frames.frames import EndFrame, EndTaskFrame, TextFrame, TTSSpeakFrame
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -19,6 +19,7 @@ from pipecat.processors.aggregators.gated_openai_llm_context import GatedOpenAIL
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.filters.null_filter import NullFilter
 from pipecat.processors.filters.wake_notifier_filter import WakeNotifierFilter
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.user_idle_processor import UserIdleProcessor
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -31,6 +32,35 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
+class KickParticipantProcessor(FrameProcessor):
+    """This processor will kick the participant if they say something inappropriate.
+
+    This is a simple example of how to use the LLM to moderate
+    the conversation. In this case we are using the OpenAI LLM to determine if
+    the user is saying something inappropriate.
+
+    """
+
+    def __init__(self):
+        """Initialize the processor."""
+        super().__init__()
+
+    async def process_frame(self, frame, direction):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TextFrame) and frame.text == "YES":
+            await self.push_frame(
+                TTSSpeakFrame(
+                    "You are being kicked from the call because of content moderation. Have a nice day!"
+                )
+            )
+
+            # Signal that the task should end after processing this frame
+            await self.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
+
+        await self.push_frame(frame, direction)
+
+
 async def run_bot(webrtc_connection: SmallWebRTCConnection):
     logger.info(f"Starting bot")
 
@@ -101,7 +131,7 @@ community guidelines.
 
     # This a filter that will wake up the notifier if the given predicate
     # (wake_check_filter) returns true.
-    completness_check = WakeNotifierFilter(notifier, types=(TextFrame,), filter=wake_check_filter)
+    completeness_check = WakeNotifierFilter(notifier, types=(TextFrame,), filter=wake_check_filter)
 
     # This processor keeps the last context and will let it through once the
     # notifier is woken up. We start with the gate open because we send an
@@ -116,6 +146,8 @@ community guidelines.
     # moderated, this will wake up the notifier if that happens.
     user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=3.0)
 
+    kick_participant = KickParticipantProcessor()
+
     # The ParallePipeline input are the user transcripts. We have two
     # contexts. The first one will be used to determine if the user is
     # moderated and if so the notifier will be woken up. The second
@@ -129,7 +161,8 @@ community guidelines.
                 [
                     moderator_context_aggregator.user(),
                     moderator_llm,
-                    completness_check,
+                    kick_participant,
+                    completeness_check,
                     NullFilter(),
                 ],
                 [context_aggregator.user(), gated_context_aggregator, llm],