IVR Navigation

Automatically navigate phone system menus using AI-powered decision making

Overview

The IVRNavigator enables your bot to automatically navigate Interactive Voice Response (IVR) phone systems to reach specific goals. Instead of manually programming navigation paths, you provide an end goal and the bot handles the complex decision-making required to traverse phone menus using DTMF tones and conversational responses.

How IVR Navigation Works

The IVRNavigator combines several intelligent capabilities:

1. Goal-oriented navigation: You specify what you want to accomplish (e.g., “reach billing support”)
2. Automatic classification: Detects whether incoming audio is an IVR system or human conversation
3. Smart decision making: Analyzes menu options and selects the best path toward your goal
4. Multi-modal responses: Uses both DTMF tones for menu selection, natural language for prompts, and waits when no input is appropriate
5. Status tracking: Monitors progress and reports completion, waiting, or stuck states

Navigation Outcomes

The IVRNavigator can reach several outcomes during navigation:

Completed ✅

The navigator successfully reaches its goal and is ready for the next step. Common actions:

• Terminate the pipeline if the goal is complete
• Transfer the call to a human agent
• Allow your bot to start a conversation with the reached department

@ivr_navigator.event_handler("on_ivr_status_changed")
async def handle_ivr_status(processor, status):
    if status == IVRStatus.COMPLETED:
        logger.info("Successfully navigated to target department")
        # Start conversation
        # Replace the context with new messages for the upcoming conversation
        messages = [{"role": "developer", "content": "You are a helpful customer service assistant."}]
        await task.queue_frame(LLMMessagesUpdateFrame(messages))
        # Adjust VAD stop_secs for conversation
        vad_params = VADParams(stop_secs=0.8)
        await task.queue_frame(VADParamsUpdateFrame(vad_params))

Stuck ⚠️

The navigator cannot find a path forward in the IVR system. This might happen when:

• Required information (account numbers, PINs) isn’t available
• The menu options don’t align with the stated goal
• The system encounters errors or invalid selections

@ivr_navigator.event_handler("on_ivr_status_changed")
async def handle_ivr_status(processor, status):
    if status == IVRStatus.STUCK:
        logger.warning("IVR navigation stuck - terminating call")
        # Log the issue and clean up
        await log_navigation_failure()
        await task.queue_frame(EndFrame())

Flexible Entry Points

One of the IVRNavigator’s key features is flexible entry point handling. When you dial a phone number, you might encounter either:

• An IVR system with menu options
• A direct connection to a human

The navigator automatically detects which scenario occurs and emits appropriate events:

Human Conversation Detected

When a human answers instead of an IVR system, an on_conversation_detected event is emitted. You can handle that event to transition to a conversation.

@ivr_navigator.event_handler("on_conversation_detected")
async def on_conversation_detected(processor, conversation_history):
    # Set up conversation prompt and preserve conversation history
    messages = [
        {
            "role": "developer",
            "content": "You are an assistant calling to check on the prescription for John Smith, date of birth 01/01/1990.",
        }
    ]

    # Add preserved conversation history if available
    if conversation_history:
        messages.extend(conversation_history)

    await task.queue_frame(LLMMessagesUpdateFrame(messages=messages, run_llm=True))

Note that the on_conversation_detected event also emits a conversation_history parameter that contains the previous conversation history. This allows you to build a prompt that includes your conversation system prompt plus any conversation history up to that point in time.

IVR System Detected

When an IVR system is detected, the IVR Navigator automatically transitions into navigation mode:

• System prompt: Updates to use your specified navigation goal
• VAD timing: Adjusts to stop_secs=2.0 (or your custom ivr_vad_params) to allow time for complete menu announcements
• Navigation logic: Begins analyzing menu options and making decisions toward your goal

No additional code is required. The navigator handles this transition automatically.

Optional Event Handling

If you need to log the detection or perform custom actions, you can handle the on_ivr_status_changed event:

@ivr_navigator.event_handler("on_ivr_status_changed")
async def on_ivr_status_changed(processor, status):
    if status == IVRStatus.DETECTED:
        logger.info("IVR system detected - beginning navigation")
        # Optional: Add analytics tracking, custom setup, etc.

Basic Implementation

Step 1: Create the IVR Navigator

from pipecat.extensions.ivr.ivr_navigator import IVRNavigator
from pipecat.audio.vad.vad_analyzer import VADParams

# Define your navigation goal
ivr_goal = "Navigate to the billing department to discuss my account balance"

# Create navigator with extended response time for IVR systems
ivr_vad_params = VADParams(stop_secs=2.0)  # Longer wait for IVR menus

ivr_navigator = IVRNavigator(
    llm=your_llm_service,
    ivr_prompt=ivr_goal,
    ivr_vad_params=ivr_vad_params
)

Step 2: Set Up Event Handlers

from pipecat.frames.frames import LLMMessagesUpdateFrame
from pipecat.extensions.ivr.ivr_navigator import IVRStatus

@ivr_navigator.event_handler("on_conversation_detected")
async def on_conversation_detected(processor, conversation_history):
    """Handle when a human conversation is detected instead of IVR"""
    logger.info("Human conversation detected")

    # Set up conversation context
    messages = [
        {"role": "developer", "content": "You are a customer service representative."}
    ]
    if conversation_history:
        messages.extend(conversation_history)

    await task.queue_frame(LLMMessagesUpdateFrame(messages=messages, run_llm=True))

@ivr_navigator.event_handler("on_ivr_status_changed")
async def on_ivr_status_changed(processor, status):
    """Handle IVR navigation status changes"""
    if status == IVRStatus.COMPLETED:
        logger.info("IVR navigation completed successfully")
        # Your success handling logic here

    elif status == IVRStatus.STUCK:
        logger.warning("IVR navigation got stuck")
        # Your error handling logic here
        await handle_navigation_failure()

Step 3: Add to Pipeline

Add the IVR Navigator to your pipeline in the place where you would normally add the LLM. The IVR Navigator contains your LLM and will perform the same functions as an LLM would, but in addition it will navigate the IVR system.

from pipecat.pipeline.pipeline import Pipeline

pipeline = Pipeline([
    transport.input(),
    stt_service,
    ivr_navigator,  # Add the navigator to your pipeline
    tts_service,
    transport.output()
])

VAD Parameter Optimization

The IVRNavigator automatically optimizes Voice Activity Detection (VAD) parameters for different scenarios:

IVR Navigation Mode

• Default: stop_secs=2.0
• Purpose: Allows time to hear complete menu options before responding
• Result: Higher navigation success rates

Conversation Mode

• Recommended: stop_secs=0.8
• Purpose: Enables natural conversation flow with quick responses
• Implementation: Push VADParamsUpdateFrame when transitioning to conversation

Next Steps

Pipeline Termination

Learn how to properly terminate pipelines when IVR navigation completes

Pipecat Flows

Integrate structured conversation flows after successful IVR navigation

The IVRNavigator provides a powerful foundation for automating phone system interactions, allowing your bots to handle the complex task of menu navigation while you focus on the core conversation logic.