Build an AI Voice Assistant for Banking KYC

Step-by-step guide to build a voice assistant for banking KYC using VideoSDK.

Introduction to AI Voice Agents in Banking KYC

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a sophisticated software application designed to interact with users through voice commands. These agents leverage advanced technologies such as speech recognition, natural language processing, and speech synthesis to understand and respond to user queries. They are becoming increasingly prevalent across various industries, including banking, where they streamline customer interactions and automate routine tasks.

Why are they important for the Banking KYC Industry?

In the banking sector, Know Your Customer (KYC) processes are critical for verifying the identity of clients and preventing fraud. AI Voice Agents can significantly enhance the efficiency of KYC processes by guiding users through document submissions, answering queries, and ensuring compliance with regulatory standards. This not only improves customer experience but also reduces operational costs for banks.

Core Components of a

Voice Agent

The core components of an AI

Voice Agent

 include:
  • Speech-to-Text (STT): Converts spoken language into text.
  • Large Language Model (LLM): Processes the text and generates responses.
  • Text-to-Speech (TTS): Converts text responses back into spoken language.

What You'll Build in This Tutorial

In this tutorial, you will learn how to build an AI Voice Assistant for banking KYC using the VideoSDK framework. We will guide you through setting up the development environment, creating a custom

voice agent

, and testing it in a real-world scenario.

Architecture and Core Concepts

High-Level Architecture Overview

The AI Voice Agent architecture involves a seamless flow of data from user speech to agent response. The process begins with capturing the user's voice input, which is then processed through a series of components to generate a meaningful response. This involves a

cascading pipeline in AI voice Agents

 that ensures efficient data processing.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, responsible for managing interactions.
  • CascadingPipeline: Manages the flow of audio processing, integrating STT, LLM, and TTS components.
  • VAD & TurnDetector: These components help the agent determine when to listen and when to speak, ensuring smooth interaction.

Setting Up the Development Environment

Prerequisites

To get started, ensure you have Python 3.11+ installed and a VideoSDK account, which you can create at app.videosdk.live.

Step 1: Create a Virtual Environment

Create a virtual environment to manage dependencies:
1python3.11 -m venv venv
2source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`
3

Step 2: Install Required Packages

Install the necessary packages using pip:
1pip install videosdk-agents videosdk-plugins
2

Step 3: Configure API Keys in a .env file

Create a .env file in your project directory to store your API keys securely:
1VIDEOSDK_API_KEY=your_api_key_here
2

Building the AI Voice Agent: A Step-by-Step Guide

To build the AI Voice Agent, we will walk through the complete code and explain each part in detail.

Step 4.1: Generating a VideoSDK Meeting ID

First, generate a meeting ID using the VideoSDK API. You can use the following curl command:
1curl -X POST "https://api.videosdk.live/v1/meetings" \
2-H "Authorization: Bearer YOUR_API_KEY" \
3-H "Content-Type: application/json"
4

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where we define the behavior of our voice assistant. It inherits from the Agent class and specifies the instructions for the agent.
1class MyVoiceAgent(Agent):
2    def __init__(self):
3        super().__init__(instructions=agent_instructions)
4    async def on_enter(self): await self.session.say("Hello! How can I help?")
5    async def on_exit(self): await self.session.say("Goodbye!")
6

Step 4.3: Defining the Core Pipeline

The

CascadingPipeline

 is crucial as it defines the flow of audio processing through various plugins.
1pipeline = CascadingPipeline(
2    stt=DeepgramSTT(model="nova-2", language="en"),
3    llm=OpenAILLM(model="gpt-4o"),
4    tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
5    vad=[Silero Voice Activity Detection](https://docs.videosdk.live/ai_agents/plugins/silero-vad)(threshold=0.35),
6    turn_detector=TurnDetector(threshold=0.8)
7)
8

Step 4.4: Managing the Session and Startup Logic

The session management and startup logic are handled in the start_session function and the if __name__ == "__main__": block. This involves setting up

AI voice Agent Sessions

 to manage interactions effectively.
1async def start_session(context: JobContext):
2    agent = MyVoiceAgent()
3    conversation_flow = [Conversation Flow in AI voice Agents](https://docs.videosdk.live/ai_agents/core-components/conversation-flow)(agent)
4    session = AgentSession(
5        agent=agent,
6        pipeline=pipeline,
7        conversation_flow=conversation_flow
8    )
9    try:
10        await context.connect()
11        await session.start()
12        await asyncio.Event().wait()
13    finally:
14        await session.close()
15        await context.shutdown()
16
17def make_context() -> JobContext:
18    room_options = RoomOptions(
19        name="VideoSDK Cascaded Agent",
20        playground=True
21    )
22    return JobContext(room_options=room_options)
23
24if __name__ == "__main__":
25    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
26    job.start()
27

Running and Testing the Agent

Step 5.1: Running the Python Script

Run the script using the following command:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

After running the script, you will receive a test URL in the console. Open it in a browser to interact with your agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend the functionality of your agent by integrating custom tools using the function_tool feature of the VideoSDK framework.

Exploring Other Plugins

Explore other STT, LLM, and TTS plugins provided by VideoSDK to enhance your agent's capabilities.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly set in the .env file and that your account is active.

Audio Input/Output Problems

Verify that your microphone and speakers are functioning correctly and check your system's audio settings.

Dependency and Version Conflicts

Ensure all dependencies are installed with compatible versions as specified in the documentation.

Conclusion

Summary of What You've Built

You have successfully built an AI Voice Assistant capable of assisting users with banking KYC processes. This agent can guide users through the KYC steps, answer related questions, and ensure compliance.

Next Steps and Further Learning

Consider exploring additional features and plugins offered by VideoSDK to further enhance your agent's capabilities and adapt it to other use cases.

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