Creating a Lead Gen Type AI Chatbot with Vector Shift AI
Introduction
In this video, we will be creating a lead gen type AI chatbot using a drag and drop AI app builder called Vector Shift AI. This tutorial is a continuation of our previous video on Vector Shift platform overview. If you are new to this series, please make sure to check out the first video to get a better understanding of what we are discussing here.
Problem Statement
Meet Sam, a content creator who loves crafting tutorials and social media content for his clients. However, Sam faces a big challenge as his days are packed with manual work such as chatting with potential clients, answering emails, preparing for meetings, onboarding clients, and managing his team. This leaves him with less time to focus on delivering high-quality work to his clients. With limited tech skills, Sam is looking for AI solutions to automate these manual processes and streamline his business so he can focus on what truly matters – delivering quality work and taking care of his team’s well-being.
Solution
If you are also looking for automations for your workflows or interested in creating powerful AI agents using low code or no code AI solutions, then you’re in the right place. In this video, we will solve Sam’s problem by creating an AI chatbot to assist him in handling client inquiries and onboarding processes.
Creating a Knowledge Base
To start building our AI chatbot, we first need to create a knowledge base within the Vector Shift platform. We will input all the service details that Sam’s company offers, including different subscription packages and pricing. This knowledge base will help the AI chatbot in responding to user queries based on the service offerings.
Building the Chatbot Pipeline
Next, we will create a chatbot pipeline from scratch. The pipeline will consist of an input node for user questions, a knowledge node to search the knowledge base, an OpenAI node for generating responses, and a chat memory node to save the conversation history. By setting up these nodes, we can create a conversational AI system that can interact with users and provide relevant information based on the service offerings.
Testing and Deployment
Once the chatbot pipeline is created, we can test it within the Vector Shift platform. By running the pipeline, we can see how the AI chatbot responds to user queries and recommends subscription packages based on the services provided. After testing, we can deploy the chatbot by creating a chatbot instance that can be integrated into websites, messaging platforms, or applications.
Enhancing the Chatbot for Lead Generation
To further enhance the chatbot functionality, we can turn it into a lead generation chatbot. By integrating a Typeform for collecting user information and automating the onboarding process, we can convert potential customers into paying clients seamlessly. The chatbot can prompt users to fill out the Typeform at the end of the conversation, providing a streamlined experience for user engagement and conversion.
Conclusion
In this tutorial, we have demonstrated how to create a lead gen type AI chatbot using Vector Shift AI. By leveraging AI technology and automation tools, businesses can streamline their workflows, improve customer engagement, and drive conversions efficiently. Stay tuned for the next video in this series, where we will explore integrating Typeform and automating customer onboarding processes. Thank you for watching, and see you in the next one!
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