This is a submission for Twilio Challenge v24.06.12

What I Built

For this challenge, I developed an AI-powered eCommerce chatbot that integrates seamlessly with WhatsApp to enhance the shopping experience for users. Leveraging the capabilities of Twilio's API, my solution allows customers to easily inquire about products, receive detailed information, and be directed to relevant purchasing pages, all through a simple and intuitive chat interface on WhatsApp.

Demo

Twilio and AI

My solution leverages Twilio's robust API to facilitate communication between the AI agent and users on WhatsApp. Here’s how Twilio and AI work together in this project:

1. WhatsApp Integration: Using Twilio's API, the chatbot is integrated with WhatsApp, allowing it to send and receive messages from users. Ensuring a smooth and reliable communication channel.

2. AI-Powered Responses: The chatbot uses the Llama3-70b model running on the Groq inference engine to understand user queries and provide fast, accurate and contextually relevant responses.

3. Real-Time Product Information: The AI agent accesses the eCommerce database in real time to fetch up-to-date product information, ensuring users always get the latest details. It can read directly from the database to look for the customer’s requested items, providing accurate and timely responses. It can also read extra contextual documents provided by the retailing company to answer any question asked about the company

4. User Engagement: Twilio’s API ensures that messages are delivered promptly and reliably, enhancing user engagement and satisfaction.

Additional Prize Categories

Additional Prize Categories
My submission qualifies for the following additional prize categories:

• Twilio Times Two: By utilizing both Twilio's WhatsApp and AI capabilities, my solution demonstrates a powerful and innovative use of Twilio’s services.

• Impactful Innovators: This chatbot has the potential to significantly impact the eCommerce industry by simplifying the shopping process and enhancing customer experience.

Credits to my team members:https://dev.to/johnmuinde

Link to the code

Thanks for participating!