Martin

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  Verizon Wireless Unlimited phone plans we interpreted using conversational generative AI

FULL NO-CUTS DEMO (demo in video was cut to shorten the video): https://youtu.be/TsEjEjJ8UFM

Luke Moshos: Industrial Engineering Andrew Ruales, Hannah Hardy, Jonathan Zhang, Victor Polisetty: Computer Science

Inspiration

While browsing the subreddit r/verizon, one of the most common pain points of customers expressed is the frustration with choosing the right phone plan with Verizon as well as figuring out pricing when there are many factors at play such as discounts, premium benefits, and different numbers of lines.

We wanted to create an AI-powered chatbot that alleviates this frustration and makes the picking process easy while also breaking down the plans for the customer so that they can fully understand what they're purchasing and why it's the most optimal plan for them.

What it does

Martin surveys a prospective customer in a short user flow to determine customer attributes. Then, with a backend generative AI prompt chaining procedure, we generate an overall analysis of all of the available plans and recommend optimal choices. This explanation includes a breakdown of features, pricing, and discounts.

How we built it

We built a React.js web app with an Express.js backend. The backend REST API calls the OpenAI API to interface with our fine-tuned LLM.

Challenges we ran into

We spent lots of time deliberating how to come up with the most simplistic user flow without losing specificity in our recommendations.

We also had to wrangle with our fine-tuned model to make sure that responses had accurate recall, were not hallucinatory, and also computed pricing properly.

Accomplishments that we're proud of

Our user flow with the initial information survey is accessible, friendly, and fast. We only collect the relevant data points and do it in as little steps as possible.

We are super proud of the correctness of our responses, as our prompt chaining methodology steered the model towards accurate and organized responses and allowed us to avoid any wrong answers (that we know of).

What we learned

We learned how to use and fine-tune a LLM through OpenAI.

We learned how to efficiently engineer and chain prompts.

We learned how to connect a React.js frontend to an Express.js backend.

We learned how to create a REST API for interfacing with our model.

We learned about user intentions when determining an optimal match for a phone plan.

What's next for Martin

Right now, Martin only helps customers pick plans and evaluate their pricings. There's so much more that companies like Verizon offers such as perks (roadside assistance, Verizon messages) and devices. In the future, Martin should have multiple tracks of conversation with customers that allow for broader product exploration.

Built With

• express.js
• gpt4
• langchain
• openai
• react
• restapi