RideWise

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Inspiration

When finally deciding on the Toyota track project RideWise wanted to center-focus on how to liberate the customer experience by acknowledging frequently faced concerns expressed universally by web users. The main concerns always derived from these three categories:

Data-Protection Measures
Personalized and Data-Informed UX
Customer Service and Communication Challenges In response our goal concentrated on creating a foundation that would aid us in modernizing how TFS helps customers discover, understand, and qualify for vehicles financially, Using User friendly visuals, On-Call Integrated AI, and Enhanced personalization.

What it does

Our TFS web platform delivers a cohesive digital experience that bridges user engagement and Finanancial insight. From the clean TFS-inspired interface to the interactive quiz and data driven results, RideWise aimed to connect every component to ensure that they communicate clarity, trust, and convenience with the goal of mirroring Toyotas commitment to customer-focused innovation.

How we built it

We built the TFS web platform using a modern React Front-end and Express backend to create a data-driven experience for car buyers. It consists of a modular design, branding, a interactive quiz system that works together to simplify financing making the Toyota services more accessible online.

Challenges we ran into

Without access to the official Toyota API, we were very limited in terms of options that would replace its function. Kamilah, and Beatrice the head front and back-end programmers created their own pipeline LLM and car Query API in order to identify Toyota cars and collect their attributes. Jaylen, the head of UI design, is one of the novice programmers in RideWise, she encountered trouble exploring the unfamiliarity's of web creation. Though troublesome she progressed steadily utilizing previously existing knowledge and taught herself as she went **.

Accomplishments that we're proud of

We are proud to have developed a chatbot with a clean user interface, integrated database, and secure user sign-in. It connects to a large language model and allows customization to help users with car financing, providing better financial guidance and support.

What we learned

RideWise learned how to organize front-end and back-end complexities, manage database/security, automate CD/CI pipelines, build advanced user Interfaces, and visualizations, train AI models, define algorithms, and simulate algorithmic workflow.

What's next for RideWise

Next for RideWise, we plan to enhance security with stronger user authentication, make the chatbot more robust, and provide richer context by incorporating users’ financial information. We also aim to improve car recommendations with more advanced filtering, going beyond just a few options.