Inspiration
Car shopping is often an overwhelming experience. Between comparing trims, fuel types, and financing options, many buyers struggle to find a vehicle that truly fits their lifestyle and budget. Our team wanted to simplify that process and make it more personal. The idea for Toyota Assist came from the vision of creating a digital companion that could understand user preferences and intelligently guide them toward their ideal Toyota — like having a trusted friend who knows cars inside and out.
What it does
Toyota Assist transforms the car-shopping process into a personalized, interactive journey. Users begin by taking a short 12-question preference quiz that captures their driving habits, budget, family size, and fuel preferences. The platform’s algorithm processes their answers and ranks over 50 Toyota vehicles based on compatibility.
The system then presents the top three Toyota models that best match the user’s preferences, along with detailed reasoning for each choice. Every recommendation includes key vehicle specifications such as fuel efficiency, horsepower, MSRP, seating capacity, and drivetrain type. Users can also explore the complete Toyota lineup through an interactive vehicle grid, compare multiple models side-by-side, and use the built-in financing calculator to estimate both loan and lease payments in real time. A favorites feature allows users to save and revisit vehicles easily, creating a complete end-to-end discovery experience.
How we built it
We built Toyota Assist using a modern, efficient, and scalable tech stack. The frontend was developed using React with TypeScript and Vite for rapid performance and type-safe coding. The user interface was designed using Tailwind CSS and shadcn/ui components, ensuring consistency with Toyota’s brand identity through the use of red, charcoal, and silver tones. Lucide React icons were used to give the interface a clean, cohesive visual appeal.
For data management, we created a structured dataset (toyotaVehicles.ts) containing 56+ Toyota vehicles from model years 2024 to 2026, each annotated with attributes like MSRP, MPG, seating, powertrain type, and category. Vehicle data was collected and validated through Selenium-based web scraping from Toyota’s official site.
The algorithm recommendation system uses a score-based algorithm that evaluates each vehicle across several dimensions — size, fuel type, seating, budget, and efficiency — and generates a ranked match score from 0–100. The Gemini API is integrated to enhance personalization and generate natural-language explanations for each recommendation.
Challenges we ran into
One of the biggest challenges was handling dynamic vehicle data from Toyota’s website, which required careful tuning of Selenium scripts and efficient caching. Integrating reasoning with structured data to produce natural and meaningful recommendations also demanded creative engineering. We encountered performance issues while rendering large sets of vehicle cards, which we solved with lazy rendering and memoization using React’s useMemo hook. Finally, balancing visual polish with accessibility standards was a key focus — ensuring Toyota Assist remained fast, intuitive, and inclusive for all users.
Accomplishments that we're proud of
We’re proud of successfully integrating personalization with real vehicle data to deliver accurate, transparent car recommendations. The platform’s sleek design, smooth animations, and fast performance bring the Toyota brand to life in a digital format. We also developed a robust dual-mode calculator for financing and leasing that updates dynamically as users adjust inputs like down payment, term length, and interest rate. Building this complete, production-ready system in a short time frame demonstrated our ability to combine technical skill, design precision, and user empathy.
What we learned
Through Toyota Assist, we learned how to combine reasoning with structured automotive datasets to deliver personalized, data-backed recommendations. We deepened our understanding of frontend optimization, accessibility best practices, and UI consistency using Tailwind. Most importantly, we learned how design and technology together can make complex decisions — like buying a car — feel simple, transparent, and human.
What's next for Toyota Assist
Looking ahead, we plan to enhance Toyota Assist with live dealership integration, user accounts, and real-time inventory data. We aim to include a trade-in calculator, a 360° vehicle view, and an AI chatbot to assist users during their car search. Expanding to other manufacturers could also turn Toyota Assist into a brand-agnostic, AI-powered automotive shopping platform.
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