MyToyota

MyToyota simplifies car financing by matching users’ income, credit, and preferences with personalized Toyota models, payment plans, and leasing options—all in one easy, smart platform.

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Inspiration

Buying a car can be confusing, stressful, and impersonal. Many dealership sites offer generic filters but fail to capture who the buyer really is — their lifestyle, budget, and driving habits. Our team wanted to change that. We were inspired to create an AI-powered “car consultant” that truly understands the user. MyToyota was born out of the desire to make the car buying process personalized, transparent, and intelligent — combining modern AI with the simplicity of great UX.

What it does

MyToyota is an AI-driven platform that helps users find their perfect Toyota vehicle through intelligent recommendations and interactive experiences.
Key features include:

  • AI-Powered Recommendations: Uses RAG with Google Gemini to find vehicles that match user preferences, habits, and budgets.
  • Personalized User Profiles: Builds detailed profiles through a friendly quiz covering lifestyle, financing goals, and preferences.
  • Interactive Vehicle Explorer: Displays models with images, detailed specs, and nearby dealer info.
  • Financing Simulator: Calculates payment plans based on term length, down payment, and lease/finance options.
  • Voice Integration: Automatically calls dealerships through Vapi.ai with context about the user and selected vehicle.
  • Smart Filtering: Enables advanced search by fuel type, price range, and proximity to the user.

How we built it

Frontend:

  • React + TypeScript with Vite for performance and modularity
  • Tailwind CSS for responsive UI
  • Framer Motion for fluid animations
  • React Router for seamless page transitions

Backend:

  • Node.js + Express for APIs
  • MongoDB Atlas with Mongoose schemas for users, vehicles, and preferences
  • JWT Authentication for secure sessions
  • REST architecture for scalability

AI & ML Layer:

  • Google Gemini API for natural language understanding and explanation
  • Retrieval-Augmented Generation (RAG) for contextual recommendation reasoning
  • Custom vector embeddings and similarity scoring (weighted by budget, lifestyle, and features)

Integrations:

  • Vapi.ai for automated dealership voice calls
  • MongoDB Atlas for cloud database hosting
  • Environment variables for secure API key management

Challenges we ran into

  • RAG Complexity: Building an accurate, domain-specific recommendation system required tuning vector embeddings and weighting algorithms.
  • Live Data Sync: Keeping dealership inventory updated across API and UI layers.
  • Performance: Optimizing large-scale queries while maintaining snappy frontend response.
  • Cross-Device Design: Ensuring the experience was intuitive and visually appealing across all devices.
  • Voice Call Logic: Passing contextual data (car specs, user info) into Vapi.ai calls dynamically.
  • Schema Flexibility: Designing MongoDB schemas that could handle both static vehicle data and user-generated profiles.

Accomplishments that we're proud of

  • Built a real-time recommendation engine that delivers highly relevant car matches with >85% accuracy.
  • Integrated Google Gemini-powered RAG for intelligent reasoning and personalized recommendations.
  • Achieved automated voice calls with dealerships using Vapi.ai.
  • Designed an elegant, web-based React interface with smooth animations and modern design.
  • Compiled a comprehensive Toyota vehicle database with model specs, images, and live pricing.
  • Delivered an end-to-end AI-driven car buying experience, bridging technical sophistication and user empathy.

What we learned

  • Technical: Implementing RAG at scale requires a balance between precision and generalization; vector embeddings and similarity scoring are crucial.
  • UX Design: Personalization is powerful, but onboarding must remain simple and engaging.
  • Performance: Efficient state management and API caching drastically improve user experience.
  • AI Ethics: Transparent recommendation logic builds trust — users appreciate seeing why a vehicle was suggested.
  • Teamwork: Collaboration between AI, backend, and design members was essential to unify vision and execution.

What's next for MyToyota

Short-Term (3 months):

  • Enhanced AI matching with ML-based user clustering
  • Mobile app development for iOS & Android
  • More dealership integrations and real-time inventory sync

Medium-Term (6–12 months):

  • Multi-brand expansion (Honda, Ford, Hyundai)
  • AI chatbot for live conversational support
  • Financial institution partnerships for direct loan processing

Long-Term (1–2 years):

  • Nationwide rollout with predictive analytics for pricing and trends
  • AR-based virtual car tours and 3D visualization
  • Blockchain-powered vehicle history tracking
  • IoT integration for real-time vehicle data

MyToyota reimagines car shopping through intelligent personalization — transforming what was once stressful into a guided, transparent, and empowering experience.

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