Most car platforms still rely on rigid filters.
I wanted to explore something better:
👉 What if users could just talk to the system?
So I built a platform where you can type:
“BMW E92 under $20k, manual, 70 miles”
…and the system understands and returns relevant cars instantly.
🧠 What makes it interesting?
Instead of simple keyword matching, the system extracts structured data from natural conversation:
- Core vehicle → make, model, generation
- Time & usage → year range, mileage
- Preferences → transmission, color
- Market constraints → location, price range This allows transforming messy human language into precise database queries.
⚙️ Tech Stack
- Next.js (frontend)
- FastAPI (backend)
- PostgreSQL (data layer)
- LLM (intent + entity extraction)
- Web scraping pipeline (real listings)
🔄 How it works
- User enters natural language
- LLM extracts structured fields
- Backend converts to query filters
- PostgreSQL returns matching vehicles
- Results improve through conversation
💡 Why this matters
This approach replaces:
❌ Manual filters
❌ Trial-and-error search
With:
✅ Natural interaction
✅ Faster discovery
✅ Smarter recommendations
🚀 Try it here:
https://askdrive-web.vercel.app/
I’m exploring how LLMs can redefine search UX in marketplaces.
Would love to hear your thoughts or feedback👇