Case study · AI product
Car shopping is fragmented by design. Car Finder replaces it with a conversation that learns what you want, checks what you can afford, and comes back with cars that fit both.
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The average American buyer spends over fourteen hours searching across multiple car websites, and by one AutoTrader study 89% of buyers feel overwhelmed by how fragmented the process is.
The problem is not a lack of inventory data. It is that every site dumps inventory on the buyer and leaves them to work out which of it is relevant, affordable and financeable.
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Existing car sites optimise for listing volume. A buyer arriving with a real-world constraint — a monthly payment ceiling, a family of five, a need for something they can park in a city — has to translate that themselves into filters, then repeat it site by site.
Financing makes it worse. Sticker price is not the number that matters; the monthly payment is. A search that ignores financing shows people cars they cannot buy.
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We built the product around the conversation rather than the catalogue. Preferences are collected the way a person would ask for them, and financing is treated as a first-class filter rather than a detail revealed at the end.
The design principle: never show a car the buyer cannot afford. Budget is captured early and applied to everything after it, which is why the results are short — and why they are useful.
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Target time to a matched car, from 14 hours of searching
Every recommendation inside the buyer's actual affordability
Recommendations improve as preferences and inventory change
More work
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