The Problem
The collector market runs on who knows more. Buyers who can read a car win. Buyers who cannot get taken, on price, on history, on condition. Dealers carry years of it in their heads.
One tool that names the car, prices it and reads the market. It has to work for a first-time buyer standing in front of a car they cannot place, and as well for a pro moving fast at an auction.
The hardest part was designing five input modes that each fit how a person meets a car in the moment, and routing between them without exposing the fallback logic.
How It Was Built
Research
First we watched how collectors and dealers do it. Naming a car is never one question. It is a look, a half-remembered spec and a sense of the market, all at once. The five ways in came from that.
AI Architecture
One model is one point of failure. So the engine runs three in a chain, and each hands off to the next when it is not sure enough. When it is unsure it says so instead of guessing wrong.
Data Integration
A price is only as good as the sales behind it. SupercarIQ keeps its own store of auction results, 4,131 of them across 22 makes as of October 6, and prices a car against what cars like it have sold for.
Delivery
It had to pass two tests at once. A first-time buyer should get an answer in thirty seconds. A pro should trust that answer at a live auction. Both need the same thing: hide the hard parts, but never dumb down the result.
Architecture
Each model, lookup and fallback is chosen on one assumption: you are standing in front of the car right now.
The Result
SupercarIQ gives you five ways in, each built for a different moment. Prices come from auction results the app has stored. Every answer carries a confidence level, so you know how far to trust it.
The goal was to give someone with no expertise the same footing as someone with twenty years of it.
AI earns its place when it is built for real speed, known failure modes and real users. Start with a Fit Call.
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