Damage photos turned into pricing signal.
Discrepancies down, confidence up.
A predictive model reads car damage photos with the ChatGPT Vision API, then fuses those visual features with vehicle specs, historical prices and market trends to forecast auction prices more accurately.
into one model
photos directly
discrepancies
reputation
Client
A leading vehicle auction and pricing analytics firm.
Goal
To improve pricing accuracy by building a predictive model that analyses car damage images and forecasts auction prices more effectively.
Damage photos held pricing clues nobody was reading.
Auction pricing relied on vehicle specifications, historical prices and market trends, but none of it accounted for what a damage photo actually showed. That gap between predicted and actual auction prices was the problem to close.
The firm needed a system that could pull visual data out of damaged-vehicle images and combine it with the numerical and market data already feeding its pricing model, without one data type drowning out the other.
Three constraints were non-negotiable:
- Incorporating visual data from damaged vehicles
- Integrating that visual data with existing pricing features
- Improving the accuracy of vehicle price estimation
Vision, spec sheet and market data, one model.
The ChatGPT Vision API analyses car damage images and extracts the features relevant to price, turning photos into structured pricing signal rather than an unread attachment.
A dented panel and a decade of auction history now feed the same price forecast.
Those extracted features are combined with vehicle specifications, historical prices and market trends already in the pricing pipeline, so a single model sees the whole picture instead of numbers alone.
A machine learning model was designed specifically to combine this visual and textual data, producing the most accurate auction price forecast the platform could generate from the inputs available.
Predicted prices catch up with actual ones.
Reading damage photos as data, not just images, closed the gap between what the model predicted and what vehicles actually sold for, making every stage of the auction more accurate and more trusted.
* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.
More case studies.
Your biggest pain point.
Fixed in 14 days. 50% off.
This started with one conversation. Book a 30 minute brainstorm call — we’ll plan your first AI project together and issue your 50% discount code. No payment today.
*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.