Naming a thing exactly — a breed, a lens, a lighting setup — bought him consistency that no amount of extra description could, and he turned that observation into a method he could repeat.
RevOS
Krishna Penmetsa · Sarvesh Madanagopal
The problem they picked
A used-car listing is six photographs and a price. Everything that decides whether it is a good buy — the open recall, the failure that model is known for at ninety thousand miles, what the noise on the test drive will cost — sits somewhere the buyer does not know to look. The team's own framing was that you meet the car the way you meet an online date, except the surprises run into the thousands.
RevOS pulls federal recall and safety data onto the listing itself, maps symptoms to their likely causes and repair costs, and keeps each car's history in a garage that is still there long after the sale.
“Buying a vehicle online is kind of like online dating — you see a couple of pictures, but when you meet in person there's a couple of surprises. For used vehicles, these surprises can cost upwards of thousands of dollars.”
RevOS, at the showcase
How it was built
API Integration
NHTSA vehicle data pulled into the health report, so recalls and safety ratings sit alongside the listing itself.
Data Modeling
A diagnostic reference they researched and built by hand, mapping each symptom to its likely causes, repair cost, and severity.
Front-End Development
A digital garage that keeps each car's history between visits, so the maintenance log is still there the next time you open it.
NHTSA APIGovernment Data API
ViteBuild Tool
Persistent digital garageClient Persistence
Also in the buildVehicle health analysis · Symptom-to-cause diagnostics · Digital garage and maintenance log
The description of Sarvesh Madanagopal above was written by studio staff rather than by the student. Students can request a change at [email protected].
