Crib Checker

Uploading the property images
Insights of the property
Analysis

Inspiration

Buying a home should feel stable, but the process is often stressful and uncertain—especially in Germany, where buyers may need to put down up to 40%. Hidden issues or unexpected repair costs can quickly become financial risks. Nearly half of buyers worry they’re making the wrong decision. We built Crib Checker to replace that anxiety with clarity.

What it does

Lets users upload images of a property to detect visible damage using AI.
Analyzes the condition of the home to estimate purchase price, repair costs, ROI, and long-term financial projections.

How we built it

Used the Gemini Vision model to identify and classify damage in property images.
Combined results with existing datasets and Interhype API to generate financial insights.

Challenges we ran into

Choosing a vision model that accurately detects damage and its severity.
Finding reliable datasets linking types of damage to real repair costs.

Accomplishments

Successfully integrated AI vision for fast, accurate property condition feedback.
Built a pipeline connecting image analysis to pricing and investment predictions.