Case study
AI Property Image Intelligence for Listing Optimization & Compliance
- Client: PropTexx
- Industry: Real Estate & PropTech
- AI capability: Computer Vision
- Property features tagged automatically
- 50+
- Compliance violation types detected
- 15+
- Processing time per image
- ~250ms
- Lift in buyer click-through rate
- 15%
The challenge
Millions of broker photos, none of them structured
Marketplaces take in millions of broker photos, and each one is an unstructured file. Faces, licence plates and watermarks create compliance risk, while the features buyers search for are never recorded.
Manual review is too slow and too expensive at that volume, so the engine has to read indoor and outdoor scenes across varied lighting and cameras in well under a second.
What we built
Detection and segmentation for listing photos
One pass per image returns both the feature tags and the compliance flags.
Room type: kitchen
1/4Fine-tuned detection and segmentation models read the scene: room type, fixtures, finishes and furniture.
Listing photos arrive as unstructured files. The pipeline reads each one in about 250 milliseconds: what room it is, what is in it, and whether anything in the frame breaks listing rules.
Room and scene understanding
Detection and segmentation models classify indoor and outdoor scenes across varied lighting and cameras.Fine-tuned YOLOSemantic segmentationFeature inventory
More than 50 property features are tagged automatically, from kitchen islands to flooring types.Privacy and compliance
Faces, licence plates and watermarks are detected so listings can be redacted or rejected.Built for marketplace volume
A distributed pipeline that keeps up with millions of broker photos a month.~250ms per image
The impact
Moderation stops being a manual job
- 50+ features tagged per photo
- 15+ compliance violation types detected
- ~250ms per image
- 15% lift in buyer click-through
Over 50 property features are tagged automatically and more than 15 compliance violation types are flagged, which removed most manual moderation.
Richer tagging improved listing quality and search relevance, with a 15% lift in buyer click-through rates.
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