Restb.ai provides computer vision AI for real estate that tags property photos, values condition and powers visual search across listings.
Restb.ai analyzes listing photos to recognize room types, features, condition and style, turning unstructured imagery into structured property data. Its models power auto-tagging, image quality scoring and room-level insights for portals, MLSs and proptech platforms. The company also offers visual search so buyers can find homes that match a look they like. Because the API is designed for integration, listing platforms embed Restb.ai capabilities directly into their own products.
Restb.ai is licensed to portals, MLSs and proptech firms through enterprise contracts, priced by image volume and modules.
Real estate portals, MLSs, brokerages and proptech developers that want computer vision without building models in-house.
No. It is a B2B API platform with negotiated enterprise pricing.
Yes. The API is the core product, with SDKs and documentation for developers.
Restb.ai specializes in property imagery, outperforming general vision APIs on real-estate-specific features and condition assessment.
Portals auto-tag millions of listing photos with room types and features, powering better filters and search. MLSs use image moderation to catch violations like watermark spam automatically. Proptech apps add visual search so buyers upload inspiration photos and find matching homes. Insurers and assessors use condition scoring to estimate property state from imagery.
Performance depends on photo quality, and unusual property types can confuse models. Integration requires engineering effort, and per-image pricing adds up at portal scale. HouseCanary covers valuation while general vision APIs handle less real-estate-specific tasks.
Restb.ai integrates through REST APIs and SDKs, processing images as they are uploaded to listing platforms. Deployment involves image pipeline wiring, model configuration per market and confidence tuning for tagging or moderation. Visual search adds an embedding index over the photo library. Enterprise onboarding includes volume planning and support for portal-scale throughput. Ongoing operations focus on model performance monitoring across photo sources and expanding recognized feature taxonomies as catalogs evolve.