Power real estate and proptech AI from property search to closing automation
Zilliz Cloud is a fully managed vector database that powers semantic property search, visual listing matching, and document intelligence for real estate platforms â with sub-10ms latency at million-listing scale. Proptech teams use it to build natural-language property discovery, match buyers to homes by visual preference, and automate document-heavy closing workflows. Proven in production at Beike (3M+ property vectors, 113ms average query), Rexera (350+ firms, 40% accuracy lift), and VerbaFlo (sub-10ms latency for conversational real estate AI). SOC 2 Type II certified.
AI Capabilities Transforming Real Estate and Proptech
Every major real estate challenge is fundamentally a matching problem: find properties like the ones a buyer loves, find buyers whose preferences match a listing, find comparable sales for accurate valuations, find relevant clauses across thousands of closing documents. Zilliz Cloud gives proptech platforms the infrastructure to solve these with true semantic similarity â at interactive speed across millions of listings.
Let Buyers Search by Intent, Not Just Filters
Encode property listings â descriptions, features, neighborhood context â into semantic embeddings and let buyers search by meaning. 'Quiet neighborhood near good schools with a modern kitchen' returns relevant results even when listings never use those exact words. Move from rigid dropdown filters to natural-language discovery that understands what buyers actually want.
Match Listings by How Properties Look, Not Just What They List
Embed property photos using vision models and find visually similar homes across your entire inventory. A buyer who favorites a mid-century modern living room sees more listings with that aesthetic â even if the listing description says nothing about style. Extract floor plan, interior finishes, architectural style, and paint colors as visual features for similarity search across millions of images.
Match Buyers to Properties Based on Behavioral Preference
Encode buyer behavior â search history, saved listings, viewing patterns, stated preferences â into preference vectors. Continuously match against your listing inventory to surface high-affinity properties before buyers find them manually. Replace static saved-search alerts with dynamic recommendations that learn from every interaction.
Extract and Retrieve Across Thousands of Transaction Documents
Embed lease agreements, title documents, HOA disclosures, inspection reports, and closing packages into searchable vector indexes. Agents and attorneys find relevant clauses, comparable terms, and compliance issues in seconds instead of hours â across document types that keyword search cannot reliably parse. Rexera processes millions of pages monthly with 40% higher retrieval accuracy using hybrid search.
Find True Comparables Beyond Simple Proximity and Square Footage
Embed property attributes â condition, renovation quality, neighborhood character, lot features â as dense vectors and find genuinely comparable sales. Go beyond the radius-and-bedroom-count approach to identify comps that actually match the subject property's appeal, even in heterogeneous neighborhoods where no two homes are alike.
Detect Emerging Patterns Across Listings, Sales, and Sentiment
Embed market signals â listing descriptions, pricing trends, neighborhood reviews, permit data â and search for semantic patterns that structured analytics miss. Identify emerging micro-markets, shifting buyer preferences, and pricing anomalies before they appear in traditional reports. Turn unstructured market data into actionable intelligence for investors and brokers.
Why Zilliz?
Why proptech platforms choose Zilliz Cloud
Real estate technology has three hard requirements that most infrastructure cannot meet simultaneously: semantic understanding to interpret the subjective language of property descriptions and buyer preferences, multimodal capability to process photos, floor plans, documents, and text together, and scale to search across millions of listings with sub-second response times. On top of that, closing workflows demand document retrieval accuracy where errors mean real financial and legal risk â and traditional keyword search falls short on the diverse, inconsistent language found in real estate documents. Beike built their intelligent property search on Milvus, searching 3M+ listing vectors with 113ms average query time. Rexera replaced Elasticsearch with Zilliz Cloud for closing document intelligence, achieving a 40% accuracy improvement with hybrid search. VerbaFlo powers real-time conversational AI for property management with sub-10ms query latency on Zilliz Cloud.
3M+Listings
Search across millions of property listings in milliseconds
Real estate platforms manage millions of active listings, each with dozens of attributes, multiple photos, and lengthy descriptions. Zilliz Cloud indexes all of this as vectors and returns similarity results in under 200ms â proven at Beike's scale with 3M+ property vectors and 113ms average query time. Fast enough for interactive search experiences that feel instant.
<10msLatency
Power real-time conversational AI for property interactions
Modern real estate platforms need AI that responds in real time â chatbots answering property questions, voice assistants handling leasing inquiries, agents processing live requests. Zilliz Cloud delivers sub-10ms P99 vector search, proven in VerbaFlo's production conversational AI system serving 40,000+ property entities.
40%Accuracy Lift
Hybrid search that combines meaning and keywords in one query
Real estate documents and listings use inconsistent terminology â the same feature described differently across MLS systems, legal documents, and marketing copy. Zilliz Cloud's hybrid search combines dense semantic vectors with sparse keyword matching and metadata filters in a single query. Rexera measured a 40% retrieval accuracy improvement over their previous Elasticsearch-based system.
-50%Cost
Consolidate search infrastructure and cut operational costs
Many proptech platforms run separate systems for text search, image search, and document retrieval. Zilliz Cloud consolidates semantic, keyword, and filtered search into a single managed service. Rexera achieved a 50% total cost reduction by replacing Elasticsearch and Deep Lake with Zilliz Cloud â while improving accuracy and eliminating infrastructure management overhead.
Hybrid search with metadata filtering
Combine semantic vector similarity with structured property filters â match listings filtered by price range, bedrooms, and geography, or retrieve documents filtered by transaction type and date. One query handles both meaning and metadata.
Multimodal search across text, images, and documents
Embed property photos, listing descriptions, floor plans, and legal documents into a shared vector space. Search across modalities â find listings that look like a reference photo, or retrieve documents that relate to a specific property feature.
Real-time index updates
Listings change constantly â new properties, price adjustments, status updates, photo additions. Zilliz Cloud supports continuous vector updates without performance degradation, keeping search results current as your inventory changes throughout the day.
Multi-tenant architecture
Serve multiple brokerages, property management firms, or MLS systems from a single deployment. VerbaFlo chose Zilliz Cloud specifically for its sophisticated partitioning and namespace isolation capabilities that match multi-tenant requirements.
Automatic and elastic scaling
Real estate traffic is seasonal and event-driven â spring buying season, interest rate changes, new development launches. Scale compute automatically for peak periods and back down during quiet months, paying only for what you use.
Enterprise-grade compliance and reliability
SOC 2 Type II certified, GDPR compliant, 99.95% SLA. BYOC deployment available for real estate platforms with strict data residency requirements and sensitive transaction data under state and federal privacy regulations.
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Resources
Essential reading for proptech AI teams
Explore how real estate platforms use vector search for property discovery, document intelligence, and personalized recommendations â with production case studies and technical guides.