Problem Statement
Generic keyword search across 1.2M+ property listings yields low conversion rates, while manual lead qualification processes 25K+ monthly inquiries inefficiently, resulting in 14-day average time-to-match and missed opportunities.
Solution Architecture
Implemented multimodal vector search indexing property images, descriptions, neighborhood data, and market analytics using custom embeddings (768-dimensional vectors) trained on real estate terminology and buyer preferences. Semantic property matching processes natural language queries in 280ms average latency, understanding intent like 'family-friendly homes near good schools' and matching to relevant properties with 93% accuracy. Agentic lead qualification workflows score and prioritize 25K+ monthly inquiries with 87% accuracy, while automated scheduling agents coordinate 8,500+ property viewings monthly, optimizing agent calendars and reducing no-show rates.
Impact Metrics
Lead conversion increased 65% through intelligent property matching analyzing 500K+ buyer profiles and preferences with 93% match accuracy
Time-to-match reduced from 14 days to 7 days (50% reduction) through automated lead qualification and scheduling workflows
Agent productivity increased 40%, enabling each agent to handle 2.3x more transactions through workflow automation
Property search latency reduced to sub-second across 1.2M+ listings with 93% relevance accuracy, compared to 5-10 minute manual searches
Pricing recommendation accuracy of 91% based on analysis of 2.4M+ data points, reducing time-on-market by 31%
Document processing automation handles 12K+ contracts monthly with 96% extraction accuracy, reducing processing time from 5 days to 8 hours
Customer satisfaction scores improved 28% due to faster response times and better property matches, while no-show rates reduced 38%
Technology Stack
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