AI for Real Estate
Property Search, Valuation & Automation

AI recommendation engines analyze buyer preferences, search history, budget, location requirements, and similar buyer profiles to surface the most relevant listings. We build these using LangChain with Qdrant vector search on your property database, so buyers find relevant properties even with natural language queries like 'quiet neighborhood near good schools'.

An AVM uses machine learning to estimate property values based on comparable sales, location data, property features, and market trends. Modern AI-powered AVMs achieve median error rates of 3–5% on residential properties with sufficient comparable data. We build AVMs using PyTorch regression models trained on your local market data.

Yes. A LangGraph agent can conduct a full lead qualification conversation , asking about budget, timeline, property type, location preferences, and financing status , then score the lead, update your CRM, and route hot leads to agents immediately. This runs 24/7 and ensures no lead goes uncontacted.

A RAG chatbot connects an LLM to your property listings, neighborhood data, and FAQ documentation. Buyers and renters get instant answers about properties, availability, pricing, and neighborhood details , grounded in your actual data. Built with LangChain and Qdrant, it handles inquiries 24/7 without agent involvement.

Yes. We build NLP pipelines that extract key terms from lease agreements, purchase contracts, and property disclosures , identifying rent amounts, lease dates, renewal clauses, and obligations automatically. This eliminates manual document review and reduces errors in property management workflows.

A focused project like a lead qualification chatbot or property recommendation engine typically takes 6–10 weeks. A more comprehensive system with AVM, semantic search, and CRM integration takes 12–16 weeks. We use agile sprints with bi-weekly demos throughout.