AI-Powered Property Search
We built a semantic search engine that allows buyers to find homes using natural language.No checkboxes. No complex filters. Just conversation.
Try the Semantic Search
Type a natural language query below. This is a simulation of how our engine processes intent.
Results will appear here...
The Problem
Traditional real estate search relies on rigid filters (Bedrooms, Bathrooms, Zip Code).
But buyers don't think in filters. They think in lifestyles:
- "I want a modern townhouse with good light, walking distance to coffee shops, under $700k."
- "Fixer-upper with a large backyard for a garden."
Legacy search engines return 0 results for these queries.
Our Solution
We ingested active MLS data into a vector database, allowing us to search by meaning, not just keywords.
- Understands "walking distance" & location context
- Analyzes image descriptions (modern, bright, etc.)
- Ranks by relevance to the user's specific intent
How It Works
This same architecture applies to legal discovery, engineering parts search, and corporate knowledge bases.
1. Ingest
Connect to MLS listings via RETS/API or CSV export.
2. Embed
Convert descriptions & features into vector embeddings (OpenAI/Cohere).
3. Search
Perform semantic similarity search against user query.
4. Rank
LLM reranks results and generates a natural language summary.
Imagine this for your data.
We can build this exact system for your engineering files, legal contracts, or marketing assets.