Real Estate AI Chatbot with Property Matching
Receives real estate inquiries via webhook, extracts preferences using LLMs, matches properties from PostgreSQL.
What it does
You get instant property matches for your real estate inquiries as the agent extracts your details from incoming messages and searches available listings. It saves your preferences in our database and handles appointment scheduling via email, freeing you to focus on finding your perfect home without manual follow-ups.
Step by step
- Set up a PostgreSQL database with tables for user profiles, property listings, and conversation history.
- Configure a webhook endpoint to receive incoming JSON payloads containing user inquiries.
- Integrate SerpApi credentials into the backend service to execute structured search queries.
- Implement Gmail API OAuth2 flow to authenticate and send emails programmatically.
Where the LLM does the work
- Prompt engineering is required to accurately classify user intent such as buying, renting, or scheduling.
- The LLM must extract specific constraints like budget, location, and bedroom count from unstructured natural language.
- Generate personalized property recommendations that synthesize search results with the user's stated preferences.
Watch out for
Storing sensitive user data in plain text is a security risk so encrypt PII before writing to PostgreSQL.
Email delivery can fail silently if Gmail scopes are misconfigured so add explicit error logging for send operations.
Tools that fit
Webhook API API Receiving initial customer chat messages
OpenAI Chat Model LLM Processing inquiries and generating responses
Postgres Service Storing customer data and conversation history
SerpApi (Google Search) API Searching for property listings online
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Real Estate AI Chatbot with Property Matching: Receives real estate inquiries via webhook, extracts preferences using LLMs, matches properties from PostgreSQL. ## Specification - What it does: You get instant property matches for your real estate inquiries as the agent extracts your details from incoming messages and searches available listings. It saves your preferences in our database and handles appointment scheduling via email, freeing you to focus on finding your perfect home without manual follow-ups. - Trigger: Triggered by an event (Event · on customer message via webhook) - Autonomy: Fully hands-off - Expected setup effort: a few focused days - Tools/services involved: - Webhook API: Receiving initial customer chat messages - OpenAI Chat Model: Processing inquiries and generating responses - Postgres: Storing customer data and conversation history - SerpApi (Google Search): Searching for property listings online - Gmail: Monitoring appointment confirmations and sending emails ## Known pitfalls, handle each one explicitly in your implementation 1. Storing sensitive user data in plain text is a security risk so encrypt PII before writing to PostgreSQL. 2. Email delivery can fail silently if Gmail scopes are misconfigured so add explicit error logging for send operations. ## Reference implementation https://n8n.io/workflows/7250 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/real-estate-ai-chatbot-with-property-matching/ via usecasesforagents.com
Frequently asked questions
Can I use a different LLM than OpenAI?
Yes. OpenAI is only the example LLM in this recipe. The same flow works with Anthropic, Cohere, DeepSeek and Google Gemini. Swap the LLM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Also fits Real Estate.