Automating property data extraction saves time and reduces manual entry errors. This guide explains how to configure Gemini AI to search official real estate databases, extract key property specifications, and standardize the results for your monday.com boards.

flowchart TD
    A[Address Input] --> B["Gemini 2.5 Flash"]
    B --> C[Raw JSON Response]
    C --> D["Data Standardization (JS)"]
    D --> E["Update monday.com Board"]

Configuration Guide

Follow these steps to set up your AI extraction and data formatting pipeline.

1. 1

   Configure the Gemini AI Prompt

   To get the most accurate data, we use Gemini 2.5 Flash with Google Search Grounding enabled. This allows the AI to browse live County Tax Assessor, CAMA, and GIS databases rather than relying solely on its training data.

   Set your AI Temperature and Top-P to 0.0. This forces the AI to be as deterministic and factual as possible, which is critical when extracting numerical data.

   System Instructions:

   You are a high-precision National Real Estate Data Auditor. Your primary goal is to locate and extract data from official County Tax Assessor, CAMA, or GIS databases for the provided address. You must bypass marketing descriptions on consumer sites and prioritize the legal "Source of Truth." If multiple sources conflict, prioritize the official government record. Ensure all land measurements are standardized to Acres.

   User Prompt Template:

   Task: Get property specs for {{address}}.
   Goal: Identify Square Footage, Lot Acreage, and Subdivision Name.
   
   Instructions:
   Search for the property on official tax or real estate sites.
   Extract sqft, lot_acres, and neighborhood.
   If a value is missing, return 0 for numbers or "N/A" for text.
   No conversational text. Output JSON only.
   
   Output JSON:
   {
     "sqft": [number],
     "lot_acres": [number],
     "type": "Detached/Townhome/Condo/Multi-Family/Commercial/Land",
     "neighborhood": [string]
   }

2. 2

   Apply Data Guardrails

   Real estate data can be messy, and AI might use interchangeable terms (like "Single Family" instead of "Detached"). To ensure the AI's output perfectly matches your database dropdowns, run the JSON response through a code step to standardize the terminology and apply logical guardrails.

   Here is the JavaScript logic used to enforce property types based on lot size:

   // Parse the AI Response
   let cleaned = aiResponse.replace(/```json|```/gi, "").trim();
   const data = JSON.parse(cleaned);
   
   let lotSize = data.lot_acres ? parseFloat(data.lot_acres) : 0;
   let houseSize = parseInt(data.sqft) || 0;
   
   // Standardize terminology
   let aiType = data.type === "Townhouse" ? "Townhome" : data.type;
   if (aiType === "Single Family") aiType = "Detached";
   let finalType = aiType;
   
   // --- THE PATTERN GUARDRAILS ---
   
   // 1. The Condo Rule
   if (lotSize === 0) {
       // Detached homes on 0 acres are legally 'Site Condos'
       finalType = (aiType === "Townhome") ? "Townhome" : "Condo";
   } 
   // 2. The Townhome Rule
   else if (lotSize > 0 && lotSize < 0.10) {
       // Force small-lot detached homes under 2400 sqft into Townhome
       if (aiType === "Detached" && houseSize < 2400) {
           finalType = "Townhome";
       }
   }
   // 3. The Flexible Rule
   else if (lotSize >= 0.10) {
       // Trust the AI's research for larger lots
       finalType = aiType;
   }
   
   // Fallback for invalid types
   const validTypes = ["Detached", "Townhome", "Condo", "Multi-Family", "Commercial", "Land"];
   if (!validTypes.includes(finalType)) finalType = "Detached";

3. 3

   Update monday.com

   Finally, map your standardized data to your target database. In this workflow, the cleaned variables are sent to the API Realtor Data board in monday.com.

   Map the following fields in your update step:

   • Status: Set to Done

   • Location: Map the lat, lng, and original address

   • Property Type: Map to your standardized finalType variable

   • Neighborhood: Map to the title-cased neighborhood string

   • Lot Acres: Map to lot_acres

   • Square Footage: Map to sqft

Property Type Guardrails Explained

The code step in this workflow enforces strict rules to prevent bad data from entering your system. Here is a breakdown of how the guardrails classify properties when the AI's raw output is ambiguous: