Overview
AI Listing Extraction Concept is a practical workflow for extracting listing information from unstructured pages and documents into structured records.
Role / Contribution
- Outlined field schema, validation rules, review conditions, and storage-ready JSON for listing extraction.
System Architecture
Step 01
Listing Source
Step 02
Parser
Step 03
Extractor
Step 04
Schema Validator
Step 05
Database Record
Primary Flow
Listing Source→Parser→Extractor→Schema Validator→Database Record
Data Flow
- Listing content is captured from a source page or document.
- Parsing separates description, facts, and broker-provided details.
- Extraction maps values into a normalized property schema.
- Validators flag missing fields, inconsistent units, and low-confidence values.
Technical Components
Web/API intake concept
Structured output schema
Field-level confidence
PostgreSQL-ready records
Review routing
JSON Output Example
{
"property_type": "industrial",
"market": "Miami-Dade",
"size_sf": 52000,
"clear_height_ft": 28,
"confidence": {
"size_sf": 0.92,
"clear_height_ft": 0.78
},
"review_required": true
}Engineering Notes
- Listing data can be inconsistent across sources, so normalization should record units and original text context.
- Low-confidence fields should remain useful by moving into review states instead of being discarded.
Key Takeaways
- Demonstrates structured extraction design.
- Shows practical database and validation awareness.
- Connects CRE data needs with AI workflow implementation.