OpenAI GPT-6

Luna Decisions Integration with n8n for Real Estate Listing Parsing: Workflow Architecture, Limitations and Open Questions

A detailed technical discussion explores the use of n8n workflows to monitor real estate advertisements by combining scheduled data collection, normalization, and comparison logic with potential AI-driven decision layers. The article examines the boundary between raw parsing and actionable decisions, highlighting how simple code-based event detection can be augmented by structured outputs from models such as OpenAI GPT-6 Luna Decisions. Key components include a Dispatcher node that identifies new listings, price drops, and removals, while storing state in Google Sheets and generating Telegram summaries. Limitations around data completeness, currency conversion, and false positives for sold status are analyzed in depth. The author proposes an experimental branch that routes validated price-change events to Luna Decisions API for typed scoring before any human notification. Overall the piece invites community feedback on whether a dedicated Decisions API provides measurable advantages over rule-based conditions or standard structured LLM outputs.