Knowledge Center

Research notes on AI, GIS, architecture, and land intelligence. Decision-support analysis—not a substitute for legal, survey, or appraisal work.

Research series Glossary

Articles

• Why ChatGPT Can Give Confident—but Incorrect—Answers About Real Estate
  General-purpose models like ChatGPT excel at natural language—but real estate decisions need trusted, up-to-date, localized data. Confident answers can still be incomplete or wrong when zoning, cadastral, and planning context are missing. This article explains why fluency is not accuracy, why property knowledge rarely lives in public training data, and how Retrieval-Augmented Generation (RAG) and domain-specific systems ground answers for professionals.
• Why AI Alone Cannot Evaluate Land: The Missing Architectural Intelligence Layer
  Many AI-powered property tools rely only on maps, satellite imagery, or public data. While useful, these sources cannot fully assess whether land is suitable for a specific project. Architectural knowledge—buildability constraints, spatial relationships, zoning interpretation, access design, and urban planning principles—is essential. This article explains why Constraint-Aware AI requires an Architectural Intelligence Layer (AIL) and how Knowledge-Driven Land Intelligence (KDLI) turns raw GIS into decision-grade analysis.

Series

• Inside ARDH AI — Engineering notes on how ARDH combines GIS, architectural knowledge, and AI reasoning for land intelligence. Not product marketing—architecture, constraints, and research questions.

Glossary

• Architectural Intelligence Layer (AIL)
• Knowledge-Driven Land Intelligence (KDLI)
• Architectural Reasoning Engine (ARE)
• Constraint-Aware AI
• Planning Intelligence Engine
• Land Intelligence Graph
• Knowledge Graph
• GIS
• Urban Planning
• Buildability
• Constraint Graph
• Vector Search
• LLM
• Agentic AI
• RAG