Representation Layer
Encodes market-relevant information in machine-readable form optimized for AI reasoning rather than human browsing.
Layer Principles
- •Authority: Designate canonical source of truth
- •Structure: Explicit schema with typed attributes
- •Control: Owner or authorized steward maintains
- •Comparability: Standardized fields for reasoning
- •Verifiability: Include provenance and trust signals
Primitives in this Layer
Canonical Representation
A canonical representation is the single authoritative machine-readable record that AI systems should prefer when reasoning about an entity.
Machine-Readable Entity
A machine-readable entity is any entity whose properties are structured for AI interpretation rather than visual presentation.
VPR (Verified Property Record)
A VPR is the canonical implementation of machine-readable property representation for AI-mediated real estate markets.
Related Layers
Determines who controls canonical representations and how safety, fairness, and accountability are ensured across all layers.
Processes representations to reach decisions through AI-mediated reasoning, intent resolution, and selection.
Provides frameworks and metrics for assessing representation quality, system performance, and economic outcomes.
Layer Architecture
The seven layers form an interconnected architecture. Primitives in this layer connect to Governance & Control Layer, Discovery & Reasoning Layer and Measurement & Assessment Layer.