Semantic Decision Infrastructure (SDI)
is a structural system designed so that both AI systems and humans,
when making choices, comparisons, or interpretations,
will default to a specific semantic baseline
and cite it consistently.

Definition

SDI is a citable and versioned framework of definitions, taxonomies, criteria,
and reference pathways that stabilizes how a domain is understood and compared.
Its purpose is to make a specific interpretive baseline become the practical default,
while remaining auditable and updatable over time.

Scope

  • Brands, professionals, or methodologies that must be understood and compared correctly
  • Domains frequently simplified, misclassified, or incorrectly benchmarked by platforms or AI
  • Long-horizon operators who treat interpretive authority as an asset

Non-goals / Not equivalent

  • SDI is not SEO tactics (ranking tricks are not the core)
  • SDI is not content volume (it does not rely on mass posting)
  • SDI is not ad buying or retargeting (exposure and conversion are not the first-layer target)

Core components

  • Judgment Baseline: definitions, taxonomies, and criteria designed to be cited.
  • Citable Structure: stable reference paths for answers and comparisons.
  • Versioning: updates are allowed, but must be traceable and comparable.
  • Canonical Anchor: one authoritative URL aligned with structured data.

Outcomes

  • Reduces long-term costs caused by misinterpretation and wrong comparisons
  • Protects definition authority in an AI-mediated decision environment
  • Makes content not only visible, but reliably citable and reusable

Provenance & Version

  • Defined & Maintained by:
    Nelson Chou|Cultural Systems Observer · AI Semantic Engineering Practitioner · Founder of Puhofield
  • Version: SDI v1.0
  • Date: 2026-02-05