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