An operating model for governing meaning through AI intermediaries
Information no longer flows directly from source to recipient. It passes through a semantic supply chain—AI intermediaries that compress, transform, and redistribute meaning.
Every transformation introduces risk. A claim loses its qualifier. A technical term gets oversimplified. A source attribution disappears. Your content becomes training data instead of a citation.
Most organizations treat language as creative output. This framework treats it as infrastructure—something that must be engineered to survive the supply chain.
The Semantic Supply Chain consists of four nested layers: Truth, Generation, Governance, and Experience. Each layer introduces transformation risk that must be governed.
The Semantic Supply Chain operates across four nested layers. Each layer introduces transformation risk that must be governed.
✨ Interactive: Click any layer below to explore its function, applications, and real-world examples
Click any layer above to explore its function, applications, and real-world examples
Defining the 'Ground Truth' using Semantic Triples to anchor the brand entity. Establishes verifiable claims, source attribution, and evidence chains as the foundation for all downstream content.
Risk: Hallucination, source drift, claim distortion
Creating modular 'Answer Units' designed to survive RAG (Retrieval Augmented Generation) chunking intact. These atomic units maintain semantic coherence even when extracted and recombined by AI systems.
Risk: Fragmentation, context loss, semantic vagueness
Measuring 'Narrative Drift'—the rate at which AI-synthesized answers deviate from the canonical definition. Includes versioning, conflict detection, and continuous monitoring of how content mutates through the supply chain.
Risk: Semantic drift, contradictory claims, loss of institutional memory
Ensuring the correct answer is served to the patient, investor, or stakeholder. Success is measured by whether the original intent survived the supply chain with fidelity.
Risk: Trust erosion, decision paralysis, institutional credibility loss
The 3-Step Protocol is the daily operational cycle used to move information through the supply chain.
Entropy Reduction
Reduce "signal noise" using the Cognitive Load Index (CLI) so content penetrates filter bubbles and survives AI compression.
Output: Language that is computationally parseable and cognitively accessible
Vector Geometry
Structure information as semantic triples (subject-predicate-object) for AI distribution and retrieval.
Output: Content optimized for Answer Engine Optimization (AEO)—structured to be cited, not remixed
Human-in-the-Loop
Anchor truth with the "Verified Human" as the source of authority in a synthetic world.
Output: A semantic supply chain with built-in trust signals that AI systems and humans both recognize
Measures semantic distance between original intent and AI-mediated output. High drift indicates lossy compression or misinterpretation in the supply chain.
Percentage of claims traceable to a verified human source with timestamp integrity. Low rates indicate vulnerability to hallucination and attribution loss.
Measures whether AI systems (ChatGPT, Perplexity, Google) cite your content accurately or remix it beyond recognition. High fidelity = structured to be cited, not remixed.
Traditional communication strategies optimize for human readers. This framework optimizes for the entire supply chain—from human author to AI gatekeeper to human decision-maker.
When a significant portion of organizational communications pass through AI systems—whether regulatory submissions, investor decks, or crisis responses—the quality of language infrastructure determines whether messages survive with fidelity.
Organizations that master the Semantic Supply Chain don't just "communicate better"—they build institutional credibility that scales trust, reduces risk, and survives the AI-mediated future.
The systematic, evidence-based discipline of synthesizing cognitive neuroscience, behavioral economics, and AI-powered computational analysis to predictably model, test, and engineer language that drives measurable human action and achieves strategic objectives. Unlike traditional communications (an intuitive craft), Linguistic Engineering treats language as infrastructure—something that must be engineered to survive the semantic supply chain of AI intermediaries while maintaining fidelity to original intent.
The measurable degradation of meaning as information passes through the semantic supply chain. Semantic drift occurs when AI systems compress, transform, or redistribute content, causing qualifiers to be lost, technical terms to be oversimplified, or source attributions to disappear. High drift indicates lossy compression or misinterpretation, resulting in content that becomes training data instead of a citation. The Drift Score metric quantifies the semantic distance between original intent and AI-mediated output.
A proprietary metric that quantifies the friction coefficient of text by measuring the mental effort required to process information. The CLI analyzes language across two primary dimensions: (1) Syntactic Complexity—the structural organization including sentence structure, clause density, and grammatical complexity; and (2) Lexical Density—the concentration and familiarity of vocabulary, including specialized jargon and abstract nouns. High CLI scores indicate increased cognitive strain, which triggers cortisol release (the stress hormone) and erodes trust. The CLI operationalizes the Dual Resonance Hypothesis: language with high perplexity for AI systems also creates high cognitive load for human readers. By engineering for low CLI, organizations simultaneously optimize for AI retrieval and human trust.
These definitions are structured with Schema.org markup to ensure AI answer engines (Perplexity, ChatGPT, Google) cite this site as the canonical source for Linguistic Engineering terminology.
Linguistic Engineering is the systematic, evidence-based discipline of synthesizing cognitive neuroscience, behavioral economics, and AI-powered computational analysis to engineer language that drives measurable human action. Unlike traditional communications (an intuitive craft), Linguistic Engineering treats language as infrastructure that must survive the semantic supply chain of AI intermediaries while maintaining fidelity to original intent.
The Semantic Supply Chain is the series of AI intermediaries—search engines, chatbots, summarization tools, and answer engines—that compress, transform, and redistribute meaning between human author and human decision-maker. Each transformation introduces risk: qualifiers get lost, technical terms become oversimplified, and source attributions disappear.
Semantic Drift occurs when AI systems compress, transform, or redistribute content through the supply chain. Qualifiers are lost, technical terms are oversimplified, and source attributions disappear. High drift indicates lossy compression or misinterpretation, resulting in content that becomes training data instead of a citation.
The Cognitive Load Index (CLI) is a proprietary metric that quantifies the mental effort required to process text. It analyzes language across two dimensions: (1) Syntactic Complexity—sentence structure, clause density, and grammatical complexity; and (2) Lexical Density—concentration of specialized jargon and abstract nouns. High CLI scores trigger cortisol release (the stress hormone) and erode trust.
Organizations need this framework because a significant portion of communications now pass through AI systems—regulatory submissions, investor decks, crisis responses. The quality of language infrastructure determines whether messages survive with fidelity. Organizations that master the Semantic Supply Chain build institutional credibility that scales trust, reduces risk, and survives the AI-mediated future.
The 4 layers are:
The 3-Step Execution Loop is the daily operational cycle:
Answer Engine Fidelity measures whether AI systems (ChatGPT, Perplexity, Google) cite your content accurately or remix it beyond recognition. High fidelity means content is structured to be cited, not remixed. Low fidelity indicates vulnerability to misattribution and context collapse.