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        "markdown": "---\ntitle: AI Visibility Dictionary | Key Terms for AI Search & Citations | NRLC.ai\ndescription: Definitions for grounding queries, citation surfaces, prechunking, retrieval share, and 35+ terms used in AI search visibility and AI citations. Canonical reference for teams.\norganization: Neural Command LLC\ncanonical: https://nrlc.ai/en-us/ai-visibility-dictionary/\n---\n\nThis dictionary defines the key terms used in AI search visibility, AI citations, and retrieval-based ranking. Use this page as the canonical reference for your team's terminology.\n\nMaintained by Joel Maldonado · Schema SEO, ontology & visibility research\n\n## Breadth\n\nDefinition: Breadth is the number of distinct topics, entities, and query clusters your site covers in a way that can be retrieved and summarized by AI systems.\n\nExample: A DNS cluster that includes TTL, MX priority, propagation, DoH vs DoT, and dig commands has more breadth than a single \"DNS basics\" article.\n\nSo what: Breadth increases the number of entry points where AI systems can discover and cite you across a topic area.\n\n## Depth\n\nDefinition: Depth is how completely a single topic is answered, including steps, edge cases, examples, and failure modes.\n\nExample: A TLS guide that includes certificate types, common misconfigurations, renewal strategy, and troubleshooting is deeper than a definition-only page.\n\nSo what: Depth increases the chance your page is selected as the \"best single source\" for a query.\n\n## Grounding query\n\nDefinition: A grounding query is a search an AI system runs to confirm facts from sources before producing an answer.\n\nExample: For \"DoH vs DoT,\" the system may run grounding queries like \"DoH vs DoT privacy performance differences\" to fetch authoritative explanations.\n\nSo what: If your page aligns with grounding queries, you get retrieved more often and cited more consistently.\n\n## Fan-out query\n\nDefinition: A fan-out query is a follow-up query an AI system runs to expand coverage around the main question (related subtopics, alternatives, definitions).\n\nExample: After retrieving \"DoH vs DoT,\" it may fan out to \"DNS leakage,\" \"enterprise policy,\" or \"mobile captive portals.\"\n\nSo what: Fan-out coverage is where breadth wins; it's how you show up across the surrounding questions.\n\n## Citation surface\n\nDefinition: A citation surface is a URL that repeatedly gets cited by AI systems as a source for answers.\n\nExample: A \"Best domain marketplaces\" page becomes a citation surface if it's referenced across many marketplace-related prompts.\n\nSo what: Citation surfaces are your distribution hubs; improving them multiplies impact.\n\n## Citation gravity\n\nDefinition: Citation gravity is the compounding effect where already-cited pages keep getting cited more because they're repeatedly selected and reinforced.\n\nExample: A hub page that's cited in many answers becomes the default retrieval target for that topic.\n\nSo what: Protect and refresh pages with citation gravity; they're compounding assets.\n\n## Citation piggybacking\n\nDefinition: Citation piggybacking is routing new pages through already-cited pages by adding a tight, relevant section and a small number of internal links near the top.\n\nExample: Add \"New in 2026: Marketplace safety checklist\" with a link to your new checklist article inside a highly cited marketplace guide.\n\nSo what: It accelerates discovery, crawling, and AI selection for new content.\n\n## Chunking\n\nDefinition: Chunking is how search and AI systems split a page into retrievable sections.\n\nExample: A long article may be retrieved as separate passages: definition, steps, FAQ, troubleshooting.\n\nSo what: You must write sections that stand alone; weak chunking reduces citations.\n\n## Citation chunk\n\nDefinition: A citation chunk is a section engineered to be quoted: direct answer, explicit entities, one concrete example, and why it matters.\n\nExample: An H2 that defines \"MX priority,\" shows a sample record, then explains what breaks if mis-set.\n\nSo what: Strong citation chunks increase liftability and attribution.\n\n## Prechunking\n\nDefinition: Prechunking is writing content so the \"chunks\" are already optimal before systems split them.\n\nExample: Each H2 includes a 1–2 sentence answer, an example, and a \"so what\" line.\n\nSo what: Prechunking improves retrieval success and reduces summarization errors.\n\n## Retrieval window\n\nDefinition: The retrieval window is the limited amount of source text the AI can bring into context for answering.\n\nExample: The system may retrieve a few passages from 3–10 pages, not entire sites.\n\nSo what: If your key answer isn't early and self-contained, it may never enter the window.\n\n## Attention window\n\nDefinition: The attention window is the smaller subset of retrieved text that actually influences the final answer most.\n\nExample: The model may focus heavily on the first clean definition it sees and ignore later paragraphs.\n\nSo what: Put the best answer first and keep it compact.\n\n## Entity salience\n\nDefinition: Entity salience is how clearly your page signals the primary entities involved (products, protocols, standards, tools, brands).\n\nExample: A page that repeatedly names \"DoH,\" \"DoT,\" \"TLS,\" \"DNS resolver,\" and \"RFC\" has stronger salience than vague wording.\n\nSo what: High salience improves correct retrieval and reduces mismatch.\n\n## Entity disambiguation\n\nDefinition: Entity disambiguation is removing ambiguity so the AI knows which \"X\" you mean.\n\nExample: \"Domain registry (Verisign) vs domain registrar (NameSilo)\" prevents confusion with generic \"registration.\"\n\nSo what: Disambiguation prevents wrong retrieval and wrong summaries.\n\n## Entity graph\n\nDefinition: An entity graph is the network of relationships among entities on your site (orgs, products, concepts, locations, attributes).\n\nExample: Domain marketplace ↔ escrow ↔ transfer lock ↔ WHOIS privacy ↔ registrar policies.\n\nSo what: Strong graphs help AI systems understand coverage and trustworthiness.\n\n## Canonical cluster\n\nDefinition: A canonical cluster is a set of near-duplicate URLs where one should be the authoritative canonical.\n\nExample: /pricing and /pricing?rid=123 are duplicates that should resolve to one canonical URL.\n\nSo what: Canonical clusters dilute signals and citations unless consolidated.\n\n## Param pollution\n\nDefinition: Param pollution is when URL parameters create indexable duplicates that split ranking and citation signals.\n\nExample: Tracking params create multiple versions of the homepage that get cited instead of the clean URL.\n\nSo what: Fixing param pollution concentrates authority and improves consistent citations.\n\n## Index bloat\n\nDefinition: Index bloat is having too many low-value pages indexed, reducing crawl efficiency and overall site quality signals.\n\nExample: Infinite /whois?query= variants or thin tag pages being indexed.\n\nSo what: Less bloat means more crawl and weight on pages that matter.\n\n## Crawl budget\n\nDefinition: Crawl budget is how much crawling search engines allocate to your site over time.\n\nExample: A site with many duplicates wastes crawl budget and gets important pages refreshed less often.\n\nSo what: Better crawl efficiency helps content refreshes show up faster.\n\n## Re-crawl trigger\n\nDefinition: A re-crawl trigger is a meaningful change that increases the likelihood a page is revisited soon.\n\nExample: Updating title/H1, adding new sections, improving internal links, and refreshing timestamps.\n\nSo what: Useful when you're trying to push updates into search and AI retrieval quickly.\n\n## Snippetability\n\nDefinition: Snippetability is how easily text can be lifted as a clean answer without extra context.\n\nExample: \"TTL controls how long resolvers cache DNS answers; lower TTL before migrations to reduce downtime.\"\n\nSo what: High snippetability increases selection in AI answers and featured snippets.\n\n## Answer box\n\nDefinition: An answer box is a 40–60 word summary placed near the top designed for copy/paste retrieval.\n\nExample: A single paragraph that defines a concept, states the decision rule, and includes a constraint.\n\nSo what: This is the most \"quotable\" unit on a page.\n\n## Source grounding\n\nDefinition: Source grounding is when an AI ties its claims to retrieved sources rather than model memory.\n\nExample: It cites a page for \"DoH vs DoT\" instead of guessing.\n\nSo what: Grounding is where citations come from; your goal is to be the grounded source.\n\n## Hallucination pressure\n\nDefinition: Hallucination pressure is when the system is forced to guess because sources are missing, vague, or contradictory.\n\nExample: A page that never gives concrete steps causes the model to improvise.\n\nSo what: Reduce hallucination pressure with explicit steps, examples, and definitions.\n\n## Query framing\n\nDefinition: Query framing is structuring queries with entities, constraints, and context to force better retrieval.\n\nExample: \"DoH vs DoT for enterprise networks: performance, policy control, and security tradeoffs.\"\n\nSo what: Framing determines what sources get pulled in.\n\n## Query expansion\n\nDefinition: Query expansion is adding related terms and synonyms to broaden retrieval coverage.\n\nExample: \"WHOIS privacy\" + \"domain privacy\" + \"redacted WHOIS\" + \"ICANN policy.\"\n\nSo what: Expansion helps you cover variations and long-tail prompts.\n\n## Freshness bias\n\nDefinition: Freshness bias is a preference for newer sources when topics change quickly.\n\nExample: A \"2026 marketplace\" update can outrank and out-cite a \"2024\" guide.\n\nSo what: Refresh top citation surfaces on a predictable cadence.\n\n## Content decay\n\nDefinition: Content decay is performance loss over time as information becomes outdated or competitors publish stronger answers.\n\nExample: A \"2025 trends\" page stops being cited when 2026 sources exist.\n\nSo what: Refresh prevents decay and preserves citation gravity.\n\n## Provenance\n\nDefinition: Provenance is the traceable origin of a claim: who said it, where, and when.\n\nExample: Citing official protocol docs, policy pages, or primary sources for rules and standards.\n\nSo what: Strong provenance increases trust and citation likelihood.\n\n## Attribution likelihood\n\nDefinition: Attribution likelihood is how often an AI will name your brand when it uses your content.\n\nExample: A named framework and a clearly branded definition increases attribution vs generic phrasing.\n\nSo what: Attribution is the difference between \"being used\" and \"being credited.\"\n\n## Link adjacency\n\nDefinition: Link adjacency is how close an internal link is to the extractable answer text.\n\nExample: A link placed immediately after an answer box is more likely to be followed than one in a footer.\n\nSo what: Adjacency is how you route new pages through citation surfaces.\n\n## Distribution hub\n\nDefinition: A distribution hub is a page designed to send authority, crawl, and users into related pages.\n\nExample: A DNS hub that links to TTL, MX priority, propagation, and troubleshooting guides.\n\nSo what: Hubs turn one strong surface into many strong pages.\n\n## Topical moat\n\nDefinition: A topical moat is owning a dense, interlinked cluster so retrieval and citations default to your site.\n\nExample: Multiple DNS pages with consistent internal linking and distinct coverage.\n\nSo what: Moats reduce competitor displacement.\n\n## Zero-click capture\n\nDefinition: Zero-click capture is winning exposure through AI answers/snippets even when users don't click.\n\nExample: Being cited or named inside the AI response.\n\nSo what: Visibility becomes brand demand and downstream conversion.\n\n## Citation share\n\nDefinition: Citation share is your proportion of citations in a topic cluster versus competitors.\n\nExample: 40% of cited sources for \"domain marketplaces\" come from your site.\n\nSo what: Citation share is the KPI for AI visibility dominance.\n\n## Retrieval share\n\nDefinition: Retrieval share is how often your pages are retrieved as sources, even if not always cited.\n\nExample: Your page is frequently fetched but another page is the one cited.\n\nSo what: Improving snippetability and provenance can turn retrieval into citations.\n\n## AI Labs\n\nDefinition: NRLC AI Labs is the category layer for retrieval, agent, and schema infrastructure — the research and implementation practice that lets machines understand, retrieve, cite, compare, localize, and act on enterprise data.\n\nExample: A team maps diagnostics findings to AI Labs pillars before scoping schema or agent work.\n\nSo what: AI Labs organizes infrastructure work into intelligence, agents, and schema categories.\n\nPillar: AI Labs guide →\n\n## LLM Compute Leak\n\nDefinition: LLM Compute Leak is the observable exposure of market demand, intent, brand preference, and citation pathways through AI retrieval behavior across answer surfaces, prompt fan-out, and agentic browsing. NRLC strategic framing unless separately documented as a proprietary product.\n\nExample: Prompt fan-out reveals related sub-queries where a brand never enters the retrieval window.\n\nSo what: Useful for prioritizing where retrieval infrastructure investment matters most.\n\nPillar: LLM Compute Leak guide →\n\n## AI Agent Optimization\n\nDefinition: AI Agent Optimization is preparing entities, products, locations, services, pricing, availability, policies, and transaction paths so autonomous agents can discover, verify, compare, and complete actions without ambiguous inference.\n\nExample: A product page with stable SKU IDs, currency-aware offers, and explicit checkout routes.\n\nSo what: Reduces inference cost and failed agent actions on commerce and service paths.\n\nPillar: AI Agent Optimization guide →\n\n## Enterprise Schema Implementation\n\nDefinition: Large-scale schema implementation is enterprise entity architecture: JSON-LD and source-of-truth records for catalogs, locations, services, people, offers, reviews, currencies, languages, and regions.\n\nExample: Shade-level Product nodes with per-locale WebPage canonicals and store-level Offer availability.\n\nSo what: Schema at catalog scale prevents entity conflation in retrieval and agentic commerce.\n\nPillar: Enterprise Schema Implementation guide →\n\n## Agent action route\n\nDefinition: An agent action route is the machine-readable path from discovery to completion — compare, verify stock, book, checkout, or procure — without human interpretation of ambiguous HTML.\n\nExample: Service lookup → policy confirmation → booking URL with structured availability.\n\nSo what: Broken routes cause agent failures even when content ranks well.\n\nPillar: Agent action route guide →\n\n## Entity and source architecture\n\nDefinition: Entity and source architecture is governed entity IDs, JSON-LD graphs, and authoritative source pages that define how AI systems interpret products, services, people, and places as verifiable facts.\n\nExample: A catalog where every variant has a stable @id aligned to its canonical source page.\n\nSo what: Foundation for retrieval, citation, and agent verification.\n\nPillar: Entity and source architecture guide →\n\n## Currency-aware product schema\n\nDefinition: Currency-aware product schema is Product and Offer markup with explicit currency, regional price, and locale alignment so retrieval systems cite the correct regional price without conflating entities.\n\nExample: Offer.priceCurrency and Offer.price per market on variant-level Product nodes.\n\nSo what: Prevents wrong-price citations and agent checkout errors in multi-market catalogs.\n\nPillar: Currency-aware product schema guide →\n\n## Language-aware entity schema\n\nDefinition: Language-aware entity schema uses localized names, descriptions, and per-locale canonical URLs so retrieval systems compare the correct regional entity.\n\nExample: Product.name and WebPage inLanguage per locale with hreflang-aligned canonicals.\n\nSo what: Reduces cross-locale entity conflation in AI answers and agent comparisons.\n\nPillar: Language-aware entity schema guide →\n\n## Ontological SEO\n\nDefinition: Ontological SEO is NRLC's practice of optimizing the entity and truth layer that search and AI systems resolve against — so brands are retrieved, attributed, and cited as the correct thing, not merely ranked as a page.\n\nExample: A brand with stable entity IDs, consistent JSON-LD across source pages, and citation-ready passages that answer engines can attribute without conflating competitors.\n\nSo what: Without a governed ontology, SEO, AEO, and GEO can surface the wrong brand facts even when pages rank.\n\nPillar: Ontological SEO guide →\n\n← Back to AI Search Glossary\n\n\n\n---\n\nSource: https://nrlc.ai/en-us/ai-visibility-dictionary/\nPublisher: Neural Command LLC\nLicense: Editorial use with attribution\n"
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                            "@id": "https://nrlc.ai/topics/entity-optimization/#term",
                            "url": "https://nrlc.ai/topics/entity-optimization/",
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                            "name": "Entity disambiguation",
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                            "url": "https://nrlc.ai/topics/entity-optimization/",
                            "inDefinedTermSet": "https://nrlc.ai/topics/#termset"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Entity graph",
                            "description": "An entity graph is the network of relationships among entities on your site (orgs, products, concepts, locations, attributes).",
                            "@id": "https://nrlc.ai/topics/knowledge-graphs/#term",
                            "url": "https://nrlc.ai/topics/knowledge-graphs/",
                            "inDefinedTermSet": "https://nrlc.ai/topics/#termset"
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                            "@type": "DefinedTerm",
                            "name": "Canonical cluster",
                            "description": "A canonical cluster is a set of near-duplicate URLs where one should be the authoritative canonical.",
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                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Param pollution",
                            "description": "Param pollution is when URL parameters create indexable duplicates that split ranking and citation signals.",
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                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Index bloat",
                            "description": "Index bloat is having too many low-value pages indexed, reducing crawl efficiency and overall site quality signals.",
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                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Crawl budget",
                            "description": "Crawl budget is how much crawling search engines allocate to your site over time.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-crawl-budget"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Re-crawl trigger",
                            "description": "A re-crawl trigger is a meaningful change that increases the likelihood a page is revisited soon.",
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                        {
                            "@type": "DefinedTerm",
                            "name": "Snippetability",
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                        {
                            "@type": "DefinedTerm",
                            "name": "Answer box",
                            "description": "An answer box is a 40–60 word summary placed near the top designed for copy/paste retrieval.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-answer-box"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Source grounding",
                            "description": "Source grounding is when an AI ties its claims to retrieved sources rather than model memory.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-source-grounding"
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                        {
                            "@type": "DefinedTerm",
                            "name": "Hallucination pressure",
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                        {
                            "@type": "DefinedTerm",
                            "name": "Query framing",
                            "description": "Query framing is structuring queries with entities, constraints, and context to force better retrieval.",
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                        {
                            "@type": "DefinedTerm",
                            "name": "Query expansion",
                            "description": "Query expansion is adding related terms and synonyms to broaden retrieval coverage.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-query-expansion"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Freshness bias",
                            "description": "Freshness bias is a preference for newer sources when topics change quickly.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-freshness-bias"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Content decay",
                            "description": "Content decay is performance loss over time as information becomes outdated or competitors publish stronger answers.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-content-decay"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Provenance",
                            "description": "Provenance is the traceable origin of a claim: who said it, where, and when.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-provenance"
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                        {
                            "@type": "DefinedTerm",
                            "name": "Attribution likelihood",
                            "description": "Attribution likelihood is how often an AI will name your brand when it uses your content.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-attribution-likelihood"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Link adjacency",
                            "description": "Link adjacency is how close an internal link is to the extractable answer text.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-link-adjacency"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Distribution hub",
                            "description": "A distribution hub is a page designed to send authority, crawl, and users into related pages.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-distribution-hub"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Topical moat",
                            "description": "A topical moat is owning a dense, interlinked cluster so retrieval and citations default to your site.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-topical-moat"
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                        {
                            "@type": "DefinedTerm",
                            "name": "Zero-click capture",
                            "description": "Zero-click capture is winning exposure through AI answers/snippets even when users don't click.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-zero-click-capture"
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                        {
                            "@type": "DefinedTerm",
                            "name": "Citation share",
                            "description": "Citation share is your proportion of citations in a topic cluster versus competitors.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-citation-share"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Retrieval share",
                            "description": "Retrieval share is how often your pages are retrieved as sources, even if not always cited.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-retrieval-share"
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                        {
                            "@type": "DefinedTerm",
                            "name": "AI Labs",
                            "description": "NRLC AI Labs is the category layer for retrieval, agent, and schema infrastructure — the research and implementation practice that lets machines understand, retrieve, cite, compare, localize, and act on enterprise data.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-ai-labs"
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                        {
                            "@type": "DefinedTerm",
                            "name": "LLM Compute Leak",
                            "description": "LLM Compute Leak is the observable exposure of market demand, intent, brand preference, and citation pathways through AI retrieval behavior across answer surfaces, prompt fan-out, and agentic browsing. NRLC strategic framing unless separately documented as a proprietary product.",
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                            "@type": "DefinedTerm",
                            "name": "AI Agent Optimization",
                            "description": "AI Agent Optimization is preparing entities, products, locations, services, pricing, availability, policies, and transaction paths so autonomous agents can discover, verify, compare, and complete actions without ambiguous inference.",
                            "@id": "https://nrlc.ai/topics/ai-agent-optimization/#term",
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                            "@type": "DefinedTerm",
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                            "description": "Large-scale schema implementation is enterprise entity architecture: JSON-LD and source-of-truth records for catalogs, locations, services, people, offers, reviews, currencies, languages, and regions.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-enterprise-schema-implementation"
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                        {
                            "@type": "DefinedTerm",
                            "name": "Agent action route",
                            "description": "An agent action route is the machine-readable path from discovery to completion — compare, verify stock, book, checkout, or procure — without human interpretation of ambiguous HTML.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-agent-action-route"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Entity and source architecture",
                            "description": "Entity and source architecture is governed entity IDs, JSON-LD graphs, and authoritative source pages that define how AI systems interpret products, services, people, and places as verifiable facts.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-entity-and-source-architecture"
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                        {
                            "@type": "DefinedTerm",
                            "name": "Currency-aware product schema",
                            "description": "Currency-aware product schema is Product and Offer markup with explicit currency, regional price, and locale alignment so retrieval systems cite the correct regional price without conflating entities.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-currency-aware-product-schema"
                        },
                        {
                            "@type": "DefinedTerm",
                            "name": "Language-aware entity schema",
                            "description": "Language-aware entity schema uses localized names, descriptions, and per-locale canonical URLs so retrieval systems compare the correct regional entity.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-language-aware-entity-schema"
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                        {
                            "@type": "DefinedTerm",
                            "name": "Ontological SEO",
                            "description": "Ontological SEO is NRLC's practice of optimizing the entity and truth layer that search and AI systems resolve against — so brands are retrieved, attributed, and cited as the correct thing, not merely ranked as a page.",
                            "@id": "https://nrlc.ai/en-us/ai-visibility-dictionary/#term-ontological-seo"
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                    "@type": [
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                        "WebPage"
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                    "url": "https://nrlc.ai/en-us/ai-visibility-dictionary/",
                    "name": "AI Visibility Dictionary",
                    "description": "Definitions for AI search visibility, AI citations, and retrieval-based ranking. Each entry is written to be liftable by AI systems: direct definition, concrete example, and when it matters.",
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                        "@type": "Organization",
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            "label": "Ontological SEO guide →"
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