feed.json

{
  "_note": "PUBLIC LINKS MUST point to https://insights.rhinegold.de/... — NEVER rhinegold.de/... The rhinegold.de/compendium/* Framer redirect is broken (:splat not substituted) and returns 404 on every URL until the Framer routing is fixed. After editing this file, purge the jsDelivr cache: https://purge.jsdelivr.net/gh/MHZach/mapgap@main/feed.json . Verify every new URL with curl (HTTP 200) before announcing it live.",
  "updated": "2026-06-13",
  "items": [
    {
      "title": "Aided vs Unaided Brand Recall in GEO",
      "url": "https://insights.rhinegold.de/compendium/aided-unaided-brand-recall-geo/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "Classical brand-equity research separates unaided brand recall from aided brand recognition. Most current GEO measurement tools collapse the two, producing systematically inflated mention rates. A three-stage taxonomy (unaided · category-aided · brand-aided) is the cleanest available correction — and rhinegold openly discloses how its own pipeline carried the same blind spot."
    },
    {
      "title": "Mention Quality",
      "url": "https://insights.rhinegold.de/compendium/mention-quality/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "The weighted-value layer above raw mention counts: grading each AI mention by position, framing, source authority and tone. A brand mentioned first in a list with confident positive framing contributes more than the same brand named parenthetically or dismissed in a comparison."
    },
    {
      "title": "Competitive Mention Map",
      "url": "https://insights.rhinegold.de/compendium/competitive-mention-map/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "A structured view of which competitors a brand is co-mentioned with in AI answers, by query cluster and platform. Makes the consideration set the AI projects to buyers visible — and reveals whether the AI-perceived peer group matches the brand's intended positioning."
    },
    {
      "title": "AI Mention Velocity",
      "url": "https://insights.rhinegold.de/compendium/ai-mention-velocity/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "The rate at which a brand begins appearing in AI answers for emerging topics — the first derivative of Mention Rate. Capturing rising-topic velocity is the AI-era equivalent of being first to rank for a breakout keyword before search volume peaks."
    },
    {
      "title": "LLM Brand Tracking",
      "url": "https://insights.rhinegold.de/compendium/llm-brand-tracking/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "The continuous discipline of monitoring how a brand appears in AI-generated responses across ChatGPT, Claude, Gemini, Perplexity and Copilot. The operational successor to classic survey-based brand tracking in an era where machine answers mediate discovery before any human actively searches."
    },
    {
      "title": "Prompt Coverage",
      "url": "https://insights.rhinegold.de/compendium/prompt-coverage/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "Share of Voice scores presence within prompts you already test. Prompt Coverage asks the prior question: across all the prompts that matter for the buying decision, in how many does the brand appear at all? A denominator question that reveals the structural blind spots SoV cannot see."
    },
    {
      "title": "Source Authority",
      "url": "https://insights.rhinegold.de/compendium/source-authority/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "Which third-party domains do AI systems treat as ground truth when answering brand and category questions? Unlike Citation Rate (how often your URL appears), Source Authority is the upstream trust layer — and for many B2B categories, indirect representation in authority sources beats trying to be cited directly."
    },
    {
      "title": "Brand Voice Match",
      "url": "https://insights.rhinegold.de/compendium/brand-voice-match/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "The degree to which AI descriptions of a brand reproduce its own voice, positioning vocabulary and distinctive attribute language — as opposed to defaulting to generic category phrasing. The distinctiveness check that sits above sentiment: a brand can be neutral-to-positive and simultaneously commoditised."
    },
    {
      "title": "AI Visibility Audit",
      "url": "https://insights.rhinegold.de/compendium/ai-visibility-audit/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "A scoped, time-bounded assessment of how a brand appears across AI-generated answers — covering mention rate, citation rate, sentiment, share of voice and source authority. The deliverable: baseline plus gap analysis plus prioritised intervention list, not a subscription dashboard."
    },
    {
      "title": "AI Reputation Risk",
      "url": "https://insights.rhinegold.de/compendium/ai-reputation-risk/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "The cumulative brand exposure a company carries from how it is represented across AI answers — aggregating hallucinated facts, phantom URLs, negated mentions, sentiment and harmful co-mentions into one governance-level risk view. Failure modes compound; a single-issue lens misses the curve."
    },
    {
      "title": "Sentiment Drift",
      "url": "https://insights.rhinegold.de/compendium/sentiment-drift/",
      "date": "2026-06-15",
      "type": "compendium",
      "teaser": "The temporal change in how AI answers characterise a brand — the directional derivative of AI Answer Sentiment. A static score is a lagging indicator; drift is the early-warning reading that lets operators intervene before negative framings compound at scale."
    },
    {
      "title": "AI Mode",
      "url": "https://insights.rhinegold.de/compendium/ai-mode/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Google's conversational search surface reasons over many parallel sub-searches and answers with citations — shifting the unit of B2B exposure from ranked link to cited passage."
    },
    {
      "title": "Microsoft Copilot",
      "url": "https://insights.rhinegold.de/compendium/microsoft-copilot/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "An AI answer surface embedded in Microsoft 365, grounded in Bing and enterprise data — a decision-stage citation surface that leaves no public results page to audit."
    },
    {
      "title": "Mistral & Le Chat",
      "url": "https://insights.rhinegold.de/compendium/mistral-le-chat/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Mistral plays two roles for European operators: Le Chat (now Vibe) as an emerging answer surface, and Mistral as the EU-jurisdiction model provider regulated enterprises weigh — two separate decisions."
    },
    {
      "title": "Agentic Commerce",
      "url": "https://insights.rhinegold.de/compendium/agentic-commerce/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Beyond AI-assisted research: an authorised agent selects, checks out and completes the purchase. With payment protocols in production, being findable and being buyable-by-machine split apart."
    },
    {
      "title": "Chunking & Passage Retrieval",
      "url": "https://insights.rhinegold.de/compendium/chunking-passage-retrieval/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "AI search retrieves and cites at the passage level, not the page. Whether each passage is self-contained decides what gets found — the most direct GEO lever an author controls."
    },
    {
      "title": "Placebo Test",
      "url": "https://insights.rhinegold.de/compendium/placebo-test/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "A falsification check: run your estimator where no effect should exist. If it still finds one, the design — not the intervention — produced the result. The minimum credibility gate for any GEO/SEO claim."
    },
    {
      "title": "Synthetic Control",
      "url": "https://insights.rhinegold.de/compendium/synthetic-control/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Build a weighted blend of untreated units to approximate what a treated brand or page would have done without an intervention — the honest counterfactual when no single control fits."
    },
    {
      "title": "Spillover & Contamination",
      "url": "https://insights.rhinegold.de/compendium/spillover-contamination/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "When a treatment leaks into the comparison group, SUTVA breaks and the measured effect shrinks or inverts. In SEO/GEO it is structural: shared links, clusters and sitewide signals."
    },
    {
      "title": "AI Vendor Sovereignty",
      "url": "https://insights.rhinegold.de/compendium/ai-vendor-sovereignty/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Choosing an LLM provider is now two decisions, not one — visibility in the models your buyers use, and control over the models you process with. The 12 June 2026 US suspension of Anthropic's Fable 5 and Mythos 5 turned vendor sovereignty from theory into a dated event."
    },
    {
      "title": "AI Agents in the Buyer Journey",
      "url": "https://insights.rhinegold.de/compendium/ai-agents-buyer-journey/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "AI agents are tool-using systems that carry out multi-step tasks — increasingly part of vendor research. When an agent builds the shortlist, the audience is no longer only human, and the criteria that win are machine-legible facts."
    },
    {
      "title": "Sentiment in AI Answers",
      "url": "https://insights.rhinegold.de/compendium/ai-answer-sentiment/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Sentiment in AI answers is the qualitative tone with which a brand is introduced — endorsed, described neutrally, or hedged. It is the layer above every counting metric."
    },
    {
      "title": "Co-Mentions",
      "url": "https://insights.rhinegold.de/compendium/co-mentions/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Co-mentions are the brands named alongside yours in the same AI answer. They define the competitive set the model puts you in — and a brand is positioned by the company it keeps."
    },
    {
      "title": "Content Decay",
      "url": "https://insights.rhinegold.de/compendium/content-decay/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Content decay is the gradual loss of a page's traffic and visibility over time. In an AI-answer world it is sharper and quieter: a page can keep its ranking yet drop out of the answers that now intercept the click."
    },
    {
      "title": "Embedding",
      "url": "https://insights.rhinegold.de/compendium/embedding/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "An embedding is a representation of text as a vector of numbers, positioned so that things with similar meaning sit close together. It is how machines compute semantic similarity."
    },
    {
      "title": "Featured Snippet",
      "url": "https://insights.rhinegold.de/compendium/featured-snippet/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "A featured snippet is the boxed answer Google lifts to the top of results, quoting a page directly. It was the first mass surface where the engine answered instead of linking — the ancestor of AI Overviews."
    },
    {
      "title": "Grounded Response Rate",
      "url": "https://insights.rhinegold.de/compendium/grounded-response-rate/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Grounded Response Rate is the share of AI answers about a topic backed by retrieved sources rather than model memory alone — the dial that decides which optimisation levers can move a brand."
    },
    {
      "title": "Lead Value",
      "url": "https://insights.rhinegold.de/compendium/lead-value/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Lead value is the expected monetary worth of a lead — the probability it converts times what it is worth if it does. It is what lead scoring should optimise toward."
    },
    {
      "title": "LLM Referral Traffic",
      "url": "https://insights.rhinegold.de/compendium/llm-referral-traffic/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "LLM referral traffic is the visits that arrive when a user clicks a link inside an AI answer — the click-side counterpart to AI visibility, usually small and systematically under-measured."
    },
    {
      "title": "Query Fan-Out",
      "url": "https://insights.rhinegold.de/compendium/query-fan-out/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Query fan-out is the technique by which an AI search surface answers one question by silently issuing many related sub-queries, then synthesising. The brand competes across searches the user never typed."
    },
    {
      "title": "Retrieval-Augmented Generation",
      "url": "https://insights.rhinegold.de/compendium/retrieval-augmented-generation/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Retrieval-Augmented Generation (RAG) is the architecture in which a language model fetches relevant documents at answer time and conditions its response on them. It is the machinery underneath AI Overviews and answer engines."
    },
    {
      "title": "Self-Reported Attribution",
      "url": "https://insights.rhinegold.de/compendium/self-reported-attribution/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Self-reported attribution asks the buyer directly — “How did you hear about us?” It is the complement to tracked attribution, and in an AI-mediated journey often the only instrument that sees what analytics cannot."
    },
    {
      "title": "Structured Data",
      "url": "https://insights.rhinegold.de/compendium/structured-data/",
      "date": "2026-06-13",
      "type": "compendium",
      "teaser": "Structured data is machine-readable markup — usually Schema.org vocabulary in JSON-LD — that states what a page's entities are. It is how a page declares its facts to machines instead of leaving them to be inferred."
    },
    {
      "title": "Phantom URL",
      "url": "https://insights.rhinegold.de/compendium/phantom-url/",
      "date": "2026-06-12",
      "type": "compendium",
      "teaser": "A phantom URL is a web address an AI presents as a source that does not exist — fabricated at answer time, plausible in structure, resolving to nothing. When the fabricated address carries a brand's domain, the brand inherits the dead end."
    },
    {
      "title": "Negated Mention",
      "url": "https://insights.rhinegold.de/compendium/negated-mention/",
      "date": "2026-06-12",
      "type": "compendium",
      "teaser": "A negated mention is an AI answer that names a brand in order to advise against it, exclude it, or mark what it lacks. Counting metrics record it as visibility; commercially it works in the opposite direction."
    },
    {
      "title": "Brand Rank",
      "url": "https://insights.rhinegold.de/compendium/brand-rank/",
      "date": "2026-06-12",
      "type": "compendium",
      "teaser": "Brand Rank is the position a brand occupies when an AI answer lists options. Being named first and being named last are different commercial events that binary presence metrics record identically. Top-3 presence is the operative shortlist threshold."
    },
    {
      "title": "Mention Intensity",
      "url": "https://insights.rhinegold.de/compendium/mention-intensity/",
      "date": "2026-06-12",
      "type": "compendium",
      "teaser": "Mention Intensity measures how much of an AI answer is built around a brand — how often and how substantially it is named within a single response. It is the depth dimension that binary presence metrics flatten."
    },
    {
      "title": "Counterfactual",
      "url": "https://insights.rhinegold.de/compendium/counterfactual/",
      "date": "2026-06-12",
      "type": "compendium",
      "teaser": "The counterfactual is what would have happened without the intervention. Every claim of marketing effect is a comparison against a counterfactual, whether the person making the claim states one or not."
    },
    {
      "title": "Difference-in-Differences",
      "url": "https://insights.rhinegold.de/compendium/difference-in-differences/",
      "date": "2026-06-12",
      "type": "compendium",
      "teaser": "Difference-in-Differences (DiD) measures an effect by comparing the change in a treated group to the change in an untreated group over the same window — subtracting market drift from measured effect."
    },
    {
      "title": "Control Group",
      "url": "https://insights.rhinegold.de/compendium/control-group/",
      "date": "2026-06-12",
      "type": "compendium",
      "teaser": "A control group is the set of units deliberately left untreated so that the counterfactual becomes observable. Its quality, not its existence, decides what the measurement is worth."
    },
    {
      "title": "Semantic Anchoring",
      "url": "https://insights.rhinegold.de/compendium/semantic-anchoring/",
      "date": "2026-06-11",
      "type": "compendium",
      "teaser": "How deeply a brand is embedded in an AI model's trained knowledge — as opposed to appearing because a document was retrieved. High Mention Rate does not imply anchoring."
    },
    {
      "title": "The Invisible Shortlist",
      "url": "https://insights.rhinegold.de/compendium/invisible-shortlist/",
      "date": "2026-06-09",
      "type": "compendium",
      "teaser": "More than half of B2B buyers now start vendor research with an AI — and one in three deals goes to a brand they had no awareness of before the AI named it."
    },
    {
      "title": "Content Architecture for LLM Authority",
      "url": "https://insights.rhinegold.de/episode5.html",
      "date": "2026-06-04",
      "type": "insights",
      "teaser": "Four episodes diagnosed the new reality of AI search. The fifth turns to construction: how to architect content so LLMs treat your brand as a primary source."
    },
    {
      "title": "Lead Scoring",
      "url": "https://insights.rhinegold.de/compendium/lead-scoring/",
      "date": "2026-06-09",
      "type": "compendium",
      "teaser": "Its failure mode is almost never the algorithm — it's the quality of the firmographic data the algorithm is fed."
    },
    {
      "title": "MQL — Marketing Qualified Lead",
      "url": "https://insights.rhinegold.de/compendium/mql/",
      "date": "2026-06-09",
      "type": "compendium",
      "teaser": "The reliability of the MQL signal depends directly on the quality of the data the scoring system operates on."
    },
    {
      "title": "SQL — Sales Qualified Lead",
      "url": "https://insights.rhinegold.de/compendium/sql/",
      "date": "2026-06-09",
      "type": "compendium",
      "teaser": "The MQL-to-SQL handoff rate is the sharpest diagnostic signal for the scoring system upstream."
    },
    {
      "title": "Touchpoints",
      "url": "https://insights.rhinegold.de/compendium/touchpoints/",
      "date": "2026-06-09",
      "type": "compendium",
      "teaser": "In B2B, the path from first contact to a committed decision runs through a sequence of interactions — and each one leaves a trace."
    },
    {
      "title": "How to Measure What You Can't See",
      "url": "https://insights.rhinegold.de/episode4.html",
      "date": "2026-02-25",
      "type": "insights",
      "teaser": "GEO metrics for the B2B practitioner: when buyers research through ChatGPT, Perplexity and Gemini before visiting your site, standard analytics are blind."
    },
    {
      "title": "The Invisible Buyer Journey",
      "url": "https://insights.rhinegold.de/episode3.html",
      "date": "2026-02-18",
      "type": "insights",
      "teaser": "Your prospect compared you to three competitors and read six LLM summaries before ever visiting your site. You saw none of it."
    },
    {
      "title": "Why Your Analytics Stack Is Measuring the Wrong Layer",
      "url": "https://insights.rhinegold.de/blog_ep02_wrong_layer.html",
      "date": "2026-02-11",
      "type": "insights",
      "teaser": "Keywords, rankings and clicks tell you where you appeared — not what the market actually heard."
    },
    {
      "title": "The Two-Year Window and Why Most Teams Will Miss It",
      "url": "https://insights.rhinegold.de/episode1.html",
      "date": "2026-02-04",
      "type": "insights",
      "teaser": "A 12–24 month lead in semantic intelligence doesn't give a proportional advantage — it compounds into structural market position."
    },
    {
      "title": "AI Overviews vs AI Mode",
      "url": "https://insights.rhinegold.de/compendium/ai-overview-vs-ai-mode/",
      "date": "2026-05-20",
      "type": "compendium",
      "teaser": "Two distinct Google surfaces with similar answers but largely different cited sources — they must be measured separately."
    },
    {
      "title": "Attribution",
      "url": "https://insights.rhinegold.de/compendium/attribution/",
      "date": "2026-05-10",
      "type": "compendium",
      "teaser": "The discipline of estimating how marketing contacts and channels contribute to pipeline and revenue."
    },
    {
      "title": "Platform Divergence",
      "url": "https://insights.rhinegold.de/compendium/platform-divergence/",
      "date": "2026-04-28",
      "type": "compendium",
      "teaser": "A brand can be prominently visible on one AI platform and absent from another. Single-platform measurement doesn't represent the full picture."
    },
    {
      "title": "Hallucination — Brand Risk",
      "url": "https://insights.rhinegold.de/compendium/hallucination-brand-risk/",
      "date": "2026-04-15",
      "type": "compendium",
      "teaser": "A brand with high AI visibility but high hallucination rate is being introduced incorrectly at the most influential moment in the buyer journey."
    },
    {
      "title": "Generative Engine Optimization",
      "url": "https://insights.rhinegold.de/compendium/generative-engine-optimization/",
      "date": "2026-03-20",
      "type": "compendium",
      "teaser": "The practice of improving how a brand is surfaced, cited, and recommended inside AI-generated answers — not in the ten blue links."
    },
    {
      "title": "Zero-Click Search",
      "url": "https://insights.rhinegold.de/compendium/zero-click-search/",
      "date": "2026-03-05",
      "type": "compendium",
      "teaser": "For brands, the question is not only how many clicks fell off, but what the brand says at the moment the answer is served."
    }
  ]
}