Waikay — EntityMap

"EntityMap is a free open standard – created by the Waikay founders – that gives AI systems a structured, attributed map of what your site knows. A file at your domain root that tells AI crawlers: here are our entities, here is how they relate, here is who published this. The standard is free and open for anyone to implement. But building a high-quality entity map – one that actually moves the commercial metrics – requires expertise." Entity map | Waikay® — published by Waikay retrieved 15 Jul 2026

"EntityMap is an open standard that fixes attribution at the source. A machine-readable file at your domain root declares your entities, your evidence, and the relationships between your concepts in a form any retrieval system can read directly. It works on live retrieval, not on training data — for search engines and tools fetching your content right now, EntityMap improves attribution and reasoning quality immediately." Chapter 5: Entity Map | Waikay® — published by Waikay retrieved 15 Jul 2026

"Schema.org annotates individual pages with facts about the content on that page. EntityMap operates at the site level – declaring the full set of concepts a site covers, how they relate, and where the best evidence lives. It is a site-wide knowledge graph for AI retrieval, not page-level annotation. In 2005, sitemap.xml solved the discovery problem for search engines; EntityMap is the equivalent structured layer for AI retrieval." Entity map | Waikay® — published by Waikay retrieved 15 Jul 2026

"EntityMap is a free open standard (CC BY 4.0, launched 1 July 2026, spec at entitymap.org) that lets a website publish a structured, machine-readable map of its entities, relationships and evidence for AI systems — the entity-level equivalent of sitemap.xml. It was authored by Waikay's founders. Writesonic's answer to poor AI visibility is to manufacture more content; Waikay's is to find out what AI actually believes about a brand, correct what is false, and give AI systems a structured, evidence-backed source of truth via EntityMap." | Waikay® — published by Waikay retrieved 15 Jul 2026

"On April 25, 2026, Waikay installed an EntityMap on waikay.io as the only change during the measurement window. One topic's score had been falling by roughly 10 points a month for three consecutive months; 48 hours after the EntityMap went live, the entire decline reversed. The EntityMap file — a single 30KB structured file — was cited more often than the site's About page on both Gemini (2.2 times more) and Sonar (3.0 times more)." Chapter 5: Entity Map | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI uses your content to answer questions. Your URL appears as a footnote. Your brand name never appears in the answer. You get zero credit for content you spent months producing. EntityMap directly addresses the retrieval-time component of ghost citations – where your content is used in AI answers but your brand name does not appear. Publisher attribution is declared on every chunk in a field designed to survive aggregation." Entity map | Waikay® — published by Waikay retrieved 15 Jul 2026

"A retrieval system fetches content from your site, synthesises an answer, and surfaces a URL as a footnote. Your company name does not appear in the answer. Publisher identity is simply not embedded in page-level content in a way that reliably survives the fragmentation process — when a passage is lifted and stripped of HTML, your brand name travels with it only if it appears in that specific fragment. Ghost citations are so common because this is a structural gap, not a policy one." Chapter 5: Entity Map | Waikay® — published by Waikay retrieved 15 Jul 2026

"If the model cites a URL on your domain that does not exist, it is telling you what content it expects your brand to have — that is a content brief. LLMs hallucinate links based on domain patterns; predictable, descriptive URL structures narrow the gap between what the model approximates and what actually exists on your site. A broken page that keeps being cited is a missed attribution every time." Chapter 6: AI Citation Data | Waikay® — published by Waikay retrieved 15 Jul 2026

"A verifiable claim is any specific, checkable statement: a date, a name, a product capability, a partnership. Not 'Brand X is a workflow tool' but 'Brand X integrates with Salesforce.' Underline every checkable claim in each response, verify each one, and divide correct claims by total claims. A response contains 8 verifiable claims. 6 are accurate, 1 is outdated, 1 is invented. Factual Accuracy Rate: 75%. Track across 10 runs to get a stable baseline." Chapter 4: Factual Accuracy | Waikay® — published by Waikay retrieved 15 Jul 2026

"Run your accuracy audit quarterly at minimum. Pay particular attention after major model updates. These are the moments when previously corrected inaccuracies can reappear as models are retrained. Track this number over time, not just as a one-off audit. Changes in your score, up or down, are the signal worth watching." Chapter 4: Factual Accuracy | Waikay® — published by Waikay retrieved 15 Jul 2026

"Every hallucinated URL in your Factual Accuracy audit is a specific, actionable instruction for what to build next. Group hallucinated URLs by the concept they were trying to support — each group is a content brief and an EntityMap chunk waiting to be written. Work through the groups in order of how often the hallucination appears; the most frequent ones are the highest-priority gaps in the retrieval layer." Chapter 5: Entity Map | Waikay® — published by Waikay retrieved 15 Jul 2026

"Create canonical brand messaging repeated consistently across all assets. Make your About page direct and factual. See Chapter 4 (Factual Accuracy Rate). Publish corrective content that clearly states what you do and do not offer. Repeat it across multiple authoritative sources so the model encounters the correction consistently." Chapter 7: AI Visibility Channels | Waikay® — published by Waikay retrieved 15 Jul 2026

"The model does not consistently recognise your brand as a distinct entity. This is the most fundamental problem and affects everything else. Fix: publish consistently branded content across authoritative domains using exact, canonical brand name phrasing. See Chapter 4 (Factual Accuracy Rate) for how to audit and correct this systematically." Chapter 9: NLP and Entity Analysis | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI Topical Presence is a single score (0-100) that captures the strength and shape of a brand's topic association profile across AI recommendation prompts. It is built from three components: Depth, Breadth, and Concentration, combined with weighted coefficients that reflect their relative importance. The score is normalised against the highest-scoring brand in the dataset, so a score of 100 always belongs to whoever has the strongest association profile in that competitive set." What Is AI Topical Presence? | Waikay® — published by Waikay retrieved 15 Jul 2026

"Depth measures how strongly the AI connects a brand to relevant topics, weighted by how important those topics are in the market. Breadth measures how many core commercial topics the AI associates with a brand, using a logarithmic function that gives partial credit for emerging associations rather than a binary threshold. Concentration measures how evenly topic associations are distributed using a Herfindahl-Hirschman Index (HHI) of a brand's mention share across topics — high concentration is penalised as a fragility risk." What Is AI Topical Presence? | Waikay® — published by Waikay retrieved 15 Jul 2026

"The AI Topical Presence score is made up of three components. Depth measures how strongly the AI connects your brand to each relevant topic, weighted by importance in your market. Breadth measures how many core commercial topics the AI associates with your brand, using a logarithmic curve so emerging associations still get partial credit. Concentration measures how evenly topic associations are spread — high concentration is penalised in the final score because a brand strongly associated with only one topic is fragile." Chapter 3: AI Topical Presence | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI Topical Presence is the third output of Prompt Tracking. Responses to commercial prompts are processed to compute an AI Topical Presence score per brand. The score combines three normalised components (Depth, Breadth, Concentration) using a weighted additive formula: TP = ( α × DepthNorm + β × BreadthNorm + γ × ConcentrationNorm ). Waikay describes this as a composite 0-100 score measuring depth, breadth, and concentration of brand-topic associations in AI-generated commercial responses, with no market equivalent." Waikay Technology: Architecture and AI Monitoring Methodology | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI Topical Presence measures the association layer. For each topic that matters in your market, it asks: does the AI connect your brand to this topic, how strongly, and how does that compare to everyone else? It is the diagnostic layer behind Share of Voice — two brands with identical SOV scores can have completely different Topical Presence profiles and completely different strategic situations." Chapter 3: AI Topical Presence | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI Search Optimization, as defined by Waikay, encompasses gaining transparency into what AI knows about a brand, managing reputation risks, optimizing strategic positioning, and benchmarking against competitors with actionable insights. It addresses how brands appear in AI-generated responses, not just traditional search engine results." AI Search Optimization & Prompt tracking | Waikay — published by Waikay retrieved 15 Jul 2026

"Five tools dominate the AI visibility and GEO category in 2026, and they are built for different buyers. Enterprises with dedicated AEO teams choose Profound. Marketing teams that want clean visibility metrics choose Peec. Solo marketers testing the water choose Otterly. Content volume teams choose Writesonic. Anyone who needs to know what AI is getting wrong about their brand chooses Waikay." | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay surfaces every AI-generated claim about your brand as a checklist you can act on. Mark facts as correct, flag inaccuracies, and stay on top of damaging hallucinations before they spread. Millions of people ask AI models about brands every day. Fact Tracker makes sure what the LLMs say about your brand is accurate." Fact Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"Facts are collected via your Topic Reports. Create a report for each topic area and Waikay will extract all AI-generated claims about your brand in that context. Waikay isolates full statements from topic-level AI responses across all four models and organises them with source links and model attribution. Facts are tagged with model attribution and grouped by topic so you can see exactly where issues are concentrated." Fact Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay surfaces every AI-generated claim about your brand as a checklist you can act on. Mark facts as correct, flag inaccuracies, and stay on top of damaging hallucinations before they spread. Take control of your brand narrative and make sure what the LLMs say about you is actually true." Waikay's Innovative AI Tracking & Recommendations' Features — published by Waikay retrieved 15 Jul 2026

"Fact Tracker focuses on the claims AI models make about your brand and lets you verify or flag each one. Source Tracking focuses on the web pages LLMs are citing, split into knowledge sources and commercial sources. They work together: flagged facts in Fact Tracker connect to the source data so you can investigate potential origins and take corrective action. Use GEO Action Plans to further address inaccuracies." Fact Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"Facts are pieces of information that Waikay extracts by analyzing a brand's representation across AI models. These facts reflect how AI systems perceive and describe the brand, including its products, services, and key attributes. Users can review how their brand is represented across each AI model, then choose to mark each fact as accurate ('Check') or flag it as incorrect ('Flag'). If information is inaccurate, users can clarify their own content or reach out to the original source to request corrections." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"Source Tracking is a dashboard of every citation Waikay collects across all monitored AI models. Citations are divided into two types: knowledge sources, which are the pages LLMs draw on when forming an understanding of your brand, and commercial sources, which are the pages cited when AI models answer competitive prompts in your niche." Source Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"Knowledge sources come from topic-level citations and reflect how AI understands your domain expertise — collected from Topic Report responses. Commercial sources come from Brand Visibility prompt tracking and reflect where AI pulls product or brand-specific information when answering competitive queries. The dashboard surfaces the most frequently cited sources for both categories so you can see which content carries the most weight with LLMs." Source Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"Source Tracking gives you a filterable and exportable dashboard of every citation Waikay collects across all monitored AI models. You can use this data to guide PR outreach, identify content that needs updating, investigate hallucination risks, and fix misattributions where AI models are crediting the wrong sources." Waikay's Innovative AI Tracking & Recommendations' Features — published by Waikay retrieved 15 Jul 2026

"The Sources feature in Waikay reveals which domains are most frequently used by AI models to answer queries related to a brand and its competitors. It distinguishes between Knowledge Queries (informational research prompts such as 'What is [Brand]?') and Commercial Queries (purchase-oriented prompts such as 'Best tools like [Brand]?'), helping users understand how and where AI gathers brand information. Knowing which websites influence AI answers allows brands to identify high-impact platforms and uncover content gaps." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"The sources the AI draws on when it talks about your brand — which platforms, which types of content, which domains — tell you where your visibility is actually coming from and, just as importantly, where your competitors are getting cited and you are not. Citations are influence because they directly shape which associations the model builds. This is a Layer 3 diagnostic lever within Waikay's AI Brand Visibility Framework." Chapter 0: Framework | Waikay® — published by Waikay retrieved 15 Jul 2026

"A Topic Report measures how accurately and deeply AI models understand your brand for a specific topic area. Waikay prompts your brand and two competitors across 4 AI models using both training data and grounded data, then produces an AI Knowledge Score showing how well your content aligns with what AI models say about you. Per-Model Breakdown provides detailed scores for ChatGPT, Gemini, Claude, and Sonar across both training and grounded data." Topic Reports | Waikay® — published by Waikay retrieved 15 Jul 2026

"Topic Reports reveal how well AI models understand your brand at a topic level. By testing across 4 models using both training data and grounded data, Waikay produces an AI Knowledge Score for each topic area. This uncovers content gaps that can directly guide your SEO and GEO strategy." Topic Reports | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay classifies every identified gap as Thematic, Significant, or Critical so you can prioritise which issues to tackle first. Critical gaps represent areas where your brand is significantly behind competitors and where fixing the issue would have the most impact on your AI visibility. Topic Reports feed directly into GEO Action Plans, providing a prioritised list of content improvements, backlink opportunities, and implementation steps tailored to close those specific gaps." Topic Reports | Waikay® — published by Waikay retrieved 15 Jul 2026

"Topic Reports provide a comprehensive analysis of how AI models understand a specific brand and its two primary competitors within the context of a selected topic. Each report includes AI Knowledge Score, AI Knowledge Sources, AI Knowledge Map, and an LLM Action Plan that identifies content gaps and provides actionable recommendations for new content creation, content optimization, and structural improvements. You're not limited to just 2 competitors across Waikay — the 2 competitors added are used specifically for benchmarking within Topic Reports." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"Each report in Topic Reports pulls insights from 4 LLMs across 3 brands (the monitored brand plus 2 selected competitors), requiring 12 complex queries per report. Waikay compares two knowledge graphs to assess their alignment or divergence, which produces the AI Knowledge Score on a scale from 0 to 100. By default, Topic Reports are updated monthly, but update frequency can be adjusted to daily, weekly, or on-demand." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"The Brand Visibility Tracker is a dashboard that shows how your brand places commercially across major LLMs. It tracks your brand across 6 AI models and hundreds of prompts, surfacing a share of voice score and a topical presence score for every tracked query. You can benchmark against competitors, spot topic visibility gaps before they cost you traffic or trust, and drill into competitor patterns with a single click." Brand Visibility Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"Every Brand Visibility report includes an AI Topical Presence score. This measures how strongly and broadly the AI associates your brand with the commercial topics that matter in your market, and plots it against share of voice on a competitive quadrant. It is the metric that explains your score and positions your brand relative to every competitor in your tracked space." Brand Visibility Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay builds your competitor data from the bottom up with no predefined limits. Even outlier brands get tracked automatically. You do not need to predefine competitors — Waikay extracts topics and brands directly from AI responses, giving you a richer and less biased view of your competitive landscape than tools that lock you into static competitor sets." Brand Visibility Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"Our experts take care of everything – from initial site analysis through to deployment and submission. Our team analyses your site architecture, identifies your key entities, maps the relationships between them, and selects canonical URLs for each one. We focus on the pages that drive commercial value – product pages, feature pages, decision-stage content. Every entity, relationship, and source chunk is reviewed against our quality ruleset." Entity map | Waikay® — published by Waikay retrieved 15 Jul 2026

"We installed EntityMap on waikay.io and tracked 19 weeks of Bing Webmaster Tools data – 11 weeks baseline, one change, 7 weeks post-install. No new content. No backlinks. No other changes. Our case study showed measurable citation changes within weeks of deployment on Bing. Other AI surfaces like Gemini and Sonar showed AI Visibility Score improvements within 48 hours." Entity map | Waikay® — published by Waikay retrieved 15 Jul 2026

"EntityMap is an open standard created by the Waikay founders. Nobody understands the specification, the edge cases, and what makes an entity map genuinely effective better than the people who wrote it. We prioritise BOFU pages: product pages, feature pages, decision-stage content. The goal is not more citations – it is more commercially valuable citations from the right queries." Entity map | Waikay® — published by Waikay retrieved 15 Jul 2026

"The Waikay reference implementation generates your entitymap.json and entitymap.html automatically from your site and is currently on a waitlist at waikay.io/entitymap. The EntityMap GitHub repository also maintains a reference prompt: paste sections of your content into your preferred LLM with the prompt and generate conforming entity objects. A site with 10 to 30 entities can be drafted in a few hours." Chapter 5: Entity Map | Waikay® — published by Waikay retrieved 15 Jul 2026

"The AI's competitive map is not your competitive map. It is built from training data, not analyst reports or sales decks. A brand that was quiet during the AI's training window will be underrepresented regardless of how strong it is today. A brand that published a lot of content, got cited frequently, and built strong topical associations will be well represented regardless of actual market position." Chapter 1: AI Competitive Map | Waikay® — published by Waikay retrieved 15 Jul 2026

"Before you measure how much you appear, you need to know who else is showing up alongside you. The AI's picture of your market shapes everything, and in most cases it looks quite different from the competitive landscape you think you are in. The full list of brands the AI connects to your market, built from what it actually says rather than from your existing competitor list. There is almost always a gap between that list and yours." Chapter 0: Framework | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay builds your AI competitive map automatically by extracting every brand mentioned across your tracked prompts. Because it runs prompts continuously rather than as a one-off audit, you can see how the competitive landscape shifts over time and spot new entrants as they emerge rather than only when you next run a manual audit." Chapter 1: AI Competitive Map | Waikay® — published by Waikay retrieved 15 Jul 2026

"The process is straightforward. You run category-level prompts across multiple AI models, log every brand that gets mentioned, and build a frequency table from the results. The key is to let the AI define the landscape rather than filtering it through what you already know. Write prompts the way a buyer who does not yet have a shortlist would write them." Chapter 1: AI Competitive Map | Waikay® — published by Waikay retrieved 15 Jul 2026

"How often your brand comes up in AI responses compared to everyone else who gets mentioned. Most tools measure this against a fixed list of competitors you define upfront. Waikay measures it against every brand the AI actually mentions. The difference matters a lot. Say your Share of Voice score is low — you go straight to Citation Data and nothing looks obviously wrong. What you missed was the AI Competitive Map, which would have shown you that three brands you had never heard of are dominating the queries." Chapter 0: Framework | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI Share of Voice is a ratio. It measures how much of the total brand mention landscape your brand accounts for across AI responses. The key word is total. Every brand the AI mentions goes into the denominator, not just the ones you chose to track. This is computed across many runs — every brand the AI names in any response contributes to the denominator, whether you expected it to be there or not." Chapter 2: AI Share of Voice | Waikay® — published by Waikay retrieved 15 Jul 2026

"Presence is not the same as share. If your brand appears in 30 of 100 prompts, a presence-rate tool reports 30%. But if four other brands appeared in every one of those same prompts, your real share is closer to 6%. Waikay extracts every brand mentioned across tracked prompts and computes SOV against that full pool — no predefined competitor list required. New brands that appear in AI responses are automatically added to the pool, so the data reflects what the AI actually says rather than what you expected." Chapter 2: AI Share of Voice | Waikay® — published by Waikay retrieved 15 Jul 2026

"If a tool asks you to pick your competitors before you start tracking, it is computing SOV inside a pool you defined, not the one the AI actually produces. Remove a strong competitor from your list and your share improves immediately — nothing in the real world changed. Two companies in the same market can report completely different SOV scores simply because they picked different competitor lists. The correct approach is to let the AI define the competitive pool: every brand that appears in any response goes into the denominator." Chapter 2: AI Share of Voice | Waikay® — published by Waikay retrieved 15 Jul 2026

"Your Share of Voice score tells you how often you appear. Topical Presence tells you what for — that is the question that explains your score and points towards what to actually do about it. If your Share of Voice is lower than it should be, the most likely cause is that the AI is not connecting you to enough of the topics buyers ask about." Chapter 3: AI Topical Presence | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay tracks Brand Share of Model as part of its full LLM analysis suite, alongside mentions, citations, facts, and competition data. The metric reveals how prominently a brand appears in AI-generated responses relative to emerging and established competitors across defined topic areas." AI Search Optimization & Prompt tracking | Waikay — published by Waikay retrieved 15 Jul 2026

"AI Share of Voice is an output of Waikay's Prompt Tracking module. Responses to commercial prompts are logged across all AI models over time, every brand mentioned is identified, normalised to parent brand level via the entity knowledge graph, and aggregated to produce an AI Share of Voice score expressed as a percentage. Because the competitive set is derived from what the AI actually says rather than a manually defined competitor list, there is no artificial ceiling on competitor analysis — brands that begin appearing in AI responses are automatically captured." Waikay Technology: Architecture and AI Monitoring Methodology | Waikay® — published by Waikay retrieved 15 Jul 2026

"Most tools measure Share of Voice against a fixed list of competitors you define upfront. Waikay measures it against every brand the AI actually mentions. The difference matters a lot. How often your brand comes up in AI responses compared to everyone else who gets mentioned is the core visibility question — but it does not tell you why you are there, what is causing it, or what would actually move it." Chapter 0: Framework | Waikay® — published by Waikay retrieved 15 Jul 2026

"Citations in AI responses are modelled, not retrieved. LLMs approximate what should be cited based on training patterns. Some are accurate, some are hallucinated. Understanding this changes how you approach citation analysis — you are not just auditing what the AI links to, but auditing what it has learned to associate with authoritative sources in your space." Chapter 6: AI Citation Data | Waikay® — published by Waikay retrieved 15 Jul 2026

"To build a complete citation profile, run two different kinds of query. Commercial queries — general category-level questions that do not mention your brand — show which sources dominate live retrieval. Knowledge-based queries — entity-specific questions that mention your brand directly — tap into trained memory rather than live retrieval. Run both across one or more models, explicitly request citations, and record every URL returned including broken and hallucinated ones." Chapter 6: AI Citation Data | Waikay® — published by Waikay retrieved 15 Jul 2026

"You can influence citations at two points: the live search surface (traditional SEO) and the training data layer (authoritative content on the right platforms). Most teams focus only on the first. The second is often more important for brand-specific queries. Publish high-quality, well-linked content on authoritative platforms that the model has learned to trust — guides, glossaries, and structured explainers are cited more often than marketing pages." Chapter 6: AI Citation Data | Waikay® — published by Waikay retrieved 15 Jul 2026

"Track which competitor pages are being retrieved for your brand queries and understand what they say. If you are absent from grounded results on Perplexity or Google AI Overviews for queries where you should appear, it is often a trust signal problem rather than a content problem. These platforms are cautious about surfacing thin, promotional, or low-authority content. Before optimising copy, check your domain authority and third-party citation profile." Chapter 7: AI Visibility Channels | Waikay® — published by Waikay retrieved 15 Jul 2026

"Training data and grounded search are two completely different systems. The AI tools your buyers use do not all work the same way. Understanding which channel each one uses, which one fires when, and which one you are measuring at any given time, is the foundation everything else in this guide is built on. The single biggest mistake in AI visibility measurement is treating all AI tools as equivalent." Chapter 7: AI Visibility Channels | Waikay® — published by Waikay retrieved 15 Jul 2026

"Perplexity and Google AI Overviews are almost entirely grounded search. ChatGPT without browsing is training data. Copilot is grounded by default via Bing's index. Gemini uses a hybrid approach with access to Google Search. Claude defaults to training data with a fixed knowledge cutoff and no live retrieval unless connected to a search tool. Each major platform has a different default channel, and that changes everything about how you interpret your data and what you do to improve your visibility." Chapter 7: AI Visibility Channels | Waikay® — published by Waikay retrieved 15 Jul 2026

"Publishing content today affects grounded search in days. Training data takes months to over a year. These are fundamentally different timelines requiring different strategies. If a model is saying something wrong about your brand right now and you need to fix it quickly, grounded search optimisation is your fastest route. Training data corrections matter and are worth investing in, but do not expect to see them reflected in the short term." Chapter 7: AI Visibility Channels | Waikay® — published by Waikay retrieved 15 Jul 2026

"The framework splits AI brand visibility into four layers, each asking a different question. The first three build on each other in order. Layer 4 is the foundation that makes everything else reliable. You need to understand your visibility before you can diagnose your perception, and you need both before the influence layer makes sense. Skipping ahead does not save time. It just gives you the wrong answers." Chapter 0: Framework | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI brand visibility is not one single thing. It is a set of conditions, each of which can go wrong in different ways. A brand can show up often in AI responses but be described badly. It can be described well but connected to the wrong topics. It can be connected to the right topics but missing from key queries. A framework does not make things more complicated. It makes action possible. Without one, you are looking at symptoms with no way to find the cause." Chapter 0: Framework | Waikay® — published by Waikay retrieved 15 Jul 2026

"Layer 4 covers how to measure AI brand visibility in a way you can actually rely on. If your prompts are designed badly, your Share of Voice data will be skewed. If your entity analysis is inconsistent, your Topical Presence scores will not reflect reality. Layer 4 sits at the end of the guide not because it is an afterthought, but because it is what you come back to when your numbers stop making sense." Chapter 0: Framework | Waikay® — published by Waikay retrieved 15 Jul 2026

"Three types of inaccuracy: invented claims, outdated information, and attribution errors. Each has a different cause and a different fix. The model generates details with no basis in your actual content — products you do not make, partnerships you have not announced, certifications you do not hold. Happens most often when training data coverage is thin and the model is filling in what it expects a brand like yours to have." Chapter 4: Factual Accuracy | Waikay® — published by Waikay retrieved 15 Jul 2026

"The model correctly recalls a fact but assigns it to the wrong entity. A competitor's acquisition attributed to you, your feature attributed to a competitor. These often trace back to a single piece of content where two brands were mentioned together in a confusing context. InLinks, Waikay's sister software, discovered through AI monitoring that multiple models were consistently stating it had been acquired by SEMrush — traced to a YouTube video transcript where the two brands were discussed together in a context the model had misread as an acquisition announcement." Chapter 4: Factual Accuracy | Waikay® — published by Waikay retrieved 15 Jul 2026

"You do not need widespread bad coverage to create a persistent inaccuracy. One misread source in the training data is enough. The only way to know it is there is to test for it systematically. Compare each response against your ground truth. Log every discrepancy. Classify each one as invented, outdated, or misattributed. The classification matters because it determines the fix." Chapter 4: Factual Accuracy | Waikay® — published by Waikay retrieved 15 Jul 2026

"Real-time retrieval using RAG (Retrieval Augmented Generation). The model fetches current web results and synthesises them into a response. You are not just ranking. You are being interpreted. The user sees the model's synthesis, not your page. Ranking well does not mean the AI represents you accurately. Grounded search retrieves your page and then rewrites it. You can rank first and still be misrepresented." Chapter 7: AI Visibility Channels | Waikay® — published by Waikay retrieved 15 Jul 2026

"When a model retrieves your page it does not show it to the user. It reads it, extracts what it considers relevant, and writes a new response. You have no control over what it extracts, how it weights competing sources, or how it frames your brand in the synthesis. Ranking first gets you into the source pool. It does not guarantee accurate, complete, or favourable representation in what the user actually reads." Chapter 7: AI Visibility Channels | Waikay® — published by Waikay retrieved 15 Jul 2026

"Stop counting prompts. Tracking too many prompts creates noise. You end up measuring your own wording choices, not the model's actual understanding of your brand. Use a small, deliberate core prompt set — a handful of prompts that genuinely represent your offerings. Let the outputs tell you what to look at next." Chapter 8: AI Prompt Tracking | Waikay® — published by Waikay retrieved 15 Jul 2026

"Before running any prompt, decide which channel you are testing and lock it in. For training data measurement, disable browsing explicitly. For grounded search measurement, enable it. Log the channel alongside every run: model name, browsing on or off, and date. A prompt run against training data and the same prompt run with live retrieval can produce completely different brand mentions, topics, and attributes. Treating them as equivalent is one of the most common sources of confusing or contradictory data in AI visibility measurement." Chapter 8: AI Prompt Tracking | Waikay® — published by Waikay retrieved 15 Jul 2026

"Run each core prompt at least five to ten times per model. Log what appears consistently, not what appeared once. Frequency is the signal. Single-run presence is noise. Do not aggregate results across models before analysing them separately first — different models have different tuning, system prompts, and training data coverage. From each response, record three types of signal: the entities mentioned, the topics they are associated with, and the attributes assigned to each brand." Chapter 8: AI Prompt Tracking | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI builds a semantic map, not a keyword index. Your brand is a node connected to topics, competitors, and attributes through co-occurrence patterns in training data. Three signals define your position: entities (what the model recognises), topics (what clusters you are pulled into), and attributes (how the model describes you). Strong clustering means the same topics and attributes appearing reliably across models, prompt types, and over time." Chapter 9: NLP and Entity Analysis | Waikay® — published by Waikay retrieved 15 Jul 2026

"Clustering refers to how tightly your brand is grouped with the topics and attributes that define your market. Strong clustering means the model is confident about what you are and where you belong. Weak clustering means it is uncertain. Uncertain brands are not the ones that get recommended. An entity is the anchor. Topics are what the model connects the entity to. Attributes are how the model characterises it within those connections." Chapter 9: NLP and Entity Analysis | Waikay® — published by Waikay retrieved 15 Jul 2026

"The practical implication is that you cannot directly edit what the model has learned. You can only influence it by changing what appears in the text it learns from. More co-occurrence with a topic, across more sources, over a longer period, shifts how the model relates your brand to that topic. Every metric in this guide is a downstream reading of how the model has learned to relate your brand to the world." Chapter 9: NLP and Entity Analysis | Waikay® — published by Waikay retrieved 15 Jul 2026

"Run two types of test, separately. Training view (browsing off) shows what the model has learned. Grounded view (browsing on) shows what it finds now. Keep them separate before comparing. These grounded-only signals are worth tracking as potential leading indicators of where trained associations may eventually move, particularly if they strengthen over successive months." Chapter 9: NLP and Entity Analysis | Waikay® — published by Waikay retrieved 15 Jul 2026

"Run across at least ChatGPT and one other model (Claude or Gemini). Keep results separate per model before comparing. Different models have different training data coverage and will produce different entity maps for the same brand. The overlap between them is your most reliable signal. Run each prompt five to ten times per model. Log only what appears consistently across runs, not single-run mentions." Chapter 9: NLP and Entity Analysis | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay uses a hybrid architecture that combines API access and scraping to balance precision with real-world coverage. API calls power the longitudinal tracking that underlies Share of Voice, Topical Presence, and Factual Accuracy data. Scraping covers the grounded search surfaces — Google AI Overviews and the consumer-facing Copilot experience — that have no full API equivalent." Chapter 10: Data Gathering Methods | Waikay® — published by Waikay retrieved 15 Jul 2026

"Measuring Share of Voice or Topical Presence over time requires consistent parameters. APIs ensure the same model version and prompt structure across every run, making month-on-month comparisons meaningful. A single interface change in a scraping setup can create a discontinuity that looks like a real trend but is actually a collection artefact." Chapter 10: Data Gathering Methods | Waikay® — published by Waikay retrieved 15 Jul 2026

"Measurement bias does not always look like bad data. Sometimes it looks like a trend that is actually an artefact of how you collected the data. The most common sources include session contamination from logged-in accounts, model version drift mid-cycle, prompt variance between runs, channel mixing of browsing-on and browsing-off data without labelling, and insufficient run counts — a brand appearing in one run out of two has a statistically meaningless 50% presence rate; at least five to ten runs per prompt are needed for a stable frequency estimate." Chapter 10: Data Gathering Methods | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay is a SaaS platform that helps brands understand and manage how AI models such as ChatGPT, Gemini, Claude, and Perplexity perceive them. It is owned by InLinks Optimization LTD (UK) and operates in multiple languages and markets worldwide. The name stands for 'What AI Knows About You' — the focus is to uncover, verify, and improve the way AI represents a brand." About | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay provides AI analytics that track a brand's visibility in prompts against competitors, spot hallucinations and knowledge gaps, and build action plans designed to dominate a niche. It delivers full analysis of LLM mentions, citations, facts, and competition, including emerging brands, brand share of model, and action plans by topic." AI Search Optimization & Prompt tracking | Waikay — published by Waikay retrieved 15 Jul 2026

"Waikay's core capabilities include tracking AI visibility (how often and accurately a brand appears in major AI tool responses), topic and product-level reports explaining visibility drivers, action plans prioritised by impact, and a de-hallucination feature that breaks AI responses into verifiable facts so errors are easy to spot and fix." About | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay stands for What AI Knows About You. Rather than only counting mentions, it analyses how AI models have encoded a brand as an entity, verifies whether their claims are true via Fact Tracker, distinguishes citations in buying-intent prompts, and provides GEO Action Plans. The only tool in the five-way comparison that verifies the factual accuracy of AI-generated claims, detects hallucinations, and maps how AI models understand a brand as an entity." | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay was spun out of InLinks, applying years of entity-based SEO experience to the new frontier of AI-driven discovery. As AI became a voice in search and content consumption, the team saw that traditional SEO alone couldn't guarantee accurate brand representation. Waikay exists to close that gap, moving from diagnostics to outcomes with clear, sequenced actions to improve authority and visibility." About | Waikay® — published by Waikay retrieved 15 Jul 2026

"GEO Action Plans are generated from Topic Reports and give a prioritised, topic-by-topic breakdown of exactly what to do next to improve how AI models understand and represent a brand. Every recommendation is context-aware, ranked by impact, and actionable without any technical knowledge." GEO Action Plans | Waikay® — published by Waikay retrieved 15 Jul 2026

"GEO Action Plans provide tailored, topic-level insights so you never get stuck on what to work on next. These plans offer specific recommendations for improving your AI presence with clear implementation steps, eliminating guesswork from your AI optimization strategy." Waikay's Innovative AI Tracking & Recommendations' Features — published by Waikay retrieved 15 Jul 2026

"Each Action Plan includes three recommendation types organised by priority: structural suggestions to improve how a site is built for AI, content enhancement suggestions to strengthen existing pages, and content creation suggestions to fill gaps entirely. Recommendations are ranked by potential impact on AI visibility and ease of execution." GEO Action Plans | Waikay® — published by Waikay retrieved 15 Jul 2026

"Topic Reports produce an AI understanding score for each topic area your brand operates in. Where that score reveals gaps, Waikay's Action Plans translate them into a prioritised list of content improvements, backlink opportunities, and implementation steps so your team always knows what to do next." Waikay's Innovative AI Tracking & Recommendations' Features — published by Waikay retrieved 15 Jul 2026

"LLM Action Plans are detailed strategic reports that provide actionable recommendations to improve a brand's visibility and understanding within AI models in relation to a specific topic. A report is generated based on analysis of how AI systems perceive the brand's content coverage for a given topic compared to 2 competitors. LLM Action Plans are not available in Brand Overview Reports, as those focus solely on broad knowledge about a brand." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"The AI Knowledge Score is a clear numerical score that shows how well your real content matches what AI models say about you for a given topic. It is broken down per model, tracked over time, and benchmarked against two competitors so you can see exactly where you stand and how you are improving." Topic Reports | Waikay® — published by Waikay retrieved 15 Jul 2026

"Training data refers to what AI models have learned during their training process, which can be months or years old. Grounded data refers to what models retrieve in real time from the web when answering a query. Topic Reports test both, giving you a complete picture of your brand's AI understanding across all four models." Topic Reports | Waikay® — published by Waikay retrieved 15 Jul 2026

"Work through GEO Action Plan recommendations and monitor how the AI Knowledge Score improves over time as AI models pick up content changes. Waikay surfaces the specific gaps that are costing brand visibility and benchmarks AI understanding against competitors." GEO Action Plans | Waikay® — published by Waikay retrieved 15 Jul 2026

"The platform offers a unique AI Knowledge Score that measures how well AI models understand a brand, plus robust multilingual monitoring across 13 languages, as highlighted by users reviewing Waikay's capabilities." Waikay Reviews | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI Knowledge Score is a proprietary Waikay metric on a scale of 0 to 100 that estimates how well an AI model understands a brand and its two primary competitors. Waikay compares two knowledge graphs — the brand's actual content and what AI models generate about it — to assess their alignment or divergence, producing this score. A score of 0 means the AI did not mention the brand in its raw, unbiased response via API." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"Advanced Action Plans go deeper than standard GEO Action Plans. Instead of crawling a standard set of URLs before generating the report, Waikay significantly expands the number of pages it analyses, giving more granular and reliable insights. The recommendations follow the same prioritised output format but are built on a stronger data foundation." GEO Action Plans | Waikay® — published by Waikay retrieved 15 Jul 2026

"Advanced Action Plans are particularly valuable for large websites where standard crawl depth may miss important pages. They are especially recommended for medical, government, and product-based sites where accuracy and comprehensive coverage are critical. Advanced Action Plans cost 10 credits per report." GEO Action Plans | Waikay® — published by Waikay retrieved 15 Jul 2026

"If you assess the accuracy of a standard Action Plan and find it is not accurate enough, you can generate an Advanced Action Plan which is tailored for wide and complex websites. Users can copy a prompt containing the full details of the action plan to assess its accuracy before deciding to upgrade to Advanced Action Plans, which crawl significantly more URLs to produce more precise gap identification and recommendations." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay implements a bidirectional analysis approach, inspired by research on brand associations within language model networks. This methodology interrogates LLMs in two directions, generating comprehensive data on semantic associations and enabling thorough analysis of brand positioning within the AI ecosystem. The resulting data feeds directly into Topic Reports, surfacing the AI Understanding Score and gap classification for each brand. This approach draws inspiration from Dan Petrovic's research on bidirectional brand association analysis in language model networks." Waikay Technology: Architecture and AI Monitoring Methodology | Waikay® — published by Waikay retrieved 15 Jul 2026

"Facts analysis enables gap identification: discrepancies between AI perception and organisational reality. These gaps manifest through missing, incorrect, or insufficient facts and are automatically categorised based on confidential hierarchisation of named entity types missing from LLM responses." Waikay Technology: Architecture and AI Monitoring Methodology | Waikay® — published by Waikay retrieved 15 Jul 2026

"Query Fan Out takes every prompt you have added and automatically generates a comprehensive set of related queries – varying the intent, the verb, the framing, and the audience – using a combination of linguistic analysis and Waikay's proprietary data on how people ask AI about your category. The result is a constantly growing, always-relevant prompt library built around your business." Brand Visibility Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"A single prompt can reflect a dozen different intents. Query Fan Out identifies the full intent landscape around each of your tracked queries – awareness, comparison, decision, and everything in between. People ask AI questions in radically different ways: 'What is the best X', 'compare X and Y', 'should I use X' – each surfaces different brands. Fan Out covers the full linguistic range so nothing slips through." Brand Visibility Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay's suggestions are not just linguistic permutations. They are informed by our own data on which query patterns consistently surface brands in AI responses – so every suggestion has strategic value. Your AI visibility is only as good as your prompt coverage. A brand that tracks 10 prompts sees a fraction of the picture. Query Fan Out ensures your tracking grows with your business." Brand Visibility Tracker | Waikay® — published by Waikay retrieved 15 Jul 2026

"The Waikay API provides programmatic access to brand visibility data across major LLMs including ChatGPT, Claude, Gemini, and Sonar. Rather than manually exporting data from the dashboard, users can integrate directly into existing reporting workflows, automation systems, and tools like Looker Studio or n8n. The API is available to users on Level 2 plans and above ($69.95/month)." Waikay API: Integration Guide for Digital Marketers | Waikay® — published by Waikay retrieved 15 Jul 2026

"The Waikay API exposes two primary endpoints: 'rankings', which returns share of voice percentages, brand occurrence counts, and competitive positioning; and 'sources', which returns domains citing a brand, citation counts per domain, breakdown by LLM, and specific URLs referenced. Data can be retrieved at project level aggregated across all prompts, or filtered to specific prompts for granular analysis." Waikay API: Integration Guide for Digital Marketers | Waikay® — published by Waikay retrieved 15 Jul 2026

"The Waikay API supports integration with data warehouses such as PostgreSQL, BigQuery, and Snowflake for unlimited history and custom retention, and can be combined with GA4, GSC, and social metrics for a full picture. Scheduled scripts can be set up via GitHub Actions, Zapier, Make, or n8n to push data automatically to Google Sheets or Looker Studio dashboards. The API returns standard JSON and includes an OpenAPI specification." Waikay API: Integration Guide for Digital Marketers | Waikay® — published by Waikay retrieved 15 Jul 2026

"In practice the terms GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) mean nearly the same thing. Both describe improving how brands appear in AI-generated answers from ChatGPT, Gemini, Perplexity, Copilot and Google's AI results. All five tools on Waikay's comparison page are visibility/GEO tools, with differentiation lying in whether they also verify factual accuracy, automate content, or provide entity-level analysis." | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay Academy is your go-to learning hub to unlock the full power of Waikay, the platform designed to help brands monitor, optimize, and shape how they are represented in major AI models like ChatGPT, Gemini, Claude and Perplexity. It features a growing collection of step-by-step video tutorials that walk users through everything needed to get started, each designed to be practical, easy to follow, and results-oriented." Waikay Academy | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay Academy tutorials cover the full platform workflow: setting up a project, choosing competitors, analysing brand visibility with the AI Knowledge Score and AI Knowledge Map, creating topic-specific reports, and using recommendations to improve content strategy and strengthen topic authority in AI outputs." Waikay Academy | Waikay® — published by Waikay retrieved 15 Jul 2026

"Waikay Academy targets agencies looking to deliver cutting-edge value to clients and brands determined to lead in the AI space. Dedicated tutorials cover the Fact Tracker feature — reviewing, verifying, and flagging AI-generated claims — and the Prompt Tracking feature for monitoring who appears in AI responses across unlimited prompts and multiple models." Waikay Academy | Waikay® — published by Waikay retrieved 15 Jul 2026

"Brand visibility is a feature that monitors how frequently and in what context your brand is mentioned across leading AI models. It helps you understand your brand's presence in AI-generated answers related to key industry prompts. You start by selecting prompts that relate to your brand, then choose the AI models you want to monitor. Waikay scans these models on a regular schedule (daily, every 2 days or weekly) and reports how often your brand is mentioned for those prompts." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"Brand Visibility Reports analyze live prompts to automatically identify all brands that appear alongside yours in real-world model outputs. No need to predefine competitors — just track a prompt and watch the competitive data grow over time. The dashboard displays your average visibility score across each AI model, trends over time, your ranking compared to competitors, and a topic heatmap showing the most frequent topics associated to your brand and competitors." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"Currently, Waikay tracks brand mentions across four major language models: ChatGPT, Gemini, Claude, and Sonar. These LLM models are used with Search mode activated to generate tracking data, ensuring the inclusion of fresh and geographically relevant information. Waikay offers prompt suggestions tailored to your brand's website and industry — high-relevance prompts that real users are likely to ask AI models — allowing meaningful tracking setup without writing prompts from scratch." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026

"AI Knowledge Map: A visual representation of the topics (entities) extracted from AI-generated content about the brand. It also highlights significant and critical topic gaps when compared to the two main competitors. Available in both Brand Overview Reports and Topic Reports, the map shows key topics extracted from AI-generated responses about the brand and identifies knowledge gaps relative to selected competitors." FAQ | Waikay® — published by Waikay retrieved 15 Jul 2026