Knowledge base
When someone asks ChatGPT "what's the best CRM for small teams?" or searches Google and gets an AI Overview, your website doesn't get a click. The AI just answers.
AEO (Answer Engine Optimization) is how you become the source those AIs cite. Learn how to structure content, build authority signals, and measure your visibility across AI engines.
- PlaybooksIntermediate
Agent Discovery Optimization (ADO)
Agent discovery optimization (ADO) is the practice of making your brand easy for AI agents to find, trust, and choose when they autonomously research, compare, and take actions on a user’s behalf.
- CitationsIntermediate
Agentic Search Optimization (ASO)
Agentic search optimization (ASO) is the practice of making your brand and content easy for AI agents to retrieve, verify, and act on when they research, compare, and complete tasks on a user’s behalf.
- MetricsIntermediate
AI Answer Penetration
AI Answer Penetration measures how often your brand appears inside AI-generated answers for the topics you care about, not just as a link but as a cited or clearly referenced source in the response.
- EnginesIntermediate
AI Answer Ranking
AI Answer Ranking is how an AI assistant decides which sources and passages to use first when it generates an answer to your customer’s question.
- PlaybooksIntermediate
AI Answer Recall
AI answer recall measures how often an AI assistant includes your brand or content in its generated answers across the set of prompts that matter to your market.
- FundamentalsBeginner
AI Brand Presence
AI brand presence is how consistently and accurately AI search and answer tools mention, describe, and cite your brand when people ask questions related to your category, problems, and products.
- PlaybooksIntermediate
AI Brand Sentiment
AI brand sentiment is how AI search and chat assistants interpret and describe your brand’s reputation based on the mix of sources they read and the language patterns they learn from those sources.
- CitationsIntermediate
AI Citation Authority
AI citation authority measures how strongly AI answer engines trust your brand as a source worth citing, based on how often you are selected, how consistently you are attributed, and how reliable your evidence looks at retrieval time.
- MetricsIntermediate
AI Citation Influence
AI citation influence measures how much a citation of your content inside AI answers changes what the model says next, including which brands it recommends, what claims it repeats, and which sources it keeps trusting for similar questions.
- CitationsBeginner
AI Citations
How an AI points to the sources it used when giving information.
- MetricsIntermediate
AI Competitive Saturation
AI Competitive Saturation measures how crowded an AI answer surface is with competitor brands for a given topic, meaning how hard it is for your brand to be mentioned or cited because the model keeps selecting the same few sources.
- PlaybooksIntermediate
AI Consensus Optimization
AI consensus optimization is the practice of getting multiple independent, trusted sources to describe your brand the same way so AI engines repeat that shared “consensus” in answers instead of guessing or borrowing a competitor’s framing.
- FundamentalsIntermediate
AI Content Extractability
AI Content Extractability is how easily AI search and chat tools can pull a clean, accurate, self-contained answer from your page and confidently cite your brand as the source.
- FundamentalsIntermediate
AI Entity Authority
AI entity authority is the strength of your brand’s identity and credibility as a clearly defined “thing” (an entity like a company, product, or person) that AI systems recognize, retrieve, and trust when generating answers.
- EnginesIntermediate
AI Grounding
AI grounding is the practice of tying an AI’s answer to specific, checkable sources and known facts so the model stays accurate, attributable, and on-brand.
- MetricsIntermediate
AI Impression Share
AI Impression Share measures how often your brand appears in AI-generated answers for a set of tracked prompts compared to how often it could have appeared in that same answer space.
- FundamentalsBeginner
AI Knowledge Footprint
AI knowledge footprint is the total set of signals across the web that teaches AI answer engines what your brand is, what it does, and which claims about it are safe to repeat.
- MetricsIntermediate
AI Mention Coverage
AI Mention Coverage measures how often and in what contexts AI search and answer engines mention your brand, products, or key topics when users ask relevant questions.
- MetricsIntermediate
AI Observability
AI observability is the practice of monitoring how AI engines find, interpret, and present your brand’s content so you can spot visibility issues early and fix them with data, not guesses.
- PlaybooksIntermediate
AI-Ready Content
Content written and structured so AI can find direct answers, verify facts, and cite clear sources.
- FundamentalsIntermediate
AI Reputation Risk
AI reputation risk is the likelihood that AI answers misrepresent your brand, repeat outdated or negative claims, or omit crucial context in ways that change how customers and buyers perceive you.
- MetricsIntermediate
AI Reputation Score
AI reputation score is a practical way to quantify how much AI answer engines trust your brand as a source worth citing, based on the quality, consistency, and sentiment of what they retrieve about you across the web.
- EnginesIntermediate
AI Retrieval Layer
AI Retrieval Layer describes the part of an AI search or chat experience that finds and ranks the best sources to pull answers from before the model writes a response.
- EnginesBeginner
AI Retrieval Optimization (AIRO)
AI retrieval optimization (AIRO) is the practice of making your pages easier for AI search tools to find, select, and quote by improving how your content appears in retrieval results like embeddings, passage indexes, and RAG source lists.
- PlaybooksIntermediate
AI Retrieval Resilience
AI retrieval resilience is your brand’s ability to keep getting retrieved, quoted, and cited by AI answer engines even when prompts, competitors, and the engines’ source selection behavior change.
- MetricsIntermediate
AI Sentiment Analysis
AI Sentiment Analysis uses machine learning to classify how people feel about your brand or topic across text like reviews, social posts, and articles so you can quantify perception and act on it.
- FundamentalsIntermediate
AI SERP Feature
An AI SERP feature is a search results element where an AI system generates a direct answer, summary, or recommendation on the results page, often pulling from multiple sources and sometimes showing citations instead of just a list of links.
- FundamentalsBeginner
AI Visibility
How often and how prominently your brand or content appears in AI-generated answers, measured as mentions over total relevant responses.
- MetricsIntermediate
AI Visibility Score
AI Visibility Score is a metric that estimates how often your brand appears and gets cited in AI-generated answers across search assistants, chatbots, and answer engines for the topics you care about.
- FundamentalsIntermediate
Algorithmic Trinity
Algorithmic trinity describes the three forces that decide whether AI answer engines surface your brand: how well your content can be retrieved, how cleanly it can be extracted into an answer, and how strongly it earns trust and citations.
- MetricsIntermediate
Answer Extraction Rate
Answer extraction rate measures how often an AI engine can pull a clean, standalone answer from your content and use it in its response to real user questions.
- PlaybooksIntermediate
Answer Formatting Signals
Answer Formatting Signals are the visible structure cues on a page, like headings, lists, tables, and labeled Q&A blocks, that make it easy for AI answer engines to extract a clean, quote-ready response and attribute it to your brand.
- CitationsIntermediate
Answer Inclusion Criteria
Answer Inclusion Criteria are the specific content signals an AI answer engine looks for before it will pull your page into a generated response, such as a clear direct answer, trustworthy sourcing, and easy-to-extract structure.
- PlaybooksIntermediate
Answer-Optimized Content
Answer-Optimized Content is content designed to get quoted as the direct answer in AI search and search assistants by stating the key point upfront and backing it with clear, verifiable support.
- PlaybooksIntermediate
Answer Positioning
Answer positioning is the practice of shaping your content so AI answer engines can confidently select, quote, and attribute your brand as the best direct answer for a specific question.
- EnginesIntermediate
Answer Reliability Signals
Answer reliability signals are the cues AI answer engines use to decide whether a claim is safe to include, how confidently to phrase it, and which sources to cite.
- MetricsIntermediate
Answer Sentiment Distribution
Answer Sentiment Distribution measures how often AI-generated answers describe your brand or category in positive, neutral, or negative terms across a set of prompts.
- MetricsIntermediate
Answer Share
Answer share measures how often your brand becomes the actual answer an AI engine gives for a set of tracked prompts, not just a link, mention, or citation.
- MetricsIntermediate
Answer surface area
Answer surface area measures how many places across AI answer engines and search experiences your brand can realistically be selected, quoted, or recommended for a given topic.
- CitationsIntermediate
Authoritative Source Attribution
Authoritative source attribution is the practice of making it easy for AI answer engines to credit your brand as the trusted origin of a specific claim, definition, or dataset by clearly tying statements to verifiable sources, owners, and context.
- PlaybooksIntermediate
Brand Context Optimization (BCO)
Brand context optimization (BCO) is the practice of shaping the specific facts and framing AI systems pull about your brand so your name shows up with the right meaning, category, and proof across answer engines.
- FundamentalsIntermediate
Brand Framing in AI Answers
Brand framing in AI answers is how an AI assistant describes your brand’s role, category, strengths, and tradeoffs in its generated response, shaping perception even when you are not directly cited or linked.
- PlaybooksAdvanced
Canonical Answer Design
A method for crafting one clear, sourced answer with exact wording, atomic facts, evidence blocks and canonical links for reliable AI citation.
- EnginesBeginner
ChatGPT
ChatGPT is an AI conversational engine that generates responses from trained models and can fetch live web sources or use plugins.
- CitationsIntermediate
Citation Absorption
Citation absorption measures how often an AI engine uses information from your content in its answers even when it does not visibly link to or name your brand as a source.
- CitationsIntermediate
Citation Completeness
Citation completeness measures how often an AI answer includes enough clear, specific sources (links or named publications) to justify the key claims it makes about your topic or brand.
- CitationsIntermediate
Citation Confidence
Citation confidence measures how likely an AI answer engine is to quote and link to your brand’s content for a specific question because it views your page as clear, verifiable, and trustworthy.
- MetricsIntermediate
Citation Eligibility Score
Citation eligibility score measures how likely your page is to be selected and attributed as a source when AI answer engines generate a response to relevant prompts.
- CitationsIntermediate
Citation Precision
Citation precision measures how accurately an AI answer attributes a specific claim to the exact source and passage that supports it, rather than vaguely citing a page that only loosely relates.
- CitationsIntermediate
Citation Probability
Citation probability measures how likely an AI answer engine is to quote and link to your page for a specific prompt, based on whether your content is easy to extract, trustworthy, and clearly relevant.
- MetricsAdvanced
Citation Share
Share of cited links pointing to your sources among all citation links in relevant AI responses.
- MetricsIntermediate
Citation Stability
Citation stability measures how consistently AI assistants cite your brand or your pages for the same topics across many prompts, days, and engines, instead of swapping you in and out unpredictably.
- MetricsIntermediate
Citation Velocity
Citation velocity measures how quickly your brand earns new AI citations over time across answer engines like ChatGPT, Perplexity, and Google AI Overviews.
- MetricsIntermediate
Competitive AI Visibility
Competitive AI Visibility is how often your brand gets mentioned or cited by AI search and answer tools compared to your direct competitors for the topics that drive your revenue.
- FundamentalsIntermediate
Content Freshness & Recency Signals
Signals that show how recent content is and which items were updated, helping AI prefer newer sources for timely answers.
- MetricsIntermediate
Content Reusability Score
Content reusability score measures how easily your existing content can be extracted, remixed, and reused by AI answer engines and your own team across multiple questions, formats, and channels without losing accuracy or brand intent.
- PlaybooksIntermediate
Context Window Optimization
Context Window Optimization is the practice of packaging and structuring the information an AI model needs so it fits inside the model’s limited “reading memory” (the context window) and still produces accurate, on-brand answers.
- PlaybooksIntermediate
Conversational Content Design
Creating content for multi-turn conversations that gives concise core answers, expandable detail, and clear follow-ups.
- PlaybooksIntermediate
Conversational Intent Mapping
Mapping user queries, prompts, and follow-ups into a conversation map that guides answers, content structure, and microcopy.
- MetricsIntermediate
Conversational Query Coverage
Conversational Query Coverage measures how well your content answers the real questions people ask in natural, chat-style language across AI assistants and search, including follow-ups and nuanced variations.
- MetricsIntermediate
Conversational Share of Voice (cSoV)
Conversational Share of Voice (cSoV) measures how often your brand shows up in AI-generated conversations (like ChatGPT or Perplexity answers) for a defined set of prompts, compared with your competitors.
- CitationsIntermediate
Dense Retrieval
Dense retrieval is a way AI systems find the best sources by matching the meaning of a question to the meaning of content, not just matching the exact keywords.
- EnginesIntermediate
Deterministic Generation
Deterministic generation refers to the practice of configuring an AI system to produce the same output every time for the same input, so your brand’s answers and copy are consistent, testable, and easier to govern across channels.
- FundamentalsIntermediate
Digital Authority Management
Digital Authority Management is the strategic discipline of building, monitoring, and protecting a brand's authority signals across search engines and AI systems to ensure consistent citation, accurate representation, and long-term visibility.
- FundamentalsIntermediate
E-E-A-T
E-E-A-T judges content by the creator's first-hand experience, expertise, recognition by others, and overall trustworthiness.
- CitationsBeginner
Embeddings
Embeddings are numeric “meaning fingerprints” that AI systems use to match your content to a question or concept even when the wording is different.
- EnginesIntermediate
Entity Collision
Entity collision happens when AI systems confuse your brand, product, or people with another similarly named “entity” (a recognized thing like a company or person), causing the wrong information to show up in answers and recommendations.
- FundamentalsIntermediate
Entity Disambiguation
Entity disambiguation is the process AI systems use to correctly identify which real-world “thing” your content refers to (like the company Apple vs. the fruit) so your brand gets attributed, cited, and surfaced in the right context.
- FundamentalsIntermediate
Entity & Knowledge Graph Optimization
Making public profiles and linked data accurate so AI and search systems recognize and attribute brands and topics correctly.
- CitationsIntermediate
Entity Linking
Entity linking is the process of connecting the names in your content (like your brand, product, people, and locations) to the correct real-world entities in knowledge bases so AI systems know exactly who or what you mean.
- FundamentalsIntermediate
Entity split
Entity split happens when AI systems and search engines treat one real-world thing (like your brand, product, or spokesperson) as multiple different “entities,” which fragments your visibility, citations, and trust signals across answers.
- CitationsIntermediate
Evidence Density
Evidence density measures how much verifiable, citable proof your content packs into the exact passages AI systems extract when they generate answers about your brand.
- EnginesIntermediate
Evidence Graph
An evidence graph is the map of sources, passages, and entity relationships that an AI answer engine uses to decide what facts to trust, how to connect them, and which brands to cite.
- CitationsIntermediate
Evidence Layer Optimization
Evidence layer optimization is the practice of packaging your claims with clear, machine-readable proof (sources, dates, and entity-level context) so AI answer engines can verify, retrieve, and confidently cite your brand.
- PlaybooksIntermediate
Forced Consensus Spam
Forced consensus spam is a manipulation tactic where many low-quality pages repeat the same claim or phrasing so AI answer engines treat it as “widely agreed” and surface it as the default answer.
- FundamentalsBeginner
Generative Engine Optimization (GEO)
Generative Engine Optimization (GEO) makes content cited in AI answers instead of ranked as links, urgent with 200M+ ChatGPT users and Google AI.
- FundamentalsIntermediate
Generative Hallucination Risk
Generative hallucination risk is the chance an AI answer engine will confidently state incorrect or unsupported information about your brand, category, or products because it is predicting text instead of verifying facts.
- FundamentalsBeginner
GEO vs SEO
GEO aims for ranking and click rate with keyword pages vs rivals; SEO aims to be cited in answers, tracks mentions and favors conversational text.
- EnginesIntermediate
Google AI Overviews
Google's AI-generated search summaries that provide concise answers with source links and expandable citations in results.
- EnginesAdvanced
Hybrid Engine Optimization (HEO)
Hybrid Engine Optimization is a framework coined by Jori Ford that blends traditional SEO with AI discovery tools to maximize brand presence across both search engines and AI agents, prioritizing visibility over rankings.
- FundamentalsBeginner
Hybrid Search
Hybrid search is a search experience that blends classic keyword results with AI-generated answers, pulling from multiple sources like web pages, knowledge graphs, and retrieval systems to respond in the format the user wants.
- MetricsIntermediate
Inclusion rate
Cited inclusion rate measures how often an AI engine (like ChatGPT, Google AI Overviews, or Perplexity) includes your brand, product, or content in its answers for the prompts you care about.
- CitationsIntermediate
LLM Source Selection
LLM source selection is the process an AI assistant uses to choose which web pages, documents, or databases to trust and cite when it generates an answer about your brand or category.
- EnginesIntermediate
LLM Training Cutoff
LLM training cutoff is the most recent date a language model’s built-in knowledge was updated, which limits how reliably it can answer questions about newer events, products, and changes without using live sources.
- EnginesIntermediate
Model preference bias
Model Preference Bias is the tendency for an AI system to repeatedly favor certain sources, brands, formats, or viewpoints in its answers, even when other relevant options exist.
- PlaybooksIntermediate
Modular Content Design
Modular content design is a way of creating content as reusable, self-contained blocks (answers, definitions, proof points, steps, tables) that can be recombined across pages so AI systems can extract, verify, and cite your brand more reliably.
- EnginesIntermediate
Multi-Engine Optimization Matrix
A matrix comparing which signals and behaviors matter across major AI engines to guide optimization priorities.
- CitationsIntermediate
Multi-Hop Retrieval
Multi-hop retrieval is when an AI assistant gathers an answer by pulling information from multiple sources in sequence, where each source helps it figure out what to fetch next.
- PlaybooksIntermediate
Multi-Turn Query Optimization
Multi-turn query optimization is the practice of shaping your content and signals so AI assistants keep selecting your brand as the conversation evolves across follow-up questions, comparisons, and constraints.
- CitationsIntermediate
Narrative Control Signals
Narrative control signals are the on-page and off-page cues that steer AI engines toward describing your brand with the right framing, facts, and comparisons when they generate answers.
- MetricsIntermediate
Negative Answer Rate
Negative answer rate measures how often an AI assistant answers a relevant question with a “no,” “not recommended,” or “doesn’t exist” type response about your brand, product, or category.
- CitationsIntermediate
Owned vs Earned Mentions
Owned mentions are AI citations of your content; earned mentions are AI references to third-party coverage or reviews about you.
- EnginesIntermediate
Passage-Level Indexing
Passage-level indexing is Google’s ability to understand and rank a specific section of a page for a query, even if the rest of the page covers broader or different topics.
- PlaybooksIntermediate
Perception Anchoring
Perception anchoring is the practice of deliberately shaping the first, most quotable idea AI answer engines repeat about your brand so later answers stay consistent, accurate, and favorable.
- EnginesBeginner
Perplexity
Perplexity is a search-first AI engine that answers queries using real-time web search and shows clear source links.
- MetricsIntermediate
Positive Mention Rate
Positive mention rate measures how often AI answers and other AI-driven surfaces talk about your brand in a favorable way compared to all mentions of your brand for a defined set of prompts or topics.
- CitationsIntermediate
Primary Source Preference
Primary source preference is the tendency of AI answer engines to favor information that comes directly from the most authoritative, closest-to-origin publisher for a fact, like an official brand page, a standards body, or the original study, when selecting what to cite and summarize.
- PlaybooksIntermediate
Prompt Coverage Mapping
Prompt Coverage Mapping is the process of cataloging the real questions people ask AI assistants about your category and checking whether your content gives clear, citable answers for each one.
- PlaybooksIntermediate
Prompt Mining
Prompt mining is the process of collecting and analyzing the real prompts people type into AI assistants to uncover the questions, wording, and brand comparisons that shape whether your content gets mentioned or cited.
- EnginesIntermediate
Prompt path dependency
Prompt Path Dependency describes how an AI assistant’s final answer can change based on the exact wording, order, and context of the prompts a user gives it, even when they’re asking “the same” question.
- PlaybooksIntermediate
Prompt Research
Studying how people phrase AI queries to identify common prompts, phrasing patterns, and effective wording for a given topic.
- MetricsIntermediate
Prompt Variability Impact
Prompt variability impact describes how much your brand’s visibility and citations change when the same underlying question is asked in different ways across AI assistants and answer engines.
- FundamentalsBeginner
Prompts vs Search Queries
Prompts are conversational requests that give context and tasks for AI, while search queries are concise keyword strings to find links.
- CitationsIntermediate
Query Decomposition
Query decomposition is the way AI search assistants break one big question into smaller sub-questions so they can retrieve, verify, and assemble a confident answer from multiple sources.
- CitationsIntermediate
Query Expansion
Query expansion is the way search and answer engines rewrite a user’s question by adding related words, synonyms, and implied details so they can retrieve more relevant sources and generate a better answer.
- CitationsIntermediate
Query Rewriting
Query rewriting is the behind-the-scenes process where a search engine or AI assistant rephrases what someone typed into a clearer, more specific request so it can retrieve better sources and generate a more accurate answer.
- MetricsIntermediate
Query-to-Answer Coverage
Query-to-Answer Coverage measures how often your content can directly satisfy a real user question with a clear, quotable answer that AI search assistants can confidently use and cite.
- PlaybooksIntermediate
Relevance Engineering
Relevance Engineering is a methodology developed by Mike King for optimizing content around how modern search and AI retrieval systems determine relevance.
- EnginesIntermediate
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a way AI assistants answer questions by first fetching relevant information from selected sources (like web pages or your docs) and then writing a response grounded in what they retrieved.
- EnginesIntermediate
Retrieval Confidence
Retrieval confidence is a measure of how sure an AI system is that the sources it pulled from its retrieval layer actually contain the best evidence to answer a specific query.
- MetricsIntermediate
Retrieval Exclusion Rate
Retrieval exclusion rate measures how often your pages fail to make it into the AI retrieval layer, meaning the model never even considers your content when generating an answer.
- EnginesIntermediate
Retrieval Priority
Retrieval priority is the likelihood that an AI system pulls your page or passage into its research set before it writes an answer, based on how relevant, accessible, and trustworthy your content looks to the retrieval layer.
- FundamentalsIntermediate
Retrieval Readiness
Retrieval readiness is how prepared your content is to be found, selected, and safely quoted by AI answer engines when they build responses from a retrieval layer (the system that fetches sources in real time).
- FundamentalsIntermediate
SameAs links
SameAs links are identity links in your structured data that tell search and AI systems which official profiles and listings refer to the exact same brand, person, or organization.
- FundamentalsIntermediate
Semantic Data Poisoning
Semantic data poisoning is the deliberate manipulation of how facts and entities are described online so AI systems learn, retrieve, or summarize a distorted version of reality about your brand, products, or category.
- MetricsIntermediate
Sentiment Share
Sentiment share measures how much of the overall conversation about your brand in AI-generated answers is positive, neutral, or negative compared to competitors.
- MetricsIntermediate
Share of Voice
Percentage of AI response mentions for your topic that name your brand out of all brand mentions.
- PlaybooksAdvanced
Snippet-Level Structured Fact Cards
Compact fact cards that pair a single claim with brief evidence and a source URL for easy extraction and citation by LLMs.
- CitationsIntermediate
Source Eligibility
Source eligibility is the set of signals that determine whether an AI answer engine will consider your page a safe, relevant, and extractable source to quote or cite for a given question.
- PlaybooksIntermediate
Source Of Truth Page
A Source Of Truth Page is the one page on your site that AI assistants and humans can reliably use to verify your brand’s core facts, positioning, and claims without hunting across conflicting pages.
- FundamentalsIntermediate
Source Trust Signals for AI
Signals like author info, citations, metadata, backlinks and clear edit history that show AI how trustworthy a source is.
- CitationsIntermediate
Sparse Retrieval
Sparse retrieval is a search method many AI systems use to pull a small set of highly relevant pages or passages by matching key terms and entities, before an LLM writes the final answer.
- FundamentalsIntermediate
Stochastic generation
Stochastic generation is when an AI model produces text by sampling from multiple plausible next words (with some randomness) rather than always choosing the single most likely option, which means answers can vary even for the same prompt.
- PlaybooksIntermediate
Structured Data for GEO
Adding simple schema.org JSON-LD markup to web pages so AI systems can parse, verify, and cite content.
- MetricsIntermediate
Synthetic Query Coverage
Synthetic Query Coverage measures how well your content answers the full range of questions AI search tools might generate about your product or topic, using model-created “synthetic” questions as a proxy for real demand.
- FundamentalsBeginner
Top-P sampling
Top-P sampling (also called nucleus sampling) is a setting in generative AI that controls how “adventurous” a model’s word choices are by limiting it to the smallest set of likely next words whose combined probability reaches a chosen threshold.
- CitationsIntermediate
Trust Framing Signals
Trust framing signals are the page, brand, and author cues that help AI systems and humans quickly decide your information is credible enough to quote, recommend, or use in an answer.
- CitationsIntermediate
Vector Search
Vector search is a way AI systems find the most relevant content by comparing meaning, not exact keywords, so they can retrieve the best passages to answer a question and cite sources.
- MetricsIntermediate
Visibility Volatility
Visibility Volatility is the day-to-day and engine-to-engine swing in how often your brand shows up in AI-generated answers, even when your underlying rankings or content have not changed.
- FundamentalsIntermediate
Zero-Click AI Answer
Zero-click AI answer means an AI assistant gives the user a complete, usable response directly in the interface, so they get what they need without clicking through to any website.
Frequently Asked Questions
What is Omnia?
Omnia is an AI visibility platform that shows where and how your brand appears in AI search engines like ChatGPT, Perplexity, Google AI Overviews, and Google AI mode.
Unlike most AI visibility tools, Omnia also takes a step further and shows you how to act on your data to get featured in future AI answer.
What AI engines can I track with Omnia?
Omnia tracks major AI engines like ChatGPT, Perplexity, Google AI Overviews, Google AI mode, Claude, Microsoft Copilot, and Gemini.
How often do you track prompts and refresh data?
Omnia tracks prompts every 24 hours so you can spot changes in AI as they happen. We have clients that see results in as little as 7 days, and use daily tracking to give you quick feedback loops to act on.
How do I know if AI recommends my product?
Omnia runs prompts to find out and show you whether your brand is mentioned in AI answers, including how you benchmark against competitors.
How does Omnia track brand mentions in AI answers?
Omnia mimics real user behaviour to runs prompts that users type to learn about your category and product.
How is AEO different from SEO?
SEO optimizes for clicks from search results. AEO optimizes for mentions and recommendations inside AI answers, often without a click.
How long does it take to see results from AEO?
We’ve seen results in AEO in as little as 7 days with customers. Unlike SEO, which often takes months, AI visibility can be changed and influenced much more quickly.