Last updated July 2026. This glossary covers 40 terms across brand monitoring, prompt tracking, and AI visibility measurement. Use the thematic groups below to jump to the section most relevant to your work, or search by term name.
How to use this glossary
Each entry opens with a standalone 40-to-60-word definition, followed by usage context and cross-references. The definitions are written to stand on their own so you can quote or cite them directly.
Thematic groups:
- Core metrics (1-10)
- Prompt and engine mechanics (11-20)
- Citation and retrieval (21-30)
- Sentiment and perception (31-36)
- Programme and platform concepts (37-40)
Core metrics
1. AI Share of Voice
AI share of voice is the percentage of relevant prompts, across a defined engine set and competitive category, for which your brand is mentioned in the AI-generated answer. A brand with 35 mentions out of 100 tracked prompts holds 35% AI share of voice for that category.
AI share of voice is the primary competitive benchmark in every AI visibility programme. It replaces the traditional rank-position metric: instead of tracking where you sit on page one, you track whether you appear at all when buyers ask AI engines the questions you should own.
Tools: Otterly.AI, Peec AI, Profound, Temso, SE Ranking.
Related terms: Share of model (2), Prompt coverage (6), Competitive set (38).
2. Share of Model
Share of model is an alternative name for AI share of voice, framing the metric from the perspective of a specific AI model’s response distribution. It asks: across all the times this model answers a relevant query, what share of those answers mention your brand?
The “model” framing is useful because AI engines are probabilistic. The same prompt run ten times on ChatGPT may produce your brand in seven responses and a competitor in the other three. Share of model captures that distribution rather than treating any single response as definitive.
Related terms: AI share of voice (1), Prompt entropy (12), Engine coverage (39).
3. Brand Mentions
Brand mentions in AI monitoring are the raw count of times your brand name, product name, or defined alias appears inside AI-generated answers across a tracked set of prompts and engines. Mentions are counted per response and aggregated over a reporting period.
Mentions are noisier than share of voice: a single response can contain your brand name multiple times. They are most useful for detecting sudden spikes or drops tied to external events (product launches, press coverage, controversies). Week-over-week mention velocity is a leading indicator of visibility change.
Related terms: AI share of voice (1), Citation rate (21), Named-entity recognition (25).
4. Prompt Coverage
Prompt coverage is the proportion of your tracked prompt set for which at least one response mentions your brand. A brand with 60% prompt coverage appears in answers to 60 out of every 100 queries in its monitored cluster.
Prompt coverage differs from share of voice in that it ignores frequency within a single response and asks a binary question: did your brand show up or not? It reveals which intent clusters you own and which you are missing entirely.
Related terms: Prompt cluster (13), Prompt family (14), AI share of voice (1).
5. Citation Rate
Citation rate is the percentage of AI-generated answers, across a defined prompt set, in which a specific URL or domain from your site is referenced or linked as a source. It measures how often AI engines treat your content as an authority, not merely whether they name your brand.
A high citation rate on a single piece of content is one of the strongest signals that AI models have incorporated that content into their retrieval layer. Improving citation rate is among the highest-leverage activities in an AI visibility programme.
See also: Citation Rate (21) in the citation section for a deeper treatment.
Related terms: Retrieved page (22), Source attribution (24), Earned citation (23).
6. AI Visibility Score
An AI visibility score is a composite metric, typically computed by a monitoring platform, that combines share of voice, citation rate, sentiment, and prompt coverage into a single index number. Different platforms weight and name this score differently.
Scores are useful for executive reporting and trend tracking. They are not comparable across platforms: a 72 on one tool and a 72 on another tool do not mean the same thing. Always anchor score changes to the underlying raw metrics so you can interpret what actually moved.
Related terms: AI share of voice (1), Citation rate (21), Sentiment (31), Prompt coverage (4).
7. Hallucination Rate
Hallucination rate in brand monitoring is the frequency with which AI engines state demonstrably false information about your brand in their generated answers. Common hallucinations include wrong pricing, discontinued features, incorrect founding dates, and inaccurate product descriptions.
Hallucinations are a distinct monitoring category from negative sentiment. A negative statement can be accurate. A hallucination is factually wrong and often persists across multiple responses because it reflects corrupted or missing source data rather than a biased editorial judgment.
Related terms: Accuracy monitoring (8), Source correction (28), Brand-fact drift (35).
8. Accuracy Monitoring
Accuracy monitoring is the practice of comparing the factual claims AI engines make about your brand against a verified ground truth (current pricing, feature descriptions, leadership team, certifications) and flagging discrepancies for correction.
Accuracy monitoring sits alongside share of voice and sentiment as the third pillar of brand perception monitoring. Platforms such as Temso, Profound, and Otterly.AI include hallucination detection as part of their standard monitoring output.
Related terms: Hallucination rate (7), Source correction (28), Brand-fact drift (35).
9. Mention Velocity
Mention velocity is the rate of change in raw brand mentions across a defined period, expressed as a week-over-week or month-over-month percentage. Positive velocity indicates growing AI presence; negative velocity indicates that competitors are crowding you out of answers.
Velocity catches trend reversals faster than a snapshot. A brand whose absolute share of voice is still high but whose velocity has turned negative is losing ground before the headline number reflects it.
Related terms: Brand mentions (3), Share of voice drift (10), AI share of voice (1).
10. Share of Voice Drift
Share of voice drift is the direction and magnitude of change in a brand’s AI share of voice over a defined period. Drift is a more actionable signal than an absolute snapshot because it reveals whether the brand is gaining, losing, or holding competitive ground.
Tracking drift rather than absolute position is especially important when absolute share of voice is low. A brand moving from 8% to 22% over a quarter is a meaningful competitive event even if 22% is still below the category leader.
Related terms: AI share of voice (1), Mention velocity (9), Prompt family (14).
Prompt and engine mechanics
11. Answer Engine
An answer engine is an AI system that responds to natural-language queries with a synthesized, generated answer rather than a ranked list of links. ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Microsoft Copilot, Meta AI, and Grok are the primary answer engines tracked for brand monitoring purposes.
The answer engine category matters for monitoring because each engine uses different retrieval methods, training data, and citation formats. A brand’s share of voice can vary significantly from one engine to another for the same prompt.
Related terms: Engine coverage (39), Retrieval-augmented generation (20), Cross-engine citation overlap (29).
12. Prompt Entropy
Prompt entropy describes how variable the answers to a given query are across repeated runs on the same AI engine. A high-entropy prompt produces meaningfully different brand names, framings, or recommendations each time it runs. A low-entropy prompt reliably produces the same answer.
Entropy matters for monitoring sample size. A high-entropy prompt requires more runs (five or more) before its brand-mention rate is statistically reliable. Single-run dashboards that report on high-entropy prompts are reporting noise rather than signal.
Related terms: Prompt family (14), Run count (17), Share of model (2).
13. Prompt Cluster
A prompt cluster is a group of queries organized by shared buyer intent. “Best project management tool for remote teams,” “top PM software for distributed companies,” and “what project manager should a 20-person team use” are three distinct prompts but one cluster.
Clusters are the practical unit of AI visibility management. You track coverage and share of voice at the cluster level so that wording variation across queries does not produce misleading single-prompt conclusions.
Related terms: Prompt family (14), Prompt coverage (4), Intent mapping (16).
14. Prompt Family
A prompt family is a defined set of query variants that a real buyer would phrase for the same information need. Tracking a family rather than a single phrasing produces a citation rate that reflects actual buyer behavior and accounts for the natural variation in how people word the same question.
Retiring prompts from a family is as important as adding them. When buyers stop using a phrasing, or when your share of voice on that phrasing is saturated near 100%, reinvest the run budget in prompts where the answer is still contested.
Related terms: Prompt cluster (13), Prompt entropy (12), Run cadence (18).
15. Buyer-Intent Prompt
A buyer-intent prompt is a query that reflects a purchase-stage information need: comparison shopping, vendor evaluation, pricing research, or category-level problem-solving. “Best AI visibility tool for a 10-person marketing team” is a buyer-intent prompt; “what is AI visibility” is an informational prompt.
Buyer-intent prompts are the highest-priority monitoring targets because they are the queries most likely to influence which vendor a prospect contacts. Share of voice on buyer-intent prompts is a closer proxy for pipeline contribution than share of voice on informational queries.
Related terms: Prompt cluster (13), Intent mapping (16), Conversion signal (40).
16. Intent Mapping
Intent mapping is the process of organizing a monitored prompt set by buyer-journey stage (awareness, consideration, decision) or by query intent type (informational, comparative, transactional). It produces a structured view of where a brand appears and where gaps exist at each stage.
Intent mapping reveals which stages are well covered and which are invisible. A brand that appears in 70% of awareness-stage prompts but only 12% of comparison prompts has a late-funnel visibility problem, even if its headline share of voice looks strong.
Related terms: Prompt cluster (13), Buyer-intent prompt (15), Prompt coverage (4).
17. Run Count
Run count is the number of times a given prompt is queried against an AI engine within a single measurement cycle. Because answer engines are probabilistic, a single run is one sample from a distribution. Five runs per prompt is a common minimum; high-entropy prompts warrant more.
Run count directly affects the reliability of every metric derived from it. Platforms that report share of voice from a single run per prompt are reporting the output of one sample, not a stable distribution. Always ask how many runs the platform executes before treating share-of-voice figures as actionable.
Related terms: Prompt entropy (12), Prompt family (14), AI share of voice (1).
18. Run Cadence
Run cadence is the frequency at which a monitoring platform refreshes its prompt data: daily, weekly, or monthly. Cadence determines how quickly you detect changes in visibility and how reliably you can attribute those changes to specific external events.
Daily cadence matters most in categories where competitive dynamics shift fast (new product launches, press events, algorithm updates). Monthly cadence is sufficient for established categories with low entropy and slow-moving competitive landscapes.
Related terms: Run count (17), Mention velocity (9), Share of voice drift (10).
19. Fan-Out Query
A fan-out query is an internal sub-query that an AI engine generates behind the scenes to retrieve source material before constructing its response to the user’s original prompt. Retrieval-augmented AI systems, including Perplexity and Bing Copilot, rely heavily on fan-out queries to gather citations.
Fan-out queries often diverge significantly from the user’s original wording. According to Profound’s April 2026 analysis, ChatGPT’s most divergent fan-out sub-queries share only about 13% word overlap with the original user prompt. Optimizing content for fan-out phrasing, not just the surface query, is an emerging AEO practice.
Related terms: Retrieval-augmented generation (20), Citation rate (21), Retrieval window (26).
20. Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is the architecture in which an AI system first retrieves relevant documents or passages from a corpus and then uses those retrieved passages as context for generating its response. Perplexity, Google AI Overviews, Microsoft Copilot, and the browsing modes of ChatGPT all use RAG to some degree.
RAG is the reason that content quality, authority, and citation by third parties affect AI visibility. A model operating purely on training data cannot cite a URL it has not indexed. RAG-enabled systems can, which means your citation rate directly correlates with how often your pages appear in the retrieved context for relevant prompts.
Related terms: Fan-out query (19), Citation rate (21), Retrieved page (22).
Citation and retrieval
21. Citation Rate
Citation rate is the percentage of AI-generated answers, within a defined prompt set and engine set, in which a specific URL or domain from your site is referenced as a source. High citation rate on a page signals that AI retrieval systems are treating that content as an authoritative input.
Citation rate and brand mentions are related but distinct. Your brand can be mentioned in an answer without any of your URLs being cited (the AI knows about you but draws from third-party sources). Conversely, a URL can be cited with no brand mention if the AI uses the content as a background source without naming the brand explicitly.
Related terms: Brand mentions (3), Earned citation (23), Retrieved page (22).
22. Retrieved Page
A retrieved page is a URL that an AI engine’s retrieval system selects as relevant context for a given prompt, before the model decides whether to include it in the generated answer. Being retrieved is necessary but not sufficient for being cited.
Platforms that provide retrieval-level data (separate from citation-level data) let you identify pages that are being selected by the AI but then dropped before the final answer. That drop-off is a signal that content is finding its way into the retrieval window but is not authoritative or relevant enough to make the final response.
Related terms: Citation rate (21), Retrieval window (26), RAG (20).
23. Earned Citation
An earned citation is a reference to your brand or domain that appears in an AI-generated answer because a third-party source (a review site, journalist, analyst, or community forum) mentioned you. It is earned in the same sense as earned media: you did not pay for it or publish it yourself.
Earned citations are the dominant citation type in most AI monitoring datasets. Research consistently finds that the large majority of AI citations come from third-party sources rather than brand-owned pages. Earned media placements, analyst mentions, and community-forum discussions all drive earned citations.
Related terms: Citation rate (21), Source attribution (24), Third-party authority (27).
24. Source Attribution
Source attribution is the process of identifying which domains or URLs are generating citations for your brand in AI-generated answers. A source-attribution report tells you whether your citations come from your own site, from media coverage, from review platforms, or from community content.
Source attribution matters for AEO strategy. If 80% of your citations come from two or three third-party domains, your visibility is fragile. Diversifying citation sources across more authoritative referring properties is a risk-reduction strategy as well as a growth lever.
Related terms: Earned citation (23), Third-party authority (27), Citation gap (30).
25. Named-Entity Recognition (NER)
Named-entity recognition (NER) in AI monitoring is the automated process of identifying and extracting brand names, product names, and competitor names from AI-generated text. Monitoring platforms use NER to count mentions, classify sentiment by entity, and build share-of-voice calculations at scale.
NER accuracy is a quality variable that differs across platforms. Abbreviated brand names, informal product references, and brand-adjacent terminology can be missed or misclassified. The quality of NER determines the reliability of every mention-based metric the platform produces.
Related terms: Brand mentions (3), AI share of voice (1), Accuracy monitoring (8).
26. Retrieval Window
The retrieval window is the set of documents or passages that an AI engine”s retrieval system selects as candidate context for a given prompt before the generation step. Content that does not enter the retrieval window cannot be cited in the answer.
Retrieval windows are determined by a combination of semantic relevance, crawlability, domain authority, content recency, and the retrieval architecture the engine uses. Optimizing for the retrieval window is distinct from optimizing for citation: you must first be selected, then be authoritative enough to appear in the final response.
Related terms: Retrieved page (22), RAG (20), Fan-out query (19).
27. Third-Party Authority
Third-party authority is the degree to which external sources (media outlets, review platforms, analyst firms, community forums) cite, describe, or link to your brand in ways that AI retrieval systems treat as credible. Brands with high third-party authority are more likely to be cited by AI engines even without strong direct-domain signals.
According to Profound”s analysis of 100,000 prompts across ChatGPT and Perplexity, only 11% of cited domains overlap between the two platforms. This means that third-party authority signals in different corners of the web drive citation on different engines.
Related terms: Earned citation (23), Source attribution (24), Cross-engine citation overlap (29).
28. Source Correction
Source correction is the practice of identifying the specific third-party URLs or training-data sources that are causing an AI engine to state incorrect, outdated, or negative information about your brand, and then taking action to update or replace those sources.
Source correction is the remediation step that follows accuracy monitoring. Tactics include updating your own content to contradict the inaccuracy, reaching out to the publishing site that hosts the incorrect content, and publishing authoritative new content that displaces the erroneous source in AI retrieval.
Related terms: Accuracy monitoring (8), Hallucination rate (7), Third-party authority (27).
29. Cross-Engine Citation Overlap
Cross-engine citation overlap is the proportion of URLs or domains that appear in AI-generated answers across two or more different AI engines for the same set of prompts. Low overlap means each engine is drawing from different source pools.
According to Profound”s research, only about 11% of domains cited by ChatGPT also appear in Perplexity’s citations for the same prompts. A separate Ahrefs study found only 13.7% overlap between Google AI Overviews and Google AI Mode citations. These low overlap rates mean you cannot infer performance on one engine from performance on another.
Related terms: Third-party authority (27), Source attribution (24), Engine coverage (39).
30. Citation Gap
A citation gap is a query or intent cluster for which your competitors are being cited in AI-generated answers but your brand is not. Citation gaps are identified by comparing your citation rate and prompt coverage against each competitor in your monitored set.
Citation gaps are the primary output that drives AEO action plans. Platforms such as Peec AI and Profound surface gaps at the prompt level, showing which specific queries are producing competitor citations without producing yours.
Related terms: Citation rate (21), Prompt coverage (4), AEO (37).
Sentiment and perception
31. Sentiment Analysis
Sentiment analysis in AI monitoring is the classification of language in AI-generated answers about your brand as positive, neutral, or negative. It covers adjectives and claims the AI makes about your product, pricing, support quality, ease of use, and competitive positioning.
Sentiment in AI answers is not the same as public sentiment. An AI engine can produce a negative sentiment framing for your brand based on a single critical article it has retrieved, even if the vast majority of real customer reviews are positive. The source of the sentiment signal matters as much as the signal itself.
Related terms: Sentiment decay (32), Perception monitoring (33), Brand-fact drift (35).
32. Sentiment Decay
Sentiment decay is the gradual worsening of AI-generated descriptions of your brand over time, driven by negative or outdated source material persisting in the model”s retrieval layer. Unlike a news cycle, which fades, negative AI sentiment can remain stable for months without active intervention.
Sentiment decay is particularly insidious because it is not caused by new negative coverage arriving: it is caused by old coverage remaining prominent in the retrieval pool. Correcting it requires either updating the source content, generating new authoritative content that outweighs the old, or securing earned coverage that introduces a more current framing.
Related terms: Sentiment analysis (31), Brand-fact drift (35), Source correction (28).
33. Perception Monitoring
Perception monitoring is the practice of tracking not just whether your brand is mentioned in AI answers but how it is characterized: the words used to describe it, the product attributes highlighted, the competitors it is compared against, and the use cases it is recommended for.
Perception monitoring sits above sentiment analysis. While sentiment asks “is this positive or negative?”, perception monitoring asks “what story is the AI telling about this brand?” A brand can receive a neutral sentiment score while being described in ways that undermine its positioning.
Related terms: Sentiment analysis (31), Sentiment decay (32), Accuracy monitoring (8).
34. Tone Classification
Tone classification is the granular version of sentiment analysis that distinguishes between sentiment categories such as enthusiastic, cautious, dismissive, and comparative (framing your brand relative to a named competitor). Tone is more actionable than a binary positive/negative score.
A tone classification of “cautious” on pricing-related prompts tells you something specific: AI engines are hedging on your pricing positioning. That is a different problem than a “dismissive” tone on feature-related prompts, which suggests the retrieval sources are treating your feature set as limited.
Related terms: Sentiment analysis (31), Perception monitoring (33), Competitive framing (36).
35. Brand-Fact Drift
Brand-fact drift is the divergence between the factual profile of your brand as stored in AI training data or retrieval sources and the current, accurate facts about your product. It accumulates when your brand changes (repricing, feature updates, rebranding) but the sources AI engines draw from are not updated.
Brand-fact drift is a slow-moving risk. A brand that reprices from $49 to $29 per month but whose most-cited review articles still quote $49 will see AI engines consistently understate its value proposition for months. Monitoring for factual accuracy on price, features, and positioning is the only way to detect and correct drift early.
Related terms: Hallucination rate (7), Accuracy monitoring (8), Source correction (28).
36. Competitive Framing
Competitive framing is the way an AI engine describes your brand in relation to named competitors within the same response. “Cheaper than Profound but with fewer enterprise features” is a competitive framing. It affects not just sentiment but which buyer segment the AI implicitly recommends you to.
Competitive framing is one of the most consequential perception signals in AI monitoring. It determines which customer profile the AI is steering toward you and which it is steering toward rivals. Tracking it at the prompt level reveals the implied positioning your brand holds in the AI”s model of your market.
Related terms: Perception monitoring (33), Tone classification (34), Competitive set (38).
Programme and platform concepts
37. AEO (Answer Engine Optimization)
Answer engine optimization (AEO) is the practice of creating, structuring, and distributing content so that AI answer engines cite your brand, accurately represent your product, and recommend you to buyers asking relevant questions. AEO is the AI-era successor to traditional SEO.
AEO differs from SEO in several ways: there are no ranked positions to optimize for, the target is a citation within a generated passage rather than a blue link, and the engines draw on a mix of real-time retrieval and trained priors rather than a crawled index alone. AEO is the execution layer; AI visibility monitoring is the measurement layer.
See the full AI visibility tools ranking for platforms that cover both. See /methodology for how we evaluate them.
Related terms: AI visibility score (6), Citation gap (30), AI share of voice (1).
38. Competitive Set
A competitive set in AI monitoring is the defined list of brands whose AI visibility metrics you track alongside your own. Share of voice calculations are always relative to a competitive set: your 35% share means one thing if the next-largest competitor has 20%, and another if they have 55%.
Competitive sets should reflect the brands an AI engine actually recommends in your category, which may differ from the competitors in your own mental model. Run a round of discovery prompts across your target query clusters before finalizing your competitive set.
Related terms: AI share of voice (1), Prompt coverage (4), Share of voice drift (10).
39. Engine Coverage
Engine coverage is the number and selection of AI engines a monitoring platform tracks. Platforms differ significantly: some cover 4 engines, others cover 9 or more. The engines in scope determine which buyer segments your monitoring data represents.
At minimum, track ChatGPT, Google AI Overviews, Perplexity, and Gemini. These four together cover the majority of AI-assisted research journeys for most B2B and B2C audiences. For broader coverage, add Microsoft Copilot, Google AI Mode, Meta AI, and Grok. Tools such as Peec AI and Temso offer the widest engine coverage in the category.
Related terms: Cross-engine citation overlap (29), Run cadence (18), AI share of voice (1).
40. Conversion Signal
A conversion signal in AI visibility is any downstream indicator that a brand’s presence in AI answers is influencing buyer behavior: referral traffic from an AI engine, form submissions attributed to AI-channel sources, or sales pipeline tagged to AI-influenced discovery.
Connecting AI visibility metrics to conversion signals is the emerging challenge in the discipline. Traffic attribution from AI engines is still immature compared to paid or organic search attribution. Brands that instrument their analytics to capture AI referral sources early will have a significant measurement advantage as the channel grows.
Related terms: AI share of voice (1), Prompt coverage (4), Mention velocity (9).
Quick-reference table
| Term | Group | One-line summary |
|---|---|---|
| AI Share of Voice | Core metric | % of prompts where your brand is mentioned |
| Share of Model | Core metric | Alternative name for AI share of voice, model-centric framing |
| Brand Mentions | Core metric | Raw count of brand appearances in AI answers |
| Prompt Coverage | Core metric | % of tracked prompts with at least one brand mention |
| Citation Rate | Core + Citation | % of answers where a specific URL is referenced |
| AI Visibility Score | Core metric | Composite platform index combining all core signals |
| Hallucination Rate | Core metric | Frequency of factually wrong AI claims about your brand |
| Accuracy Monitoring | Core metric | Comparing AI-stated facts against verified ground truth |
| Mention Velocity | Core metric | Rate of change in raw brand mentions week-over-week |
| Share of Voice Drift | Core metric | Directional change in AI share of voice over time |
| Answer Engine | Prompt/engine | AI system that returns synthesized answers (not just links) |
| Prompt Entropy | Prompt/engine | How much answer variation occurs across runs of one prompt |
| Prompt Cluster | Prompt/engine | Group of queries sharing the same buyer intent |
| Prompt Family | Prompt/engine | Defined set of phrasings for one information need |
| Buyer-Intent Prompt | Prompt/engine | Query reflecting a purchase-stage information need |
| Intent Mapping | Prompt/engine | Organizing prompts by buyer-journey stage |
| Run Count | Prompt/engine | Number of times a prompt is run per measurement cycle |
| Run Cadence | Prompt/engine | Frequency at which monitoring data is refreshed |
| Fan-Out Query | Prompt/engine | Internal sub-query generated by AI before responding |
| RAG | Prompt/engine | Retrieve-then-generate architecture used by most AI engines |
| Citation Rate | Citation | URL-level source reference rate in AI answers |
| Retrieved Page | Citation | URL selected by retrieval but not necessarily cited |
| Earned Citation | Citation | Brand reference originating from a third-party source |
| Source Attribution | Citation | Identifying which domains drive your brand”s citations |
| Named-Entity Recognition | Citation | Automated extraction of brand names from AI text |
| Retrieval Window | Citation | Candidate document set the AI considers before generating |
| Third-Party Authority | Citation | External source credibility that drives AI citation |
| Source Correction | Citation | Updating sources that cause AI to state incorrect facts |
| Cross-Engine Citation Overlap | Citation | % of cited URLs shared across two AI engines |
| Citation Gap | Citation | Intent clusters where rivals are cited but you are not |
| Sentiment Analysis | Sentiment | Positive/neutral/negative classification of AI brand descriptions |
| Sentiment Decay | Sentiment | Gradual worsening of AI brand sentiment without new input |
| Perception Monitoring | Sentiment | Tracking how AI characterizes your brand, not just valence |
| Tone Classification | Sentiment | Granular sentiment (cautious, enthusiastic, dismissive) |
| Brand-Fact Drift | Sentiment | Divergence between AI-stated facts and current reality |
| Competitive Framing | Sentiment | How AI describes your brand relative to named rivals |
| AEO | Programme | Answer engine optimization: the practice of earning AI citations |
| Competitive Set | Programme | The defined rival brands in your AI visibility tracking |
| Engine Coverage | Programme | Number and selection of AI engines a platform monitors |
| Conversion Signal | Programme | Downstream evidence that AI visibility affects buyer behavior |
Tools that track these metrics
The platforms below cover the core metrics in this glossary. Each tracks at least share of voice, brand mentions, and sentiment across multiple AI engines.
- Otterly.AI: Prompt-level citation tracking across 6 engines; structured GEO audit output. Entry at $29/mo.
- Peec AI: Daily tracking across 9 or more engines; unlimited seats; strong cross-engine coverage including DeepSeek and Grok. From €85/mo.
- Profound: Deep citation attribution and prompt-volume data for enterprise teams. Growth tier at $399/mo.
- Temso: All-in-one monitoring plus an action plan that converts visibility gaps into fixes, across 8 engines. From $89/mo.
- SE Ranking: AI Overviews and traditional SEO tracking combined; useful for teams that need both datasets in one workflow.
Full scoring criteria and rankings: /rankings/ai-visibility-tools. Editorial standards and methodology: /methodology. Full glossary index: /glossary.
Start measuring your AI share of voice
Understanding the terms is the first step. The second is running your brand against the prompts your buyers actually use and seeing where you appear and where rivals are ahead of you.
The AI visibility tools ranking compares every major platform on engine coverage, action depth, pricing, and third-party evidence. If you want to start with a single tool that covers monitoring and execution in one subscription, start with the ranking and filter by your team size and budget.