To monitor competitor mentions in AI search, track how often AI tools mention your competitors, where they mention them, what sources they cite, and how they describe them compared with your brand.
That means you are not just checking whether your website ranks. You are checking whether AI systems like ChatGPT, Google AI Overviews, Google AI Mode, Perplexity, Gemini, Claude, and Copilot include your competitors in the actual answer.
The basic workflow is this: create a list of buyer prompts, run those prompts across AI search platforms, record every brand mention, citation, source, position, and sentiment, then repeat the process on a schedule.
I’d look at it this way: classic SEO asks, “Where does my page rank?” AI search competitor tracking asks, “When someone asks an AI tool for advice in my category, who does it trust enough to mention?”
That is the real game. A competitor can beat you in AI search without ranking above you in the normal search results. They might appear because their brand is better understood, their content is easier to cite, or third party sources describe them more clearly than they describe you.
So the goal is not just to count mentions. You want to track mentions, citations, answer context, source ownership, LLM visibility, answer drift, and changes over time. That is where AI search monitoring and answer engine monitoring become useful as a practical workflow instead of a vague “are we in ChatGPT?” panic button.
What Competitor Mentions In AI Search Actually Means
Competitor mentions in AI search are the places where an AI generated answer names another brand, product, tool, or company in your market.
For example, someone might ask:
“Best tools for tracking brand visibility in ChatGPT.”
The AI answer might mention five tools. Some may be direct competitors. Some may be adjacent tools. Some may be companies you did not even consider competitors until the AI answer casually invited them to your party.
That is why you need to monitor competitor mentions in AI search as a separate activity from normal SEO tracking.
In traditional search, you usually track pages. In AI search, you track answers. Those answers can include:
| Signal | What It Means |
|---|---|
| Brand Mention | The AI answer names your brand or a competitor |
| Citation | The AI answer uses a website as a source |
| First Mention | A brand appears before others in the answer |
| Recommendation | The AI actively suggests one brand as a good option |
| Framing | The AI describes a brand as cheaper, stronger, easier, safer, more trusted, or more suitable |
| Omission | Your brand is missing from an answer where it should probably appear |
The omission is often the most useful signal. If your competitors appear in five answers and you appear in none, that is not just a visibility problem. It is a positioning, content, authority, or entity understanding problem.
AI systems need to understand what your brand does, who it is for, where it fits, and why it belongs in a recommendation. If that information is weak, scattered, outdated, or only obvious to people already inside your company, AI search may not connect the dots.
And no, shouting your brand name across 19 thin blog posts is not a strategy. That is just SEO cosplay with extra electricity.
How To Monitor Competitor Mentions In AI Search Step By Step
AI search competitor tracking works by testing real prompts and recording how AI systems answer them.
A good tracking process usually looks like this:
- Choose the AI platforms you care about.
- Build a prompt list based on real buyer questions.
- Define your competitors and brand variations.
- Run the same prompts in a consistent way.
- Capture the answer text, brand mentions, citations, and source links.
- Score each result.
- Repeat the process weekly or monthly.
- Compare your visibility against competitors.
The important part is consistency. One random ChatGPT answer does not tell you much. A repeated set of prompts across multiple platforms tells you a lot more.
You are looking for patterns like:
| Pattern | What It Usually Means |
|---|---|
| Competitors appear often, but you do not | AI systems do not strongly associate your brand with the category |
| Competitors are cited often | Their websites or third party profiles are seen as useful sources |
| Third party sites mention competitors but not you | Your external footprint is weak |
| You appear only for branded prompts | AI tools understand your name, but not your category relevance |
| Competitors appear first in answers | They may have stronger perceived relevance or authority |
| AI describes competitors more clearly | Their positioning is easier to understand and reuse |
The way I see it, the best AI search tracking is not about building a perfect score. It is about finding practical gaps you can fix.
If a competitor keeps appearing for “best AI visibility tools for agencies” and you do not, that gives you a clear question: what proof does the AI have that they belong in that answer, and what proof is missing for you?
Start With The Right Competitor List
Before you track anything, define the competitors properly.
Do not only use the competitors your sales team talks about. In AI search, your competitor set is wider.
You should track:
| Competitor Type | Example Of What To Look For |
|---|---|
| Direct Product Competitors | Companies selling similar software or services |
| Search Competitors | Sites ranking for the same commercial queries |
| AI Mention Competitors | Brands AI tools mention beside yours |
| Citation Competitors | Sites AI tools cite instead of yours |
| Third Party Influence Sources | Review sites, publishers, marketplaces, directories, and industry blogs |
| Adjacent Tools | Products solving part of the same problem |
This matters because AI answers often mix direct and indirect competitors.
For example, if you sell software for AI search visibility, your direct competitors might be other AI visibility platforms. But AI answers may also mention SEO tools, brand monitoring tools, rank trackers, analytics platforms, or digital PR tools.
You may think, “That is not really our competitor.”
The AI answer does not care. If it gives your buyer that option, you need to track it.
A simple starting list is:
Your brand.
Five direct competitors.
Five companies that rank for your target queries.
Five brands AI tools already mention in your first audit.
Five third party sources that appear often in citations.
That gives you a more realistic view of the market than only tracking the usual suspects. It also gives you a stronger starting point for competitor reputation in AI results, because you are measuring who AI systems seem to trust, not just who your team already knows.
Build A Prompt Set Around Real Buyer Questions
Your prompt set is the heart of the system.
Bad prompts produce bad data. If you only test generic prompts, you will miss how buyers actually use AI search.
Do not only track prompts like:
“Best [category] tools.”
That is useful, but it is not enough. People use AI tools for messy, specific, practical questions.
They ask things like:
“What is the best tool for tracking competitor visibility in ChatGPT?”
“How can I monitor whether competitors are being mentioned in AI search?”
“What tools help with AI search competitor tracking?”
“Which platform is best for tracking AI Overviews and ChatGPT mentions?”
“How do I know if my competitors are showing up more than me in Perplexity?”
You want prompts that cover different types of intent.
| Prompt Type | What It Tests |
|---|---|
| Category Prompts | Which brands AI tools recommend in your market |
| Comparison Prompts | How your brand is framed against competitors |
| Alternative Prompts | Whether you appear as a replacement option |
| Use Case Prompts | Whether you appear for specific buyer needs |
| Feature Prompts | Whether AI tools connect you to key capabilities |
| Pricing Prompts | Whether you appear for budget based decisions |
| Integration Prompts | Whether you appear for workflow specific questions |
| Problem Prompts | Whether you appear when buyers describe pain points |
| Branded Prompts | Whether AI tools understand your brand correctly |
For example, if your topic is competitor mentions in AI search, useful prompt groups could include:
| Prompt Group | Example Prompt |
|---|---|
| Category | “Best tools to monitor AI search visibility” |
| Competitor Tracking | “How do I track competitor mentions in ChatGPT?” |
| Platform Specific | “How can I monitor competitors in ChatGPT and Perplexity?” |
| Business Use Case | “How can a SaaS company track whether competitors appear in AI answers?” |
| Comparison | “Which tools are best for AI search competitor tracking?” |
| Methodology | “How do brands measure AI share of voice?” |
I would also use prompt variants and track prompt performance. AI answers can change when the wording changes, even if the intent is similar. That is why prompt sensitivity monitoring matters for serious tracking.
For example:
“How do I monitor competitor mentions in AI search?”
and
“How can I see which competitors ChatGPT recommends instead of my brand?”
Those two prompts are close, but they may trigger different answers. Track both if the intent matters.
How To Monitor Competitors In ChatGPT
To monitor competitors in ChatGPT, run your buyer prompts in the ChatGPT experience you want to measure, capture the full response, and record which competitors appear, which sources are cited, and how the answer frames each brand.
The phrase “monitor competitors in ChatGPT” sounds simple, but you need to be specific about the mode.
ChatGPT can behave differently depending on whether search is active, whether browsing is used, whether the user is asking a general question, and whether the answer includes citations. So do not mix every ChatGPT result into one pile.
This is where ChatGPT result monitoring should be treated as its own data stream, not just another tab in your normal SEO report.
At minimum, log:
| Field | What To Record |
|---|---|
| Date | When you ran the prompt |
| Exact Prompt | The exact wording used |
| Platform | ChatGPT |
| Mode | Search enabled, normal chat, or another mode |
| Your Brand Mentioned | Yes or no |
| Competitors Mentioned | All competitor names in the answer |
| First Mention | The first brand named |
| Citation URLs | Sources used in the answer |
| Citation Type | Your site, competitor site, third party site, review site, documentation, article |
| Sentiment | Positive, neutral, negative, or mixed |
| Recommendation Role | Recommended, listed, compared, dismissed, or missing |
| Notes | Any useful context |
The key is to separate answer visibility from source visibility.
A competitor might be mentioned in ChatGPT without being cited. That suggests the brand is strongly associated with the category.
A competitor might be cited without being recommended. That suggests the competitor’s content is influencing the answer, even if the product is not the final recommendation.
Both matter.
If your competitor appears often and your site is never cited, I’d check your content quality first. Is your category page clear? Do you explain what your product does in plain language? Do you have comparison pages? Do you answer the questions buyers actually ask? Do trusted third party sources mention you?
AI tools cannot cite, summarize, or recommend what they cannot easily understand.
If you only want to monitor ChatGPT answers manually, a spreadsheet is fine. Once you care about trend lines, alerts, and raw answer history, ChatGPT visibility tracking becomes the more useful frame.
Track Google AI Overviews, Gemini, Claude, And Other AI Platforms Separately
Google AI Overviews and Google AI Mode should not be treated as the same thing.
AI Overviews appear inside normal Google Search when Google decides an AI generated summary is useful for the query. AI Mode is a more conversational AI search experience. Both can surface brands, links, and source material, but the user experience is different.
That means your tracking should separate them.
For Google AI Overviews, sometimes called Google AIO in SEO teams, track search style queries like:
“Best AI visibility tools.”
“Competitor mention tracking AI search.”
“AI search competitor tracking software.”
For Google AI Mode, track conversational prompts like:
“I run a SaaS company and want to know when competitors are mentioned in AI search. What should I use?”
These are different behaviors. A normal search query may trigger one type of answer. A conversational query may trigger a more detailed recommendation.
You should also split Gemini, Claude, Perplexity, and Copilot instead of rolling them into one blob. Use Gemini search visibility alerts if Google’s AI ecosystem matters to your audience. Use a Claude answer monitoring workflow if Claude is common in your buyer’s research stack.
For each platform result, record:
| Field | Why It Matters |
|---|---|
| Query Or Prompt | Lets you repeat the check |
| AI Answer Present | Not every query triggers an AI answer |
| Platform Mode | Separates search, chat, overview, and assistant behavior |
| Brands Mentioned | Shows competitor visibility |
| Sources Shown | Shows which pages influence the answer |
| Source Type | Helps you see whether the system prefers guides, reviews, docs, or publishers |
| Your Brand Position | Shows whether you appear early, late, or not at all |
| Competitor Position | Shows who gets priority |
| Notes | Captures odd behavior or useful patterns |
This is where traditional SEO thinking can become a trap.
In classic SEO, you might care mostly about ranking number one. In AI search, the answer may be built from several sources. A competitor may not own the top blue link, but they may still get mentioned because a trusted third party comparison page includes them.
So your action is not always “write another blog post.” Sometimes it is:
Improve your product page.
Create a clearer comparison page.
Update documentation.
Get included in relevant third party lists.
Improve review profiles.
Publish original research.
Make your brand entity easier to understand.
That last one sounds fancy, but it is simple: make it painfully clear who you are, what you do, who you serve, and how you compare.
Track Mentions And Citations Separately
This is one of the most important parts of AI search competitor tracking.
Mentions and citations are different signals.
A mention means the AI answer named a brand.
A citation means the AI answer used a source.
You need both because they tell you different things.
| Result | What It Means |
|---|---|
| Competitor Mentioned And Cited | Strong visibility and source influence |
| Competitor Mentioned But Not Cited | Strong brand association |
| Competitor Cited But Not Mentioned Prominently | Their content may influence the answer |
| Your Brand Mentioned But Not Cited | You have visibility, but weak source ownership |
| Your Site Cited But Competitor Recommended | Your content may be useful, but your positioning may be losing |
| Third Party Source Cited | External content may be shaping the answer |
If you only count mentions, you may miss the source layer.
For example, an AI answer might recommend Competitor A, but cite a review site. In that case, Competitor A did not win only because of its own website. It won because a third party source helped make the case.
That gives you a different action plan. You may need better third party coverage, stronger reviews, clearer listings, or more useful comparison content.
This is also where brand mention tracking and AI search tracking start to overlap. Mentions tell you who is visible. Citations tell you what the system leaned on. Context tells you whether that visibility is actually helping.
I’d also track citation ownership:
| Citation Owner | What To Do With It |
|---|---|
| Your Website | Improve and protect useful pages |
| Competitor Website | Study what their page explains better |
| Review Site | Check your profile, category, reviews, and descriptions |
| Publisher | Look for inclusion or coverage opportunities |
| Marketplace | Check your listing accuracy |
| Documentation | Improve technical clarity |
| Forum Or Community | Understand real user language and complaints |
You are not trying to manipulate every source. You are trying to understand which sources AI tools trust when they answer your buyer’s questions.
Score Competitor Visibility With Simple Metrics
You do not need a complicated scoring model at the start.
Use simple metrics that are easy to explain. Think of these as your competitor AI reach metrics, not a magic number with a tuxedo on.
| Metric | Simple Formula | What It Tells You |
|---|---|---|
| Appearance Rate | Prompts where brand appears divided by total prompts | How often a brand shows up |
| AI Share Of Voice | Brand mentions divided by all tracked brand mentions | How visible a brand is compared with competitors |
| Citation Share | Brand owned citations divided by total citations | How often owned sources are used |
| First Mention Rate | Prompts where brand appears first divided by prompts with any brand mention | Which brand gets priority |
| Recommendation Rate | Prompts where brand is actively recommended divided by total prompts | Whether the AI actually suggests the brand |
| Sentiment Score | Positive, neutral, negative, or mixed | How the answer frames the brand |
| Source Influence | Number of times a source appears across answers | Which websites shape responses |
| Trust Gap | Competitor citation share minus your citation share | Whether competitors own more of the trusted source layer |
AI share of voice is especially useful because it gives you a simple benchmark.
If your brand appears in 8 out of 50 relevant AI answers, and your main competitor appears in 31 out of 50, you have a visibility gap.
But be careful. The score only means something inside your tracking setup. If your prompt list is too narrow, your numbers will be narrow too. If your prompts are mostly branded, your brand may look stronger than it really is. If your prompts are mostly comparison queries around one competitor, that competitor may look artificially dominant.
So when you report AI search competitor tracking, always include the context:
Number of prompts.
Platforms tested.
Date range.
Country or region if relevant.
Prompt groups.
Competitor set.
Whether citations were included.
Whether the results were manually checked.
Without that, the score can become dashboard theater. Very shiny, very confident, and possibly very wrong.
For tone, do not stop at positive, neutral, and negative labels. A proper competitor AI sentiment comparison should also ask what the AI praises, what it criticizes, and which use cases it keeps attaching to each brand.
Use A Manual Spreadsheet Before Buying A Tool
A manual spreadsheet is a good first step because it forces you to understand what is actually happening.
Start with 30 to 50 prompts. Run them across ChatGPT Search, Perplexity, and one Google AI surface. That is enough to see patterns without drowning yourself in data.
Your spreadsheet can look like this:
| Column | Example |
|---|---|
| Date | May 17, 2026 |
| Prompt Group | Comparison |
| Prompt | “Best tools for AI search competitor tracking” |
| Platform | ChatGPT Search |
| Your Brand Mentioned | Yes |
| Competitors Mentioned | Competitor A, Competitor B |
| First Mention | Competitor B |
| Your Citation URLs | yoursite.com/example-page |
| Competitor Citation URLs | competitor.com/example-page |
| Third Party Sources | reviewsite.com, publisher.com |
| Sentiment | Neutral |
| Recommendation Role | Listed, not strongly recommended |
| Notes | Competitor cited from a comparison article |
The first pass should answer a few practical questions:
Which competitors appear most often?
Which platforms mention you?
Which platforms ignore you?
Which prompts trigger competitors but not your brand?
Which sources are cited repeatedly?
Are competitors described more clearly than you?
Is your brand being misunderstood?
Is your website being cited at all?
That last one is big. If your brand appears but your website is never cited, you may have decent awareness but weak source authority. If your website is cited but your brand is not recommended, your content might be useful but your positioning may not be persuasive.
A spreadsheet also helps you spot messy issues tools may hide. For example, maybe an AI answer mentions your old product name. Maybe it confuses you with another company. Maybe it cites an outdated article. Maybe it says you lack a feature you launched six months ago.
Those are not abstract “AI visibility” problems. Those are fixable information problems.
When An AI Search Tracking Tool Makes Sense
A spreadsheet works for a baseline audit. A tool makes sense when you need scale.
Use a tool when you want:
Automated prompt runs.
Tracking across many AI platforms.
Daily or weekly trend reports.
Competitor benchmarking.
Citation tracking.
Source influence reports.
Raw answer storage.
Prompt grouping.
Market or language segmentation.
Alerts when visibility changes.
Exports for analysis.
Reports for clients or leadership.
The main thing I’d check before trusting any tool is methodology.
Ask questions like:
Where do the prompts come from?
Can you add your own prompts?
Which platforms are tracked?
How often are prompts rerun?
Does the tool use APIs, browser based checks, or modeled estimates?
Does it separate mentions from citations?
Does it show raw answer text?
Can you see the cited URLs?
Can you track brand variants?
Can you track domains separately from brand names?
Can you compare prompt groups?
Can you export the data?
If a tool only gives you one mysterious “AI visibility score,” be careful. A score is useful only if you can inspect what created it.
I would rather have a boring table with raw answers than a beautiful score with no explanation. The boring table can help you make decisions. The unexplained score mostly helps someone sell a dashboard.
If your market changes fast, add competitor AI platform monitoring to the setup. That helps you catch new platforms, new AI features, pricing changes, and positioning shifts before they show up in normal reports.
What To Do When Competitors Appear More Than You
When competitors appear more than you, do not jump straight into panic publishing.
First, diagnose the gap.
There are usually four common causes.
| Gap | What Is Happening | What To Do |
|---|---|---|
| Content Gap | You do not answer the buyer question clearly | Create or improve the relevant page |
| Source Gap | Trusted third party sources mention competitors but not you | Improve profiles, reviews, listings, and coverage |
| Entity Gap | AI tools do not understand your brand or category fit | Clarify your about page, product pages, schema, and naming |
| Proof Gap | Competitors have stronger evidence | Add case studies, reviews, data, examples, and documentation |
Let’s say competitors keep appearing for:
“Best tools to monitor competitors in ChatGPT.”
But your brand does not.
I’d check these things first:
Do you have a page that directly answers that use case?
Does your site clearly say you help monitor competitors in ChatGPT?
Do you explain the workflow in plain language?
Do third party sources describe you that way?
Do review sites list you in the right category?
Do comparison pages mention you?
Do your pages include examples, screenshots, or proof?
Is your product name consistent everywhere?
Do your titles and headings match the way buyers ask the question?
The fix might be content. But it might also be external visibility.
If every AI answer cites third party review sites, and your profile is empty or poorly described, your website alone may not fix the issue. You need to improve the source layer AI tools are already using. That may mean better profiles, better review data, or actual review monitoring tools so you are not discovering reputation problems three months late with a sad coffee in your hand.
How To Improve Your Chances Of Being Mentioned
To improve your chances of being mentioned in AI search, make your brand easy to understand, easy to cite, and clearly relevant to the prompts you care about.
That sounds simple because it is. The execution is where most teams get lazy.
You should have:
Clear product pages.
Use case pages.
Comparison pages.
Alternative pages where appropriate.
Documentation.
Pricing clarity if possible.
Customer proof.
Review profiles.
Accurate directory listings.
Helpful third party coverage.
Original data or research.
Consistent brand descriptions.
Good internal linking.
Pages that answer specific buyer questions.
For example, if you want to show up for “AI search competitor tracking,” do not bury that concept inside a vague paragraph about “next generation digital intelligence.”
Say what you do.
Explain the workflow.
Show what gets tracked.
Mention platforms where relevant.
Explain the metrics.
Show screenshots or examples if you can.
Make it clear who the product is for.
This is not just content cleanup. It is AI brand reputation tracking in practice, because you are shaping the information AI systems use to describe your brand.
AI systems are not impressed by vague positioning. Neither are humans, which is convenient.
Also, avoid writing pages only for AI systems. The safest approach is to create pages that are genuinely useful to buyers and easy for machines to parse.
That means clear headings, direct answers, specific claims, current information, and enough context for the page to stand on its own.
Use Competitor Content Reach To Find The Missing Layer
Sometimes your competitor is not winning because their product page is better. They are winning because their content travels further.
That is where competitor AI content reach helps.
You are not just asking whether a competitor wrote a good article. You are asking whether that article gets referenced, shared, cited, summarized, quoted, or used as a source in AI generated answers.
Look for:
Which competitor pages get cited.
Which formats show up most often.
Which third party sites repeat their positioning.
Which topics are attached to their brand.
Which pages explain the category better than yours.
Which content types AI answers seem to prefer.
If a competitor’s comparison guide, documentation, or research report keeps appearing in AI answers, study the structure. Do not copy it. Figure out why it is extractable.
Usually, it has a few boring but powerful traits:
Clear headings.
Direct answers.
Specific claims.
Examples.
Fresh information.
Readable formatting.
A clean connection between topic, entity, and proof.
That is the stuff AI systems can reuse without needing to solve a mystery. And if your page makes the AI solve a mystery, the AI may politely choose someone else.
Account For Region, Model, And Prompt Differences
AI visibility is not always global.
A brand can show up in one country, disappear in another, and get described differently in a third. That matters for multi location brands, agencies, franchises, global SaaS companies, and anyone selling into more than one market.
Use localized AI search tracking when location affects the answer.
For example, track:
Country level prompts.
City level prompts.
Language variants.
Local competitor substitutions.
Regional source citations.
Local review and directory influence.
Different spelling or terminology.
You should also compare models. ChatGPT, Gemini, Claude, Perplexity, and Copilot can disagree because they use different retrieval systems, data sources, tuning choices, and product experiences. That is where AI model comparison analytics becomes useful.
The practical rule is simple: do not collapse everything into one average too early.
Separate the data first. Then roll it up.
Otherwise, you may hide the most useful finding. Your overall score might look fine while Gemini ignores you, Claude misframes you, or Perplexity cites a competitor every time. Aggregates are sneaky like that. They put on a nice suit and hide the problem.
Avoid Trying To Game AI Search
Trying to game AI search is a bad plan.
Do not create fake reviews, spammy comparison pages, doorway pages, keyword stuffed content, or low quality pages designed only to force a brand mention.
That might create short term noise, but it is weak strategically. It also creates reputation risk.
The better approach is to improve the information environment around your brand.
That means:
Publish useful content that answers real questions.
Keep product information accurate.
Make comparisons fair and specific.
Fix outdated pages.
Improve third party profiles.
Earn real reviews.
Get mentioned in relevant industry sources.
Use consistent brand and product naming.
Create content that is worth citing.
Use sentiment analysis to separate “people are talking” from “people trust us.” Those are not the same thing, and pretending they are is how dashboards become decorative furniture.
If your competitor is winning because the AI answer has better evidence for them, give the system better evidence for you. Not fake evidence. Better evidence.
That is the boring answer, but boring is often where the money is.
Common Mistakes In AI Search Competitor Tracking
The first mistake is treating AI search like normal rank tracking.
You are not just tracking position. You are tracking generated answers, brand mentions, citations, and framing.
The second mistake is using too few prompts. Five prompts can give you a quick signal, but not a reliable view. You need enough prompts to cover category, comparison, alternatives, use cases, features, pain points, pricing, and integrations.
The third mistake is ignoring citations. Mentions tell you who got named. Citations tell you what the AI relied on.
The fourth mistake is mixing platforms too early. ChatGPT, Perplexity, Gemini, Claude, Copilot, Google AI Overviews, and Google AI Mode can all behave differently. Look at them separately first, then roll them up later.
The fifth mistake is not tracking prompt wording. If you do not save the exact prompt, you cannot repeat the test properly.
The sixth mistake is treating one result as truth. AI answers can vary. You need repeat runs and trend data.
The seventh mistake is only tracking direct competitors. AI search may surface adjacent tools, review sites, publishers, and marketplaces that influence the buyer more than a direct competitor does.
The eighth mistake is obsessing over a single score. A score is fine, but only if you can see the raw answers behind it.
A Simple Setup You Can Use This Week
If you want a practical setup, start with this.
Create 40 prompts:
10 category prompts.
10 comparison prompts.
10 use case prompts.
10 problem or pain point prompts.
Run them across:
ChatGPT Search.
Perplexity.
Google AI Overviews or Google AI Mode.
Gemini.
Claude.
Track these fields:
Your brand mentioned.
Competitors mentioned.
First brand mentioned.
Recommendation role.
Citation URLs.
Citation owner.
Sentiment.
Source type.
Notes.
Then calculate:
Appearance rate.
AI share of voice.
Citation share.
First mention rate.
Recommendation rate.
Trust gap.
Repeat the same process every week for a month.
After four runs, you will know much more than you would from a one time audit. You will see which competitor mentions in AI search are stable, which ones are random, and which sources keep shaping the answers.
That is when the work gets useful.
You can stop asking, “Are we visible in AI search?”
You can start asking, “Which exact prompts are we losing, who is beating us, what source helped them win, and what do we need to fix?”