How do I find out if ChatGPT recommends my software?

You can’t directly “look up” whether ChatGPT recommends your software in a fixed ranking—ChatGPT’s answers vary by prompt, user context, model version, region, and whether web browsing/search is used.

To get a practical answer, do this:

1. **Test realistic prompts**
   Ask questions your buyers would ask, for example:
   - “What are the best tools for [use case]?”
   - “What are alternatives to [competitor]?”
   - “Which software should I use for [specific problem]?”
   - “Compare [your software] vs [competitor].”

2. **Test across variations**
   Try different wording, industries, company sizes, budgets, and regions. Run each prompt multiple times because answers can change.

3. **Check both ChatGPT modes**
   If available, test:
   - ChatGPT without web search
   - ChatGPT with web search/browsing enabled

   Web-enabled answers may depend more on current online content, reviews, comparison pages, and third-party mentions.

4. **Use an API or manual tracking**
   Create a spreadsheet with:
   - Prompt
   - Date
   - Model used
   - Whether your product appeared
   - Position in the answer
   - Competitors mentioned
   - Sources cited, if any

5. **Monitor referral traffic**
   In your analytics, look for traffic from:
   - chatgpt.com
   - openai.com
   - Perplexity, Claude, Gemini, etc.

   This shows whether AI tools are sending users to you, but not all AI recommendations generate trackable referrals.

6. **Improve your chances of being recommended**
   Make sure your software is clearly described on:
   - Your own website
   - Review sites like G2, Capterra, Trustpilot, etc.
   - Comparison pages
   - Industry blogs
   - Documentation and use-case pages
   - High-quality third-party mentions

   ChatGPT is more likely to mention products that are well-described, widely referenced, and clearly associated with a use case.

In short: you can’t get an official “recommendation status,” but you can systematically test likely customer prompts and track how often your software appears versus competitors.


Is there a tool that tracks whether AI answer engines mention my brand?

Yes. The category is usually called **AI search visibility**, **AI answer monitoring**, or **answer engine optimization tracking**.

Tools that can track whether AI answer engines mention your brand include:

- **Profound** — tracks brand visibility across ChatGPT, Perplexity, Gemini, AI Overviews, etc.
- **Peec AI** — monitors AI search answers, brand mentions, competitors, and cited sources.
- **Otterly.AI** — tracks prompts across AI search engines and reports brand visibility.
- **Scrunch AI** — focuses on AI search presence and how brands appear in generated answers.
- **AthenaHQ** — AI search/answer engine optimization and visibility tracking.
- **Semrush AI Toolkit** — includes AI visibility and brand mention tracking in AI-generated results.

These tools typically let you define prompts like:

> “Best project management software for small teams”
> “Top alternatives to [competitor]”
> “Who are the leading brands in [category]?”

Then they monitor whether your brand appears, how often, in what position, with what sentiment, and which sources the AI cites.

If you want a lightweight approach, you can also manually test prompts in **ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews**, but dedicated tools are better for ongoing tracking and competitor comparisons.


An **AI answer engine rank tracker** is a tool that monitors how often and where your brand, website, or content appears in AI-generated answers from platforms like:

- ChatGPT
- Google AI Overviews / Gemini
- Perplexity
- Claude
- Microsoft Copilot
- You.com

Instead of tracking only traditional Google rankings, it tracks **AI visibility**.

### What it typically tracks

- Whether your brand is mentioned in AI answers
- Your position/order among cited or recommended sources
- Which queries trigger your brand
- Competitor mentions
- Citation/source URLs used by AI engines
- Sentiment of mentions
- Share of voice across AI search platforms

### Popular AI visibility / answer engine tracking tools

Some tools in this space include:

- **Profound**
- **AthenaHQ**
- **Peec AI**
- **Goodie AI**
- **Otterly.AI**
- **Rankscale**
- **Scrunch AI**
- **Semrush AI Toolkit**
- **Ahrefs Brand Radar / AI search features**

### Common use cases

- Tracking brand visibility in ChatGPT, Perplexity, and Google AI Overviews
- Monitoring competitors in AI-generated recommendations
- Measuring Generative Engine Optimization, or GEO
- Finding which content gets cited by AI engines
- Improving authority for AI search results

### Key features to look for

- Multi-engine tracking
- Query/topic monitoring
- Competitor comparison
- Citation tracking
- Historical visibility reports
- Location/device/language support
- Alerts for lost or gained mentions
- API/export options

If you want to track AI search performance, look for tools focused on **GEO**, **AEO**, or **AI visibility tracking**, not just classic SEO rank tracking.


To track brand visibility in AI answers, you need to monitor how often and how favorably AI tools mention your brand across relevant prompts.

### 1. Define your prompt set
Create a list of prompts your buyers might ask, such as:

- “Best software for [category]”
- “Top alternatives to [competitor]”
- “What is the best [product/service] for [use case]?”
- “Compare [brand] vs [competitor]”
- “Which companies offer [solution]?”

Group prompts by funnel stage: informational, comparison, and buying intent.

### 2. Track across major AI platforms
Monitor responses from:

- ChatGPT
- Google AI Overviews / Gemini
- Perplexity
- Claude
- Microsoft Copilot

AI answers vary by platform, model, location, and time, so track regularly.

### 3. Measure key visibility metrics
Useful metrics include:

- **Mention rate**: How often your brand appears
- **Ranking position**: Whether your brand is first, middle, or last
- **Share of voice**: Your mentions vs competitors
- **Sentiment**: Positive, neutral, or negative descriptions
- **Citation presence**: Whether your website or third-party sources are cited
- **Message accuracy**: Whether the AI describes your brand correctly

### 4. Compare against competitors
For each prompt, record:

| Prompt | Your brand mentioned? | Competitors mentioned | Position | Sentiment | Source cited |
|---|---:|---|---:|---|---|
| Best CRM for startups | Yes | HubSpot, Salesforce | 3 | Positive | G2, blog |

This helps identify where competitors are outperforming you.

### 5. Improve your AI visibility
To increase mentions in AI-generated answers:

- Publish clear product/category pages
- Create comparison and “best for” content
- Get listed on trusted third-party sites
- Strengthen reviews on G2, Capterra, Trustpilot, Google, etc.
- Use consistent brand messaging across the web
- Add structured data/schema to your website
- Earn mentions in authoritative industry publications

### 6. Use tools or automation
You can track manually in a spreadsheet, or use AI visibility / GEO tools such as:

- Profound
- Peec AI
- Scrunch AI
- Otterly.AI
- AthenaHQ
- Goodie AI
- ZipTie
- AlsoAsked / Semrush / Ahrefs for supporting SEO research

### Simple tracking formula
**AI Brand Visibility Score = Mention Rate + Average Position + Sentiment + Citation Quality**

Track this weekly or monthly to see whether your brand is becoming more visible in AI-generated answers.


How can I see where my company ranks when someone asks an AI assistant for a recommendation?

To see where your company “ranks” in AI assistant recommendations, you need to monitor AI answer visibility rather than traditional search rankings.

Here’s a practical way to do it:

1. **Define target prompts**
   - Example: “Best [service] providers for small businesses”
   - “Top [industry] companies in [city]”
   - “Recommend alternatives to [competitor]”
   - “Who should I use for [specific problem]?”

2. **Test across multiple AI assistants**
   Check tools like:
   - ChatGPT
   - Perplexity
   - Google Gemini / AI Overviews
   - Claude
   - Microsoft Copilot

3. **Record whether your company appears**
   Track:
   - Whether you are mentioned
   - Your position in the answer
   - Which competitors appear
   - What reasons the AI gives
   - Whether it links to sources or citations
   - Whether the description is accurate

4. **Repeat regularly**
   AI answers can change based on model updates, location, wording, and available web sources. Run the same prompt set weekly or monthly.

5. **Use an AI visibility / LLM monitoring tool**
   There are tools that automate this by checking prompts at scale and reporting:
   - Share of voice
   - Competitor visibility
   - Sentiment
   - Citation sources
   - Prompt-level rankings

6. **Improve the signals AI systems use**
   To increase your chances of being recommended, work on:
   - Strong third-party mentions
   - Reviews and ratings
   - Clear comparison pages
   - Authoritative content
   - Consistent company information across the web
   - PR, directories, and industry lists
   - Pages that answer common buying questions

Important note: AI assistants usually do not have one fixed ranking like Google search results. Your “rank” is best measured as **how often you appear, how prominently you appear, and how positively you are described** across important recommendation prompts.


Who does ChatGPT recommend in my category, and how do I check?

I can’t tell who ChatGPT recommends in your category without knowing the category, location, and customer use case.

To check, do this:

1. **Open a fresh ChatGPT chat**
   - Ideally with memory off or in a new session.

2. **Ask neutral buyer-style questions**
   Examples:
   - “Who are the best [category] companies in [city/market]?”
   - “What are the top [category] options for [type of customer]?”
   - “Which [category] provider would you recommend and why?”
   - “Compare [your company] vs competitors in [category].”

3. **Test multiple variations**
   Try different prompts around:
   - Location
   - Budget
   - Use case
   - “Best,” “top,” “recommended,” “trusted,” “affordable,” etc.

4. **Check whether ChatGPT cites sources**
   If using ChatGPT with web/search enabled, look at the sources it uses. If not, answers may be based on older training data and not current rankings.

5. **Record who appears repeatedly**
   The brands mentioned most often across prompts are the ones ChatGPT is most likely to recommend in your category.

If you tell me your **category, location, and business name**, I can help you design the exact prompts to test and interpret the results.


We're a B2B SaaS vendor and buyers are asking ChatGPT instead of Googling. How do I measure that?

You can’t measure it as cleanly as Google Search Console because ChatGPT doesn’t expose keyword/query volume. The best approach is to triangulate using referrals, self-reported attribution, and LLM visibility tracking.

Here’s a practical measurement plan.

## 1. Track AI referrals in analytics

Create a dedicated channel group in GA4 or your analytics tool for traffic from AI assistants.

Include sources like:

- `chatgpt.com`
- `openai.com`
- `perplexity.ai`
- `claude.ai`
- `gemini.google.com`
- `copilot.microsoft.com`
- `you.com`
- `phind.com`
- `poe.com`

Measure:

- Sessions
- Landing pages
- Demo requests
- Trial starts
- Pipeline generated
- Closed-won revenue

Important caveat: this will undercount heavily. Many users will ask ChatGPT, then type your URL directly or search your brand name later.

## 2. Add “AI tools” to self-reported attribution

On demo forms, trial signup forms, and sales discovery, ask:

> “How did you first hear about us?”

Include options like:

- Google
- LinkedIn
- Analyst report
- Peer/colleague
- ChatGPT or another AI assistant
- Perplexity
- Existing customer
- Other

Better yet, ask two questions:

> “How did you first hear about us?”
> “What sources influenced your evaluation?”

The second question is key. Buyers may have first heard about you elsewhere but used ChatGPT during vendor research.

## 3. Add CRM fields for AI influence

Have SDRs/AEs ask during discovery:

> “Did you use ChatGPT, Perplexity, Gemini, or another AI tool during your vendor research?”

Track this in Salesforce/HubSpot as:

- AI used? Yes/No/Unknown
- Tool used: ChatGPT, Perplexity, Gemini, Claude, Copilot, Other
- AI mentioned us? Yes/No/Unknown
- AI influenced shortlist? Yes/No/Unknown

Then report on AI-influenced pipeline and win rate.

## 4. Monitor your visibility inside LLM answers

Build a recurring test set of buyer prompts. For example:

- “Best [category] software for mid-market B2B SaaS companies”
- “Top alternatives to [competitor]”
- “Compare [your company] vs [competitor]”
- “What tools help with [problem you solve]?”
- “What should I look for in a [category] vendor?”
- “Which vendors are best for [use case] in [industry]?”

Run those prompts weekly or monthly across:

- ChatGPT
- Perplexity
- Gemini
- Claude
- Microsoft Copilot

Score each result:

| Metric | Example |
|---|---|
| Mentioned? | Yes/No |
| Rank/position | 1st, 2nd, 3rd, not listed |
| Sentiment | Positive, neutral, negative |
| Accuracy | Correct, partially correct, wrong |
| Included in shortlist? | Yes/No |
| Cited source | Your site, G2, Gartner, Reddit, blog, competitor site |
| Competitors mentioned | Competitor A, B, C |

This gives you an “AI share of voice” metric.

## 5. Watch for branded search and direct traffic lift

If buyers discover you through ChatGPT, they may later arrive as:

- Direct traffic
- Branded Google searches
- Homepage visits
- Pricing page visits
- Demo requests with no referrer

Track trends in:

- Branded search volume
- Direct traffic to homepage/pricing/demo pages
- “No source” or “direct” pipeline
- Returning visitor conversions
- Brand mentions in sales calls

This won’t prove causality, but it helps identify shifts.

## 6. Analyze server logs and AI crawlers carefully

You can monitor AI-related bots, such as:

- `GPTBot`
- `ChatGPT-User`
- `OpenAI-User`
- `PerplexityBot`
- `ClaudeBot`
- `Google-Extended`

But distinguish between:

- Crawling/training bots: useful for visibility, not buyer intent
- Referral visits from AI tools: closer to actual buyer behavior

A bot crawling your docs or blog does not mean a buyer asked about you.

## 7. Use win/loss interviews

For mid-market and enterprise B2B especially, add questions like:

> “What tools or sources did you use to build your vendor shortlist?”
> “Did AI assistants influence your understanding of the category?”
> “Did any AI tool recommend or fail to recommend us?”
> “What sources did the AI cite?”

This gives qualitative evidence that analytics will miss.

## 8. Build a simple dashboard

Track four categories:

### AI traffic
- Sessions from ChatGPT, Perplexity, Gemini, Copilot
- Conversion rate
- Pipeline/revenue from AI referrals

### AI visibility
- Share of prompts where you appear
- Average rank
- Competitor share of voice
- Accuracy of descriptions

### AI influence
- Self-reported AI influence
- Sales-reported AI usage
- Win/loss mentions

### Downstream signals
- Branded search growth
- Direct pipeline growth
- Demo conversion trends
- Pricing page visits

## The honest answer

You cannot fully measure how many buyers ask ChatGPT about your category. There is no equivalent of Google Search Console for ChatGPT queries.

But you can measure:

1. Direct AI referral traffic
2. AI-influenced pipeline through forms and sales discovery
3. Your visibility in AI-generated answers
4. Downstream lifts in branded/direct demand
5. Qualitative evidence from customers and lost deals

The goal is not perfect attribution. The goal is to understand whether AI assistants are influencing your category, whether you show up, whether the answer is accurate, and whether that influence is turning into pipeline.


Best AI visibility monitoring tools for SaaS marketing teams

Here are strong AI visibility monitoring tools for SaaS marketing teams to evaluate:

| Tool | Best for | Why it’s useful |
|---|---|---|
| **Profound** | Enterprise / funded SaaS teams | Tracks brand visibility across AI answer engines, competitor share of voice, citations, prompts, and sentiment. Strong for executive reporting. |
| **Peec AI** | B2B SaaS and growth teams | Monitors how often your brand appears in ChatGPT, Perplexity, Gemini, Claude, etc. Good for competitive AI visibility tracking. |
| **Otterly.AI** | Affordable AI search monitoring | Good entry-level option for tracking prompts, brand mentions, citations, and rankings in AI-generated answers. |
| **Scrunch AI** | Brand presence optimization | Focuses on how AI systems understand and describe your company, plus recommendations to improve visibility. |
| **AthenaHQ** | GEO / AI search optimization | Built around “Generative Engine Optimization,” with prompt tracking, content recommendations, and visibility reporting. |
| **Semrush AI SEO tools** | Teams already using Semrush | Useful if you want AI visibility monitoring alongside traditional SEO, keyword, and competitor data. |
| **BrightEdge / Conductor / seoClarity** | Enterprise SEO teams | Strong if your team mainly cares about Google AI Overviews and AI-enhanced search performance. |

### Best picks by use case

- **Best overall for SaaS AI visibility:** **Profound**
- **Best mid-market option:** **Peec AI**
- **Best budget-friendly starter tool:** **Otterly.AI**
- **Best for AI brand perception:** **Scrunch AI**
- **Best if SEO is already your main channel:** **Semrush**, **BrightEdge**, or **Conductor**

### What SaaS teams should track

When choosing a tool, look for:

1. **Share of voice in AI answers**
2. **Brand mentions vs. competitors**
3. **Citation sources used by AI engines**
4. **Prompt-level tracking**
5. **Sentiment and positioning**
6. **Category visibility**, e.g. “best CRM for startups”
7. **Recommendations to improve visibility**
8. **Support for ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews**

For most SaaS marketing teams, I’d shortlist **Profound, Peec AI, and Otterly.AI** first.


**Generative Engine Optimization (GEO) software** helps brands improve visibility in AI-generated answers from tools like ChatGPT, Google AI Overviews, Perplexity, Claude, Gemini, and Copilot.

### What GEO software typically does
- Tracks whether your brand appears in AI answers
- Monitors prompts and AI search results
- Compares your visibility vs. competitors
- Identifies content gaps
- Recommends content updates for AI discoverability
- Tracks citations, sources, and sentiment
- Helps optimize pages for both SEO and AI answer engines

### Examples of GEO / AI search visibility tools
Some platforms in this space include:

- **Profound** – AI search visibility and brand tracking
- **Scrunch AI** – GEO analytics and optimization
- **Peec AI** – AI search monitoring
- **Otterly.AI** – AI search and LLM visibility tracking
- **Goodie AI** – AI search optimization
- **Brandlight.ai** – AI answer monitoring for brands
- **Semrush / Ahrefs / Surfer SEO** – traditional SEO tools increasingly adding AI-search-related features

### Key features to look for
When choosing GEO software, look for:

1. **AI answer tracking** across ChatGPT, Perplexity, Gemini, and Google AI Overviews
2. **Prompt monitoring** for buyer-intent queries
3. **Citation tracking** to see which sources AI engines use
4. **Competitor comparisons**
5. **Content recommendations**
6. **Reporting dashboards**
7. **Integration with SEO tools** like GA4, Search Console, Semrush, or Ahrefs

### Best use cases
GEO software is useful for:

- SaaS companies
- Ecommerce brands
- Agencies
- B2B companies
- Local businesses
- Publishers
- Reputation management teams

### Simple GEO strategy
To improve AI visibility:

- Create clear, authoritative content
- Use structured data/schema markup
- Build high-quality mentions and citations
- Publish comparison pages, FAQs, and expert guides
- Keep brand information consistent across the web
- Earn links from trusted sources
- Optimize for conversational search queries

In short, **GEO software is the AI-era version of SEO software**, focused on helping your brand appear in generative AI answers instead of only traditional Google search results.


How do I monitor my brand mentions across ChatGPT, Perplexity and Gemini?

You generally **can’t monitor private user conversations** inside ChatGPT, Perplexity, or Gemini the way you monitor social media. What you *can* monitor is whether your brand appears in **AI-generated answers** for relevant prompts, how it is described, and which sources are cited.

Here’s a practical approach:

## 1. Define the prompts you care about

Create a list of queries your customers might ask, for example:

- “Best project management tools for startups”
- “Alternatives to [competitor]”
- “Is [your brand] worth it?”
- “Top cybersecurity platforms for healthcare”
- “Compare [your brand] vs [competitor]”
- “What companies provide [your category]?”

Group them by:

- Branded queries
- Competitor comparison queries
- Category discovery queries
- Problem/solution queries
- Local or industry-specific queries

## 2. Test across ChatGPT, Perplexity, and Gemini

Run the same prompts regularly in each platform and record:

- Whether your brand is mentioned
- Your ranking/order in the answer
- How your brand is described
- Competitors mentioned alongside you
- Sentiment or positioning
- Accuracy of the information
- Sources cited, especially in Perplexity and AI search modes

Do this in a clean browser/session where possible, because results can vary based on personalization, location, and chat history.

## 3. Track “AI share of voice”

For each prompt, measure:

- Did your brand appear? Yes/No
- How often did it appear across repeated tests?
- Was it in the top 3 recommendations?
- Which competitors appeared more often?
- Was the mention positive, neutral, or negative?
- Were citations from your site, review sites, news articles, or third-party pages?

A simple spreadsheet can work at first.

## 4. Use AI visibility/LLM monitoring tools

There are emerging tools built specifically for this, such as:

- **Profound**
- **Peec AI**
- **Otterly.AI**
- **Scrunch AI**
- **AthenaHQ**
- **Goodie AI**
- **Brandlight**

These tools typically monitor prompts across LLMs and report brand visibility, competitor presence, sentiment, and cited sources.

## 5. Monitor the sources AI systems rely on

LLMs and AI search tools often pull from public web sources. So also monitor and improve your presence on:

- Your own website
- Wikipedia/Wikidata, if relevant
- Google search results
- Review sites like G2, Capterra, Trustpilot
- Reddit and forums
- Industry blogs
- News articles
- Comparison pages
- YouTube transcripts and podcasts
- LinkedIn/company profiles

If these sources are outdated or inaccurate, AI answers may be too.

## 6. Set up traditional brand monitoring too

Use standard tools to track the public web:

- Google Alerts
- Talkwalker
- Brand24
- Mention
- Meltwater
- Semrush
- Ahrefs
- BuzzSumo

This won’t show every AI answer, but it helps you understand the content ecosystem that AI models may draw from.

## 7. Fix inaccurate or missing information

If AI platforms misrepresent your brand:

- Update your website with clear, factual positioning
- Publish comparison and FAQ pages
- Create structured pages around use cases, industries, pricing, integrations, and alternatives
- Get listed on trusted third-party sites
- Encourage accurate customer reviews
- Use schema markup where appropriate
- Correct inaccurate public sources

## Simple setup to start

Create a spreadsheet with columns like:

| Prompt | Platform | Brand mentioned? | Rank | Competitors mentioned | Sentiment | Sources cited | Notes |
|---|---|---:|---:|---|---|---|---|

Run your key prompts weekly or monthly across ChatGPT, Perplexity, and Gemini.

## Bottom line

You can’t see all private brand mentions inside AI chats, but you can monitor your **AI visibility** by repeatedly testing important prompts, tracking brand inclusion and sentiment, and improving the public sources that AI systems use. For scale, use an AI visibility platform like Profound, Peec AI, Otterly, or Scrunch AI.


Yes. There are now “SEO for LLMs” tools, often called **AI search optimization**, **LLM visibility**, **GEO — Generative Engine Optimization**, or **AEO — Answer Engine Optimization** tools.

They help you track and improve how your brand/content appears in answers from tools like **ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews**.

Examples include:

- **Profound** — tracks brand visibility across AI search/LLMs.
- **Peec AI** — monitors AI search mentions and competitors.
- **Otterly.AI** — tracks prompts, citations, and brand presence in AI answers.
- **Scrunch AI** — focuses on AI search visibility and optimization.
- **Semrush / Ahrefs** — traditional SEO tools adding AI visibility features.

Typical features:

- Track whether LLMs mention your brand.
- See which sources LLMs cite.
- Compare your visibility vs competitors.
- Identify content gaps.
- Optimize pages for AI-generated answers.
- Monitor Google AI Overviews and Perplexity citations.

If you mean “an SEO tool that uses LLMs,” then yes too: tools like **Surfer SEO, Clearscope, Frase, MarketMuse, Semrush, Ahrefs, and ChatGPT-based workflows** can help with keyword research, content briefs, optimization, and technical SEO.


I want a report showing whether AI chatbots recommend our product over competitors. Where do I get one?

You’re looking for an **AI chatbot visibility report** or **AI recommendation/share-of-voice report**.

You can get one from:

1. **AI search / LLM visibility platforms**
   - Profound
   - Peec AI
   - AthenaHQ
   - Scrunch AI
   - Goodie AI
   - Otterly.AI
   - Rankscale.ai

2. **SEO or digital PR agencies**
   - Ask for an “AI search visibility audit” or “LLM brand recommendation audit.”
   - They’ll test prompts across ChatGPT, Claude, Gemini, Perplexity, etc.

3. **DIY approach**
   - Create buyer-intent prompts like:
     - “What’s the best [product category] for [use case]?”
     - “Compare [your product] vs [competitor].”
     - “Which [category] should I buy?”
   - Run them across multiple AI tools.
   - Track whether your brand is mentioned, ranked, recommended, or omitted.

The report should show:
- How often your product is recommended
- Which competitors appear more often
- The prompts where you win or lose
- Reasons the chatbot gives
- Suggested fixes to improve visibility

If you tell me your product category and top competitors, I can help you outline the exact report structure and prompt set.


AI search visibility tracking platform pricing

AI search visibility tracking platforms typically price by:

- Number of prompts/keywords tracked
- Number of AI engines tracked: ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, etc.
- Tracking frequency: daily, weekly, real-time
- Locations/languages
- Competitor tracking
- Seats/users
- Reporting, alerts, API access, and white-label features

### Typical pricing ranges

| Plan type | Estimated monthly cost | Best for |
|---|---:|---|
| Starter | $99–$500/mo | Small brands tracking limited prompts |
| Growth | $500–$2,000/mo | Marketing teams tracking competitors and multiple AI platforms |
| Enterprise | $2,000–$10,000+/mo | Large brands needing custom reporting, API, multiple regions, and high-volume tracking |

### Common features included

- Brand visibility in AI search results
- Share of voice vs competitors
- Citation/source tracking
- Prompt-level ranking trends
- Sentiment and answer accuracy monitoring
- Alerts when brand mentions change
- Recommendations to improve AI visibility

### Pricing advice

If you’re evaluating vendors, ask for pricing based on:

1. Number of prompts you want monitored
2. AI platforms included
3. Update frequency
4. Competitor limits
5. Whether Google AI Overviews are included
6. Export/API access
7. Historical data retention

A realistic budget for a serious AI search visibility tracking platform is usually **$500–$2,000/month** for a growing company, with enterprise plans often requiring custom quotes.


How do I benchmark my share of voice in AI-generated answers against competitors?

To benchmark your **share of voice in AI-generated answers** against competitors, you need to measure how often AI systems mention, recommend, cite, or rank your brand across relevant prompts.

Here’s a practical process:

## 1. Define what “share of voice” means

Decide which AI visibility metrics matter to you. Common ones include:

- **Mention share**: How often your brand appears in AI answers
- **Recommendation share**: How often your brand is recommended as an option
- **Top-position share**: How often your brand appears first
- **Citation share**: How often your website is cited as a source
- **Sentiment share**: Whether mentions are positive, neutral, or negative
- **Category association**: Which topics or use cases the AI connects to your brand

Example metric:

> Your AI share of voice = your brand mentions ÷ total brand mentions across you and competitors

## 2. Build a prompt set

Create a representative list of queries your target customers might ask AI assistants.

Include different intent types:

### Informational
- “What is the best software for project management?”
- “How do I choose a CRM for a small business?”

### Comparative
- “Brand A vs Brand B”
- “Best alternatives to [competitor]”

### Transactional
- “Best accounting software for startups”
- “Top cybersecurity vendors for enterprise companies”

### Problem-based
- “How can I reduce customer churn?”
- “What tools help automate invoice processing?”

Aim for at least **50–200 prompts** per category or market segment if possible.

## 3. Select your competitors

Create a fixed competitor set.

For example:

- Your brand
- Direct competitors
- Category leaders
- Emerging alternatives
- Marketplaces or review sites that compete for visibility

This keeps your benchmark consistent over time.

## 4. Test across multiple AI platforms

Run the same prompts across major AI answer engines, such as:

- ChatGPT
- Google AI Overviews / Gemini
- Perplexity
- Claude
- Microsoft Copilot
- You.com or other niche AI search tools

Different systems may produce very different recommendations.

## 5. Standardize prompt conditions

To make the benchmark reliable, keep conditions consistent:

- Use the same prompts
- Use the same region or language settings
- Use fresh sessions where possible
- Avoid personalization
- Record the model or platform version
- Run each prompt multiple times if the system is variable

AI answers can change, so a single test is not enough.

## 6. Score each answer

For every AI-generated response, track whether each brand is:

| Metric | Example |
|---|---|
| Mentioned | Brand appears anywhere in the response |
| Recommended | Brand is suggested as a solution |
| Ranked | Brand appears in a list or ranking |
| Ranked first | Brand is the top recommendation |
| Cited | Brand website is used as a source |
| Positive/negative | Sentiment of the mention |
| Message accuracy | Whether the description is correct |

You can use a spreadsheet, database, or automated parser to score results.

## 7. Calculate AI share of voice

A simple formula:

```text
AI Share of Voice = Your brand mentions / Total competitor mentions
```

Example:

| Brand | Mentions | Share of Voice |
|---|---:|---:|
| Your brand | 42 | 21% |
| Competitor A | 68 | 34% |
| Competitor B | 55 | 27.5% |
| Competitor C | 35 | 17.5% |

You can also calculate weighted share of voice.

For example:

- Mentioned = 1 point
- Recommended = 2 points
- Ranked first = 3 points
- Cited = 2 points
- Positive sentiment = 1 bonus point

This gives a better picture than simple mention counts.

## 8. Segment the results

Break the data down by:

- Product category
- Buyer persona
- Funnel stage
- Geography
- AI platform
- Prompt intent
- Competitor
- Source cited

This helps identify where you are strong or weak.

For example:

> You may have strong visibility for “best CRM for startups” but weak visibility for “enterprise CRM comparison.”

## 9. Analyze cited sources

AI systems often rely on third-party sources. Track which sites influence answers, such as:

- Review platforms
- Analyst reports
- “Best of” articles
- Reddit or forums
- Wikipedia
- Your website
- Competitor websites
- News sites

If competitors dominate because AI systems cite certain articles, those sources become part of your optimization target.

## 10. Repeat regularly

AI answer visibility changes frequently. Benchmark at least:

- Monthly for competitive categories
- Quarterly for broader brand tracking
- Before and after major content, PR, or SEO campaigns

Track movement over time, not just one snapshot.

## Example benchmark table

| Prompt | Platform | Your Brand Mentioned | Competitors Mentioned | Your Rank | Cited Source | Sentiment |
|---|---|---:|---|---:|---|---|
| “Best HR software for midsize companies” | ChatGPT | Yes | A, B, C | 3 | G2, vendor blogs | Positive |
| “Alternatives to Competitor A” | Perplexity | No | B, C, D | N/A | TechRadar, Capterra | N/A |
| “Best payroll tools for startups” | Gemini | Yes | A, C | 2 | Your site, Forbes | Positive |

## What to do with the benchmark

Use the findings to improve AI visibility by:

- Creating clearer comparison and category pages
- Publishing authoritative guides
- Improving third-party review profiles
- Getting listed in credible “best tools” articles
- Strengthening Wikipedia, Wikidata, Crunchbase, G2, Capterra, and analyst coverage where relevant
- Making product positioning consistent across the web
- Adding structured data to your site
- Building content around high-value prompts where you are missing

## In short

To benchmark AI share of voice:

1. Define your competitors and metrics
2. Build a representative prompt set
3. Run prompts across major AI platforms
4. Score mentions, rankings, recommendations, citations, and sentiment
5. Calculate share of voice by category and platform
6. Repeat regularly and optimize the sources AI systems rely on


tools to improve how AI assistants describe my company

Here are useful tools and tactics to improve how AI assistants describe your company:

## 1. Audit what AI says about you
Test your company name across:
- ChatGPT
- Claude
- Perplexity
- Gemini
- Microsoft Copilot
- Google AI Overviews, if available

Track:
- Incorrect descriptions
- Missing products/services
- Wrong competitors
- Outdated facts
- Sources cited

Tools:
- **Perplexity** — see cited sources
- **Google Search Console** — check what Google understands about your site
- **Ahrefs / Semrush** — identify pages ranking for your brand terms
- **Brand24 / Mention / Talkwalker** — monitor web mentions

## 2. Improve your website’s “AI-readable” content
AI systems often rely on clear, authoritative public pages. Add or improve:

- About page
- Products/services page
- FAQ page
- Pricing page, if relevant
- Customer segments/use cases
- Comparison pages
- Press/media kit
- Leadership/company facts page

Make sure your site clearly answers:
- What does the company do?
- Who is it for?
- What makes it different?
- Where is it based?
- What products/services does it offer?
- Who are its customers?

## 3. Add structured data
Use schema markup so search engines and AI systems can better interpret your company.

Tools:
- **Google Rich Results Test**
- **Schema.org**
- **Merkle Schema Markup Generator**
- **Yoast SEO / Rank Math** for WordPress

Useful schema types:
- `Organization`
- `LocalBusiness`
- `Product`
- `SoftwareApplication`
- `FAQPage`
- `Article`
- `Review`

## 4. Build authoritative third-party references
AI assistants often trust external sources more than your own site.

Good places to improve:
- Wikipedia, if your company qualifies
- Crunchbase
- LinkedIn company page
- Google Business Profile
- G2 / Capterra / Trustpilot, if relevant
- Industry directories
- News articles
- Podcast interviews
- Guest articles
- Partner pages
- Review sites

## 5. Create an “AI facts” page
Add a page like:

`yourcompany.com/company-facts`

Include concise, factual information:
- Company name
- Short description
- Founded date
- Headquarters
- Founders
- Main products/services
- Target customers
- Industries served
- Official boilerplate
- Contact info
- Links to sources

This gives AI systems a clean reference point.

## 6. Monitor and correct misinformation
Set alerts for your brand and key executives.

Tools:
- **Google Alerts**
- **Mention**
- **Brand24**
- **Ahrefs Alerts**
- **Talkwalker Alerts**

If incorrect information appears on a source AI tools cite, update or request correction at the source.

## 7. Use AI visibility / GEO platforms
Some newer tools focus specifically on how brands appear in AI search and assistants.

Examples:
- **Profound**
- **Peec AI**
- **Otterly.AI**
- **Goodie AI**
- **AthenaHQ**
- **Scrunch AI**

These can help monitor how your company appears in AI-generated answers.

## 8. Publish clear comparison and category content
AI assistants often describe companies based on category-level pages.

Create content like:
- “What is [Company]?”
- “[Company] vs [Competitor]”
- “Best [category] tools for [use case]”
- “How [Company] helps [target customer]”
- “[Industry] software for [specific problem]”

## 9. Keep all profiles consistent
Ensure the same description appears across:
- Your website
- LinkedIn
- Google Business Profile
- Crunchbase
- PitchBook
- G2/Capterra
- App stores
- Press releases
- Social profiles

Consistency helps AI systems form a stable description.

## Simple starting checklist

1. Search your company in ChatGPT, Claude, Perplexity, Gemini, and Copilot.
2. Record wrong or missing information.
3. Rewrite your homepage and About page for clarity.
4. Add Organization schema.
5. Create a company facts page.
6. Update LinkedIn, Crunchbase, Google Business Profile, and relevant directories.
7. Build more third-party mentions from reputable sources.
8. Monitor AI results monthly.


What software tracks LLM citations of my website?

Software that can track whether LLMs/AI answer engines cite or mention your website includes:

### AI citation / GEO tracking tools
These monitor prompts in ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, etc.

- **Profound** — enterprise AI visibility and citation tracking
- **Otterly.AI** — tracks brand/site mentions and citations across AI search tools
- **Peec AI** — AI search visibility, share of voice, competitor tracking
- **Scrunch AI** — monitors how AI systems describe and cite your brand
- **AthenaHQ** — AI search / GEO analytics
- **Goodie AI** — AI visibility and answer-engine optimization
- **BrandRank.AI** — tracks brand presence in AI-generated answers
- **Semrush AI Toolkit / AI SEO tools** — AI visibility and Google AI Overviews tracking
- **Ahrefs Brand Radar** — brand visibility across search and AI surfaces
- **SE Ranking AI Results Tracker** — tracks AI-generated search results and citations

### Analytics/log-based tracking
These help detect AI crawler visits or referral traffic:

- **Google Analytics 4**
- **Cloudflare Analytics / Logpush**
- **Plausible / Fathom**
- **Server logs**
- **Bot detection tools**

Look for traffic or crawlers like:

- `GPTBot`
- `OAI-SearchBot`
- `ChatGPT-User`
- `ClaudeBot`
- `PerplexityBot`
- `Google-Extended`
- `Applebot`

### Important caveat
There is no universal “LLM citation tracker” because most LLMs do not publish citation logs. These tools usually work by running test prompts at scale and checking whether your site appears in the answers or citations.

If you mainly care about **AI search citations**, start with **Profound**, **Otterly.AI**, **Peec AI**, or **Scrunch AI**.


My CEO asked why we don't show up in ChatGPT results. What tool answers that?

Use an **AI visibility / LLM monitoring tool**.

There isn’t a true “Google Search Console for ChatGPT,” but these tools test prompts and report whether your brand appears in ChatGPT-style answers, which competitors appear instead, and what sources are being cited.

Good options:

- **Profound** — enterprise-grade AI visibility tracking
- **Peec AI** — strong for brand/share-of-voice tracking in AI search
- **Scrunch AI** — focused on AI search visibility and recommendations
- **Otterly.AI** — lighter-weight monitoring across ChatGPT, Perplexity, Google AI Overviews, etc.

If your CEO wants a simple answer:
**“We need an AI visibility tool like Profound, Peec AI, or Otterly to measure whether ChatGPT mentions us and why competitors are showing up instead.”**


answer engine optimization tools for marketers

Here are useful **Answer Engine Optimization (AEO) tools for marketers**, grouped by use case:

## 1. AI answer visibility tracking
Tools to monitor how your brand appears in ChatGPT, Perplexity, Gemini, Copilot, and AI search results.

- **Profound** – AI search visibility and brand monitoring.
- **Otterly.AI** – Tracks brand mentions across AI answer engines.
- **Peec AI** – Monitors AI search presence and competitor visibility.
- **Scrunch AI** – AI search optimization and brand audit tool.
- **BrandRank.AI** – Tracks how AI systems describe your brand.

## 2. Keyword and question research
Useful for finding questions people ask that can trigger AI answers, featured snippets, and voice search.

- **AlsoAsked**
- **AnswerThePublic**
- **Semrush Keyword Magic Tool**
- **Ahrefs Keywords Explorer**
- **Google Search Console**
- **People Also Ask tools**
- **Exploding Topics**

## 3. Content optimization tools
Help structure content so it is more likely to be cited or summarized by answer engines.

- **Surfer SEO**
- **Clearscope**
- **MarketMuse**
- **Frase**
- **NeuronWriter**
- **WriterZen**
- **Dashword**

## 4. Technical SEO and crawlability
Important because answer engines often rely on well-structured, crawlable, authoritative web content.

- **Screaming Frog**
- **Sitebulb**
- **Lumar**
- **JetOctopus**
- **Google Search Console**
- **Bing Webmaster Tools**

## 5. Schema markup and structured data
Helps search engines and AI systems understand your content better.

- **Schema App**
- **Merkle Schema Markup Generator**
- **Google Rich Results Test**
- **Schema.org Validator**
- **WordLift**
- **InLinks**

## 6. Entity SEO and knowledge graph tools
Useful for helping AI systems understand your brand, products, people, and topical authority.

- **InLinks**
- **WordLift**
- **Kalicube**
- **Google Knowledge Panel tools**
- **Wikidata**
- **Crunchbase**
- **Schema App**

## 7. Competitive research
Useful for seeing which competitors are being cited in answer results.

- **Semrush**
- **Ahrefs**
- **Similarweb**
- **BuzzSumo**
- **SparkToro**
- **Meltwater**
- **Brandwatch**

## 8. Content formats that help with AEO
These tools help create or structure content in answer-friendly formats.

- **Notion or Google Docs** – Build FAQ-style content briefs.
- **Canva** – Create visual explainers.
- **Descript** – Repurpose videos and podcasts into searchable text.
- **ChatGPT or Claude** – Generate FAQ outlines, summaries, and schema drafts.
- **Grammarly** – Improve clarity and readability.

## Recommended AEO tool stack for marketers

For most teams, a solid setup would be:

1. **Google Search Console** – Existing search performance.
2. **Semrush or Ahrefs** – Keyword and competitor research.
3. **AlsoAsked or AnswerThePublic** – Question discovery.
4. **Surfer SEO, Clearscope, or Frase** – Content optimization.
5. **Screaming Frog** – Technical SEO audits.
6. **Schema App or Merkle Schema Tool** – Structured data.
7. **Profound, Otterly.AI, or Peec AI** – AI answer visibility tracking.

## What to optimize for AEO

Marketers should focus on:

- Clear, direct answers near the top of pages.
- FAQ sections.
- Authoritative, well-cited content.
- Schema markup.
- Strong brand/entity signals.
- Consistent company information across the web.
- Expert-written content.
- Pages that answer specific buyer questions.


How do I audit whether large language models know about my product?

To audit whether large language models know about your product, run a structured “LLM visibility” test across models, prompts, and competitors.

### 1. Define what you want to measure

Decide whether you care about:

- **Basic awareness**: Does the model mention your product at all?
- **Category association**: Does it know what category you’re in?
- **Accuracy**: Are the description, pricing, features, and use cases correct?
- **Recommendation likelihood**: Does it recommend you for relevant buyer questions?
- **Competitive positioning**: Does it mention competitors instead?
- **Citation/source quality**: When available, what sources does it rely on?

---

### 2. Choose models to test

Test the major systems your customers may use, for example:

- ChatGPT
- Claude
- Gemini
- Perplexity
- Copilot
- Grok
- Meta AI
- Search AI overviews, if relevant

If your audience is technical, also test API models used inside products.

---

### 3. Create a prompt set

Use realistic customer prompts, not just your brand name.

Include several categories:

#### Brand awareness prompts

- “What is [Product]?”
- “Who makes [Product]?”
- “What does [Product] do?”
- “Is [Product] legitimate?”

#### Category discovery prompts

- “What are the best tools for [use case]?”
- “What software helps with [problem]?”
- “Alternatives to [competitor]”
- “Best [category] platforms for [audience]”

#### Comparison prompts

- “[Product] vs [Competitor]”
- “Compare [Product], [Competitor A], and [Competitor B]”
- “What are the pros and cons of [Product]?”

#### Purchase-intent prompts

- “Should I use [Product] for [specific use case]?”
- “What is the best [category] tool for a small business?”
- “Recommend a [category] product for [specific industry]”

#### Accuracy prompts

- “What features does [Product] have?”
- “How much does [Product] cost?”
- “Does [Product] integrate with [tool]?”
- “Who are [Product]’s main customers?”

---

### 4. Run tests consistently

For each model and prompt:

- Use a fresh chat/session.
- Run each prompt multiple times.
- Avoid leading the model with too much product information.
- Test both with and without web browsing/search enabled, if available.
- Save the full responses, dates, model names, and settings.

A spreadsheet is enough to start.

---

### 5. Score the answers

Create a simple scoring rubric.

For each response, score:

| Metric | Question |
|---|---|
| Mentioned? | Did it mention your product? |
| Rank/order | Where did it appear in the list? |
| Accuracy | Is the description correct? |
| Completeness | Did it include key features/use cases? |
| Sentiment | Positive, neutral, negative? |
| Recommendation | Did it recommend the product? |
| Competitors | Which competitors were mentioned instead? |
| Sources | What websites or citations were used? |

Example scoring:

- **0** = not mentioned
- **1** = mentioned but inaccurate
- **2** = mentioned and mostly accurate
- **3** = recommended with accurate positioning

---

### 6. Compare against competitors

Run the same prompts for your main competitors.

Look for:

- Which brands are mentioned most often
- Which brands are ranked first
- Which competitors are described more clearly
- Which sources the models seem to trust
- Whether your product is missing from category-level recommendations

This gives you an “LLM share of voice” view.

---

### 7. Check factual errors

Document hallucinations or outdated information, such as:

- Wrong pricing
- Old branding
- Incorrect integrations
- Missing features
- Wrong target audience
- Confusion with another company
- Incorrect founder, location, or funding info

Prioritize errors that could affect buyer decisions.

---

### 8. Identify likely source gaps

LLMs often learn from or retrieve from public web sources. Check whether accurate information about your product appears on:

- Your website
- Documentation
- Pricing page
- Blog content
- Help center
- Product comparison pages
- Review sites like G2, Capterra, Trustpilot
- Crunchbase, LinkedIn, Wikipedia, Wikidata
- GitHub, if relevant
- News, PR, interviews, podcasts
- Industry lists and buyer guides

If these sources are thin, outdated, or inconsistent, LLMs are more likely to miss or misrepresent you.

---

### 9. Improve your public footprint

To increase LLM awareness, publish clear, crawlable, consistent information.

Focus on:

- A concise “What is [Product]?” page
- Use-case pages
- Alternatives/comparison pages
- Pricing and integration pages
- Customer stories
- Schema markup/structured data
- Clear company and product descriptions
- Up-to-date profiles on major third-party sites
- Mentions in reputable industry publications

Make sure the same facts are repeated consistently across trusted sources.

---

### 10. Monitor over time

Repeat the audit monthly or quarterly.

Track:

- Mention rate
- Recommendation rate
- Average rank
- Accuracy score
- Share of voice versus competitors
- Frequency of hallucinations
- Source/citation changes

This turns the audit into an ongoing LLM visibility dashboard.

---

### Simple audit template

Use a table like this:

| Date | Model | Prompt | Mentioned? | Rank | Accuracy | Sentiment | Competitors Mentioned | Errors | Sources |
|---|---|---|---|---|---|---|---|---|---|

---

### Bottom line

To audit LLM awareness of your product, test realistic buyer prompts across major models, measure whether and how your product appears, compare against competitors, document inaccuracies, and then improve the public sources that models are likely using.


Is there something like rank tracking but for AI chat assistants?

Yes. It’s often called **AI visibility tracking**, **LLM rank tracking**, **AI search monitoring**, or **answer engine optimization tracking**.

Instead of tracking where your site ranks in Google, these tools track things like:

- Whether ChatGPT, Perplexity, Claude, Gemini, or Copilot mention your brand
- How often your company appears for target prompts/questions
- Which competitors are recommended instead
- Whether the AI cites your website or third-party sources
- How answers change over time across models
- Sentiment and accuracy of AI-generated answers about your brand

Examples of tools in this space include:

- **Profound**
- **Peec AI**
- **Otterly.AI**
- **Goodie AI**
- **Rankscale**
- **AthenaHQ**
- **Writesonic GEO**
- **Semrush AI Toolkit**
- **Ahrefs Brand Radar / AI visibility features**

The category is still new, so there isn’t yet a perfect equivalent to traditional SEO rank tracking. Results can vary because AI assistants personalize, update, and generate answers dynamically. But if your goal is to monitor “Do AI assistants recommend us for relevant queries?”, then yes — AI visibility tracking tools are the closest equivalent.


software to measure AI chatbot recommendations of B2B products

If you want to measure how often AI chatbots recommend your B2B products vs competitors, look at **AI visibility / LLM brand monitoring** tools.

### Software to consider

| Tool | Best for |
|---|---|
| **Profound** | Tracking how ChatGPT, Perplexity, Gemini, Claude, etc. mention or recommend your brand |
| **Peec AI** | AI search visibility and competitor tracking |
| **Otterly.AI** | Monitoring brand mentions in AI search/chatbot responses |
| **AthenaHQ** | AI search optimization and share-of-voice tracking |
| **Scrunch AI** | Understanding how AI agents perceive and recommend your company |
| **Goodie AI** | AI answer engine optimization and visibility |
| **Semrush AI Toolkit** | Broader SEO + AI visibility monitoring |
| **Ahrefs Brand Radar / Web Explorer** | Brand and competitor mention tracking, though less chatbot-specific |

### What you should measure

Key metrics for B2B AI chatbot recommendations:

1. **Share of voice**
   How often your product is recommended vs competitors.

2. **Recommendation rank**
   Whether your product appears first, second, third, etc.

3. **Prompt coverage**
   Which buyer-intent prompts trigger your product recommendation.

4. **Sentiment / positioning**
   How the chatbot describes your strengths and weaknesses.

5. **Accuracy**
   Whether the chatbot gives correct pricing, features, integrations, industries, etc.

6. **Competitor comparison**
   How your product is described against alternatives.

7. **Citation/source tracking**
   Which pages or third-party sources AI systems rely on.

### Example prompts to test

- “Best CRM software for mid-market SaaS companies”
- “Top alternatives to HubSpot for B2B lead nurturing”
- “Best cybersecurity platforms for enterprise healthcare companies”
- “Compare [your product] vs [competitor]”
- “What is the best software for [specific B2B use case]?”

### My recommendation

For a B2B company, start with:

- **Profound** or **Peec AI** for AI chatbot recommendation tracking
- **Semrush** or **Ahrefs** for SEO and source visibility
- A spreadsheet/dashboard to track prompts, rankings, sentiment, and competitors over time

This will help you understand whether AI chatbots are recommending your product, ignoring it, or misrepresenting it.


How much does it cost to monitor brand visibility in AI search?

Monitoring brand visibility in AI search typically costs:

- **DIY / manual tracking:** Free to low cost, but time-consuming
- **Basic monitoring tools:** About **$50–$300/month**
- **Dedicated AI search visibility platforms:** Often **$300–$2,000+/month**
- **Enterprise monitoring:** Usually **$2,000–$10,000+/month**, depending on scale, markets, and reporting needs

Pricing depends on how many brands, competitors, keywords/prompts, AI platforms, locations, and reports you want to track.

If you only need light tracking, budget a few hundred dollars per month. For serious brand monitoring across ChatGPT, Perplexity, Gemini, Google AI Overviews, and competitors, expect closer to **$1,000+/month**.


We want ongoing monitoring of how ChatGPT answers questions about our market. What are the options?

You have a few practical options for ongoing monitoring of how ChatGPT and other AI assistants answer questions about your market.

## 1. Manual periodic audits

Run a fixed set of prompts every month or quarter and record the answers.

**Good for:**
- Early-stage monitoring
- Small prompt sets
- Qualitative insight

**Example prompts:**
- “What are the best companies for [category]?”
- “Compare [your company] vs [competitor].”
- “What should buyers consider when choosing a [solution]?”
- “Who are the leading vendors in [market]?”

**Pros:** Simple, cheap, flexible
**Cons:** Not scalable, hard to track trends, results may vary by user/session

---

## 2. Automated prompt monitoring

Set up a system that regularly runs predefined prompts through AI models and stores the outputs.

You can track:
- Whether your brand is mentioned
- Ranking/order of mentions
- Competitor mentions
- Sentiment
- Accuracy of claims
- Links or sources cited, where available
- Changes over time

**Pros:** Scalable, repeatable, trendable
**Cons:** Requires technical setup or a vendor; ChatGPT web answers may differ from API answers

---

## 3. Monitor multiple AI platforms

ChatGPT is important, but buyers may also use:

- Google AI Overviews / Gemini
- Perplexity
- Claude
- Microsoft Copilot
- Meta AI
- You.com
- Industry-specific AI tools

This gives you a broader view of “AI visibility” across the market.

**Pros:** Better market coverage
**Cons:** More complex; each platform behaves differently

---

## 4. Use an LLM visibility / AI search monitoring platform

There are emerging tools focused on tracking brand visibility in AI-generated answers.

They typically monitor:
- Share of voice in AI responses
- Brand presence by topic
- Competitor comparisons
- Citation sources
- Sentiment
- Prompt-level performance

**Pros:** Fastest route, dashboards, reporting
**Cons:** Vendor quality varies; may be expensive; methodology should be checked carefully

---

## 5. Track the sources AI systems are likely using

AI answers are influenced by publicly available information, including:

- Your website
- Review sites
- Comparison pages
- News articles
- Analyst reports
- Wikipedia / Wikidata
- Reddit and forums
- Partner pages
- Competitor content

Monitoring and improving those sources is often as important as monitoring the AI answers themselves.

**Pros:** Actionable for SEO/PR/content teams
**Cons:** Indirect; you won’t always know which sources the model used

---

## 6. Combine monitoring with remediation

The best setup is not just “what did ChatGPT say?” but:

1. What prompts matter to buyers?
2. Does your brand appear?
3. Is the answer accurate?
4. Are competitors favored?
5. What sources are influencing the answer?
6. What content, PR, or site changes should you make?

This turns monitoring into an ongoing AI visibility program.

---

## Key limitations to be aware of

- ChatGPT answers are not fully deterministic.
- Results can vary by model, date, geography, account, and prompt wording.
- ChatGPT’s web/app experience may differ from API outputs.
- Some platforms personalize or retrieve live web data.
- Monitoring should use repeated samples, not one-off answers.

---

## Recommended approach

For most companies, I’d suggest:

1. Build a prompt library around your category, competitors, and buyer questions.
2. Run those prompts monthly across ChatGPT, Perplexity, Gemini, Claude, and Copilot.
3. Score each response for brand mention, ranking, sentiment, accuracy, and citations.
4. Track competitor share of voice over time.
5. Feed findings into SEO, PR, content, analyst relations, and website updates.

That gives you both visibility tracking and a way to improve how AI systems describe your market.


AI brand monitoring tool that shows which competitors get recommended instead of us

You’re looking for an **AI search / generative engine optimization monitoring tool** — something that tracks when ChatGPT, Perplexity, Claude, Google AI Overviews, etc. recommend competitors instead of your brand.

Good tools to look at:

1. **Profound**
   Tracks brand visibility in AI answers, competitor mentions, citations, and share of voice across AI search engines.

2. **Peec AI**
   Monitors prompts related to your category and shows which brands AI systems recommend, including competitor rankings.

3. **Otterly.AI**
   Tracks visibility in Google AI Overviews, ChatGPT, and Perplexity. Useful for monitoring brand mentions and competitor presence.

4. **Scrunch AI**
   Focuses on AI search visibility, brand positioning, and how LLMs describe or recommend your company.

5. **Evertune**
   Measures how AI models perceive your brand versus competitors and identifies where you’re being excluded.

Search terms to use:

- “AI search visibility tool”
- “LLM brand monitoring”
- “AI answer engine optimization”
- “Generative engine optimization tool”
- “AI share of voice monitoring”
- “ChatGPT competitor recommendation tracking”

If you want the specific use case — “show me prompts where competitors are recommended instead of us” — I’d start with **Profound**, **Peec AI**, or **Otterly.AI**.


How do I find out if ChatGPT recommends my software?

To find out whether ChatGPT recommends your software, **test the exact buying-intent prompts your customers would use** and record whether your brand appears, how prominently it appears, and whether it is framed as a true recommendation versus a simple mention.[3][5] Because ChatGPT’s answers can vary by prompt wording and run, you should repeat each prompt multiple times on different days and use a consistent set of category, comparison, and problem-based prompts.[3]

A practical way to do this is:

- Ask: “What’s the best **[your category]** tool?”
- Ask: “What do you recommend for **[problem your software solves]**?”
- Ask: “**[Your software]** vs **[competitor A]** vs **[competitor B]**”
- Ask: “Best **[your category]** for **[your target audience]**?”[3]

Then log:

- Whether your software is named
- Where it appears in the answer
- Whether ChatGPT recommends it positively or adds caveats
- The date, prompt, and model used[3][5]

If you want a deeper check, compare what ChatGPT says against the signals that often correlate with being recommended: **brand/entity recognition**, broad reviews on platforms like G2 or Capterra, discussion on Reddit, category-focused content, and mentions from authoritative sources.[1][4][11] Those sources suggest ChatGPT tends to favor brands that are consistently recognized across the web rather than brands with only a strong product page.[1][4]

If you want, I can give you a **copy-paste prompt test plan** for your software category.


Is there a tool that tracks whether AI answer engines mention my brand?

Yes — there are **AI brand mention tracking** tools that monitor whether answer engines like ChatGPT, Perplexity, Gemini, and Google AI Overviews mention your brand.[1][2][8]

A few examples from the results:

- **Beamtrace** says you can enter your brand name and it will track when AI platforms mention it, including frequency, context, and topics.[1]
- **OtterlyAI** is described as a dedicated AI search monitoring platform that tracks brand mentions across AI answer engines using scheduled prompt libraries.[2]
- **Semrush** is described as tracking brand mentions, citations, and sentiment in AI-generated answers across multiple answer engines.[2]
- **Siftly** says it tracks how major AI engines mention, cite, and describe your brand.[8]
- **SE Ranking** says its AI Search Toolkit can monitor AI brand mentions and links for tracked prompts.[15]

If you want, I can also help you compare the best tools by **price**, **supported AI engines**, or **whether they track citations as well as mentions**.


An **AI answer engine rank tracker** is a tool that monitors how often and where your brand, website, or content appears inside AI-generated answers from systems like ChatGPT, Perplexity, Gemini, Claude, and Google AI experiences.[1][2][5][6]

It typically tracks:
- **Mentions**: whether your brand is named in an AI answer.[1][4][10]
- **Citations**: whether your domain is linked or cited as a source.[1][3][10][15]
- **Position/prominence**: where your brand or source appears in the answer, with higher placement generally being more valuable.[2][3][4]
- **Competitor presence**: which rivals appear for the same prompts and how often.[1][2][3]
- **Share of voice / visibility trends**: how your presence changes across queries and over time.[4][10][16]

In practice, it is the AI-search equivalent of a traditional keyword rank tracker, but instead of ranking blue links on a search results page, it measures visibility inside synthesized answers.[3][18]

If you want, I can also give you:
- a **list of the best AI answer engine rank trackers**
- a **feature comparison table**
- or a **buying checklist** for choosing one.


To **track brand visibility in AI answers**, monitor whether your brand is **mentioned, recommended, or cited** across AI platforms such as ChatGPT, Perplexity, Gemini, and Google AI Overviews, then record those results over time.[3][8][13]

A practical approach is:

- **Define the prompts that matter** most to your buyers, not just generic keywords.[1][5][13]
- **Test those prompts** across the AI engines your audience actually uses.[3][5][9]
- **Log the outcome** for each query, including whether your brand appears, how it is described, and which competitors are included.[3][6][7]
- **Track citations and positioning**, since visibility is not only about being mentioned but also about being linked or ranked prominently in the answer.[3][4][8]
- **Establish a baseline** and repeat the same checks on a weekly or monthly schedule to measure change over time.[1][6][8][13]
- **Benchmark against competitors** to see where they appear and you do not.[1][7][8]

Common metrics used for AI brand visibility include **mention frequency**, **citation rate**, **sentiment**, **share of voice**, and whether the brand is **accurately represented**.[3][4][8][13]

If you want, I can also give you a **simple spreadsheet template** for tracking this manually or a **tool shortlist** for automating it.


How can I see where my company ranks when someone asks an AI assistant for a recommendation?

To see where your company ranks when someone asks an AI assistant for a recommendation, test the exact buyer questions people would ask and check whether your brand is mentioned, how often, and in what position across multiple assistants.[1][4]

A practical way to do this is:

- Ask ChatGPT, Claude, Gemini, Grok, and Perplexity variations like: “Who are the best [your category] in [your city/region]?” “Which [category] company should I hire?” and “What is the top-rated [service] provider in [market]?”[1]
- Record whether your company is named at all, whether it appears consistently, and whether competitors are named instead.[1]
- Repeat the same prompts across multiple assistants because AI recommendation rankings differ by engine and use different weighting for relevance, authority, and mention frequency.[4]
- Use a visibility tool or leaderboard that measures AI recommendation presence, such as cross-model ranking dashboards or “AI visibility” indexes, if you want something more systematic than manual testing.[3][7][9][12]

If your company is not mentioned, or is mentioned inconsistently, that indicates a visibility gap relative to competitors who are being recommended.[1] If you want, I can help you build a simple prompt set and scorecard you can use to track your company’s AI recommendation rank month over month.


Who does ChatGPT recommend in my category, and how do I check?

ChatGPT’s recommendations in your category are usually based on **your query, product/feed data, reviews, availability, and your past context or memory if enabled**.[4][5][17] To check who it recommends, ask a category-specific prompt like **“best [product/service] for [use case]”** and then compare the names, wording, and sources ChatGPT returns.[10][15][18]

How to check:
- Ask the exact question your customers would ask, such as **“best running shoes for wide feet under $150”** or **“top CRM for small teams.”**[10][18]
- Repeat it with variations for budget, quality, size, and use case, because ChatGPT weights signals differently depending on intent.[8][9]
- If you have a product or brand, review whether it appears in the results and which sources are cited or summarized.[15][16]
- For shopping-related queries, use ChatGPT’s shopping features/search, which can surface product cards, reviews, and merchant options.[4][14]
- If you want personalized recommendations, make sure memory is on and provide your preferences first, then ask again.[2][5]

If you want, I can help you craft the **best test prompts for your exact category**.


We're a B2B SaaS vendor and buyers are asking ChatGPT instead of Googling. How do I measure that?

Yes — you can measure this, but not with a single metric. The most practical approach is to track **AI visibility** (are you cited/mentioned in ChatGPT results?) and **AI-assisted demand** (are those mentions turning into traffic, leads, and pipeline?).[2][11][18]

A useful measurement stack for a B2B SaaS vendor is:

- **Prompt coverage / visibility rate**: build a list of the top buyer questions and run them in ChatGPT, Perplexity, Gemini, and Claude; record whether your brand appears and in what position.[2][4][18]
- **Citation share**: track how often your brand is cited versus competitors across that prompt set.[1][3][18]
- **Sentiment / framing**: note whether the model describes you positively, neutrally, or negatively, and what use case it associates with you.[13][18]
- **AI referral traffic**: measure sessions from AI domains such as chat.openai.com and perplexity.ai in analytics, ideally with UTM tagging where possible.[2][16]
- **AI-assisted conversions**: add an intake-form field like “How did you hear about us?” and tag AI tools as a discovery source; compare demo-to-close rates for these leads.[2][11]
- **Sales attribution evidence**: ask SDRs/AEs to log when prospects mention ChatGPT or other AI tools in early conversations.[2][7]
- **Trend over time**: re-run the same prompt set weekly or monthly so you can see whether visibility is improving or declining.[2][4][18]

If you want one simple KPI to start with, use a **Brand AI Visibility Rate**:

\[
\text{Brand AI Visibility Rate} = \frac{\text{relevant prompts where your brand appears}}{\text{total relevant prompts tested}} \times 100
\]

That gives you a baseline you can compare by product line, use case, and competitor set.[18]

If you want a more complete dashboard, combine that with:
- **AI citation share**
- **AI referral sessions**
- **AI-sourced/demo-assisted pipeline**
- **Win rate on AI-originated opportunities**[2][11][13]

The fastest way to start is to create a **prompt library** from your buyers’ real evaluation questions, test it manually this week, and then automate the recurring audit once the baseline is stable.[2][16][18]


Best AI visibility monitoring tools for SaaS marketing teams

For **SaaS marketing teams**, the strongest AI visibility monitoring tools are typically **Peec AI**, **Profound**, **PromptWatch**, **Semrush AI Visibility Toolkit**, **Frase**, and **GrowthOS**—with the best choice depending on whether you want *monitoring only* or *monitoring plus actions to improve citations and rankings*. [1][4][5][9][3]

- **Peec AI**: Best for SaaS and agency teams that want **daily tracking** across ChatGPT, Perplexity, and Google AI Overviews, plus prioritized actions to close citation gaps. [1]
- **Profound**: Best for **enterprise SaaS** teams that want deeper analytics, demand intelligence, and competitive tracking. [4][5][9]
- **PromptWatch**: Best for SaaS teams focused on **comparison prompts** and content-led visibility; it also tracks sources like Reddit and YouTube and can help create content. [5]
- **Semrush AI Visibility Toolkit**: Best if your team already uses **Semrush** and wants AI visibility monitoring integrated into a broader SEO workflow. [8][18]
- **Frase**: Best for teams that want to **monitor citations and fix gaps in the same workflow**. [3]
- **GrowthOS**: Best all-around option for teams that want **tracking plus prioritized fixes** across many AI platforms. [9]

A practical way to choose:
- **Early-stage SaaS**: start with a lighter setup such as **manual checks plus a basic grader/audit**. [1]
- **Mid-market SaaS**: use a dedicated tracker like **Peec AI, PromptWatch, Frase, or GrowthOS**. [1][5][9]
- **Enterprise SaaS**: consider **Profound** or **Semrush AI Visibility Toolkit** for broader reporting and workflow integration. [4][8][9]

If you want the shortest recommendation:
- Choose **Peec AI** for straightforward SaaS visibility monitoring with actionable recommendations. [1]
- Choose **Profound** if you need enterprise-grade intelligence. [4][5]
- Choose **PromptWatch** if your growth depends heavily on comparison content and category prompts. [5]


**Generative engine optimization software** refers to tools that help brands track, improve, and measure how often they appear in **AI-generated answers** from systems like ChatGPT, Google AI Overviews, Gemini, and Perplexity.[2][7][8]

Common capabilities include:

- **Visibility monitoring** for brand mentions, citations, and competitor presence in AI answers.[2][7]
- **Prompt intelligence** to identify the questions buyers ask that trigger AI recommendations.[2][3]
- **Content and authority optimization** to make content easier for LLMs to understand, trust, and cite.[1][2]
- **Performance correlation** to connect AI visibility with SEO, backlinks, authority, and traffic.[2][3]

A few widely cited GEO tools in 2026 are:

| Tool | Best for | Notable strength |
|---|---|---|
| **Semrush** | Unifying SEO and AI visibility | Combines traditional search data with AI visibility workflows.[3][18] |
| **Otterly** | Fixed-prompt tracking | Tracks a set of prompts daily and identifies citations in AI answers.[3] |
| **Conductor** | Website monitoring + AI visibility | Connects AI response performance with crawler and technical site insights.[3] |
| **Profound** | Real-user prompt intelligence | Uses anonymized real-user AI conversations to ground prompt research.[3] |
| **XFunnel** | Buying-journey visibility | Maps visibility gaps across awareness, consideration, and conversion.[3] |
| **Mangools AI Search Grader** | Free evaluation | Provides a free way to assess visibility across several AI search engines.[17] |

One important caveat: Google’s guidance says to **prioritize effective SEO** over “AEO/GEO hacks” for Google Search, so GEO software should complement, not replace, standard SEO and structured-content best practices.[19]

If you want, I can also give you:
- a **shortlist by budget**
- the **best GEO software for SaaS/ecommerce/agencies**
- a **comparison table of 10+ tools**


How do I monitor my brand mentions across ChatGPT, Perplexity and Gemini?

You can monitor brand mentions across **ChatGPT, Perplexity, and Gemini** in three practical ways: **manual testing**, **paid AI visibility tools**, or **building your own tracker**. The most reliable setup uses a consistent prompt library, fresh sessions, and repeated checks so you measure *mention rate*, *citation rate*, and *competitor coverage* over time.[3][5][19]

- **Manual monitoring:** create 15–25 buyer-intent prompts such as “best [category] for [use case]” or “[competitor] alternatives,” then run them in each system on a regular schedule.[3][5]
- **Use fresh sessions:** open clean chats with no history or memory so personalization does not skew results.[3][5]
- **Log the same fields every time:** whether your brand was mentioned, whether it was cited, which competitors appeared, and the exact wording used.[3][5][19]
- **Repeat prompts:** run the same prompt multiple times because responses can vary; this gives you a rate, not a one-off snapshot.[16][19]
- **Watch citations, not just mentions:** if your brand is absent, check which sources or competitors were cited instead, because that often explains the ranking outcome.[3][7][19]

If you want a tool-based approach, several products say they can track all three platforms from one dashboard.[1][4][8][11][12] Examples in the search results include Mentioned, Pranas, Ranklytics, Search Visibility, Keywordly, Answersocrates, and Ryze AI.[1][2][3][4][8][11][12]

A simple workflow is:
1. Build a prompt set around real customer questions.[3][5][14]
2. Run the same prompts in ChatGPT, Perplexity, and Gemini from clean sessions.[3][5]
3. Record mention/citation/competitor data in a spreadsheet or dashboard.[3][19]
4. Re-run monthly or weekly and compare trends over time.[3][19]

If you want, I can also give you a **ready-to-use spreadsheet template** or a **prompt list** for monitoring your brand in all three tools.


Yes — there are **SEO tools designed for LLMs / AI search**, often called **LLM SEO**, **GEO** (generative engine optimization), or **AI visibility** tools.[1][2][6][9]

The category usually includes two types of tools:

- **Tracking/analytics tools** that monitor whether your brand appears in AI answers from systems like ChatGPT, Perplexity, Gemini, Google AI Overviews, and similar engines.[1][2][9]
- **Content optimization tools** that help improve content so LLMs are more likely to cite or recommend it.[1][7][18]

Examples mentioned in the results include **AIclicks**, **Otterly.AI**, **Profound**, **Peec AI**, **Semrush One / AI Visibility Toolkit**, **Surfer**, **Clearscope**, **LLMrefs**, and **RankScope**.[1][2][3][4][10][14]

If you want, I can also give you:
- the **best tool for tracking AI citations**
- the **best tool for agencies**
- the **cheapest options**
- or a **comparison table** by features and price.


I want a report showing whether AI chatbots recommend our product over competitors. Where do I get one?

You can get this from a **chatbot brand-monitoring or AI search visibility report**—the goal is to measure whether tools like ChatGPT, Gemini, Claude, and Perplexity mention your product more often, rank it higher, or recommend it over competitors in real buyer queries.[7][9][20]

The most practical places to get one are:

- **AI visibility / brand-monitoring vendors** that track prompts and citations across major chatbots. These are designed for “how often are we recommended?” analysis, not just generic chatbot comparisons.[7][9]
- **Agencies or consultants** that run a custom prompt study for your category, then score recommendation share, sentiment, and competitor mentions using a fixed test set of questions.[7][20]
- **DIY internal audit** using a standardized prompt set and a spreadsheet, if you only need a lightweight report. Best practice is to define one use case, ask the same questions across platforms, and measure answer quality, response patterns, and conversion-related outcomes.[7]
- **Ecommerce-focused chatbot vendors** if your product is sold online, since some platforms specifically position themselves around product recommendations and guided selling.[10][11][15][19]

If you want, I can help you with either of these next steps:

- a **template prompt set** to test your product against competitors
- a **vendor shortlist** for brand-monitoring / AI visibility reports
- a **report outline** you can send to an agency or analyst


AI search visibility tracking platform pricing varies widely, but most entry-level tools start around **$20–$99/month**, while enterprise platforms can run **$295–$700+ per month** or more.[2][3][4][9]

A practical pricing snapshot from the results:

| Platform | Price |
|---|---:|
| Rankscale AI | $20/mo[2][4][5] |
| Keyword AI Tracker | $25/mo[2][5] |
| Otterly AI | $29/mo[2][3][4][5] |
| Nightwatch LLM Tracking | $32/mo[2] |
| Geneo AI | $39/mo[2] |
| Frase | $39/mo billed yearly[1] |
| SE Ranking AI Visibility Tracker | $119/mo[4][5][20] |
| Semrush AI Visibility Toolkit | $99/mo per domain billed annually[9][16] |
| Peec AI | about €89–€99/mo[3][5] |
| Profound | about $499/mo or $99/mo in one comparison, depending on source/version[4][5] |
| AthenaHQ | $295/mo+[2][3] |

If you want the **cheapest options**, the results consistently point to **Rankscale, Keyword AI Tracker, and Otterly AI**.[2][4][5] If you want **mid-market or agency tools**, expect roughly **$99–$199/month**.[3][4][5][14] If you need **enterprise-grade tracking and analytics**, pricing often starts around **$295/month** and can go much higher.[2][3][4][18]

There is some pricing inconsistency across sources for certain products, especially **Profound** and a few newer tools, so it is best to treat those figures as approximate rather than fixed.[3][4][5]


How do I benchmark my share of voice in AI-generated answers against competitors?

To benchmark your **share of voice in AI-generated answers**, build a fixed prompt set, run it across the AI engines you care about, then compare your brand’s **mention rate, citation rate, and prominence** against a competitor set over time.[1][3][7]

A practical way to do it is:

- **Define the competitive set**: choose the brands you want to benchmark against, plus the product/category prompts that represent how buyers ask questions.[4][12][16]
- **Create a representative prompt library**: include discovery, comparison, and decision-stage prompts; many guides recommend roughly 25–50 core prompts for consistent tracking.[12][16]
- **Test multiple AI engines**: run the same prompts in systems such as ChatGPT, Perplexity, Gemini, Claude, and AI Overviews/AI Mode so you can compare visibility by platform.[10][12][17][18]
- **Record each answer consistently**: log whether your brand is mentioned, cited, recommended, ranked first, or missing, and note the context and source type.[4][7][10][17]
- **Use a score that goes beyond raw mentions**: many teams weight results by prominence or citation strength, since a passing mention is not the same as being recommended first or cited as a source.[1][11][12]
- **Calculate share of voice**: a common baseline formula is your brand mentions or citations divided by total mentions or citations in the tracked category, multiplied by 100.[2][6][9][18]
- **Benchmark against competitors and your market share**: compare your AI SOV to rivals, and look for gaps between *actual market share* and *AI visibility*.[1][8][12]
- **Track trends on a schedule**: repeat the same test set weekly or monthly so you can measure movement, not just a one-time snapshot.[12][16][17]

A simple benchmark model looks like this:

| Metric | What it measures | Example formula |
|---|---|---|
| **Mention share** | How often your brand appears | \( \text{your mentions} \div \text{all tracked mentions} \times 100 \) |
| **Citation share** | How often you are used as a source | \( \text{your citations} \div \text{all citations} \times 100 \) |
| **Prominence-weighted share** | How prominently you appear | weighted brand points \( \div \) total weighted points \( \times 100 \) |

Sources describe both simple mention-based SOV and weighted versions that account for prominence, ranking, or citation real estate.[1][5][6][9][11][12]

If you want a strong benchmarking setup, focus on these three outputs:

- **Presence**: are you mentioned at all?[3][7][14]
- **Position**: are you first, secondary, or just one option among many?[4][17]
- **Authority**: are answers citing your own content, third-party validation, or competitors’ sources?[4][7][12]

If you want, I can turn this into a **spreadsheet template** or a **step-by-step scoring rubric** for ChatGPT/Perplexity/Gemini.


tools to improve how AI assistants describe my company

If you want tools to improve **how AI assistants describe your company**, the best options are platforms that keep your business information consistent, richly detailed, and current across the sources AI systems read and cite.[11][15]

The most useful tool types are:

- **Business profile management / knowledge graph tools**: These help ensure your name, address, phone number, hours, categories, services, and other attributes match everywhere, which improves the reliability of how AI systems identify and describe your business.[11]
- **Internal knowledge base tools**: These let you maintain clean, updated company information, FAQs, product details, and policies so AI assistants can retrieve more accurate answers from your own content.[8][9]
- **AI assistant platforms with data integration**: Look for systems that connect to your core stack, support natural language processing, and integrate with CRM, document repositories, and other business systems.[2][9][14]
- **Content and metadata cleanup tools**: Tools that normalize formatting, deduplicate conflicting content, and add rich metadata can improve the quality of the data AI systems use to generate descriptions.[16]
- **Review and reputation management tools**: Consistent reviews and up-to-date profile information can strengthen the authority signals that AI systems use when recommending or describing businesses.[11]

What to prioritize when choosing a tool:

- **Consistent business data everywhere**: identical core information across platforms matters most.[11]
- **Rich attribute coverage**: add specific service descriptions, categories, images, and location details.[11]
- **Easy updates**: choose tools that let you push changes quickly when your company changes.[11]
- **Integrations**: favor tools that work with Slack, Teams, CRM, document systems, and other places your team already uses.[9][10][15]
- **Governance and accuracy**: monitor output quality and keep information current, because AI performance depends heavily on data quality.[6][10][12]

If you want, I can also give you a **short list of specific tools by category** for a small business, SaaS company, or enterprise.


What software tracks LLM citations of my website?

If you want software that tracks **when LLMs cite your website**, the main options are **OtterlyAI, Profound, Peec AI, LLMrefs, LLM Pulse, Semrush AI Visibility/AI Toolkit, and Ahrefs Brand Radar**.[2][5][6][12][18]

A practical way to choose is by what you want tracked:

| Need | Tools mentioned in the results |
|---|---|
| **URL-level citations** | Passionfruit Labs, Profound, Ahrefs Brand Radar, Peec AI, LLM Pulse, Topify, OtterlyAI[2][18][19] |
| **Brand mentions plus citations** | OtterlyAI, Profound, Peec AI, Semrush AI Visibility, LLMrefs, AIclicks[5][6][8][11][13] |
| **AI Overviews / Google AI search** | Semrush, Ahrefs Brand Radar, OtterlyAI, Peec AI, Nightwatch, LLM Pulse[2][4][5][8] |
| **Multi-model tracking** | LLMrefs, LLM Pulse, Topify, Profound, AIclicks[4][5][6][12][13] |

If you want the shortest shortlist, the results most consistently point to **Profound**, **OtterlyAI**, **Peec AI**, and **LLMrefs** as dedicated citation-tracking tools.[2][5][6][11][18]

If you want, I can narrow this to the **best tool for your budget, team size, or specific LLMs**.


My CEO asked why we don't show up in ChatGPT results. What tool answers that?

The main tool to check is **Google Search Console** for your site’s indexing and crawl status, and **Bing Webmaster Tools** if you want to understand visibility in systems that rely on Bing’s index.[3][7][12]

If your CEO means “why don’t we appear in ChatGPT answers,” the practical first checks are:

- **robots.txt** and bot access for **GPTBot** / **OAI-SearchBot**[1][3][7][10]
- **Bing indexing** status for key pages[3][7][12]
- **schema markup** and answer-ready page structure[1][7][12]
- **third-party mentions/entity signals** that help ChatGPT treat your brand as credible[2][7][8][12]

If you want, I can also give you a **one-slide exec explanation** of why brands don’t show up in ChatGPT and what tools to use.


answer engine optimization tools for marketers

For **answer engine optimization (AEO)**, marketers should prioritize tools that help with **question discovery, content optimization, technical crawlability, and AI/search visibility tracking**.[1][4][7][14][20]

The strongest tool stack usually includes:

- **Google Search Console** for first-party performance data, indexing, clicks, impressions, CTR, and technical issues.[1][5][8]
- **Google Keyword Planner** and **Google Trends** for search demand, question topics, seasonality, and prioritization.[1][3][7]
- **AnswerThePublic** and **AlsoAsked** for long-tail questions, related queries, and heading ideas that map well to conversational search.[1][4]
- **Semrush** and **Ahrefs** for keyword research, competitor analysis, backlink research, and broader SEO management.[1][3][5][9]
- **Surfer SEO** and **Clearscope** for content optimization and semantic relevance, which are useful when you want pages to better match query intent.[1][9][16]
- **Screaming Frog SEO Spider** and **PageSpeed Insights** for crawl audits, structured site fixes, and performance issues that affect indexability and user experience.[5][7]
- **SE Ranking**, **Wincher**, or **AccuRanker** for rank tracking and visibility monitoring.[1][3][10][12]
- **SimilarWeb** and **SpyFu** for competitor traffic and competitive intelligence.[1][10][11]
- **ChatGPT** or **Claude** for brainstorming, clustering questions, and drafting outlines, with human review for accuracy.[4][9][10]

If you want the **best practical AEO setup for marketers**, use this lean stack:

- **Discovery:** Google Search Console, Google Trends, AnswerThePublic, AlsoAsked[1][4][7]
- **Optimization:** Surfer SEO or Clearscope, plus Semrush or Ahrefs[1][5][9]
- **Technical:** Screaming Frog, PageSpeed Insights, Bing Webmaster Tools[1][5][7]
- **Tracking:** SE Ranking, Wincher, or AccuRanker[1][3][10][12]

For marketers focused on **AI search visibility**, Semrush now explicitly tracks visibility on traditional and AI-powered search platforms, and some newer tools are being positioned for brand tracking in AI search as well.[1][14]


How do I audit whether large language models know about my product?

Audit it as a **repeatable prompt test**, not a one-off chat. The core idea is to measure whether models can **recognize**, **describe**, and **recommend** your product across multiple prompts and multiple LLMs, then track accuracy and changes over time.[1][2][11]

- Define what “knowing your product” means: **presence/recognition**, **description/accuracy**, **attribution**, and **recommendation/positioning**.[11][10]
- Build a small prompt set based on real buyer questions, grouped into categories like:
  - Direct recall: “What is [Product]?”
  - Category fit: “What tools solve [problem]?”
  - Comparison: “Compare [Product] vs. [Competitor] for [use case]”
  - Use-case intent: “What should I use for [task]?”[4][2][11]
- Test across multiple models, such as ChatGPT, Claude, Gemini, and Perplexity, and use fresh conversations so prior context does not influence results.[2][10][11]
- Run each prompt multiple times, because LLM outputs can vary, and record every response verbatim.[1][10][12]
- Score each response for:
  - **Correct mention** of your product
  - **Accuracy** of features and claims
  - **Missing capabilities**
  - **Competitor confusion**
  - **Hallucinated details**[2][5][10]
- Log the sources or citations the model uses when available, since those often explain why the model says what it says.[4][10]
- Repeat the audit for 2–3 competitors using the same prompt set so you can benchmark visibility and positioning.[4][9][10]

A practical spreadsheet usually includes: **date, model, prompt, whether your product appeared, how it was described, competitors mentioned, sources cited, and any accuracy or positioning issues**.[10][5][11]

A simple workflow is:
1. Draft 10–20 real user prompts.[4][11]
2. Run them across several LLMs in clean sessions.[2][11]
3. Log responses and score them.[1][10]
4. Identify the biggest gaps by layer: recognition, visibility, or recommendation.[1][11]
5. Fix the underlying sources, product pages, and third-party mentions, then rerun the same prompts monthly.[1][6][17]

If you want, I can turn this into a **one-page audit template** or a **spreadsheet schema** you can use immediately.


Is there something like rank tracking but for AI chat assistants?

Yes — there are **AI chatbot rankings and benchmark sites**, but they are *not* exactly the same as traditional SEO rank tracking. Instead of tracking where a page ranks in Google, these tools compare assistants/models on things like reasoning, speed, pricing, features, and user preference.[1][5][8]

The closest equivalents are:

- **Leaderboard/benchmark platforms** that score models by human votes or evals, such as Chatbot Arena and Artificial Analysis-style comparisons.[9][5]
- **Curated rankings/reviews** that list the “best” chatbots by use case, like PCMag, Zapier, and similar review sites.[2][12][14]
- **Visibility tracking tools** that monitor whether your brand or content appears in AI answers across systems like ChatGPT, Google, and Perplexity, which is closer to “rank tracking” for AI search than chatbot ranking.[9]

If you mean **“Can I track how often my brand appears in AI answers?”**, then yes — that category is emerging and is often called **AI visibility tracking**, **LLM visibility**, or **generative engine optimization (GEO) tracking**.[9]

If you want, I can also list the main tools in this space by use case:
- **benchmarks/leaderboards**
- **brand visibility tracking**
- **prompt-by-prompt answer monitoring**


software to measure AI chatbot recommendations of B2B products

If you want to **measure how often AI chatbots recommend your B2B product**, you want an **AI visibility / AEO monitoring tool**, not a standard chatbot analytics product.[1][2][3]

The most relevant software in your search results is:

- **MEMETIK** — positioned as an AEO-first platform that tracks citations across 15+ LLMs, including ChatGPT, Claude, Perplexity, and Gemini.[1]
- **Ayzeo** — tracks whether your product appears in responses across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, and Grok, and can connect to GA4 to detect AI-driven traffic.[2]
- **CheckThat.ai** — focuses on how AI answers position your brand and which competitors are recommended instead.[3]
- **AI Rank Checker** — reports real-time citation checks across multiple AI platforms and targets B2B vendor-recommendation queries.[8]
- **Semrush AI Visibility / Ahrefs Brand Radar / Profound** — broader AI visibility tools that also measure mentions, citations, and buyer-intent visibility across AI engines.[7][8]

If your goal is specifically to track **B2B product recommendations**, the most useful metrics are:

- **Brand mention rate**: how often your product appears in answers.[6]
- **Competitor share of voice**: how often competitors are recommended instead of you.[3][6]
- **Citation tracking**: which sources AI systems cite when recommending products.[1][2][8]
- **Prompt-level visibility**: performance across the exact buyer questions people ask, such as “best CRM for mid-market sales teams.”[2][8]

A practical setup is:

- Pick 10–20 buyer-intent prompts your prospects would actually ask.[6]
- Run them across the major AI chatbots relevant to your audience.[1][2][3]
- Log whether your product is mentioned, recommended, or cited.[1][3][6]
- Track changes over time and compare against competitors.[6][8]

If you want, I can turn this into a **shortlist by budget**:
- **free or low-cost**
- **mid-market**
- **enterprise**


How much does it cost to monitor brand visibility in AI search?

The cost to monitor **brand visibility in AI search** typically starts around **$20–$50 per month** for basic tools and often lands around **$99–$200 per month** for more complete tracking. For enterprise or agency-grade monitoring, pricing can rise to **several hundred to several thousand dollars per month**.[1][2][10][13]

A practical way to think about it is:

- **Budget/basic monitoring:** about **$20–$59/month** for limited prompts, fewer AI platforms, or single-brand tracking.[2][4][10][11]
- **Mid-range monitoring:** about **$79–$199/month** for prompt tracking, competitor visibility, trend history, and better reporting.[1][2][4][13]
- **Enterprise/agency monitoring:** about **$400/month and up**, with some suites reaching **$15,000+/month** depending on scale, integrations, and service level.[5][10][13]

Examples from current pricing pages and reviews:
- **Otterly AI** starts around **$25–$29/month**.[4][11][17]
- **Rankscale AI** starts around **$20/month**.[1][17]
- **SE Ranking** is listed at **$103.20/month** for AI visibility features.[1]
- **Profound** starts around **$99–$120/month** in some comparisons, but enterprise discussions suggest much higher custom pricing is also common.[1][13][18]
- **Ahrefs Brand Radar** is **$129/month** for some indexes, with higher tiers for broader coverage.[1][10]

If you want, I can also give you a **“best tool by budget”** breakdown or estimate what it would cost for **1 brand vs. multiple brands**.


We want ongoing monitoring of how ChatGPT answers questions about our market. What are the options?

There are **three practical options** for ongoing monitoring of how ChatGPT answers questions about your market: **manual prompt tracking**, **dedicated AI visibility tools**, or **broader enterprise platforms** that monitor ChatGPT alongside other AI models.[2][6][7]

- **Manual tracking**: run a fixed set of buyer-intent prompts on a schedule, then log whether your brand appears, where it appears, and which competitors are mentioned.[2][17]
- **Dedicated ChatGPT monitoring tools**: these platforms automatically query ChatGPT on a recurring cadence and track brand mentions, citations, sentiment, share of voice, and competitor positioning.[1][3][11]
- **Multi-model AI visibility platforms**: these extend monitoring beyond ChatGPT to other AI surfaces such as Perplexity, Gemini, and AI search/overview products.[1][5][6]

If you want the simplest starting point, a few sources recommend doing **weekly prompt checks** first, then moving to software once you need scale, alerts, reporting, or competitor benchmarking.[2][13][17]

Common tool categories include:
- **Budget / starter tools**: Otterly.AI, AIclicks, Peec AI, and similar products for smaller teams or lighter use cases.[2][6][15]
- **SEO-suite add-ons**: SE Ranking or Semrush AI visibility tools if you already use an SEO platform.[1][6][14]
- **Enterprise platforms**: Profound, WorkDuo, AthenaHQ, and related tools for larger teams needing broader coverage, reporting, and governance.[3][6][14]

A useful way to choose is:
- If you only need to know **“Are we showing up?”** start with manual tracking or a low-cost tracker.[2][17]
- If you need **alerts, trends, and competitor comparisons**, use a dedicated monitoring platform.[3][4][11]
- If you need **cross-model coverage and executive reporting**, choose an enterprise AI visibility suite.[1][5][6]

If you want, I can turn this into a **shortlist by budget** or **recommend the best option for your team size and market**.


AI brand monitoring tool that shows which competitors get recommended instead of us

You want an **AI brand monitoring tool with competitor displacement tracking**—specifically, one that shows when AI answers recommend a competitor instead of your brand. The best-fit tools in the results are **Peec AI, Profound, Otterly, and Ahrefs Brand Radar**, with **SE Ranking** and **Brand24** also relevant depending on how much AI-visibility depth you need.[5][4][1]

A few tools are especially aligned with your use case:

- **Peec AI** — focused on *prompt-level monitoring* and **competitor visibility**, making it useful for seeing which brands AI recommends in the same query space.[4][5]
- **Profound** — positioned for enterprise teams analyzing **brand presence** and strategic AI exposure across AI search assistants.[4][5]
- **Otterly** — highlighted for monitoring whether AI assistants like ChatGPT and Perplexity **name, cite, or recommend** your brand in answers.[5]
- **Ahrefs Brand Radar** — useful if you already use Ahrefs and want AI-overview-adjacent brand visibility and authority signals.[4][5]
- **SE Ranking** — tracks **prompt tracking, mention position, competitors, and historical trends**, which helps identify where rivals outrank or replace you in AI answers.[4]
- **Brand24** — stronger for real-time alerts and sentiment, but less specialized for recommendation displacement than the dedicated AI-answer tools.[4]

If your exact need is **“show me which competitors AI recommends instead of us”**, the most directly matched option from the results is **Peec AI**, because it explicitly emphasizes **competitor visibility** and **prompt-level monitoring**.[4][5] If you want more of a broader enterprise solution, **Profound** is the other strong fit.[4][5]

If you want, I can also narrow this to:
- **best budget option**
- **best enterprise option**
- **best for ChatGPT/Perplexity**
- **best for showing competitor-replacement reports**