How do I find out if ChatGPT recommends my software?

There’s no official dashboard that tells you “ChatGPT recommends your software,” and you can’t see private ChatGPT conversations. But you can estimate it in a few practical ways:

1. **Test likely buyer prompts**
   - Ask ChatGPT questions your target customers might ask, e.g.:
     - “What are the best tools for [use case]?”
     - “Recommend software for [industry/problem].”
     - “Alternatives to [competitor].”
     - “Best [category] for small businesses/enterprise/etc.”
   - Try multiple phrasings and run them in fresh chats.

2. **Repeat tests over time**
   - ChatGPT answers can vary depending on wording, model, location, browsing availability, and context.
   - Track whether your product appears, how often, and in what position.

3. **Check website analytics**
   - Look for referrals from domains like:
     - `chatgpt.com`
     - `chat.openai.com`
   - Also check server logs for OpenAI-related crawlers or user agents, though crawling does not necessarily mean recommendation.

4. **Ask leads/customers**
   - Add “How did you hear about us?” to forms.
   - Include options like “ChatGPT / AI assistant.”

5. **Use AI visibility tools**
   - Some SEO/GEO tools monitor whether brands appear in AI-generated answers across ChatGPT, Perplexity, Gemini, Claude, etc.

6. **Improve your chances of being recommended**
   - Make your site easy to crawl and understand.
   - Clearly state your category, use cases, pricing, integrations, and differentiators.
   - Publish comparison pages, case studies, docs, reviews, and third-party listings.
   - Make sure reputable external sources mention your software.

In short: you can’t know every time ChatGPT recommends you, but you can test representative prompts, monitor referral traffic, ask customers, and track AI visibility over time.


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

Yes. There are “AI visibility” or “answer engine monitoring” tools that track whether platforms like ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews mention your brand.

Examples include:

- **Profound** — tracks brand visibility across AI answer engines and compares against competitors.
- **Peec AI** — monitors prompts, brand mentions, sentiment, and source citations in AI answers.
- **Otterly.AI** — focuses on AI search visibility in ChatGPT, Perplexity, and Google AI Overviews.
- **Scrunch AI** — tracks how AI systems describe your brand and where they source information.
- **BrandRank.AI** — monitors brand presence and reputation in AI-generated answers.
- **AthenaHQ** — AI search optimization and visibility tracking.

These tools usually let you monitor prompts like:

> “Best project management tools for startups”
> “Top alternatives to [competitor]”
> “Who are the leaders in [your category]?”

They then report whether your brand appears, how often, in what position, with what sentiment, and which sources the AI used.

If you just want a lightweight setup, you can also manually test recurring prompts in ChatGPT, Perplexity, Gemini, and Claude, but dedicated tools are better for tracking trends over time.


An **AI answer engine rank tracker** monitors how your brand/website appears in AI-generated answers from platforms like **ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews**, and Bing Copilot.

Instead of traditional “rank #1–10” SEO tracking, it usually tracks:

- **Brand mentions** in AI answers
- **Citation/source inclusion**
- **Answer position or prominence**
- **Share of voice vs competitors**
- **Sentiment/context of mentions**
- **Prompt/query performance over time**
- **Which pages are cited by AI engines**

Popular use cases:

1. Track whether AI engines recommend your brand for target queries.
2. Monitor competitors in AI-generated answers.
3. See which sources AI engines cite.
4. Optimize content for AI search visibility / GEO / AEO.
5. Measure impact of PR, SEO, and content updates.

Example tracked prompt:

> “Best CRM software for small businesses”

Metrics:

| Metric | Example |
|---|---|
| Mentioned? | Yes |
| Position | 2nd brand listed |
| Cited URL | yoursite.com/crm-guide |
| Sentiment | Positive |
| Competitors | HubSpot, Salesforce, Zoho |
| Engine | Perplexity, Gemini, ChatGPT |

If you’re looking to build or choose one, the key features should be:

- Multi-engine tracking
- Scheduled prompt monitoring
- Citation detection
- Competitor benchmarking
- Geo/language/device segmentation
- Historical trend reports
- Alerts when visibility changes
- Export/API access

In short: an **AI answer engine rank tracker** is like an SEO rank tracker, but for visibility inside AI-generated answers rather than Google’s classic blue-link results.


To track brand visibility in AI answers, set up an “AI search visibility” monitoring process across major AI platforms.

### 1. Define the prompts you want to monitor
Track questions your buyers might ask, such as:

- “Best [category] tools”
- “Top alternatives to [competitor]”
- “What is the best software for [use case]?”
- “Compare [your brand] vs [competitor]”
- “How do I solve [problem]?”

Group prompts by funnel stage: awareness, comparison, purchase, support.

### 2. Monitor across AI platforms
Check visibility in:

- ChatGPT
- Google AI Overviews / Gemini
- Perplexity
- Claude
- Microsoft Copilot
- Meta AI, if relevant

Run the same prompt set regularly.

### 3. Track key metrics
Useful metrics include:

- **Mention rate**: how often your brand appears
- **Share of voice**: your mentions vs competitors
- **Ranking position**: whether you appear first, second, etc.
- **Sentiment**: positive, neutral, or negative descriptions
- **Accuracy**: whether the AI answer describes your brand correctly
- **Citation/source presence**: whether your site or third-party sources are cited
- **Competitor comparisons**: which brands are recommended instead

### 4. Track sources influencing AI answers
AI systems often rely on publicly available content. Monitor whether your brand appears in:

- Review sites
- Comparison pages
- Industry roundups
- Wikipedia/Wikidata
- Reddit and forums
- YouTube transcripts
- News articles
- Analyst reports
- Your own website and documentation

### 5. Use tools or build a tracker
You can track manually in a spreadsheet, or use tools such as:

- Profound
- Peec AI
- Scrunch AI
- Otterly.ai
- AthenaHQ
- Goodie AI
- Semrush AI visibility features
- Ahrefs/Brand Radar-style monitoring, depending on availability

A simple spreadsheet can include:

| Prompt | Platform | Brand Mentioned? | Position | Competitors Mentioned | Sentiment | Sources Cited | Notes |
|---|---|---:|---:|---|---|---|---|

### 6. Improve visibility
To increase your chances of appearing in AI answers:

- Publish clear comparison and use-case pages
- Get included in credible third-party lists and reviews
- Strengthen presence on review platforms like G2, Capterra, Trustpilot, etc.
- Create factual, structured content about your product
- Add schema markup where relevant
- Build authoritative mentions from trusted sites
- Keep pricing, features, and product descriptions consistent across the web

### Simple KPI to start with
Track this monthly:

> **AI Visibility Score = % of target prompts where your brand appears in the answer**

Then break it down by platform, category, and competitor.


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

You can check this by doing **AI recommendation tracking** (sometimes called “AI visibility” or “GEO tracking”). The goal is to see whether assistants like ChatGPT, Perplexity, Gemini, Claude, Copilot, etc. mention your company when users ask recommendation-style questions.

### Practical ways to do it

1. **Test real buyer prompts**
   - “What are the best [category] companies?”
   - “Recommend a [service/product] provider for [use case].”
   - “Who are the top alternatives to [competitor]?”
   - “Best [category] for [industry/company size/location].”

2. **Check across multiple AI assistants**
   - ChatGPT
   - Perplexity
   - Google Gemini / AI Overviews
   - Microsoft Copilot
   - Claude
   - Industry-specific AI search tools if relevant

3. **Record whether you appear**
   Track:
   - Whether your company is mentioned
   - Your position/order
   - What competitors appear
   - What reasons the AI gives
   - Which sources/citations are used
   - Whether the answer is accurate

4. **Vary the prompts**
   AI answers can change based on wording, location, industry, company size, and user intent. Test multiple versions rather than relying on one query.

5. **Use monitoring tools or automation**
   You can manually test, but for ongoing tracking you may want tools that monitor AI search visibility. These typically run scheduled prompts and report when your brand appears, competitors mentioned, and cited sources.

6. **Look at source influence**
   AI assistants often pull from:
   - Your website
   - Review sites
   - Industry lists
   - News articles
   - Reddit/forums
   - Comparison pages
   - Directories
   - Partner/vendor marketplaces

### Important caveat

AI assistants do not have one fixed “ranking” like traditional Google search. Results can vary by model, prompt, location, timing, and context. So you’re really measuring **share of recommendations across a set of relevant prompts**, not a single universal rank.

A simple scorecard would be:

| Prompt | Assistant | Mentioned? | Position | Competitors | Sources cited | Notes |
|---|---|---:|---:|---|---|---|

If you want, I can help you create a prompt set for your specific company/category to start tracking your AI recommendation visibility.


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

I don’t know your specific category yet, but you can check who ChatGPT recommends by testing the kinds of questions your customers would ask.

Try prompts like:

- “Who are the best [category] companies in [city/region]?”
- “Recommend a reliable [category] provider for [use case].”
- “What are the top-rated [category] businesses near [location]?”
- “Compare the best [category] options for [specific need].”

Then check:

1. **Which brands are named**
2. **Whether your business appears**
3. **What reasons ChatGPT gives**
4. **What sources or citations are used, if browsing/search is enabled**
5. **Whether competitors appear repeatedly across different prompts**

For more accurate results, test in:

- ChatGPT with web browsing/search enabled
- Google AI Overviews
- Perplexity
- Gemini
- Claude, if relevant

Use several variations of the prompt because AI recommendations can change based on wording, location, and available sources.

If you tell me your category, location, and business name, I can suggest exact prompts to test.


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

You can’t measure “ChatGPT usage” the same way you measure Google search volume because most AI assistants don’t expose query-volume or impression data. But you *can* measure its impact using a combination of attribution, synthetic testing, and buyer research.

Here’s a practical framework for a B2B SaaS vendor.

---

## 1. Track traffic and leads coming from AI assistants

In GA4, HubSpot, Salesforce, or your analytics tool, create a channel for AI referrals.

Common referrers to monitor:

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

Metrics to track:

- Sessions from AI tools
- Demo requests
- Trial signups
- Contact-us submissions
- Pipeline and closed-won revenue
- Conversion rate versus organic search

Important caveat: this will undercount heavily. Many AI-influenced buyers will visit you directly, through Google, or through a copied link rather than a tracked referral.

---

## 2. Add “How did you hear about us?” options

Use self-reported attribution on forms and sales calls.

Add options like:

- ChatGPT / AI assistant
- Perplexity
- Claude
- Gemini
- Google search
- LinkedIn
- Analyst report
- Peer recommendation
- Other

Also train SDRs and AEs to ask:

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

Log this in your CRM.

This is one of the best ways to detect AI-assisted buying behavior that analytics will miss.

---

## 3. Monitor your visibility in AI answers

Run recurring tests for the prompts your buyers are likely asking.

Examples:

- “Best software for [category]”
- “Top [category] platforms for enterprise companies”
- “[Competitor] alternatives”
- “[Your company] vs [Competitor]”
- “What tools help with [use case]?”
- “Which vendors are best for [industry] [problem]?”

For each prompt, record:

- Are you mentioned?
- Where are you ranked?
- Are competitors mentioned instead?
- Is the description accurate?
- Are sources cited?
- Which pages are cited?
- Is your positioning correct?
- Are there factual errors?

Test across:

- ChatGPT
- Perplexity
- Claude
- Gemini
- Microsoft Copilot
- Google AI Overviews, if available

Do this monthly or quarterly so you can trend your “AI share of voice.”

---

## 4. Create an AI visibility scorecard

You can score each prompt like this:

| Metric | Example |
|---|---|
| Mentioned? | Yes / No |
| Rank | 1st, 2nd, 3rd, not listed |
| Sentiment | Positive, neutral, negative |
| Accuracy | Accurate, partially accurate, wrong |
| Citation | Your site, third party, none |
| Competitors mentioned | Vendor A, Vendor B |
| Buyer relevance | High, medium, low |

Then roll it up into metrics:

- AI share of voice
- AI rank by prompt
- AI sentiment
- Accuracy rate
- Citation share
- Competitor overlap
- Prompt coverage

This gives you a measurable baseline even without official impression data.

---

## 5. Compare organic search trends with direct and AI-influenced demand

Look for changes in:

- Branded search volume
- Non-branded organic traffic
- Direct traffic
- Homepage visits
- Pricing page visits
- “Alternatives” page visits
- Demo conversion rates
- Sales cycle source notes

If Google organic traffic is flat or down but direct traffic, branded search, or “heard about us from AI” responses are rising, that may indicate AI-assisted discovery.

---

## 6. Analyze your website logs for AI crawlers

AI systems and answer engines use crawlers to retrieve or index content.

Watch for bots such as:

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

This does **not** prove buyers are asking about you, but it shows whether AI systems are accessing your content.

Useful metrics:

- Which pages are crawled
- Crawl frequency
- Pages excluded by robots.txt
- Documentation or comparison pages accessed
- Pricing, integrations, and blog content crawled

---

## 7. Ask customers directly during onboarding or QBRs

For new customers, ask:

> “Did you use AI tools like ChatGPT, Perplexity, Claude, or Gemini while researching vendors?”

Follow up with:

- What did you ask?
- Were we mentioned?
- Was the information accurate?
- Which competitors came up?
- Did it influence your shortlist?

This gives you qualitative intelligence that analytics will not capture.

---

## 8. Watch third-party sources AI tools often cite

AI assistants often draw from pages like:

- G2
- Capterra
- Gartner Peer Insights
- Reddit
- LinkedIn
- Analyst reports
- Wikipedia
- Product documentation
- Comparison articles
- “Best software” listicles
- Integration marketplaces

Track whether your profiles are complete, current, and competitive on those sources.

Measure:

- Review volume
- Review quality
- Category ranking
- Competitor comparisons
- Message consistency
- Presence in “best of” articles
- Accuracy of third-party descriptions

---

## 9. Build dashboards around AI-assisted demand

A simple executive dashboard could include:

1. AI referral traffic
2. AI-sourced or AI-influenced leads
3. Pipeline from AI-attributed sources
4. AI share of voice by prompt cluster
5. Ranking in AI answers versus competitors
6. Accuracy of AI-generated descriptions
7. Top cited sources
8. Bot crawl activity
9. Customer survey mentions of AI research

---

## 10. Accept that this is directional, not perfect

The honest answer: you cannot currently know exactly how many buyers asked ChatGPT about your category.

But you can measure:

- How often AI tools send traffic
- How often buyers say they used AI
- Whether you appear in AI-generated vendor recommendations
- Whether the information is accurate
- Which competitors appear more often
- Whether AI-assisted leads convert
- Whether your content is being crawled and cited

That combination gives you a practical measurement system for AI-driven discovery.


## Best AI visibility monitoring tools for SaaS marketing teams

AI visibility tools help you track how often your brand, competitors, and content appear in AI answers from tools like ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews.

### Recommended shortlist

| Tool | Best for | Why it’s useful for SaaS marketing |
|---|---|---|
| **Profound** | Enterprise SaaS and larger teams | Strong AI search/share-of-voice tracking, competitive visibility, citation monitoring, and executive-level reporting. |
| **Peec AI** | B2B SaaS growth teams | Tracks brand visibility across AI platforms, competitor mentions, prompts, and citation sources. Good for ongoing GEO/AI SEO workflows. |
| **Scrunch AI** | SaaS teams focused on AI answer optimization | Helps monitor how AI systems describe your brand and where they source information from. Useful for positioning and content gaps. |
| **Otterly.AI** | Affordable AI visibility monitoring | Tracks prompts, brand mentions, rankings, and citations across AI answer engines. Good entry point for smaller SaaS teams. |
| **AthenaHQ** | AI search optimization programs | Focuses on brand presence in generative search and helps identify which sources influence AI answers. |
| **Semrush** | Teams already using SEO tooling | Useful for traditional SEO plus monitoring Google AI Overviews and broader search visibility. Best as a complement, not a dedicated AI visibility platform. |
| **Ahrefs** | SEO-led SaaS teams | Strong for backlink/content/source analysis; useful for improving the pages AI tools are likely to cite. Less specialized for AI visibility tracking. |

## Best picks by team type

### Best overall for SaaS: **Peec AI**
Good balance of AI visibility tracking, competitor monitoring, and usability for marketing teams.

### Best for enterprise SaaS: **Profound**
Better suited for larger teams that need advanced reporting, market-level visibility, and executive dashboards.

### Best budget-friendly option: **Otterly.AI**
A practical starting point if you want to monitor prompts, mentions, and citations without a large platform commitment.

### Best if SEO is your main channel: **Semrush + a dedicated AI visibility tool**
Use Semrush or Ahrefs for SEO fundamentals, then pair with Peec AI, Profound, or Otterly.AI for AI-specific visibility.

## What to look for

For a SaaS marketing team, prioritize tools that can track:

- Brand mentions in ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews
- Competitor share of voice
- Prompt-level rankings for buyer-intent queries
- Citation/source URLs used by AI engines
- Sentiment and positioning of your brand
- Changes over time
- Regional and language differences
- Exports, Slack alerts, and dashboard reporting

## Suggested stack

For most SaaS teams:

**Peec AI or Otterly.AI** for AI visibility monitoring
+ **Ahrefs or Semrush** for SEO/content authority
+ **GA4/Search Console/CRM** for pipeline attribution

If you’re an enterprise team, replace Peec/Otterly with **Profound**.


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

### What GEO software typically does
- Tracks whether your brand appears in AI answers
- Monitors citations and sources used by AI engines
- Finds prompts/questions where competitors are mentioned
- Identifies content gaps
- Recommends pages to optimize for AI visibility
- Tracks sentiment and accuracy of AI-generated brand mentions
- Helps optimize FAQs, comparison pages, expert content, and structured data

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

- **Profound** — AI search visibility and brand tracking
- **Peec AI** — tracks brand visibility across AI answer engines
- **Otterly.AI** — monitors AI search results and citations
- **AthenaHQ** — GEO and AI answer optimization
- **Semrush** — traditional SEO with AI visibility features emerging
- **Ahrefs** — useful for content, backlinks, and authority signals that support GEO
- **BrightEdge** — enterprise SEO platform with AI search insights

### What to look for in GEO software
Choose a platform that can track:

1. **Brand mentions in AI answers**
2. **Competitor visibility**
3. **Citation sources**
4. **Prompt/query rankings**
5. **Google AI Overview presence**
6. **Content recommendations**
7. **Reporting by topic, market, and language**

### Best GEO strategy
GEO software is useful, but the core strategy is still:

- Create authoritative, well-structured content
- Answer specific questions clearly
- Build credible citations and backlinks
- Use schema markup
- Publish comparison, FAQ, and expert-led content
- Get mentioned on trusted third-party websites

In short: **GEO software helps you measure and improve how often AI engines mention, cite, and recommend your brand.**


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

You can monitor AI-brand mentions in two main ways: use a dedicated “AI visibility” tool, or build a lightweight tracking workflow yourself.

## Option 1: Use an AI visibility / GEO monitoring tool

Tools in this category track whether your brand appears in AI answers across platforms like ChatGPT, Perplexity, Gemini and sometimes Claude.

Examples to evaluate:

- **Profound**
- **Otterly.AI**
- **Peec AI**
- **Scrunch AI**
- **AthenaHQ**
- **Semrush AI visibility/AI toolkit features**
- **Ahrefs Brand Radar**, depending on coverage

When comparing tools, check whether they support:

- ChatGPT, Perplexity and Gemini specifically
- Your target countries/languages
- Scheduled prompt monitoring
- Competitor comparison
- Citation/source tracking
- Sentiment or context analysis
- Historical trend reporting
- Export/API access

This is the easiest route if you want dashboards, alerts and ongoing reporting.

## Option 2: Build your own monitoring workflow

### 1. Create a prompt set

Track prompts your potential customers might ask, for example:

- “Best software for [category]”
- “Top [category] companies”
- “[Your brand] vs [competitor]”
- “Alternatives to [competitor]”
- “Who are the leading providers of [service]?”
- “Is [your brand] good for [use case]?”

Group prompts by:

- Brand queries
- Category queries
- Competitor queries
- Use-case queries
- Local/industry-specific queries

### 2. Run them on each AI platform

Test regularly on:

- **ChatGPT** — ideally with web browsing/search enabled if relevant
- **Perplexity**
- **Gemini**

Use a clean browser/session where possible, since results can vary based on account, location, history and model version.

### 3. Track the key fields

Use a spreadsheet or database with columns like:

| Date | Platform | Prompt | Was brand mentioned? | Position/order | Sentiment | Competitors mentioned | Citations/sources | Notes |
|---|---|---|---|---|---|---|---|---|

Important metrics:

- Mention rate
- Average position in answer
- Share of voice vs competitors
- Sentiment/context
- Whether your website is cited
- Which third-party sources influence the answer

### 4. Repeat on a schedule

Run the same prompts weekly or monthly. AI answers change frequently, so one-off checks are not enough.

### 5. Improve the sources AI systems rely on

To increase mentions, focus on improving your presence in sources that AI engines cite or learn from:

- High-quality comparison pages
- Review sites
- Industry directories
- PR coverage
- Wikipedia/Wikidata, where appropriate
- Authoritative blogs and guides
- Case studies
- Clear product/service pages
- FAQ content answering category-level questions

## Important limitation

There is no universal “Google Alerts for ChatGPT/Gemini/Perplexity” that monitors every private AI conversation. You can only monitor answers generated from a defined set of prompts, locations and models.

## Best practical setup

If this is for business reporting, I’d recommend:

1. Build a list of 50–200 important prompts.
2. Use a dedicated AI visibility tool to monitor ChatGPT, Perplexity and Gemini weekly.
3. Track your brand, top competitors, sentiment and cited sources.
4. Use the citation data to guide SEO, PR and content improvements.


Yes. The category is often called **LLM SEO**, **GEO — Generative Engine Optimization**, or **AI search visibility**.

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

Examples include:

- **Profound** — AI search/LLM visibility tracking for brands.
- **Peec AI** — tracks brand mentions and competitors across AI engines.
- **AthenaHQ** — GEO/AI search optimization platform.
- **Scrunch AI** — monitors how AI agents interpret and recommend your brand.
- **Semrush / Ahrefs / BrightEdge** — traditional SEO platforms adding AI visibility features.

What they typically do:

- Track whether your brand is mentioned in LLM answers.
- Monitor competitors in AI-generated responses.
- Show which sources LLMs cite.
- Identify prompts/questions where you should appear.
- Recommend content updates to improve AI visibility.

One caveat: LLM “rankings” are less stable than Google rankings, so these tools are more about **visibility, citations, authority, and answer inclusion** than classic keyword ranking.

If you want the simple answer: **yes — look for GEO or AI search visibility tools.**


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 / AI share-of-voice report** — sometimes called a **GEO report**, **LLM visibility audit**, or **AI search visibility report**.

You can get one from:

### 1. AI visibility monitoring platforms
These tools test prompts across ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, etc., and report whether your brand is recommended vs. competitors.

Examples to look at:
- **Profound**
- **Peec AI**
- **Otterly.AI**
- **Scrunch AI**
- **AthenaHQ**
- **Goodie AI**
- **Brandlight**

### 2. SEO / content agencies offering “AI search” audits
Many SEO agencies now provide **AI visibility audits** that show:
- Which chatbots mention your product
- Whether competitors are recommended more often
- Which queries you appear for
- Sentiment around your brand
- Sources/citations influencing the answer
- Recommendations to improve visibility

Search terms:
- “AI search visibility audit”
- “LLM brand visibility report”
- “GEO agency”
- “Generative engine optimization report”
- “AI chatbot brand monitoring”

### 3. DIY lightweight version
You can also create a basic version by testing prompts manually across tools like ChatGPT, Claude, Gemini, and Perplexity, for example:

> “What is the best [product category] for [use case]?”
> “Compare [your product] vs [competitor].”
> “Which [category] tool should I buy?”
> “Recommend a [product category] for [target customer].”

Track:
- Whether your brand appears
- Ranking/order of recommendation
- Competitors mentioned
- Accuracy of the description
- Sources cited, if any

If you want a proper business-ready report, I’d look for an **AI visibility monitoring platform** or an agency offering a **Generative Engine Optimization / LLM visibility audit**.


AI search visibility tracking platform pricing

Typical pricing for an **AI search visibility tracking platform** varies by tracked prompts, brands, competitors, search engines, and reporting frequency.

| Tier | Typical Price | Best For | Common Limits |
|---|---:|---|---|
| Starter | $99–$300/month | Small brands, consultants | Limited prompts, weekly tracking, 1–3 competitors |
| Growth | $300–$1,000/month | SEO/content teams | More prompts, daily tracking, multiple AI engines |
| Pro / Agency | $1,000–$3,000/month | Agencies, multi-brand teams | Client dashboards, exports, more seats |
| Enterprise | $3,000–$10,000+/month | Large brands | Custom prompts, API, SSO, advanced analytics, support |

Common features included:

- Tracking brand visibility in ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, etc.
- Share-of-voice reporting
- Competitor comparisons
- Citation/source tracking
- Prompt and topic monitoring
- GEO/AI SEO recommendations
- Alerts and trend reports
- White-label or client reporting for agencies

Most platforms price based on:

1. Number of prompts tracked
2. Number of AI/search engines monitored
3. Tracking frequency: weekly, daily, or real-time
4. Number of brands or domains
5. Competitors tracked
6. Seats/users
7. API access and integrations
8. Reporting/white-label needs

If you’re evaluating tools, expect many AI visibility platforms to use **custom pricing**, especially for enterprise or agency plans.


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, treat AI assistants like a new search/discovery channel and measure how often, where, and how prominently your brand appears in their responses.

## 1. Define your benchmark set

Start with the questions your buyers are likely to ask AI tools.

Group prompts by intent, for example:

- “Best tools for X”
- “Top alternatives to [competitor]”
- “How do I solve [problem]?”
- “Compare [your brand] vs [competitor]”
- “What companies offer [category]?”
- “Which [product/service] is best for [industry/use case]?”

For each prompt, define:

- Target market or region
- Buyer persona
- Industry or use case
- Funnel stage
- Competitor set

Aim for at least **50–200 representative prompts** to start.

## 2. Choose the AI platforms to test

Benchmark across the assistants your audience is likely to use, such as:

- ChatGPT
- Google AI Overviews / Gemini
- Perplexity
- Claude
- Microsoft Copilot
- You.com or other answer engines

If possible, test both **general AI answers** and **AI search answers with citations**.

## 3. Run prompts consistently

For each prompt, collect multiple outputs because AI answers can vary.

Best practices:

- Use the same prompt wording each time
- Test from the same location/language where possible
- Run prompts at regular intervals, e.g. weekly or monthly
- Capture full answers, citations, ranking/order of mentions, and URLs referenced
- Use APIs or approved tools where possible rather than violating platform terms

## 4. Measure brand presence

For each answer, track whether your brand and competitors appear.

Key metrics include:

### Basic share of voice

```text
Brand SOV = Brand mentions / Total competitor category mentions
```

Example:
If your brand is mentioned 30 times and all tracked brands are mentioned 150 times total:

```text
30 / 150 = 20% AI share of voice
```

### Prompt-level visibility

```text
Visibility Rate = Prompts where brand appears / Total prompts tested
```

### Prominence score

Give higher weight to brands mentioned earlier or recommended more strongly.

Example scoring:

- First recommended brand: 5 points
- Second: 4 points
- Third: 3 points
- Mentioned but not recommended: 1 point
- Not mentioned: 0 points

### Citation share

For AI search engines:

```text
Citation Share = Your URLs cited / Total URLs cited for your category
```

### Sentiment or recommendation strength

Classify each mention as:

- Strong recommendation
- Neutral mention
- Negative mention
- Comparison only
- Not mentioned

## 5. Compare against competitors

Create a table like this:

| Brand | Mention Share | Visibility Rate | Avg. Rank | Citation Share | Sentiment |
|---|---:|---:|---:|---:|---|
| Your brand | 22% | 38% | 3.1 | 18% | Mostly positive |
| Competitor A | 35% | 55% | 1.8 | 32% | Positive |
| Competitor B | 25% | 44% | 2.6 | 24% | Neutral-positive |
| Competitor C | 18% | 29% | 4.0 | 12% | Mixed |

This lets you see not just whether you are mentioned, but whether AI systems are **recommending you prominently**.

## 6. Analyze the sources behind AI answers

For cited answers, identify which sources influence visibility:

- Review sites
- Analyst reports
- Comparison pages
- Reddit or forums
- Wikipedia
- News articles
- Partner pages
- Your own website
- Competitor websites

Look for patterns such as:

- Competitors appearing on more “best of” lists
- Your product pages not being cited
- Missing comparison content
- Weak third-party validation
- Outdated or inconsistent brand descriptions

## 7. Track over time

AI share of voice should be monitored like SEO rankings.

Recommended cadence:

- Monthly for stable categories
- Weekly for highly competitive or fast-moving categories
- Before and after major content, PR, or product launches

Track:

- SOV trend
- Prompt categories where you win or lose
- Competitors gaining visibility
- Changes in citations
- Changes in positioning or messaging

## 8. Improve your AI visibility

To increase your share of voice:

- Publish clear category and use-case pages
- Create comparison pages against competitors
- Earn mentions in authoritative third-party sources
- Keep listings updated on review sites
- Strengthen schema markup and structured data
- Make pricing, features, integrations, and positioning easy to parse
- Build high-quality “best tools for X” and “alternatives to X” content
- Ensure your brand is consistently described across the web

## Simple benchmark framework

Use this scorecard:

```text
AI Visibility Score =
40% Mention Share
+ 25% Prominence / Rank
+ 20% Citation Share
+ 15% Sentiment / Recommendation Strength
```

This gives you a more complete view than mentions alone.

In short: build a representative prompt set, test it across major AI platforms, measure mentions, ranking, citations, and sentiment, then compare those metrics against competitors over time.


tools to improve how AI assistants describe my company

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

### 1. Update your public web presence
AI assistants often rely on publicly available information.

- Your homepage and “About” page
- Product/service pages
- FAQ pages
- Press/news pages
- Leadership/team bios
- Customer case studies

Make sure your company description is clear, consistent, and repeated across trusted pages.

### 2. Add structured data to your website
Use schema markup so search engines and AI systems can better understand your company.

Useful schema types:

- `Organization`
- `LocalBusiness`
- `Product`
- `Service`
- `FAQPage`
- `Review`
- `SameAs` links to social profiles

Tools:
- Google Rich Results Test
- Schema.org Validator
- Merkle Schema Markup Generator
- WordPress plugins like Rank Math or Yoast SEO

### 3. Improve your company’s knowledge graph presence
AI tools often pull from sources connected to knowledge graphs.

Focus on:

- Google Business Profile
- LinkedIn company page
- Crunchbase
- Wikidata
- Wikipedia, if notable enough
- OpenCorporates
- G2, Capterra, Trustpilot, or relevant industry directories

### 4. Publish a clear “AI-friendly” company summary
Create a page such as:

`yourcompany.com/about/company-overview`

Include:

- What your company does
- Who you serve
- Main products/services
- Industries
- Locations
- Founding year
- Differentiators
- Official boilerplate description

Example:

> [Company] is a [category] company that helps [audience] solve [problem] using [product/service]. Founded in [year], the company serves [markets/customers] and is known for [differentiator].

### 5. Monitor how AI assistants describe you
Test your company name in:

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

Ask prompts like:

- “What does [Company] do?”
- “Describe [Company] in one paragraph.”
- “Who are [Company]’s competitors?”
- “Is [Company] legitimate?”
- “What is [Company] best known for?”

Track inaccuracies and update your public sources accordingly.

### 6. Use SEO and brand monitoring tools
Helpful tools include:

- Semrush
- Ahrefs
- Moz
- Google Search Console
- Google Alerts
- Brand24
- Mention
- SparkToro

These help you find outdated, inaccurate, or weak descriptions of your company online.

### 7. Create authoritative content
Publish content that clearly explains your positioning:

- Comparison pages
- Use-case pages
- Industry pages
- Customer stories
- Research reports
- Glossary pages
- Founder/CEO interviews

AI assistants tend to favor clear, well-linked, authoritative sources.

### 8. Get listed in trusted third-party sources
AI systems are more likely to trust descriptions from independent sources.

Examples:

- Industry directories
- Analyst reports
- Review platforms
- News articles
- Podcast interviews
- Partner marketplaces
- Association websites

### 9. Keep descriptions consistent everywhere
Use the same core positioning across:

- Website
- LinkedIn
- Crunchbase
- Google Business Profile
- Press releases
- Social profiles
- App marketplaces
- Review sites

Inconsistency makes AI-generated descriptions less accurate.

### 10. Build an LLM-ready brand facts page
Create a simple public page titled something like:

**“Company Facts”** or **“Official Company Information”**

Include:

- Legal company name
- Brand name
- Short description
- Long description
- Founding date
- Headquarters
- Target customers
- Products/services
- Pricing model
- Executive team
- Official social links
- Media contact

This gives AI systems and journalists a clean source to reference.


What software tracks LLM citations of my website?

Yes—there are now several “AI search visibility” / “GEO” tools that track when LLMs cite, mention, or link to your website.

Popular options:

- **Profound** — Tracks brand mentions, citations, and visibility across ChatGPT, Perplexity, Google AI Overviews, etc.
- **Otterly.AI** — Monitors prompts and shows whether your site is cited in ChatGPT, Perplexity, and AI search results.
- **Peec AI** — Tracks LLM visibility, citations, competitors, and share of voice.
- **Scrunch AI** — AI search analytics for tracking brand mentions and citations in LLM answers.
- **AthenaHQ** — Monitors how AI systems describe and reference your brand/site.
- **Brandlight** — Enterprise-oriented AI visibility and brand monitoring.
- **Semrush AI Toolkit / AI Overview tracking** — Useful if you already use Semrush, especially for Google AI Overviews.
- **Ahrefs Brand Radar** — Helpful for tracking brand visibility and AI Overview presence, depending on your use case.
- **ZipTie.dev** — Focused on monitoring AI search visibility and citations.

Important note: most of these tools do **not** receive private citation data directly from OpenAI, Google, Anthropic, etc. They typically work by repeatedly querying LLMs with target prompts and tracking whether your domain appears in answers.

If you want to track actual traffic from LLMs, also use:

- **GA4**
- **Plausible / Fathom**
- **Cloudflare Web Analytics**
- **Server logs**

and filter referrers such as `chatgpt.com`, `perplexity.ai`, `gemini.google.com`, `claude.ai`, etc.


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

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

If you want one name to give your CEO: **Profound** is one of the best-known tools for tracking whether your brand appears in ChatGPT, Perplexity, Gemini, etc.

Other options: **Peec AI**, **AthenaHQ**, **OtterlyAI**, **Goodie**, **Semrush AI SEO Toolkit**.

These tools answer questions like:

- Do we show up in ChatGPT answers for target prompts?
- Which competitors show up instead?
- What sources is ChatGPT citing?
- What content do we need to create or improve?
- How is our “AI share of voice” changing over time?

Important caveat: there’s no official “Google Search Console for ChatGPT.” Results can vary by user, prompt, location, and whether browsing is enabled. These tools give monitored estimates across repeatable prompt sets.


answer engine optimization tools for marketers

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

## 1. AI Search & Answer Visibility Tracking
Tools to monitor how your brand appears in AI answers.

- **Profound** – Tracks brand visibility across AI search engines like ChatGPT, Perplexity, and Google AI Overviews.
- **Peec AI** – Monitors how often and how positively AI platforms mention your brand.
- **Otterly.AI** – Tracks AI search visibility, prompts, citations, and competitor mentions.
- **Scrunch AI** – Helps brands understand how AI systems interpret and recommend them.
- **Rankscale.ai** – Tracks AI search rankings and answer engine performance.

## 2. Traditional SEO Tools Useful for AEO
These help identify questions, intent, and content gaps.

- **Semrush** – Keyword research, SERP features, content gaps, and topic clustering.
- **Ahrefs** – Competitor research, backlink analysis, keyword questions, and content opportunities.
- **Moz Pro** – SEO tracking, domain authority, and keyword insights.
- **Similarweb** – Competitive traffic and market intelligence.
- **Screaming Frog** – Technical SEO audits and structured data checks.

## 3. Content Optimization Tools
Useful for creating content that answer engines can easily understand and cite.

- **Surfer SEO** – Content optimization based on search intent and competitor pages.
- **Clearscope** – NLP-based content optimization and topical coverage.
- **MarketMuse** – Content planning, authority building, and topic gap analysis.
- **Frase** – Great for question-based content and answer-focused briefs.
- **NeuronWriter** – Semantic SEO and content optimization.

## 4. Question & Intent Research Tools
AEO depends heavily on answering real user questions clearly.

- **AnswerThePublic** – Finds common questions around topics.
- **AlsoAsked** – Maps People Also Ask-style question relationships.
- **Google Search Console** – Reveals actual queries driving impressions.
- **Google Trends** – Tracks topic demand over time.
- **Reddit / Quora / TikTok search** – Useful for finding natural-language questions.

## 5. Structured Data & Schema Tools
Schema helps answer engines understand your content.

- **Schema.org Validator** – Tests structured data markup.
- **Google Rich Results Test** – Checks eligibility for rich search results.
- **Merkle Schema Markup Generator** – Easy schema creation.
- **Yoast SEO** – WordPress SEO and schema support.
- **Rank Math** – WordPress SEO plugin with strong schema features.

## 6. AI Content & Workflow Tools
Helpful for drafting, summarizing, and formatting answer-friendly content.

- **ChatGPT** – Content outlines, FAQs, schema drafts, and answer optimization.
- **Claude** – Long-form content refinement and summarization.
- **Perplexity** – Research and citation discovery.
- **Jasper** – AI marketing content workflows.
- **Writer.com** – Brand-safe AI content creation for teams.

## 7. Analytics & Conversion Tracking
AEO should be tied to business outcomes, not just visibility.

- **GA4** – Tracks traffic, engagement, and conversions.
- **Looker Studio** – Builds SEO and AEO dashboards.
- **HubSpot** – Connects content performance to leads and CRM.
- **Adobe Analytics** – Enterprise analytics.
- **CallRail** – Tracks phone leads from organic and AI-assisted discovery.

## Recommended Starter Stack

For most marketing teams:

1. **Semrush or Ahrefs** – SEO and keyword research
2. **Frase or Clearscope** – Answer-focused content optimization
3. **AlsoAsked or AnswerThePublic** – Question research
4. **Schema.org Validator + Rank Math/Yoast** – Structured data
5. **Otterly.AI, Profound, or Peec AI** – AI answer visibility tracking
6. **GA4 + Looker Studio** – Reporting and performance measurement

AEO is still emerging, so the best approach is to combine **SEO fundamentals, structured content, authority building, and AI visibility tracking**.


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

To audit whether LLMs “know” about your product, test three things separately:

1. **Awareness** — Does the model mention your product at all?
2. **Understanding** — Does it describe the product accurately?
3. **Recommendation behavior** — Does it suggest your product when users ask for solutions in your category?

Here’s a practical audit process.

---

## 1. Define the product facts you expect models to know

Create a short reference document with:

- Product name
- Company name
- Category
- Target customers
- Core features
- Pricing, if relevant
- Differentiators
- Competitors
- Use cases
- Common misconceptions
- Recent launches or changes

This becomes your “ground truth” for scoring model answers.

---

## 2. Test multiple model types

Audit both:

### General LLMs
Examples:

- ChatGPT
- Claude
- Gemini
- Perplexity
- Microsoft Copilot
- Meta AI

### Search-augmented or browsing models
These may know about you through live web retrieval rather than training data.

You should test them separately because “the model knows” and “the model found you online” are different things.

---

## 3. Use several prompt categories

Do not only ask, “Do you know Product X?” That is too narrow.

Test prompts like these:

### Direct awareness prompts

- “What is [Product Name]?”
- “Tell me about [Product Name] by [Company].”
- “What does [Company] do?”
- “Is [Product Name] a legitimate product?”

### Category recommendation prompts

- “What are the best tools for [job to be done]?”
- “What software helps with [problem your product solves]?”
- “Compare tools for [category].”
- “What are alternatives to [competitor]?”

### Comparison prompts

- “[Product Name] vs [Competitor]”
- “How does [Product Name] compare to [Competitor]?”
- “What are the pros and cons of [Product Name]?”

### Buyer-intent prompts

- “I need a tool that can [specific use case]. What should I consider?”
- “Recommend vendors for [category] for a mid-sized company.”
- “What is the best [category] software for [target customer]?”

### Misconception prompts

- “Is [Product Name] only for [incorrect use case]?”
- “Does [Product Name] support [feature]?”
- “Who uses [Product Name]?”

---

## 4. Score the responses

Use a simple scoring table.

| Metric | Question |
|---|---|
| Awareness | Did the model mention your product? |
| Accuracy | Were the facts correct? |
| Positioning | Did it describe the product the way you would? |
| Feature coverage | Did it mention key features? |
| Differentiation | Did it explain why you are different? |
| Recommendation | Did it recommend you for relevant use cases? |
| Competitor context | Did it compare you fairly to alternatives? |
| Hallucination | Did it invent false claims, features, pricing, or customers? |
| Freshness | Did it know about recent updates? |
| Source quality | If citations are shown, are they accurate and authoritative? |

A simple 0–3 scale works well:

- **0** = missing or completely wrong
- **1** = mentioned but mostly incomplete or inaccurate
- **2** = mostly accurate but missing important context
- **3** = accurate, useful, and well-positioned

---

## 5. Run the audit repeatedly

LLM outputs vary. Run each prompt multiple times, especially with different wording.

For example:

- 20–50 prompts total
- 3–5 runs per prompt
- Across 4–6 major AI systems

Then calculate:

- Product mention rate
- Accuracy score
- Recommendation rate
- Hallucination rate
- Competitor mention rate
- Share of voice versus competitors

---

## 6. Separate organic knowledge from web retrieval

For tools with browsing or citations, check where the answer came from.

Useful questions:

- Is the model citing your website?
- Is it citing third-party reviews?
- Is it citing outdated articles?
- Is it pulling from competitor pages?
- Is it relying on directories like G2, Capterra, Product Hunt, Crunchbase, or Wikipedia?

If it only mentions you when web browsing is enabled, then your web presence matters more than model training data.

---

## 7. Compare yourself against competitors

Create category prompts where your product should reasonably appear.

Example:

> “What are the top tools for [category]?”

Track:

- Which competitors are named
- Their order
- How often you appear
- What differentiators the model assigns to each vendor
- Whether you are omitted from lists where you should appear

This gives you an “AI visibility” benchmark.

---

## 8. Look for incorrect or outdated information

Common issues include:

- Old pricing
- Old product names
- Missing recent features
- Wrong target market
- Confusion with similarly named companies
- Invented integrations
- False claims about funding, customers, or availability
- Incorrect comparisons with competitors

Document these issues with screenshots, model name, date, prompt, and response.

---

## 9. Improve the source material models may use

If the audit shows poor awareness or inaccurate answers, improve your public information footprint.

Prioritize:

- Clear product pages
- “What is [Product]?” pages
- Comparison pages
- Use-case pages
- Documentation
- Pricing pages
- FAQ pages
- Schema markup
- Case studies
- Third-party reviews
- Press coverage
- Directory listings
- Wikipedia or Wikidata, if notable and appropriate
- Consistent naming across the web

For search-augmented models, high-quality public pages and authoritative third-party sources are especially important.

---

## 10. Repeat quarterly

LLM visibility changes over time due to:

- New model releases
- Search index updates
- Retrieval changes
- New content
- Competitor activity
- Product updates

Run a baseline audit now, then repeat every quarter or after major launches.

---

## Simple audit template

You can track results like this:

| Prompt | Model | Mentioned? | Accurate? | Recommended? | Hallucinations | Notes |
|---|---|---:|---:|---:|---|---|
| “What is [Product]?” | ChatGPT | Yes | 2/3 | N/A | Minor | Missed enterprise features |
| “Best tools for [category]” | Claude | No | N/A | No | None | Competitors mentioned |
| “[Product] vs [Competitor]” | Perplexity | Yes | 3/3 | Yes | None | Cited official docs |

---

In short: build a prompt set, test across major AI systems, score awareness and accuracy, compare against competitors, identify hallucinations, and improve the public sources that models and retrieval systems rely on.


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

Yes. It’s often called **AI visibility tracking**, **LLM brand monitoring**, **AI search tracking**, or **GEO tracking** — short for **Generative Engine Optimization**.

It’s similar to SEO rank tracking, but instead of checking where you rank in Google, it tracks how AI assistants like **ChatGPT, Perplexity, Claude, Gemini, Copilot**, etc. answer prompts related to your category.

Typically it measures things like:

- Whether your brand/product is mentioned
- Where you appear compared with competitors
- Whether the AI recommends you
- The wording/sentiment around your brand
- Which sources/citations the AI uses
- How results vary by prompt, model, location, or time
- “Share of voice” across AI-generated answers

Example prompts might be:

- “Best CRM for small businesses”
- “Top alternatives to [competitor]”
- “What is the best project management software?”
- “Compare [your brand] vs [competitor]”

There are already tools in this space, often positioned as **AI search visibility** or **answer engine optimization** platforms. Some SEO platforms are also adding this type of tracking.

One important difference from traditional rank tracking: AI answers are less deterministic. The same prompt can produce different responses depending on model version, context, personalization, geography, and retrieval sources. So the tracking is usually based on repeated prompt testing and aggregate visibility rather than a single fixed “rank.”


software to measure AI chatbot recommendations of B2B products

If you want to measure **how AI chatbots recommend B2B products** — for example ChatGPT, Claude, Gemini, Perplexity, Copilot — you likely need an **AI visibility / LLM monitoring / answer engine optimization** tool.

Here are relevant software options:

### AI chatbot / LLM visibility tracking tools

1. **Profound**
   - Tracks how brands appear in AI answers.
   - Useful for measuring share of voice, recommendation frequency, sentiment, and competitors.

2. **Scrunch AI**
   - Monitors brand visibility across AI search and chatbot platforms.
   - Good for tracking whether your company is cited or recommended.

3. **Peec AI**
   - Focused on AI search visibility and brand monitoring.
   - Can show how often your product appears for target prompts.

4. **Otterly.AI**
   - Tracks brand mentions across ChatGPT, Perplexity, Google AI Overviews, and other AI surfaces.
   - Useful for SEO and AI answer monitoring.

5. **Rankscale AI**
   - Measures AI search visibility and competitive positioning.
   - Helpful for B2B categories and prompt tracking.

6. **AthenaHQ**
   - AI search optimization and visibility analytics.
   - Tracks how brands show up in generative AI results.

7. **Goodie AI**
   - Focuses on AI search / GEO visibility.
   - Can help measure recommendations across conversational engines.

8. **Semrush Enterprise AI Optimization**
   - Semrush has started adding AI visibility and AI search tracking features.
   - Useful if you already use Semrush for SEO.

9. **Brandwatch / Sprinklr**
   - Not chatbot-specific, but useful for broader brand and sentiment monitoring.
   - Can complement AI recommendation tracking.

---

### What to measure

For B2B product recommendations, track:

- **Recommendation frequency**: How often your product is recommended.
- **Share of voice**: Your mentions vs. competitors.
- **Ranking position**: Whether you are listed first, second, etc.
- **Prompt coverage**: Which buyer-intent questions trigger your brand.
- **Sentiment**: Positive, neutral, or negative description.
- **Accuracy**: Whether pricing, features, integrations, and positioning are correct.
- **Citation sources**: Which pages or websites influence the AI answer.
- **Competitor comparisons**: How your product is described against alternatives.

---

### Example prompts to monitor

- “Best CRM software for mid-market B2B SaaS companies”
- “Top alternatives to Salesforce for B2B sales teams”
- “Best customer support platforms for enterprise companies”
- “Compare HubSpot, Salesforce, and Pipedrive”
- “What is the best software for B2B lead scoring?”
- “Which account-based marketing platforms are best for enterprise?”

---

### Best approach

Use one of the AI visibility tools above, then create a fixed set of buyer-intent prompts and monitor them weekly across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews.

For a B2B company, I’d shortlist:

**Profound**, **Scrunch AI**, **Peec AI**, and **Otterly.AI**.


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

Costs vary by tool and scope, but typical pricing for monitoring brand visibility in AI search is:

- **DIY/manual monitoring:** Free, but time-consuming
- **Basic tools:** About **$50–$200/month**
- **AI search visibility platforms:** Around **$300–$1,500/month**
- **Enterprise monitoring:** Often **$2,000+/month**, usually custom-priced

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

If you only need a light view of how your brand appears in ChatGPT, Gemini, Perplexity, and Google AI Overviews, expect to start in the low hundreds per month.


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

There are a few practical options, depending on how rigorous you want the monitoring to be.

## 1. Manual spot checks

You periodically ask ChatGPT questions relevant to your market, such as:

- “Who are the leading vendors in [category]?”
- “What are the best alternatives to [competitor]?”
- “What should I consider when buying [product]?”
- “Compare [your company] vs [competitor].”

**Pros:** Simple, low cost, fast to start.
**Cons:** Not systematic, hard to track changes over time, results may vary by model, location, personalization, and phrasing.

---

## 2. Structured prompt testing

Create a fixed set of questions and test them on a recurring schedule across ChatGPT and other AI systems.

You would track things like:

- Whether your company is mentioned
- How you are described
- Which competitors are recommended
- Accuracy of claims
- Sentiment and positioning
- Sources cited, if using search-enabled answers
- Changes over time

**Pros:** More consistent and measurable.
**Cons:** Requires setup and ongoing analysis.

---

## 3. Automated monitoring using the API

You can build an automated system that runs a library of market-relevant prompts through AI models on a schedule, then scores the responses.

Example scoring dimensions:

- Brand visibility
- Share of voice
- Competitor mentions
- Accuracy
- Message alignment
- Risky or outdated claims
- Citation quality
- Buying-journey coverage

**Pros:** Scalable, repeatable, good for dashboards and alerts.
**Cons:** API responses are not always identical to consumer ChatGPT, especially when browsing/search is involved.

---

## 4. AI visibility / “GEO” monitoring tools

There are emerging tools for monitoring brand visibility in generative AI platforms. These are sometimes described as:

- Generative Engine Optimization, or GEO
- AI search visibility
- LLM brand monitoring
- AI answer monitoring

These platforms typically run repeated prompts across ChatGPT, Claude, Gemini, Perplexity, Copilot, and others, then report brand visibility and competitor comparisons.

**Pros:** Faster than building your own, usually includes dashboards.
**Cons:** Market is still new; methodologies vary; you should validate how they collect and score results.

---

## 5. Monitor cited sources and web presence

For ChatGPT answers that use web search or cite sources, the underlying sources matter a lot.

You can monitor:

- Which websites are cited for your category
- Whether review sites, analyst reports, Reddit, Wikipedia, blogs, or competitor pages are influencing answers
- Whether your own pages are accessible, clear, and authoritative
- Whether third-party descriptions of your company are accurate

**Pros:** Helps you improve the inputs AI systems rely on.
**Cons:** Does not fully explain answers from models that rely on training data rather than live search.

---

## 6. Competitive prompt panels

Another option is to maintain a “prompt panel” that represents different buyer personas and stages.

For example:

- Early-stage buyer: “What is [category]?”
- Evaluation-stage buyer: “Top tools for [use case].”
- Comparison-stage buyer: “[Your company] vs [competitor].”
- Procurement-stage buyer: “Which vendors support [requirement]?”
- Risk-stage buyer: “What are the downsides of [your company]?”

**Pros:** Closely maps to real buyer behavior.
**Cons:** Needs regular updating as your market changes.

---

## 7. Internal sales and customer feedback loop

Ask sales, support, and customers what AI-generated answers they are seeing.

For example:

- Are prospects referencing ChatGPT during evaluations?
- Are they asking about competitors surfaced by AI?
- Are they repeating inaccurate claims?
- Are they using AI-generated RFP shortlists?

**Pros:** Captures real-world impact.
**Cons:** Anecdotal unless paired with structured monitoring.

---

## Important limitation

You generally cannot see what every ChatGPT user is asking or how ChatGPT answers them. Monitoring is done through representative testing, not direct access to private user interactions.

So the best approach is usually:

1. Define your most important buyer questions
2. Run them repeatedly across AI platforms
3. Track brand visibility, accuracy, sentiment, and competitors
4. Monitor cited sources
5. Improve the content ecosystem that AI systems learn from or retrieve from
6. Repeat monthly or weekly

For most companies, I would recommend starting with a structured prompt set and monthly reporting, then moving to automated monitoring once the question set and scoring criteria are proven.


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

Yes — you’re looking for an **AI search / LLM brand monitoring tool** that tracks when AI assistants recommend competitors instead of your brand.

Good tools to evaluate:

1. **Profound**
   - Tracks brand visibility across ChatGPT, Perplexity, Gemini, Claude, etc.
   - Shows competitor share of voice and which brands are recommended for key prompts.
   - Strong for enterprise AI search analytics.

2. **Peec AI**
   - Monitors how often your brand appears in AI-generated answers.
   - Compares you against competitors.
   - Good for tracking prompt-level visibility.

3. **Otterly.AI**
   - Tracks AI search results across ChatGPT, Perplexity, and Google AI Overviews.
   - Shows where your brand appears, citations, and competitor mentions.
   - Useful for SEO and content teams.

4. **Scrunch AI**
   - Focuses on how AI agents perceive and recommend your brand.
   - Helps identify gaps in brand positioning and content.

5. **AthenaHQ**
   - AI visibility and answer engine optimization platform.
   - Tracks brand recommendations, competitors, and prompts.

What you want the tool to show:

- Prompts where your brand should appear but doesn’t
- Competitors recommended instead of you
- Your share of voice vs. competitors
- Which sources/citations AI tools use
- Sentiment or positioning of your brand
- Changes over time across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews

If you want the simplest starting point, look at **Otterly.AI** or **Peec AI**. For a more advanced enterprise setup, look at **Profound**.


How do I find out if ChatGPT recommends my software?

To find out whether **ChatGPT recommends your software**, test it with a small, repeatable set of buying-intent prompts and log whether your product appears, where it appears, and how it is described.[4][6]

A practical method is:

- Ask category prompts like “What’s the best [your category] tool?” and “What do you recommend for [problem your software solves]?”[4]
- Compare your software against competitors using prompts like “[Your software] vs [competitor] vs [competitor].”[4]
- Run each prompt multiple times on different days, since recommendation outputs can vary.[4]
- Record whether your software is mentioned, how prominently it is placed, and whether ChatGPT frames it as a recommendation or just a mention.[4][7]

If you want to understand *why* it is or isn’t recommended, the strongest signals reported in these analyses are **brand/entity recognition**, **cross-platform consensus**, **review volume**, **Reddit discussion**, and **high-authority media or category-content mentions**.[1][6] In other words, ChatGPT tends to surface brands that are broadly and consistently represented across the web rather than only on the company’s own site.[1][6]

If you want, I can also give you a **copy-paste prompt set** and a **simple tracking sheet** you can use to test your software.


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

Yes — there are tools specifically built to track whether AI answer engines mention your brand. These are usually described as **AI brand monitoring**, **AI visibility**, or **answer engine optimization (AEO)** tools, and they monitor mentions across systems like ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, and Copilot.[2][6][8][9][10]

A few examples from the results include:

- **Beamtrace** — tracks when AI platforms mention your brand in their answers and reports frequency, context, and topics.[1]
- **OtterlyAI** — monitors scheduled prompt libraries and captures which brands appear in AI responses.[2]
- **Semrush** — tracks brand mentions, citations, and sentiment in AI-generated answers across multiple engines.[2]
- **Siftly** — says it tracks how major AI engines mention, cite, and describe your brand.[9]
- **SE Ranking** — says its AI Search Toolkit can monitor linked and unlinked brand mentions triggered by tracked prompts.[14]
- **HubSpot AEO** — shows how often your brand appears in AI answers and includes brand mention monitoring.[8][16]

The usual workflow is to build a prompt set based on real buyer questions, run it on a schedule across multiple AI engines, and record whether your brand appears, where it appears, and how it is described.[3][6][19]

If you want, I can also help you narrow these down by **budget**, **enterprise vs. SMB**, or whether you want **monitoring only** versus **monitoring plus fixes/actions**.


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

In practice, these tools track metrics such as **citation presence**, **citation position**, **mention rate**, **share of voice**, and sometimes **competitor presence** across a set of prompts you choose.[2][3][4][7][12]

If you want, I can also help you with one of these:
- a **list of the best AI answer engine rank trackers**
- a **comparison of free vs paid tools**
- a **recommendation based on your budget and platforms**


To **track brand visibility in AI answers**, monitor whether your brand is **mentioned, cited, recommended, or negatively/positively described** in responses from platforms like ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot.[3][6][7]

A practical way to do it is:

- **Choose high-value prompts** your customers actually ask, rather than generic SEO keywords.[1][3][8]
- **Run the same prompts across AI platforms** on a fixed schedule and log whether your brand appears.[3][7][9]
- **Record key metrics** such as mention frequency, citation rate, position in the answer, sentiment, and competitor mentions.[3][5][6][13]
- **Establish a baseline**, then compare future checks against it to measure gains or losses over time.[1][3][6][13]
- **Benchmark against competitors** to see who dominates the answers for each topic.[1][4][6][9]
- **Use a tracker or spreadsheet** if you need scale; several tools are built specifically for AI visibility monitoring.[2][4][5][12][14]

A simple starting template is:
- Query
- Platform
- Brand mentioned? \(Y/N\)
- Brand cited? \(Y/N\)
- Position in answer
- Competitors mentioned
- Sentiment
- Date tested

If you want, I can turn this into a **ready-to-use tracking spreadsheet** or a **weekly monitoring workflow**.


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

You can check your company’s **AI recommendation visibility** in two practical ways: use a dedicated visibility/ranking tool, and run the same recommendation prompts across major assistants yourself.[2][10][3]

- **Use an AI visibility/ranking dashboard:** Tools like Trakkr and GoAI publish rankings or visibility scores showing which brands AI engines recommend most, including movement over time and category-level leaderboards.[2][10]
- **Test the assistants directly:** Ask ChatGPT, Claude, Gemini, Grok, and similar tools the same buyer-intent prompts, such as “Who are the best [your category] in [your market]?” or “Which [service] company should I hire?” If your company is missing or appears inconsistently, that indicates low recommendation visibility.[3]
- **Compare by prompt and market:** The useful measurement is not a single global rank; it’s whether your company is named for the specific category, location, and use case your buyers care about.[3][4]
- **Track share of mentions over time:** Some dashboards measure a **visibility score** or **citation share** across prompts and models, which is closer to “where you rank” in AI assistants than a traditional SEO position.[2][15]

If you want, I can help you design a simple prompt set to test your company across ChatGPT, Claude, Gemini, and Perplexity in 10 minutes.


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

ChatGPT does **not have a single built-in “my category” list** it recommends from; it generates recommendations based on the specific query, your constraints, and, for shopping, the products and sources available through its shopping/retrieval pipeline.[3][10][16]

To check **who it recommends in your category**, ask ChatGPT a query like: “**Best [category] for [your customer type/use case]**,” then repeat with variations such as price range, use case, and comparison prompts, and record which names recur.[12][13][18]

A practical way to audit this is:
- Ask the exact prompt your customers would use, such as “best [category] for [ICP].”[12]
- Try comparison prompts like “[your product] vs [competitor]” and “affordable options for [need].”[13]
- Run the same checks across several days and note the brands, descriptions, and sources that appear.[11][13]
- If you want personalized recommendations, provide your preferences or past favorites and use ChatGPT’s memory if available in your account.[3]

If you want, I can help you build a **copy-paste prompt set** to test your category in ChatGPT.


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

To measure whether buyers are asking ChatGPT instead of Googling, track **three layers**: **AI visibility** (are you mentioned in answers), **AI referral traffic** (do visits come from AI tools), and **self-reported influence** in forms or interviews (did AI start the buying journey).[1][6][8][12]

A practical measurement stack looks like this:

- **AI visibility / share of voice**
  - Build a list of 20–100 buyer-intent prompts that your prospects would actually ask.
  - Run them weekly in ChatGPT and other major AI tools.
  - Record whether your brand appears, its position, the sentiment/context, and which competitors appear.
  - Convert this into a simple score such as brand mention rate or AI share of voice \( \text{Brand Citations} / \text{Total Category Citations} \times 100 \).[2][6][9][13][17]

- **AI referral traffic**
  - In analytics, filter sessions from AI sources such as `chat.openai.com`, `perplexity.ai`, `claude.ai`, and `gemini.google.com`.
  - Track sessions, landing pages, bounce rate, pages per session, and conversion rate from those sources.
  - Compare AI traffic conversion against other channels so you can tell whether it is high-quality demand or just curiosity.[1][7][8]

- **Declared attribution**
  - Add a form field like “How did you first hear about us?” with AI tools as an option.
  - Ask demo requests, trial signups, and closed-won customers whether ChatGPT or another AI assistant was part of their research.
  - Review closed-won deals monthly to count how many started with AI-assisted research.[1][12][16]

- **Pipeline impact**
  - Connect AI-referred sessions to CRM records.
  - Track AI-sourced or AI-influenced leads, opportunities, and closed-won revenue.
  - If you want a simple financial estimate, some teams calculate pipeline contribution as:
    \[
    (\text{AI-referred form completions}) \times (\text{average deal value}) \times (\text{close rate})
    \]
    which gives a directional estimate of AI-driven pipeline value.[8]

If you want the fastest starting point, use this weekly cadence:

- Pick your top 10–20 buyer prompts.
- Test them in ChatGPT, Perplexity, Claude, and Gemini.
- Log mention rate, rank, and competitors.
- Watch AI referral traffic in analytics.
- Add one self-reported attribution question to forms.
- Reconcile all three views in a monthly dashboard.[1][6][9][13]

The key limitation is that there is no perfect “ChatGPT search console” for all users, so the best measurement is a **triangulation** of manual prompt audits, referral analytics, and first-party attribution.[1][4][6][9]


Best AI visibility monitoring tools for SaaS marketing teams

For **SaaS marketing teams**, the best AI visibility monitoring tools are typically **Frase**, **Semrush**, **Profound**, **Otterly AI**, **Peec AI**, and **Wellows**, depending on whether you need *monitoring only* or a combined *monitoring + optimization* workflow.[3][1][4][15]

- **Frase** is a strong choice for content and marketing teams because it monitors where AI search cites your brand and helps close the gap in the same workflow.[3]
- **Semrush** is a good fit if you want **AI visibility tracking inside a broader SEO suite**, with large data coverage and integrated SEO workflows.[1]
- **Profound** is positioned for **enterprise-scale** teams that want AI visibility plus demand or business-outcome data.[4][15]
- **Otterly AI** is often a practical option for **simple, low-cost tracking** and monitoring AI search mentions/citations.[15][16]
- **Peec AI** is commonly recommended for **daily prompt monitoring** and specialist AI search analytics.[15][13]
- **Wellows** is useful if your team cares about **daily monitoring, historical trend tracking, and content/outreach opportunities**.[1][15]

If you want a quick shortlist by use case:

- **Best all-around for SaaS marketing:** **Frase**[3]
- **Best for existing SEO teams:** **Semrush**[1]
- **Best for enterprise visibility programs:** **Profound**[4][15]
- **Best budget/simple tracker:** **Otterly AI**[15][16]
- **Best for daily monitoring depth:** **Peec AI**[15][13]
- **Best for actioning insights into content/outreach:** **Wellows**[1][15]

What matters most for SaaS teams is whether the tool tracks *brand citations, competitor presence, and prompt-level visibility* across AI systems like ChatGPT, Perplexity, Claude, Gemini, and AI Overviews.[3][7][16] Tools that also connect insights to content fixes or SEO workflows tend to be more useful for marketing teams than monitoring-only tools.[3][8]


**Generative engine optimization software** refers to tools that help you track, improve, and measure how your brand appears in AI-generated answers from systems like ChatGPT, Google AI Overviews, Gemini, Perplexity, and Claude.[2][5][14]

These tools typically do three things:
- **Monitor visibility**: track whether your brand is mentioned, cited, or recommended in AI answers.[2][5][14]
- **Analyze prompts and citations**: identify the questions buyers ask and which sources AI models use in responses.[2][6]
- **Improve content strategy**: help structure content so it is easier for AI systems to understand, trust, and cite.[1][2][17]

Commonly cited GEO software in 2026 includes:
- **Semrush** — useful for combining SEO and AI visibility in one workflow.[6][17]
- **Otterly** — strong for fixed-prompt tracking and citation monitoring.[6]
- **Profound** — focused on real-user prompt intelligence.[6]
- **Conductor** — combines AI visibility with website monitoring and technical insights.[6]
- **XFunnel** — maps AI visibility across the buying journey.[6]
- **Mangools AI Search Grader** — a free tool for evaluating AI search visibility.[18]

If you want, I can also give you:
- a **shortlist of the best GEO software by use case**
- a **comparison table of top tools**
- or **recommendations for SaaS, ecommerce, or agencies**


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

To monitor your brand mentions across **ChatGPT, Perplexity, and Gemini**, use a **repeatable prompt set** and either track it manually or with a multi-engine monitoring tool that supports all three platforms.[1][2][10] The most reliable setup is to build a library of **20–50 buyer-intent queries**, run them on a schedule, and record whether your brand is mentioned, its position, sentiment, and cited sources.[1][4][18]

- **Manual method:** Open each platform, enter the same customer-style prompts, and log the results in a spreadsheet.[1][19]
- **What to track:** brand mention, rank/position in the answer, sentiment, competitors mentioned, and citations/sources.[2][4][19]
- **How often:** many guides recommend running the same prompts weekly, and repeating each prompt 3–5 times because responses can vary between runs.[4][19]
- **Best practice:** start with a **baseline** before changing content, so you can measure improvement over time.[5]

If you want software, several tools are built for cross-platform AI brand monitoring:

- **Pranas**: tracks ChatGPT, Gemini, and Perplexity across multiple model variants with a custom prompt library.[2]
- **Peec AI**: positioned for competitor analysis and visibility scoring across ChatGPT, Perplexity, Gemini, and AI Overviews.[1]
- **Profound**: broad enterprise monitoring with 10+ AI engines, including ChatGPT, Perplexity, and Gemini.[18]
- **OtterlyAI**: budget-friendly monitoring for ChatGPT and Perplexity, with Gemini available as an add-on in some plans.[18]
- **Riff Analytics / Ryze AI / similar multi-engine tools**: also advertised as tracking ChatGPT, Perplexity, and Gemini from one dashboard.[10][15]

A practical workflow is:

1. Create a list of **buyer-intent prompts** your customers would actually ask.[4][18]
2. Run them on **ChatGPT, Perplexity, and Gemini** with the same wording each time.[1][19]
3. Log the outputs in a spreadsheet or dashboard.[1][19]
4. Track changes in **mentions, citations, and competitors** over time.[2][4]
5. Use the findings to update content, FAQs, and source coverage where your brand is missing.[4][6]

If you want, I can also give you a **ready-to-use spreadsheet template** and a **prompt library** for monitoring these three platforms.


Yes — there are **SEO tools built specifically for LLMs / AI search**. These tools track how often your brand appears in answers from systems like **ChatGPT, Perplexity, Gemini, and Google AI Overviews**, and many also help with content optimization and citation tracking.[1][2][9]

Common examples include:

- **AIclicks** — AI visibility tracking and analysis across multiple LLMs.[1][11]
- **Otterly.AI** — GEO/LLM tracking and audit tools.[1][2]
- **Profound** — tracking plus optimization recommendations.[1]
- **Peec AI** — competitive intelligence and visibility monitoring.[1][11]
- **Semrush One / AI Visibility Toolkit** — AI visibility add-ons inside a broader SEO suite.[1][6][11]
- **Surfer** — content optimization with AI tracking features.[1][18]
- **LLMrefs** — keyword-first AI search visibility and citation tracking.[6][17]

If by “SEO tool for LLMs” you mean *a tool that helps optimize content so LLMs cite or recommend your site*, that category is usually called **LLM SEO** or **GEO** (generative engine optimization).[3][9][20]

If you want, I can also give you:
- the **best free options**
- the **best tools for agencies**
- the **best tools for tracking brand mentions in ChatGPT/Perplexity**


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

You can get this in one of two ways: a **vendor benchmark/report** from an AI-chatbot analytics or commerce vendor, or a **custom brand-monitoring study** that tests prompts across chatbots and compares whether they recommend your product vs. competitors. Public comparison guides exist for general chatbot and ecommerce chatbot selection, but they are not the same as a branded “who gets recommended” report.[1][6][10][15]

For your specific need, the most relevant options are:

- **AI-chatbot analytics vendors** that score chatbot answers, recommendation frequency, or visibility across models; the search results point to companies and comparison frameworks that track chatbot performance, connectors, privacy, and feature scores, but not a universal off-the-shelf “brand recommendation share” report.[6][7]
- **Ecommerce chatbot platforms** if your product is sold online and you want product recommendations inside shopping flows; several vendors focus on product recommendation chatbots and compare conversion impact, integrations, and recommendation capabilities.[8][10][13][15][17][20]
- **A custom study/agency report** if you want a direct answer to “Do chatbots recommend us over competitors?” The most useful methodology is to run a pilot with real prompts, measure response quality and conversion-related outcomes, and compare outputs across platforms and prompt sets.[7]

If you want a ready-made report, ask for one using terms like:

- **AI chatbot brand visibility report**
- **LLM recommendation share report**
- **Generative engine optimization (GEO) audit**
- **AI answer monitoring for your brand**
- **Competitor recommendation benchmarking in chatbots**

If you tell me your industry, product category, and target chatbots, I can help you identify the right vendor type or draft a request for proposal.


AI search visibility tracking platform pricing typically starts around **$20–$39/month** for entry-level tools and can rise to **$99–$299/month** for more advanced plans, with enterprise offerings often going much higher.[1][3][4]

A quick pricing snapshot from the results:

| Platform | Starting price | Notes |
|---|---:|---|
| Rankscale AI | **$20/mo** | Low-cost entry option.[3][5] |
| Keyword AI Tracker | **$25/mo** | Basic monitoring.[3][5] |
| Otterly AI | **$29/mo** | Starter tier.[1][5][11] |
| Nightwatch | **$39/mo** + AI add-on | SEO + AI visibility.[1][4] |
| Frase | **$39/mo billed yearly** | AI visibility tracking included.[2] |
| Peec AI | **€89–€99/mo** | More advanced multi-engine tracking.[1][4][5] |
| SE Ranking AI Visibility Tracker | **$119/mo** | Included in SEO suite.[3][5] |
| Searchable | **$125/mo** | AI search optimization.[1] |
| AthenaHQ | **Free / $295+** | Free plan plus paid tiers.[1][3] |
| Semrush AI Toolkit | **$745/mo** or **$99/mo per domain** | Higher-end / add-on pricing appears in different listings.[3][16] |

What the market looks like overall:
- Entry-level tools generally start at **$20–$39/month**.[1][3][5]
- Mid-market tools are commonly **$99–$199/month**.[3][4][5]
- Enterprise plans can exceed **$1,000/month**.[1]
- One pricing guide estimates the *average* cost at about **$337/month**.[1][7]

Pricing is usually based on **prompt volume**, **number of AI engines tracked**, **refresh frequency**, and sometimes **seats, domains, or dashboards** rather than traditional keyword-count pricing.[11]

If you want, I can also turn this into a **best-value shortlist by budget**: under **$50**, **$100**, or **enterprise**.


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

Benchmark your **AI share of voice** by testing the *same prompt set* across the major AI answer engines you care about, then measuring how often your brand appears and how prominently it appears relative to competitors.[1][3][8]

A practical approach is:

- **Define the competitive set**: choose the brands you want to compare against.[8][13]
- **Build a representative prompt library**: use category-relevant prompts that reflect buyer intent, usually split across informational, commercial, and navigational queries.[3][16]
- **Run the prompts across multiple models**: track the same prompts in engines like ChatGPT, Perplexity, Gemini, and others you care about.[10][12][18]
- **Record presence and prominence**: for each answer, note whether your brand was mentioned, cited, recommended, or used as a source, and whether it appeared early or prominently in the response.[1][7][12]
- **Calculate share of voice**: a simple version is your brand mentions divided by total brand mentions across the tracked category; a weighted version also scores prominence or citations.[2][5][11]
- **Compare by segment**: break results out by engine, prompt cluster, and intent so you can see where you win or lose against competitors.[3][7][18]
- **Track over time**: rerun on a weekly or monthly cadence so you can benchmark trend lines, not just a one-time snapshot.[3][12][16]

Two common formulas are:

- **Simple AI SOV** = \( \frac{\text{your brand mentions}}{\text{total brand mentions across tracked competitors}} \times 100 \)[2][5][11]
- **Weighted AI SOV** = \( \frac{\text{your weighted citations or prominence score}}{\text{total weighted score for all tracked brands}} \times 100 \)[1][5][12]

To make the benchmark more useful, include:

- **Mentions**: how often your brand appears.[2][11]
- **Citations**: whether the model sources you directly.[3][7][10]
- **Positioning**: whether you appear first, in a shortlist, or only in passing.[10][12][18]
- **Comparison rate**: how often you appear versus each named competitor in the same answer set.[6][13][19]

If you want a clean competitor benchmark, the most defensible setup is to run an identical prompt set across the same AI engines, score each answer with a consistent rubric, and compare each brand’s percentage of total visibility over time.[3][8][12]


tools to improve how AI assistants describe my company

To improve how **AI assistants describe your company**, use tools that keep your business information **consistent, current, and richly structured** across the sources those assistants read.[11][13][15]

The most useful tool categories are:

- **Business profile management tools**: These help keep your name, address, phone number, hours, categories, and service descriptions identical across platforms, which improves how AI systems surface and summarize your business.[11]
- **Knowledge base / documentation tools**: AI assistants perform better when they can draw from clean, updated internal content, so tools that organize FAQs, help articles, and company docs can improve descriptions and answers.[8][13][18]
- **AI visibility / listing optimization tools**: Tools that audit your presence across platforms and push updates help reduce inconsistencies that can cause assistants to deprioritize your business.[11]
- **CRM and workflow integration tools**: Assistants integrated with your existing systems can pull more relevant, accurate company details from the places your team already uses.[4][12][13]
- **Governance and review tools**: Security, compliance, validation, and content review features help ensure the AI is using approved information and not outdated claims.[2][9][15]

If your goal is specifically to improve **how AI assistants describe the company publicly**, focus first on:
- **consistent business listings**
- **structured service descriptions**
- **fresh FAQs and product/service pages**
- **customer reviews**
- **regular content audits**[11][15]

If you want, I can also give you a shortlist of specific tools by category for **small business**, **local business**, or **enterprise** use.


What software tracks LLM citations of my website?

If you want software that tracks when **LLMs cite your website or URLs**, the main options in the results are **Profound, OtterlyAI, Peec AI, LLMrefs, Ahrefs Brand Radar, Semrush AI Visibility/Toolkit, AthenaHQ, and LLM Pulse**.[4][6][7][12][18][19]

A few useful distinctions:

- **Page/URL-level citation tracking**: **Profound**, **OtterlyAI**, **Peec AI**, **LLM Pulse**, and **LLMrefs** are described as tracking citations or extracting the specific URLs AI systems use.[4][6][13][18][19]
- **Brand-level visibility with citation support**: **Ahrefs Brand Radar** and **Semrush AI Visibility/Toolkit** are presented more as broader AI visibility tools that also surface citations or source data.[2][6][8][18]
- **Enterprise / broader workflow tools**: **AthenaHQ** and **Profound** appear in the results as tools used for citation analysis and monitoring across multiple models.[1][15][18]

If you want the shortest shortlist to evaluate first, I’d start with **Profound**, **LLMrefs**, **OtterlyAI**, and **Peec AI** because the results explicitly describe them as citation-focused rather than just general AI visibility tools.[4][6][13][18]

If you want, I can turn this into a **comparison table by price, engines tracked, and whether they track exact URLs vs. brand mentions**.


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

The tool you want is **an AI visibility audit / Answer Engine Optimization (AEO) audit**: it checks whether your site is crawlable, indexable, and easy for ChatGPT-style systems to quote.[1][2][9]

In practice, the most important checks are:
- **robots.txt / crawler access** for GPTBot or OAI-SearchBot[2][3][13][14]
- **Bing indexing** and page accessibility, since ChatGPT Search relies on web retrieval rather than just your site alone[2][9]
- **schema markup** and clear, answer-first content structure so the model can extract a usable answer[2][3][12]
- **entity / authority signals** across third-party sources, not just your own website[8][9][10]

If your CEO wants a single phrase, say: **“We need an AI search visibility tool or AEO audit tool, not a traditional SEO rank tracker.”**


answer engine optimization tools for marketers

For marketers, the best **answer engine optimization (AEO) tools** are the ones that help you research question-based demand, optimize content for direct answers, and track visibility across search and AI-powered results. The most commonly recommended stack includes **Google Search Console**, **Google Keyword Planner**, **Google Trends**, **AnswerThePublic**, **AlsoAsked**, **Semrush**, **Ahrefs**, and **Surfer**.[1][3][4][9][13]

A practical marketer-focused breakdown:

- **Research user questions and intent:** Google Autocomplete, AnswerThePublic, AlsoAsked, Google Keyword Planner, Google Trends.[1][3]
- **Optimize content for answer-style pages:** Surfer, Clearscope, MarketMuse, HubSpot’s SEO tools.[1][6][7][9]
- **Track visibility and technical health:** Google Search Console, Bing Webmaster Tools, PageSpeed Insights, Screaming Frog.[1][3][5][9]
- **Analyze competitors and backlinks:** Ahrefs, Semrush, SimilarWeb, SpyFu.[1][3][4][13]
- **Automate workflows and monitoring:** Gumloop, AirOps, SEO.ai, Yoast SEO, Rank Math.[1][4][13]

If you want a lean stack for a marketing team, a strong starting set is:

1. **Google Search Console** for first-party performance and indexing data.[1][9]
2. **AnswerThePublic** or **AlsoAsked** for question discovery and FAQ/topic ideas.[1][3][4]
3. **Semrush** or **Ahrefs** for keyword, competitor, and gap analysis.[1][3][4][7][9]
4. **Surfer** or **Clearscope** for content optimization against search intent.[1][7][9]
5. **Screaming Frog** for technical audits and crawl issues.[3][5][7][9]

For AEO specifically, the tools most aligned with answer-driven search are those that surface *questions, related subtopics, and content structure*: AnswerThePublic, AlsoAsked, Google Autocomplete, Keyword Insights, Surfer, and Clearscope.[1][3][7][13] Semrush now also emphasizes visibility on “traditional and AI-powered search platforms,” which makes it relevant for teams tracking both SEO and AI search visibility.[11]

If you want, I can turn this into a **beginner**, **mid-market**, or **enterprise** AEO tool stack.


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

You can audit this by treating it as a **structured visibility test**, not a single prompt. The most useful approach is to define a prompt set, test multiple models repeatedly, record whether your product is mentioned and described accurately, then compare gaps over time.[1][5][10]

A practical audit framework looks like this:

- **Define what “knowing your product” means**: test for **recognition** (does the model know you exist?), **description** (does it explain what you do correctly?), **attribution** (does it connect the right features to you?), and **recommendation** (does it surface you when users ask for solutions or comparisons?).[7][11]
- **Build a real buyer-intent prompt set**: use prompts your customers would actually ask, including awareness, category, comparison, and use-case questions.[2][5][10]
- **Run the same prompts across multiple LLMs**: common choices are ChatGPT, Claude, Gemini, and Perplexity, because different models may have different knowledge and citation behavior.[2][5][10]
- **Repeat each prompt several times**: one-shot answers can vary, so repeated runs help you see whether results are stable or random.[1][12]
- **Log each response in a spreadsheet**: record the prompt, model, date, whether your product appeared, where it appeared, the exact wording, competitors mentioned, cited sources, and any factual errors or hallucinations.[5][7][10]
- **Score the outputs**: mark **accuracy**, **missing capabilities**, **competitor conflation**, **outdated information**, and **fabricated details**.[2][7]
- **Benchmark against competitors**: run the same prompt set for 2–3 direct competitors to see whether the model prefers them, ignores you, or confuses you with them.[5][9]
- **Track source patterns**: if the model cites sources, note which domains or publications it relies on, because those sources often shape what it “knows.”[5][10]
- **Repeat monthly or after major product/SEO/content changes** so you can see whether your visibility improves.[6][14]

A simple starter template is:

- **What is [your product]?**
- **What problems does [your product] solve?**
- **Compare [your product] vs. [competitor].**
- **What are the best tools for [use case]?**
- **Which product should I use if I need [specific capability]?**[5][6][11]

If you want a fast manual audit, start with 10–20 prompts across 3–4 models and score each response for **mention**, **accuracy**, and **recommendation strength**.[5][10] If you want a more robust program, use the 4-step loop: **set up prompts, run and track, analyze and diagnose, fix and repeat**.[1]

If you’d like, I can turn this into a one-page audit spreadsheet template with scoring columns and sample prompts.


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

Yes — there are **rankings, benchmarks, and tracking tools** for AI chat assistants, but they are *not yet as standardized* as traditional SEO rank tracking for Google. Existing options tend to fall into three buckets: **leaderboards/benchmarks**, **comparison sites**, and **visibility tracking tools**.[8][9][18]

- **Benchmarks and leaderboards:** LMSYS Chatbot Arena is a widely used crowdsourced leaderboard for LLMs, using human pairwise comparisons and Elo-style ratings.[8]
- **Comparison/ranking sites:** Sites like Artificial Analysis, PCMag, Zapier, and similar review pages publish comparative rankings of chatbots and assistants, usually based on their own testing criteria.[3][9][11]
- **Tool-based rankings:** Some platforms specifically market themselves as chatbot ranking tools, such as BotRanker and RankmyAI, which organize and score AI tools across categories.[4][16]
- **Visibility tracking:** Some SEO/content platforms now track visibility across AI assistants; one example in the results claims to track visibility across Google, ChatGPT, and Perplexity.[8]

If you mean **“rank tracking” in the SEO sense** — i.e., monitoring where your brand/content appears in responses from ChatGPT, Gemini, Claude, or Perplexity — then the category exists, but it is still emerging and fragmented rather than a mature, universal standard.[8]

If you want, I can also list the **best current tools for tracking visibility in AI assistants** by use case: SEO, brand monitoring, or product benchmarking.


software to measure AI chatbot recommendations of B2B products

If you want to **measure how often AI chatbots recommend your B2B products**, the most relevant software category is **AI visibility / AEO monitoring tools** rather than traditional chatbot analytics tools.[1][3][4]

Common options mentioned in the results include:

- **MEMETIK** — positioned as an AEO-first platform that tracks citations across 15+ LLMs, including ChatGPT, Claude, Perplexity, and Gemini.[1]
- **Ayzeo** — tracks brand presence and citations across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, and Grok, with GA4 integration to separate AI-driven traffic.[2]
- **CheckThat.ai** — focuses on how AI answers position your brand and which competitors are recommended instead.[3]
- **Sight AI** — recommends manual tracking, API-based custom solutions, or dedicated AI visibility platforms, and suggests measuring mentions, sentiment, competitor comparison, and trends.[4]
- **AI Rank Checker / Rankability / Peec AI / Profound / Semrush AI Visibility** — listed as B2B-focused AEO tools for tracking visibility, citations, and recommendation framing across multiple AI platforms.[8]

If your goal is specifically to answer questions like *“Which software products do ChatGPT or Perplexity recommend for this use case?”*, prioritize tools that measure:

- **Brand mentions**
- **Competitor mentions**
- **Citation sources**
- **Recommendation position**
- **Sentiment / framing**
- **Traffic from AI referrals**[1][2][4][8]

If you want, I can also give you a **shortlist by budget** or a **comparison table for B2B SaaS use cases**.


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

Monitoring **brand visibility in AI search** typically costs **about $25–$150 per month** for basic to mid-range tools, while **enterprise or agency-grade** platforms can run **$500 to several thousand dollars per month**.[1][3][10][12]

A practical way to think about pricing is:

- **Entry-level:** about **$20–$59/month** for limited prompt tracking or single-brand monitoring.[1][3][5]
- **Mid-range:** about **$79–$200/month** for broader coverage, more prompts, competitors, and analytics.[1][3][12]
- **Premium/enterprise:** about **$239/month to $3,000+ per month**, depending on scale, team seats, and custom reporting.[3][10][12]

Examples from current tools show this spread clearly: **Otterly AI** starts around **$25–$29/month**, **SE Ranking** starts at about **$103.20/month**, **Profound** starts around **$99–$120+/month**, and **Ahrefs Brand Radar** is **$129/month** for core indexes with **add-ons up to $699/month** for all indexes.[1][5][12][15]

If you want, I can also break this down by **small business vs agency vs enterprise**, or recommend the **cheapest tools** for monitoring AI visibility.