Key takeaways
- AI search engines like ChatGPT, Perplexity, and Gemini are now a real discovery channel -- Google AI Overviews alone processes roughly 15 billion queries per day, and 56% of AI referral traffic flows through ChatGPT.
- Tracking works at the prompt level, not the keyword level. You need to know which specific questions trigger your brand to appear (or not appear).
- Citation sources differ sharply by engine: ChatGPT pulls heavily from Reddit, Wikipedia, and news; Google AI Overviews leans on brand sites. You need multi-engine coverage.
- Manual tracking with a spreadsheet is a legitimate starting point -- it forces you to understand what you're measuring before you automate it.
- Purpose-built GEO platforms go beyond monitoring to show you what content to create, which gaps to close, and how visibility translates to traffic.
Why tracking AI visibility matters now
A year ago, most marketing teams treated AI search as a curiosity. That's no longer a defensible position. ChatGPT processes around 2.5 billion queries per day. Google AI Overviews is now embedded in the default search experience for billions of users. Perplexity has carved out a loyal base of research-oriented users who trust its citations more than a standard SERP.
The problem: none of this shows up in Google Search Console. Your organic traffic reports look fine while a competitor is getting recommended by name in every "best [your category] tool" prompt your potential customers are typing.
That's the gap this guide is about closing.
Understanding what you're actually measuring
Before you set up any tracking, it helps to be clear on what "brand visibility in AI search" actually means. It's not the same as ranking on page one.
When someone asks ChatGPT "what's the best project management tool for remote teams?", the model generates a paragraph or two and might name three or four tools. Your brand either appears in that answer or it doesn't. If it does appear, you want to know:
- Where in the answer (first mention, buried in a list, or a footnote)
- How it's described (positively, neutrally, or with caveats)
- Whether a citation link is included
- Which source the model is drawing from
That last point matters more than most people realize. 95% of citations in AI answers come from third-party websites -- review sites, Reddit threads, news articles, Wikipedia. You don't control those sources. So tracking AI visibility is partly about monitoring surfaces you don't own.
Step 1: Build your prompt list
The unit of work in AI search tracking is the prompt, not the keyword. A keyword like "project management software" is too vague. The prompts real users type are longer and more specific -- research from Otterly.AI found that real user prompts average 15.1 words versus 8.8 words for estimated prompts, and skew heavily toward tool-finding intent.
Start by building a list of 20-50 prompts that represent how your target customers actually ask questions. Think in categories:
- Category prompts: "What's the best [your category] for [use case]?"
- Comparison prompts: "How does [your brand] compare to [competitor]?"
- Problem prompts: "How do I solve [specific problem your product addresses]?"
- Brand prompts: "Tell me about [your brand name]" or "Is [your brand] trustworthy?"
Write these out as full sentences, the way a real person would type them into ChatGPT or Perplexity. Avoid keyword-style fragments.
Step 2: Manual tracking with a spreadsheet
If you're just starting out, don't buy a tool yet. A spreadsheet forces you to understand what you're measuring, and the patterns you notice manually will make you a much better user of any platform you eventually adopt.
Set up columns for:
- Prompt text
- AI engine tested (ChatGPT, Perplexity, Gemini, etc.)
- Date tested
- Brand mentioned? (Yes / No)
- Position in answer (first, second, mentioned in passing, not mentioned)
- Sentiment (positive, neutral, negative, mixed)
- Citation included? (Yes / No)
- Citation source (if yes, what domain)
- Competitor brands mentioned
- Notes (anything unusual about the framing)
Run each prompt manually in each engine and fill in the row. Do this weekly or bi-weekly for your top 20 prompts. After a month, you'll have enough data to see patterns: which engines mention you, which prompts you're winning, and which competitors keep showing up where you don't.
This is tedious. It's also genuinely useful because you'll start to notice things that automated tools summarize away -- like the specific phrasing an AI uses when it describes your brand, or the fact that one particular Reddit thread keeps getting cited.
Step 3: What to look for in each engine
Each major AI engine has its own citation behavior and answer style. Treating them as interchangeable will lead you to wrong conclusions.
ChatGPT
ChatGPT's answers are conversational and tend to synthesize across many sources. It pulls heavily from Reddit, Wikipedia, and news sites -- one analysis found these three categories account for roughly 39.5% of ChatGPT's citations combined. Brand websites matter less here than third-party coverage and community discussions.
When testing ChatGPT, pay attention to whether your brand appears in the narrative text versus just a citation link. Also note whether the model hedges ("some users report...") or states things confidently. Hedging often signals thin or conflicting source material.
Perplexity
Perplexity is more citation-forward than ChatGPT. It shows numbered source links inline with the answer, which makes it easier to see exactly what it's drawing from. This is actually useful for tracking: when your brand appears, you can usually trace it back to a specific source.
Perplexity tends to favor recent, authoritative content. If you published something in the last 90 days that directly answers a common question in your category, it has a reasonable chance of being cited.
Gemini and Google AI Overviews
Google AI Overviews pulls 59.8% of its citations from brand sites -- a much higher proportion than ChatGPT. This means your own website content matters more here. If you're not appearing in Google AI Overviews for category queries, the fix is often on-site: structured content, clear entity signals, and pages that directly answer the questions being asked.
Gemini (the standalone product) behaves somewhat differently from AI Overviews and is worth testing separately.
A note on consistency
AI answers are not deterministic. The same prompt can produce different answers on different days, or even in different sessions on the same day. This is why tracking requires repeated testing over time, not a one-off snapshot.
Step 4: Moving to a dedicated tracking tool
Manual tracking works up to a point. Once you're monitoring more than 30-40 prompts across multiple engines, the spreadsheet becomes a bottleneck. That's when a dedicated platform starts paying for itself.
Here's what to look for when evaluating tools:
| Feature | Why it matters |
|---|---|
| Multi-engine coverage | Citation patterns differ by engine; single-engine data is misleading |
| Prompt-level tracking | Topic-level summaries hide the specific gaps you need to fix |
| Competitor share of voice | Knowing you appear 40% of the time means nothing without knowing competitors appear 80% |
| Citation source attribution | You need to know which pages are driving your visibility |
| Sentiment framing | Appearing with negative framing is worse than not appearing |
| Historical trends | A single snapshot doesn't tell you if you're improving or declining |
| Content recommendations | Monitoring without action is just expensive anxiety |
| Crawler / agent logs | Shows when AI bots are crawling your site and which pages they read |
Tools worth knowing about
Otterly.AI is a solid entry point for teams that want multi-engine monitoring without a heavy price tag. It covers ChatGPT, Perplexity, Google AI Overviews, Gemini, and a few others, with competitive benchmarking built in.
Peec AI focuses on AI visibility tracking with suggestions for improvement -- useful if you want some guidance on what to do with the data.
AthenaHQ is monitoring-focused and works well for teams that primarily want to understand their current visibility picture.
Profound is built for enterprise teams and has strong analytics depth, though it comes at a higher price point.
For teams that want to go beyond monitoring into actually fixing visibility gaps, Promptwatch is the most complete option available. It tracks across 10 AI models (ChatGPT, Perplexity, Gemini, Claude, Grok, DeepSeek, Copilot, Meta AI, Google AI Overviews, and Google AI Mode), but the differentiating feature is the action loop: it shows you which prompts competitors rank for that you don't, generates content to close those gaps, and then tracks whether the new content gets cited. It also includes real AI crawler logs -- you can see when ChatGPT or Perplexity's bots crawl your pages and whether those crawls lead to citations.
Step 5: Tracking citations you don't control
This is the part most teams skip, and it's a mistake. Because AI engines draw so heavily from third-party sources, your AI visibility is partly a function of what Reddit, G2, Capterra, industry blogs, and news sites say about you.
Practically, this means:
- Monitor which third-party pages are being cited when your brand appears. If a specific G2 review page keeps showing up, that page is doing work for you -- protect it, and consider whether you can influence its content.
- Watch for Reddit threads that mention your brand. Reddit has become a significant citation source for ChatGPT in particular. A thread with negative framing about your brand can suppress your visibility even if your own site content is excellent.
- Track brand mentions in listicles and comparison articles. "Best [category] tools" articles on third-party sites are frequently cited by AI engines. If you're not on those lists, you're invisible in those answers.
Some platforms track offsite citations natively. If yours doesn't, you can supplement with a tool like Google Alerts or a media monitoring service to catch new mentions as they appear.
Step 6: Interpreting the data and taking action
Tracking data is only useful if it leads to decisions. Here's how to translate what you find into action:
You're not mentioned at all for a category prompt. This usually means AI engines don't have enough source material connecting your brand to that topic. The fix is content: publish something that directly and clearly answers the question, then build third-party coverage around it.
You're mentioned but with caveats or negative framing. Dig into the citation sources. If a specific review or article is driving the negative framing, that's your target. You can't delete it, but you can create better content that gives AI engines an alternative source to draw from.
Competitors consistently appear before you. Look at what sources are driving their visibility. Are they being cited from a specific publication, a Reddit thread, or their own blog? That tells you where to focus your own content and PR efforts.
You appear in some engines but not others. This is common and usually reflects the different citation preferences of each engine. If you're visible in Google AI Overviews but not ChatGPT, you probably need more third-party coverage (Reddit, news, reviews) rather than more on-site content.
Putting it all together: a simple weekly workflow
You don't need to make this complicated. Here's a sustainable weekly routine that works whether you're doing this manually or with a tool:
- Run your top 20 prompts across ChatGPT, Perplexity, and Gemini (or let your platform do it automatically).
- Log any changes from the previous week -- new appearances, lost mentions, new competitor appearances.
- Note any new citation sources you haven't seen before.
- Pick one gap to address: either a prompt where you're not appearing, or a citation source that's working against you.
- Create or update one piece of content to address that gap.
That last step is what separates teams that improve their AI visibility from teams that just watch it. Monitoring tells you where you stand. Acting on it is what changes the number.
Comparison: manual tracking vs. dedicated platforms
| Approach | Cost | Time per week | Engines covered | Action guidance |
|---|---|---|---|---|
| Manual spreadsheet | Free | 3-5 hours | As many as you test | None |
| Otterly.AI | From ~$49/mo | 30-60 min | 6 engines | Basic |
| Peec AI | Paid | 30-60 min | Multiple | Suggestions |
| AthenaHQ | Paid | 30-60 min | Multiple | Monitoring-focused |
| Profound | Enterprise pricing | Low | Multiple | Strong analytics |
| Promptwatch | From $99/mo | 15-30 min | 10 engines | Full action loop: gap analysis, content generation, crawler logs |
The right choice depends on where you are. If you're just starting to understand AI visibility, the spreadsheet is genuinely the right move for the first month. Once you have a clear picture of what you're tracking and why, a platform that automates the data collection and surfaces actionable gaps will save you significant time and probably find things you'd miss manually.
One thing most guides don't mention
AI search visibility is not stable. The same prompt can return different results week to week as models are updated, new content gets indexed, and citation sources shift. This means tracking is not a one-time audit -- it's an ongoing process, more like social media monitoring than a technical SEO crawl.
The teams that build durable AI visibility treat it the same way they treat content marketing: consistent effort over time, with a feedback loop between what they publish and what gets cited. The data tells you what's working. The content is what actually moves the number.
Start simple, stay consistent, and follow the citations wherever they lead.